diff --git a/docs/configuration/prompts.md b/docs/configuration/prompts.md
index 14454d7c..e4c428cf 100644
--- a/docs/configuration/prompts.md
+++ b/docs/configuration/prompts.md
@@ -57,7 +57,7 @@ prompts:
 
     Process:
     1. Search for relevant documents using the search_documents tool
-    2. Review results and their relevance scores
+    2. Review results ordered by relevance (rank 1 = most relevant)
     3. Provide a brief, direct answer based on retrieved content
 
     Guidelines:
diff --git a/evaluations/evaluations/prompts.py b/evaluations/evaluations/prompts.py
index e8bc81f6..0a0e1b83 100644
--- a/evaluations/evaluations/prompts.py
+++ b/evaluations/evaluations/prompts.py
@@ -4,7 +4,7 @@ You are a WIX technical support expert helping users with questions about the WI
 Your process:
 1. When a user asks a question, use the search_documents tool to find relevant information
 2. Search with specific keywords and phrases from the user's question
-3. Review the search results and their relevance scores
+3. Review the search results ordered by relevance (rank 1 = most relevant)
 4. If you need additional context, perform follow-up searches with different keywords
 5. Provide a short and to the point comprehensive answer based only on the retrieved documents
 
diff --git a/haiku_rag_slim/haiku/rag/agents/qa/agent.py b/haiku_rag_slim/haiku/rag/agents/qa/agent.py
index c8d16a90..2cce2ca8 100644
--- a/haiku_rag_slim/haiku/rag/agents/qa/agent.py
+++ b/haiku_rag_slim/haiku/rag/agents/qa/agent.py
@@ -48,7 +48,7 @@ class QuestionAnswerAgent:
         ) -> str:
             """Search the knowledge base for relevant documents.
 
-            Returns results with chunk IDs and relevance scores.
+            Returns results with chunk IDs and rank positions.
             Reference results by their chunk_id in cited_chunks.
             """
             results = await ctx.deps.client.search(
diff --git a/tests/cassettes/test_chat_agent/test_chat_agent_ask_adds_citations.yaml b/tests/cassettes/test_chat_agent/test_chat_agent_ask_adds_citations.yaml
index 43396119..557ac230 100644
--- a/tests/cassettes/test_chat_agent/test_chat_agent_ask_adds_citations.yaml
+++ b/tests/cassettes/test_chat_agent/test_chat_agent_ask_adds_citations.yaml
@@ -180,7 +180,7 @@ interactions:
   response:
     headers:
       content-length:
-      - '523'
+      - '520'
       content-type:
       - application/json
     parsed_body:
@@ -189,24 +189,24 @@ interactions:
         index: 0
         message:
           content: ''
-          reasoning: We need ask.
+          reasoning: Need ask.
           role: assistant
           tool_calls:
           - function:
               arguments: '{"document_name":null,"question":"What is the highest count class in the DocLayNet dataset?"}'
               name: ask
-            id: call_afzr6lnr
+            id: call_9p1yei96
             index: 0
             type: function
-      created: 1768997465
-      id: chatcmpl-648
+      created: 1769001268
+      id: chatcmpl-844
       model: gpt-oss
       object: chat.completion
       system_fingerprint: fp_ollama
       usage:
-        completion_tokens: 44
+        completion_tokens: 43
         prompt_tokens: 850
-        total_tokens: 894
+        total_tokens: 893
     status:
       code: 200
       message: OK
@@ -300,7 +300,7 @@ interactions:
   response:
     headers:
       content-length:
-      - '560'
+      - '514'
       content-type:
       - application/json
     parsed_body:
@@ -309,24 +309,24 @@ interactions:
         index: 0
         message:
           content: ''
-          reasoning: We need to call gather_context on the main question.
+          reasoning: Need to gather context.
           role: assistant
           tool_calls:
           - function:
-              arguments: '{"limit":5,"query":"What is the highest count class in the DocLayNet dataset?"}'
+              arguments: '{"limit":null,"query":"DocLayNet dataset highest count class"}'
               name: gather_context
-            id: call_o696xo4a
+            id: call_4txvh12j
             index: 0
             type: function
-      created: 1768997467
-      id: chatcmpl-112
+      created: 1769001269
+      id: chatcmpl-805
       model: gpt-oss
       object: chat.completion
       system_fingerprint: fp_ollama
       usage:
-        completion_tokens: 55
+        completion_tokens: 41
         prompt_tokens: 430
-        total_tokens: 485
+        total_tokens: 471
     status:
       code: 200
       message: OK
@@ -339,7 +339,7 @@ interactions:
       connection:
       - keep-alive
       content-length:
-      - '127'
+      - '107'
       content-type:
       - application/json
       host:
@@ -348,7 +348,7 @@ interactions:
     parsed_body:
       encoding_format: base64
       input:
-      - What is the highest count class in the DocLayNet dataset?
+      - DocLayNet dataset highest count class
       model: qwen3-embedding:4b
     uri: http://localhost:11434/v1/embeddings
   response:
@@ -359,14 +359,14 @@ interactions:
       - chunked
     parsed_body:
       data:
-      - embedding: 6ZXRuYTGg7zwxo48CioGPa163rrrJkA90msfPVW8xzk9cAA8RNCauzuppzvlqZg8AQ+MO+I5YDzB2uu8qtxfvRBB7TyGg288OL76O/uFkbv9Y9e7szYIPXp/Ij042pM7x8ZVvfhtN72Nqsi8pZCMvBey2zsO7u08yPWhPF86HL27Ijy7V8ZhvHLMMjs19bK7BYIQvGlxCrv+qO+7dXNBvZ1f4jsiBW68iKTdOUJiADz3LoI5zbG3O6OiFTzezyK9yUPvvKfPZrzrtwM8nhojPBqrz7zbS4C7JVEZPbIhITrMzgw9DhNUOkWvlLtrXJc8XyRpu7u2s7k/EKu8bi4QvCAANbxRT8i8Ok6WPG07krtfgQc8a8AxvPVpM73fl9W5au8nvIVFFDzdjDw9dQravCNNNrxergQ8uNeCu/Qtwjz+hqk80jLZOhvvHTtPeg49drV9u81pObwurXo8e3BdPJ9aN7x/o1s8t2qfPKNNjDzg7IK86V+kPPchMrvgZwg8dYm+vEpQKbwnYJK7laSHu0Ilcbv/F8W8cg+Iu8Yum7mtxME82CbxvH2Utbz0lIk6YNtSvJzBijvVh446LreOu3cC5LzLnOu6YWQsucsWibpSJTi7qjyTPKM2VTz3k8Y8XCf6u6bscjsPwJy81d6Du+x/BDsZ9EG9JbCdu61Vzrwtxi097RJGu8GzHzxlVbC8yJ/gPMwkk7wsMZo7MVM1u3zHgbzgzJ87tqiCu0+AkDwmVdC7IFftu5XLwbp+R888l3NNvNtCKb3OlEk82oExvXueNjwtcDM7PxnEPFGJobxcPos8QkUdPAj+OjwSIKU8/tykvLwWrjxHet87ngdJPFn2QbyxkK48JdgmvGzDIT38mbY6EDccPEuQsjyKeDk79HL9u5TPFL3eAeu78pFPvBjA87u8HYy8ii35vN4dC7yhqYC8DWhHvIAZjrul/cQ7N02NOy1pET0exL8897oiuwCjcjxOhIi8s/CzuwQfSbxLPbw7o4atPFKUAztpuEI8AhhZvB47LjynloO7H9anvNRqrruOG3g8wzm2vN43HjyEK6M8gHf3OtM6tDwuKUm8MJnAu6ARJ7xJLgK8YBOSvPo/yTsrS4+8EQ3kOsJTZLxoaXe8iH6WOqZw2jv0tVE83qnKvHyCR7wIxbI8lk5xvN5wwTyZ0oi8uMclvCAQRTycoHq8OHtLPALAiTovgqe8SXM0vLb66Lsn9NA8IrqYPBbmHbqj8yy8WG7PO9WGtTvtcgu9aKovPGoCgDwFuCa9DRFDPVdViryzaFG8EkD8u2qPfrynC4y6EMaIOxNJuLzztX68nX3evKrdmrs7MQE8Y6KJO1It2rzGA3u8xDmTvN8SZLzXOTy78VrEu9W0Z7zXHNu56sKuvDZo1LvUm/m7MIqQu1LqlLyjoas8UcW8vHiJCrv3Me87/N19PN05RTrK98c77ec1uvcb7jyW+nO8qxIwPP58fjzGAOY61karPC9zTbz5SZ47Yy8Nu2YGwbucfaK78Ar+uysP7Tyj5oe7JTEqvEqnMDy9f4s8ggCqOoL9Rzxnb0q8nGSju90YVDwHJ2Q7Lx2mOlzum7vfx3G7VQh2vE0I8Dq4OII8vihJO5XpK7wr8Hg8IeXUO0YMHrxAArc8Ix9QPCzxKDxaJF08PncvvPZorjkxghg9yRmdvL4Xi7v+L5K6cfSyvH8nEjq2Doq8NzY4vbNw7LyfKjC7Vo0jvDeo1TxTn9E8clcePXHXGj3JSIG7vcEfvC70QD3Z6GO9tT3hO160g7zZJ5I82xyzvIILvDwijGs8aGPrO98Q4rtg5IA7UyWhuYkBlLxtnpq8NUKruxPdQ7xjFh48LGGqvIILqjyNywm99t+7u9wPeryh5SM8kq0ePUOUG7oUDbq7UjbgO/KQNz3gXce8j5CFvG62u7zU2L073Hn2OzxFAb1hdqO8CAlpvJubdTxDv6M6OKijvGqUsDxxaxY8lasTPZOtTDxw9r+843+1uyLcVLyGOm28fSOuuu8KYbz7+2I7RjzvuqE4Lrx6r4g8AmnlvHkJWzvt2F+879SFvNxuNLwN/V+70QwFPKEfxDxw5G488GOTu3ugM729eOe7mETouc2UszzBWcQ8G1LwvKl8qLqecSA8Ti3cvNuLfLzZhoY8XMtEvbNd2rwzBtM8zTHuu+xlqzvciha8JZasvNvIJzyzRJi8kuINvfn7djsb8B88WYk2vBjlDb1Zo8Q7ZakGvHLDvbxBx5E8X7c6vI8zUTuBCAI9+plouyHFHT1A/1E8L6CDvF4mODo9FSw9HHscPQ1LMD2+6J28MfRBvKL5a7wUMgQ8l5JKO+dLNb3/SME85rxJO6acOTxY/JM80N22vNTg0zmDKAg87RcdPb4o1zyf7Va9LaaAvAdokzt4ZZq7bnQ4vFFNML1Bvzy8PGulN8cQDzzF9h29tj0nvJQSGb3Dm0s74QNOvKLdRrzWhSm7RXVIvPoH0bsuMIa8zCgAvXLCAz1hQZg7NfpPu6vpHTxOrM28wwYdPLRvQj2uy607Kh18PLk9NbxSmiY8ZnQRPCU9IjydC7M8qIY4PVpBQ7u9L+w8llADPbsylLy2Cci8ogLuPIO57jxBg7W8oEeBvP0kxbwMIxU889WYui5hBzwSSY47ywsPvP4lybuFg3y8GCCnvG1MJDzEFDu8AS4tvGL4JD2EW5K7EhN6u+qQ8bxDgrY8BbJ1PE5drjvRzDa7ehGwvJEryLtio6y8pFjdu3vAA7zmXO673ribPPkGGrzyAHG7qkCeuyaciDrR0LK6+PDCPMlTQD0HfSm7S/CGPOrO4zyGCQg8qOrZO+uuhTy0Uwm8qg+OvK3pbbtMooo6i2bour6zt7lhUqI8tA2Bu+6i/jyftl28zwOnu6+6orwCuI68K50rOnx5C7wdY+28CzJAvIKhATxpd5C8koFXuyDvNrwKKRq82roTPJeAC70zWNs8HCm7ulP0B7u9kIS8PD6TPCUbaDtV6pY8idQ3PI7Qfrw/csE8QuqlvLbyEb3HOJu89eyqvARjxLvuz7Q8VqdcuzsBsTyGjo88clTaOuNOuLsz5uy8Hk1BPF5FQDyKhaU70gSYvLqxKTwZUW+8vJiKu76ynjwJ9aY8yGEDPcPxQ7yXcS+83xKMO4jZ0buOg927HBsLvIzpHbw0J7+6BKX+vCS/wzsNBo+7A7piOySNsryBdgo8P6C+PGGb7TtT62Q6bQ8QPebpHTwQTSa8a0T8u37X7Tzmvmk7XW/9utSM+jzR8e88kwqqvLD2BLwkPbm8JeGBvCGgHb0Oi767KGJYPIeb0rhhlEq71NOuvBzcFrvOjfe881iVvNbluDvCkoq7ZqqCvF6XlrwTM589UAr4PPUkZ7sEPnm85/VCvWnlwLukj6K7KjEQPLXETTwU1Zo8fOnWPIS+qLz4u+88sbihu9C8eL11UsO8SAEWPdu29LwogV68UGnTPIb9obx8ldU8h/GovHPJorpPC9k8Hln5u1koID3vv9s3ykJLu+5An7zs+zO9Fg1LPJyl/LuR0No8a/uyvB27ibxG4AW8gO3VvDouLDwnP3A8FU8HPXVanLtWD0U8rbMovYHYrDts42C794N1PGNYE7wagyQ8G7hBPEewGbyzaAy83cVwu3uSTLyGkZU7t8mzujcRbryjsfI8T4T6O89cRr2RVC68RbAOPet2gDruvdM8qmiBPGyJirwRpKe8tUAVuK0GoLwOOc66x2GAPCzPK71dqnm8MPZpvH7uSzwXj4S8j73TPKSrHz3Ps8I8z5nevOvf77xIF7U84RLivJoPajyyxKO87LEWPd/IUDkLg5C74lxgPMfxyztbe/S7H3+/PD1KnbvK55w89+CBvNyMdzyzA7q81TOIPR12LbugkzO8MAsBPPtiJ7scYDS9oQpxO2VnCD2sZrO8IahevE8JPTwQKDI96SeBPBAdSjzzhX651Z9/vJKf/DtfFUy82ZBzPCF/87vaV388yr0sPdU1HToggh66nmrAvMrc1ryem688LN2APPPyC7zs8Re7Rdx1PBExsjs1Qc28qi2LvC+xIzzO0Ay7uLuivB+VZzyi6Fm8cI4dPFT9xTzqUK46wFE+vEB2gTxpa5A77qlkPRn9cTxZS9g71BLxumjWDz1J/hq8LfvEvOg70rxfre88qByMvLFeNzvGHD68sSydvBFXITvghSk7q0fovOfx67xQEjy8N5JGuwOzGD1usrm5oaxLvKX7ibwYMw68n+pKvZ/GjDvkmLA8Y5pjO6yguDt/Fg46BibhvAnbqzwiDyE9l6k1PMJY0bzhFBw8QcHVPOANJrxprSw81CybPDhbBLwyhpW7yFEmPc4rObxaggy85KaOu+ppYjzSles8+E2sOlH08rseKzY8Fd2SPJ2gET3pxlC8ariNPD78/TswFb+7BGfqPLwtg7wr7cs86TKyPOGoBLtVDio9iMkQvS4W/7yxYhK9yL8lvFDeDL2INtE8xTsIO+6oPTwBnUI84vGWu/fezzzDQZw6T8ryuz6uz7ue5G49TAsRPRBhQjx2C3u89PltPLOMCz063pW7fH0fPIHHlLsBJXS8bm6dO0KNFbzpAMA7cuplu8g8cDu9zNI7+5qEvPuz/Ltp3C+9R++LPLbNxrwHngE7h/nyu0SnSz053gO8rgOmvMOtYjxqI4A8rSSxuzU1JD3Jgi+8bsigPH11Jz2saBy8pbCbPB47ErxQz/C6adgFPDDIPrw3J5C83YnkO6kRB7zyPsc7j3thvFiinjyzHXe8PFLkvJqptjwTS6Q7eJoQvBCMCj3t6sS8nayJPN0XLD2t6qm6G2i5vFKB7jzH5Io8v6aXugIGI71dD5u6dIHHOmA51zxtBxO9mfWyO/mAAb2fWWU8ccCMvGZgijtm0pQ8vvy3u1TSMb3M+e86pEpKPIVL/js1iwK8y3YBvLrvtLqj4jY8K1PavMd4ljzECh27oHGGPIrJJD3RyOo5VBoevCC3UTy526U8x43YvLvwy7wz1y09F6JkPFGCsDw28iY6oqerO882L7ieXIg84nI1OxCLIr0svIw7qidyPEVbiLyukxg7a/mIvAWs17xfTTe8+aAtPB/HwjyC6uo61OCjuzvPR7sarFW9h95TO30hkjy1OQs7jjurPM7ySrxFme682wmouziT7Luf9uu7BDL6u2B2TTyse2a8gD8Xu8/FxLxAiAA90Ju4u1hqKj3j26e7jywfOnTdtzxgT0k8euqYPIzilrxQdKa7ipPfuxvFkbwZoYo8gUuVvL/WrztBWZQ7szuvPC14Bj3mDSc9UKCCPOGrTDwwIww7ruLauzSlDb0YUrE8awhLOzqcBbzz+PG8NlrGOi/SkDsrE9m7yTmsPNJU8rwWtcG8Z/vHuolaQjuhUHQ8nxvhu7KY57zuMf07Gm8RPZDcr7vSs/w88H9UvK2qU7zB6Ti6kmAaPTUapznVZaC7UDWzPPuN3jwsNb87s/ITPY1GGDwGy0M7soswvQGYCDwCVFS7f1oBvXgOOjsn+gq8wAXjutuWv7qJiLm6O8oLPRmNRjzHeqO828MxvDGcQjyVLDs8CZyfu4VQRrw7U6y88RZ0Owp0ojuMM8Q7FaT8PFZOv7zZQx27leGPPJDNXjxpHlM6yGUgPb6voboTK128+UpCvNhK4LwlFwq8u8/cvNE8r7uQ6tk7cS46PBuLgzvUVAq8FmtFPI+eKjv2tAM6RVd0POPBqrxU83i8vRLOvPLbbLxaXsc5Ls2IPOmhh7urikO8nVQXPLRPnrwV/uM8p8uRPGWPG7wbNMe8eHLPPGpLzzqGa707pYaJPNjZ5Lwsl8w8HrFtvNKXbrzxj6I7g6b2vAS1I7zfpLA7jxq7OyyeNLwqeyU8BrfnvEGhD718K7c7nIBrvA3hLDvKs7s8mGE1vQK5GzzBhrS8tMBmvCw3Drxx6KO87l8Cvedocb0Ox9463cepvOPAUDoLGHo89dlqvMJr6DxbVRC8hDkqvCiSmjxZY9A7hxZhPMB6OzxyShS9uDn9ul4ILrr8PpG7527GuzwmkTyviQG9LjxUPHLIEbhUVna88MKEvPEqD70BjBw9pa45vem3Aj2eJhs91aUPvIzeAD2nyx68Ha7nPCrSC73L2Yo8hw+TuzUFjzwmeLI7ccOoPEXxRbtpwbE8DysKPWcfB7x5kDc8IYApvI6NojsmuAw7qeevvASptjzzFva78beYvCL7djvl1n07xJUSO1CEBr0BWyI6O1JuPCwa8bqE6y+86S4sO4WRmrwEIk08G7HtuoBnI7qbHEm6pXbZPMfUtby1KA+96vb0PC0iozzCoeI83VM6vF/HCz1clTM9EPsxvL+afDuKWuK8J/81Onp/EbufZGS77LLbO1ilhL3e7Gy8xdOCvNktwLz73Yw6FBkbvJNvzLloc0c8fvaPvH+axTxU0ZW7G0JOPWyLGTyKpnY7VxEWvNppKryZYwO7PXjUObJL3zwC8ce81W6QvE0OPLycmEu8ti/ivBUvtDxyADQ883GOOzRRAL2FUKk8D6coPatkWj1gr/k6a86qPNLX9Dx3tK48Wme2O3PhXbt6PDm8xk+0vNX2sTrWksw8yikcO513lbtjDQG75K36PLpKGbxGkls9O+cSvXYtHTz0LgQ9vyjCuw2AgDxfQUG98RHFPNjSybzyidQ7Rnq6O3S+z7sEopI7K8LIvMOirrx+mSs9jXonvClmijrY84u86Hi7PKyECj3+UA68izwTPSl+R7zbdOu7Y022u3iFeTyl/Pm8wuyTPFiqYz0G4PI7dO9RvPVKKTweqe67bNXmvCixwby3oZq783KjvNBVjDza1eu8NCSquwNGb7yjiTC9e128PEKZ+7yj+w+9yWQ+PJ3vR7twCdq8IOM6PCyYRLvKc1k8HT9LPA3ORbwKR3M8qjkgO8WsgLy+u+I7zD68OhgIejwdpbA8TkkavQjSqbouYje8WPZduzKB1Dvydp08t77BvPa/Ujx++8Y78ItaPCTiIb136qC8brXQvEQCBb22tWe79cM9vJXGabyY8G682kj1OSIWGzwujqu6hzKQO8B74TyYnBy8n9/Iu2rInzuWCsw8+BvEO1oDqDwAvHa8NxwdPaeoVjvPNAo8BENLPcY8JrxbGQO9L1vvu1nDNb3p1fs77zVlvMeggDzGVSq9H7InvJfLm7v1BOC8tEaePG6ykjwgRQ+8Sd3wO7uE6DzLtWQ8VqbuOyRv/zvjY2i8MZZGvGR2AT207QK9MOdEPCK0EDxpId07CPtsPPjm9zxdnBU9tT2AvFIwLbzUc8E8ImoSOpiwMzzxPfy8N9y9uwu3yzoIxBm8vbRDPV1vUr3Ktw+9eBa8udjoNjtpYzk80lMyvFTtWT2FCNY82iW/vJ9EmzwrDTy5n3xFOoM0iTv8sX285G8ovd2KID2OL5Y8YLIVvdQsojw9Gck8ztFrOwh1hzyPgIQ8TJoRvMqdIb1aWwo80LfFOy/6N7zrgAM9hcWNPH2AUrxdWgy8vDzSvI6kKT3W7VC9En5ZvGtiybtqSL28BM8KPeHWp7yemPS7Y+fSOIgChzyFuhm9B9YHvV8dgTqgJAW77+VpvKoQfLt9LpO7inkJvNepYTpqKT+82sqwvO9uBb0udAW8z+KPPOSaqTxqsji8B77yO0NeJbu6JzG721IzvKoLQLz2DwY88E/YPMcGFb1HffK7hkcNO08AebzojOQ8Fuu1uhgI4bvEggA8d1PUPORmP7yuclq7SpxLPNpenLzz+pO8L9LAPL/2TrwC67m7ZGI0PJb547xvi9k6jKoFPVO03LvdJIy89qe7vJmaIbyRTj+8OEsUvBQZwDzQHka72tn5u9wFAL0bxdI8iuGPvB4PzbwmqBW7VuSxvNHWCT0ohNw6xEkHPV7+p7q/J8+8XhvvPAZu47zpetU8SPdTPIXXAryHFO68sRyVugEKmDzGHVS7cotEPdjatLyarMS8hUrzvBpVgryLsvO8gdJfPBwbTLw6wQa7C9AUu0fZNLvu+rC85+wSvCQ9LDuaZsk6RiMEPIGLGL3Kqwk8a+yfPJrMGjzYwTO8zCzivCiHATy4Dio8AMmmvAzOKDu197C7UxwSvWwdxbsVZQy9zKnaurk357tAL7Y8/sFAvNiPmjuKF8i6gxSsPCOeUbzhwSe7LBTOvADk1ryjTIi8tzOEOy8YazxI+xA9THrMvPUqOTwGvAg9OM+7O8FM87ueU5I7HEWcu3l/FT3T6pM8E8fou0QVJDymL2I7kEpqvK1g1ztNEf27fcohvTO2CjzW1rs8Lw0dPHBtmjyb8oM8Ef1vPKzATz0ifSi9RDHNOsspQbzWgy286Bewu8+ACD0pWcY7Y7LUO8Sd1LyU3DE9j3vZu9bQIbw46Ko7inskO15fEDv7jV68oNFjPFZKrjyGOPk8VhaLOiaBs7wjKMo6SwayunkLNjpESJE8CG3/PM0F6Ls1qcK6CkWKvLRTaDswbE682yBsPPv8Uzwl27a8N8ZAvCFFhjtf8648QJnVPPuzrLxxg287xsKLvL57Dz158SS9Lan7O8Ssljuj9Lu8K7tovONerjnHUAo8hwMzuzm0ITvvHHI9YUIevMMAX7x6XoY8MhYBPRmS9zz6Iws9ELayPM+oCz1po3k80NOtvAGx/Tu/ZQo9mHwXvL1lIby491k80Qr7u4hNGj3gt6C7FRgVvPDjzruklCq8tcvfuzMLcrymRAs9F5nau1/s8ztoMI48ijSYOzzAzDyZVDS9A9y4vMHlAr3ZQ4e8is0APd88ezz7i6U7oR36PIMbCbwIsCw9d4jaPL5qYDzq65o5RQAjvK7PqbvX4ns8jGcDvC8QnTnnRaS8/lOdPMh14jyg94q8n9StO4CV0Ty6BeU64SkWvIpvMjwRSTq9Vb5ovAop9jv2OYM8TD9SPUSgcrxFKRS9zIruu6XafTqx9KS8POYBPHFsKr2CNgM8/RMPPZJlhbyPXPO7DHf4u9+YaLwkuU+8VouMPHjC3Txtwc084JlAu3dM7bouL6Q6fNKPPFC/+Ty0d/O8F6AXvLxEDDupnKs8mpznPN+fATrAjG69EsA4PIwCKzsbht07S0aGu8VboLz8hgG9hguTOTiJ1jsK/qC8DzINPJvemzsce788g4NfvCVznTyLlxk8CSXuvLcWjDxL4Fc87i4KvAbSlbxiGYQ8drZvPAV4Uj3V8cE8sMKVu6xZP7qtM0k7CQRVPKstqbxmfyc7CubNPFUXnrzVXT68RySQvFFssbsd18g7pjYdvMOd1jm5/Lu810QXPIqOYTzRFoS8EjNQvI0P1TwcDzA9jJWkuSyjYTyEFQg8e2wVu6Q16ztrMxC9OeH6O5B1F7xGdh28HLJ3vBDiEjzba5A7WzN4u9aEEL1ZrYO5aHzquSscf7zyisK8SUjRvM3NIrxeOAS95ZFtuzCsijwVu4Q8a9sqPVZMLjtiCQs8PhU6PC6Lk7yG/ZO6goQmvQOh+7oNjr66EeKkvL5z5DtedG48+YmsuzVNArzreXy8PgNovOvCsLywjki9QQWMOsuZGTzSi5m8ZTZtvCuRhDxU/Ei974irvJOUGb3Cg4w8gMYivNNN5TmSTYU70t2DvCfdqLoFARA9Ibqtu0gPtrynjp67unkbvO3/MzwTHis8+0+Ru1omiLwVGGk7m9aGPK3AD73PzYU8Fs8PPLml1zwRWEC8PoEqOsxmorzRKho8fxULvQeVrDxPhYK8i9JqO40PEry5gC08lhOguwH2BTyZUWq8qyQ7vfWfbju78Fq80iPMPGifKr3l9SQ8GBYDPF1PGTuXc3W6kNEcu6BZZjwddG68TZxyu8dKxLk39eU8GOs4vS40mTyNudU8PF9ovDqtmjx/zZk85JQWPGy5YjyurQo6EkOkvN9sV7z3TQq9ds2/vGOA4Lx85De8quOUOo+QITwVDeQ7YQoBPJsaQ7spCz+8ReIYvAiK47wIIYi70n0/u812rLxFUJI89CojPZi2xDwj67S7OBt2PNebajy0NZE8cweWO8OqOrzjcKA8bMSuPAj67TtgL/g8dK3uPJFnZDwhrCu9HUxCvOrfxjvANpI80dZpPASlkrweMOS7VXSFvIvG0DykGV08zF1VPO+3Ajz7+dC8MGgHvcPKCD3HCv28O3WAPN6EILsw3I28zZJauhUC6Ls5AzI8xMWnvKlGgjss6ZA8KrvAu5GVsjrEiNU7X/1RPG72vbmXJuO7WU/suqUemzzT8Ja8kinyumapP7uYD/u7XsTJt6OqSbsF8y08Ye1evE3aujsqfWO8w/couYtkQjxHiJK8yC23uvXFcbyamZg8OQe3O75bMzwBDjg8GO+7PAPlkLw2Pke9eLVTu8YBS7xvUBO81pDNPJbAhDxq7iq7QAenvKxW9jzvism7G8j1PD0UJDyS8LK4mZoDvLaMw7w/fko7mTgPPIfOHTyrCQA9FTDvu5/iZLzncdi7vmgAPCvmmTxo7hO9HP7PvMTLBrxxA+c6Sd8KPM9LGjxx9cO6gVBQuxBQ47sVZ688jlhquo1Hj7w7t/m8mfkHPC0c1zvjW448lkhCPONyBDyVUzG8/qM3PWdYJ7303yy7hD+zOy8mRDuWGpc8fpzqOawkDb2jcZM8wTdkPBTh6zwxCgE9dFCOu2gZvrrpDmK82N1OvJulIz2/O8q81OdqvDtNrrt8tJu8EfMYvCIADr3kyi+8eHktvGBKRr3QwFi8GAetu44gFr1NHTu845HsujraUzsiQ+W6YlPAOxmgZbn0EM47I6fWu/ZN1TyydE+7ZeDWu7uEZDuMJUe7gOyRvCaNTzxttOW81K3Wuz4gNL1g/Wc8fr0XPRZUkjxYMj28tXnGOz/YDT3fM5i7pzjDO5rK2DrVJge9FGBaPPauhTxPxI28y2JFumBjDr1TTty8pIdKPIxBmrz6Ovs80/5VvJAPvLuNEgm96vBou94bFT0rmzU8RnSBvFTlhjwFSTW7lFcXPQkC5bsbifi8vQO2O7FgHL3/NF+8cJlyPIzvKzxkQ5q8oyIjvLc6iDx9weO8cgD7PP0rDjyTEbc80xLYvABpGryHFRA7S1kWvJvcabzg4Qw8Xs0fvG8jsTxyg307rUxWuy03eTsPuSM8ylh5vOIbkbwGfNO7Ve+xOyIh3rz67wc9qnITPZG/2Dzb0W07R+2aOjfShTxzPhe87p4ivJRpf7yniB+8VHIsPTa+o7yH+1U8DcRRPKqsFr0CPlk7ZM6wuwSWhLzwKJ28jcL+u8vIBTyTtWE8sg/GOwTu/zrlQG+8MpTNPJ51Gbz3l608qpdYPG2WcrwQXES8G5sPPC6jYjk54We6QNj3u9jyx7zCcJY79rXEvJpoXbxfUti5RN7kucKSrbyjCzC9o1DXPL3PizxTdFE7WN+fPLk3nzztISu8ljZ9PDsWPjxFeHI8/zsNPLayRbz4wcw7pDPPPAu0rTt23zO8d3s6O1CxOLyCpqS8uUeFuooaF7xLahY9aDnsu2Zw4rxTDs08W2zLOpk40zuSeYE8W3AovEZMnjyijQu8bFyZvNFlAb0RXFo7qPNKvNGb1LvMc5O8pO5pOvDQnDxoyrk83G6qvC91DT2DyRO72FqtO2edAD2NoC+8JLxjOkWnubynges8k3bouw3fC716OrO7rGRIu8J9kTyuVl89F8lEPGwUhDyFad88wS8IvZNqnzzCSLi8hwNXulTrG73Dfdq7LzuTvPkem7x7oH48zDvWuYpiyjnQyuK7drBhvEh3Wz2dQBS8Do5ZPK3IFDyLQKi8R9PfOQLe17x2DOg7Ts4UvSUnOD0BAiI81VNGvN1Diry0uIE84RAJPJN9/rrIGAa7B9uvO0mIyTzZbxy7AmgWPBzLOjwlhpk8ohWyvMHl4DuJ/dY7qkbaO4DfOTz8ZK48hMeHvM0uLrw+yAS8CuXsvJh5yjzLz8G8r8yGPGeaCL2A/zI7k83tvN8YnrwHvxu9D4uVPDwUkjt+qTY8/ZAivE+1RLwKs2W84orSO8FlJzyjsME7rPfvumsLMDtKLcG8q3yVPJsjFj0zBWa8i7zbvGrk6Twbj6K8MvI9PPQlbj29cpC7nNqeO5xhQ7y1z2C68dYIveoMk7xEJ/K6o8qKPEbhs7sScxa9ISyDPI090LyOhCM9q4WDPEOOqrwcjT887ge9O/CzVjxM2f+8gPkQvSs1Bb2SVaU87GYWuxa0j7wQrTc9TFhgPGa4xTv9Hss7kQOTvPzl7Dw6JDS9tBO9u0PhLDyu5o08Q7+DvMyB5rzVzZ28U4IjPWt0qzyrJZ+8g6VePOH2FzwQJ+g8lHanOxRBI7shXJg84qbVvPwA3byF4CE640LrO5Q/1zzx4Yw7l1jePNZMKbu46eE85u17ug8wQLt9iLC7G17bOpWPn7sImes8dLlgvPcXXLzYtNm8yI6QvBOkBbxdPPo7JySOu7R7yTtmLwi8BO6WvGeUDj1ZNZK8RC1KvNZFLDpFrT481sRpvAvY2bzno1k8dRmdvJgX57wUyqm8Alh2u0OLrjuOzzY7sTcnuprZU7tgM7q8czLluszO2zvl44+6KyopPGayArxrSZu8J6WEPF4K9jyyqIc71mcyPJATR7sBLRW8/+WSPFZ8l7tC3G+8jqQgPAtoJT1EP5i8b7kEPLXRAzv3lLE8Pb24POl387tNtzU9xeIvPdnsxzys2JM8VE3TO08VMTwVSm08D66VvKEb1DzFsAW7wKbpvJ8KOTy02Ac9WJ/4ODPhobsB3O486C7/OybhgTp8EHA8Okg/PNlDazouIbm8M0rMPJbJQ7u5dCM7qUbdOoLISrzSc4w8tHtBvOqSAz3RAZQ8wXQZvF/0S7zp8Bo8Nij3vBvORzydL4C8iGZLPH+9hrphjFS6NLqZvEluNrxnrNQ8vItwvPDi1LxUVgE9Toivu9XZAb1XaoI7ztEcvM63g7zZ0Io6Q8IoOxrBubzzRTc9gs49vVeltTxPfC2982VCO2VX1btedym7+2cvPQ1hpzyBjy86TEFXvGVjszwJgYa8bY4KPC+dYryukJ8819onuynziby0Qae7Dg8/PZnSM7yaOMW6HxeUO4+HLjwvcbg8ZXKGvCKnvTzvjYA8XjsFPfb1Dzz8/hs9LwHLPKT+8rwXiIM883cau0ejhjyc1zK8twmiPPlt3DvChYQ8UtNTva7JIbtV9Tu9cl8xOjk5+DxJL6u8mYgqvW+BULxHeFG7Eg7NPGmsjzvRyC68QLMKPMZf+Ly2gTW8DwG0OusWrDwcFYq7dEo7vEKs7rzhLXc9ED6KOwK3STxsW7U7rspLvc1qA7xkKHy7yFgvPaexvLyAwzE82pHgOnOvfryDxKa8ypftu1iTJTtqofy8Vg7pPFXt0jwK0Zu8gPCjPLPSajx3NjO8iyb/uzKOpTzHGBO8dUU0vYUcdLxno+W7AiXLu7831bwQBYG808VmO9rkvLmtn5I8Pt7SOwm6bzzlr1M82mEOO6jFMDwdncM8QwHQvKRkqjj+DZw7wpndvHFpirtbQ148SfdrvDIsVryt+707GXh0ubaz4TvkRru84LYRvJQ3ortD0wq9yrWCvD0esjs+agw7LmMLOyBXlrtPXR48PwENPMOYijz4zLo89E5IvMlGILwosT08FCsvOw==
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
         index: 0
         object: embedding
       model: qwen3-embedding:4b
       object: list
       usage:
-        prompt_tokens: 15
-        total_tokens: 15
+        prompt_tokens: 9
+        total_tokens: 9
     status:
       code: 200
       message: OK
@@ -379,7 +379,7 @@ interactions:
       connection:
       - keep-alive
       content-length:
-      - '2946'
+      - '2900'
       content-type:
       - application/json
       host:
@@ -420,14 +420,14 @@ interactions:
         role: user
       - content: |-
           <think>
-          We need to call gather_context on the main question.
+          Need to gather context.
           </think>
         role: assistant
         tool_calls:
         - function:
-            arguments: '{"limit":5,"query":"What is the highest count class in the DocLayNet dataset?"}'
+            arguments: '{"limit":null,"query":"DocLayNet dataset highest count class"}'
             name: gather_context
-          id: call_o696xo4a
+          id: call_4txvh12j
           type: function
       - content: |-
           DocLayNet defines 11 distinct class labels for document layout analysis:
@@ -450,7 +450,7 @@ interactions:
 
           Table - Tabular data
         role: tool
-        tool_call_id: call_o696xo4a
+        tool_call_id: call_4txvh12j
       model: gpt-oss
       reasoning_effort: low
       stream: false
@@ -493,7 +493,7 @@ interactions:
   response:
     headers:
       content-length:
-      - '902'
+      - '782'
       content-type:
       - application/json
     parsed_body:
@@ -501,26 +501,22 @@ interactions:
       - finish_reason: stop
         index: 0
         message:
-          content: |-
-            **Sub-questions (in priority order)**
-
-            1. In the official DocLayNet distribution, which class label has the largest number of annotated instances across all partitions (train, validation, test)?
-
-            2. What are the exact instance counts for each of the 11 class labels in the DocLayNet dataset as recorded in the public metadata files?
-
-            3. Is the class with the maximum instance count the same across all split phases (train, validation, and test) of DocLayNet, or does it vary by split?
-          reasoning: Now produce up to 3 sub-questions. The answer likely is "Caption" or "Picture". Provide questions to
-            verify.
+          content: "1. In the DocLayNet dataset, which of the 11 layout classes has the largest number of annotated instances?
+            \ \n2. What is the exact instance count for the class identified as having the highest occurrence in DocLayNet?
+            \ \n3. What percentage of the total annotated instances does this most frequent class represent in the DocLayNet
+            dataset?"
+          reasoning: 'Now produce 3 sub_questions, high value: 1) Which class has the highest number of instances? 2) exact
+            count for that class? 3) maybe proportion.'
           role: assistant
-      created: 1768997471
-      id: chatcmpl-785
+      created: 1769001271
+      id: chatcmpl-196
       model: gpt-oss
       object: chat.completion
       system_fingerprint: fp_ollama
       usage:
-        completion_tokens: 142
-        prompt_tokens: 584
-        total_tokens: 726
+        completion_tokens: 117
+        prompt_tokens: 572
+        total_tokens: 689
     status:
       code: 200
       message: OK
@@ -533,7 +529,7 @@ interactions:
       connection:
       - keep-alive
       content-length:
-      - '4351'
+      - '4029'
       content-type:
       - application/json
       host:
@@ -574,14 +570,14 @@ interactions:
         role: user
       - content: |-
           <think>
-          We need to call gather_context on the main question.
+          Need to gather context.
           </think>
         role: assistant
         tool_calls:
         - function:
-            arguments: '{"limit":5,"query":"What is the highest count class in the DocLayNet dataset?"}'
+            arguments: '{"limit":null,"query":"DocLayNet dataset highest count class"}'
             name: gather_context
-          id: call_o696xo4a
+          id: call_4txvh12j
           type: function
       - content: |-
           DocLayNet defines 11 distinct class labels for document layout analysis:
@@ -604,19 +600,12 @@ interactions:
 
           Table - Tabular data
         role: tool
-        tool_call_id: call_o696xo4a
-      - content: |-
-          <think>
-          Now produce up to 3 sub-questions. The answer likely is "Caption" or "Picture". Provide questions to verify.
-          </think>
-
-          **Sub-questions (in priority order)**
-
-          1. In the official DocLayNet distribution, which class label has the largest number of annotated instances across all partitions (train, validation, test)?
-
-          2. What are the exact instance counts for each of the 11 class labels in the DocLayNet dataset as recorded in the public metadata files?
-
-          3. Is the class with the maximum instance count the same across all split phases (train, validation, and test) of DocLayNet, or does it vary by split?
+        tool_call_id: call_4txvh12j
+      - content: "<think>\nNow produce 3 sub_questions, high value: 1) Which class has the highest number of instances? 2)
+          exact count for that class? 3) maybe proportion.\n</think>\n\n1. In the DocLayNet dataset, which of the 11 layout
+          classes has the largest number of annotated instances?  \n2. What is the exact instance count for the class identified
+          as having the highest occurrence in DocLayNet?  \n3. What percentage of the total annotated instances does this
+          most frequent class represent in the DocLayNet dataset?"
         role: assistant
       - content: |-
           1 validation error:
@@ -625,8 +614,8 @@ interactions:
             {
               "type": "json_invalid",
               "loc": [],
-              "msg": "Invalid JSON: expected value at line 1 column 1",
-              "input": "**Sub-questions (in priority order)**\n\n1. In the official DocLayNet distribution, which class label has the largest number of annotated instances across all partitions (train, validation, test)?\n\n2. What are the exact instance counts for each of the 11 class labels in the DocLayNet dataset as recorded in the public metadata files?\n\n3. Is the class with the maximum instance count the same across all split phases (train, validation, and test) of DocLayNet, or does it vary by split?"
+              "msg": "Invalid JSON: invalid number at line 1 column 3",
+              "input": "1. In the DocLayNet dataset, which of the 11 layout classes has the largest number of annotated instances?  \n2. What is the exact instance count for the class identified as having the highest occurrence in DocLayNet?  \n3. What percentage of the total annotated instances does this most frequent class represent in the DocLayNet dataset?"
             }
           ]
           ```
@@ -675,37 +664,29 @@ interactions:
   response:
     headers:
       content-length:
-      - '918'
+      - '706'
       content-type:
       - application/json
     parsed_body:
       choices:
-      - finish_reason: tool_calls
+      - finish_reason: stop
         index: 0
         message:
-          content: ''
-          reasoning: Need to output JSON object.
+          content: '{"sub_questions":["In the DocLayNet dataset, which of the 11 layout classes has the largest number of
+            annotated instances?","What is the exact instance count for the class identified as having the highest occurrence
+            in DocLayNet?","What percentage of the total annotated instances does this most frequent class represent in the
+            DocLayNet dataset?"]}'
+          reasoning: Need to return JSON with key sub_questions array.
           role: assistant
-          tool_calls:
-          - function:
-              arguments: '{"sub_questions":["In the official DocLayNet distribution, which class label has the largest number
-                of annotated instances across all partitions (train, validation, test)?","What are the exact instance counts
-                for each of the 11 class labels in the DocLayNet dataset as recorded in the public metadata files?","Is the
-                class with the maximum instance count the same across all split phases (train, validation, and test) of DocLayNet,
-                or does it vary by split?"]}'
-              name: final_result
-            id: call_3y8ea2um
-            index: 0
-            type: function
-      created: 1768997484
-      id: chatcmpl-213
+      created: 1769001274
+      id: chatcmpl-948
       model: gpt-oss
       object: chat.completion
       system_fingerprint: fp_ollama
       usage:
-        completion_tokens: 123
-        prompt_tokens: 902
-        total_tokens: 1025
+        completion_tokens: 102
+        prompt_tokens: 827
+        total_tokens: 929
     status:
       code: 200
       message: OK
@@ -718,7 +699,7 @@ interactions:
       connection:
       - keep-alive
       content-length:
-      - '2942'
+      - '2893'
       content-type:
       - application/json
       host:
@@ -771,8 +752,7 @@ interactions:
           - Be concise and direct; avoid meta commentary about the process.
           - Results are ordered by relevance, with rank 1 being most relevant.
         role: system
-      - content: In the official DocLayNet distribution, which class label has the largest number of annotated instances across
-          all partitions (train, validation, test)?
+      - content: In the DocLayNet dataset, which of the 11 layout classes has the largest number of annotated instances?
         role: user
       model: gpt-oss
       reasoning_effort: low
@@ -828,7 +808,7 @@ interactions:
   response:
     headers:
       content-length:
-      - '582'
+      - '554'
       content-type:
       - application/json
     parsed_body:
@@ -841,21 +821,20 @@ interactions:
           role: assistant
           tool_calls:
           - function:
-              arguments: '{"limit":5,"query":"DocLayNet distribution class label largest number of annotated instances across
-                all partitions train validation test"}'
+              arguments: '{"query":"DocLayNet dataset layout classes largest number of annotated instances 11 layout classes","limit":5}'
               name: search_and_answer
-            id: call_ij6twkqk
+            id: call_hl5bq575
             index: 0
             type: function
-      created: 1768997495
-      id: chatcmpl-150
+      created: 1769001275
+      id: chatcmpl-249
       model: gpt-oss
       object: chat.completion
       system_fingerprint: fp_ollama
       usage:
-        completion_tokens: 48
-        prompt_tokens: 643
-        total_tokens: 691
+        completion_tokens: 46
+        prompt_tokens: 637
+        total_tokens: 683
     status:
       code: 200
       message: OK
@@ -868,7 +847,7 @@ interactions:
       connection:
       - keep-alive
       content-length:
-      - '186'
+      - '158'
       content-type:
       - application/json
       host:
@@ -877,7 +856,7 @@ interactions:
     parsed_body:
       encoding_format: base64
       input:
-      - DocLayNet distribution class label largest number of annotated instances across all partitions train validation test
+      - DocLayNet dataset layout classes largest number of annotated instances 11 layout classes
       model: qwen3-embedding:4b
     uri: http://localhost:11434/v1/embeddings
   response:
@@ -888,14 +867,14 @@ interactions:
       - chunked
     parsed_body:
       data:
-      - embedding: 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
         index: 0
         object: embedding
       model: qwen3-embedding:4b
       object: list
       usage:
-        prompt_tokens: 19
-        total_tokens: 19
+        prompt_tokens: 18
+        total_tokens: 18
     status:
       code: 200
       message: OK
@@ -908,7 +887,7 @@ interactions:
       connection:
       - keep-alive
       content-length:
-      - '3880'
+      - '3803'
       content-type:
       - application/json
       host:
@@ -961,8 +940,7 @@ interactions:
           - Be concise and direct; avoid meta commentary about the process.
           - Results are ordered by relevance, with rank 1 being most relevant.
         role: system
-      - content: In the official DocLayNet distribution, which class label has the largest number of annotated instances across
-          all partitions (train, validation, test)?
+      - content: In the DocLayNet dataset, which of the 11 layout classes has the largest number of annotated instances?
         role: user
       - content: |-
           <think>
@@ -971,13 +949,12 @@ interactions:
         role: assistant
         tool_calls:
         - function:
-            arguments: '{"limit":5,"query":"DocLayNet distribution class label largest number of annotated instances across
-              all partitions train validation test"}'
+            arguments: '{"query":"DocLayNet dataset layout classes largest number of annotated instances 11 layout classes","limit":5}'
             name: search_and_answer
-          id: call_ij6twkqk
+          id: call_hl5bq575
           type: function
       - content: |-
-          [dbebd025-21b1-4b94-8ce4-c553f20a85ec] [rank 1 of 1]
+          [8bde9c2a-808a-4c65-8d7c-c9e2985b57fc] [rank 1 of 1]
           Source: "DocLayNet Class Labels"
           Type: list_item
           Content:
@@ -1001,7 +978,7 @@ interactions:
 
           Table - Tabular data
         role: tool
-        tool_call_id: call_ij6twkqk
+        tool_call_id: call_hl5bq575
       model: gpt-oss
       reasoning_effort: low
       stream: false
@@ -1056,271 +1033,7 @@ interactions:
   response:
     headers:
       content-length:
-      - '572'
-      content-type:
-      - application/json
-    parsed_body:
-      choices:
-      - finish_reason: tool_calls
-        index: 0
-        message:
-          content: ''
-          reasoning: The result lists labels but not counts. Need more search.
-          role: assistant
-          tool_calls:
-          - function:
-              arguments: '{"limit":5,"query":"DocLayNet annotation statistics number of instances per class"}'
-              name: search_and_answer
-            id: call_pc44qwhk
-            index: 0
-            type: function
-      created: 1768997501
-      id: chatcmpl-964
-      model: gpt-oss
-      object: chat.completion
-      system_fingerprint: fp_ollama
-      usage:
-        completion_tokens: 50
-        prompt_tokens: 842
-        total_tokens: 892
-    status:
-      code: 200
-      message: OK
-- request:
-    headers:
-      accept:
-      - application/json
-      accept-encoding:
-      - gzip, deflate, zstd
-      connection:
-      - keep-alive
-      content-length:
-      - '131'
-      content-type:
-      - application/json
-      host:
-      - localhost:11434
-    method: POST
-    parsed_body:
-      encoding_format: base64
-      input:
-      - DocLayNet annotation statistics number of instances per class
-      model: qwen3-embedding:4b
-    uri: http://localhost:11434/v1/embeddings
-  response:
-    headers:
-      content-type:
-      - application/json
-      transfer-encoding:
-      - chunked
-    parsed_body:
-      data:
-      - embedding: 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
-        index: 0
-        object: embedding
-      model: qwen3-embedding:4b
-      object: list
-      usage:
-        prompt_tokens: 12
-        total_tokens: 12
-    status:
-      code: 200
-      message: OK
-- request:
-    headers:
-      accept:
-      - application/json
-      accept-encoding:
-      - gzip, deflate, zstd
-      connection:
-      - keep-alive
-      content-length:
-      - '4808'
-      content-type:
-      - application/json
-      host:
-      - localhost:11434
-    method: POST
-    parsed_body:
-      messages:
-      - content: |-
-          You are a search and question-answering specialist.
-
-          Process:
-          1. Call search_and_answer with relevant keywords from the question.
-          2. Review the results ordered by relevance.
-          3. If needed, perform follow-up searches with different keywords (max 3 total).
-          4. Provide a concise answer based strictly on the retrieved content.
-
-          The search tool returns results like:
-          [9bde5847-44c9-400a-8997-0e6b65babf92] [rank 1 of 5]
-          Source: "Document Title" > Section > Subsection
-          Type: paragraph
-          Content:
-          The actual text content here...
-
-          [d5a63c82-cb40-439f-9b2e-de7d177829b7] [rank 2 of 5]
-          Source: "Another Document"
-          Type: table
-          Content:
-          | Column 1 | Column 2 |
-          ...
-
-          Each result includes:
-          - chunk_id in brackets and rank position (rank 1 = most relevant)
-          - Source: document title and section hierarchy (when available)
-          - Type: content type like paragraph, table, code, list_item (when available)
-          - Content: the actual text
-
-          Output format:
-          - query: Echo the question you are answering
-          - answer: Your concise answer based on the retrieved content
-          - cited_chunks: List of plain strings containing only the chunk UUIDs (not objects)
-          - confidence: A score from 0.0 to 1.0 indicating answer confidence
-
-          IMPORTANT: Use the EXACT, COMPLETE chunk ID (full UUID). Do NOT truncate IDs.
-
-          Guidelines:
-          - Base answers strictly on retrieved content - do not use external knowledge.
-          - Use the Source and Type metadata to understand context.
-          - If multiple results are relevant, synthesize them coherently.
-          - If information is insufficient, say so clearly.
-          - Be concise and direct; avoid meta commentary about the process.
-          - Results are ordered by relevance, with rank 1 being most relevant.
-        role: system
-      - content: In the official DocLayNet distribution, which class label has the largest number of annotated instances across
-          all partitions (train, validation, test)?
-        role: user
-      - content: |-
-          <think>
-          Need search.
-          </think>
-        role: assistant
-        tool_calls:
-        - function:
-            arguments: '{"limit":5,"query":"DocLayNet distribution class label largest number of annotated instances across
-              all partitions train validation test"}'
-            name: search_and_answer
-          id: call_ij6twkqk
-          type: function
-      - content: |-
-          [dbebd025-21b1-4b94-8ce4-c553f20a85ec] [rank 1 of 1]
-          Source: "DocLayNet Class Labels"
-          Type: list_item
-          Content:
-          DocLayNet defines 11 distinct class labels for document layout analysis:
-
-          Caption - Text describing figures or tables
-
-          Footnote - Notes at the bottom of pages
-
-          Formula - Mathematical expressions
-
-          List-item - Items in bulleted or numbered lists
-
-          Page-footer - Footer content on pages
-
-          Page-header - Header content on pages
-
-          Picture - Images and diagrams
-
-          Section-header - Headings for document sections
-
-          Table - Tabular data
-        role: tool
-        tool_call_id: call_ij6twkqk
-      - content: |-
-          <think>
-          The result lists labels but not counts. Need more search.
-          </think>
-        role: assistant
-        tool_calls:
-        - function:
-            arguments: '{"limit":5,"query":"DocLayNet annotation statistics number of instances per class"}'
-            name: search_and_answer
-          id: call_pc44qwhk
-          type: function
-      - content: |-
-          [dbebd025-21b1-4b94-8ce4-c553f20a85ec] [rank 1 of 1]
-          Source: "DocLayNet Class Labels"
-          Type: list_item
-          Content:
-          DocLayNet defines 11 distinct class labels for document layout analysis:
-
-          Caption - Text describing figures or tables
-
-          Footnote - Notes at the bottom of pages
-
-          Formula - Mathematical expressions
-
-          List-item - Items in bulleted or numbered lists
-
-          Page-footer - Footer content on pages
-
-          Page-header - Header content on pages
-
-          Picture - Images and diagrams
-
-          Section-header - Headings for document sections
-
-          Table - Tabular data
-        role: tool
-        tool_call_id: call_pc44qwhk
-      model: gpt-oss
-      reasoning_effort: low
-      stream: false
-      tool_choice: auto
-      tools:
-      - function:
-          description: Search the knowledge base for relevant documents.
-          name: search_and_answer
-          parameters:
-            additionalProperties: false
-            properties:
-              limit:
-                anyOf:
-                - type: integer
-                - type: 'null'
-                default: null
-              query:
-                type: string
-            required:
-            - query
-            type: object
-        type: function
-      - function:
-          description: Answer to a search query with chunk references.
-          name: final_result
-          parameters:
-            additionalProperties: false
-            properties:
-              answer:
-                description: The answer to the question
-                type: string
-              cited_chunks:
-                description: IDs of chunks used to form the answer
-                items:
-                  type: string
-                type: array
-              confidence:
-                default: 1.0
-                description: Confidence score for this answer (0-1)
-                maximum: 1.0
-                minimum: 0.0
-                type: number
-              query:
-                description: The question that was answered
-                type: string
-            required:
-            - query
-            - answer
-            type: object
-        type: function
-    uri: http://localhost:11434/v1/chat/completions
-  response:
-    headers:
-      content-length:
-      - '370'
+      - '804'
       content-type:
       - application/json
     parsed_body:
@@ -1328,2198 +1041,24 @@ interactions:
       - finish_reason: stop
         index: 0
         message:
-          content: I couldn’t locate the quantitative counts for each class in the distribution data.
+          content: |-
+            **query:** In the DocLayNet dataset, which of the 11 layout classes has the largest number of annotated instances?
+
+            **answer:** The DocLayNet dataset does not provide, within the publicly available documentation, any enumeration or comparison of the number of annotated instances per class. Consequently, it is not possible to determine from the retrieved information which of the 11 layout classes has the largest instance count.
+
+            **cited_chunks:** ["8bde9c2a-808a-4c65-8d7c-c9e2985b57fc"]
+
+            **confidence:** 0.2
           role: assistant
-      created: 1768997505
-      id: chatcmpl-405
-      model: gpt-oss
-      object: chat.completion
-      system_fingerprint: fp_ollama
-      usage:
-        completion_tokens: 19
-        prompt_tokens: 1043
-        total_tokens: 1062
-    status:
-      code: 200
-      message: OK
-- request:
-    headers:
-      accept:
-      - application/json
-      accept-encoding:
-      - gzip, deflate, zstd
-      connection:
-      - keep-alive
-      content-length:
-      - '5054'
-      content-type:
-      - application/json
-      host:
-      - localhost:11434
-    method: POST
-    parsed_body:
-      messages:
-      - content: |-
-          You are a search and question-answering specialist.
-
-          Process:
-          1. Call search_and_answer with relevant keywords from the question.
-          2. Review the results ordered by relevance.
-          3. If needed, perform follow-up searches with different keywords (max 3 total).
-          4. Provide a concise answer based strictly on the retrieved content.
-
-          The search tool returns results like:
-          [9bde5847-44c9-400a-8997-0e6b65babf92] [rank 1 of 5]
-          Source: "Document Title" > Section > Subsection
-          Type: paragraph
-          Content:
-          The actual text content here...
-
-          [d5a63c82-cb40-439f-9b2e-de7d177829b7] [rank 2 of 5]
-          Source: "Another Document"
-          Type: table
-          Content:
-          | Column 1 | Column 2 |
-          ...
-
-          Each result includes:
-          - chunk_id in brackets and rank position (rank 1 = most relevant)
-          - Source: document title and section hierarchy (when available)
-          - Type: content type like paragraph, table, code, list_item (when available)
-          - Content: the actual text
-
-          Output format:
-          - query: Echo the question you are answering
-          - answer: Your concise answer based on the retrieved content
-          - cited_chunks: List of plain strings containing only the chunk UUIDs (not objects)
-          - confidence: A score from 0.0 to 1.0 indicating answer confidence
-
-          IMPORTANT: Use the EXACT, COMPLETE chunk ID (full UUID). Do NOT truncate IDs.
-
-          Guidelines:
-          - Base answers strictly on retrieved content - do not use external knowledge.
-          - Use the Source and Type metadata to understand context.
-          - If multiple results are relevant, synthesize them coherently.
-          - If information is insufficient, say so clearly.
-          - Be concise and direct; avoid meta commentary about the process.
-          - Results are ordered by relevance, with rank 1 being most relevant.
-        role: system
-      - content: In the official DocLayNet distribution, which class label has the largest number of annotated instances across
-          all partitions (train, validation, test)?
-        role: user
-      - content: |-
-          <think>
-          Need search.
-          </think>
-        role: assistant
-        tool_calls:
-        - function:
-            arguments: '{"limit":5,"query":"DocLayNet distribution class label largest number of annotated instances across
-              all partitions train validation test"}'
-            name: search_and_answer
-          id: call_ij6twkqk
-          type: function
-      - content: |-
-          [dbebd025-21b1-4b94-8ce4-c553f20a85ec] [rank 1 of 1]
-          Source: "DocLayNet Class Labels"
-          Type: list_item
-          Content:
-          DocLayNet defines 11 distinct class labels for document layout analysis:
-
-          Caption - Text describing figures or tables
-
-          Footnote - Notes at the bottom of pages
-
-          Formula - Mathematical expressions
-
-          List-item - Items in bulleted or numbered lists
-
-          Page-footer - Footer content on pages
-
-          Page-header - Header content on pages
-
-          Picture - Images and diagrams
-
-          Section-header - Headings for document sections
-
-          Table - Tabular data
-        role: tool
-        tool_call_id: call_ij6twkqk
-      - content: |-
-          <think>
-          The result lists labels but not counts. Need more search.
-          </think>
-        role: assistant
-        tool_calls:
-        - function:
-            arguments: '{"limit":5,"query":"DocLayNet annotation statistics number of instances per class"}'
-            name: search_and_answer
-          id: call_pc44qwhk
-          type: function
-      - content: |-
-          [dbebd025-21b1-4b94-8ce4-c553f20a85ec] [rank 1 of 1]
-          Source: "DocLayNet Class Labels"
-          Type: list_item
-          Content:
-          DocLayNet defines 11 distinct class labels for document layout analysis:
-
-          Caption - Text describing figures or tables
-
-          Footnote - Notes at the bottom of pages
-
-          Formula - Mathematical expressions
-
-          List-item - Items in bulleted or numbered lists
-
-          Page-footer - Footer content on pages
-
-          Page-header - Header content on pages
-
-          Picture - Images and diagrams
-
-          Section-header - Headings for document sections
-
-          Table - Tabular data
-        role: tool
-        tool_call_id: call_pc44qwhk
-      - content: I couldn’t locate the quantitative counts for each class in the distribution data.
-        role: assistant
-      - content: |-
-          Validation feedback:
-          Please include your response in a tool call.
-
-          Fix the errors and try again.
-        role: user
-      model: gpt-oss
-      reasoning_effort: low
-      stream: false
-      tool_choice: auto
-      tools:
-      - function:
-          description: Search the knowledge base for relevant documents.
-          name: search_and_answer
-          parameters:
-            additionalProperties: false
-            properties:
-              limit:
-                anyOf:
-                - type: integer
-                - type: 'null'
-                default: null
-              query:
-                type: string
-            required:
-            - query
-            type: object
-        type: function
-      - function:
-          description: Answer to a search query with chunk references.
-          name: final_result
-          parameters:
-            additionalProperties: false
-            properties:
-              answer:
-                description: The answer to the question
-                type: string
-              cited_chunks:
-                description: IDs of chunks used to form the answer
-                items:
-                  type: string
-                type: array
-              confidence:
-                default: 1.0
-                description: Confidence score for this answer (0-1)
-                maximum: 1.0
-                minimum: 0.0
-                type: number
-              query:
-                description: The question that was answered
-                type: string
-            required:
-            - query
-            - answer
-            type: object
-        type: function
-    uri: http://localhost:11434/v1/chat/completions
-  response:
-    headers:
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-      content-type:
-      - application/json
-    parsed_body:
-      choices:
-      - finish_reason: tool_calls
-        index: 0
-        message:
-          content: ''
-          reasoning: Need to output via final_result.
-          role: assistant
-          tool_calls:
-          - function:
-              arguments: '{"answer":"I couldn’t locate the quantitative counts for each class in the distribution data.","cited_chunks":[],"confidence":0.5,"query":"In
-                the official DocLayNet distribution, which class label has the largest number of annotated instances across
-                all partitions (train, validation, test)?"}'
-              name: final_result
-            id: call_uxrsa1c7
-            index: 0
-            type: function
-      created: 1768997507
-      id: chatcmpl-42
-      model: gpt-oss
-      object: chat.completion
-      system_fingerprint: fp_ollama
-      usage:
-        completion_tokens: 86
-        prompt_tokens: 1087
-        total_tokens: 1173
-    status:
-      code: 200
-      message: OK
-- request:
-    headers:
-      accept:
-      - application/json
-      accept-encoding:
-      - gzip, deflate, zstd
-      connection:
-      - keep-alive
-      content-length:
-      - '2923'
-      content-type:
-      - application/json
-      host:
-      - localhost:11434
-    method: POST
-    parsed_body:
-      messages:
-      - content: |-
-          You are a search and question-answering specialist.
-
-          Process:
-          1. Call search_and_answer with relevant keywords from the question.
-          2. Review the results ordered by relevance.
-          3. If needed, perform follow-up searches with different keywords (max 3 total).
-          4. Provide a concise answer based strictly on the retrieved content.
-
-          The search tool returns results like:
-          [9bde5847-44c9-400a-8997-0e6b65babf92] [rank 1 of 5]
-          Source: "Document Title" > Section > Subsection
-          Type: paragraph
-          Content:
-          The actual text content here...
-
-          [d5a63c82-cb40-439f-9b2e-de7d177829b7] [rank 2 of 5]
-          Source: "Another Document"
-          Type: table
-          Content:
-          | Column 1 | Column 2 |
-          ...
-
-          Each result includes:
-          - chunk_id in brackets and rank position (rank 1 = most relevant)
-          - Source: document title and section hierarchy (when available)
-          - Type: content type like paragraph, table, code, list_item (when available)
-          - Content: the actual text
-
-          Output format:
-          - query: Echo the question you are answering
-          - answer: Your concise answer based on the retrieved content
-          - cited_chunks: List of plain strings containing only the chunk UUIDs (not objects)
-          - confidence: A score from 0.0 to 1.0 indicating answer confidence
-
-          IMPORTANT: Use the EXACT, COMPLETE chunk ID (full UUID). Do NOT truncate IDs.
-
-          Guidelines:
-          - Base answers strictly on retrieved content - do not use external knowledge.
-          - Use the Source and Type metadata to understand context.
-          - If multiple results are relevant, synthesize them coherently.
-          - If information is insufficient, say so clearly.
-          - Be concise and direct; avoid meta commentary about the process.
-          - Results are ordered by relevance, with rank 1 being most relevant.
-        role: system
-      - content: What are the exact instance counts for each of the 11 class labels in the DocLayNet dataset as recorded in
-          the public metadata files?
-        role: user
-      model: gpt-oss
-      reasoning_effort: low
-      stream: false
-      tool_choice: auto
-      tools:
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-          description: Search the knowledge base for relevant documents.
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-          parameters:
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-            properties:
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-                - type: integer
-                - type: 'null'
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-              query:
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-            properties:
-              answer:
-                description: The answer to the question
-                type: string
-              cited_chunks:
-                description: IDs of chunks used to form the answer
-                items:
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-                type: array
-              confidence:
-                default: 1.0
-                description: Confidence score for this answer (0-1)
-                maximum: 1.0
-                minimum: 0.0
-                type: number
-              query:
-                description: The question that was answered
-                type: string
-            required:
-            - query
-            - answer
-            type: object
-        type: function
-    uri: http://localhost:11434/v1/chat/completions
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-      - application/json
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-      choices:
-      - finish_reason: tool_calls
-        index: 0
-        message:
-          content: ''
-          reasoning: Need search.
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-              name: search_and_answer
-            id: call_imywt5yz
-            index: 0
-            type: function
-      created: 1768997509
-      id: chatcmpl-991
-      model: gpt-oss
-      object: chat.completion
-      system_fingerprint: fp_ollama
-      usage:
-        completion_tokens: 45
-        prompt_tokens: 643
-        total_tokens: 688
-    status:
-      code: 200
-      message: OK
-- request:
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-      - application/json
-      accept-encoding:
-      - gzip, deflate, zstd
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-      - application/json
-      host:
-      - localhost:11434
-    method: POST
-    parsed_body:
-      encoding_format: base64
-      input:
-      - DocLayNet instance counts 11 class labels public metadata files
-      model: qwen3-embedding:4b
-    uri: http://localhost:11434/v1/embeddings
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-    headers:
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-      - application/json
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
-        index: 0
-        object: embedding
-      model: qwen3-embedding:4b
-      object: list
-      usage:
-        prompt_tokens: 15
-        total_tokens: 15
-    status:
-      code: 200
-      message: OK
-- request:
-    headers:
-      accept:
-      - application/json
-      accept-encoding:
-      - gzip, deflate, zstd
-      connection:
-      - keep-alive
-      content-length:
-      - '3811'
-      content-type:
-      - application/json
-      host:
-      - localhost:11434
-    method: POST
-    parsed_body:
-      messages:
-      - content: |-
-          You are a search and question-answering specialist.
-
-          Process:
-          1. Call search_and_answer with relevant keywords from the question.
-          2. Review the results ordered by relevance.
-          3. If needed, perform follow-up searches with different keywords (max 3 total).
-          4. Provide a concise answer based strictly on the retrieved content.
-
-          The search tool returns results like:
-          [9bde5847-44c9-400a-8997-0e6b65babf92] [rank 1 of 5]
-          Source: "Document Title" > Section > Subsection
-          Type: paragraph
-          Content:
-          The actual text content here...
-
-          [d5a63c82-cb40-439f-9b2e-de7d177829b7] [rank 2 of 5]
-          Source: "Another Document"
-          Type: table
-          Content:
-          | Column 1 | Column 2 |
-          ...
-
-          Each result includes:
-          - chunk_id in brackets and rank position (rank 1 = most relevant)
-          - Source: document title and section hierarchy (when available)
-          - Type: content type like paragraph, table, code, list_item (when available)
-          - Content: the actual text
-
-          Output format:
-          - query: Echo the question you are answering
-          - answer: Your concise answer based on the retrieved content
-          - cited_chunks: List of plain strings containing only the chunk UUIDs (not objects)
-          - confidence: A score from 0.0 to 1.0 indicating answer confidence
-
-          IMPORTANT: Use the EXACT, COMPLETE chunk ID (full UUID). Do NOT truncate IDs.
-
-          Guidelines:
-          - Base answers strictly on retrieved content - do not use external knowledge.
-          - Use the Source and Type metadata to understand context.
-          - If multiple results are relevant, synthesize them coherently.
-          - If information is insufficient, say so clearly.
-          - Be concise and direct; avoid meta commentary about the process.
-          - Results are ordered by relevance, with rank 1 being most relevant.
-        role: system
-      - content: What are the exact instance counts for each of the 11 class labels in the DocLayNet dataset as recorded in
-          the public metadata files?
-        role: user
-      - content: |-
-          <think>
-          Need search.
-          </think>
-        role: assistant
-        tool_calls:
-        - function:
-            arguments: '{"limit":null,"query":"DocLayNet instance counts 11 class labels public metadata files"}'
-            name: search_and_answer
-          id: call_imywt5yz
-          type: function
-      - content: |-
-          [dbebd025-21b1-4b94-8ce4-c553f20a85ec] [rank 1 of 1]
-          Source: "DocLayNet Class Labels"
-          Type: list_item
-          Content:
-          DocLayNet defines 11 distinct class labels for document layout analysis:
-
-          Caption - Text describing figures or tables
-
-          Footnote - Notes at the bottom of pages
-
-          Formula - Mathematical expressions
-
-          List-item - Items in bulleted or numbered lists
-
-          Page-footer - Footer content on pages
-
-          Page-header - Header content on pages
-
-          Picture - Images and diagrams
-
-          Section-header - Headings for document sections
-
-          Table - Tabular data
-        role: tool
-        tool_call_id: call_imywt5yz
-      model: gpt-oss
-      reasoning_effort: low
-      stream: false
-      tool_choice: auto
-      tools:
-      - function:
-          description: Search the knowledge base for relevant documents.
-          name: search_and_answer
-          parameters:
-            additionalProperties: false
-            properties:
-              limit:
-                anyOf:
-                - type: integer
-                - type: 'null'
-                default: null
-              query:
-                type: string
-            required:
-            - query
-            type: object
-        type: function
-      - function:
-          description: Answer to a search query with chunk references.
-          name: final_result
-          parameters:
-            additionalProperties: false
-            properties:
-              answer:
-                description: The answer to the question
-                type: string
-              cited_chunks:
-                description: IDs of chunks used to form the answer
-                items:
-                  type: string
-                type: array
-              confidence:
-                default: 1.0
-                description: Confidence score for this answer (0-1)
-                maximum: 1.0
-                minimum: 0.0
-                type: number
-              query:
-                description: The question that was answered
-                type: string
-            required:
-            - query
-            - answer
-            type: object
-        type: function
-    uri: http://localhost:11434/v1/chat/completions
-  response:
-    headers:
-      content-length:
-      - '532'
-      content-type:
-      - application/json
-    parsed_body:
-      choices:
-      - finish_reason: tool_calls
-        index: 0
-        message:
-          content: ''
-          reasoning: No counts. need another query.
-          role: assistant
-          tool_calls:
-          - function:
-              arguments: '{"limit":null,"query":"DocLayNet metadata instance counts 11 classes"}'
-              name: search_and_answer
-            id: call_c4x5ekef
-            index: 0
-            type: function
-      created: 1768997511
-      id: chatcmpl-876
-      model: gpt-oss
-      object: chat.completion
-      system_fingerprint: fp_ollama
-      usage:
-        completion_tokens: 44
-        prompt_tokens: 837
-        total_tokens: 881
-    status:
-      code: 200
-      message: OK
-- request:
-    headers:
-      accept:
-      - application/json
-      accept-encoding:
-      - gzip, deflate, zstd
-      connection:
-      - keep-alive
-      content-length:
-      - '115'
-      content-type:
-      - application/json
-      host:
-      - localhost:11434
-    method: POST
-    parsed_body:
-      encoding_format: base64
-      input:
-      - DocLayNet metadata instance counts 11 classes
-      model: qwen3-embedding:4b
-    uri: http://localhost:11434/v1/embeddings
-  response:
-    headers:
-      content-type:
-      - application/json
-      transfer-encoding:
-      - chunked
-    parsed_body:
-      data:
-      - embedding: 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
-        index: 0
-        object: embedding
-      model: qwen3-embedding:4b
-      object: list
-      usage:
-        prompt_tokens: 12
-        total_tokens: 12
-    status:
-      code: 200
-      message: OK
-- request:
-    headers:
-      accept:
-      - application/json
-      accept-encoding:
-      - gzip, deflate, zstd
-      connection:
-      - keep-alive
-      content-length:
-      - '4699'
-      content-type:
-      - application/json
-      host:
-      - localhost:11434
-    method: POST
-    parsed_body:
-      messages:
-      - content: |-
-          You are a search and question-answering specialist.
-
-          Process:
-          1. Call search_and_answer with relevant keywords from the question.
-          2. Review the results ordered by relevance.
-          3. If needed, perform follow-up searches with different keywords (max 3 total).
-          4. Provide a concise answer based strictly on the retrieved content.
-
-          The search tool returns results like:
-          [9bde5847-44c9-400a-8997-0e6b65babf92] [rank 1 of 5]
-          Source: "Document Title" > Section > Subsection
-          Type: paragraph
-          Content:
-          The actual text content here...
-
-          [d5a63c82-cb40-439f-9b2e-de7d177829b7] [rank 2 of 5]
-          Source: "Another Document"
-          Type: table
-          Content:
-          | Column 1 | Column 2 |
-          ...
-
-          Each result includes:
-          - chunk_id in brackets and rank position (rank 1 = most relevant)
-          - Source: document title and section hierarchy (when available)
-          - Type: content type like paragraph, table, code, list_item (when available)
-          - Content: the actual text
-
-          Output format:
-          - query: Echo the question you are answering
-          - answer: Your concise answer based on the retrieved content
-          - cited_chunks: List of plain strings containing only the chunk UUIDs (not objects)
-          - confidence: A score from 0.0 to 1.0 indicating answer confidence
-
-          IMPORTANT: Use the EXACT, COMPLETE chunk ID (full UUID). Do NOT truncate IDs.
-
-          Guidelines:
-          - Base answers strictly on retrieved content - do not use external knowledge.
-          - Use the Source and Type metadata to understand context.
-          - If multiple results are relevant, synthesize them coherently.
-          - If information is insufficient, say so clearly.
-          - Be concise and direct; avoid meta commentary about the process.
-          - Results are ordered by relevance, with rank 1 being most relevant.
-        role: system
-      - content: What are the exact instance counts for each of the 11 class labels in the DocLayNet dataset as recorded in
-          the public metadata files?
-        role: user
-      - content: |-
-          <think>
-          Need search.
-          </think>
-        role: assistant
-        tool_calls:
-        - function:
-            arguments: '{"limit":null,"query":"DocLayNet instance counts 11 class labels public metadata files"}'
-            name: search_and_answer
-          id: call_imywt5yz
-          type: function
-      - content: |-
-          [dbebd025-21b1-4b94-8ce4-c553f20a85ec] [rank 1 of 1]
-          Source: "DocLayNet Class Labels"
-          Type: list_item
-          Content:
-          DocLayNet defines 11 distinct class labels for document layout analysis:
-
-          Caption - Text describing figures or tables
-
-          Footnote - Notes at the bottom of pages
-
-          Formula - Mathematical expressions
-
-          List-item - Items in bulleted or numbered lists
-
-          Page-footer - Footer content on pages
-
-          Page-header - Header content on pages
-
-          Picture - Images and diagrams
-
-          Section-header - Headings for document sections
-
-          Table - Tabular data
-        role: tool
-        tool_call_id: call_imywt5yz
-      - content: |-
-          <think>
-          No counts. need another query.
-          </think>
-        role: assistant
-        tool_calls:
-        - function:
-            arguments: '{"limit":null,"query":"DocLayNet metadata instance counts 11 classes"}'
-            name: search_and_answer
-          id: call_c4x5ekef
-          type: function
-      - content: |-
-          [dbebd025-21b1-4b94-8ce4-c553f20a85ec] [rank 1 of 1]
-          Source: "DocLayNet Class Labels"
-          Type: list_item
-          Content:
-          DocLayNet defines 11 distinct class labels for document layout analysis:
-
-          Caption - Text describing figures or tables
-
-          Footnote - Notes at the bottom of pages
-
-          Formula - Mathematical expressions
-
-          List-item - Items in bulleted or numbered lists
-
-          Page-footer - Footer content on pages
-
-          Page-header - Header content on pages
-
-          Picture - Images and diagrams
-
-          Section-header - Headings for document sections
-
-          Table - Tabular data
-        role: tool
-        tool_call_id: call_c4x5ekef
-      model: gpt-oss
-      reasoning_effort: low
-      stream: false
-      tool_choice: auto
-      tools:
-      - function:
-          description: Search the knowledge base for relevant documents.
-          name: search_and_answer
-          parameters:
-            additionalProperties: false
-            properties:
-              limit:
-                anyOf:
-                - type: integer
-                - type: 'null'
-                default: null
-              query:
-                type: string
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-          - Be concise and direct; avoid meta commentary about the process.
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-      - content: What are the exact instance counts for each of the 11 class labels in the DocLayNet dataset as recorded in
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-        role: user
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-            name: search_and_answer
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-          type: function
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-          Caption - Text describing figures or tables
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-          Footnote - Notes at the bottom of pages
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-          Formula - Mathematical expressions
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-          List-item - Items in bulleted or numbered lists
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-          Page-footer - Footer content on pages
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-          Page-header - Header content on pages
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-          Table - Tabular data
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-            name: search_and_answer
-          id: call_c4x5ekef
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-          [dbebd025-21b1-4b94-8ce4-c553f20a85ec] [rank 1 of 1]
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-        role: tool
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-      - content: |-
-          <think>
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-        role: assistant
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-            name: search_and_answer
-          id: call_c4x5ekef
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-      - content: |-
-          [dbebd025-21b1-4b94-8ce4-c553f20a85ec] [rank 1 of 1]
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-          Footnote - Notes at the bottom of pages
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-          Formula - Mathematical expressions
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-          List-item - Items in bulleted or numbered lists
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-          Page-footer - Footer content on pages
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
-        index: 0
-        object: embedding
-      model: qwen3-embedding:4b
-      object: list
-      usage:
-        prompt_tokens: 15
-        total_tokens: 15
-    status:
-      code: 200
-      message: OK
-- request:
-    headers:
-      accept:
-      - application/json
-      accept-encoding:
-      - gzip, deflate, zstd
-      connection:
-      - keep-alive
-      content-length:
-      - '3836'
-      content-type:
-      - application/json
-      host:
-      - localhost:11434
-    method: POST
-    parsed_body:
-      messages:
-      - content: |-
-          You are a search and question-answering specialist.
-
-          Process:
-          1. Call search_and_answer with relevant keywords from the question.
-          2. Review the results ordered by relevance.
-          3. If needed, perform follow-up searches with different keywords (max 3 total).
-          4. Provide a concise answer based strictly on the retrieved content.
-
-          The search tool returns results like:
-          [9bde5847-44c9-400a-8997-0e6b65babf92] [rank 1 of 5]
-          Source: "Document Title" > Section > Subsection
-          Type: paragraph
-          Content:
-          The actual text content here...
-
-          [d5a63c82-cb40-439f-9b2e-de7d177829b7] [rank 2 of 5]
-          Source: "Another Document"
-          Type: table
-          Content:
-          | Column 1 | Column 2 |
-          ...
-
-          Each result includes:
-          - chunk_id in brackets and rank position (rank 1 = most relevant)
-          - Source: document title and section hierarchy (when available)
-          - Type: content type like paragraph, table, code, list_item (when available)
-          - Content: the actual text
-
-          Output format:
-          - query: Echo the question you are answering
-          - answer: Your concise answer based on the retrieved content
-          - cited_chunks: List of plain strings containing only the chunk UUIDs (not objects)
-          - confidence: A score from 0.0 to 1.0 indicating answer confidence
-
-          IMPORTANT: Use the EXACT, COMPLETE chunk ID (full UUID). Do NOT truncate IDs.
-
-          Guidelines:
-          - Base answers strictly on retrieved content - do not use external knowledge.
-          - Use the Source and Type metadata to understand context.
-          - If multiple results are relevant, synthesize them coherently.
-          - If information is insufficient, say so clearly.
-          - Be concise and direct; avoid meta commentary about the process.
-          - Results are ordered by relevance, with rank 1 being most relevant.
-        role: system
-      - content: Is the class with the maximum instance count the same across all split phases (train, validation, and test)
-          of DocLayNet, or does it vary by split?
-        role: user
-      - content: |-
-          <think>
-          Need search.
-          </think>
-        role: assistant
-        tool_calls:
-        - function:
-            arguments: '{"query":"DocLayNet class maximum instance count split train validation test same class","limit":5}'
-            name: search_and_answer
-          id: call_lnz8uutd
-          type: function
-      - content: |-
-          [dbebd025-21b1-4b94-8ce4-c553f20a85ec] [rank 1 of 1]
-          Source: "DocLayNet Class Labels"
-          Type: list_item
-          Content:
-          DocLayNet defines 11 distinct class labels for document layout analysis:
-
-          Caption - Text describing figures or tables
-
-          Footnote - Notes at the bottom of pages
-
-          Formula - Mathematical expressions
-
-          List-item - Items in bulleted or numbered lists
-
-          Page-footer - Footer content on pages
-
-          Page-header - Header content on pages
-
-          Picture - Images and diagrams
-
-          Section-header - Headings for document sections
-
-          Table - Tabular data
-        role: tool
-        tool_call_id: call_lnz8uutd
-      model: gpt-oss
-      reasoning_effort: low
-      stream: false
-      tool_choice: auto
-      tools:
-      - function:
-          description: Search the knowledge base for relevant documents.
-          name: search_and_answer
-          parameters:
-            additionalProperties: false
-            properties:
-              limit:
-                anyOf:
-                - type: integer
-                - type: 'null'
-                default: null
-              query:
-                type: string
-            required:
-            - query
-            type: object
-        type: function
-      - function:
-          description: Answer to a search query with chunk references.
-          name: final_result
-          parameters:
-            additionalProperties: false
-            properties:
-              answer:
-                description: The answer to the question
-                type: string
-              cited_chunks:
-                description: IDs of chunks used to form the answer
-                items:
-                  type: string
-                type: array
-              confidence:
-                default: 1.0
-                description: Confidence score for this answer (0-1)
-                maximum: 1.0
-                minimum: 0.0
-                type: number
-              query:
-                description: The question that was answered
-                type: string
-            required:
-            - query
-            - answer
-            type: object
-        type: function
-    uri: http://localhost:11434/v1/chat/completions
-  response:
-    headers:
-      content-length:
-      - '560'
-      content-type:
-      - application/json
-    parsed_body:
-      choices:
-      - finish_reason: tool_calls
-        index: 0
-        message:
-          content: ''
-          reasoning: No info on counts per split. Need another search.
-          role: assistant
-          tool_calls:
-          - function:
-              arguments: '{"limit":5,"query":"DocLayNet train validation test instance counts per class"}'
-              name: search_and_answer
-            id: call_2u0xuflc
-            index: 0
-            type: function
-      created: 1768997523
-      id: chatcmpl-922
-      model: gpt-oss
-      object: chat.completion
-      system_fingerprint: fp_ollama
-      usage:
-        completion_tokens: 49
-        prompt_tokens: 844
-        total_tokens: 893
-    status:
-      code: 200
-      message: OK
-- request:
-    headers:
-      accept:
-      - application/json
-      accept-encoding:
-      - gzip, deflate, zstd
-      connection:
-      - keep-alive
-      content-length:
-      - '127'
-      content-type:
-      - application/json
-      host:
-      - localhost:11434
-    method: POST
-    parsed_body:
-      encoding_format: base64
-      input:
-      - DocLayNet train validation test instance counts per class
-      model: qwen3-embedding:4b
-    uri: http://localhost:11434/v1/embeddings
-  response:
-    headers:
-      content-type:
-      - application/json
-      transfer-encoding:
-      - chunked
-    parsed_body:
-      data:
-      - embedding: VHlJuesdITucRB075bsgPac3Q7pXK449pJcbPcZ6UTya9qQ8HJ68vNnllTzphns8v5InuQYvS7qXYxC9xodVvTswID0LYz28QWxEu4UYnrvWdA+82w9NPM1zizyyi+I8kv+cvPQBFb1RBrW8yrU1vKJqpLmdKRw9BucCuxQGCr0rMbY8FxAHvLgFkjsvA5O8oW57vKFuRbsq4Xq7qcWJvQkDODwirA47Z5RcO7l7zDsvvXO7pmj+u4eadTyGFs28qj0fvdV3+7ltuZY7lhdZPHnDT7zNe6W8Jq1UPW8i87sH9cM8pv62u1GuCLxCccY8p3XSu+1UBbufYiS84HVovIizEbzhDoK8O0PCPBV5DrwlQJ08Cygeu5oT4byTyQK8iIOJvPLSIjzOm/M8huv4vJIuOrwO45A80ppmvHX0szyTeGg8XyOku0YGortpniQ98bWFuwuOOLxZFKk8uEmnO0H2o7z+O8I8BsAJPBFFPzjYAQG6Ap59POrn8Do/xFA8oUlfvAxkdbxh5Py71bhmOm+7s7xJdTC84quoPCGDZrsC/to8I78BvcWcq7wRiDS7jaTOOUOlNzytkra6pZ3/u6QTJzuOPZ07FvKROyAUCrzYCT+8/83+POWwoTx90xE9qbUMvBCGWTxAzVe8iGHHuUnTmTwnF069Y+ZKuxao5rxCn9Q8MbpRu3PnzTyv7m28SIMNPQZoJ7z09gC6nRafO/p8r7x26aU7OlrSulDk7Ty7Hh28l4/iOuvJ7zoK8fI8BxmHvI6PYb3pnGy7oyILvTzEAzxO0n27QOzAPH63wLzrzMU8AfUXvDjVgbgx7hk9J4mdvLbckjwxBY48aHYoO6vAu7tVfdM8Qz7XvBUUDz2rugE7CWyEPOl6vDx+ZcW7DuiAu+YH8ryhlKE7JDfnvEoFc7xBOG+7M5F3vEQZM7ySA/e7Dn/dO9wEebw1IoU8KUlYPDfuIT1zWgE9f10Wu6WsUTy8yuS7GJ2ZOoUfmLtsWXk8eDHlOp8T6TvOBIo8pI+SvIw2dTwYd9i7fTmHvMSjB7wIp8+7dwu0urVHlzx0YYI8iC6WO6CUELzTVCG8shf8u+TEfrzeqxq7Ts3QvNPqjjutmPW7aCAWO2gXTLx/lF+87h02vMhTOTySsZA8E6PPvOayXrzAedw8yIu3vGlTBDsLAJ07jvwku5sbnzuatbq8o0gUPAhutToKDpS8kn5YPDIpR7xhRtk8e1CoPJ5R+7txK+y6DEoKPCyGJjyzYhS9f4tWPEuMhDwo2Wm8AZMYPTUHqLxKFY+89XwwO4TfPLyx8Si8mUJ7PGs+3LzTxci8DLORvMHw1DlV0EA7ajCUPORnzLzvqKq8sozpu+KQgLy4bzO9ytAvvFO2Hb0agRe7gLbju8MkjLx1vm68RcbzOqjRMjzPNYY8lS7evNRrwrs/CPk74VPgPNc68rkxaOw6ZUTXO665xroLhqW81BAHPKl0OzzWflk8CyLxPB2IKrvBsZq7KZh+vKto/7qy2JE6Orw4vBwIOT1cw1a8fxpOu1ZSNzxSz988bs2CvKbWiTveXbW8k9jXvAOdg7sJS6g8DF2tuwfbODwOFYO8bnK6u/l8rrsLJLM7HO+1PC9eZ7xpk/E884QWvP59LLxB8Is8Z3cFPHE/UDwFsxa8ub3gu8llRTyQhJo87TK8vEsUBrwMvgq8SD/BvG+Bd7wufmu7BaXLvNQ0bLye/Zq7If9RO+nTMTxpaQA9AZrNPETUZDwgBVe8Wa2au62JEj3xnZ69KKhuu1oIjrsjt347dkaRuu1x1zxwGJS7ogTKOwsEQbs0dMo6NdqMu1XH4rz3v/m7dmuku16NDDz1YKO7LUhfvGo8pTwzHHm8KgFnvLzEO7zeVqC7maiTOoR8nTuoxd67eOcJO3LTlzyRcse8LobJvAUBgbwCWFK8T2MoPB0BDb0rhZe8LwLXvMtwzzwA3Hw8K5IuvF89qjw+mtY7z4fiPHgupTttPZ87a7kAvOxEmLsw0IK8qLmSuyPXabvJXfU7Y6DPPKKVqrsGsbI8+UTrvE2Euzod8Uy7wr+hvBxUB7vRR7q8nmsGu70fGz13IGA8tQWDvLI8Jr3tSYk8m3Anuwu2tDw/bs487tYyvf3yJbyb9h087yTYvODl9rohg4A8va01vWkr4bwPbfM84HMGO46cNrqJqYu54F0dPO7MhTy/eb68gC0GvWRJZzsJSL481MzlvOF+hbv9iBe7CpaeO3VKMr338C88ooWwuvHoBzx9RZ08zimqO9uKsTz4iUm5reQwvTk/KDyXrbY8N7UBPZlhUz0ydzC8+PISO3mwO7ypdwk8P/68OyKO9Lw58Rq5Y0ImvJJfXzws5Qc9RSD0vPmsqbwLvTI87gpHPOmUmTy+4oG8UYFdvEkB57xFp4g42mjGu8GhsLwo8A+8G3CmO7F3hbqNzRG94JuTPB2vir1+Nzw818x6uxzQDbwIbMs7f8BbvA09grz4iOq76UvYvPB0CD1nc407jf0MvFg3Q7uq08Q6hY05PDMC9DyL6Yg83vQZunw1eryYJAc8qDKOO89Nyjy5yeI8fRIkPVY3jDqAxw09h13FPGpC4zuAI/S8iaYKvG9M/TxY6KC8Rnv+vDoJMLxtdR+7DJ5lPP9xbjx0zfm8qGGPvLrcNzxSJfu8NiKBvEXMbjxSBQI7I1X9OgJ2pjz5Mcc8HLx2vIW07bzEdXo8XXUkPKyUhTz8i+a7PMXsvH77pDwGEbm8ApIwOzPXDTx+zNY7Bk/NPJik7rywVHq8IX6EvChLtrvF8ka78LHLPPpI3jwwoW68dk3lvGO4KjvqkfS6mJkmOyaUDjyJlpi8HRhdvOGVNLvfOvi66OuSPGdDELxrJxE8wxWMu6iGoTzRkc+7oEUlPFxw6rtQk4q8ldcxPPdjjzx3xCi9WfwXu+PChjxKyQi91MJPutUjursmJA68hU2kPKD4B70ZXP08ELsmPGOFIjzeX9u8xz+iPLtszDyQAsk8D1s+u0SZQ7yDUvi7SzAGu1IJRr2DgwK8vdOcvOrNrrtIMqA8a5xCvLJEsTyVuAM93bEbvIaIsDrwK5i8zL06ukXKzTyCur48K7/HvJ2xlDzWHDY78rESPEWDmzyjwEk7LkCzPK2SpjqNXUS73ZNxPI51QLvqO6K8UnjvusRyUrwiDU68vSHSvIHLUrtoRAI83U64O+dWz7uUNA482/kBPYOP3brNT+a6Bx8OPagUdTzi2+i8a2cdOXObnTz0m1c8dlLyO+mJCD2Sl7k8BNUZvSSjm7zKHKu8AniRu8fxVLw8hve898dXPP+0MDmiWYS87cBAOpPpiLxFGWe8VE0DvW6z2Lvky528XgoBvSAlPrw2OSA9vVwaPGjGXLwC8aC8/MFgvVt8VzwawzQ8oNCJuy3W+Dy/g3o88J+DPIPfubuekCU9/k1ku0LQaL1DnaC8BHoAPeQFyzujIva7GbxoPAljgLxu36A8EQcMvJvYnby7BBI98eQOvHRUrzycfco7riKcO/THbrs85Mm85EkVPSfCzrtpJiA9kbAcvbf0JbxHYoo79Mk4vApc37viMAk9EEIOPSPJprztErs8vcqUvGpLkjz2GVe8ktWXO7xTMzvm6sM8IuoUPKq1VzwtFky8Z1Hfup8tiryZhIM8K8SPu8i73btzu+I89KRLO9/QFryoUMK8f9rzPCGslzzAHSk9Bq9XPEyaDryb6oW83wI4vHGGO7wdfCC8+kkUvNJnpLz4fOQ71LE1upfBGrz8NJe56KCuPEAZFjxOWPs7EA+luyWdTb0Kd4g8gi+WvE+eJjxji8e74dKZPK+ugLwp0YK8OP8FvOPt1DxMkpC8YeUoPJsByLxGwP478H9lOxmLNzx2w/u7pDCZPeM7mLs/uyG8yNjZOxndZbrxthq91ceAO2VK1zzwiHq7GF+mvJCWubqD53M93eOTO1MoZjzvBpQ8SAW7O4ptrzzvTqm8//C4u01wdDvxKdQ8vGmSPDGwlbwjX466xKh0vBR+xDqI9TI8GgAAPBvl1buDfeE8Zz2VPAYoETxBS9a8RzHzu2jHQLsTLma8F47svN4WrDxJoPM7NhjQunSu9Tts4sS8A3+ePOnWXjzoc167y4RTPfzjgbsZx5u8gg/Ouma7fT0CgQq8vHnkvEEjNrwTm4w8WwGxvDIjCTuDJWm8BE8/vOi05rx6eNu7Be32vP0voLwfYgq7FI2YO3+fvjzGnpe7lBJLvE2v6zsNMC68hLLhvC81Q7uVHhA9l+K3OxBnSDxUsaw8iaSpvCY/5jyvAxM9PBnnO26aRrwsJb88yz29O7ABA7u3egS7RVkPPWNQM7zFspu8HB4vPWJGSLzcrJ47+ZqOvNihMDwXNfg88baBO3mBrTvg6DM8qAkJPNKcyzzWniC6l/kHPREUTTshSj68tr69PDsXf7x2v1w7ulurPLSC6bpLZi09JUEtvZIiw7wDBe+8kjULvapxBr3s7ag87Czqu6tqAjymR3k7rtGmvEWxrjs/m8I7JjwIO+C1hDz+Nl498UsgPQiAsTu+wou8FiKxOi5BFT091Dm88AiuuyaOarvkHTs83Ko4vDURW7sAnjW7DZgOvOvMVrvlw768MBgMvXWhNru0iYq93MUBPW8wBjy03hO88V4OO9w3jz37QJy8d4qyvGydQbwhVsw8blRovAYnpzxSp428CVvSPIXpAz1rs5q7Ix7HPH43uLx+s54807/GPFqYxjvb/Im8q9TAOxp5G7wGE8u7UkN6O+uHDjzxQr87Jl23vFS3ozu9vXS8mICHPNi1vzy+TqK8isTBPDS58TzgxkQ80PnbvMbHkjvVJoY8U3RgvApBFL3X4We85yGuO+WPsTwUU3O9d6v7O8Unlrwy/QE8Z3sDvUTgrbsJkpM7aHkYvF9WG73VlIQ8Bv+EPB47ZDnMOZS8JafIvMUbhLzoU5+7/aAlvaRwfDyEP5a8ubzDPCA8vTwOgKe7cJsvPKrhgDvDp5U8kgkivM9VILx1tco8YygTPTj12zzW06U72IyIu938lDzqaME70k/ROwUIdr2AnmM6Wft2PNAqYrzWDIW7WK6NvH3YgbyPfkW8l65Du0GfTDxmdpK7Q1JrPF0V3DsTGA+9ohw9PINogTwyf8I7MCqEPP2pg7zrvZy7qM/LOz2WprvmgO28/K3XO86x7LsQO5M7wWrtO5dDwbtXgvc8FQTKuz1n+jtDUDO8RSnTup4OgzwepzA8BtqeO9R1Mb04e7s69QhBPBhN3juj1/E7zb4CvDlrOzskBQs8pLydOxabxTxvWFA9H7jRPLmCnTxwSoQ8shS8O7bJM70WtOs8OMLOu9eT3ToDEJe86v0XvYV6wjx1RzW82MJoPFPAWbvfFpG8u8vNulpVjTvevmk8sWX6vOxIvLw68Lk8ai8LPdV+bbxZmjU9jlo/OyE1x7s8q0K8WKh6POqKUTypkqq7oF4kPW+T+jsOIzg8OMk9PUIxPzwWNLs8NNpIvUEZTrz9iWw6+rZlux8L+bt9fYy8zgCOux/5sLxNuRE8bXW8PNa5WTtICbu8toADvJo3zTtZh8s8iv4RvGNAUTr/1RW8ROJjuonyfzsAj4s7X2DnPKf5C71gC6a7my1FOxS7FzyCcJs75lz8PBefgzsPA5q6IFOMPFvU87wfp3M7VQf8vNAF8bu+CFw8Ei3/O8tae7uzwsW87e8gPc3qm7vDJI27ismSPPUo+bysrZe80tm/vG7JTLyJrbK7C20CvD0XHjz1/sy8M4MPu5yIGLwg1lc8r72MPKzQvztQq1i67EsmPEAtkzxV9O46KfG/PPoIwbvF0vM8aq1VvN5LrLxjuWa8NzzTvNj+CrxsVHe7A7SJu58rPzuZTL476RjpvJNvlrz7vsA8Dh7jOtBqHDyLR8Y8RkU+vZdw+zvJ3t27TVZgPK1jXDuRyKi8Xon0vCiyPr07n4W8PurcvIc5DTxuUZA8U4o0vIHAET1Kqbg7MV3Tuo2TmjzKv8e7bADDu9Jv3zwZIeS8mUWUOzjSRrsBbp28J9jPO3XOZTwY/yq8A7LAPLLdDzzRMLC8kCeYvH6qCrwALwE9+wsWvXTa/zwTnqs8+ZuFuu0nrTwMYaQ8JESeu6MhCr0dFzO6I7VrPBCnojymnxU8y6YNPD83sDtADYw8j6RJPR3EojpQtZO8meBJO+fUujpSwOS7dx+9vJ1zsjzGHuU6NJMSvJsCnjtCEf27hskYPMQHdbyJprE8DDqWPD7PfTu2CN+73BMHO/5sr7x6mQu7CATUvMIRarzPqtQ6OKtJPZUcg7vM4iS9+twFPaB8JDy5hN08N4q1vMAl4jzmvx89MO/8O0C7ITwBtMe8lGMRPJQsJrtcpZC8SxvcuykNgb1PRmu8F4E5u6HWEr3oNQe8xgPavOrbjDvSsX27uSnSvKYIELw29je8RDZCPYdDLLtsmGw7Rb2vvK2tIzz/71m8TMNXPKwvBj1cYRO9VizqO80xoLwi51S8vHTzvH0NnDxhjjG8JM2AvIoplrwqkFA8z4YYPXr/+Tymem87PMC7PCYj7jw/g6w8kftGO/fJ2LyKIeo7MRQxvDjHSTwcNgA9KrN+uwnotrtX354721wLPamCHbwi9yU9ctpjOoCdNrxroqE7yT8ZvMoeYDyjiV+9VBqWPNHwtLwEz0y8zfOOOx0PjbxL1MA477ElvKlzHTuAORM9NTcFvKh92Dtpgd68t7ZuPAIhozyrHzy8vxrdPKxkcbxKAGy8KpRXO41XsjzOrbO8Y1fUPMZ7pTwRG6W7UDTHvFDueTwP3Sm8+ARrvGZGB73ebWg7tNIOvDeUsjzJEBi7+A+Ous+1ersmNLq81uJGPHRN4rtWs++86w5TPNlFpruckEy94KV5vJ8rFL1Vc1A8AAjIO0LVjbtxuEY5v5NQu7hTsrwUJ1Y8UvQiO6bkALvSFKg8pHMzvYTi4zyQxEY74l8HvFUorDxGIJE8QRK4vM8BajusciI8vBgTPFS/Lr0U2JK85+EgvQ2HC71vvme8UzzVuwpAN7v0dZk7i2tNPOTSbrtFf5i6IRENPFIYozssawC9DPllvOTOKTzsT0a7Zw8juyhjGj3vu8i8zzllPLHREz0+n1s8pOAdPcFm6LyyvRm9vEtWO67etbzsv7U8ZYu+vMRW9DvBjX29Wj2cPDoj+LugEdy85NiIPE6Dfzyvseo72K02Oxqj6jxGoXU8MobPuqSUzrtfv9G72JJdO/ncwzx+G+y8TiTCPKj7H7tqQSw8qxKNPPNltTxV6+c8kURwvNjf47tX4xk9oQ+IuBRQvbyktte8Su9/uphxLTzXFLK7pcqhPF1hCb2ZdZK8/KeBO7YHMjwsdZI874reuwBnIjzpDiU9K+k5vVFBKT1ypZe7i8OJPDOiJbyMfoi7OVEdvGIh4Dw26LE8YLoWvdbFGD3ugnI8Wz6HvE6GEzvBskU83ykVu0VSDb35nwc90uOtOtN4n7xGZSs93rcaPQ3R0bzfuZK8jkXhvBRe7DxwPU29LzW3u2qmMrwW2eS8gc3APL3YVbx3RBi8lZcZu8AHmTtIxEq9BVb5vHVgKTtolRU8F1bouh9paDwub/a7719fPDHTjbrWnlu8stAJvPFs87wb5TK71gNDPDIyCTtDkaS6eXcJPPuKkzwp4kU8b6j+vJojh7waUy49wkSiO+sAqbwFM6y8/JJUPGpCNbwr2688wRvwO/Vv57xkG3q7bcI0PSGXTLkJBxm8aPV4PLEtvbwj4x28Kyr7PJQ3bTuz6yu7qWavPLVfvry2UeI7phYDPcf15rtvVqu8r98OveaQD7zXq4O840h+vJ99qDy0RTU7un0wO7+p8LuwyEQ8+5WGu1RPHrw1hAY8vJWBvDX1Pzz5Igq8ERjWPH8pm7wBFt68QDsCPQvf07r/Kpk8PNZVPHnW1TxY76a82P0BOyI1mTy+X5G8o5zHPOfXIL2CnXO8YYwnvbeMozowXAa9LwUKPPXllrxsP+W70xbou3wQJLzvANK8UbAmvBkQUzxqDYq7FtEMPA2KhLxXEGO8s6QMPQ4plDzWjSy72jmpvNBOajtSI5K7niqCOhcqz7te+qE7+zEEvdqagTo7a/i8xcpou3UqNbwItgI9AC59u8rqiLvmFnM8AVylPKQKxrsg2aQ75kLpvO9DgLw9KpC80jFJu98pTzyBZ7E8maP5u4+/xTySoGQ7QkmGO0MxiDsde3u8BSCpvG7gcDx5AH48OkadueNwhToN+qI8oG2evGF2/zzz3mU7CtkevQ/zjTt/Qmk8H6W/O2jvlTy7Fdk8f90TvKTuXT0bGdu8S4BivGo7WLx9+4+7NmeNvF4zODxIu3y7GQ6JPGf2+LyXodM8U4oHPFWR/DvFrvc7D4jkPJ3oQDz0Hh47xvHHPNPEZDslf9g8MQwqu5QkqryWkVi7IFqOul3KizwuMM48jvj+OxQYBb2ySAe7ThzzO7PXIztNfim831bdOzZ2kTzwnkC8fg9HO/uMOjwPeNg84DIpPX/Nlrx/kaO74SHLu9rIYz3hefy8eJ2IvPzigDxhIQC9gM7KvAJQbrv6neK54oFIu/Sa9DqCKQ49f2uAvF41orzVpwy8DvuuO/KDwzwkpYs8RetTPUx/Pjx41ou7Up8MvDep7TyjqCk9LljUOobhWLkD/o08AEh9PGXlHz1DmrO8Y3dZvCWZaLw/My+853ZyOmnmqbywuc08XTFRu0Nw2zoGBYQ8mIQruXKVuDxxoVi9U3KwvNpAN72O8Re8Wa2kPPWepToqJO+74SfqPJ8xozyVKNY8h0NzPLQWrjqC2kU81nksPNVEgzsmyuY7tFJJu/C6NzyoMSe8v9sAPRpohjzg/eq8xv65OzZRcDzLb0m8mEb7u0xjG7raafa8USguvexTUTxp2Kc8N5uOPC6X5zujPQm9ToOCvLaveLwUBje8kRcJO/kFB71+tHe8+3D7PGTwhLyXw4O8vVtUvGk3u7xqOxM7ZPFTPJyfzDzyfiU9vF6pOJr8ODwjHOc7iRTDPOPjujyBLC69SdYYPOFl17uoZ3C8j0/WO0o/0jriGdi8VCspPFqGNLzG+iq7sIMwvMI7i7ySxwC8OnKpuxcFmzyp1Z+8Mt0ouiEyb7yatZw87eswvKNYkTzoCyQ8//ETvF0ltDz+5Ig8iJMwu+CkrroPKg68gvZIPNfGIj1lElE8wpNLvO6IHDzRxyg8dtBXvFOfpTxDF4+7FzOyPILCibxvN/W862RgvMYd0bxbnB28N7RsvOwIkDtKoKW8ihUQu7pzQjw4Bu68wzNIvJ+MxzzbWNk8N1W5O6Z1AbtppL87mUcZvH1a5ztGxgG91hU4PKhRjrs84Ia80MOyu498Ljx2hOw71CmFvNCGK73qOcS84AZ4vBJOmbwxqzK9mf/Ku00HNryU1GW8TC/DuwjYtDwd7Ta888AiPWjjJDs+e0U8GziVPB0dDDzblBe8e75lvJPY9rqeJZ689KEUvY9zNju3Do87MqX+OvMugbw+pLM6QMckvAe1r7wDVmW99h3Cu4jciDyCeAO9OKjivPmZuzwukvm82aMIOvuV0Lz1g0U87B6nvIazK7xEtVA8lDjzvKBXnzzAnx896Teju3cxxrxZ9Zk8Pf5vO+ogqzxzjQQ9cYx6vOMJ6LtNBPg6DE9YO6P5Er0NzDU8Sy0YPJ+birtMQPq7l8NavNRSAL3fwUI7yrsvvYm2UztKmoG8IFY8PJV2Ajv4dcW7QDnLvCy0IzyqMhi8m/EgvUpD+Toinaa80cDFPNCrL7133bk8gY+buYIkmDy4yim89bzYu2F/qDtLBJG8+r4MPG2axruA8gg8yuEIvb2U9TsE4WI8jDvFuypWSTzrV6A8roWDPA9S2Tu7e0K7ha64POMWgLxQvPm83RHXvItL2rzU7Le7m3evPBYmpDxE/Vq8pubzOsJEiLxgCYu8a80IvVgM6Lxt1NQ7rJOZPL9aDL2cW3Y80Py/PAZyvjwBTmO645z7PJt0MzycHpI8zMcuPCbMo7t0Jao8aY+8O53EqDxVu8I8I8scPTLRhTwtfRK9voUIvBIz4Dzm+8Y83aHSPPdTh7xlu8C6/H8lu2BwsDz2EqC7gIKJPOaKRry3jJe85aFJvXlFWTwh6nS8eAU3PIw7LzwKMdm7ouppPArIQbyCpgI8LSSxvOmOnDvfW2i7JvOVOoR/njzfnae67iYXPIZuW7tWKVm82JLquwMsPzzAKII8PpWjPAUAxbxgl2y8FpCjuy2PjbyUv9m7FXseO6EBqzvajvu7+eDhOaiydzw78A284DYEPH2Bw7zYcwY9yN8oOy94jLtHOGy7hV3vPKpHfLyJDmM8f4MFPMcAaLzfJCC7tFNRPD3Icjx9rDo8cTUmu8bqBjzE/UO8L6NEPVZHkbtZQs065hpYO2Vr07yNVli862ESPGP+cDzoQkE8eGqSvDp2Eb2Q4RC8CM5rPH/AOj1eWAu9IzMrvKIMxrwll4682OdDvNLDmzywXWK8ANMFPGpU97vC0EQ4oEmOPBLYbLxrfoC9Z7T8PGQI6jt0wh475LS4PGrXjjyj9e+7S8YaPfw9HL3OwgC9mwU7PG5csDz3wR48nVswukeBWL2dYGc84Ks9PHTGJT0Iaf88NkkGOqwcWbxl9SW8oxljvG/e1jypqqw7NCoxPEtNIrxUbza8bm/gO8cOI703NiK8D4m4ullUKr3Y1XS8vXkOvJsA5LxGLNK8jYemNngJzDuUvjG7CkN7u3oQAD0JT0Y79h8TOvSbvzwwr/w6E+KKu8jmXjsQAWA81A8XvPiXJjzs7iO8vco8u6YoZL0pNrs8nX/rPBBhHDxgwji9gFSDu17vNDzsRqC8/BxzPGnC7jxXioM8TAI2uzMCpTrANy48PEuSOiJm+bz1Pm68I58NPP4y6rtxZNg85m0ZvCW+j7wH10m9S5SivJe/9Dx5FFw89HSQumggZTwbBLa8Se2KPEJcArznCKO8i+2SPEqrHL2QH5O8p+zePLptxTxYMs64TcEPvARDADz2rom8YlAyPFRAxztFcig8q0EBvRDsCTxdz6S88r8gvM9qBLz3qBy7n4bcu0jHMT1j0BU8bh+ivIpXA7oYC3W6hzz0uz+TEr1QYBu8my7wOxTGGLwNDJY87T0nPeNl3Tw1a4u8+8SAvAB3BjtznSY8MgpMvOgVELyTZTO8Nl7vPAUvsLz1z0u8HievPHjXv7xknyM8ddEdOyIYFr1hy4i8f+bTu0R7o7sFDn67D6CgO9u3c7v1mYO8SbBAPNH+ULySaqw8bFAlPJA9grw47gI88IqsPAihObxKyV685uncu/y/bbwkm7+7z8gyvBcpJ7yHbX48PFTcuzsmpbu3fTS9y5KUPFCtGz0AsKM7PUnYO7agGrrRo3W8B5qEPGB9dzuuBA865r6cPOJ9abuONII8KEVFPPBNSzyi0/M7jZBVPKvHfrwYVva7qlS7vFLscrz/vO48F9hXvAE2wLykuLc8ilUCPf+wezzY/lI7ehBtvFFbzDxQvNO7vHWgvHFB7LxEh4e7bv5kvKM3k7r6fxW8FjFFvKW+xDsx9Is8CrnbvETKBz2M+hg8jDCGOGhBSjzWf9y7NNnku5eC/byNKVw89/kwu6ZN/bvcPPy6ApvLOx+BuzwyfAY9LIB0PIk6R7t+/iA9GBo8vYUGhzuJuMK8a8GbPBkxYb2OlJW8zQhpvPLm7bwQjuw8czXAvAQ3ijvR9Cy8PosBuxr6Vj1M7Pm7mh3CPBevrTzTfy+9REs0utQtIr2cHmQ8Khd5vKErQT3P4907T0iousPH8LtMLPw6lOfUPGzOojsHbo66+CpfvAKwnTyw+ZY8dkOGPHtKlTxHhV08ZDe0vEYmL7vcp9M7+LBtu6sohzwmUx497vLbu6uk6rzQ1nE8uMZmvLN1STxy0Qe9KoDKujo5RL0TOuk76yfqu9mZprsOu8m8mlREPN1k/Dvn3eM8sD23vEVf4Lz2AUG8gtb2Ozv6oDyW7z67J30ZvFr5njw0Zow72JGvOzvNwzwMaFa8IdObOzjQ+jwcBD28Un57O/siSD0IRYK7v7GGO+j2CjxC52O8+cQOuzN4zrykpn87UC20PAteFbx3Y4y8KGIpPB0DPzsuwQg9SaPEPPt0kLwW0Tc8IJk2vHPur7vOSQK9JkIUvcqJprzAxL88NFHMPBEtCb33Zik9YBebPC8BhzwcC0A8DXTEvIXTAD0OMSy9QdTJO0jcGzywTJG60LKtu3yDTryY5jm9WIaFPA+0GT0UkL28nv8Cux3ESLz3XMc8H7GoO5Ol1Loq7He7arQdvXDqy7zxYWi86IWrPJIZ8zysM3U8WV6TPMlyWrtbYf48VfKYu3+f3Lt065a8qn0rvIDQOrwiqg48+IsmvDu+vTtCBzG9snEPvJYNlbyZLxK7oELcO8MPsjxYvCy4/od/vGbETz3yDSi8Hb0lvEmXTLsRNMi7pHM+PIZuebyKDtw7N6NRvPer17tdEA+9OfzTO/PCDTn4UO07Y1cWPKYLn7vuDBy9MJIgvAIB0LugI2q7DjxTOxSierpe6o284Z3jPGyDDT24pgI7CEliOpRSN7z0nVG8oq+PPAGlwLuR7Ti7qlm4PMQnAD359a+8oOa7PAKBZrsKJ4s86gfSPLVWSLz9vws9e10JPQJUkjxpw+Y8F6UYPPFZgzzZL9K8h9qLvBeIGT2wQiO7MqyZuzj7BTtCM3U95qF9PHxTrLu1G6k8ej/3PIEUdDzyCQ09wsYFu0IXsryT6Cq7EX8NPdgt5zkXLp+5TMOFuzYlZbqAC7k6MqE9vIC5Az2yBs47IPiHvCtNszs37ga8+XODvGXdYTyO1fm8iac2vHBsfzz1W9w8jHW/vE9WAzqrhYA6IJaPvFxssLz7Nyg9V2y3u1kD+rzKoNq7imIMO4BYHbzMnaK8z48bu4E6ULwpPYU8UClEvYsO7zu/3we9WlDAPPkZC7wlhBk8d6FzPEQ4uzxSajE5r+eZPJCLwjsBJ6y6GqoWPIP+8LxymTw8fmTgvEJ4qjwFmHS7Ip4FPYeNcrwyRqm8aDGaugARHDxPMba5dVhDvGcCwTy5gsc6GHaaPMISHLs8Twk9ozjpPD38g7wCigw94o8kOwAJZjyF93m7JbQDPCEbTTw5wXA7tBBmvZLncbzSeRK97R1VvG21WTwRT1+8XHYRvX7EWTzroug7qIuqPNEg4rvFC+C8I4KwO0VuOLyV0Iy8TsA0vHniPjwzq1C9M1O4vCowGL3xVSk9bZYhOBkAiDyQza47gYoRvQWibbvpG2i8sFopPb5uSTs2k3A7NgGcvGNAWbz55VG67lDauxKqa7sxXua8uLHxPDpLvjvKBMa7+9mHPN2/ozu/M966ey8avbVQbDzGhOU6IovovCAVsLzTcDq6u1VjO+cQbbzUt8M59K2dOsYKWjwnU6482Iq9uwleNDziem08g5eKu3iBH7t4VR48hGCsu9X6VLzfR5G7YJg6vC0lQLylwa88BjdTvPKdk7uk7pO8VhcRvBlvjjwJR9y7RUGJu0kikrrBPIi8LbVnvGIz4bpFC+K8Eze/u9lDSDwf01w8pzdaO3fotTv3w9o71r/ZO/aIBb1iICe8FvY1Og==
-        index: 0
-        object: embedding
-      model: qwen3-embedding:4b
-      object: list
-      usage:
-        prompt_tokens: 12
-        total_tokens: 12
-    status:
-      code: 200
-      message: OK
-- request:
-    headers:
-      accept:
-      - application/json
-      accept-encoding:
-      - gzip, deflate, zstd
-      connection:
-      - keep-alive
-      content-length:
-      - '4752'
-      content-type:
-      - application/json
-      host:
-      - localhost:11434
-    method: POST
-    parsed_body:
-      messages:
-      - content: |-
-          You are a search and question-answering specialist.
-
-          Process:
-          1. Call search_and_answer with relevant keywords from the question.
-          2. Review the results ordered by relevance.
-          3. If needed, perform follow-up searches with different keywords (max 3 total).
-          4. Provide a concise answer based strictly on the retrieved content.
-
-          The search tool returns results like:
-          [9bde5847-44c9-400a-8997-0e6b65babf92] [rank 1 of 5]
-          Source: "Document Title" > Section > Subsection
-          Type: paragraph
-          Content:
-          The actual text content here...
-
-          [d5a63c82-cb40-439f-9b2e-de7d177829b7] [rank 2 of 5]
-          Source: "Another Document"
-          Type: table
-          Content:
-          | Column 1 | Column 2 |
-          ...
-
-          Each result includes:
-          - chunk_id in brackets and rank position (rank 1 = most relevant)
-          - Source: document title and section hierarchy (when available)
-          - Type: content type like paragraph, table, code, list_item (when available)
-          - Content: the actual text
-
-          Output format:
-          - query: Echo the question you are answering
-          - answer: Your concise answer based on the retrieved content
-          - cited_chunks: List of plain strings containing only the chunk UUIDs (not objects)
-          - confidence: A score from 0.0 to 1.0 indicating answer confidence
-
-          IMPORTANT: Use the EXACT, COMPLETE chunk ID (full UUID). Do NOT truncate IDs.
-
-          Guidelines:
-          - Base answers strictly on retrieved content - do not use external knowledge.
-          - Use the Source and Type metadata to understand context.
-          - If multiple results are relevant, synthesize them coherently.
-          - If information is insufficient, say so clearly.
-          - Be concise and direct; avoid meta commentary about the process.
-          - Results are ordered by relevance, with rank 1 being most relevant.
-        role: system
-      - content: Is the class with the maximum instance count the same across all split phases (train, validation, and test)
-          of DocLayNet, or does it vary by split?
-        role: user
-      - content: |-
-          <think>
-          Need search.
-          </think>
-        role: assistant
-        tool_calls:
-        - function:
-            arguments: '{"query":"DocLayNet class maximum instance count split train validation test same class","limit":5}'
-            name: search_and_answer
-          id: call_lnz8uutd
-          type: function
-      - content: |-
-          [dbebd025-21b1-4b94-8ce4-c553f20a85ec] [rank 1 of 1]
-          Source: "DocLayNet Class Labels"
-          Type: list_item
-          Content:
-          DocLayNet defines 11 distinct class labels for document layout analysis:
-
-          Caption - Text describing figures or tables
-
-          Footnote - Notes at the bottom of pages
-
-          Formula - Mathematical expressions
-
-          List-item - Items in bulleted or numbered lists
-
-          Page-footer - Footer content on pages
-
-          Page-header - Header content on pages
-
-          Picture - Images and diagrams
-
-          Section-header - Headings for document sections
-
-          Table - Tabular data
-        role: tool
-        tool_call_id: call_lnz8uutd
-      - content: |-
-          <think>
-          No info on counts per split. Need another search.
-          </think>
-        role: assistant
-        tool_calls:
-        - function:
-            arguments: '{"limit":5,"query":"DocLayNet train validation test instance counts per class"}'
-            name: search_and_answer
-          id: call_2u0xuflc
-          type: function
-      - content: |-
-          [dbebd025-21b1-4b94-8ce4-c553f20a85ec] [rank 1 of 1]
-          Source: "DocLayNet Class Labels"
-          Type: list_item
-          Content:
-          DocLayNet defines 11 distinct class labels for document layout analysis:
-
-          Caption - Text describing figures or tables
-
-          Footnote - Notes at the bottom of pages
-
-          Formula - Mathematical expressions
-
-          List-item - Items in bulleted or numbered lists
-
-          Page-footer - Footer content on pages
-
-          Page-header - Header content on pages
-
-          Picture - Images and diagrams
-
-          Section-header - Headings for document sections
-
-          Table - Tabular data
-        role: tool
-        tool_call_id: call_2u0xuflc
-      model: gpt-oss
-      reasoning_effort: low
-      stream: false
-      tool_choice: auto
-      tools:
-      - function:
-          description: Search the knowledge base for relevant documents.
-          name: search_and_answer
-          parameters:
-            additionalProperties: false
-            properties:
-              limit:
-                anyOf:
-                - type: integer
-                - type: 'null'
-                default: null
-              query:
-                type: string
-            required:
-            - query
-            type: object
-        type: function
-      - function:
-          description: Answer to a search query with chunk references.
-          name: final_result
-          parameters:
-            additionalProperties: false
-            properties:
-              answer:
-                description: The answer to the question
-                type: string
-              cited_chunks:
-                description: IDs of chunks used to form the answer
-                items:
-                  type: string
-                type: array
-              confidence:
-                default: 1.0
-                description: Confidence score for this answer (0-1)
-                maximum: 1.0
-                minimum: 0.0
-                type: number
-              query:
-                description: The question that was answered
-                type: string
-            required:
-            - query
-            - answer
-            type: object
-        type: function
-    uri: http://localhost:11434/v1/chat/completions
-  response:
-    headers:
-      content-length:
-      - '498'
-      content-type:
-      - application/json
-    parsed_body:
-      choices:
-      - finish_reason: stop
-        index: 0
-        message:
-          content: I couldn’t find any documentation that lists the instance counts for each class per split, so I can’t confirm
-            whether the class with the highest count is identical across the train, validation, and test sets.
-          role: assistant
-      created: 1768997530
-      id: chatcmpl-864
-      model: gpt-oss
-      object: chat.completion
-      system_fingerprint: fp_ollama
-      usage:
-        completion_tokens: 45
-        prompt_tokens: 1044
-        total_tokens: 1089
-    status:
-      code: 200
-      message: OK
-- request:
-    headers:
-      accept:
-      - application/json
-      accept-encoding:
-      - gzip, deflate, zstd
-      connection:
-      - keep-alive
-      content-length:
-      - '5126'
-      content-type:
-      - application/json
-      host:
-      - localhost:11434
-    method: POST
-    parsed_body:
-      messages:
-      - content: |-
-          You are a search and question-answering specialist.
-
-          Process:
-          1. Call search_and_answer with relevant keywords from the question.
-          2. Review the results ordered by relevance.
-          3. If needed, perform follow-up searches with different keywords (max 3 total).
-          4. Provide a concise answer based strictly on the retrieved content.
-
-          The search tool returns results like:
-          [9bde5847-44c9-400a-8997-0e6b65babf92] [rank 1 of 5]
-          Source: "Document Title" > Section > Subsection
-          Type: paragraph
-          Content:
-          The actual text content here...
-
-          [d5a63c82-cb40-439f-9b2e-de7d177829b7] [rank 2 of 5]
-          Source: "Another Document"
-          Type: table
-          Content:
-          | Column 1 | Column 2 |
-          ...
-
-          Each result includes:
-          - chunk_id in brackets and rank position (rank 1 = most relevant)
-          - Source: document title and section hierarchy (when available)
-          - Type: content type like paragraph, table, code, list_item (when available)
-          - Content: the actual text
-
-          Output format:
-          - query: Echo the question you are answering
-          - answer: Your concise answer based on the retrieved content
-          - cited_chunks: List of plain strings containing only the chunk UUIDs (not objects)
-          - confidence: A score from 0.0 to 1.0 indicating answer confidence
-
-          IMPORTANT: Use the EXACT, COMPLETE chunk ID (full UUID). Do NOT truncate IDs.
-
-          Guidelines:
-          - Base answers strictly on retrieved content - do not use external knowledge.
-          - Use the Source and Type metadata to understand context.
-          - If multiple results are relevant, synthesize them coherently.
-          - If information is insufficient, say so clearly.
-          - Be concise and direct; avoid meta commentary about the process.
-          - Results are ordered by relevance, with rank 1 being most relevant.
-        role: system
-      - content: Is the class with the maximum instance count the same across all split phases (train, validation, and test)
-          of DocLayNet, or does it vary by split?
-        role: user
-      - content: |-
-          <think>
-          Need search.
-          </think>
-        role: assistant
-        tool_calls:
-        - function:
-            arguments: '{"query":"DocLayNet class maximum instance count split train validation test same class","limit":5}'
-            name: search_and_answer
-          id: call_lnz8uutd
-          type: function
-      - content: |-
-          [dbebd025-21b1-4b94-8ce4-c553f20a85ec] [rank 1 of 1]
-          Source: "DocLayNet Class Labels"
-          Type: list_item
-          Content:
-          DocLayNet defines 11 distinct class labels for document layout analysis:
-
-          Caption - Text describing figures or tables
-
-          Footnote - Notes at the bottom of pages
-
-          Formula - Mathematical expressions
-
-          List-item - Items in bulleted or numbered lists
-
-          Page-footer - Footer content on pages
-
-          Page-header - Header content on pages
-
-          Picture - Images and diagrams
-
-          Section-header - Headings for document sections
-
-          Table - Tabular data
-        role: tool
-        tool_call_id: call_lnz8uutd
-      - content: |-
-          <think>
-          No info on counts per split. Need another search.
-          </think>
-        role: assistant
-        tool_calls:
-        - function:
-            arguments: '{"limit":5,"query":"DocLayNet train validation test instance counts per class"}'
-            name: search_and_answer
-          id: call_2u0xuflc
-          type: function
-      - content: |-
-          [dbebd025-21b1-4b94-8ce4-c553f20a85ec] [rank 1 of 1]
-          Source: "DocLayNet Class Labels"
-          Type: list_item
-          Content:
-          DocLayNet defines 11 distinct class labels for document layout analysis:
-
-          Caption - Text describing figures or tables
-
-          Footnote - Notes at the bottom of pages
-
-          Formula - Mathematical expressions
-
-          List-item - Items in bulleted or numbered lists
-
-          Page-footer - Footer content on pages
-
-          Page-header - Header content on pages
-
-          Picture - Images and diagrams
-
-          Section-header - Headings for document sections
-
-          Table - Tabular data
-        role: tool
-        tool_call_id: call_2u0xuflc
-      - content: I couldn’t find any documentation that lists the instance counts for each class per split, so I can’t confirm
-          whether the class with the highest count is identical across the train, validation, and test sets.
-        role: assistant
-      - content: |-
-          Validation feedback:
-          Please include your response in a tool call.
-
-          Fix the errors and try again.
-        role: user
-      model: gpt-oss
-      reasoning_effort: low
-      stream: false
-      tool_choice: auto
-      tools:
-      - function:
-          description: Search the knowledge base for relevant documents.
-          name: search_and_answer
-          parameters:
-            additionalProperties: false
-            properties:
-              limit:
-                anyOf:
-                - type: integer
-                - type: 'null'
-                default: null
-              query:
-                type: string
-            required:
-            - query
-            type: object
-        type: function
-      - function:
-          description: Answer to a search query with chunk references.
-          name: final_result
-          parameters:
-            additionalProperties: false
-            properties:
-              answer:
-                description: The answer to the question
-                type: string
-              cited_chunks:
-                description: IDs of chunks used to form the answer
-                items:
-                  type: string
-                type: array
-              confidence:
-                default: 1.0
-                description: Confidence score for this answer (0-1)
-                maximum: 1.0
-                minimum: 0.0
-                type: number
-              query:
-                description: The question that was answered
-                type: string
-            required:
-            - query
-            - answer
-            type: object
-        type: function
-    uri: http://localhost:11434/v1/chat/completions
-  response:
-    headers:
-      content-length:
-      - '926'
-      content-type:
-      - application/json
-    parsed_body:
-      choices:
-      - finish_reason: tool_calls
-        index: 0
-        message:
-          content: ''
-          reasoning: I need to reply with a function call providing answer. Use final_result.
-          role: assistant
-          tool_calls:
-          - function:
-              arguments: '{"answer":"I couldn’t find any documentation that lists the instance counts for each class per split,
-                so I can’t confirm whether the class with the highest count is identical across the train, validation, and
-                test sets.","cited_chunks":[],"confidence":0.4,"query":"Is the class with the maximum instance count the same
-                across all split phases (train, validation, and test) of DocLayNet, or does it vary by split?"}'
-              name: final_result
-            id: call_k5lxlwnc
-            index: 0
-            type: function
-      created: 1768997539
-      id: chatcmpl-391
+      created: 1769001279
+      id: chatcmpl-412
       model: gpt-oss
       object: chat.completion
       system_fingerprint: fp_ollama
       usage:
         completion_tokens: 126
-        prompt_tokens: 1114
-        total_tokens: 1240
+        prompt_tokens: 838
+        total_tokens: 964
     status:
       code: 200
       message: OK
@@ -3532,7 +1071,1935 @@ interactions:
       connection:
       - keep-alive
       content-length:
-      - '3625'
+      - '4484'
+      content-type:
+      - application/json
+      host:
+      - localhost:11434
+    method: POST
+    parsed_body:
+      messages:
+      - content: |-
+          You are a search and question-answering specialist.
+
+          Process:
+          1. Call search_and_answer with relevant keywords from the question.
+          2. Review the results ordered by relevance.
+          3. If needed, perform follow-up searches with different keywords (max 3 total).
+          4. Provide a concise answer based strictly on the retrieved content.
+
+          The search tool returns results like:
+          [9bde5847-44c9-400a-8997-0e6b65babf92] [rank 1 of 5]
+          Source: "Document Title" > Section > Subsection
+          Type: paragraph
+          Content:
+          The actual text content here...
+
+          [d5a63c82-cb40-439f-9b2e-de7d177829b7] [rank 2 of 5]
+          Source: "Another Document"
+          Type: table
+          Content:
+          | Column 1 | Column 2 |
+          ...
+
+          Each result includes:
+          - chunk_id in brackets and rank position (rank 1 = most relevant)
+          - Source: document title and section hierarchy (when available)
+          - Type: content type like paragraph, table, code, list_item (when available)
+          - Content: the actual text
+
+          Output format:
+          - query: Echo the question you are answering
+          - answer: Your concise answer based on the retrieved content
+          - cited_chunks: List of plain strings containing only the chunk UUIDs (not objects)
+          - confidence: A score from 0.0 to 1.0 indicating answer confidence
+
+          IMPORTANT: Use the EXACT, COMPLETE chunk ID (full UUID). Do NOT truncate IDs.
+
+          Guidelines:
+          - Base answers strictly on retrieved content - do not use external knowledge.
+          - Use the Source and Type metadata to understand context.
+          - If multiple results are relevant, synthesize them coherently.
+          - If information is insufficient, say so clearly.
+          - Be concise and direct; avoid meta commentary about the process.
+          - Results are ordered by relevance, with rank 1 being most relevant.
+        role: system
+      - content: In the DocLayNet dataset, which of the 11 layout classes has the largest number of annotated instances?
+        role: user
+      - content: |-
+          <think>
+          Need search.
+          </think>
+        role: assistant
+        tool_calls:
+        - function:
+            arguments: '{"query":"DocLayNet dataset layout classes largest number of annotated instances 11 layout classes","limit":5}'
+            name: search_and_answer
+          id: call_hl5bq575
+          type: function
+      - content: |-
+          [8bde9c2a-808a-4c65-8d7c-c9e2985b57fc] [rank 1 of 1]
+          Source: "DocLayNet Class Labels"
+          Type: list_item
+          Content:
+          DocLayNet defines 11 distinct class labels for document layout analysis:
+
+          Caption - Text describing figures or tables
+
+          Footnote - Notes at the bottom of pages
+
+          Formula - Mathematical expressions
+
+          List-item - Items in bulleted or numbered lists
+
+          Page-footer - Footer content on pages
+
+          Page-header - Header content on pages
+
+          Picture - Images and diagrams
+
+          Section-header - Headings for document sections
+
+          Table - Tabular data
+        role: tool
+        tool_call_id: call_hl5bq575
+      - content: |-
+          **query:** In the DocLayNet dataset, which of the 11 layout classes has the largest number of annotated instances?
+
+          **answer:** The DocLayNet dataset does not provide, within the publicly available documentation, any enumeration or comparison of the number of annotated instances per class. Consequently, it is not possible to determine from the retrieved information which of the 11 layout classes has the largest instance count.
+
+          **cited_chunks:** ["8bde9c2a-808a-4c65-8d7c-c9e2985b57fc"]
+
+          **confidence:** 0.2
+        role: assistant
+      - content: |-
+          Validation feedback:
+          Please include your response in a tool call.
+
+          Fix the errors and try again.
+        role: user
+      model: gpt-oss
+      reasoning_effort: low
+      stream: false
+      tool_choice: auto
+      tools:
+      - function:
+          description: Search the knowledge base for relevant documents.
+          name: search_and_answer
+          parameters:
+            additionalProperties: false
+            properties:
+              limit:
+                anyOf:
+                - type: integer
+                - type: 'null'
+                default: null
+              query:
+                type: string
+            required:
+            - query
+            type: object
+        type: function
+      - function:
+          description: Answer to a search query with chunk references.
+          name: final_result
+          parameters:
+            additionalProperties: false
+            properties:
+              answer:
+                description: The answer to the question
+                type: string
+              cited_chunks:
+                description: IDs of chunks used to form the answer
+                items:
+                  type: string
+                type: array
+              confidence:
+                default: 1.0
+                description: Confidence score for this answer (0-1)
+                maximum: 1.0
+                minimum: 0.0
+                type: number
+              query:
+                description: The question that was answered
+                type: string
+            required:
+            - query
+            - answer
+            type: object
+        type: function
+    uri: http://localhost:11434/v1/chat/completions
+  response:
+    headers:
+      content-length:
+      - '887'
+      content-type:
+      - application/json
+    parsed_body:
+      choices:
+      - finish_reason: tool_calls
+        index: 0
+        message:
+          content: ''
+          reasoning: Need to use function final_result.
+          role: assistant
+          tool_calls:
+          - function:
+              arguments: '{"answer":"The available documentation does not report the number of annotated instances for each
+                of the 11 DocLayNet layout classes, so from the retrieved information it cannot be determined which class
+                has the most instances.","cited_chunks":["8bde9c2a-808a-4c65-8d7c-c9e2985b57fc"],"confidence":0.2,"query":"In
+                the DocLayNet dataset, which of the 11 layout classes has the largest number of annotated instances?"}'
+              name: final_result
+            id: call_atquzjw5
+            index: 0
+            type: function
+      created: 1769001282
+      id: chatcmpl-836
+      model: gpt-oss
+      object: chat.completion
+      system_fingerprint: fp_ollama
+      usage:
+        completion_tokens: 132
+        prompt_tokens: 989
+        total_tokens: 1121
+    status:
+      code: 200
+      message: OK
+- request:
+    headers:
+      accept:
+      - application/json
+      accept-encoding:
+      - gzip, deflate, zstd
+      connection:
+      - keep-alive
+      content-length:
+      - '2894'
+      content-type:
+      - application/json
+      host:
+      - localhost:11434
+    method: POST
+    parsed_body:
+      messages:
+      - content: |-
+          You are a search and question-answering specialist.
+
+          Process:
+          1. Call search_and_answer with relevant keywords from the question.
+          2. Review the results ordered by relevance.
+          3. If needed, perform follow-up searches with different keywords (max 3 total).
+          4. Provide a concise answer based strictly on the retrieved content.
+
+          The search tool returns results like:
+          [9bde5847-44c9-400a-8997-0e6b65babf92] [rank 1 of 5]
+          Source: "Document Title" > Section > Subsection
+          Type: paragraph
+          Content:
+          The actual text content here...
+
+          [d5a63c82-cb40-439f-9b2e-de7d177829b7] [rank 2 of 5]
+          Source: "Another Document"
+          Type: table
+          Content:
+          | Column 1 | Column 2 |
+          ...
+
+          Each result includes:
+          - chunk_id in brackets and rank position (rank 1 = most relevant)
+          - Source: document title and section hierarchy (when available)
+          - Type: content type like paragraph, table, code, list_item (when available)
+          - Content: the actual text
+
+          Output format:
+          - query: Echo the question you are answering
+          - answer: Your concise answer based on the retrieved content
+          - cited_chunks: List of plain strings containing only the chunk UUIDs (not objects)
+          - confidence: A score from 0.0 to 1.0 indicating answer confidence
+
+          IMPORTANT: Use the EXACT, COMPLETE chunk ID (full UUID). Do NOT truncate IDs.
+
+          Guidelines:
+          - Base answers strictly on retrieved content - do not use external knowledge.
+          - Use the Source and Type metadata to understand context.
+          - If multiple results are relevant, synthesize them coherently.
+          - If information is insufficient, say so clearly.
+          - Be concise and direct; avoid meta commentary about the process.
+          - Results are ordered by relevance, with rank 1 being most relevant.
+        role: system
+      - content: What is the exact instance count for the class identified as having the highest occurrence in DocLayNet?
+        role: user
+      model: gpt-oss
+      reasoning_effort: low
+      stream: false
+      tool_choice: auto
+      tools:
+      - function:
+          description: Search the knowledge base for relevant documents.
+          name: search_and_answer
+          parameters:
+            additionalProperties: false
+            properties:
+              limit:
+                anyOf:
+                - type: integer
+                - type: 'null'
+                default: null
+              query:
+                type: string
+            required:
+            - query
+            type: object
+        type: function
+      - function:
+          description: Answer to a search query with chunk references.
+          name: final_result
+          parameters:
+            additionalProperties: false
+            properties:
+              answer:
+                description: The answer to the question
+                type: string
+              cited_chunks:
+                description: IDs of chunks used to form the answer
+                items:
+                  type: string
+                type: array
+              confidence:
+                default: 1.0
+                description: Confidence score for this answer (0-1)
+                maximum: 1.0
+                minimum: 0.0
+                type: number
+              query:
+                description: The question that was answered
+                type: string
+            required:
+            - query
+            - answer
+            type: object
+        type: function
+    uri: http://localhost:11434/v1/chat/completions
+  response:
+    headers:
+      content-length:
+      - '515'
+      content-type:
+      - application/json
+    parsed_body:
+      choices:
+      - finish_reason: tool_calls
+        index: 0
+        message:
+          content: ''
+          reasoning: Need search.
+          role: assistant
+          tool_calls:
+          - function:
+              arguments: '{"limit":5,"query":"DocLayNet class highest occurrence instance count"}'
+              name: search_and_answer
+            id: call_mq88lla6
+            index: 0
+            type: function
+      created: 1769001283
+      id: chatcmpl-329
+      model: gpt-oss
+      object: chat.completion
+      system_fingerprint: fp_ollama
+      usage:
+        completion_tokens: 39
+        prompt_tokens: 635
+        total_tokens: 674
+    status:
+      code: 200
+      message: OK
+- request:
+    headers:
+      accept:
+      - application/json
+      accept-encoding:
+      - gzip, deflate, zstd
+      connection:
+      - keep-alive
+      content-length:
+      - '119'
+      content-type:
+      - application/json
+      host:
+      - localhost:11434
+    method: POST
+    parsed_body:
+      encoding_format: base64
+      input:
+      - DocLayNet class highest occurrence instance count
+      model: qwen3-embedding:4b
+    uri: http://localhost:11434/v1/embeddings
+  response:
+    headers:
+      content-type:
+      - application/json
+      transfer-encoding:
+      - chunked
+    parsed_body:
+      data:
+      - embedding: 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
+        index: 0
+        object: embedding
+      model: qwen3-embedding:4b
+      object: list
+      usage:
+        prompt_tokens: 10
+        total_tokens: 10
+    status:
+      code: 200
+      message: OK
+- request:
+    headers:
+      accept:
+      - application/json
+      accept-encoding:
+      - gzip, deflate, zstd
+      connection:
+      - keep-alive
+      content-length:
+      - '3765'
+      content-type:
+      - application/json
+      host:
+      - localhost:11434
+    method: POST
+    parsed_body:
+      messages:
+      - content: |-
+          You are a search and question-answering specialist.
+
+          Process:
+          1. Call search_and_answer with relevant keywords from the question.
+          2. Review the results ordered by relevance.
+          3. If needed, perform follow-up searches with different keywords (max 3 total).
+          4. Provide a concise answer based strictly on the retrieved content.
+
+          The search tool returns results like:
+          [9bde5847-44c9-400a-8997-0e6b65babf92] [rank 1 of 5]
+          Source: "Document Title" > Section > Subsection
+          Type: paragraph
+          Content:
+          The actual text content here...
+
+          [d5a63c82-cb40-439f-9b2e-de7d177829b7] [rank 2 of 5]
+          Source: "Another Document"
+          Type: table
+          Content:
+          | Column 1 | Column 2 |
+          ...
+
+          Each result includes:
+          - chunk_id in brackets and rank position (rank 1 = most relevant)
+          - Source: document title and section hierarchy (when available)
+          - Type: content type like paragraph, table, code, list_item (when available)
+          - Content: the actual text
+
+          Output format:
+          - query: Echo the question you are answering
+          - answer: Your concise answer based on the retrieved content
+          - cited_chunks: List of plain strings containing only the chunk UUIDs (not objects)
+          - confidence: A score from 0.0 to 1.0 indicating answer confidence
+
+          IMPORTANT: Use the EXACT, COMPLETE chunk ID (full UUID). Do NOT truncate IDs.
+
+          Guidelines:
+          - Base answers strictly on retrieved content - do not use external knowledge.
+          - Use the Source and Type metadata to understand context.
+          - If multiple results are relevant, synthesize them coherently.
+          - If information is insufficient, say so clearly.
+          - Be concise and direct; avoid meta commentary about the process.
+          - Results are ordered by relevance, with rank 1 being most relevant.
+        role: system
+      - content: What is the exact instance count for the class identified as having the highest occurrence in DocLayNet?
+        role: user
+      - content: |-
+          <think>
+          Need search.
+          </think>
+        role: assistant
+        tool_calls:
+        - function:
+            arguments: '{"limit":5,"query":"DocLayNet class highest occurrence instance count"}'
+            name: search_and_answer
+          id: call_mq88lla6
+          type: function
+      - content: |-
+          [8bde9c2a-808a-4c65-8d7c-c9e2985b57fc] [rank 1 of 1]
+          Source: "DocLayNet Class Labels"
+          Type: list_item
+          Content:
+          DocLayNet defines 11 distinct class labels for document layout analysis:
+
+          Caption - Text describing figures or tables
+
+          Footnote - Notes at the bottom of pages
+
+          Formula - Mathematical expressions
+
+          List-item - Items in bulleted or numbered lists
+
+          Page-footer - Footer content on pages
+
+          Page-header - Header content on pages
+
+          Picture - Images and diagrams
+
+          Section-header - Headings for document sections
+
+          Table - Tabular data
+        role: tool
+        tool_call_id: call_mq88lla6
+      model: gpt-oss
+      reasoning_effort: low
+      stream: false
+      tool_choice: auto
+      tools:
+      - function:
+          description: Search the knowledge base for relevant documents.
+          name: search_and_answer
+          parameters:
+            additionalProperties: false
+            properties:
+              limit:
+                anyOf:
+                - type: integer
+                - type: 'null'
+                default: null
+              query:
+                type: string
+            required:
+            - query
+            type: object
+        type: function
+      - function:
+          description: Answer to a search query with chunk references.
+          name: final_result
+          parameters:
+            additionalProperties: false
+            properties:
+              answer:
+                description: The answer to the question
+                type: string
+              cited_chunks:
+                description: IDs of chunks used to form the answer
+                items:
+                  type: string
+                type: array
+              confidence:
+                default: 1.0
+                description: Confidence score for this answer (0-1)
+                maximum: 1.0
+                minimum: 0.0
+                type: number
+              query:
+                description: The question that was answered
+                type: string
+            required:
+            - query
+            - answer
+            type: object
+        type: function
+    uri: http://localhost:11434/v1/chat/completions
+  response:
+    headers:
+      content-length:
+      - '561'
+      content-type:
+      - application/json
+    parsed_body:
+      choices:
+      - finish_reason: tool_calls
+        index: 0
+        message:
+          content: ''
+          reasoning: Only list of classes, not counts. Need data source. Search more.
+          role: assistant
+          tool_calls:
+          - function:
+              arguments: '{"limit":5,"query":"DocLayNet dataset distribution class counts"}'
+              name: search_and_answer
+            id: call_ojcd5ee6
+            index: 0
+            type: function
+      created: 1769001284
+      id: chatcmpl-236
+      model: gpt-oss
+      object: chat.completion
+      system_fingerprint: fp_ollama
+      usage:
+        completion_tokens: 50
+        prompt_tokens: 829
+        total_tokens: 879
+    status:
+      code: 200
+      message: OK
+- request:
+    headers:
+      accept:
+      - application/json
+      accept-encoding:
+      - gzip, deflate, zstd
+      connection:
+      - keep-alive
+      content-length:
+      - '113'
+      content-type:
+      - application/json
+      host:
+      - localhost:11434
+    method: POST
+    parsed_body:
+      encoding_format: base64
+      input:
+      - DocLayNet dataset distribution class counts
+      model: qwen3-embedding:4b
+    uri: http://localhost:11434/v1/embeddings
+  response:
+    headers:
+      content-type:
+      - application/json
+      transfer-encoding:
+      - chunked
+    parsed_body:
+      data:
+      - embedding: 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
+        index: 0
+        object: embedding
+      model: qwen3-embedding:4b
+      object: list
+      usage:
+        prompt_tokens: 9
+        total_tokens: 9
+    status:
+      code: 200
+      message: OK
+- request:
+    headers:
+      accept:
+      - application/json
+      accept-encoding:
+      - gzip, deflate, zstd
+      connection:
+      - keep-alive
+      content-length:
+      - '4682'
+      content-type:
+      - application/json
+      host:
+      - localhost:11434
+    method: POST
+    parsed_body:
+      messages:
+      - content: |-
+          You are a search and question-answering specialist.
+
+          Process:
+          1. Call search_and_answer with relevant keywords from the question.
+          2. Review the results ordered by relevance.
+          3. If needed, perform follow-up searches with different keywords (max 3 total).
+          4. Provide a concise answer based strictly on the retrieved content.
+
+          The search tool returns results like:
+          [9bde5847-44c9-400a-8997-0e6b65babf92] [rank 1 of 5]
+          Source: "Document Title" > Section > Subsection
+          Type: paragraph
+          Content:
+          The actual text content here...
+
+          [d5a63c82-cb40-439f-9b2e-de7d177829b7] [rank 2 of 5]
+          Source: "Another Document"
+          Type: table
+          Content:
+          | Column 1 | Column 2 |
+          ...
+
+          Each result includes:
+          - chunk_id in brackets and rank position (rank 1 = most relevant)
+          - Source: document title and section hierarchy (when available)
+          - Type: content type like paragraph, table, code, list_item (when available)
+          - Content: the actual text
+
+          Output format:
+          - query: Echo the question you are answering
+          - answer: Your concise answer based on the retrieved content
+          - cited_chunks: List of plain strings containing only the chunk UUIDs (not objects)
+          - confidence: A score from 0.0 to 1.0 indicating answer confidence
+
+          IMPORTANT: Use the EXACT, COMPLETE chunk ID (full UUID). Do NOT truncate IDs.
+
+          Guidelines:
+          - Base answers strictly on retrieved content - do not use external knowledge.
+          - Use the Source and Type metadata to understand context.
+          - If multiple results are relevant, synthesize them coherently.
+          - If information is insufficient, say so clearly.
+          - Be concise and direct; avoid meta commentary about the process.
+          - Results are ordered by relevance, with rank 1 being most relevant.
+        role: system
+      - content: What is the exact instance count for the class identified as having the highest occurrence in DocLayNet?
+        role: user
+      - content: |-
+          <think>
+          Need search.
+          </think>
+        role: assistant
+        tool_calls:
+        - function:
+            arguments: '{"limit":5,"query":"DocLayNet class highest occurrence instance count"}'
+            name: search_and_answer
+          id: call_mq88lla6
+          type: function
+      - content: |-
+          [8bde9c2a-808a-4c65-8d7c-c9e2985b57fc] [rank 1 of 1]
+          Source: "DocLayNet Class Labels"
+          Type: list_item
+          Content:
+          DocLayNet defines 11 distinct class labels for document layout analysis:
+
+          Caption - Text describing figures or tables
+
+          Footnote - Notes at the bottom of pages
+
+          Formula - Mathematical expressions
+
+          List-item - Items in bulleted or numbered lists
+
+          Page-footer - Footer content on pages
+
+          Page-header - Header content on pages
+
+          Picture - Images and diagrams
+
+          Section-header - Headings for document sections
+
+          Table - Tabular data
+        role: tool
+        tool_call_id: call_mq88lla6
+      - content: |-
+          <think>
+          Only list of classes, not counts. Need data source. Search more.
+          </think>
+        role: assistant
+        tool_calls:
+        - function:
+            arguments: '{"limit":5,"query":"DocLayNet dataset distribution class counts"}'
+            name: search_and_answer
+          id: call_ojcd5ee6
+          type: function
+      - content: |-
+          [8bde9c2a-808a-4c65-8d7c-c9e2985b57fc] [rank 1 of 1]
+          Source: "DocLayNet Class Labels"
+          Type: list_item
+          Content:
+          DocLayNet defines 11 distinct class labels for document layout analysis:
+
+          Caption - Text describing figures or tables
+
+          Footnote - Notes at the bottom of pages
+
+          Formula - Mathematical expressions
+
+          List-item - Items in bulleted or numbered lists
+
+          Page-footer - Footer content on pages
+
+          Page-header - Header content on pages
+
+          Picture - Images and diagrams
+
+          Section-header - Headings for document sections
+
+          Table - Tabular data
+        role: tool
+        tool_call_id: call_ojcd5ee6
+      model: gpt-oss
+      reasoning_effort: low
+      stream: false
+      tool_choice: auto
+      tools:
+      - function:
+          description: Search the knowledge base for relevant documents.
+          name: search_and_answer
+          parameters:
+            additionalProperties: false
+            properties:
+              limit:
+                anyOf:
+                - type: integer
+                - type: 'null'
+                default: null
+              query:
+                type: string
+            required:
+            - query
+            type: object
+        type: function
+      - function:
+          description: Answer to a search query with chunk references.
+          name: final_result
+          parameters:
+            additionalProperties: false
+            properties:
+              answer:
+                description: The answer to the question
+                type: string
+              cited_chunks:
+                description: IDs of chunks used to form the answer
+                items:
+                  type: string
+                type: array
+              confidence:
+                default: 1.0
+                description: Confidence score for this answer (0-1)
+                maximum: 1.0
+                minimum: 0.0
+                type: number
+              query:
+                description: The question that was answered
+                type: string
+            required:
+            - query
+            - answer
+            type: object
+        type: function
+    uri: http://localhost:11434/v1/chat/completions
+  response:
+    headers:
+      content-length:
+      - '402'
+      content-type:
+      - application/json
+    parsed_body:
+      choices:
+      - finish_reason: stop
+        index: 0
+        message:
+          content: I couldn’t find the specific instance count for the most frequent class in DocLayNet from the retrieved
+            documents.
+          role: assistant
+      created: 1769001285
+      id: chatcmpl-685
+      model: gpt-oss
+      object: chat.completion
+      system_fingerprint: fp_ollama
+      usage:
+        completion_tokens: 26
+        prompt_tokens: 1034
+        total_tokens: 1060
+    status:
+      code: 200
+      message: OK
+- request:
+    headers:
+      accept:
+      - application/json
+      accept-encoding:
+      - gzip, deflate, zstd
+      connection:
+      - keep-alive
+      content-length:
+      - '4960'
+      content-type:
+      - application/json
+      host:
+      - localhost:11434
+    method: POST
+    parsed_body:
+      messages:
+      - content: |-
+          You are a search and question-answering specialist.
+
+          Process:
+          1. Call search_and_answer with relevant keywords from the question.
+          2. Review the results ordered by relevance.
+          3. If needed, perform follow-up searches with different keywords (max 3 total).
+          4. Provide a concise answer based strictly on the retrieved content.
+
+          The search tool returns results like:
+          [9bde5847-44c9-400a-8997-0e6b65babf92] [rank 1 of 5]
+          Source: "Document Title" > Section > Subsection
+          Type: paragraph
+          Content:
+          The actual text content here...
+
+          [d5a63c82-cb40-439f-9b2e-de7d177829b7] [rank 2 of 5]
+          Source: "Another Document"
+          Type: table
+          Content:
+          | Column 1 | Column 2 |
+          ...
+
+          Each result includes:
+          - chunk_id in brackets and rank position (rank 1 = most relevant)
+          - Source: document title and section hierarchy (when available)
+          - Type: content type like paragraph, table, code, list_item (when available)
+          - Content: the actual text
+
+          Output format:
+          - query: Echo the question you are answering
+          - answer: Your concise answer based on the retrieved content
+          - cited_chunks: List of plain strings containing only the chunk UUIDs (not objects)
+          - confidence: A score from 0.0 to 1.0 indicating answer confidence
+
+          IMPORTANT: Use the EXACT, COMPLETE chunk ID (full UUID). Do NOT truncate IDs.
+
+          Guidelines:
+          - Base answers strictly on retrieved content - do not use external knowledge.
+          - Use the Source and Type metadata to understand context.
+          - If multiple results are relevant, synthesize them coherently.
+          - If information is insufficient, say so clearly.
+          - Be concise and direct; avoid meta commentary about the process.
+          - Results are ordered by relevance, with rank 1 being most relevant.
+        role: system
+      - content: What is the exact instance count for the class identified as having the highest occurrence in DocLayNet?
+        role: user
+      - content: |-
+          <think>
+          Need search.
+          </think>
+        role: assistant
+        tool_calls:
+        - function:
+            arguments: '{"limit":5,"query":"DocLayNet class highest occurrence instance count"}'
+            name: search_and_answer
+          id: call_mq88lla6
+          type: function
+      - content: |-
+          [8bde9c2a-808a-4c65-8d7c-c9e2985b57fc] [rank 1 of 1]
+          Source: "DocLayNet Class Labels"
+          Type: list_item
+          Content:
+          DocLayNet defines 11 distinct class labels for document layout analysis:
+
+          Caption - Text describing figures or tables
+
+          Footnote - Notes at the bottom of pages
+
+          Formula - Mathematical expressions
+
+          List-item - Items in bulleted or numbered lists
+
+          Page-footer - Footer content on pages
+
+          Page-header - Header content on pages
+
+          Picture - Images and diagrams
+
+          Section-header - Headings for document sections
+
+          Table - Tabular data
+        role: tool
+        tool_call_id: call_mq88lla6
+      - content: |-
+          <think>
+          Only list of classes, not counts. Need data source. Search more.
+          </think>
+        role: assistant
+        tool_calls:
+        - function:
+            arguments: '{"limit":5,"query":"DocLayNet dataset distribution class counts"}'
+            name: search_and_answer
+          id: call_ojcd5ee6
+          type: function
+      - content: |-
+          [8bde9c2a-808a-4c65-8d7c-c9e2985b57fc] [rank 1 of 1]
+          Source: "DocLayNet Class Labels"
+          Type: list_item
+          Content:
+          DocLayNet defines 11 distinct class labels for document layout analysis:
+
+          Caption - Text describing figures or tables
+
+          Footnote - Notes at the bottom of pages
+
+          Formula - Mathematical expressions
+
+          List-item - Items in bulleted or numbered lists
+
+          Page-footer - Footer content on pages
+
+          Page-header - Header content on pages
+
+          Picture - Images and diagrams
+
+          Section-header - Headings for document sections
+
+          Table - Tabular data
+        role: tool
+        tool_call_id: call_ojcd5ee6
+      - content: I couldn’t find the specific instance count for the most frequent class in DocLayNet from the retrieved documents.
+        role: assistant
+      - content: |-
+          Validation feedback:
+          Please include your response in a tool call.
+
+          Fix the errors and try again.
+        role: user
+      model: gpt-oss
+      reasoning_effort: low
+      stream: false
+      tool_choice: auto
+      tools:
+      - function:
+          description: Search the knowledge base for relevant documents.
+          name: search_and_answer
+          parameters:
+            additionalProperties: false
+            properties:
+              limit:
+                anyOf:
+                - type: integer
+                - type: 'null'
+                default: null
+              query:
+                type: string
+            required:
+            - query
+            type: object
+        type: function
+      - function:
+          description: Answer to a search query with chunk references.
+          name: final_result
+          parameters:
+            additionalProperties: false
+            properties:
+              answer:
+                description: The answer to the question
+                type: string
+              cited_chunks:
+                description: IDs of chunks used to form the answer
+                items:
+                  type: string
+                type: array
+              confidence:
+                default: 1.0
+                description: Confidence score for this answer (0-1)
+                maximum: 1.0
+                minimum: 0.0
+                type: number
+              query:
+                description: The question that was answered
+                type: string
+            required:
+            - query
+            - answer
+            type: object
+        type: function
+    uri: http://localhost:11434/v1/chat/completions
+  response:
+    headers:
+      content-length:
+      - '740'
+      content-type:
+      - application/json
+    parsed_body:
+      choices:
+      - finish_reason: tool_calls
+        index: 0
+        message:
+          content: ''
+          reasoning: Need to call final_result.
+          role: assistant
+          tool_calls:
+          - function:
+              arguments: '{"answer":"I couldn’t find the specific instance count for the most frequent class in DocLayNet
+                from the retrieved documents.","cited_chunks":[],"confidence":0.3,"query":"What is the exact instance count
+                for the class identified as having the highest occurrence in DocLayNet?"}'
+              name: final_result
+            id: call_wl6kbizd
+            index: 0
+            type: function
+      created: 1769001287
+      id: chatcmpl-995
+      model: gpt-oss
+      object: chat.completion
+      system_fingerprint: fp_ollama
+      usage:
+        completion_tokens: 84
+        prompt_tokens: 1085
+        total_tokens: 1169
+    status:
+      code: 200
+      message: OK
+- request:
+    headers:
+      accept:
+      - application/json
+      accept-encoding:
+      - gzip, deflate, zstd
+      connection:
+      - keep-alive
+      content-length:
+      - '2904'
+      content-type:
+      - application/json
+      host:
+      - localhost:11434
+    method: POST
+    parsed_body:
+      messages:
+      - content: |-
+          You are a search and question-answering specialist.
+
+          Process:
+          1. Call search_and_answer with relevant keywords from the question.
+          2. Review the results ordered by relevance.
+          3. If needed, perform follow-up searches with different keywords (max 3 total).
+          4. Provide a concise answer based strictly on the retrieved content.
+
+          The search tool returns results like:
+          [9bde5847-44c9-400a-8997-0e6b65babf92] [rank 1 of 5]
+          Source: "Document Title" > Section > Subsection
+          Type: paragraph
+          Content:
+          The actual text content here...
+
+          [d5a63c82-cb40-439f-9b2e-de7d177829b7] [rank 2 of 5]
+          Source: "Another Document"
+          Type: table
+          Content:
+          | Column 1 | Column 2 |
+          ...
+
+          Each result includes:
+          - chunk_id in brackets and rank position (rank 1 = most relevant)
+          - Source: document title and section hierarchy (when available)
+          - Type: content type like paragraph, table, code, list_item (when available)
+          - Content: the actual text
+
+          Output format:
+          - query: Echo the question you are answering
+          - answer: Your concise answer based on the retrieved content
+          - cited_chunks: List of plain strings containing only the chunk UUIDs (not objects)
+          - confidence: A score from 0.0 to 1.0 indicating answer confidence
+
+          IMPORTANT: Use the EXACT, COMPLETE chunk ID (full UUID). Do NOT truncate IDs.
+
+          Guidelines:
+          - Base answers strictly on retrieved content - do not use external knowledge.
+          - Use the Source and Type metadata to understand context.
+          - If multiple results are relevant, synthesize them coherently.
+          - If information is insufficient, say so clearly.
+          - Be concise and direct; avoid meta commentary about the process.
+          - Results are ordered by relevance, with rank 1 being most relevant.
+        role: system
+      - content: What percentage of the total annotated instances does this most frequent class represent in the DocLayNet
+          dataset?
+        role: user
+      model: gpt-oss
+      reasoning_effort: low
+      stream: false
+      tool_choice: auto
+      tools:
+      - function:
+          description: Search the knowledge base for relevant documents.
+          name: search_and_answer
+          parameters:
+            additionalProperties: false
+            properties:
+              limit:
+                anyOf:
+                - type: integer
+                - type: 'null'
+                default: null
+              query:
+                type: string
+            required:
+            - query
+            type: object
+        type: function
+      - function:
+          description: Answer to a search query with chunk references.
+          name: final_result
+          parameters:
+            additionalProperties: false
+            properties:
+              answer:
+                description: The answer to the question
+                type: string
+              cited_chunks:
+                description: IDs of chunks used to form the answer
+                items:
+                  type: string
+                type: array
+              confidence:
+                default: 1.0
+                description: Confidence score for this answer (0-1)
+                maximum: 1.0
+                minimum: 0.0
+                type: number
+              query:
+                description: The question that was answered
+                type: string
+            required:
+            - query
+            - answer
+            type: object
+        type: function
+    uri: http://localhost:11434/v1/chat/completions
+  response:
+    headers:
+      content-length:
+      - '538'
+      content-type:
+      - application/json
+    parsed_body:
+      choices:
+      - finish_reason: tool_calls
+        index: 0
+        message:
+          content: ''
+          reasoning: We need search.
+          role: assistant
+          tool_calls:
+          - function:
+              arguments: '{"query":"DocLayNet most frequent class percentage of total annotated instances","limit":5}'
+              name: search_and_answer
+            id: call_47zg1dmj
+            index: 0
+            type: function
+      created: 1769001289
+      id: chatcmpl-459
+      model: gpt-oss
+      object: chat.completion
+      system_fingerprint: fp_ollama
+      usage:
+        completion_tokens: 43
+        prompt_tokens: 635
+        total_tokens: 678
+    status:
+      code: 200
+      message: OK
+- request:
+    headers:
+      accept:
+      - application/json
+      accept-encoding:
+      - gzip, deflate, zstd
+      connection:
+      - keep-alive
+      content-length:
+      - '139'
+      content-type:
+      - application/json
+      host:
+      - localhost:11434
+    method: POST
+    parsed_body:
+      encoding_format: base64
+      input:
+      - DocLayNet most frequent class percentage of total annotated instances
+      model: qwen3-embedding:4b
+    uri: http://localhost:11434/v1/embeddings
+  response:
+    headers:
+      content-type:
+      - application/json
+      transfer-encoding:
+      - chunked
+    parsed_body:
+      data:
+      - embedding: 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
+        index: 0
+        object: embedding
+      model: qwen3-embedding:4b
+      object: list
+      usage:
+        prompt_tokens: 13
+        total_tokens: 13
+    status:
+      code: 200
+      message: OK
+- request:
+    headers:
+      accept:
+      - application/json
+      accept-encoding:
+      - gzip, deflate, zstd
+      connection:
+      - keep-alive
+      content-length:
+      - '3798'
+      content-type:
+      - application/json
+      host:
+      - localhost:11434
+    method: POST
+    parsed_body:
+      messages:
+      - content: |-
+          You are a search and question-answering specialist.
+
+          Process:
+          1. Call search_and_answer with relevant keywords from the question.
+          2. Review the results ordered by relevance.
+          3. If needed, perform follow-up searches with different keywords (max 3 total).
+          4. Provide a concise answer based strictly on the retrieved content.
+
+          The search tool returns results like:
+          [9bde5847-44c9-400a-8997-0e6b65babf92] [rank 1 of 5]
+          Source: "Document Title" > Section > Subsection
+          Type: paragraph
+          Content:
+          The actual text content here...
+
+          [d5a63c82-cb40-439f-9b2e-de7d177829b7] [rank 2 of 5]
+          Source: "Another Document"
+          Type: table
+          Content:
+          | Column 1 | Column 2 |
+          ...
+
+          Each result includes:
+          - chunk_id in brackets and rank position (rank 1 = most relevant)
+          - Source: document title and section hierarchy (when available)
+          - Type: content type like paragraph, table, code, list_item (when available)
+          - Content: the actual text
+
+          Output format:
+          - query: Echo the question you are answering
+          - answer: Your concise answer based on the retrieved content
+          - cited_chunks: List of plain strings containing only the chunk UUIDs (not objects)
+          - confidence: A score from 0.0 to 1.0 indicating answer confidence
+
+          IMPORTANT: Use the EXACT, COMPLETE chunk ID (full UUID). Do NOT truncate IDs.
+
+          Guidelines:
+          - Base answers strictly on retrieved content - do not use external knowledge.
+          - Use the Source and Type metadata to understand context.
+          - If multiple results are relevant, synthesize them coherently.
+          - If information is insufficient, say so clearly.
+          - Be concise and direct; avoid meta commentary about the process.
+          - Results are ordered by relevance, with rank 1 being most relevant.
+        role: system
+      - content: What percentage of the total annotated instances does this most frequent class represent in the DocLayNet
+          dataset?
+        role: user
+      - content: |-
+          <think>
+          We need search.
+          </think>
+        role: assistant
+        tool_calls:
+        - function:
+            arguments: '{"query":"DocLayNet most frequent class percentage of total annotated instances","limit":5}'
+            name: search_and_answer
+          id: call_47zg1dmj
+          type: function
+      - content: |-
+          [8bde9c2a-808a-4c65-8d7c-c9e2985b57fc] [rank 1 of 1]
+          Source: "DocLayNet Class Labels"
+          Type: list_item
+          Content:
+          DocLayNet defines 11 distinct class labels for document layout analysis:
+
+          Caption - Text describing figures or tables
+
+          Footnote - Notes at the bottom of pages
+
+          Formula - Mathematical expressions
+
+          List-item - Items in bulleted or numbered lists
+
+          Page-footer - Footer content on pages
+
+          Page-header - Header content on pages
+
+          Picture - Images and diagrams
+
+          Section-header - Headings for document sections
+
+          Table - Tabular data
+        role: tool
+        tool_call_id: call_47zg1dmj
+      model: gpt-oss
+      reasoning_effort: low
+      stream: false
+      tool_choice: auto
+      tools:
+      - function:
+          description: Search the knowledge base for relevant documents.
+          name: search_and_answer
+          parameters:
+            additionalProperties: false
+            properties:
+              limit:
+                anyOf:
+                - type: integer
+                - type: 'null'
+                default: null
+              query:
+                type: string
+            required:
+            - query
+            type: object
+        type: function
+      - function:
+          description: Answer to a search query with chunk references.
+          name: final_result
+          parameters:
+            additionalProperties: false
+            properties:
+              answer:
+                description: The answer to the question
+                type: string
+              cited_chunks:
+                description: IDs of chunks used to form the answer
+                items:
+                  type: string
+                type: array
+              confidence:
+                default: 1.0
+                description: Confidence score for this answer (0-1)
+                maximum: 1.0
+                minimum: 0.0
+                type: number
+              query:
+                description: The question that was answered
+                type: string
+            required:
+            - query
+            - answer
+            type: object
+        type: function
+    uri: http://localhost:11434/v1/chat/completions
+  response:
+    headers:
+      content-length:
+      - '541'
+      content-type:
+      - application/json
+    parsed_body:
+      choices:
+      - finish_reason: tool_calls
+        index: 0
+        message:
+          content: ''
+          reasoning: Need percentage. Search more.
+          role: assistant
+          tool_calls:
+          - function:
+              arguments: '{"limit":5,"query":"DocLayNet dataset annotation class distribution percentage"}'
+              name: search_and_answer
+            id: call_21w67mpq
+            index: 0
+            type: function
+      created: 1769001290
+      id: chatcmpl-650
+      model: gpt-oss
+      object: chat.completion
+      system_fingerprint: fp_ollama
+      usage:
+        completion_tokens: 42
+        prompt_tokens: 833
+        total_tokens: 875
+    status:
+      code: 200
+      message: OK
+- request:
+    headers:
+      accept:
+      - application/json
+      accept-encoding:
+      - gzip, deflate, zstd
+      connection:
+      - keep-alive
+      content-length:
+      - '128'
+      content-type:
+      - application/json
+      host:
+      - localhost:11434
+    method: POST
+    parsed_body:
+      encoding_format: base64
+      input:
+      - DocLayNet dataset annotation class distribution percentage
+      model: qwen3-embedding:4b
+    uri: http://localhost:11434/v1/embeddings
+  response:
+    headers:
+      content-type:
+      - application/json
+      transfer-encoding:
+      - chunked
+    parsed_body:
+      data:
+      - embedding: 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
+        index: 0
+        object: embedding
+      model: qwen3-embedding:4b
+      object: list
+      usage:
+        prompt_tokens: 10
+        total_tokens: 10
+    status:
+      code: 200
+      message: OK
+- request:
+    headers:
+      accept:
+      - application/json
+      accept-encoding:
+      - gzip, deflate, zstd
+      connection:
+      - keep-alive
+      content-length:
+      - '4695'
+      content-type:
+      - application/json
+      host:
+      - localhost:11434
+    method: POST
+    parsed_body:
+      messages:
+      - content: |-
+          You are a search and question-answering specialist.
+
+          Process:
+          1. Call search_and_answer with relevant keywords from the question.
+          2. Review the results ordered by relevance.
+          3. If needed, perform follow-up searches with different keywords (max 3 total).
+          4. Provide a concise answer based strictly on the retrieved content.
+
+          The search tool returns results like:
+          [9bde5847-44c9-400a-8997-0e6b65babf92] [rank 1 of 5]
+          Source: "Document Title" > Section > Subsection
+          Type: paragraph
+          Content:
+          The actual text content here...
+
+          [d5a63c82-cb40-439f-9b2e-de7d177829b7] [rank 2 of 5]
+          Source: "Another Document"
+          Type: table
+          Content:
+          | Column 1 | Column 2 |
+          ...
+
+          Each result includes:
+          - chunk_id in brackets and rank position (rank 1 = most relevant)
+          - Source: document title and section hierarchy (when available)
+          - Type: content type like paragraph, table, code, list_item (when available)
+          - Content: the actual text
+
+          Output format:
+          - query: Echo the question you are answering
+          - answer: Your concise answer based on the retrieved content
+          - cited_chunks: List of plain strings containing only the chunk UUIDs (not objects)
+          - confidence: A score from 0.0 to 1.0 indicating answer confidence
+
+          IMPORTANT: Use the EXACT, COMPLETE chunk ID (full UUID). Do NOT truncate IDs.
+
+          Guidelines:
+          - Base answers strictly on retrieved content - do not use external knowledge.
+          - Use the Source and Type metadata to understand context.
+          - If multiple results are relevant, synthesize them coherently.
+          - If information is insufficient, say so clearly.
+          - Be concise and direct; avoid meta commentary about the process.
+          - Results are ordered by relevance, with rank 1 being most relevant.
+        role: system
+      - content: What percentage of the total annotated instances does this most frequent class represent in the DocLayNet
+          dataset?
+        role: user
+      - content: |-
+          <think>
+          We need search.
+          </think>
+        role: assistant
+        tool_calls:
+        - function:
+            arguments: '{"query":"DocLayNet most frequent class percentage of total annotated instances","limit":5}'
+            name: search_and_answer
+          id: call_47zg1dmj
+          type: function
+      - content: |-
+          [8bde9c2a-808a-4c65-8d7c-c9e2985b57fc] [rank 1 of 1]
+          Source: "DocLayNet Class Labels"
+          Type: list_item
+          Content:
+          DocLayNet defines 11 distinct class labels for document layout analysis:
+
+          Caption - Text describing figures or tables
+
+          Footnote - Notes at the bottom of pages
+
+          Formula - Mathematical expressions
+
+          List-item - Items in bulleted or numbered lists
+
+          Page-footer - Footer content on pages
+
+          Page-header - Header content on pages
+
+          Picture - Images and diagrams
+
+          Section-header - Headings for document sections
+
+          Table - Tabular data
+        role: tool
+        tool_call_id: call_47zg1dmj
+      - content: |-
+          <think>
+          Need percentage. Search more.
+          </think>
+        role: assistant
+        tool_calls:
+        - function:
+            arguments: '{"limit":5,"query":"DocLayNet dataset annotation class distribution percentage"}'
+            name: search_and_answer
+          id: call_21w67mpq
+          type: function
+      - content: |-
+          [8bde9c2a-808a-4c65-8d7c-c9e2985b57fc] [rank 1 of 1]
+          Source: "DocLayNet Class Labels"
+          Type: list_item
+          Content:
+          DocLayNet defines 11 distinct class labels for document layout analysis:
+
+          Caption - Text describing figures or tables
+
+          Footnote - Notes at the bottom of pages
+
+          Formula - Mathematical expressions
+
+          List-item - Items in bulleted or numbered lists
+
+          Page-footer - Footer content on pages
+
+          Page-header - Header content on pages
+
+          Picture - Images and diagrams
+
+          Section-header - Headings for document sections
+
+          Table - Tabular data
+        role: tool
+        tool_call_id: call_21w67mpq
+      model: gpt-oss
+      reasoning_effort: low
+      stream: false
+      tool_choice: auto
+      tools:
+      - function:
+          description: Search the knowledge base for relevant documents.
+          name: search_and_answer
+          parameters:
+            additionalProperties: false
+            properties:
+              limit:
+                anyOf:
+                - type: integer
+                - type: 'null'
+                default: null
+              query:
+                type: string
+            required:
+            - query
+            type: object
+        type: function
+      - function:
+          description: Answer to a search query with chunk references.
+          name: final_result
+          parameters:
+            additionalProperties: false
+            properties:
+              answer:
+                description: The answer to the question
+                type: string
+              cited_chunks:
+                description: IDs of chunks used to form the answer
+                items:
+                  type: string
+                type: array
+              confidence:
+                default: 1.0
+                description: Confidence score for this answer (0-1)
+                maximum: 1.0
+                minimum: 0.0
+                type: number
+              query:
+                description: The question that was answered
+                type: string
+            required:
+            - query
+            - answer
+            type: object
+        type: function
+    uri: http://localhost:11434/v1/chat/completions
+  response:
+    headers:
+      content-length:
+      - '456'
+      content-type:
+      - application/json
+    parsed_body:
+      choices:
+      - finish_reason: stop
+        index: 0
+        message:
+          content: I’m sorry, but I couldn’t find a source that gives the exact percentage of the most frequent class relative
+            to the total annotated instances in the DocLayNet dataset.
+          role: assistant
+      created: 1769001291
+      id: chatcmpl-876
+      model: gpt-oss
+      object: chat.completion
+      system_fingerprint: fp_ollama
+      usage:
+        completion_tokens: 38
+        prompt_tokens: 1030
+        total_tokens: 1068
+    status:
+      code: 200
+      message: OK
+- request:
+    headers:
+      accept:
+      - application/json
+      accept-encoding:
+      - gzip, deflate, zstd
+      connection:
+      - keep-alive
+      content-length:
+      - '5027'
+      content-type:
+      - application/json
+      host:
+      - localhost:11434
+    method: POST
+    parsed_body:
+      messages:
+      - content: |-
+          You are a search and question-answering specialist.
+
+          Process:
+          1. Call search_and_answer with relevant keywords from the question.
+          2. Review the results ordered by relevance.
+          3. If needed, perform follow-up searches with different keywords (max 3 total).
+          4. Provide a concise answer based strictly on the retrieved content.
+
+          The search tool returns results like:
+          [9bde5847-44c9-400a-8997-0e6b65babf92] [rank 1 of 5]
+          Source: "Document Title" > Section > Subsection
+          Type: paragraph
+          Content:
+          The actual text content here...
+
+          [d5a63c82-cb40-439f-9b2e-de7d177829b7] [rank 2 of 5]
+          Source: "Another Document"
+          Type: table
+          Content:
+          | Column 1 | Column 2 |
+          ...
+
+          Each result includes:
+          - chunk_id in brackets and rank position (rank 1 = most relevant)
+          - Source: document title and section hierarchy (when available)
+          - Type: content type like paragraph, table, code, list_item (when available)
+          - Content: the actual text
+
+          Output format:
+          - query: Echo the question you are answering
+          - answer: Your concise answer based on the retrieved content
+          - cited_chunks: List of plain strings containing only the chunk UUIDs (not objects)
+          - confidence: A score from 0.0 to 1.0 indicating answer confidence
+
+          IMPORTANT: Use the EXACT, COMPLETE chunk ID (full UUID). Do NOT truncate IDs.
+
+          Guidelines:
+          - Base answers strictly on retrieved content - do not use external knowledge.
+          - Use the Source and Type metadata to understand context.
+          - If multiple results are relevant, synthesize them coherently.
+          - If information is insufficient, say so clearly.
+          - Be concise and direct; avoid meta commentary about the process.
+          - Results are ordered by relevance, with rank 1 being most relevant.
+        role: system
+      - content: What percentage of the total annotated instances does this most frequent class represent in the DocLayNet
+          dataset?
+        role: user
+      - content: |-
+          <think>
+          We need search.
+          </think>
+        role: assistant
+        tool_calls:
+        - function:
+            arguments: '{"query":"DocLayNet most frequent class percentage of total annotated instances","limit":5}'
+            name: search_and_answer
+          id: call_47zg1dmj
+          type: function
+      - content: |-
+          [8bde9c2a-808a-4c65-8d7c-c9e2985b57fc] [rank 1 of 1]
+          Source: "DocLayNet Class Labels"
+          Type: list_item
+          Content:
+          DocLayNet defines 11 distinct class labels for document layout analysis:
+
+          Caption - Text describing figures or tables
+
+          Footnote - Notes at the bottom of pages
+
+          Formula - Mathematical expressions
+
+          List-item - Items in bulleted or numbered lists
+
+          Page-footer - Footer content on pages
+
+          Page-header - Header content on pages
+
+          Picture - Images and diagrams
+
+          Section-header - Headings for document sections
+
+          Table - Tabular data
+        role: tool
+        tool_call_id: call_47zg1dmj
+      - content: |-
+          <think>
+          Need percentage. Search more.
+          </think>
+        role: assistant
+        tool_calls:
+        - function:
+            arguments: '{"limit":5,"query":"DocLayNet dataset annotation class distribution percentage"}'
+            name: search_and_answer
+          id: call_21w67mpq
+          type: function
+      - content: |-
+          [8bde9c2a-808a-4c65-8d7c-c9e2985b57fc] [rank 1 of 1]
+          Source: "DocLayNet Class Labels"
+          Type: list_item
+          Content:
+          DocLayNet defines 11 distinct class labels for document layout analysis:
+
+          Caption - Text describing figures or tables
+
+          Footnote - Notes at the bottom of pages
+
+          Formula - Mathematical expressions
+
+          List-item - Items in bulleted or numbered lists
+
+          Page-footer - Footer content on pages
+
+          Page-header - Header content on pages
+
+          Picture - Images and diagrams
+
+          Section-header - Headings for document sections
+
+          Table - Tabular data
+        role: tool
+        tool_call_id: call_21w67mpq
+      - content: I’m sorry, but I couldn’t find a source that gives the exact percentage of the most frequent class relative
+          to the total annotated instances in the DocLayNet dataset.
+        role: assistant
+      - content: |-
+          Validation feedback:
+          Please include your response in a tool call.
+
+          Fix the errors and try again.
+        role: user
+      model: gpt-oss
+      reasoning_effort: low
+      stream: false
+      tool_choice: auto
+      tools:
+      - function:
+          description: Search the knowledge base for relevant documents.
+          name: search_and_answer
+          parameters:
+            additionalProperties: false
+            properties:
+              limit:
+                anyOf:
+                - type: integer
+                - type: 'null'
+                default: null
+              query:
+                type: string
+            required:
+            - query
+            type: object
+        type: function
+      - function:
+          description: Answer to a search query with chunk references.
+          name: final_result
+          parameters:
+            additionalProperties: false
+            properties:
+              answer:
+                description: The answer to the question
+                type: string
+              cited_chunks:
+                description: IDs of chunks used to form the answer
+                items:
+                  type: string
+                type: array
+              confidence:
+                default: 1.0
+                description: Confidence score for this answer (0-1)
+                maximum: 1.0
+                minimum: 0.0
+                type: number
+              query:
+                description: The question that was answered
+                type: string
+            required:
+            - query
+            - answer
+            type: object
+        type: function
+    uri: http://localhost:11434/v1/chat/completions
+  response:
+    headers:
+      content-length:
+      - '811'
+      content-type:
+      - application/json
+    parsed_body:
+      choices:
+      - finish_reason: tool_calls
+        index: 0
+        message:
+          content: ''
+          reasoning: We need to use final_result tool.
+          role: assistant
+          tool_calls:
+          - function:
+              arguments: '{"answer":"I’m sorry, but I couldn’t find a source that gives the exact percentage of the most frequent
+                class relative to the total annotated instances in the DocLayNet dataset.","cited_chunks":[],"confidence":0.1,"query":"What
+                percentage of the total annotated instances does this most frequent class represent in the DocLayNet dataset?"}'
+              name: final_result
+            id: call_tnz25g3j
+            index: 0
+            type: function
+      created: 1769001294
+      id: chatcmpl-289
+      model: gpt-oss
+      object: chat.completion
+      system_fingerprint: fp_ollama
+      usage:
+        completion_tokens: 98
+        prompt_tokens: 1093
+        total_tokens: 1191
+    status:
+      code: 200
+      message: OK
+- request:
+    headers:
+      accept:
+      - application/json
+      accept-encoding:
+      - gzip, deflate, zstd
+      connection:
+      - keep-alive
+      content-length:
+      - '3606'
       content-type:
       - application/json
       host:
@@ -3566,21 +3033,21 @@ interactions:
             <question>What is the highest count class in the DocLayNet dataset?</question>
             <prior_answers>
               <item>
-                <question>In the official DocLayNet distribution, which class label has the largest number of annotated instances across all partitions (train, validation, test)?</question>
-                <answer>I couldn’t locate the quantitative counts for each class in the distribution data.</answer>
-                <confidence>0.5</confidence>
+                <question>In the DocLayNet dataset, which of the 11 layout classes has the largest number of annotated instances?</question>
+                <answer>The available documentation does not report the number of annotated instances for each of the 11 DocLayNet layout classes, so from the retrieved information it cannot be determined which class has the most instances.</answer>
+                <confidence>0.2</confidence>
+                <source>DocLayNet Class Labels</source>
+              </item>
+              <item>
+                <question>What is the exact instance count for the class identified as having the highest occurrence in DocLayNet?</question>
+                <answer>I couldn’t find the specific instance count for the most frequent class in DocLayNet from the retrieved documents.</answer>
+                <confidence>0.3</confidence>
                 <source>null</source>
               </item>
               <item>
-                <question>What are the exact instance counts for each of the 11 class labels in the DocLayNet dataset as recorded in the public metadata files?</question>
-                <answer>I’m sorry, but I couldn’t find the exact instance counts for each of the 11 class labels in the public DocLayNet metadata files.</answer>
-                <confidence>0.0</confidence>
-                <source>null</source>
-              </item>
-              <item>
-                <question>Is the class with the maximum instance count the same across all split phases (train, validation, and test) of DocLayNet, or does it vary by split?</question>
-                <answer>I couldn’t find any documentation that lists the instance counts for each class per split, so I can’t confirm whether the class with the highest count is identical across the train, validation, and test sets.</answer>
-                <confidence>0.4</confidence>
+                <question>What percentage of the total annotated instances does this most frequent class represent in the DocLayNet dataset?</question>
+                <answer>I’m sorry, but I couldn’t find a source that gives the exact percentage of the most frequent class relative to the total annotated instances in the DocLayNet dataset.</answer>
+                <confidence>0.1</confidence>
                 <source>null</source>
               </item>
             </prior_answers>
@@ -3654,7 +3121,184 @@ interactions:
   response:
     headers:
       content-length:
-      - '1435'
+      - '876'
+      content-type:
+      - application/json
+    parsed_body:
+      choices:
+      - finish_reason: tool_calls
+        index: 0
+        message:
+          content: ''
+          reasoning: We need answer based on gathered evidence. We have no evidence of counts. So answer that unknown. Provide
+            confidence low.
+          role: assistant
+          tool_calls:
+          - function:
+              arguments: '{"answer":"I’m sorry, but I couldn’t find any source that reports the actual numbers of instances
+                per layout class in DocLayNet, so I don’t know which class has the highest count. It would require looking
+                at the dataset’s annotations or an official statistics table to determine that.","citations":[],"confidence":0.1}'
+              name: final_result
+            id: call_0fpwdoq1
+            index: 0
+            type: function
+      created: 1769001297
+      id: chatcmpl-92
+      model: gpt-oss
+      object: chat.completion
+      system_fingerprint: fp_ollama
+      usage:
+        completion_tokens: 112
+        prompt_tokens: 650
+        total_tokens: 762
+    status:
+      code: 200
+      message: OK
+- request:
+    headers:
+      accept:
+      - application/json
+      accept-encoding:
+      - gzip, deflate, zstd
+      connection:
+      - keep-alive
+      content-length:
+      - '4714'
+      content-type:
+      - application/json
+      host:
+      - localhost:11434
+    method: POST
+    parsed_body:
+      messages:
+      - content: |-
+          You are a helpful research assistant powered by haiku.rag, a knowledge base system.
+
+          You have access to a knowledge base of documents. Use your tools to search and answer questions.
+
+          CRITICAL RULES:
+          1. For greetings or casual chat: respond directly WITHOUT using any tools
+          2. For questions: Use the "ask" tool EXACTLY ONCE - it handles query expansion internally
+          3. For searches: Use the "search" tool EXACTLY ONCE - it handles multi-query expansion internally
+          4. NEVER call the same tool multiple times for a single user message
+          5. NEVER make up information - always use tools to get facts from the knowledge base
+
+          How to decide which tool to use:
+          - "get_document" - Use when the user references a SPECIFIC document by name, title, or URI (e.g., "summarize document X", "get the paper about Y", "fetch 2412.00566"). Retrieves the full document content.
+          - "ask" - Use for general questions about topics in the knowledge base when no specific document is named. It searches across all documents and returns answers with citations.
+          - "search" - Use when the user explicitly asks to search/find/explore documents. Call it ONCE. After calling search, copy the ENTIRE tool response to your output INCLUDING the content snippets. Do NOT shorten, summarize, or omit any part of the results.
+
+          IMPORTANT - When user mentions a document in search/ask:
+          - If user says "search in <doc>", "find in <doc>", "answer from <doc>", or "<topic> in <doc>":
+            - Extract the TOPIC as `query`/`question`
+            - Extract the DOCUMENT NAME as `document_name`
+          - Examples for search:
+            - "search for embeddings in the ML paper" → query="embeddings", document_name="ML paper"
+            - "find transformer architecture in 2412.00566" → query="transformer architecture", document_name="2412.00566"
+          - Examples for ask:
+            - "what does the ML paper say about embeddings?" → question="what are the embedding methods?", document_name="ML paper"
+            - "answer from 2412.00566 about model training" → question="how is the model trained?", document_name="2412.00566"
+
+          Be friendly and conversational. When you use the "ask" tool, summarize the key findings for the user.
+        role: system
+      - content: What is the highest count class in the DocLayNet dataset?
+        role: user
+      - content: |-
+          <think>
+          Need ask.
+          </think>
+        role: assistant
+        tool_calls:
+        - function:
+            arguments: '{"document_name":null,"question":"What is the highest count class in the DocLayNet dataset?"}'
+            name: ask
+          id: call_9p1yei96
+          type: function
+      - content: |-
+          I’m sorry, but I couldn’t find any source that reports the actual numbers of instances per layout class in DocLayNet, so I don’t know which class has the highest count. It would require looking at the dataset’s annotations or an official statistics table to determine that.
+
+          Sources: [1]
+        role: tool
+        tool_call_id: call_9p1yei96
+      model: gpt-oss
+      reasoning_effort: low
+      stream: false
+      tool_choice: auto
+      tools:
+      - function:
+          description: |-
+            Search the knowledge base for relevant documents.
+
+            Use this when you need to find documents or explore the knowledge base.
+            Results are displayed to the user - just list the titles found.
+          name: search
+          parameters:
+            additionalProperties: false
+            properties:
+              document_name:
+                anyOf:
+                - type: string
+                - type: 'null'
+                default: null
+                description: Optional document name/title to search within
+              limit:
+                anyOf:
+                - type: integer
+                - type: 'null'
+                default: null
+                description: 'Number of results to return (default: 5)'
+              query:
+                description: The search query (what to search for)
+                type: string
+            required:
+            - query
+            type: object
+        type: function
+      - function:
+          description: |-
+            Answer a specific question using the knowledge base.
+
+            Use this for direct questions that need a focused answer with citations.
+            Uses a research graph for planning, searching, and synthesis.
+          name: ask
+          parameters:
+            additionalProperties: false
+            properties:
+              document_name:
+                anyOf:
+                - type: string
+                - type: 'null'
+                default: null
+                description: Optional document name/title to search within (e.g., "tbmed593", "army manual")
+              question:
+                description: The question to answer
+                type: string
+            required:
+            - question
+            type: object
+        type: function
+      - function:
+          description: |-
+            Retrieve a specific document by title or URI.
+
+            Use this when the user wants to fetch/get/retrieve a specific document.
+          name: get_document
+          parameters:
+            additionalProperties: false
+            properties:
+              query:
+                description: The document title or URI to look up
+                type: string
+            required:
+            - query
+            type: object
+          strict: true
+        type: function
+    uri: http://localhost:11434/v1/chat/completions
+  response:
+    headers:
+      content-length:
+      - '566'
       content-type:
       - application/json
     parsed_body:
@@ -3662,26 +3306,19 @@ interactions:
       - finish_reason: stop
         index: 0
         message:
-          content: '{"answer":"In the official DocLayNet release, the class that appears most frequently is **Text‐blocks**.
-            Across the entire dataset (train, validation, and test combined), Text blocks amount to approximately 1.1 million
-            annotated instances, which outnumbers all other categories such as Title, Section, List, Table, Graph, Image,
-            Chart, Figure, Note and Caption.  The figure below shows the relative proportions of each of the 11 classes, demonstrating
-            that Text is the dominant label.\n\n- **Text** – ~1,114,000 instances (≈49 % of all annotations)\n- **Title**
-            – ~250,000\n- **Section** – ~240,000\n- **Figure** – ~130,000\n- **List** – ~80,000\n- **Table** – ~65,000\n-
-            **Graph** – ~45,000\n- **Image** – ~35,000\n- **Chart** – ~25,000\n- **Note** – ~15,000\n- **Caption** – ~10,000\n\nThese
-            counts are taken from the public JSON metadata files that accompany the distribution, where each image’s annotations
-            are enumerated by class.  Thus, the highest‑count class in DocLayNet is Text.\n","confidence":0.92}'
-          reasoning: We need answer. likely class 'Text' has highest count. Provide counts.
+          content: I’m sorry, but I couldn’t find any source that reports the actual numbers of instances per layout class
+            in DocLayNet, so I don’t know which class has the highest count. It would require looking at the dataset’s annotations
+            or an official statistics table to determine that.
           role: assistant
-      created: 1768997550
-      id: chatcmpl-461
+      created: 1769001299
+      id: chatcmpl-506
       model: gpt-oss
       object: chat.completion
       system_fingerprint: fp_ollama
       usage:
-        completion_tokens: 308
-        prompt_tokens: 665
-        total_tokens: 973
+        completion_tokens: 60
+        prompt_tokens: 970
+        total_tokens: 1030
     status:
       code: 200
       message: OK
diff --git a/tests/cassettes/test_chat_agent/test_chat_agent_ask_with_state_key.yaml b/tests/cassettes/test_chat_agent/test_chat_agent_ask_with_state_key.yaml
new file mode 100644
index 00000000..0b447e0e
--- /dev/null
+++ b/tests/cassettes/test_chat_agent/test_chat_agent_ask_with_state_key.yaml
@@ -0,0 +1,3355 @@
+interactions:
+- request:
+    headers:
+      accept:
+      - application/json
+      accept-encoding:
+      - gzip, deflate, zstd
+      connection:
+      - keep-alive
+      content-length:
+      - '730'
+      content-type:
+      - application/json
+      host:
+      - localhost:11434
+    method: POST
+    parsed_body:
+      encoding_format: base64
+      input:
+      - |-
+        DocLayNet Dataset - Class Labels
+        DocLayNet defines 11 distinct class labels for document layout analysis:
+        1. Caption - Text describing figures or tables
+        2. Footnote - Notes at the bottom of pages
+        3. Formula - Mathematical expressions
+        4. List-item - Items in bulleted or numbered lists
+        5. Page-footer - Footer content on pages
+        6. Page-header - Header content on pages
+        7. Picture - Images and diagrams
+        8. Section-header - Headings for document sections
+        9. Table - Tabular data
+        10. Text - Regular paragraph text (highest count: 510,377 instances)
+        11. Title - Document titles
+        The Text class has the highest count with 510,377 instances in the dataset.
+      model: qwen3-embedding:4b
+    uri: http://localhost:11434/v1/embeddings
+  response:
+    headers:
+      content-type:
+      - application/json
+      transfer-encoding:
+      - chunked
+    parsed_body:
+      data:
+      - embedding: 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
+        index: 0
+        object: embedding
+      model: qwen3-embedding:4b
+      object: list
+      usage:
+        prompt_tokens: 166
+        total_tokens: 166
+    status:
+      code: 200
+      message: OK
+- request:
+    headers:
+      accept:
+      - application/json
+      accept-encoding:
+      - gzip, deflate, zstd
+      connection:
+      - keep-alive
+      content-length:
+      - '4099'
+      content-type:
+      - application/json
+      host:
+      - localhost:11434
+    method: POST
+    parsed_body:
+      messages:
+      - content: |-
+          You are a helpful research assistant powered by haiku.rag, a knowledge base system.
+
+          You have access to a knowledge base of documents. Use your tools to search and answer questions.
+
+          CRITICAL RULES:
+          1. For greetings or casual chat: respond directly WITHOUT using any tools
+          2. For questions: Use the "ask" tool EXACTLY ONCE - it handles query expansion internally
+          3. For searches: Use the "search" tool EXACTLY ONCE - it handles multi-query expansion internally
+          4. NEVER call the same tool multiple times for a single user message
+          5. NEVER make up information - always use tools to get facts from the knowledge base
+
+          How to decide which tool to use:
+          - "get_document" - Use when the user references a SPECIFIC document by name, title, or URI (e.g., "summarize document X", "get the paper about Y", "fetch 2412.00566"). Retrieves the full document content.
+          - "ask" - Use for general questions about topics in the knowledge base when no specific document is named. It searches across all documents and returns answers with citations.
+          - "search" - Use when the user explicitly asks to search/find/explore documents. Call it ONCE. After calling search, copy the ENTIRE tool response to your output INCLUDING the content snippets. Do NOT shorten, summarize, or omit any part of the results.
+
+          IMPORTANT - When user mentions a document in search/ask:
+          - If user says "search in <doc>", "find in <doc>", "answer from <doc>", or "<topic> in <doc>":
+            - Extract the TOPIC as `query`/`question`
+            - Extract the DOCUMENT NAME as `document_name`
+          - Examples for search:
+            - "search for embeddings in the ML paper" → query="embeddings", document_name="ML paper"
+            - "find transformer architecture in 2412.00566" → query="transformer architecture", document_name="2412.00566"
+          - Examples for ask:
+            - "what does the ML paper say about embeddings?" → question="what are the embedding methods?", document_name="ML paper"
+            - "answer from 2412.00566 about model training" → question="how is the model trained?", document_name="2412.00566"
+
+          Be friendly and conversational. When you use the "ask" tool, summarize the key findings for the user.
+        role: system
+      - content: What is the highest count class in the DocLayNet dataset?
+        role: user
+      model: gpt-oss
+      reasoning_effort: low
+      stream: false
+      tool_choice: auto
+      tools:
+      - function:
+          description: |-
+            Search the knowledge base for relevant documents.
+
+            Use this when you need to find documents or explore the knowledge base.
+            Results are displayed to the user - just list the titles found.
+          name: search
+          parameters:
+            additionalProperties: false
+            properties:
+              document_name:
+                anyOf:
+                - type: string
+                - type: 'null'
+                default: null
+                description: Optional document name/title to search within
+              limit:
+                anyOf:
+                - type: integer
+                - type: 'null'
+                default: null
+                description: 'Number of results to return (default: 5)'
+              query:
+                description: The search query (what to search for)
+                type: string
+            required:
+            - query
+            type: object
+        type: function
+      - function:
+          description: |-
+            Answer a specific question using the knowledge base.
+
+            Use this for direct questions that need a focused answer with citations.
+            Uses a research graph for planning, searching, and synthesis.
+          name: ask
+          parameters:
+            additionalProperties: false
+            properties:
+              document_name:
+                anyOf:
+                - type: string
+                - type: 'null'
+                default: null
+                description: Optional document name/title to search within (e.g., "tbmed593", "army manual")
+              question:
+                description: The question to answer
+                type: string
+            required:
+            - question
+            type: object
+        type: function
+      - function:
+          description: |-
+            Retrieve a specific document by title or URI.
+
+            Use this when the user wants to fetch/get/retrieve a specific document.
+          name: get_document
+          parameters:
+            additionalProperties: false
+            properties:
+              query:
+                description: The document title or URI to look up
+                type: string
+            required:
+            - query
+            type: object
+          strict: true
+        type: function
+    uri: http://localhost:11434/v1/chat/completions
+  response:
+    headers:
+      content-length:
+      - '519'
+      content-type:
+      - application/json
+    parsed_body:
+      choices:
+      - finish_reason: tool_calls
+        index: 0
+        message:
+          content: ''
+          reasoning: Need ask.
+          role: assistant
+          tool_calls:
+          - function:
+              arguments: '{"document_name":null,"question":"What is the highest count class in the DocLayNet dataset?"}'
+              name: ask
+            id: call_f61rh3p7
+            index: 0
+            type: function
+      created: 1769001195
+      id: chatcmpl-92
+      model: gpt-oss
+      object: chat.completion
+      system_fingerprint: fp_ollama
+      usage:
+        completion_tokens: 43
+        prompt_tokens: 850
+        total_tokens: 893
+    status:
+      code: 200
+      message: OK
+- request:
+    headers:
+      accept:
+      - application/json
+      accept-encoding:
+      - gzip, deflate, zstd
+      connection:
+      - keep-alive
+      content-length:
+      - '2147'
+      content-type:
+      - application/json
+      host:
+      - localhost:11434
+    method: POST
+    parsed_body:
+      messages:
+      - content: |-
+          You are the research orchestrator for a focused workflow.
+
+          If a <background> section is provided, use it to understand the domain context.
+
+          Responsibilities:
+          1. Understand and decompose the main question
+          2. Propose a minimal, high-leverage plan
+          3. Coordinate specialized agents to gather evidence
+
+          Plan requirements:
+          - Produce at most 3 sub_questions that together cover the main question.
+          - sub_questions must be a list of plain strings, where each string is a complete
+            question. Do NOT use objects with nested fields like {question, details}.
+          - Each sub_question must be a standalone, self-contained query that can run
+            without extra context. Include concrete entities, scope, timeframe, and any
+            qualifiers. Avoid ambiguous pronouns (it/they/this/that).
+          - Prioritize the highest-value aspects first; avoid redundancy and overlap.
+          - Prefer questions that are likely answerable from the current knowledge base;
+            if coverage is uncertain, make scopes narrower and specific.
+          - Order sub_questions by execution priority (most valuable first).
+
+          Use the gather_context tool once on the main question before planning.
+
+          Use the gather_context tool once on the main question before planning.
+        role: system
+      - content: |-
+          Plan a focused approach for the main question.
+
+          Main question: What is the highest count class in the DocLayNet dataset?
+        role: user
+      model: gpt-oss
+      reasoning_effort: low
+      stream: false
+      tool_choice: auto
+      tools:
+      - function:
+          description: ''
+          name: gather_context
+          parameters:
+            additionalProperties: false
+            properties:
+              limit:
+                anyOf:
+                - type: integer
+                - type: 'null'
+                default: null
+              query:
+                type: string
+            required:
+            - query
+            type: object
+        type: function
+      - function:
+          description: A structured research plan with sub-questions to explore.
+          name: final_result
+          parameters:
+            additionalProperties: false
+            properties:
+              sub_questions:
+                description: Specific questions to research, phrased as complete questions
+                items:
+                  type: string
+                type: array
+            required:
+            - sub_questions
+            type: object
+          strict: true
+        type: function
+    uri: http://localhost:11434/v1/chat/completions
+  response:
+    headers:
+      content-length:
+      - '530'
+      content-type:
+      - application/json
+    parsed_body:
+      choices:
+      - finish_reason: tool_calls
+        index: 0
+        message:
+          content: ''
+          reasoning: We need to gather context first.
+          role: assistant
+          tool_calls:
+          - function:
+              arguments: '{"limit":null,"query":"highest count class in the DocLayNet dataset"}'
+              name: gather_context
+            id: call_6j83gec4
+            index: 0
+            type: function
+      created: 1769001196
+      id: chatcmpl-223
+      model: gpt-oss
+      object: chat.completion
+      system_fingerprint: fp_ollama
+      usage:
+        completion_tokens: 44
+        prompt_tokens: 430
+        total_tokens: 474
+    status:
+      code: 200
+      message: OK
+- request:
+    headers:
+      accept:
+      - application/json
+      accept-encoding:
+      - gzip, deflate, zstd
+      connection:
+      - keep-alive
+      content-length:
+      - '114'
+      content-type:
+      - application/json
+      host:
+      - localhost:11434
+    method: POST
+    parsed_body:
+      encoding_format: base64
+      input:
+      - highest count class in the DocLayNet dataset
+      model: qwen3-embedding:4b
+    uri: http://localhost:11434/v1/embeddings
+  response:
+    headers:
+      content-type:
+      - application/json
+      transfer-encoding:
+      - chunked
+    parsed_body:
+      data:
+      - embedding: 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
+        index: 0
+        object: embedding
+      model: qwen3-embedding:4b
+      object: list
+      usage:
+        prompt_tokens: 11
+        total_tokens: 11
+    status:
+      code: 200
+      message: OK
+- request:
+    headers:
+      accept:
+      - application/json
+      accept-encoding:
+      - gzip, deflate, zstd
+      connection:
+      - keep-alive
+      content-length:
+      - '2916'
+      content-type:
+      - application/json
+      host:
+      - localhost:11434
+    method: POST
+    parsed_body:
+      messages:
+      - content: |-
+          You are the research orchestrator for a focused workflow.
+
+          If a <background> section is provided, use it to understand the domain context.
+
+          Responsibilities:
+          1. Understand and decompose the main question
+          2. Propose a minimal, high-leverage plan
+          3. Coordinate specialized agents to gather evidence
+
+          Plan requirements:
+          - Produce at most 3 sub_questions that together cover the main question.
+          - sub_questions must be a list of plain strings, where each string is a complete
+            question. Do NOT use objects with nested fields like {question, details}.
+          - Each sub_question must be a standalone, self-contained query that can run
+            without extra context. Include concrete entities, scope, timeframe, and any
+            qualifiers. Avoid ambiguous pronouns (it/they/this/that).
+          - Prioritize the highest-value aspects first; avoid redundancy and overlap.
+          - Prefer questions that are likely answerable from the current knowledge base;
+            if coverage is uncertain, make scopes narrower and specific.
+          - Order sub_questions by execution priority (most valuable first).
+
+          Use the gather_context tool once on the main question before planning.
+
+          Use the gather_context tool once on the main question before planning.
+        role: system
+      - content: |-
+          Plan a focused approach for the main question.
+
+          Main question: What is the highest count class in the DocLayNet dataset?
+        role: user
+      - content: |-
+          <think>
+          We need to gather context first.
+          </think>
+        role: assistant
+        tool_calls:
+        - function:
+            arguments: '{"limit":null,"query":"highest count class in the DocLayNet dataset"}'
+            name: gather_context
+          id: call_6j83gec4
+          type: function
+      - content: |-
+          DocLayNet defines 11 distinct class labels for document layout analysis:
+
+          Caption - Text describing figures or tables
+
+          Footnote - Notes at the bottom of pages
+
+          Formula - Mathematical expressions
+
+          List-item - Items in bulleted or numbered lists
+
+          Page-footer - Footer content on pages
+
+          Page-header - Header content on pages
+
+          Picture - Images and diagrams
+
+          Section-header - Headings for document sections
+
+          Table - Tabular data
+        role: tool
+        tool_call_id: call_6j83gec4
+      model: gpt-oss
+      reasoning_effort: low
+      stream: false
+      tool_choice: auto
+      tools:
+      - function:
+          description: ''
+          name: gather_context
+          parameters:
+            additionalProperties: false
+            properties:
+              limit:
+                anyOf:
+                - type: integer
+                - type: 'null'
+                default: null
+              query:
+                type: string
+            required:
+            - query
+            type: object
+        type: function
+      - function:
+          description: A structured research plan with sub-questions to explore.
+          name: final_result
+          parameters:
+            additionalProperties: false
+            properties:
+              sub_questions:
+                description: Specific questions to research, phrased as complete questions
+                items:
+                  type: string
+                type: array
+            required:
+            - sub_questions
+            type: object
+          strict: true
+        type: function
+    uri: http://localhost:11434/v1/chat/completions
+  response:
+    headers:
+      content-length:
+      - '1267'
+      content-type:
+      - application/json
+    parsed_body:
+      choices:
+      - finish_reason: stop
+        index: 0
+        message:
+          content: "**Plan for researching “What is the highest count class in the DocLayNet dataset?”**\n\n1. **Confirm the
+            official list of class labels and their counts**  \n   *Query:* “DocLayNet dataset class distribution – number
+            of instances per class (Caption, Footnote, Formula, List‑item, Page‑footer, Page‑header, Picture, Section‑header,
+            Table, …).”\n\n2. **Identify the class with the maximum count**  \n   *Query:* “Which DocLayNet class has the
+            highest instance count according to the official statistics?”\n\n3. **Validate the answer with a secondary source**
+            \ \n   *Query:* “External reference or research paper confirming the most frequent class in DocLayNet (e.g., citation
+            of the dataset paper).”\n\nThese three sub‑questions will locate the official class counts, determine the maximum,
+            and ensure the information is reliable."
+          reasoning: 'We have doc. Now propose plan: ask about class distribution stats, or which class largest. Need sub_questions.'
+          role: assistant
+      created: 1769001201
+      id: chatcmpl-248
+      model: gpt-oss
+      object: chat.completion
+      system_fingerprint: fp_ollama
+      usage:
+        completion_tokens: 213
+        prompt_tokens: 576
+        total_tokens: 789
+    status:
+      code: 200
+      message: OK
+- request:
+    headers:
+      accept:
+      - application/json
+      accept-encoding:
+      - gzip, deflate, zstd
+      connection:
+      - keep-alive
+      content-length:
+      - '5058'
+      content-type:
+      - application/json
+      host:
+      - localhost:11434
+    method: POST
+    parsed_body:
+      messages:
+      - content: |-
+          You are the research orchestrator for a focused workflow.
+
+          If a <background> section is provided, use it to understand the domain context.
+
+          Responsibilities:
+          1. Understand and decompose the main question
+          2. Propose a minimal, high-leverage plan
+          3. Coordinate specialized agents to gather evidence
+
+          Plan requirements:
+          - Produce at most 3 sub_questions that together cover the main question.
+          - sub_questions must be a list of plain strings, where each string is a complete
+            question. Do NOT use objects with nested fields like {question, details}.
+          - Each sub_question must be a standalone, self-contained query that can run
+            without extra context. Include concrete entities, scope, timeframe, and any
+            qualifiers. Avoid ambiguous pronouns (it/they/this/that).
+          - Prioritize the highest-value aspects first; avoid redundancy and overlap.
+          - Prefer questions that are likely answerable from the current knowledge base;
+            if coverage is uncertain, make scopes narrower and specific.
+          - Order sub_questions by execution priority (most valuable first).
+
+          Use the gather_context tool once on the main question before planning.
+
+          Use the gather_context tool once on the main question before planning.
+        role: system
+      - content: |-
+          Plan a focused approach for the main question.
+
+          Main question: What is the highest count class in the DocLayNet dataset?
+        role: user
+      - content: |-
+          <think>
+          We need to gather context first.
+          </think>
+        role: assistant
+        tool_calls:
+        - function:
+            arguments: '{"limit":null,"query":"highest count class in the DocLayNet dataset"}'
+            name: gather_context
+          id: call_6j83gec4
+          type: function
+      - content: |-
+          DocLayNet defines 11 distinct class labels for document layout analysis:
+
+          Caption - Text describing figures or tables
+
+          Footnote - Notes at the bottom of pages
+
+          Formula - Mathematical expressions
+
+          List-item - Items in bulleted or numbered lists
+
+          Page-footer - Footer content on pages
+
+          Page-header - Header content on pages
+
+          Picture - Images and diagrams
+
+          Section-header - Headings for document sections
+
+          Table - Tabular data
+        role: tool
+        tool_call_id: call_6j83gec4
+      - content: "<think>\nWe have doc. Now propose plan: ask about class distribution stats, or which class largest. Need
+          sub_questions.\n</think>\n\n**Plan for researching “What is the highest count class in the DocLayNet dataset?”**\n\n1.
+          **Confirm the official list of class labels and their counts**  \n   *Query:* “DocLayNet dataset class distribution
+          – number of instances per class (Caption, Footnote, Formula, List‑item, Page‑footer, Page‑header, Picture, Section‑header,
+          Table, …).”\n\n2. **Identify the class with the maximum count**  \n   *Query:* “Which DocLayNet class has the highest
+          instance count according to the official statistics?”\n\n3. **Validate the answer with a secondary source**  \n
+          \  *Query:* “External reference or research paper confirming the most frequent class in DocLayNet (e.g., citation
+          of the dataset paper).”\n\nThese three sub‑questions will locate the official class counts, determine the maximum,
+          and ensure the information is reliable."
+        role: assistant
+      - content: |-
+          1 validation error:
+          ```json
+          [
+            {
+              "type": "json_invalid",
+              "loc": [],
+              "msg": "Invalid JSON: expected value at line 1 column 1",
+              "input": "**Plan for researching “What is the highest count class in the DocLayNet dataset?”**\n\n1. **Confirm the official list of class labels and their counts**  \n   *Query:* “DocLayNet dataset class distribution – number of instances per class (Caption, Footnote, Formula, List‑item, Page‑footer, Page‑header, Picture, Section‑header, Table, …).”\n\n2. **Identify the class with the maximum count**  \n   *Query:* “Which DocLayNet class has the highest instance count according to the official statistics?”\n\n3. **Validate the answer with a secondary source**  \n   *Query:* “External reference or research paper confirming the most frequent class in DocLayNet (e.g., citation of the dataset paper).”\n\nThese three sub‑questions will locate the official class counts, determine the maximum, and ensure the information is reliable."
+            }
+          ]
+          ```
+
+          Fix the errors and try again.
+        role: user
+      model: gpt-oss
+      reasoning_effort: low
+      stream: false
+      tool_choice: auto
+      tools:
+      - function:
+          description: ''
+          name: gather_context
+          parameters:
+            additionalProperties: false
+            properties:
+              limit:
+                anyOf:
+                - type: integer
+                - type: 'null'
+                default: null
+              query:
+                type: string
+            required:
+            - query
+            type: object
+        type: function
+      - function:
+          description: A structured research plan with sub-questions to explore.
+          name: final_result
+          parameters:
+            additionalProperties: false
+            properties:
+              sub_questions:
+                description: Specific questions to research, phrased as complete questions
+                items:
+                  type: string
+                type: array
+            required:
+            - sub_questions
+            type: object
+          strict: true
+        type: function
+    uri: http://localhost:11434/v1/chat/completions
+  response:
+    headers:
+      content-length:
+      - '923'
+      content-type:
+      - application/json
+    parsed_body:
+      choices:
+      - finish_reason: tool_calls
+        index: 0
+        message:
+          content: ''
+          reasoning: Need to output JSON with sub_questions array.
+          role: assistant
+          tool_calls:
+          - function:
+              arguments: '{"sub_questions":["What is the distribution of instances for each class in the official DocLayNet
+                dataset (Caption, Footnote, Formula, List‑item, Page‑footer, Page‑header, Picture, Section‑header, Table,
+                etc.)?","Which class in DocLayNet has the highest instance count according to the dataset’s official statistics?","Does
+                a peer‑reviewed article or dataset documentation confirm that this class is the most frequent in DocLayNet?"]}'
+              name: final_result
+            id: call_8wikfwfp
+            index: 0
+            type: function
+      created: 1769001204
+      id: chatcmpl-918
+      model: gpt-oss
+      object: chat.completion
+      system_fingerprint: fp_ollama
+      usage:
+        completion_tokens: 125
+        prompt_tokens: 1050
+        total_tokens: 1175
+    status:
+      code: 200
+      message: OK
+- request:
+    headers:
+      accept:
+      - application/json
+      accept-encoding:
+      - gzip, deflate, zstd
+      connection:
+      - keep-alive
+      content-length:
+      - '2989'
+      content-type:
+      - application/json
+      host:
+      - localhost:11434
+    method: POST
+    parsed_body:
+      messages:
+      - content: |-
+          You are a search and question-answering specialist.
+
+          Process:
+          1. Call search_and_answer with relevant keywords from the question.
+          2. Review the results ordered by relevance.
+          3. If needed, perform follow-up searches with different keywords (max 3 total).
+          4. Provide a concise answer based strictly on the retrieved content.
+
+          The search tool returns results like:
+          [9bde5847-44c9-400a-8997-0e6b65babf92] [rank 1 of 5]
+          Source: "Document Title" > Section > Subsection
+          Type: paragraph
+          Content:
+          The actual text content here...
+
+          [d5a63c82-cb40-439f-9b2e-de7d177829b7] [rank 2 of 5]
+          Source: "Another Document"
+          Type: table
+          Content:
+          | Column 1 | Column 2 |
+          ...
+
+          Each result includes:
+          - chunk_id in brackets and rank position (rank 1 = most relevant)
+          - Source: document title and section hierarchy (when available)
+          - Type: content type like paragraph, table, code, list_item (when available)
+          - Content: the actual text
+
+          Output format:
+          - query: Echo the question you are answering
+          - answer: Your concise answer based on the retrieved content
+          - cited_chunks: List of plain strings containing only the chunk UUIDs (not objects)
+          - confidence: A score from 0.0 to 1.0 indicating answer confidence
+
+          IMPORTANT: Use the EXACT, COMPLETE chunk ID (full UUID). Do NOT truncate IDs.
+
+          Guidelines:
+          - Base answers strictly on retrieved content - do not use external knowledge.
+          - Use the Source and Type metadata to understand context.
+          - If multiple results are relevant, synthesize them coherently.
+          - If information is insufficient, say so clearly.
+          - Be concise and direct; avoid meta commentary about the process.
+          - Results are ordered by relevance, with rank 1 being most relevant.
+        role: system
+      - content: What is the distribution of instances for each class in the official DocLayNet dataset (Caption, Footnote,
+          Formula, List‑item, Page‑footer, Page‑header, Picture, Section‑header, Table, etc.)?
+        role: user
+      model: gpt-oss
+      reasoning_effort: low
+      stream: false
+      tool_choice: auto
+      tools:
+      - function:
+          description: Search the knowledge base for relevant documents.
+          name: search_and_answer
+          parameters:
+            additionalProperties: false
+            properties:
+              limit:
+                anyOf:
+                - type: integer
+                - type: 'null'
+                default: null
+              query:
+                type: string
+            required:
+            - query
+            type: object
+        type: function
+      - function:
+          description: Answer to a search query with chunk references.
+          name: final_result
+          parameters:
+            additionalProperties: false
+            properties:
+              answer:
+                description: The answer to the question
+                type: string
+              cited_chunks:
+                description: IDs of chunks used to form the answer
+                items:
+                  type: string
+                type: array
+              confidence:
+                default: 1.0
+                description: Confidence score for this answer (0-1)
+                maximum: 1.0
+                minimum: 0.0
+                type: number
+              query:
+                description: The question that was answered
+                type: string
+            required:
+            - query
+            - answer
+            type: object
+        type: function
+    uri: http://localhost:11434/v1/chat/completions
+  response:
+    headers:
+      content-length:
+      - '614'
+      content-type:
+      - application/json
+    parsed_body:
+      choices:
+      - finish_reason: tool_calls
+        index: 0
+        message:
+          content: ''
+          reasoning: Need to search.
+          role: assistant
+          tool_calls:
+          - function:
+              arguments: '{"query":"DocLayNet dataset class distribution Caption Footnote Formula List‑item Page‑footer Page‑header
+                Picture Section‑header Table distribution","limit":5}'
+              name: search_and_answer
+            id: call_sa4rp6ua
+            index: 0
+            type: function
+      created: 1769001206
+      id: chatcmpl-579
+      model: gpt-oss
+      object: chat.completion
+      system_fingerprint: fp_ollama
+      usage:
+        completion_tokens: 58
+        prompt_tokens: 662
+        total_tokens: 720
+    status:
+      code: 200
+      message: OK
+- request:
+    headers:
+      accept:
+      - application/json
+      accept-encoding:
+      - gzip, deflate, zstd
+      connection:
+      - keep-alive
+      content-length:
+      - '215'
+      content-type:
+      - application/json
+      host:
+      - localhost:11434
+    method: POST
+    parsed_body:
+      encoding_format: base64
+      input:
+      - DocLayNet dataset class distribution Caption Footnote Formula List‑item Page‑footer Page‑header Picture Section‑header
+        Table distribution
+      model: qwen3-embedding:4b
+    uri: http://localhost:11434/v1/embeddings
+  response:
+    headers:
+      content-type:
+      - application/json
+      transfer-encoding:
+      - chunked
+    parsed_body:
+      data:
+      - embedding: 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
+        index: 0
+        object: embedding
+      model: qwen3-embedding:4b
+      object: list
+      usage:
+        prompt_tokens: 27
+        total_tokens: 27
+    status:
+      code: 200
+      message: OK
+- request:
+    headers:
+      accept:
+      - application/json
+      accept-encoding:
+      - gzip, deflate, zstd
+      connection:
+      - keep-alive
+      content-length:
+      - '3959'
+      content-type:
+      - application/json
+      host:
+      - localhost:11434
+    method: POST
+    parsed_body:
+      messages:
+      - content: |-
+          You are a search and question-answering specialist.
+
+          Process:
+          1. Call search_and_answer with relevant keywords from the question.
+          2. Review the results ordered by relevance.
+          3. If needed, perform follow-up searches with different keywords (max 3 total).
+          4. Provide a concise answer based strictly on the retrieved content.
+
+          The search tool returns results like:
+          [9bde5847-44c9-400a-8997-0e6b65babf92] [rank 1 of 5]
+          Source: "Document Title" > Section > Subsection
+          Type: paragraph
+          Content:
+          The actual text content here...
+
+          [d5a63c82-cb40-439f-9b2e-de7d177829b7] [rank 2 of 5]
+          Source: "Another Document"
+          Type: table
+          Content:
+          | Column 1 | Column 2 |
+          ...
+
+          Each result includes:
+          - chunk_id in brackets and rank position (rank 1 = most relevant)
+          - Source: document title and section hierarchy (when available)
+          - Type: content type like paragraph, table, code, list_item (when available)
+          - Content: the actual text
+
+          Output format:
+          - query: Echo the question you are answering
+          - answer: Your concise answer based on the retrieved content
+          - cited_chunks: List of plain strings containing only the chunk UUIDs (not objects)
+          - confidence: A score from 0.0 to 1.0 indicating answer confidence
+
+          IMPORTANT: Use the EXACT, COMPLETE chunk ID (full UUID). Do NOT truncate IDs.
+
+          Guidelines:
+          - Base answers strictly on retrieved content - do not use external knowledge.
+          - Use the Source and Type metadata to understand context.
+          - If multiple results are relevant, synthesize them coherently.
+          - If information is insufficient, say so clearly.
+          - Be concise and direct; avoid meta commentary about the process.
+          - Results are ordered by relevance, with rank 1 being most relevant.
+        role: system
+      - content: What is the distribution of instances for each class in the official DocLayNet dataset (Caption, Footnote,
+          Formula, List‑item, Page‑footer, Page‑header, Picture, Section‑header, Table, etc.)?
+        role: user
+      - content: |-
+          <think>
+          Need to search.
+          </think>
+        role: assistant
+        tool_calls:
+        - function:
+            arguments: '{"query":"DocLayNet dataset class distribution Caption Footnote Formula List‑item Page‑footer Page‑header
+              Picture Section‑header Table distribution","limit":5}'
+            name: search_and_answer
+          id: call_sa4rp6ua
+          type: function
+      - content: |-
+          [52cb239c-ba6f-42cb-a8ae-8d06437b2c30] [rank 1 of 1]
+          Source: "DocLayNet Class Labels"
+          Type: list_item
+          Content:
+          DocLayNet defines 11 distinct class labels for document layout analysis:
+
+          Caption - Text describing figures or tables
+
+          Footnote - Notes at the bottom of pages
+
+          Formula - Mathematical expressions
+
+          List-item - Items in bulleted or numbered lists
+
+          Page-footer - Footer content on pages
+
+          Page-header - Header content on pages
+
+          Picture - Images and diagrams
+
+          Section-header - Headings for document sections
+
+          Table - Tabular data
+        role: tool
+        tool_call_id: call_sa4rp6ua
+      model: gpt-oss
+      reasoning_effort: low
+      stream: false
+      tool_choice: auto
+      tools:
+      - function:
+          description: Search the knowledge base for relevant documents.
+          name: search_and_answer
+          parameters:
+            additionalProperties: false
+            properties:
+              limit:
+                anyOf:
+                - type: integer
+                - type: 'null'
+                default: null
+              query:
+                type: string
+            required:
+            - query
+            type: object
+        type: function
+      - function:
+          description: Answer to a search query with chunk references.
+          name: final_result
+          parameters:
+            additionalProperties: false
+            properties:
+              answer:
+                description: The answer to the question
+                type: string
+              cited_chunks:
+                description: IDs of chunks used to form the answer
+                items:
+                  type: string
+                type: array
+              confidence:
+                default: 1.0
+                description: Confidence score for this answer (0-1)
+                maximum: 1.0
+                minimum: 0.0
+                type: number
+              query:
+                description: The question that was answered
+                type: string
+            required:
+            - query
+            - answer
+            type: object
+        type: function
+    uri: http://localhost:11434/v1/chat/completions
+  response:
+    headers:
+      content-length:
+      - '567'
+      content-type:
+      - application/json
+    parsed_body:
+      choices:
+      - finish_reason: tool_calls
+        index: 0
+        message:
+          content: ''
+          reasoning: Only list labels, no distribution. Need another search.
+          role: assistant
+          tool_calls:
+          - function:
+              arguments: '{"limit":5,"query":"DocLayNet dataset statistics number of instances per class"}'
+              name: search_and_answer
+            id: call_87xsuib1
+            index: 0
+            type: function
+      created: 1769001207
+      id: chatcmpl-100
+      model: gpt-oss
+      object: chat.completion
+      system_fingerprint: fp_ollama
+      usage:
+        completion_tokens: 49
+        prompt_tokens: 869
+        total_tokens: 918
+    status:
+      code: 200
+      message: OK
+- request:
+    headers:
+      accept:
+      - application/json
+      accept-encoding:
+      - gzip, deflate, zstd
+      connection:
+      - keep-alive
+      content-length:
+      - '128'
+      content-type:
+      - application/json
+      host:
+      - localhost:11434
+    method: POST
+    parsed_body:
+      encoding_format: base64
+      input:
+      - DocLayNet dataset statistics number of instances per class
+      model: qwen3-embedding:4b
+    uri: http://localhost:11434/v1/embeddings
+  response:
+    headers:
+      content-type:
+      - application/json
+      transfer-encoding:
+      - chunked
+    parsed_body:
+      data:
+      - embedding: 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
+        index: 0
+        object: embedding
+      model: qwen3-embedding:4b
+      object: list
+      usage:
+        prompt_tokens: 12
+        total_tokens: 12
+    status:
+      code: 200
+      message: OK
+- request:
+    headers:
+      accept:
+      - application/json
+      accept-encoding:
+      - gzip, deflate, zstd
+      connection:
+      - keep-alive
+      content-length:
+      - '4882'
+      content-type:
+      - application/json
+      host:
+      - localhost:11434
+    method: POST
+    parsed_body:
+      messages:
+      - content: |-
+          You are a search and question-answering specialist.
+
+          Process:
+          1. Call search_and_answer with relevant keywords from the question.
+          2. Review the results ordered by relevance.
+          3. If needed, perform follow-up searches with different keywords (max 3 total).
+          4. Provide a concise answer based strictly on the retrieved content.
+
+          The search tool returns results like:
+          [9bde5847-44c9-400a-8997-0e6b65babf92] [rank 1 of 5]
+          Source: "Document Title" > Section > Subsection
+          Type: paragraph
+          Content:
+          The actual text content here...
+
+          [d5a63c82-cb40-439f-9b2e-de7d177829b7] [rank 2 of 5]
+          Source: "Another Document"
+          Type: table
+          Content:
+          | Column 1 | Column 2 |
+          ...
+
+          Each result includes:
+          - chunk_id in brackets and rank position (rank 1 = most relevant)
+          - Source: document title and section hierarchy (when available)
+          - Type: content type like paragraph, table, code, list_item (when available)
+          - Content: the actual text
+
+          Output format:
+          - query: Echo the question you are answering
+          - answer: Your concise answer based on the retrieved content
+          - cited_chunks: List of plain strings containing only the chunk UUIDs (not objects)
+          - confidence: A score from 0.0 to 1.0 indicating answer confidence
+
+          IMPORTANT: Use the EXACT, COMPLETE chunk ID (full UUID). Do NOT truncate IDs.
+
+          Guidelines:
+          - Base answers strictly on retrieved content - do not use external knowledge.
+          - Use the Source and Type metadata to understand context.
+          - If multiple results are relevant, synthesize them coherently.
+          - If information is insufficient, say so clearly.
+          - Be concise and direct; avoid meta commentary about the process.
+          - Results are ordered by relevance, with rank 1 being most relevant.
+        role: system
+      - content: What is the distribution of instances for each class in the official DocLayNet dataset (Caption, Footnote,
+          Formula, List‑item, Page‑footer, Page‑header, Picture, Section‑header, Table, etc.)?
+        role: user
+      - content: |-
+          <think>
+          Need to search.
+          </think>
+        role: assistant
+        tool_calls:
+        - function:
+            arguments: '{"query":"DocLayNet dataset class distribution Caption Footnote Formula List‑item Page‑footer Page‑header
+              Picture Section‑header Table distribution","limit":5}'
+            name: search_and_answer
+          id: call_sa4rp6ua
+          type: function
+      - content: |-
+          [52cb239c-ba6f-42cb-a8ae-8d06437b2c30] [rank 1 of 1]
+          Source: "DocLayNet Class Labels"
+          Type: list_item
+          Content:
+          DocLayNet defines 11 distinct class labels for document layout analysis:
+
+          Caption - Text describing figures or tables
+
+          Footnote - Notes at the bottom of pages
+
+          Formula - Mathematical expressions
+
+          List-item - Items in bulleted or numbered lists
+
+          Page-footer - Footer content on pages
+
+          Page-header - Header content on pages
+
+          Picture - Images and diagrams
+
+          Section-header - Headings for document sections
+
+          Table - Tabular data
+        role: tool
+        tool_call_id: call_sa4rp6ua
+      - content: |-
+          <think>
+          Only list labels, no distribution. Need another search.
+          </think>
+        role: assistant
+        tool_calls:
+        - function:
+            arguments: '{"limit":5,"query":"DocLayNet dataset statistics number of instances per class"}'
+            name: search_and_answer
+          id: call_87xsuib1
+          type: function
+      - content: |-
+          [52cb239c-ba6f-42cb-a8ae-8d06437b2c30] [rank 1 of 1]
+          Source: "DocLayNet Class Labels"
+          Type: list_item
+          Content:
+          DocLayNet defines 11 distinct class labels for document layout analysis:
+
+          Caption - Text describing figures or tables
+
+          Footnote - Notes at the bottom of pages
+
+          Formula - Mathematical expressions
+
+          List-item - Items in bulleted or numbered lists
+
+          Page-footer - Footer content on pages
+
+          Page-header - Header content on pages
+
+          Picture - Images and diagrams
+
+          Section-header - Headings for document sections
+
+          Table - Tabular data
+        role: tool
+        tool_call_id: call_87xsuib1
+      model: gpt-oss
+      reasoning_effort: low
+      stream: false
+      tool_choice: auto
+      tools:
+      - function:
+          description: Search the knowledge base for relevant documents.
+          name: search_and_answer
+          parameters:
+            additionalProperties: false
+            properties:
+              limit:
+                anyOf:
+                - type: integer
+                - type: 'null'
+                default: null
+              query:
+                type: string
+            required:
+            - query
+            type: object
+        type: function
+      - function:
+          description: Answer to a search query with chunk references.
+          name: final_result
+          parameters:
+            additionalProperties: false
+            properties:
+              answer:
+                description: The answer to the question
+                type: string
+              cited_chunks:
+                description: IDs of chunks used to form the answer
+                items:
+                  type: string
+                type: array
+              confidence:
+                default: 1.0
+                description: Confidence score for this answer (0-1)
+                maximum: 1.0
+                minimum: 0.0
+                type: number
+              query:
+                description: The question that was answered
+                type: string
+            required:
+            - query
+            - answer
+            type: object
+        type: function
+    uri: http://localhost:11434/v1/chat/completions
+  response:
+    headers:
+      content-length:
+      - '484'
+      content-type:
+      - application/json
+    parsed_body:
+      choices:
+      - finish_reason: stop
+        index: 0
+        message:
+          content: I couldn't find the exact instance counts for each class in the official DocLayNet dataset. The available
+            public sources only list the 11 class labels but do not provide the distribution statistics.
+          role: assistant
+      created: 1769001209
+      id: chatcmpl-968
+      model: gpt-oss
+      object: chat.completion
+      system_fingerprint: fp_ollama
+      usage:
+        completion_tokens: 41
+        prompt_tokens: 1068
+        total_tokens: 1109
+    status:
+      code: 200
+      message: OK
+- request:
+    headers:
+      accept:
+      - application/json
+      accept-encoding:
+      - gzip, deflate, zstd
+      connection:
+      - keep-alive
+      content-length:
+      - '5242'
+      content-type:
+      - application/json
+      host:
+      - localhost:11434
+    method: POST
+    parsed_body:
+      messages:
+      - content: |-
+          You are a search and question-answering specialist.
+
+          Process:
+          1. Call search_and_answer with relevant keywords from the question.
+          2. Review the results ordered by relevance.
+          3. If needed, perform follow-up searches with different keywords (max 3 total).
+          4. Provide a concise answer based strictly on the retrieved content.
+
+          The search tool returns results like:
+          [9bde5847-44c9-400a-8997-0e6b65babf92] [rank 1 of 5]
+          Source: "Document Title" > Section > Subsection
+          Type: paragraph
+          Content:
+          The actual text content here...
+
+          [d5a63c82-cb40-439f-9b2e-de7d177829b7] [rank 2 of 5]
+          Source: "Another Document"
+          Type: table
+          Content:
+          | Column 1 | Column 2 |
+          ...
+
+          Each result includes:
+          - chunk_id in brackets and rank position (rank 1 = most relevant)
+          - Source: document title and section hierarchy (when available)
+          - Type: content type like paragraph, table, code, list_item (when available)
+          - Content: the actual text
+
+          Output format:
+          - query: Echo the question you are answering
+          - answer: Your concise answer based on the retrieved content
+          - cited_chunks: List of plain strings containing only the chunk UUIDs (not objects)
+          - confidence: A score from 0.0 to 1.0 indicating answer confidence
+
+          IMPORTANT: Use the EXACT, COMPLETE chunk ID (full UUID). Do NOT truncate IDs.
+
+          Guidelines:
+          - Base answers strictly on retrieved content - do not use external knowledge.
+          - Use the Source and Type metadata to understand context.
+          - If multiple results are relevant, synthesize them coherently.
+          - If information is insufficient, say so clearly.
+          - Be concise and direct; avoid meta commentary about the process.
+          - Results are ordered by relevance, with rank 1 being most relevant.
+        role: system
+      - content: What is the distribution of instances for each class in the official DocLayNet dataset (Caption, Footnote,
+          Formula, List‑item, Page‑footer, Page‑header, Picture, Section‑header, Table, etc.)?
+        role: user
+      - content: |-
+          <think>
+          Need to search.
+          </think>
+        role: assistant
+        tool_calls:
+        - function:
+            arguments: '{"query":"DocLayNet dataset class distribution Caption Footnote Formula List‑item Page‑footer Page‑header
+              Picture Section‑header Table distribution","limit":5}'
+            name: search_and_answer
+          id: call_sa4rp6ua
+          type: function
+      - content: |-
+          [52cb239c-ba6f-42cb-a8ae-8d06437b2c30] [rank 1 of 1]
+          Source: "DocLayNet Class Labels"
+          Type: list_item
+          Content:
+          DocLayNet defines 11 distinct class labels for document layout analysis:
+
+          Caption - Text describing figures or tables
+
+          Footnote - Notes at the bottom of pages
+
+          Formula - Mathematical expressions
+
+          List-item - Items in bulleted or numbered lists
+
+          Page-footer - Footer content on pages
+
+          Page-header - Header content on pages
+
+          Picture - Images and diagrams
+
+          Section-header - Headings for document sections
+
+          Table - Tabular data
+        role: tool
+        tool_call_id: call_sa4rp6ua
+      - content: |-
+          <think>
+          Only list labels, no distribution. Need another search.
+          </think>
+        role: assistant
+        tool_calls:
+        - function:
+            arguments: '{"limit":5,"query":"DocLayNet dataset statistics number of instances per class"}'
+            name: search_and_answer
+          id: call_87xsuib1
+          type: function
+      - content: |-
+          [52cb239c-ba6f-42cb-a8ae-8d06437b2c30] [rank 1 of 1]
+          Source: "DocLayNet Class Labels"
+          Type: list_item
+          Content:
+          DocLayNet defines 11 distinct class labels for document layout analysis:
+
+          Caption - Text describing figures or tables
+
+          Footnote - Notes at the bottom of pages
+
+          Formula - Mathematical expressions
+
+          List-item - Items in bulleted or numbered lists
+
+          Page-footer - Footer content on pages
+
+          Page-header - Header content on pages
+
+          Picture - Images and diagrams
+
+          Section-header - Headings for document sections
+
+          Table - Tabular data
+        role: tool
+        tool_call_id: call_87xsuib1
+      - content: I couldn't find the exact instance counts for each class in the official DocLayNet dataset. The available
+          public sources only list the 11 class labels but do not provide the distribution statistics.
+        role: assistant
+      - content: |-
+          Validation feedback:
+          Please include your response in a tool call.
+
+          Fix the errors and try again.
+        role: user
+      model: gpt-oss
+      reasoning_effort: low
+      stream: false
+      tool_choice: auto
+      tools:
+      - function:
+          description: Search the knowledge base for relevant documents.
+          name: search_and_answer
+          parameters:
+            additionalProperties: false
+            properties:
+              limit:
+                anyOf:
+                - type: integer
+                - type: 'null'
+                default: null
+              query:
+                type: string
+            required:
+            - query
+            type: object
+        type: function
+      - function:
+          description: Answer to a search query with chunk references.
+          name: final_result
+          parameters:
+            additionalProperties: false
+            properties:
+              answer:
+                description: The answer to the question
+                type: string
+              cited_chunks:
+                description: IDs of chunks used to form the answer
+                items:
+                  type: string
+                type: array
+              confidence:
+                default: 1.0
+                description: Confidence score for this answer (0-1)
+                maximum: 1.0
+                minimum: 0.0
+                type: number
+              query:
+                description: The question that was answered
+                type: string
+            required:
+            - query
+            - answer
+            type: object
+        type: function
+    uri: http://localhost:11434/v1/chat/completions
+  response:
+    headers:
+      content-length:
+      - '1318'
+      content-type:
+      - application/json
+    parsed_body:
+      choices:
+      - finish_reason: tool_calls
+        index: 0
+        message:
+          content: ''
+          reasoning: We must output via final_result. Need to call final_result with answer. Use cited_chunks. We have only
+            two chunk IDs. But answer says distribution not found. We'll provide that.
+          role: assistant
+          tool_calls:
+          - function:
+              arguments: '{"query":"What is the distribution of instances for each class in the official DocLayNet dataset
+                (Caption, Footnote, Formula, List‑item, Page‑footer, Page‑header, Picture, Section‑header, Table, etc.)?","answer":"The
+                publicly available information about DocLayNet only lists the 11 class labels (Caption, Footnote, Formula,
+                List‑item, Page‑footer, Page‑header, Picture, Section‑header, Table, etc.). It does **not** provide the number
+                of instances or distribution statistics for each class in the dataset. Consequently, the exact distribution
+                of instances per class cannot be reported from the available sources.","cited_chunks":["52cb239c-ba6f-42cb-a8ae-8d06437b2c30"],"confidence":0.9}'
+              name: final_result
+            id: call_cijvm31f
+            index: 0
+            type: function
+      created: 1769001214
+      id: chatcmpl-714
+      model: gpt-oss
+      object: chat.completion
+      system_fingerprint: fp_ollama
+      usage:
+        completion_tokens: 225
+        prompt_tokens: 1134
+        total_tokens: 1359
+    status:
+      code: 200
+      message: OK
+- request:
+    headers:
+      accept:
+      - application/json
+      accept-encoding:
+      - gzip, deflate, zstd
+      connection:
+      - keep-alive
+      content-length:
+      - '2895'
+      content-type:
+      - application/json
+      host:
+      - localhost:11434
+    method: POST
+    parsed_body:
+      messages:
+      - content: |-
+          You are a search and question-answering specialist.
+
+          Process:
+          1. Call search_and_answer with relevant keywords from the question.
+          2. Review the results ordered by relevance.
+          3. If needed, perform follow-up searches with different keywords (max 3 total).
+          4. Provide a concise answer based strictly on the retrieved content.
+
+          The search tool returns results like:
+          [9bde5847-44c9-400a-8997-0e6b65babf92] [rank 1 of 5]
+          Source: "Document Title" > Section > Subsection
+          Type: paragraph
+          Content:
+          The actual text content here...
+
+          [d5a63c82-cb40-439f-9b2e-de7d177829b7] [rank 2 of 5]
+          Source: "Another Document"
+          Type: table
+          Content:
+          | Column 1 | Column 2 |
+          ...
+
+          Each result includes:
+          - chunk_id in brackets and rank position (rank 1 = most relevant)
+          - Source: document title and section hierarchy (when available)
+          - Type: content type like paragraph, table, code, list_item (when available)
+          - Content: the actual text
+
+          Output format:
+          - query: Echo the question you are answering
+          - answer: Your concise answer based on the retrieved content
+          - cited_chunks: List of plain strings containing only the chunk UUIDs (not objects)
+          - confidence: A score from 0.0 to 1.0 indicating answer confidence
+
+          IMPORTANT: Use the EXACT, COMPLETE chunk ID (full UUID). Do NOT truncate IDs.
+
+          Guidelines:
+          - Base answers strictly on retrieved content - do not use external knowledge.
+          - Use the Source and Type metadata to understand context.
+          - If multiple results are relevant, synthesize them coherently.
+          - If information is insufficient, say so clearly.
+          - Be concise and direct; avoid meta commentary about the process.
+          - Results are ordered by relevance, with rank 1 being most relevant.
+        role: system
+      - content: Which class in DocLayNet has the highest instance count according to the dataset’s official statistics?
+        role: user
+      model: gpt-oss
+      reasoning_effort: low
+      stream: false
+      tool_choice: auto
+      tools:
+      - function:
+          description: Search the knowledge base for relevant documents.
+          name: search_and_answer
+          parameters:
+            additionalProperties: false
+            properties:
+              limit:
+                anyOf:
+                - type: integer
+                - type: 'null'
+                default: null
+              query:
+                type: string
+            required:
+            - query
+            type: object
+        type: function
+      - function:
+          description: Answer to a search query with chunk references.
+          name: final_result
+          parameters:
+            additionalProperties: false
+            properties:
+              answer:
+                description: The answer to the question
+                type: string
+              cited_chunks:
+                description: IDs of chunks used to form the answer
+                items:
+                  type: string
+                type: array
+              confidence:
+                default: 1.0
+                description: Confidence score for this answer (0-1)
+                maximum: 1.0
+                minimum: 0.0
+                type: number
+              query:
+                description: The question that was answered
+                type: string
+            required:
+            - query
+            - answer
+            type: object
+        type: function
+    uri: http://localhost:11434/v1/chat/completions
+  response:
+    headers:
+      content-length:
+      - '524'
+      content-type:
+      - application/json
+    parsed_body:
+      choices:
+      - finish_reason: tool_calls
+        index: 0
+        message:
+          content: ''
+          reasoning: Need search.
+          role: assistant
+          tool_calls:
+          - function:
+              arguments: '{"query":"DocLayNet highest instance count class official statistics","limit":5}'
+              name: search_and_answer
+            id: call_p9zy6129
+            index: 0
+            type: function
+      created: 1769001215
+      id: chatcmpl-915
+      model: gpt-oss
+      object: chat.completion
+      system_fingerprint: fp_ollama
+      usage:
+        completion_tokens: 40
+        prompt_tokens: 634
+        total_tokens: 674
+    status:
+      code: 200
+      message: OK
+- request:
+    headers:
+      accept:
+      - application/json
+      accept-encoding:
+      - gzip, deflate, zstd
+      connection:
+      - keep-alive
+      content-length:
+      - '128'
+      content-type:
+      - application/json
+      host:
+      - localhost:11434
+    method: POST
+    parsed_body:
+      encoding_format: base64
+      input:
+      - DocLayNet highest instance count class official statistics
+      model: qwen3-embedding:4b
+    uri: http://localhost:11434/v1/embeddings
+  response:
+    headers:
+      content-type:
+      - application/json
+      transfer-encoding:
+      - chunked
+    parsed_body:
+      data:
+      - embedding: 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
+        index: 0
+        object: embedding
+      model: qwen3-embedding:4b
+      object: list
+      usage:
+        prompt_tokens: 11
+        total_tokens: 11
+    status:
+      code: 200
+      message: OK
+- request:
+    headers:
+      accept:
+      - application/json
+      accept-encoding:
+      - gzip, deflate, zstd
+      connection:
+      - keep-alive
+      content-length:
+      - '3775'
+      content-type:
+      - application/json
+      host:
+      - localhost:11434
+    method: POST
+    parsed_body:
+      messages:
+      - content: |-
+          You are a search and question-answering specialist.
+
+          Process:
+          1. Call search_and_answer with relevant keywords from the question.
+          2. Review the results ordered by relevance.
+          3. If needed, perform follow-up searches with different keywords (max 3 total).
+          4. Provide a concise answer based strictly on the retrieved content.
+
+          The search tool returns results like:
+          [9bde5847-44c9-400a-8997-0e6b65babf92] [rank 1 of 5]
+          Source: "Document Title" > Section > Subsection
+          Type: paragraph
+          Content:
+          The actual text content here...
+
+          [d5a63c82-cb40-439f-9b2e-de7d177829b7] [rank 2 of 5]
+          Source: "Another Document"
+          Type: table
+          Content:
+          | Column 1 | Column 2 |
+          ...
+
+          Each result includes:
+          - chunk_id in brackets and rank position (rank 1 = most relevant)
+          - Source: document title and section hierarchy (when available)
+          - Type: content type like paragraph, table, code, list_item (when available)
+          - Content: the actual text
+
+          Output format:
+          - query: Echo the question you are answering
+          - answer: Your concise answer based on the retrieved content
+          - cited_chunks: List of plain strings containing only the chunk UUIDs (not objects)
+          - confidence: A score from 0.0 to 1.0 indicating answer confidence
+
+          IMPORTANT: Use the EXACT, COMPLETE chunk ID (full UUID). Do NOT truncate IDs.
+
+          Guidelines:
+          - Base answers strictly on retrieved content - do not use external knowledge.
+          - Use the Source and Type metadata to understand context.
+          - If multiple results are relevant, synthesize them coherently.
+          - If information is insufficient, say so clearly.
+          - Be concise and direct; avoid meta commentary about the process.
+          - Results are ordered by relevance, with rank 1 being most relevant.
+        role: system
+      - content: Which class in DocLayNet has the highest instance count according to the dataset’s official statistics?
+        role: user
+      - content: |-
+          <think>
+          Need search.
+          </think>
+        role: assistant
+        tool_calls:
+        - function:
+            arguments: '{"query":"DocLayNet highest instance count class official statistics","limit":5}'
+            name: search_and_answer
+          id: call_p9zy6129
+          type: function
+      - content: |-
+          [52cb239c-ba6f-42cb-a8ae-8d06437b2c30] [rank 1 of 1]
+          Source: "DocLayNet Class Labels"
+          Type: list_item
+          Content:
+          DocLayNet defines 11 distinct class labels for document layout analysis:
+
+          Caption - Text describing figures or tables
+
+          Footnote - Notes at the bottom of pages
+
+          Formula - Mathematical expressions
+
+          List-item - Items in bulleted or numbered lists
+
+          Page-footer - Footer content on pages
+
+          Page-header - Header content on pages
+
+          Picture - Images and diagrams
+
+          Section-header - Headings for document sections
+
+          Table - Tabular data
+        role: tool
+        tool_call_id: call_p9zy6129
+      model: gpt-oss
+      reasoning_effort: low
+      stream: false
+      tool_choice: auto
+      tools:
+      - function:
+          description: Search the knowledge base for relevant documents.
+          name: search_and_answer
+          parameters:
+            additionalProperties: false
+            properties:
+              limit:
+                anyOf:
+                - type: integer
+                - type: 'null'
+                default: null
+              query:
+                type: string
+            required:
+            - query
+            type: object
+        type: function
+      - function:
+          description: Answer to a search query with chunk references.
+          name: final_result
+          parameters:
+            additionalProperties: false
+            properties:
+              answer:
+                description: The answer to the question
+                type: string
+              cited_chunks:
+                description: IDs of chunks used to form the answer
+                items:
+                  type: string
+                type: array
+              confidence:
+                default: 1.0
+                description: Confidence score for this answer (0-1)
+                maximum: 1.0
+                minimum: 0.0
+                type: number
+              query:
+                description: The question that was answered
+                type: string
+            required:
+            - query
+            - answer
+            type: object
+        type: function
+    uri: http://localhost:11434/v1/chat/completions
+  response:
+    headers:
+      content-length:
+      - '605'
+      content-type:
+      - application/json
+    parsed_body:
+      choices:
+      - finish_reason: tool_calls
+        index: 0
+        message:
+          content: ''
+          reasoning: We need highest instance count per official stats. The snippet doesn't include counts. Need further search.
+          role: assistant
+          tool_calls:
+          - function:
+              arguments: '{"limit":10,"query":"DocLayNet class distribution instance count"}'
+              name: search_and_answer
+            id: call_oc5dleh2
+            index: 0
+            type: function
+      created: 1769001216
+      id: chatcmpl-761
+      model: gpt-oss
+      object: chat.completion
+      system_fingerprint: fp_ollama
+      usage:
+        completion_tokens: 54
+        prompt_tokens: 824
+        total_tokens: 878
+    status:
+      code: 200
+      message: OK
+- request:
+    headers:
+      accept:
+      - application/json
+      accept-encoding:
+      - gzip, deflate, zstd
+      connection:
+      - keep-alive
+      content-length:
+      - '113'
+      content-type:
+      - application/json
+      host:
+      - localhost:11434
+    method: POST
+    parsed_body:
+      encoding_format: base64
+      input:
+      - DocLayNet class distribution instance count
+      model: qwen3-embedding:4b
+    uri: http://localhost:11434/v1/embeddings
+  response:
+    headers:
+      content-type:
+      - application/json
+      transfer-encoding:
+      - chunked
+    parsed_body:
+      data:
+      - embedding: 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
+        index: 0
+        object: embedding
+      model: qwen3-embedding:4b
+      object: list
+      usage:
+        prompt_tokens: 9
+        total_tokens: 9
+    status:
+      code: 200
+      message: OK
+- request:
+    headers:
+      accept:
+      - application/json
+      accept-encoding:
+      - gzip, deflate, zstd
+      connection:
+      - keep-alive
+      content-length:
+      - '4736'
+      content-type:
+      - application/json
+      host:
+      - localhost:11434
+    method: POST
+    parsed_body:
+      messages:
+      - content: |-
+          You are a search and question-answering specialist.
+
+          Process:
+          1. Call search_and_answer with relevant keywords from the question.
+          2. Review the results ordered by relevance.
+          3. If needed, perform follow-up searches with different keywords (max 3 total).
+          4. Provide a concise answer based strictly on the retrieved content.
+
+          The search tool returns results like:
+          [9bde5847-44c9-400a-8997-0e6b65babf92] [rank 1 of 5]
+          Source: "Document Title" > Section > Subsection
+          Type: paragraph
+          Content:
+          The actual text content here...
+
+          [d5a63c82-cb40-439f-9b2e-de7d177829b7] [rank 2 of 5]
+          Source: "Another Document"
+          Type: table
+          Content:
+          | Column 1 | Column 2 |
+          ...
+
+          Each result includes:
+          - chunk_id in brackets and rank position (rank 1 = most relevant)
+          - Source: document title and section hierarchy (when available)
+          - Type: content type like paragraph, table, code, list_item (when available)
+          - Content: the actual text
+
+          Output format:
+          - query: Echo the question you are answering
+          - answer: Your concise answer based on the retrieved content
+          - cited_chunks: List of plain strings containing only the chunk UUIDs (not objects)
+          - confidence: A score from 0.0 to 1.0 indicating answer confidence
+
+          IMPORTANT: Use the EXACT, COMPLETE chunk ID (full UUID). Do NOT truncate IDs.
+
+          Guidelines:
+          - Base answers strictly on retrieved content - do not use external knowledge.
+          - Use the Source and Type metadata to understand context.
+          - If multiple results are relevant, synthesize them coherently.
+          - If information is insufficient, say so clearly.
+          - Be concise and direct; avoid meta commentary about the process.
+          - Results are ordered by relevance, with rank 1 being most relevant.
+        role: system
+      - content: Which class in DocLayNet has the highest instance count according to the dataset’s official statistics?
+        role: user
+      - content: |-
+          <think>
+          Need search.
+          </think>
+        role: assistant
+        tool_calls:
+        - function:
+            arguments: '{"query":"DocLayNet highest instance count class official statistics","limit":5}'
+            name: search_and_answer
+          id: call_p9zy6129
+          type: function
+      - content: |-
+          [52cb239c-ba6f-42cb-a8ae-8d06437b2c30] [rank 1 of 1]
+          Source: "DocLayNet Class Labels"
+          Type: list_item
+          Content:
+          DocLayNet defines 11 distinct class labels for document layout analysis:
+
+          Caption - Text describing figures or tables
+
+          Footnote - Notes at the bottom of pages
+
+          Formula - Mathematical expressions
+
+          List-item - Items in bulleted or numbered lists
+
+          Page-footer - Footer content on pages
+
+          Page-header - Header content on pages
+
+          Picture - Images and diagrams
+
+          Section-header - Headings for document sections
+
+          Table - Tabular data
+        role: tool
+        tool_call_id: call_p9zy6129
+      - content: |-
+          <think>
+          We need highest instance count per official stats. The snippet doesn't include counts. Need further search.
+          </think>
+        role: assistant
+        tool_calls:
+        - function:
+            arguments: '{"limit":10,"query":"DocLayNet class distribution instance count"}'
+            name: search_and_answer
+          id: call_oc5dleh2
+          type: function
+      - content: |-
+          [52cb239c-ba6f-42cb-a8ae-8d06437b2c30] [rank 1 of 1]
+          Source: "DocLayNet Class Labels"
+          Type: list_item
+          Content:
+          DocLayNet defines 11 distinct class labels for document layout analysis:
+
+          Caption - Text describing figures or tables
+
+          Footnote - Notes at the bottom of pages
+
+          Formula - Mathematical expressions
+
+          List-item - Items in bulleted or numbered lists
+
+          Page-footer - Footer content on pages
+
+          Page-header - Header content on pages
+
+          Picture - Images and diagrams
+
+          Section-header - Headings for document sections
+
+          Table - Tabular data
+        role: tool
+        tool_call_id: call_oc5dleh2
+      model: gpt-oss
+      reasoning_effort: low
+      stream: false
+      tool_choice: auto
+      tools:
+      - function:
+          description: Search the knowledge base for relevant documents.
+          name: search_and_answer
+          parameters:
+            additionalProperties: false
+            properties:
+              limit:
+                anyOf:
+                - type: integer
+                - type: 'null'
+                default: null
+              query:
+                type: string
+            required:
+            - query
+            type: object
+        type: function
+      - function:
+          description: Answer to a search query with chunk references.
+          name: final_result
+          parameters:
+            additionalProperties: false
+            properties:
+              answer:
+                description: The answer to the question
+                type: string
+              cited_chunks:
+                description: IDs of chunks used to form the answer
+                items:
+                  type: string
+                type: array
+              confidence:
+                default: 1.0
+                description: Confidence score for this answer (0-1)
+                maximum: 1.0
+                minimum: 0.0
+                type: number
+              query:
+                description: The question that was answered
+                type: string
+            required:
+            - query
+            - answer
+            type: object
+        type: function
+    uri: http://localhost:11434/v1/chat/completions
+  response:
+    headers:
+      content-length:
+      - '379'
+      content-type:
+      - application/json
+    parsed_body:
+      choices:
+      - finish_reason: stop
+        index: 0
+        message:
+          content: I couldn’t find the exact class with the highest instance count in the retrieved documents.
+          role: assistant
+      created: 1769001217
+      id: chatcmpl-591
+      model: gpt-oss
+      object: chat.completion
+      system_fingerprint: fp_ollama
+      usage:
+        completion_tokens: 21
+        prompt_tokens: 1028
+        total_tokens: 1049
+    status:
+      code: 200
+      message: OK
+- request:
+    headers:
+      accept:
+      - application/json
+      accept-encoding:
+      - gzip, deflate, zstd
+      connection:
+      - keep-alive
+      content-length:
+      - '4991'
+      content-type:
+      - application/json
+      host:
+      - localhost:11434
+    method: POST
+    parsed_body:
+      messages:
+      - content: |-
+          You are a search and question-answering specialist.
+
+          Process:
+          1. Call search_and_answer with relevant keywords from the question.
+          2. Review the results ordered by relevance.
+          3. If needed, perform follow-up searches with different keywords (max 3 total).
+          4. Provide a concise answer based strictly on the retrieved content.
+
+          The search tool returns results like:
+          [9bde5847-44c9-400a-8997-0e6b65babf92] [rank 1 of 5]
+          Source: "Document Title" > Section > Subsection
+          Type: paragraph
+          Content:
+          The actual text content here...
+
+          [d5a63c82-cb40-439f-9b2e-de7d177829b7] [rank 2 of 5]
+          Source: "Another Document"
+          Type: table
+          Content:
+          | Column 1 | Column 2 |
+          ...
+
+          Each result includes:
+          - chunk_id in brackets and rank position (rank 1 = most relevant)
+          - Source: document title and section hierarchy (when available)
+          - Type: content type like paragraph, table, code, list_item (when available)
+          - Content: the actual text
+
+          Output format:
+          - query: Echo the question you are answering
+          - answer: Your concise answer based on the retrieved content
+          - cited_chunks: List of plain strings containing only the chunk UUIDs (not objects)
+          - confidence: A score from 0.0 to 1.0 indicating answer confidence
+
+          IMPORTANT: Use the EXACT, COMPLETE chunk ID (full UUID). Do NOT truncate IDs.
+
+          Guidelines:
+          - Base answers strictly on retrieved content - do not use external knowledge.
+          - Use the Source and Type metadata to understand context.
+          - If multiple results are relevant, synthesize them coherently.
+          - If information is insufficient, say so clearly.
+          - Be concise and direct; avoid meta commentary about the process.
+          - Results are ordered by relevance, with rank 1 being most relevant.
+        role: system
+      - content: Which class in DocLayNet has the highest instance count according to the dataset’s official statistics?
+        role: user
+      - content: |-
+          <think>
+          Need search.
+          </think>
+        role: assistant
+        tool_calls:
+        - function:
+            arguments: '{"query":"DocLayNet highest instance count class official statistics","limit":5}'
+            name: search_and_answer
+          id: call_p9zy6129
+          type: function
+      - content: |-
+          [52cb239c-ba6f-42cb-a8ae-8d06437b2c30] [rank 1 of 1]
+          Source: "DocLayNet Class Labels"
+          Type: list_item
+          Content:
+          DocLayNet defines 11 distinct class labels for document layout analysis:
+
+          Caption - Text describing figures or tables
+
+          Footnote - Notes at the bottom of pages
+
+          Formula - Mathematical expressions
+
+          List-item - Items in bulleted or numbered lists
+
+          Page-footer - Footer content on pages
+
+          Page-header - Header content on pages
+
+          Picture - Images and diagrams
+
+          Section-header - Headings for document sections
+
+          Table - Tabular data
+        role: tool
+        tool_call_id: call_p9zy6129
+      - content: |-
+          <think>
+          We need highest instance count per official stats. The snippet doesn't include counts. Need further search.
+          </think>
+        role: assistant
+        tool_calls:
+        - function:
+            arguments: '{"limit":10,"query":"DocLayNet class distribution instance count"}'
+            name: search_and_answer
+          id: call_oc5dleh2
+          type: function
+      - content: |-
+          [52cb239c-ba6f-42cb-a8ae-8d06437b2c30] [rank 1 of 1]
+          Source: "DocLayNet Class Labels"
+          Type: list_item
+          Content:
+          DocLayNet defines 11 distinct class labels for document layout analysis:
+
+          Caption - Text describing figures or tables
+
+          Footnote - Notes at the bottom of pages
+
+          Formula - Mathematical expressions
+
+          List-item - Items in bulleted or numbered lists
+
+          Page-footer - Footer content on pages
+
+          Page-header - Header content on pages
+
+          Picture - Images and diagrams
+
+          Section-header - Headings for document sections
+
+          Table - Tabular data
+        role: tool
+        tool_call_id: call_oc5dleh2
+      - content: I couldn’t find the exact class with the highest instance count in the retrieved documents.
+        role: assistant
+      - content: |-
+          Validation feedback:
+          Please include your response in a tool call.
+
+          Fix the errors and try again.
+        role: user
+      model: gpt-oss
+      reasoning_effort: low
+      stream: false
+      tool_choice: auto
+      tools:
+      - function:
+          description: Search the knowledge base for relevant documents.
+          name: search_and_answer
+          parameters:
+            additionalProperties: false
+            properties:
+              limit:
+                anyOf:
+                - type: integer
+                - type: 'null'
+                default: null
+              query:
+                type: string
+            required:
+            - query
+            type: object
+        type: function
+      - function:
+          description: Answer to a search query with chunk references.
+          name: final_result
+          parameters:
+            additionalProperties: false
+            properties:
+              answer:
+                description: The answer to the question
+                type: string
+              cited_chunks:
+                description: IDs of chunks used to form the answer
+                items:
+                  type: string
+                type: array
+              confidence:
+                default: 1.0
+                description: Confidence score for this answer (0-1)
+                maximum: 1.0
+                minimum: 0.0
+                type: number
+              query:
+                description: The question that was answered
+                type: string
+            required:
+            - query
+            - answer
+            type: object
+        type: function
+    uri: http://localhost:11434/v1/chat/completions
+  response:
+    headers:
+      content-length:
+      - '726'
+      content-type:
+      - application/json
+    parsed_body:
+      choices:
+      - finish_reason: tool_calls
+        index: 0
+        message:
+          content: ''
+          reasoning: Need to use final_result tool.
+          role: assistant
+          tool_calls:
+          - function:
+              arguments: '{"answer":"I could not locate the specific class with the highest instance count in the documents
+                retrieved.","cited_chunks":[],"confidence":0.3,"query":"Which class in DocLayNet has the highest instance
+                count according to the dataset’s official statistics?"}'
+              name: final_result
+            id: call_pxj5e4dt
+            index: 0
+            type: function
+      created: 1769001219
+      id: chatcmpl-834
+      model: gpt-oss
+      object: chat.completion
+      system_fingerprint: fp_ollama
+      usage:
+        completion_tokens: 80
+        prompt_tokens: 1074
+        total_tokens: 1154
+    status:
+      code: 200
+      message: OK
+- request:
+    headers:
+      accept:
+      - application/json
+      accept-encoding:
+      - gzip, deflate, zstd
+      connection:
+      - keep-alive
+      content-length:
+      - '2904'
+      content-type:
+      - application/json
+      host:
+      - localhost:11434
+    method: POST
+    parsed_body:
+      messages:
+      - content: |-
+          You are a search and question-answering specialist.
+
+          Process:
+          1. Call search_and_answer with relevant keywords from the question.
+          2. Review the results ordered by relevance.
+          3. If needed, perform follow-up searches with different keywords (max 3 total).
+          4. Provide a concise answer based strictly on the retrieved content.
+
+          The search tool returns results like:
+          [9bde5847-44c9-400a-8997-0e6b65babf92] [rank 1 of 5]
+          Source: "Document Title" > Section > Subsection
+          Type: paragraph
+          Content:
+          The actual text content here...
+
+          [d5a63c82-cb40-439f-9b2e-de7d177829b7] [rank 2 of 5]
+          Source: "Another Document"
+          Type: table
+          Content:
+          | Column 1 | Column 2 |
+          ...
+
+          Each result includes:
+          - chunk_id in brackets and rank position (rank 1 = most relevant)
+          - Source: document title and section hierarchy (when available)
+          - Type: content type like paragraph, table, code, list_item (when available)
+          - Content: the actual text
+
+          Output format:
+          - query: Echo the question you are answering
+          - answer: Your concise answer based on the retrieved content
+          - cited_chunks: List of plain strings containing only the chunk UUIDs (not objects)
+          - confidence: A score from 0.0 to 1.0 indicating answer confidence
+
+          IMPORTANT: Use the EXACT, COMPLETE chunk ID (full UUID). Do NOT truncate IDs.
+
+          Guidelines:
+          - Base answers strictly on retrieved content - do not use external knowledge.
+          - Use the Source and Type metadata to understand context.
+          - If multiple results are relevant, synthesize them coherently.
+          - If information is insufficient, say so clearly.
+          - Be concise and direct; avoid meta commentary about the process.
+          - Results are ordered by relevance, with rank 1 being most relevant.
+        role: system
+      - content: Does a peer‑reviewed article or dataset documentation confirm that this class is the most frequent in DocLayNet?
+        role: user
+      model: gpt-oss
+      reasoning_effort: low
+      stream: false
+      tool_choice: auto
+      tools:
+      - function:
+          description: Search the knowledge base for relevant documents.
+          name: search_and_answer
+          parameters:
+            additionalProperties: false
+            properties:
+              limit:
+                anyOf:
+                - type: integer
+                - type: 'null'
+                default: null
+              query:
+                type: string
+            required:
+            - query
+            type: object
+        type: function
+      - function:
+          description: Answer to a search query with chunk references.
+          name: final_result
+          parameters:
+            additionalProperties: false
+            properties:
+              answer:
+                description: The answer to the question
+                type: string
+              cited_chunks:
+                description: IDs of chunks used to form the answer
+                items:
+                  type: string
+                type: array
+              confidence:
+                default: 1.0
+                description: Confidence score for this answer (0-1)
+                maximum: 1.0
+                minimum: 0.0
+                type: number
+              query:
+                description: The question that was answered
+                type: string
+            required:
+            - query
+            - answer
+            type: object
+        type: function
+    uri: http://localhost:11434/v1/chat/completions
+  response:
+    headers:
+      content-length:
+      - '572'
+      content-type:
+      - application/json
+    parsed_body:
+      choices:
+      - finish_reason: tool_calls
+        index: 0
+        message:
+          content: ''
+          reasoning: Need to search.
+          role: assistant
+          tool_calls:
+          - function:
+              arguments: '{"limit":3,"query":"DocLayNet most frequent class class most frequent DocLayNet peer-reviewed article
+                dataset documentation"}'
+              name: search_and_answer
+            id: call_9yxa0j5u
+            index: 0
+            type: function
+      created: 1769001221
+      id: chatcmpl-492
+      model: gpt-oss
+      object: chat.completion
+      system_fingerprint: fp_ollama
+      usage:
+        completion_tokens: 52
+        prompt_tokens: 638
+        total_tokens: 690
+    status:
+      code: 200
+      message: OK
+- request:
+    headers:
+      accept:
+      - application/json
+      accept-encoding:
+      - gzip, deflate, zstd
+      connection:
+      - keep-alive
+      content-length:
+      - '173'
+      content-type:
+      - application/json
+      host:
+      - localhost:11434
+    method: POST
+    parsed_body:
+      encoding_format: base64
+      input:
+      - DocLayNet most frequent class class most frequent DocLayNet peer-reviewed article dataset documentation
+      model: qwen3-embedding:4b
+    uri: http://localhost:11434/v1/embeddings
+  response:
+    headers:
+      content-type:
+      - application/json
+      transfer-encoding:
+      - chunked
+    parsed_body:
+      data:
+      - embedding: ZA+CudnsJDyoXhs8eRfJPNCqiropvXQ9XaV7PchWujxZk688S2KavJpeojzWpfs83E8AO1IA/rsP6qq8UPB2vZn44jxZ+1G8pXopPJ5DAbxIWk68yRGuPGslwDy6atg8BIi4vKkY5Ly0cKa8RiSyvLNSnzttHro8TVOUPMyVC706ANQ8XzSluoKTlzunbYe8ZGbavJ4H/ruGoyg8/ubHvMaPjjyrRQq8hE2gPCKolzsECOw8fahYvER2CTy6cby8g/sNvecybrwJJT88BwO1O6y/KL16eoW8ZbRTPd7sC7xAJsc8QND4u+MIHb2iw2E8x0dXuy4LRLxMqoC8BhS3vBnn/bvmoby86fmcPAH03LwbC4E8/VdkOdYuHL3HrEQ8w0hEOnw7TzyLb5M8KTvyvBUBAbxoMbo8N48WvCyE4DwHa1U8Wv2zu+Gn3DvZYEA9hupEPHcEtTtNzAY95M29OysS/LzRbtY8DG+VO4jQljymLZq7LqdbPEFbAjqPQ4Y7WZ7ZvMDVkryJMaq7o3dhO/cK9bszSqy8lenZPIkMWDuy5J47ypLNvJrYHbyLcbm7T+nkOr6XiDsduJ67qcTtu+axY7zRM9Y7GtwCvDzFX7xc1AQ8xZxjPJKKjTwoHSs9uTcqvDKihDwKmTu842miO0GVqjtgOHC98jUJvGON0LzPZuM8LE2BO9qNHDzTxbi8jhAnPHy93rzj8lA7AwqZOgBxSbzhjAY8uMfUu0AsbjzuHVy8gYxIOcvrAzyg3qk7vXJDvBB4W71H8oC8is6qvIxkezwXFGa7NgCwPEiejbxCSmA8q5l+PA7PNzy6cgo9SUShvFSYGTyTCdc7hbeKPFwLD7wPhq88xUTPvLR0vDyO3Mk7yH5BPLSLWLzwIM87BiU5uzK2yLz02hI8ApG1vFaNG7vyWkG871LSvPbucryA9ku8hWcGvBMoRrzqdqQ8agS9PO7iOj0NeGk6T3WEPCG+SzumCX28InsXu5F1TLpvb9M8NiFtPIZTwTsEGPo7ct8lvAYUJjy9Lh68bpfhu958SLySHMA7HIv1ukjvpDxYtvg6Wbs8PG0DGDxdtcy7q5rMu+viAbzAgoO7Y+WfvFkIjTwzdIa8Lh6YPPsT3ry5Rca8OAQLPCCWljzXFPs7cpPzvKcfgLyHZwk9OmzxO7UfDTvVjAK8bzcrvCniPbuuYLu8PHL6OnED9jvDUWC8y/WWO8QgObxDS2I83jerPNt6o7x5UME5BoeiO8H33DskhZa8juaUutAeajykNNW8f/ETPImVCr26JWC8r3GSPATfqLx/ife7G6fyO8BjtrxAI468VAesvCaRO7yK16I6+L+HPM9Y4bwZ0wa90/CUOqHi4LwL2P+81SI5vMMDCDyevsM7tFdcvATKFrypY4e8b2URvAnLqDx2DAs8gouCvHHdjrwj65c53FlWPS/hk7tDPf07nk6DO08/kjze1628eq06PMvGhDvRpFo7XgfQPG0HuLu7+HG7gSeyuz0mfzuYvSe8syXmuPmfNz2Zgnu7WQjyOxJRuzwPiq88hVtAvKZkITw2GMi8PAhCvM29STwKcVc8wfYDPC30Pjx4kwS8X46ZvAZPsDoU1kg8CocGPGk5hry6VJw8W32hO2rVRLtEBhU9G5YxO4uHCbuzdR+8C1QKvJMhMzu7AM48rrPnvGKmLDszBF08IuX1vJTpnry6xVw5GY9IvQWljLzyrO27AnHOu0HPYTynAqM8+6jNPFGGGj3DQwu8jc1WvMsiRD0W4lu9St8IOSUdjjt31CQ7GN/KunR9Jj2sXw49Vg4vPDYnkrye3TA81g10PPli2ry3c+O8TZuPuzIjlDnKVfq6Qj6tvFDaELvJAZi7+s7evPBbx7xywrq7J6+nO9ONFjw2VcG71pbpOtaErjzlj7280+2MvB02aLzIvFw7D3kTPFScJr2isH68Nh7jvMXkmTxuRvM8deSavEkskjvs7yS8OP0TPR0Imrr5qnm8JxaZu4uGHzz9qqq8pWJSOh3WDbwpaF08N/ENPdYXR7wVw7E8n64Evbp0Mzxebf67bNWPu26ra7yFo9m79kd8unl1ET0WWAg8zoRBvNHvKr1Tawo93PsNOpY2ezy9wzE9JuLhvC5a+rvkQcU79jztvCOzZbteDUg8CHIVvcmGz7ybS8M8WdL9O0iL3rvBLai88fEduViTrzyr1yW8Js4tvd2fJjypNJs89J5RvApgqrz3NTM87NPlvNVhDb1vQmg8hDXFvEWvjLvLKtk8YzwjvEJS9Txcw887JmfWvHNIQby/PsA7mxHTPKH8Rz3oj6a8K3pbuWSntLwcnNO7PveBu2yvC72j1ZU69FwRPINEHTyz9Kw8H0UsvTfRJrzY5AC6WXIIPSCXDj2KsFW9QAOUvENBSrtJJZC7hI6VOrbGprxQQEU7QBMHu8kVC7uX1S29xA88PHEJu70M2aE8N3wguwUU4Lx8W6Q7BL6svMmMjrxRvKK8QX+UvIOzND1puw28nGxFvHk6UTtiKqs75IagO9qMGj2WA1a7hUJkPImC6bsJRIM8C8T4PGnsnTzQBwc9Oh3KPGf4pTyoy0k9r/eCPHi0Kzxg/Mw7bA8cOh1orTyRsqy8QbvqvATsjTxjsEu8GT7UPPDzFDyV6+e8hsk6vLUvcbwM1fK8ZjykvFQIBTyE1gu8jpjUvCCuwjwckhE8RtOWOxjeXrzmQIQ8Ea5cPJjTgDzUu6G8aGcdvQUvNzy9U0+8ZF3tu2xEAryGqp28E3eePGEZkLzh0Yk8UvA+PLfYrrvCzDS81/zGPEhE5zyWHda7q5gjPIIeBjx4GBs7VyW7PC7G+budlY27YdgQvSVI2TlvwJI7pKSHPDfRADy6fZw8IUfmuwKlwTt1bYk8L3n9u5yWiLugarW8ro5nvG0J7boVe/G8uOupvKHrAjzN9x29lGxoO9R/Vrz5lja8xhCPPL7SHb0Zwww9cpB/PAyL1zuDdva8u0WBPIwZGzzUuIo8w5KCPBLRgrwflD48HAwGPGBArrwfXIe7lzNRvOrjMDsUpmE7UEq1OxaTfDxPmn46k3mKu6OE07w6Z9e8mod1O38+wjzR6MY8vq8tvAbmrDksRm28uOy4u5JenrsH+OA757IxPaqQP7wcvCK8KDm3u/Y1FDyKShS87dtHO4MwbLw1htq83ogUvfUmijg8aDK8Tm8CPegekrzoiXO8v05APBcGp7ziqDm8jEOnPDpgbzwYbAC9cKy0uwhFeTyNTOs7Hki4O0bdpDyfGOe7UOy5vAIhVbwWCpq8PUanvE8e0bvPNKe8IcRfPHjAkrxq05480lzyO4eT3Ly0ifS887q4vKfVS7yolzM6f3q0vA5tn7xP4Uo9tNERPDKzLLyZgBO8NRKMvRqk3DzydfI7c4QFPHu0Jz1wLuA8nYEJPHOH9Lv18j09dgjAu4l+dr2anPG89POeO36sSDrm0To4L+X0PKiPKrw5aRw8Syicu4Xhojo36Lg8YyxAu0a+jzxAaNY7beq2u45AnLz5ufu8Tx0cPRey4DqQzbQ8yPTgvETugLyaRsw8dOOuvDbviDtaUTM3nDa2PFHiULxiWog8imglvWTYS7lgFfQ7QbAEPQMd5LtQ+Zc8NMALPIJmjjz5c128x6vKu9//d7y78IC84uiju2ryjDqmqoE85hMFO9i+zrx7fIC7qSwNPVA11ztyQso8gN37O8maW7t9G928aVkKvJQ6cryoLFa8+ZIEPO8A37y2QdE7ueYtPI1wYDzkeoi8T4f5PMdPwTzFXHc8iAdAvLBqRr11KwA8aZWXvIIHBzxoz6C8BLkQPPbChrvUb/y8Zf5DuwdHhTvNOge9QrW4unMcnbxrqWE8kWUKPJgwAjxssge99syMPXoNsrtGknu7DkMVOw5MDTz39qK82giNOq2XWTzoZ4Y7zK+HvO3Cqzzhjhc98bD1O1/ytjubA1k87QOqu99lfzxW0DK9oZEwutaEezsAkL878U9WPEuujLvFe5I8DVxpvKu6CLwGM6Q8IVgLPMHXBruM5xc8FEUtPLV2sLsdzja82Gm3uoAWEzz1gwu9ICHHvIg4jzyk3bU6ionnOqPIAzzr2uG81fMFPFmAbTtwpqW8q5hcPUeWxTvUp6W7joIdvKUFTTyGwTq7T9ZKvFMivrwusrM8OYQCvNpZWbvDPD28abagvCLLhLtNdkA8zh3fvAISkTt3lPg6QoQMvIFtezwPB1g8IAiCvFomMzwBI2K7LDowvUKonbvHjks8yYg4Og5D0Dup89s8x1ulvPIvlDxQhgU9tPiZOkSblbwyGYs8+WQIPIX447yDlpe87YlsPJqdLbxh5+y7DEu0PPv4pbokGRq89mySO5WcUju92uE8/HlSO14WTjnH6k07QVixPPqQ3Dx3laY7L4yEPDcgPTxMVSI7W3+bPNqCwbuIoVY8Qe4JPI3QLrwBkgc9mGEKvRYQHL2cpta88yz2ugIE9bxOKTc94/N+u+wAgLk0jQw7IjJEu+uInDxI03g6hN83PFFWJjyzw2M9n3QkPcp5Qjz0Jpq76KCCPLrCKT2F+h08pupnPPLrlrxK+I08rbmKvG/e2bxthAs6dQRau351pTyA2RM890oUvdDRBrx103y99ycMPdIrk7gAtRg8dzlJvB/IPD3ZWWK88uPdvDokHLwJDR+8RuqNOwxR9Tz16sW8yfjePAXjsDxgS7K8mJCVO+G+zbxi+Hc82lWhOhgTtrv6AD28RQuaPAwsizo+poA8mJKLvOaOLbtztpu7+6bCvCm7Bj2u1ii8UVA4O0uiQTwKI9S77iYOPdYUBD2uvTo8GEDsvCMPgLs+prA84weavDiNH71035C84FAPO2F3gTz0eVm9Uhf/O9WaIrwVIH66/169vNXk/7tm9qI8KyAwO0qjrbyppe66LUoIPQUUjLyP2GC806m+vOIHoLximYe7qH8SvafqkDzZs7k7+2s5PAn79zwFC9C75B5tu3W617qn9IE81yQ8vGneo7wuAck7dd7zPNujyzzUFuM7j+4QOxlqLTwPQJO7UDcXPKraJr3K4HU7sfiGvAd0IrvDKPy8uXhcPPNxprw35mA8ZXb3O5VOGLv2lqQ7ymlUOyir0TuT3B+9j7pqPIemDTzxMOA7bluNPM+9w7wfWpK8rO+ZPBzq4Tuckz+8tnENvN9jsDmoOKc7wiuXO1mD+7tBJCI9uCvKOwEffjyCJkW8brgkvH+PezwZHSY8vX/7uQ55Ab34WZU7tdHpO8byFjw3yLc7ouXXu9bqRjzAWmw8F3BePNcV0Dxihh49kuTgOxslDT26G0o8tKqHOgLiU70i4sc8ubWMuw34qbw7puS8n6yevN/XSDyNh6c6InvVPC1z8rwF1oS839FdvENDqTtHryw6/iTJvCFPLrxibyW7tFwNPQeobryDfLU8+KiIu+FV+zs4r6+764LBPIRqnjwY9FC8gM4gPRfJ5jxIU4K8PXchPXCL0TxYdBo9oI4yvecALjztK967xu9GvAUjLDmPBum7s6gAuyeWY7ybiDG8Yw7YPMSUKjxq+368dbTAvNfAPDyBIUA7BsO/vMh4lbvnQAM8cI9+O68X2TyqlOA7kIGzPDM3Er0gApu81sVTPJVok7u90R87ByUzPWoqhjztAKa8CT2mO5zMFr07jhu88b4/vbkRgjw/ZCs8DcItvEcEHzxQZAe9rUANPbHznbx4XrW7PrFUPMhUw7zGM/+77xQivXNHqLx0I2u7SW+VvF+dNLxS5xy8JXALvJM1eLyW4o87pUqoPPwoNzxRR4O8DaIEPYrXTjym7qi6ipGYPI9imLwqlQ09i5ANvM2C1bx+0Ne5PYXcvK5KY7yxGXS8K63zO7Za6LvxS0q7RSEVvWWdpbxxk4s8dHQfPLANmbt169885IEHvRhcgDzG/hO8tD9bPF3YgjsU0HU7v+vBvH4W67wtvWe85KcWvbb5oTuuG648HMreu9t/fTxGDRO8TctlvFBtyTxBxU08HT5zu/xovjy3L9W8Led1PF9hRTyzjr682fVIPDQN5Tz7IrO8a9qwPDLxDz0M72u7FhcGvdM3nLxwaxU8rn4mu1C8gDwuOw892RPPux8mBj3fw/G7hatGPDNNHr33nwI78H1gPNdQ2jvduV67tiy6O5Xsqzvd+gM9auWDPPwZ9DtgLI+6/oPxO/+oibsA70S8xZSuvFd9Ez2tcbq7v9g5OzrForw7wJO8jkuIvE9gIb3XvLI8SGsOPIupUDyhauI710mBPIVxDrxl7Ru8+Ou/vL0r0Tu9W4K86Do9PSsbDb29ifa85O4IPUHPbbr9UNc8hQOOOvWBzDzXliM9ZsiEOxdNfjyicdS8/DP4O5LKITmZ+Dw83rq8vPi6lL1G2um8pfIUvCQPWL3k62u7IfWIvDX5nzs1t+k7coiLvIlzWzxnOfM74BV7PeCTFDwPOQo8J5apvBPuYjssK4G8zntFu6nh7DzNE3C8kqCAOpzIQrzVMlu8itpVvGO6sjyeMle80dbGvMavGrxr7QE81f4VPaM66jxvyhy7YAMiPM88kjxsWtw8DFihPJWjarwFZ5I8a9/pvLOHLLxRSwE9rc4CPKFRxbz23D48nbeVPMlKnro4m3g9b7FfOlKxOTsKmM08qggwvEUNkrxUiWG9CJ9wupXJ3bzya4O8R+AqOqhgvbts7LM8R3/Nu5fmaLzkbkk9qsqjvEJ7OLyaCZO8HQ3fOAT6pDyaoxc7C6BnPJtK+Lj6pdu76Bv9us7nnTyOhlO83O9dPKby2TzXywW8ltZjvOGtJDxzj+m7r9zQuwfxC72oifs8FiEROmM2KjsfQUS8p0wgPKtinbkzyyG9RQ3DPEN9O7xy6PS8Nj1YPIg0gzxsTwu9eeVDvD2YyLwzx0o70pnpPL6G5rsbs1E59/eSu6kdGbzjaJ87xDIyPN6/rDsPMGM85IClvKMnmjz6n208TajgvF+t9LrmmYQ8kkwOvcfqFDwSqWG70tjbOyNH5bzjFNi8PLMfvWiK2bxkp2W6zb02u81LFToSHdk6yQZju8OtcDy8zXi7duH5O6zXNru6Aeq7HBIyvDFx1Dr7S3o8P2XTO5wOEj2ic2283H4yPDnbVTxfL8M7KC5cPWQpwLyXEze9PrqtvPl+p7waWEI8vY2MO5ug2DszQ4C96VCjO0E+HLypGQu9NjBmuqhuPjxZkj474oaaPGqHQjyFyOY8AO7Xuvkni7pUwZY6KPCyOzSqFDzt2+68ZekWPQULE7yEB7a5suvqOgvNJT0wC9c8UQqFvAqQSzsXsNY86GmUPC3oT7wyc7m85SPGu3LPeTzZ8cu8YLsePbd3xbyhEwO8+8ihO/+rartx7lo8+8oavOH2Bj0M3Qk95fpOvaHV4TxnU727rUfOPNerfTqPvqO8WdyevCyfWz0CF5o8qIoAvSisJj2cuEg8O11pugIjJLsxmVI8DthFOrtVBb2i8Vo8bx6SPG9jxbyP4uA8+DVmPI85rbxj4PG8GUmKvNYgfz2o6va8ZBvCvBiopTv+ir+8A+G5PJvMyry4f1e8N7N9vPxO7zsUR1e9X570vLwVDrx/Wwc78UWCvFxgrzzo2nC84ZnsuZZzervz1qK8a8OMvGPwzLy4j5C8rOpIPGStgDwsZy47Ir23O45e7DtjhSg8nwt3vNcHYLxc0KE8UKO5O+hsjLwEBhE4qKWUuQg4bbrcXzc8gFpDPOjmeLzLOlA5QbchPQqJjzxVssy4522QPH9KXryVCc68hxCLPA1vKrxLSYk8hBuCPOjC+LwhXcI8OH4SPZQS77yZ14m8/XC3vLWfjrzsQm+8GCUtvM+1vTxkayM8dxzmuwrqhLzZaJU81iA3u3mBfLyBQbc7V0HovOFUWjz/FM46Q00DPWfwlzvunOm8k1PHPP9TgLw10rO7KogUPYddFzsb7pW8C3AivIPAmDzm32a8lS/QPHkPjry/5lK8s10DvX3xojlsGhy9CpCIPN5KvLzhdE68//7Ru6IlzbpCksa8znxTPBI54jrHsia7L08hPL95xryVA+m7Vs0UPDp/djxl5ee7TtEEvClq4zuzuE88oflRvBOmmroxaUq7O5gRvdeBurtersK8Wavhu9aSdzxQTQ09/k+Hu7wslDycWlw8Bg6cPNcNLruGgXQ82y30upHr8btvv248w7PrOzipiTzkKRs9mx0tvBhHZzwZa8Y8usL1OwioGTx40XG7KZHZuyaMrDxiNgY8+fiWPDPYCDwNY4+81pWWvE6CFz3LXZc63GtFvVJKFj3g1Bo9CWP6O1w2jDzwszW8Fo0XOoOwcD0IXRe94NjDO1Kli7yArz67SPsBu7rfyDyrXim7dnlYPEtYEb08zVI8vDjwO9VkM7x6DxE8PRRfO5ktKDwAx9u70MA6PBxVZDzihAo9VdShuz9QU7yEk6i8S4eGu+4GELyFPjQ8wjkLPXodo7xf4le7lbwUPE2gbDsYdyM8NvRDPFdv2TsVwb286BE0vPU6VjwMPAY96FvuPPVbu7yoCPK8a0Jju2p1Lj2YnR+9jcCPO4VcHzz8MhK9ZFg/u9ZTG7ycMko84uCvu/VbwrpTARY9Ha8RO0iMRLxixJS7Co9+O6zX3Dzxsbw82NcWPco5hzzOO2y8fVzTvM3AAzy2lRM9z+ytu5ONo7s5VRM8oKQLvHQ8PD1c12G8qWBNu9HzRbzAwKO8QMlEuzjkkbxue9w8W9oSO2Jf+7v2FmU8iXPCu1EcxjyN+zq9ZGiavEjVdb2N20e7nhygPOlb4TvewE07U8wNPbB1wjubXhk95fxqPLL6YjyBGHA7NGvbvDUN7zv73x07GQigOz0KKDxb9hi8GC38PKoKrzwDwZ68mo+4PLpVVrojmYs7fM2XumzZbTsvWdO86hfovF5gHj0tCnc8aBW4PNQk8rub7+C8RUY4u716lbzjReC8Qeblu+v+DL0S9pI6NqsAPRuKprwmWVy8o1X3u0jNuLzzdH28R7M7PIHAjDzP96g8HSP0u8qRljxB6Ko7AavrPO5g3zslKfu8LFs9vELr8jvx5DG8u72WuQrPOTzOKTW99jCgOw+1GbxLL8+7Bvv1vPNITLzMC8y7iQtdvNlHmjwgCnW8FKPUvCqlurpNiRM9jQQCvIqoQDzwgZE8JvKbPPCGBD1BDF08Kan+vGBaezvi0i48APFLPOadID09dWg8KDBuvOoc6zy+Ktq7pPaqvPX//zufPIk7r2hMPDAdfLwKwIi869xpvL0Zrbzn7fi7Yi3RvA+lMztDkru80xInOwenizucWBu8kfo/vJb/uzxRIr48rcDCOcxxVjuRMkM8D84BvJ7kuTpHFf28cz/QPIBTibuRw8C7/BtXu9OLfTss1jS6SVHKvP8t2ryUKaS8La0bu2kOgbph8Ri9BCIpO7r777tLMbO8kp03u41sCD1zKt+8rPQnPbxPibyXyja8RPEkPN8lGDxnXN27Z2E1vYMshbw37+A66G4UvU85wDwx6YU7suUBPDJyhbvFeJS8l9AfPEM83bvF7Qa9UcFmuwuUIzzu/u68To/GvNzYzTxC9Aq9RWzPO8oYGr39frY8yM7gvP/li7yblw282/2XvAReuDxMIdw894AlvAzTdrzF+q48sbCzu4UBBD0+kH87mVEtO8ccxzqxJCc7lvxcPDAAHr1EGVw7a98DPJCEHjxHSea8yYmivLCVCr2DwYQ7ikUEvT5ol7viLpS8NIQ2vLICX7yVcOU7BF6fvKof1DsMWxK9wlopvaV/YbtWtVS86ohqPMp8Db32Gc08CeZwvMs2JTxMKFC8H19UvE37wjxBn4S8l/lvPHN9VrwLIYM7/DP9vHhMVzw4lt88s+eUvBOVUjtUOGi6B1QTvOzFcLvVplO8ENciuYlApDrhYpW8E4uovI51yrxpYBC8x0mbO2dRpTy91XC8IR+MPPVnNLt4Jvq8yLKuvIWyubw4JyY6yox/OwdQ77zqiy88BJLZPMn96jyfcmU6BLeBPKs5XTxdVNg7RNCfPF0EEbxDN/s8QNeVPD/rTDw+2u88Mb00PZdJtjwsh/O8lGbcvExDXTwXR7U8mjIdvLTDlbyfTwc8gatGPP034zycXtq6c0SRPD79LLxOXgK9gv82vY7HVLp+C46845SYPOz+tTt2qp27+96Ju85C7rtYNig665MFvDV67DuF2q+6mHrMuzEXnDxPEGi7pqyePNcKZDt32J68yJw2u24U5TsVPLU8WlTmOwKehrrfQ347A552Oyq2k7x09hu8g8KDPLlBETyuxV+8TOoavJC49TvmCvi771UrPG/1crzulAE9gqldPITPWDys6068sbX4PLYp87sS41y8hPEpPKdnsbzAd987mt/dPC1HlTzRlrC7dosivKNzrDtlkuU6XDujPPQzyrpcEbu7nxAyu1/r47xusLm7oHQBPCvX5Dyo0ZY8rR0GvalTSL3Z0XO8Ggl5OtfMCz3msyS9fbx7Ow6mILxpWTu8gEOjuzrdjLpdetK8Ffi1O2aE8bqnwxg8tElsO9zmprxgnyS9sTXbPKsoorksEs67Z/PyO2GzJzulvNQ7KT8gPXWoCL1kPzq8+fCNPI/VWLtSAho8wKaPOwY0xrymERs8qjBWu13QFz0ESh49Uyzqu/3ke7uPrIu8GOK0vMZaCD2yMr+856+3uoYIWLzyFbu8mMItPDETfryKX5u7OyAtvPI3gr2o98q7ZNUdvNG5V70LWRW8L3upOS/mljuB2Uq7lcK5PG8E6br83o68Ej8ZPKpMbDzKAL08Oo7hvCljXTxqcpY7vbPvu3P4/js9DdC8/WcuvC6iOL1sYI88iWdHPcEq8TtMAxW9GClRu6BeID0rXuq8Hqv1O9B/gTzi0fk7A112PPiUADwdwkq8JPZMvO+fMLw6GeQ78UXiPNU8i7yTM7w8xTiYvHxk9rvMDTO90mnHvPFpDj2Y1lc7ZiELvJOqcjx2ADO8d0iuPHXInjvF9QO91EmuPEUWBb0JC92764kGPJItNj2944e7huCTvBpyLzoo3ym8hl6qPJ0Ao7t0/vw8/adxvEJY1rq2iRc8jgr8u7ccobtYJEq8M0NXvPaYOD1akHu4bJpEvN434DvRhFc8PXuMOttjM72WTTO86GQaPWh6E7sPbug8AJcJPYO8CT37jcu7yhkkvBiJlzrQbNi8uSQGOEHFmjmgT/G6ZVoZPSIRzrwAvEa8EkEhPGU32jqYRIG77icIPJ/P3rzeGwW9b9J1vK2+VLxxbIG7YG/hOqe/kznzadS8imBSPAz3bjwPjzo8CbuhPGXyl7yqlsI84HGMPN0AibwZhbK7pia+Owop27xcIea80yhmvA6lXbyt5+Y7YCu9vBZ84jl2QTK81ja6PCUX6TxJOmY8kM4YPE/zuzy27eu8TSu1PJCUQjy5yZW6G5QYPJEJpruJocw8JFHBPN9NVDy4GxS7DMfHPPOg9rsH4Uy899L5u4eAvLw3B/08rNVgO3hU0rxq2kK7emyBPMvZgrlY1Js78bpuvNsipDtpn327Pm/jvOJ/OL3m8eS8oV6CvIOc4boWkdu8TuFUO8IFKDwtxy47pObMvGQFjzwXtb+7Cgq7Oq1XvjyJ4Bq8Dvyfu0kKEr14MAA97WUwvGNc47wQSNy7qJ3AuyI45jzhHyo9EBYFPFJBCDwr7+g8Emn+vJVEhDzdJbe8khqnPO2XQb0Ywwa8cHIuvJPVIL2NupI8/2a0vFCLgTwMxNO7sl+HuwIcgT0KYlu88ltiPP5MrDuzWcG88jtUPILUpLy9g708nXeWvPsUMT0tiP87n/bWvPbr8rsjNha8AEsLPXYu8Tvyu1w8ajgZuzQhtDunkpo8r8SSPFWtNT2A8DM8yhpau5+5nrsEf1O7nZU6PEHO07u2LhU9KVCxu2V++7ys3WC7ssAnvEHsXzzJ79+833m/OpEw+7xttRE8uGOevEpSLbyRc8S83BSxPIkNQToBBtw7Wji5vAe0TbypB8a7erkHu2nFaDytd806isEivNViNTyz3108D9SSOxTxhjzlIIW8pMDfO9ntDT3954a8tFgsPKSlFT2eR+I7A2pTvKqNmrwDPQC7sUWnus8YyrzMksI7VfPdPLNsoTsejKK8FnrXPNxpG7yevKM8fX/GPHeaQjy51Vs8ISdNuqc23Dv77UO92PYcvc1ixrzpvFg8Tw3DPEIUurv12kQ95GT1O350ajzWi5A8jHD+vBRZCT14+fC8BNIUu5M71jy0gN87JjdeuBlL7LyJII28pgOcPDnfjjxJlLK8BBTkuxv/n7zkdZw8UVvhOyb+kjznvFg7PhABvdLHk7yU3r2714D+OxHDCT0e5aM8AZouPAhMn7ra+ZU8LM2OuyruNDukRYa7suJbvJE1v7sTQj48e0NRu1dqPLzGnhK9brpIvE/tQbptUFs7VGS+O4f/7jwjbEY8EjW2vEgTKD2Z+tm7vKSBOw+K7DuNO6C7qJRbuikixbyyAWm7WHxfvLuYj7wpZzG9GWeHvPXPKLwDpzY81K8gPCkmDbwIEqC8VcL8u2fuGzy8ZZG70HutPPQ1KrxaSpm8kYQPPF1J0zxhuMa7XpGtPDNoFbyUlu47QhiiPHUxrLxfqFa8Xy25PFIglzzcD4e8DjbUPOJy4Tvstm48GesEPXQVujppJzw9gIkOPRh4tjxgPpg89X4rPJylhzzaA7a7JV8LvN0hDz0SYo+7vXc3u6LBEDwo1F89Kxywu2EhQLw8cG08Uex7PAmI3zp+mfI8LJvOOuPIwbtAAO2710y2PNrGVjwHmkc7T0+9O6oiKLwyZJQ7D0OGu7f9eTxzfHE8T66NvGBFNrxFsLu8NK7fvPD6IrsZxN+8cMl6uGY6H7uXGYI8XteIvFLkkrlygoC8oK//u3dgMLzJORM9q+tfvNsu/LzHc568+gtku3DYALrcgCU5To3Iu+wLnrxGLB89ceBAvemaCT1LyQ+7EepGPLPBsrx63XI5blcnPbRLwTkzWYe7gZdbOURxSzy3gmC7MxWrO65kfbzGVlU81WIVvQI80TqKHh26iqfoPLiNc7tczBm8sTuHu2Ugh7undOg8cZ8MvRKrPD1r64G8Hu2IPIsAVLxQZcs8P4RpPL3vsDvgDY88XoBvO9MoFLxYyo27hImAuy/FKDynEAE9P24evZcGwbsmAPG8+apKu/4PiDzYVQm9+CbpvH6W0Ty0Bo25CyjTPCHeMzyKT1289YRYu/QxuLyW2+W7zpNKuxdmPjrzmsi8ebImPGwLAr31rVM9JXulO99L3LuOgdw7WpYOvU6Ur7sc1gU7Gx2cPCR1o7wvras8WF25u57TPjzqFmS8bGqrOgK0Art9QyW9TD6APMj0zDq027o7d6EAPeLHTrydW6E7A/uHvPY8qjxaOGE8cGE7vPVpJLxwaWm8urURu4CmLbzPtYo7EMy2u6Yo4DsYeI08wq3eOch1mzxpBi088GEUu25T97qOTzo8AUpLvCmBbbruHF+7LDBGvCP8LLyBw6Q8+qM7vKWAVryUhQG8cZvgu7WnGzwl2/27KP9GO6XK4TvqJZu8TqgHvPwQmTp8RTS8z7caPEU2sDwc14M7d+aRu5qUPDyGBMU8NIc7vHrmQbyevS87JHYGvA==
+        index: 0
+        object: embedding
+      model: qwen3-embedding:4b
+      object: list
+      usage:
+        prompt_tokens: 20
+        total_tokens: 20
+    status:
+      code: 200
+      message: OK
+- request:
+    headers:
+      accept:
+      - application/json
+      accept-encoding:
+      - gzip, deflate, zstd
+      connection:
+      - keep-alive
+      content-length:
+      - '3832'
+      content-type:
+      - application/json
+      host:
+      - localhost:11434
+    method: POST
+    parsed_body:
+      messages:
+      - content: |-
+          You are a search and question-answering specialist.
+
+          Process:
+          1. Call search_and_answer with relevant keywords from the question.
+          2. Review the results ordered by relevance.
+          3. If needed, perform follow-up searches with different keywords (max 3 total).
+          4. Provide a concise answer based strictly on the retrieved content.
+
+          The search tool returns results like:
+          [9bde5847-44c9-400a-8997-0e6b65babf92] [rank 1 of 5]
+          Source: "Document Title" > Section > Subsection
+          Type: paragraph
+          Content:
+          The actual text content here...
+
+          [d5a63c82-cb40-439f-9b2e-de7d177829b7] [rank 2 of 5]
+          Source: "Another Document"
+          Type: table
+          Content:
+          | Column 1 | Column 2 |
+          ...
+
+          Each result includes:
+          - chunk_id in brackets and rank position (rank 1 = most relevant)
+          - Source: document title and section hierarchy (when available)
+          - Type: content type like paragraph, table, code, list_item (when available)
+          - Content: the actual text
+
+          Output format:
+          - query: Echo the question you are answering
+          - answer: Your concise answer based on the retrieved content
+          - cited_chunks: List of plain strings containing only the chunk UUIDs (not objects)
+          - confidence: A score from 0.0 to 1.0 indicating answer confidence
+
+          IMPORTANT: Use the EXACT, COMPLETE chunk ID (full UUID). Do NOT truncate IDs.
+
+          Guidelines:
+          - Base answers strictly on retrieved content - do not use external knowledge.
+          - Use the Source and Type metadata to understand context.
+          - If multiple results are relevant, synthesize them coherently.
+          - If information is insufficient, say so clearly.
+          - Be concise and direct; avoid meta commentary about the process.
+          - Results are ordered by relevance, with rank 1 being most relevant.
+        role: system
+      - content: Does a peer‑reviewed article or dataset documentation confirm that this class is the most frequent in DocLayNet?
+        role: user
+      - content: |-
+          <think>
+          Need to search.
+          </think>
+        role: assistant
+        tool_calls:
+        - function:
+            arguments: '{"limit":3,"query":"DocLayNet most frequent class class most frequent DocLayNet peer-reviewed article
+              dataset documentation"}'
+            name: search_and_answer
+          id: call_9yxa0j5u
+          type: function
+      - content: |-
+          [52cb239c-ba6f-42cb-a8ae-8d06437b2c30] [rank 1 of 1]
+          Source: "DocLayNet Class Labels"
+          Type: list_item
+          Content:
+          DocLayNet defines 11 distinct class labels for document layout analysis:
+
+          Caption - Text describing figures or tables
+
+          Footnote - Notes at the bottom of pages
+
+          Formula - Mathematical expressions
+
+          List-item - Items in bulleted or numbered lists
+
+          Page-footer - Footer content on pages
+
+          Page-header - Header content on pages
+
+          Picture - Images and diagrams
+
+          Section-header - Headings for document sections
+
+          Table - Tabular data
+        role: tool
+        tool_call_id: call_9yxa0j5u
+      model: gpt-oss
+      reasoning_effort: low
+      stream: false
+      tool_choice: auto
+      tools:
+      - function:
+          description: Search the knowledge base for relevant documents.
+          name: search_and_answer
+          parameters:
+            additionalProperties: false
+            properties:
+              limit:
+                anyOf:
+                - type: integer
+                - type: 'null'
+                default: null
+              query:
+                type: string
+            required:
+            - query
+            type: object
+        type: function
+      - function:
+          description: Answer to a search query with chunk references.
+          name: final_result
+          parameters:
+            additionalProperties: false
+            properties:
+              answer:
+                description: The answer to the question
+                type: string
+              cited_chunks:
+                description: IDs of chunks used to form the answer
+                items:
+                  type: string
+                type: array
+              confidence:
+                default: 1.0
+                description: Confidence score for this answer (0-1)
+                maximum: 1.0
+                minimum: 0.0
+                type: number
+              query:
+                description: The question that was answered
+                type: string
+            required:
+            - query
+            - answer
+            type: object
+        type: function
+    uri: http://localhost:11434/v1/chat/completions
+  response:
+    headers:
+      content-length:
+      - '866'
+      content-type:
+      - application/json
+    parsed_body:
+      choices:
+      - finish_reason: stop
+        index: 0
+        message:
+          content: |-
+            **query:** Does a peer‑reviewed article or dataset documentation confirm that this class is the most frequent in DocLayNet?
+
+            **answer:** No retrieved source provides evidence that any particular class is the most frequent in DocLayNet. The only document found lists the 11 class labels but does not report class frequencies. Therefore, a confirmation of a most‑frequent class cannot be given based on the available materials.
+
+            **cited_chunks:** ["52cb239c-ba6f-42cb-a8ae-8d06437b2c30"]
+
+            **confidence:** 0.1
+          reasoning: No info about frequency. Probably not confirmed.
+          role: assistant
+      created: 1769001224
+      id: chatcmpl-119
+      model: gpt-oss
+      object: chat.completion
+      system_fingerprint: fp_ollama
+      usage:
+        completion_tokens: 139
+        prompt_tokens: 837
+        total_tokens: 976
+    status:
+      code: 200
+      message: OK
+- request:
+    headers:
+      accept:
+      - application/json
+      accept-encoding:
+      - gzip, deflate, zstd
+      connection:
+      - keep-alive
+      content-length:
+      - '4583'
+      content-type:
+      - application/json
+      host:
+      - localhost:11434
+    method: POST
+    parsed_body:
+      messages:
+      - content: |-
+          You are a search and question-answering specialist.
+
+          Process:
+          1. Call search_and_answer with relevant keywords from the question.
+          2. Review the results ordered by relevance.
+          3. If needed, perform follow-up searches with different keywords (max 3 total).
+          4. Provide a concise answer based strictly on the retrieved content.
+
+          The search tool returns results like:
+          [9bde5847-44c9-400a-8997-0e6b65babf92] [rank 1 of 5]
+          Source: "Document Title" > Section > Subsection
+          Type: paragraph
+          Content:
+          The actual text content here...
+
+          [d5a63c82-cb40-439f-9b2e-de7d177829b7] [rank 2 of 5]
+          Source: "Another Document"
+          Type: table
+          Content:
+          | Column 1 | Column 2 |
+          ...
+
+          Each result includes:
+          - chunk_id in brackets and rank position (rank 1 = most relevant)
+          - Source: document title and section hierarchy (when available)
+          - Type: content type like paragraph, table, code, list_item (when available)
+          - Content: the actual text
+
+          Output format:
+          - query: Echo the question you are answering
+          - answer: Your concise answer based on the retrieved content
+          - cited_chunks: List of plain strings containing only the chunk UUIDs (not objects)
+          - confidence: A score from 0.0 to 1.0 indicating answer confidence
+
+          IMPORTANT: Use the EXACT, COMPLETE chunk ID (full UUID). Do NOT truncate IDs.
+
+          Guidelines:
+          - Base answers strictly on retrieved content - do not use external knowledge.
+          - Use the Source and Type metadata to understand context.
+          - If multiple results are relevant, synthesize them coherently.
+          - If information is insufficient, say so clearly.
+          - Be concise and direct; avoid meta commentary about the process.
+          - Results are ordered by relevance, with rank 1 being most relevant.
+        role: system
+      - content: Does a peer‑reviewed article or dataset documentation confirm that this class is the most frequent in DocLayNet?
+        role: user
+      - content: |-
+          <think>
+          Need to search.
+          </think>
+        role: assistant
+        tool_calls:
+        - function:
+            arguments: '{"limit":3,"query":"DocLayNet most frequent class class most frequent DocLayNet peer-reviewed article
+              dataset documentation"}'
+            name: search_and_answer
+          id: call_9yxa0j5u
+          type: function
+      - content: |-
+          [52cb239c-ba6f-42cb-a8ae-8d06437b2c30] [rank 1 of 1]
+          Source: "DocLayNet Class Labels"
+          Type: list_item
+          Content:
+          DocLayNet defines 11 distinct class labels for document layout analysis:
+
+          Caption - Text describing figures or tables
+
+          Footnote - Notes at the bottom of pages
+
+          Formula - Mathematical expressions
+
+          List-item - Items in bulleted or numbered lists
+
+          Page-footer - Footer content on pages
+
+          Page-header - Header content on pages
+
+          Picture - Images and diagrams
+
+          Section-header - Headings for document sections
+
+          Table - Tabular data
+        role: tool
+        tool_call_id: call_9yxa0j5u
+      - content: |-
+          <think>
+          No info about frequency. Probably not confirmed.
+          </think>
+
+          **query:** Does a peer‑reviewed article or dataset documentation confirm that this class is the most frequent in DocLayNet?
+
+          **answer:** No retrieved source provides evidence that any particular class is the most frequent in DocLayNet. The only document found lists the 11 class labels but does not report class frequencies. Therefore, a confirmation of a most‑frequent class cannot be given based on the available materials.
+
+          **cited_chunks:** ["52cb239c-ba6f-42cb-a8ae-8d06437b2c30"]
+
+          **confidence:** 0.1
+        role: assistant
+      - content: |-
+          Validation feedback:
+          Please include your response in a tool call.
+
+          Fix the errors and try again.
+        role: user
+      model: gpt-oss
+      reasoning_effort: low
+      stream: false
+      tool_choice: auto
+      tools:
+      - function:
+          description: Search the knowledge base for relevant documents.
+          name: search_and_answer
+          parameters:
+            additionalProperties: false
+            properties:
+              limit:
+                anyOf:
+                - type: integer
+                - type: 'null'
+                default: null
+              query:
+                type: string
+            required:
+            - query
+            type: object
+        type: function
+      - function:
+          description: Answer to a search query with chunk references.
+          name: final_result
+          parameters:
+            additionalProperties: false
+            properties:
+              answer:
+                description: The answer to the question
+                type: string
+              cited_chunks:
+                description: IDs of chunks used to form the answer
+                items:
+                  type: string
+                type: array
+              confidence:
+                default: 1.0
+                description: Confidence score for this answer (0-1)
+                maximum: 1.0
+                minimum: 0.0
+                type: number
+              query:
+                description: The question that was answered
+                type: string
+            required:
+            - query
+            - answer
+            type: object
+        type: function
+    uri: http://localhost:11434/v1/chat/completions
+  response:
+    headers:
+      content-length:
+      - '828'
+      content-type:
+      - application/json
+    parsed_body:
+      choices:
+      - finish_reason: tool_calls
+        index: 0
+        message:
+          content: ''
+          reasoning: We need to output a tool call with final_result.
+          role: assistant
+          tool_calls:
+          - function:
+              arguments: '{"answer":"No source confirms a most‑frequent class in DocLayNet; the only available document lists
+                class labels but gives no frequency data.","cited_chunks":["52cb239c-ba6f-42cb-a8ae-8d06437b2c30"],"confidence":0.1,"query":"Does
+                a peer‑reviewed article or dataset documentation confirm that this class is the most frequent in DocLayNet?"}'
+              name: final_result
+            id: call_tfxiz00q
+            index: 0
+            type: function
+      created: 1769001227
+      id: chatcmpl-26
+      model: gpt-oss
+      object: chat.completion
+      system_fingerprint: fp_ollama
+      usage:
+        completion_tokens: 120
+        prompt_tokens: 1001
+        total_tokens: 1121
+    status:
+      code: 200
+      message: OK
+- request:
+    headers:
+      accept:
+      - application/json
+      accept-encoding:
+      - gzip, deflate, zstd
+      connection:
+      - keep-alive
+      content-length:
+      - '3855'
+      content-type:
+      - application/json
+      host:
+      - localhost:11434
+    method: POST
+    parsed_body:
+      messages:
+      - content: |-
+          Generate a direct, conversational answer
+          to the question based on the gathered evidence.
+
+          Output:
+          - answer: Direct, comprehensive answer with a natural, helpful tone.
+            Write the actual answer, not a description of what you found.
+            Use as many sentences as needed to fully address the question.
+          - confidence: Score from 0.0 to 1.0 indicating answer quality.
+
+          Guidelines:
+          - Base your answer solely on the evidence provided in the context.
+          - If a <background> section is provided, use it to frame your answer appropriately.
+          - Be thorough - include all relevant information from the evidence.
+          - Use formatting (bullet points, numbered lists) when it improves clarity.
+          - Do NOT use meta-commentary like "Based on the research..." or "The evidence shows..."
+            Instead, directly state the information.
+          - If the evidence is incomplete, acknowledge limitations briefly.
+        role: system
+      - content: |-
+          Answer the question based on the gathered evidence.
+
+          <context>
+            <question>What is the highest count class in the DocLayNet dataset?</question>
+            <prior_answers>
+              <item>
+                <question>What is the distribution of instances for each class in the official DocLayNet dataset (Caption, Footnote, Formula, List‑item, Page‑footer, Page‑header, Picture, Section‑header, Table, etc.)?</question>
+                <answer>The publicly available information about DocLayNet only lists the 11 class labels (Caption, Footnote, Formula, List‑item, Page‑footer, Page‑header, Picture, Section‑header, Table, etc.). It does **not** provide the number of instances or distribution statistics for each class in the dataset. Consequently, the exact distribution of instances per class cannot be reported from the available sources.</answer>
+                <confidence>0.9</confidence>
+                <source>DocLayNet Class Labels</source>
+              </item>
+              <item>
+                <question>Which class in DocLayNet has the highest instance count according to the dataset’s official statistics?</question>
+                <answer>I could not locate the specific class with the highest instance count in the documents retrieved.</answer>
+                <confidence>0.3</confidence>
+                <source>null</source>
+              </item>
+              <item>
+                <question>Does a peer‑reviewed article or dataset documentation confirm that this class is the most frequent in DocLayNet?</question>
+                <answer>No source confirms a most‑frequent class in DocLayNet; the only available document lists class labels but gives no frequency data.</answer>
+                <confidence>0.1</confidence>
+                <source>DocLayNet Class Labels</source>
+              </item>
+            </prior_answers>
+          </context>
+        role: user
+      model: gpt-oss
+      reasoning_effort: low
+      stream: false
+      tool_choice: auto
+      tools:
+      - function:
+          description: Conversational answer for chat context.
+          name: final_result
+          parameters:
+            $defs:
+              Citation:
+                additionalProperties: false
+                description: Resolved citation with full metadata for display/visual grounding.
+                properties:
+                  chunk_id:
+                    type: string
+                  content:
+                    type: string
+                  document_id:
+                    type: string
+                  document_title:
+                    anyOf:
+                    - type: string
+                    - type: 'null'
+                    default: null
+                  document_uri:
+                    type: string
+                  headings:
+                    anyOf:
+                    - items:
+                        type: string
+                      type: array
+                    - type: 'null'
+                    default: null
+                  page_numbers:
+                    items:
+                      type: integer
+                    type: array
+                required:
+                - document_id
+                - chunk_id
+                - document_uri
+                - content
+                type: object
+            additionalProperties: false
+            properties:
+              answer:
+                description: Direct answer to the question
+                type: string
+              citations:
+                description: Citations supporting the answer
+                items:
+                  $ref: '#/$defs/Citation'
+                type: array
+              confidence:
+                default: 1.0
+                description: Confidence score (0-1)
+                maximum: 1.0
+                minimum: 0.0
+                type: number
+            required:
+            - answer
+            type: object
+        type: function
+    uri: http://localhost:11434/v1/chat/completions
+  response:
+    headers:
+      content-length:
+      - '980'
+      content-type:
+      - application/json
+    parsed_body:
+      choices:
+      - finish_reason: tool_calls
+        index: 0
+        message:
+          content: ''
+          reasoning: 'Need answer: highest count class in DocLayNet dataset. Evidence shows unknown, no source. So answer:
+            not determinable.'
+          role: assistant
+          tool_calls:
+          - function:
+              arguments: '{"answer":"Based on the available public documentation, the DocLayNet dataset does not provide explicit
+                counts or a distribution for each of its 11 classes. Therefore, there is no published information that identifies
+                which class has the highest instance count. Without official statistics or a peer‑reviewed source detailing
+                the class frequencies, the most‑frequent class cannot be determined.","citations":[],"confidence":0.2}'
+              name: final_result
+            id: call_5zz8t62w
+            index: 0
+            type: function
+      created: 1769001230
+      id: chatcmpl-712
+      model: gpt-oss
+      object: chat.completion
+      system_fingerprint: fp_ollama
+      usage:
+        completion_tokens: 128
+        prompt_tokens: 713
+        total_tokens: 841
+    status:
+      code: 200
+      message: OK
+- request:
+    headers:
+      accept:
+      - application/json
+      accept-encoding:
+      - gzip, deflate, zstd
+      connection:
+      - keep-alive
+      content-length:
+      - '4820'
+      content-type:
+      - application/json
+      host:
+      - localhost:11434
+    method: POST
+    parsed_body:
+      messages:
+      - content: |-
+          You are a helpful research assistant powered by haiku.rag, a knowledge base system.
+
+          You have access to a knowledge base of documents. Use your tools to search and answer questions.
+
+          CRITICAL RULES:
+          1. For greetings or casual chat: respond directly WITHOUT using any tools
+          2. For questions: Use the "ask" tool EXACTLY ONCE - it handles query expansion internally
+          3. For searches: Use the "search" tool EXACTLY ONCE - it handles multi-query expansion internally
+          4. NEVER call the same tool multiple times for a single user message
+          5. NEVER make up information - always use tools to get facts from the knowledge base
+
+          How to decide which tool to use:
+          - "get_document" - Use when the user references a SPECIFIC document by name, title, or URI (e.g., "summarize document X", "get the paper about Y", "fetch 2412.00566"). Retrieves the full document content.
+          - "ask" - Use for general questions about topics in the knowledge base when no specific document is named. It searches across all documents and returns answers with citations.
+          - "search" - Use when the user explicitly asks to search/find/explore documents. Call it ONCE. After calling search, copy the ENTIRE tool response to your output INCLUDING the content snippets. Do NOT shorten, summarize, or omit any part of the results.
+
+          IMPORTANT - When user mentions a document in search/ask:
+          - If user says "search in <doc>", "find in <doc>", "answer from <doc>", or "<topic> in <doc>":
+            - Extract the TOPIC as `query`/`question`
+            - Extract the DOCUMENT NAME as `document_name`
+          - Examples for search:
+            - "search for embeddings in the ML paper" → query="embeddings", document_name="ML paper"
+            - "find transformer architecture in 2412.00566" → query="transformer architecture", document_name="2412.00566"
+          - Examples for ask:
+            - "what does the ML paper say about embeddings?" → question="what are the embedding methods?", document_name="ML paper"
+            - "answer from 2412.00566 about model training" → question="how is the model trained?", document_name="2412.00566"
+
+          Be friendly and conversational. When you use the "ask" tool, summarize the key findings for the user.
+        role: system
+      - content: What is the highest count class in the DocLayNet dataset?
+        role: user
+      - content: |-
+          <think>
+          Need ask.
+          </think>
+        role: assistant
+        tool_calls:
+        - function:
+            arguments: '{"document_name":null,"question":"What is the highest count class in the DocLayNet dataset?"}'
+            name: ask
+          id: call_f61rh3p7
+          type: function
+      - content: |-
+          Based on the available public documentation, the DocLayNet dataset does not provide explicit counts or a distribution for each of its 11 classes. Therefore, there is no published information that identifies which class has the highest instance count. Without official statistics or a peer‑reviewed source detailing the class frequencies, the most‑frequent class cannot be determined.
+
+          Sources: [1]
+        role: tool
+        tool_call_id: call_f61rh3p7
+      model: gpt-oss
+      reasoning_effort: low
+      stream: false
+      tool_choice: auto
+      tools:
+      - function:
+          description: |-
+            Search the knowledge base for relevant documents.
+
+            Use this when you need to find documents or explore the knowledge base.
+            Results are displayed to the user - just list the titles found.
+          name: search
+          parameters:
+            additionalProperties: false
+            properties:
+              document_name:
+                anyOf:
+                - type: string
+                - type: 'null'
+                default: null
+                description: Optional document name/title to search within
+              limit:
+                anyOf:
+                - type: integer
+                - type: 'null'
+                default: null
+                description: 'Number of results to return (default: 5)'
+              query:
+                description: The search query (what to search for)
+                type: string
+            required:
+            - query
+            type: object
+        type: function
+      - function:
+          description: |-
+            Answer a specific question using the knowledge base.
+
+            Use this for direct questions that need a focused answer with citations.
+            Uses a research graph for planning, searching, and synthesis.
+          name: ask
+          parameters:
+            additionalProperties: false
+            properties:
+              document_name:
+                anyOf:
+                - type: string
+                - type: 'null'
+                default: null
+                description: Optional document name/title to search within (e.g., "tbmed593", "army manual")
+              question:
+                description: The question to answer
+                type: string
+            required:
+            - question
+            type: object
+        type: function
+      - function:
+          description: |-
+            Retrieve a specific document by title or URI.
+
+            Use this when the user wants to fetch/get/retrieve a specific document.
+          name: get_document
+          parameters:
+            additionalProperties: false
+            properties:
+              query:
+                description: The document title or URI to look up
+                type: string
+            required:
+            - query
+            type: object
+          strict: true
+        type: function
+    uri: http://localhost:11434/v1/chat/completions
+  response:
+    headers:
+      content-length:
+      - '553'
+      content-type:
+      - application/json
+    parsed_body:
+      choices:
+      - finish_reason: stop
+        index: 0
+        message:
+          content: I couldn’t find an official ranking of class frequencies for the DocLayNet dataset. The publicly available
+            documentation lists the 11 classes but does not provide instance‑count statistics, so it’s not possible to say
+            which one is the most frequent at this time.
+          role: assistant
+      created: 1769001233
+      id: chatcmpl-964
+      model: gpt-oss
+      object: chat.completion
+      system_fingerprint: fp_ollama
+      usage:
+        completion_tokens: 56
+        prompt_tokens: 984
+        total_tokens: 1040
+    status:
+      code: 200
+      message: OK
+version: 1
diff --git a/tests/cassettes/test_client/test_client_ask.yaml b/tests/cassettes/test_client/test_client_ask.yaml
index 1c4c99a2..88bd1d25 100644
--- a/tests/cassettes/test_client/test_client_ask.yaml
+++ b/tests/cassettes/test_client/test_client_ask.yaml
@@ -48,7 +48,7 @@ interactions:
       connection:
       - keep-alive
       content-length:
-      - '2668'
+      - '2666'
       content-type:
       - application/json
       host:
@@ -106,7 +106,7 @@ interactions:
           description: |-
             Search the knowledge base for relevant documents.
 
-            Returns results with chunk IDs and relevance scores.
+            Returns results with chunk IDs and rank positions.
             Reference results by their chunk_id in cited_chunks.
           name: search_documents
           parameters:
@@ -155,7 +155,7 @@ interactions:
   response:
     headers:
       content-length:
-      - '484'
+      - '506'
       content-type:
       - application/json
     parsed_body:
@@ -164,24 +164,24 @@ interactions:
         index: 0
         message:
           content: ''
-          reasoning: Need to search.
+          reasoning: Need to search for "What is Python".
           role: assistant
           tool_calls:
           - function:
-              arguments: '{"query":"Python definition","limit":5}'
+              arguments: '{"limit":5,"query":"What is Python?"}'
               name: search_documents
-            id: call_zzot5v63
+            id: call_qr5c35xf
             index: 0
             type: function
-      created: 1768998999
-      id: chatcmpl-36
+      created: 1769001440
+      id: chatcmpl-499
       model: gpt-oss
       object: chat.completion
       system_fingerprint: fp_ollama
       usage:
-        completion_tokens: 33
+        completion_tokens: 40
         prompt_tokens: 545
-        total_tokens: 578
+        total_tokens: 585
     status:
       code: 200
       message: OK
@@ -194,7 +194,7 @@ interactions:
       connection:
       - keep-alive
       content-length:
-      - '87'
+      - '85'
       content-type:
       - application/json
       host:
@@ -203,7 +203,7 @@ interactions:
     parsed_body:
       encoding_format: base64
       input:
-      - Python definition
+      - What is Python?
       model: qwen3-embedding:4b
     uri: http://localhost:11434/v1/embeddings
   response:
@@ -214,14 +214,14 @@ interactions:
       - chunked
     parsed_body:
       data:
-      - embedding: 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
         index: 0
         object: embedding
       model: qwen3-embedding:4b
       object: list
       usage:
-        prompt_tokens: 3
-        total_tokens: 3
+        prompt_tokens: 5
+        total_tokens: 5
     status:
       code: 200
       message: OK
@@ -234,7 +234,7 @@ interactions:
       connection:
       - keep-alive
       content-length:
-      - '3071'
+      - '3090'
       content-type:
       - application/json
       host:
@@ -285,22 +285,22 @@ interactions:
         role: user
       - content: |-
           <think>
-          Need to search.
+          Need to search for "What is Python".
           </think>
         role: assistant
         tool_calls:
         - function:
-            arguments: '{"query":"Python definition","limit":5}'
+            arguments: '{"limit":5,"query":"What is Python?"}'
             name: search_documents
-          id: call_zzot5v63
+          id: call_qr5c35xf
           type: function
       - content: |-
-          [8c03f6b5-b9e5-47fa-b785-83f659ff9198] [rank 1 of 1]
+          [36ec1231-b2f5-4295-bad7-9045e947d0a0] [rank 1 of 1]
           Type: text
           Content:
           Python is a high-level programming language.
         role: tool
-        tool_call_id: call_zzot5v63
+        tool_call_id: call_qr5c35xf
       model: gpt-oss
       reasoning_effort: low
       stream: false
@@ -310,7 +310,7 @@ interactions:
           description: |-
             Search the knowledge base for relevant documents.
 
-            Returns results with chunk IDs and relevance scores.
+            Returns results with chunk IDs and rank positions.
             Reference results by their chunk_id in cited_chunks.
           name: search_documents
           parameters:
@@ -359,7 +359,7 @@ interactions:
   response:
     headers:
       content-length:
-      - '457'
+      - '454'
       content-type:
       - application/json
     parsed_body:
@@ -367,18 +367,18 @@ interactions:
       - finish_reason: stop
         index: 0
         message:
-          content: '{"answer":"Python is a high‑level programming language.","cited_chunks":["8c03f6b5-b9e5-47fa-b785-83f659ff9198"],"confidence":0.93,"query":"What
+          content: '{"answer":"Python is a high-level programming language.","cited_chunks":["36ec1231-b2f5-4295-bad7-9045e947d0a0"],"confidence":1.0,"query":"What
             is Python?"}'
           role: assistant
-      created: 1768999000
-      id: chatcmpl-740
+      created: 1769001442
+      id: chatcmpl-258
       model: gpt-oss
       object: chat.completion
       system_fingerprint: fp_ollama
       usage:
-        completion_tokens: 62
-        prompt_tokens: 642
-        total_tokens: 704
+        completion_tokens: 61
+        prompt_tokens: 649
+        total_tokens: 710
     status:
       code: 200
       message: OK
@@ -391,7 +391,7 @@ interactions:
       connection:
       - keep-alive
       content-length:
-      - '3406'
+      - '3422'
       content-type:
       - application/json
       host:
@@ -442,23 +442,23 @@ interactions:
         role: user
       - content: |-
           <think>
-          Need to search.
+          Need to search for "What is Python".
           </think>
         role: assistant
         tool_calls:
         - function:
-            arguments: '{"query":"Python definition","limit":5}'
+            arguments: '{"limit":5,"query":"What is Python?"}'
             name: search_documents
-          id: call_zzot5v63
+          id: call_qr5c35xf
           type: function
       - content: |-
-          [8c03f6b5-b9e5-47fa-b785-83f659ff9198] [rank 1 of 1]
+          [36ec1231-b2f5-4295-bad7-9045e947d0a0] [rank 1 of 1]
           Type: text
           Content:
           Python is a high-level programming language.
         role: tool
-        tool_call_id: call_zzot5v63
-      - content: '{"answer":"Python is a high‑level programming language.","cited_chunks":["8c03f6b5-b9e5-47fa-b785-83f659ff9198"],"confidence":0.93,"query":"What
+        tool_call_id: call_qr5c35xf
+      - content: '{"answer":"Python is a high-level programming language.","cited_chunks":["36ec1231-b2f5-4295-bad7-9045e947d0a0"],"confidence":1.0,"query":"What
           is Python?"}'
         role: assistant
       - content: |-
@@ -476,7 +476,7 @@ interactions:
           description: |-
             Search the knowledge base for relevant documents.
 
-            Returns results with chunk IDs and relevance scores.
+            Returns results with chunk IDs and rank positions.
             Reference results by their chunk_id in cited_chunks.
           name: search_documents
           parameters:
@@ -525,7 +525,7 @@ interactions:
   response:
     headers:
       content-length:
-      - '628'
+      - '616'
       content-type:
       - application/json
     parsed_body:
@@ -534,25 +534,25 @@ interactions:
         index: 0
         message:
           content: ''
-          reasoning: We need final result via function.
+          reasoning: Must use final_result tool.
           role: assistant
           tool_calls:
           - function:
-              arguments: '{"answer":"Python is a high‑level programming language.","cited_chunks":["8c03f6b5-b9e5-47fa-b785-83f659ff9198"],"confidence":0.93,"query":"What
+              arguments: '{"answer":"Python is a high-level programming language.","cited_chunks":["36ec1231-b2f5-4295-bad7-9045e947d0a0"],"confidence":1,"query":"What
                 is Python?"}'
               name: final_result
-            id: call_sn6do4tl
+            id: call_cjmyxq28
             index: 0
             type: function
-      created: 1768999002
-      id: chatcmpl-226
+      created: 1769001443
+      id: chatcmpl-594
       model: gpt-oss
       object: chat.completion
       system_fingerprint: fp_ollama
       usage:
-        completion_tokens: 80
-        prompt_tokens: 725
-        total_tokens: 805
+        completion_tokens: 78
+        prompt_tokens: 731
+        total_tokens: 809
     status:
       code: 200
       message: OK
diff --git a/tests/cassettes/test_qa/test_qa_anthropic.yaml b/tests/cassettes/test_qa/test_qa_anthropic.yaml
index 22858cbe..1b2fa4f7 100644
--- a/tests/cassettes/test_qa/test_qa_anthropic.yaml
+++ b/tests/cassettes/test_qa/test_qa_anthropic.yaml
@@ -86,7 +86,7 @@ interactions:
       connection:
       - keep-alive
       content-length:
-      - '2717'
+      - '2715'
       content-type:
       - application/json
       host:
@@ -146,7 +146,7 @@ interactions:
       - description: |-
           Search the knowledge base for relevant documents.
 
-          Returns results with chunk IDs and relevance scores.
+          Returns results with chunk IDs and rank positions.
           Reference results by their chunk_id in cited_chunks.
         input_schema:
           additionalProperties: false
@@ -193,7 +193,7 @@ interactions:
       connection:
       - keep-alive
       content-length:
-      - '556'
+      - '554'
       content-type:
       - application/json
       strict-transport-security:
@@ -202,12 +202,12 @@ interactions:
       - chunked
     parsed_body:
       content:
-      - id: toolu_01LridL1jpN2GNEJDdP81Yfq
+      - id: toolu_012SMbp95ixidsSfPU7Ki79V
         input:
-          query: Bintang Jakarta election civic engagement innovative feedback citizens
+          query: Bintang Jakarta election civic engagement feedback innovative method
         name: search_documents
         type: tool_use
-      id: msg_01KNhXYYKFbACTyKFEr715kV
+      id: msg_01QkAsaKBoE8MbG7KETqn15k
       model: claude-3-5-haiku-20241022
       role: assistant
       stop_reason: tool_use
@@ -219,7 +219,7 @@ interactions:
           ephemeral_5m_input_tokens: 0
         cache_creation_input_tokens: 0
         cache_read_input_tokens: 0
-        input_tokens: 1027
+        input_tokens: 1026
         output_tokens: 48
         service_tier: standard
     status:
@@ -234,7 +234,7 @@ interactions:
       connection:
       - keep-alive
       content-length:
-      - '140'
+      - '138'
       content-type:
       - application/json
       host:
@@ -243,7 +243,7 @@ interactions:
     parsed_body:
       encoding_format: base64
       input:
-      - Bintang Jakarta election civic engagement innovative feedback citizens
+      - Bintang Jakarta election civic engagement feedback innovative method
       model: qwen3-embedding:4b
     uri: http://localhost:11434/v1/embeddings
   response:
@@ -254,7 +254,7 @@ interactions:
       - chunked
     parsed_body:
       data:
-      - embedding: 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
         index: 0
         object: embedding
       model: qwen3-embedding:4b
@@ -274,7 +274,7 @@ interactions:
       connection:
       - keep-alive
       content-length:
-      - '7946'
+      - '7942'
       content-type:
       - application/json
       host:
@@ -289,15 +289,15 @@ interactions:
           type: text
         role: user
       - content:
-        - id: toolu_01LridL1jpN2GNEJDdP81Yfq
+        - id: toolu_012SMbp95ixidsSfPU7Ki79V
           input:
-            query: Bintang Jakarta election civic engagement innovative feedback citizens
+            query: Bintang Jakarta election civic engagement feedback innovative method
           name: search_documents
           type: tool_use
         role: assistant
       - content:
         - content: |-
-            [2495002c-8728-43a5-9019-715fe43a2880] [rank 1 of 1]
+            [f2e022de-3a32-47d4-914c-7f12525f4d72] [rank 1 of 1]
             Type: text
             Content:
             Jakarta Election Campaigns Heat Up: Here's How to Understand the System
@@ -342,7 +342,7 @@ interactions:
 
             Jakarta's vibrant election campaign offers an insight into its flourishing democracy. As the city looks ahead to an exciting new chapter in its political history, electoral processes demonstrate the significance of people-power in shaping our collective futures.
           is_error: false
-          tool_use_id: toolu_01LridL1jpN2GNEJDdP81Yfq
+          tool_use_id: toolu_012SMbp95ixidsSfPU7Ki79V
           type: tool_result
         role: user
       model: claude-3-5-haiku-20241022
@@ -391,7 +391,7 @@ interactions:
       - description: |-
           Search the knowledge base for relevant documents.
 
-          Returns results with chunk IDs and relevance scores.
+          Returns results with chunk IDs and rank positions.
           Reference results by their chunk_id in cited_chunks.
         input_schema:
           additionalProperties: false
@@ -438,7 +438,7 @@ interactions:
       connection:
       - keep-alive
       content-length:
-      - '1032'
+      - '1014'
       content-type:
       - application/json
       strict-transport-security:
@@ -447,19 +447,19 @@ interactions:
       - chunked
     parsed_body:
       content:
-      - id: toolu_01XfbdxN4q9LZdGEzzyLsLbf
+      - id: toolu_01YPtvsmJWh6pX7jxuj8XPFg
         input:
           answer: Bintang introduced an interactive mobile app that allowed citizens to provide real-time feedback about their
-            daily commute challenges. This was highlighted as an innovative approach to civic engagement during the Jakarta
-            election campaign, enabling direct communication between the candidate and voters about urban transportation issues.
+            daily commute challenges. This innovative digital platform was praised as a unique approach to civic engagement,
+            enabling direct communication between the candidate and voters about urban transportation issues.
           cited_chunks:
-          - 2495002c-8728-43a5-9019-715fe43a2880
+          - f2e022de-3a32-47d4-914c-7f12525f4d72
           confidence: 0.95
           query: What innovative form of civic engagement did Bintang introduce for gathering feedback from citizens during
             Jakarta's election?
         name: final_result
         type: tool_use
-      id: msg_01RnpGJaUNrdYC66vJptgxTr
+      id: msg_01YNGhM3cFaE11wjZFNWL3jU
       model: claude-3-5-haiku-20241022
       role: assistant
       stop_reason: tool_use
@@ -471,8 +471,8 @@ interactions:
           ephemeral_5m_input_tokens: 0
         cache_creation_input_tokens: 0
         cache_read_input_tokens: 0
-        input_tokens: 2081
-        output_tokens: 188
+        input_tokens: 2084
+        output_tokens: 190
         service_tier: standard
     status:
       code: 200
@@ -486,7 +486,7 @@ interactions:
       connection:
       - keep-alive
       content-length:
-      - '2017'
+      - '1999'
       content-type:
       - application/json
       host:
@@ -520,7 +520,7 @@ interactions:
       - content: |-
           QUESTION: What innovative form of civic engagement did Bintang introduce for gathering feedback from citizens during Jakarta's election?
 
-          GENERATED ANSWER: Bintang introduced an interactive mobile app that allowed citizens to provide real-time feedback about their daily commute challenges. This was highlighted as an innovative approach to civic engagement during the Jakarta election campaign, enabling direct communication between the candidate and voters about urban transportation issues.
+          GENERATED ANSWER: Bintang introduced an interactive mobile app that allowed citizens to provide real-time feedback about their daily commute challenges. This innovative digital platform was praised as a unique approach to civic engagement, enabling direct communication between the candidate and voters about urban transportation issues.
 
           EXPECTED ANSWER: Bintang introduced an interactive app for real-time feedback on daily commute challenges.
         role: user
@@ -546,7 +546,7 @@ interactions:
   response:
     headers:
       content-length:
-      - '567'
+      - '631'
       content-type:
       - application/json
     parsed_body:
@@ -555,25 +555,25 @@ interactions:
         index: 0
         message:
           content: ''
-          reasoning: We need to determine equivalence. Both say interactive app for real-time feedback on daily commute challenges.
-            So equivalent.
+          reasoning: We need to see if answers equivalent. Both say interactive app for real-time feedback on daily commute
+            challenges. One adds mobile app, digital platform, unique approach. So yes equivalent.
           role: assistant
           tool_calls:
           - function:
               arguments: '{"equivalent":true}'
               name: final_result
-            id: call_cta2dxtr
+            id: call_aaamvt6t
             index: 0
             type: function
-      created: 1768996903
-      id: chatcmpl-246
+      created: 1769001427
+      id: chatcmpl-514
       model: gpt-oss
       object: chat.completion
       system_fingerprint: fp_ollama
       usage:
-        completion_tokens: 48
-        prompt_tokens: 413
-        total_tokens: 461
+        completion_tokens: 61
+        prompt_tokens: 411
+        total_tokens: 472
     status:
       code: 200
       message: OK
diff --git a/tests/cassettes/test_qa/test_qa_ollama.yaml b/tests/cassettes/test_qa/test_qa_ollama.yaml
index d9555a40..3a0f6930 100644
--- a/tests/cassettes/test_qa/test_qa_ollama.yaml
+++ b/tests/cassettes/test_qa/test_qa_ollama.yaml
@@ -86,7 +86,7 @@ interactions:
       connection:
       - keep-alive
       content-length:
-      - '2780'
+      - '2778'
       content-type:
       - application/json
       host:
@@ -145,7 +145,7 @@ interactions:
           description: |-
             Search the knowledge base for relevant documents.
 
-            Returns results with chunk IDs and relevance scores.
+            Returns results with chunk IDs and rank positions.
             Reference results by their chunk_id in cited_chunks.
           name: search_documents
           parameters:
@@ -194,7 +194,7 @@ interactions:
   response:
     headers:
       content-length:
-      - '1139'
+      - '1430'
       content-type:
       - application/json
     parsed_body:
@@ -204,27 +204,27 @@ interactions:
         message:
           content: ''
           reasoning: |-
-            The user asks: "What innovative form of civic engagement did Bintang introduce for gathering feedback from citizens during Jakarta's election?" We need to find info about Bintang (someone) who introduced an innovative form of civic engagement for gathering feedback from citizens during Jakarta's election. Likely from a document about social or political context in Jakarta. We need to search the knowledge base. Use search_documents function.
+            We need to answer: "What innovative form of civic engagement did Bintang introduce for gathering feedback from citizens during Jakarta's election?" This is a specific question about some innovative civic engagement introduced by "Bintang" for gathering feedback from citizens during Jakarta's election. Bintang could refer to a person or a company. Might be a brand? Or maybe it's a short for Bintang, maybe a government official? The question: "What innovative form of civic engagement did Bintang introduce for gathering feedback from citizens during Jakarta's election?" So the answer: likely something like "interactive polling booth", "online civic engagement platform", "live voting kiosks" or something.
 
-            Let's call search with query: "Bintang innovative form of civic engagement gathering feedback from citizens during Jakarta election".
+            We need to search the knowledge base. Use search_documents with query: 'Bintang innovative form of civic engagement gathering feedback citizens Jakarta election'.
           role: assistant
           tool_calls:
           - function:
-              arguments: '{"query":"Bintang innovative form of civic engagement gathering feedback from citizens during Jakarta
-                election","limit":5}'
+              arguments: '{"limit":5,"query":"Bintang innovative form of civic engagement gathering feedback citizens Jakarta
+                election"}'
               name: search_documents
-            id: call_y4w5orma
+            id: call_pg1twsq1
             index: 0
             type: function
-      created: 1768995991
-      id: chatcmpl-547
+      created: 1769001375
+      id: chatcmpl-679
       model: gpt-oss
       object: chat.completion
       system_fingerprint: fp_ollama
       usage:
-        completion_tokens: 143
+        completion_tokens: 203
         prompt_tokens: 561
-        total_tokens: 704
+        total_tokens: 764
     status:
       code: 200
       message: OK
@@ -237,7 +237,7 @@ interactions:
       connection:
       - keep-alive
       content-length:
-      - '170'
+      - '158'
       content-type:
       - application/json
       host:
@@ -246,7 +246,7 @@ interactions:
     parsed_body:
       encoding_format: base64
       input:
-      - Bintang innovative form of civic engagement gathering feedback from citizens during Jakarta election
+      - Bintang innovative form of civic engagement gathering feedback citizens Jakarta election
       model: qwen3-embedding:4b
     uri: http://localhost:11434/v1/embeddings
   response:
@@ -257,14 +257,14 @@ interactions:
       - chunked
     parsed_body:
       data:
-      - embedding: 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
+      - embedding: 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
         index: 0
         object: embedding
       model: qwen3-embedding:4b
       object: list
       usage:
-        prompt_tokens: 16
-        total_tokens: 16
+        prompt_tokens: 14
+        total_tokens: 14
     status:
       code: 200
       message: OK
@@ -277,7 +277,7 @@ interactions:
       connection:
       - keep-alive
       content-length:
-      - '8609'
+      - '8898'
       content-type:
       - application/json
       host:
@@ -329,20 +329,20 @@ interactions:
         role: user
       - content: |-
           <think>
-          The user asks: "What innovative form of civic engagement did Bintang introduce for gathering feedback from citizens during Jakarta's election?" We need to find info about Bintang (someone) who introduced an innovative form of civic engagement for gathering feedback from citizens during Jakarta's election. Likely from a document about social or political context in Jakarta. We need to search the knowledge base. Use search_documents function.
+          We need to answer: "What innovative form of civic engagement did Bintang introduce for gathering feedback from citizens during Jakarta's election?" This is a specific question about some innovative civic engagement introduced by "Bintang" for gathering feedback from citizens during Jakarta's election. Bintang could refer to a person or a company. Might be a brand? Or maybe it's a short for Bintang, maybe a government official? The question: "What innovative form of civic engagement did Bintang introduce for gathering feedback from citizens during Jakarta's election?" So the answer: likely something like "interactive polling booth", "online civic engagement platform", "live voting kiosks" or something.
 
-          Let's call search with query: "Bintang innovative form of civic engagement gathering feedback from citizens during Jakarta election".
+          We need to search the knowledge base. Use search_documents with query: 'Bintang innovative form of civic engagement gathering feedback citizens Jakarta election'.
           </think>
         role: assistant
         tool_calls:
         - function:
-            arguments: '{"query":"Bintang innovative form of civic engagement gathering feedback from citizens during Jakarta
-              election","limit":5}'
+            arguments: '{"limit":5,"query":"Bintang innovative form of civic engagement gathering feedback citizens Jakarta
+              election"}'
             name: search_documents
-          id: call_y4w5orma
+          id: call_pg1twsq1
           type: function
       - content: |-
-          [33635a00-3e8a-4d08-a76e-833cf37878e5] [rank 1 of 1]
+          [e6d3622c-0d09-4caa-a7ab-ef01949233c8] [rank 1 of 1]
           Type: text
           Content:
           Jakarta Election Campaigns Heat Up: Here's How to Understand the System
@@ -387,7 +387,7 @@ interactions:
 
           Jakarta's vibrant election campaign offers an insight into its flourishing democracy. As the city looks ahead to an exciting new chapter in its political history, electoral processes demonstrate the significance of people-power in shaping our collective futures.
         role: tool
-        tool_call_id: call_y4w5orma
+        tool_call_id: call_pg1twsq1
       model: gpt-oss
       reasoning_effort: high
       stream: false
@@ -397,7 +397,7 @@ interactions:
           description: |-
             Search the knowledge base for relevant documents.
 
-            Returns results with chunk IDs and relevance scores.
+            Returns results with chunk IDs and rank positions.
             Reference results by their chunk_id in cited_chunks.
           name: search_documents
           parameters:
@@ -446,608 +446,7 @@ interactions:
   response:
     headers:
       content-length:
-      - '1144'
-      content-type:
-      - application/json
-    parsed_body:
-      choices:
-      - finish_reason: tool_calls
-        index: 0
-        message:
-          content: ''
-          reasoning: |+
-            We have a chunk: "Bintang's interactive app for gathering real-time feedback from citizens about daily commute challenges was applauded as an innovative form of civic engagement". The question: "What innovative form of civic engagement did Bintang introduce for gathering feedback from citizens during Jakarta's election?" It appears to be exactly that: Bintang introduced an interactive app for gathering real-time feedback.
-
-            Thus answer: she introduced an interactive app.
-
-            But we need to be careful: maybe there is also another similar chunk. Let's search again with other keyword "interactive app" and "Bintang" to see.
-
-          role: assistant
-          tool_calls:
-          - function:
-              arguments: '{"limit":5,"query":"Bintang interactive app feedback citizens Jakarta"}'
-              name: search_documents
-            id: call_av46l6b1
-            index: 0
-            type: function
-      created: 1768996005
-      id: chatcmpl-980
-      model: gpt-oss
-      object: chat.completion
-      system_fingerprint: fp_ollama
-      usage:
-        completion_tokens: 152
-        prompt_tokens: 1600
-        total_tokens: 1752
-    status:
-      code: 200
-      message: OK
-- request:
-    headers:
-      accept:
-      - application/json
-      accept-encoding:
-      - gzip, deflate, zstd
-      connection:
-      - keep-alive
-      content-length:
-      - '119'
-      content-type:
-      - application/json
-      host:
-      - localhost:11434
-    method: POST
-    parsed_body:
-      encoding_format: base64
-      input:
-      - Bintang interactive app feedback citizens Jakarta
-      model: qwen3-embedding:4b
-    uri: http://localhost:11434/v1/embeddings
-  response:
-    headers:
-      content-type:
-      - application/json
-      transfer-encoding:
-      - chunked
-    parsed_body:
-      data:
-      - embedding: 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-      model: qwen3-embedding:4b
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-        total_tokens: 9
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-- request:
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-          You are a knowledgeable assistant that answers questions using a document knowledge base.
-
-          Process:
-          1. Call search_documents with relevant keywords from the question
-          2. Review the results ordered by relevance
-          3. If needed, perform follow-up searches with different keywords (max 3 total)
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-
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-          Source: "Document Title" > Section > Subsection
-          Type: paragraph
-          Content:
-          The actual text content here...
-
-          [chunk_def456] [rank 2 of 5]
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-          | Column 1 | Column 2 |
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-
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-
-          Guidelines:
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-          - Be concise and direct - avoid elaboration unless asked
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-        role: system
-      - content: What innovative form of civic engagement did Bintang introduce for gathering feedback from citizens during
-          Jakarta's election?
-        role: user
-      - content: |-
-          <think>
-          The user asks: "What innovative form of civic engagement did Bintang introduce for gathering feedback from citizens during Jakarta's election?" We need to find info about Bintang (someone) who introduced an innovative form of civic engagement for gathering feedback from citizens during Jakarta's election. Likely from a document about social or political context in Jakarta. We need to search the knowledge base. Use search_documents function.
-
-          Let's call search with query: "Bintang innovative form of civic engagement gathering feedback from citizens during Jakarta election".
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-          [33635a00-3e8a-4d08-a76e-833cf37878e5] [rank 1 of 1]
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-          Jakarta Election Campaigns Heat Up: Here's How to Understand the System
-
-          As election day in Jakarta draws nearer, candidates are out in force using various strategies to woo voters and prepare to exercise their democratic rights. Citizens make their voices heard as the city vibrates with life. This coverage offers valuable insight into campaign activities while offering an in-depth guide for understanding electoral processes in Jakarta.
-
-          Initial Launch of Candidates' Campaign Plans on September 1
-
-          After September 1st, when campaign season officially kicked off, candidates have moved swiftly to engage their bases. Amira Bintang, an upstart candidate with extensive social activism experience and promising urban development and public transportation reform as key platforms of her candidacy speech in Jakarta; incumbent Rizal Harahap relies heavily on his track record and highlights all infrastructure projects completed during his term.
-
-          Campaign Strategies: From Digital Battlegrounds to Door-toDoor Outreach
-
-          Campaign strategies have taken an advanced turn as candidates leverage digital media to reach a wider audience. Hashtags, viral videos, targeted ads and targeted messaging can all play an influential role in changing public opinion with just a tweet or meme. At the grassroots level candidates engage in door-to-door campaigns personalized for individual voters in an attempt to connect.
-
-          Bintang's interactive app for gathering real-time feedback from citizens about daily commute challenges was applauded as an innovative form of civic engagement, while Harahap launched a series of webinars featuring experts discussing economic growth under his administration.
-
-          Rallies and Persuasion
-
-          Jakartan politicians know the power of an impassioned speech cannot be underrated, and candidates have been taking full advantage of its effectiveness at rallies. Rallies feature vibrant colors, banners and impassioned discourse in an attempt to win converts over. At one high-spirited rally on October 22, Bintang outlined her policy plans for improving education and healthcare to an appreciative crowd while Harahap's rallies often consist of shows of solidarity from various political allies united behind his plea for continuity and stability.
-
-          Debates: Clashes Between Visions and Policies
-
-          Debates are one of the highlights of Jakarta election campaigns, allowing candidates to outline their platforms and discuss critical issues. On November 5th, citizens witnessed an exhilarating debate between candidates Bintang and Harahap over whether the city was prepared for digital transformation in public services; Bintang advocated an aggressive move toward smart city model while Harahap advocated a more measured approach so as not to alienate less tech-savvy residents.
-
-          Voter Engagement: Making Every Vote Count
-
-          Ensuring every eligible voter is engaged and informed remains an ongoing challenge for Jakartans. Civil society groups and independent bodies host workshops and publish voter guides to inform voters of their rights and choices, while an annual democracy festival such as that held on November 20 featured interactive exhibits on Jakarta's electoral history as well as mock voting booths for first-time voters.
-
-          Campaign Financing: Transparency and Accountability
-
-          Campaign financing has always been a contentious topic in elections, and this election cycle is no exception. Bintang's campaign, funded largely through crowdfunders online supporters, stands in stark contrast with Harahap's sophisticated machine backed by both private donors and party funds. To protect democratic decision making processes from any
-
-          undue influences on decision-making processes, Jakarta Election Commission mandated strict reporting and transparency measures during campaign financing decisions.
-
-          Before Election Day: Submit Final Appeals Now
-
-          As election day nears, candidates make their last appeals to voters. Bintang plans a visit through key neighborhoods while Harahap plans a final rally scheduled for late November. Both camps are honing their messaging and policy proposals while encouraging supporters to make an appearance at polling booths on November 8th.
-
-          Polling Day: The Final Act of Campaign Activities
-
-          On December 6th, voting booths across Jakarta will open their doors, signalling the culmination of weeks of intense campaigning. Voters will cast their vote and candidates await results that depend on how effective their strategies, speeches and outreach initiatives have been.
-
-          Jakarta's vibrant election campaign offers an insight into its flourishing democracy. As the city looks ahead to an exciting new chapter in its political history, electoral processes demonstrate the significance of people-power in shaping our collective futures.
-        role: tool
-        tool_call_id: call_y4w5orma
-      - content: |-
-          <think>
-          We have a chunk: "Bintang's interactive app for gathering real-time feedback from citizens about daily commute challenges was applauded as an innovative form of civic engagement". The question: "What innovative form of civic engagement did Bintang introduce for gathering feedback from citizens during Jakarta's election?" It appears to be exactly that: Bintang introduced an interactive app for gathering real-time feedback.
-
-          Thus answer: she introduced an interactive app.
-
-          But we need to be careful: maybe there is also another similar chunk. Let's search again with other keyword "interactive app" and "Bintang" to see.
-
-
-          </think>
-        role: assistant
-        tool_calls:
-        - function:
-            arguments: '{"limit":5,"query":"Bintang interactive app feedback citizens Jakarta"}'
-            name: search_documents
-          id: call_av46l6b1
-          type: function
-      - content: |-
-          [33635a00-3e8a-4d08-a76e-833cf37878e5] [rank 1 of 1]
-          Type: text
-          Content:
-          Jakarta Election Campaigns Heat Up: Here's How to Understand the System
-
-          As election day in Jakarta draws nearer, candidates are out in force using various strategies to woo voters and prepare to exercise their democratic rights. Citizens make their voices heard as the city vibrates with life. This coverage offers valuable insight into campaign activities while offering an in-depth guide for understanding electoral processes in Jakarta.
-
-          Initial Launch of Candidates' Campaign Plans on September 1
-
-          After September 1st, when campaign season officially kicked off, candidates have moved swiftly to engage their bases. Amira Bintang, an upstart candidate with extensive social activism experience and promising urban development and public transportation reform as key platforms of her candidacy speech in Jakarta; incumbent Rizal Harahap relies heavily on his track record and highlights all infrastructure projects completed during his term.
-
-          Campaign Strategies: From Digital Battlegrounds to Door-toDoor Outreach
-
-          Campaign strategies have taken an advanced turn as candidates leverage digital media to reach a wider audience. Hashtags, viral videos, targeted ads and targeted messaging can all play an influential role in changing public opinion with just a tweet or meme. At the grassroots level candidates engage in door-to-door campaigns personalized for individual voters in an attempt to connect.
-
-          Bintang's interactive app for gathering real-time feedback from citizens about daily commute challenges was applauded as an innovative form of civic engagement, while Harahap launched a series of webinars featuring experts discussing economic growth under his administration.
-
-          Rallies and Persuasion
-
-          Jakartan politicians know the power of an impassioned speech cannot be underrated, and candidates have been taking full advantage of its effectiveness at rallies. Rallies feature vibrant colors, banners and impassioned discourse in an attempt to win converts over. At one high-spirited rally on October 22, Bintang outlined her policy plans for improving education and healthcare to an appreciative crowd while Harahap's rallies often consist of shows of solidarity from various political allies united behind his plea for continuity and stability.
-
-          Debates: Clashes Between Visions and Policies
-
-          Debates are one of the highlights of Jakarta election campaigns, allowing candidates to outline their platforms and discuss critical issues. On November 5th, citizens witnessed an exhilarating debate between candidates Bintang and Harahap over whether the city was prepared for digital transformation in public services; Bintang advocated an aggressive move toward smart city model while Harahap advocated a more measured approach so as not to alienate less tech-savvy residents.
-
-          Voter Engagement: Making Every Vote Count
-
-          Ensuring every eligible voter is engaged and informed remains an ongoing challenge for Jakartans. Civil society groups and independent bodies host workshops and publish voter guides to inform voters of their rights and choices, while an annual democracy festival such as that held on November 20 featured interactive exhibits on Jakarta's electoral history as well as mock voting booths for first-time voters.
-
-          Campaign Financing: Transparency and Accountability
-
-          Campaign financing has always been a contentious topic in elections, and this election cycle is no exception. Bintang's campaign, funded largely through crowdfunders online supporters, stands in stark contrast with Harahap's sophisticated machine backed by both private donors and party funds. To protect democratic decision making processes from any
-
-          undue influences on decision-making processes, Jakarta Election Commission mandated strict reporting and transparency measures during campaign financing decisions.
-
-          Before Election Day: Submit Final Appeals Now
-
-          As election day nears, candidates make their last appeals to voters. Bintang plans a visit through key neighborhoods while Harahap plans a final rally scheduled for late November. Both camps are honing their messaging and policy proposals while encouraging supporters to make an appearance at polling booths on November 8th.
-
-          Polling Day: The Final Act of Campaign Activities
-
-          On December 6th, voting booths across Jakarta will open their doors, signalling the culmination of weeks of intense campaigning. Voters will cast their vote and candidates await results that depend on how effective their strategies, speeches and outreach initiatives have been.
-
-          Jakarta's vibrant election campaign offers an insight into its flourishing democracy. As the city looks ahead to an exciting new chapter in its political history, electoral processes demonstrate the significance of people-power in shaping our collective futures.
-        role: tool
-        tool_call_id: call_av46l6b1
-      model: gpt-oss
-      reasoning_effort: high
-      stream: false
-      tool_choice: auto
-      tools:
-      - function:
-          description: |-
-            Search the knowledge base for relevant documents.
-
-            Returns results with chunk IDs and relevance scores.
-            Reference results by their chunk_id in cited_chunks.
-          name: search_documents
-          parameters:
-            additionalProperties: false
-            properties:
-              limit:
-                anyOf:
-                - type: integer
-                - type: 'null'
-                default: null
-              query:
-                type: string
-            required:
-            - query
-            type: object
-        type: function
-      - function:
-          description: Answer to a search query with chunk references.
-          name: final_result
-          parameters:
-            additionalProperties: false
-            properties:
-              answer:
-                description: The answer to the question
-                type: string
-              cited_chunks:
-                description: IDs of chunks used to form the answer
-                items:
-                  type: string
-                type: array
-              confidence:
-                default: 1.0
-                description: Confidence score for this answer (0-1)
-                maximum: 1.0
-                minimum: 0.0
-                type: number
-              query:
-                description: The question that was answered
-                type: string
-            required:
-            - query
-            - answer
-            type: object
-        type: function
-    uri: http://localhost:11434/v1/chat/completions
-  response:
-    headers:
-      content-length:
-      - '1019'
-      content-type:
-      - application/json
-    parsed_body:
-      choices:
-      - finish_reason: stop
-        index: 0
-        message:
-          content: "Bintang introduced an **interactive mobile app** that lets citizens give real‑time feedback on daily commute
-            challenges during the Jakarta election.  \n\n**Cited chunk:** [33635a00-3e8a-4d08-a76e-833cf37878e5]"
-          reasoning: |-
-            We've got only one chunk. It's consistent with the answer. The question: "What innovative form of civic engagement did Bintang introduce for gathering feedback from citizens during Jakarta's election?" So answer: The interactive app for real-time feedback about daily commute challenges.
-
-            Thus: "Bintang introduced an interactive app for real-time feedback from citizens about daily commute challenges." Provide chunk_id [33635a00-3e8a-4d08-a76e-833cf37878e5] as cited_chunks. Provide concise answer.
-          role: assistant
-      created: 1768996019
-      id: chatcmpl-178
-      model: gpt-oss
-      object: chat.completion
-      system_fingerprint: fp_ollama
-      usage:
-        completion_tokens: 174
-        prompt_tokens: 2648
-        total_tokens: 2822
-    status:
-      code: 200
-      message: OK
-- request:
-    headers:
-      accept:
-      - application/json
-      accept-encoding:
-      - gzip, deflate, zstd
-      connection:
-      - keep-alive
-      content-length:
-      - '15343'
-      content-type:
-      - application/json
-      host:
-      - localhost:11434
-    method: POST
-    parsed_body:
-      messages:
-      - content: |-
-          You are a knowledgeable assistant that answers questions using a document knowledge base.
-
-          Process:
-          1. Call search_documents with relevant keywords from the question
-          2. Review the results ordered by relevance
-          3. If needed, perform follow-up searches with different keywords (max 3 total)
-          4. Provide a concise answer based strictly on the retrieved content
-
-          The search tool returns results like:
-          [chunk_abc123] [rank 1 of 5]
-          Source: "Document Title" > Section > Subsection
-          Type: paragraph
-          Content:
-          The actual text content here...
-
-          [chunk_def456] [rank 2 of 5]
-          Source: "Another Document"
-          Type: table
-          Content:
-          | Column 1 | Column 2 |
-          ...
-
-          Each result includes:
-          - chunk_id in brackets and rank position (rank 1 = most relevant)
-          - Source: document title and section hierarchy (when available)
-          - Type: content type like paragraph, table, code, list_item (when available)
-          - Content: the actual text
-
-          In your response, include the chunk IDs you used in cited_chunks.
-
-          Guidelines:
-          - Base answers strictly on retrieved content - do not use external knowledge
-          - Use the Source and Type metadata to understand context
-          - If multiple results are relevant, synthesize them coherently
-          - If information is insufficient, say: "I cannot find enough information in the knowledge base to answer this question."
-          - Be concise and direct - avoid elaboration unless asked
-          - Results are ordered by relevance, with rank 1 being most relevant
-        role: system
-      - content: What innovative form of civic engagement did Bintang introduce for gathering feedback from citizens during
-          Jakarta's election?
-        role: user
-      - content: |-
-          <think>
-          The user asks: "What innovative form of civic engagement did Bintang introduce for gathering feedback from citizens during Jakarta's election?" We need to find info about Bintang (someone) who introduced an innovative form of civic engagement for gathering feedback from citizens during Jakarta's election. Likely from a document about social or political context in Jakarta. We need to search the knowledge base. Use search_documents function.
-
-          Let's call search with query: "Bintang innovative form of civic engagement gathering feedback from citizens during Jakarta election".
-          </think>
-        role: assistant
-        tool_calls:
-        - function:
-            arguments: '{"query":"Bintang innovative form of civic engagement gathering feedback from citizens during Jakarta
-              election","limit":5}'
-            name: search_documents
-          id: call_y4w5orma
-          type: function
-      - content: |-
-          [33635a00-3e8a-4d08-a76e-833cf37878e5] [rank 1 of 1]
-          Type: text
-          Content:
-          Jakarta Election Campaigns Heat Up: Here's How to Understand the System
-
-          As election day in Jakarta draws nearer, candidates are out in force using various strategies to woo voters and prepare to exercise their democratic rights. Citizens make their voices heard as the city vibrates with life. This coverage offers valuable insight into campaign activities while offering an in-depth guide for understanding electoral processes in Jakarta.
-
-          Initial Launch of Candidates' Campaign Plans on September 1
-
-          After September 1st, when campaign season officially kicked off, candidates have moved swiftly to engage their bases. Amira Bintang, an upstart candidate with extensive social activism experience and promising urban development and public transportation reform as key platforms of her candidacy speech in Jakarta; incumbent Rizal Harahap relies heavily on his track record and highlights all infrastructure projects completed during his term.
-
-          Campaign Strategies: From Digital Battlegrounds to Door-toDoor Outreach
-
-          Campaign strategies have taken an advanced turn as candidates leverage digital media to reach a wider audience. Hashtags, viral videos, targeted ads and targeted messaging can all play an influential role in changing public opinion with just a tweet or meme. At the grassroots level candidates engage in door-to-door campaigns personalized for individual voters in an attempt to connect.
-
-          Bintang's interactive app for gathering real-time feedback from citizens about daily commute challenges was applauded as an innovative form of civic engagement, while Harahap launched a series of webinars featuring experts discussing economic growth under his administration.
-
-          Rallies and Persuasion
-
-          Jakartan politicians know the power of an impassioned speech cannot be underrated, and candidates have been taking full advantage of its effectiveness at rallies. Rallies feature vibrant colors, banners and impassioned discourse in an attempt to win converts over. At one high-spirited rally on October 22, Bintang outlined her policy plans for improving education and healthcare to an appreciative crowd while Harahap's rallies often consist of shows of solidarity from various political allies united behind his plea for continuity and stability.
-
-          Debates: Clashes Between Visions and Policies
-
-          Debates are one of the highlights of Jakarta election campaigns, allowing candidates to outline their platforms and discuss critical issues. On November 5th, citizens witnessed an exhilarating debate between candidates Bintang and Harahap over whether the city was prepared for digital transformation in public services; Bintang advocated an aggressive move toward smart city model while Harahap advocated a more measured approach so as not to alienate less tech-savvy residents.
-
-          Voter Engagement: Making Every Vote Count
-
-          Ensuring every eligible voter is engaged and informed remains an ongoing challenge for Jakartans. Civil society groups and independent bodies host workshops and publish voter guides to inform voters of their rights and choices, while an annual democracy festival such as that held on November 20 featured interactive exhibits on Jakarta's electoral history as well as mock voting booths for first-time voters.
-
-          Campaign Financing: Transparency and Accountability
-
-          Campaign financing has always been a contentious topic in elections, and this election cycle is no exception. Bintang's campaign, funded largely through crowdfunders online supporters, stands in stark contrast with Harahap's sophisticated machine backed by both private donors and party funds. To protect democratic decision making processes from any
-
-          undue influences on decision-making processes, Jakarta Election Commission mandated strict reporting and transparency measures during campaign financing decisions.
-
-          Before Election Day: Submit Final Appeals Now
-
-          As election day nears, candidates make their last appeals to voters. Bintang plans a visit through key neighborhoods while Harahap plans a final rally scheduled for late November. Both camps are honing their messaging and policy proposals while encouraging supporters to make an appearance at polling booths on November 8th.
-
-          Polling Day: The Final Act of Campaign Activities
-
-          On December 6th, voting booths across Jakarta will open their doors, signalling the culmination of weeks of intense campaigning. Voters will cast their vote and candidates await results that depend on how effective their strategies, speeches and outreach initiatives have been.
-
-          Jakarta's vibrant election campaign offers an insight into its flourishing democracy. As the city looks ahead to an exciting new chapter in its political history, electoral processes demonstrate the significance of people-power in shaping our collective futures.
-        role: tool
-        tool_call_id: call_y4w5orma
-      - content: |-
-          <think>
-          We have a chunk: "Bintang's interactive app for gathering real-time feedback from citizens about daily commute challenges was applauded as an innovative form of civic engagement". The question: "What innovative form of civic engagement did Bintang introduce for gathering feedback from citizens during Jakarta's election?" It appears to be exactly that: Bintang introduced an interactive app for gathering real-time feedback.
-
-          Thus answer: she introduced an interactive app.
-
-          But we need to be careful: maybe there is also another similar chunk. Let's search again with other keyword "interactive app" and "Bintang" to see.
-
-
-          </think>
-        role: assistant
-        tool_calls:
-        - function:
-            arguments: '{"limit":5,"query":"Bintang interactive app feedback citizens Jakarta"}'
-            name: search_documents
-          id: call_av46l6b1
-          type: function
-      - content: |-
-          [33635a00-3e8a-4d08-a76e-833cf37878e5] [rank 1 of 1]
-          Type: text
-          Content:
-          Jakarta Election Campaigns Heat Up: Here's How to Understand the System
-
-          As election day in Jakarta draws nearer, candidates are out in force using various strategies to woo voters and prepare to exercise their democratic rights. Citizens make their voices heard as the city vibrates with life. This coverage offers valuable insight into campaign activities while offering an in-depth guide for understanding electoral processes in Jakarta.
-
-          Initial Launch of Candidates' Campaign Plans on September 1
-
-          After September 1st, when campaign season officially kicked off, candidates have moved swiftly to engage their bases. Amira Bintang, an upstart candidate with extensive social activism experience and promising urban development and public transportation reform as key platforms of her candidacy speech in Jakarta; incumbent Rizal Harahap relies heavily on his track record and highlights all infrastructure projects completed during his term.
-
-          Campaign Strategies: From Digital Battlegrounds to Door-toDoor Outreach
-
-          Campaign strategies have taken an advanced turn as candidates leverage digital media to reach a wider audience. Hashtags, viral videos, targeted ads and targeted messaging can all play an influential role in changing public opinion with just a tweet or meme. At the grassroots level candidates engage in door-to-door campaigns personalized for individual voters in an attempt to connect.
-
-          Bintang's interactive app for gathering real-time feedback from citizens about daily commute challenges was applauded as an innovative form of civic engagement, while Harahap launched a series of webinars featuring experts discussing economic growth under his administration.
-
-          Rallies and Persuasion
-
-          Jakartan politicians know the power of an impassioned speech cannot be underrated, and candidates have been taking full advantage of its effectiveness at rallies. Rallies feature vibrant colors, banners and impassioned discourse in an attempt to win converts over. At one high-spirited rally on October 22, Bintang outlined her policy plans for improving education and healthcare to an appreciative crowd while Harahap's rallies often consist of shows of solidarity from various political allies united behind his plea for continuity and stability.
-
-          Debates: Clashes Between Visions and Policies
-
-          Debates are one of the highlights of Jakarta election campaigns, allowing candidates to outline their platforms and discuss critical issues. On November 5th, citizens witnessed an exhilarating debate between candidates Bintang and Harahap over whether the city was prepared for digital transformation in public services; Bintang advocated an aggressive move toward smart city model while Harahap advocated a more measured approach so as not to alienate less tech-savvy residents.
-
-          Voter Engagement: Making Every Vote Count
-
-          Ensuring every eligible voter is engaged and informed remains an ongoing challenge for Jakartans. Civil society groups and independent bodies host workshops and publish voter guides to inform voters of their rights and choices, while an annual democracy festival such as that held on November 20 featured interactive exhibits on Jakarta's electoral history as well as mock voting booths for first-time voters.
-
-          Campaign Financing: Transparency and Accountability
-
-          Campaign financing has always been a contentious topic in elections, and this election cycle is no exception. Bintang's campaign, funded largely through crowdfunders online supporters, stands in stark contrast with Harahap's sophisticated machine backed by both private donors and party funds. To protect democratic decision making processes from any
-
-          undue influences on decision-making processes, Jakarta Election Commission mandated strict reporting and transparency measures during campaign financing decisions.
-
-          Before Election Day: Submit Final Appeals Now
-
-          As election day nears, candidates make their last appeals to voters. Bintang plans a visit through key neighborhoods while Harahap plans a final rally scheduled for late November. Both camps are honing their messaging and policy proposals while encouraging supporters to make an appearance at polling booths on November 8th.
-
-          Polling Day: The Final Act of Campaign Activities
-
-          On December 6th, voting booths across Jakarta will open their doors, signalling the culmination of weeks of intense campaigning. Voters will cast their vote and candidates await results that depend on how effective their strategies, speeches and outreach initiatives have been.
-
-          Jakarta's vibrant election campaign offers an insight into its flourishing democracy. As the city looks ahead to an exciting new chapter in its political history, electoral processes demonstrate the significance of people-power in shaping our collective futures.
-        role: tool
-        tool_call_id: call_av46l6b1
-      - content: "<think>\nWe've got only one chunk. It's consistent with the answer. The question: \"What innovative form
-          of civic engagement did Bintang introduce for gathering feedback from citizens during Jakarta's election?\" So answer:
-          The interactive app for real-time feedback about daily commute challenges.\n\nThus: \"Bintang introduced an interactive
-          app for real-time feedback from citizens about daily commute challenges.\" Provide chunk_id [33635a00-3e8a-4d08-a76e-833cf37878e5]
-          as cited_chunks. Provide concise answer.\n</think>\n\nBintang introduced an **interactive mobile app** that lets
-          citizens give real‑time feedback on daily commute challenges during the Jakarta election.  \n\n**Cited chunk:**
-          [33635a00-3e8a-4d08-a76e-833cf37878e5]"
-        role: assistant
-      - content: |-
-          Validation feedback:
-          Please include your response in a tool call.
-
-          Fix the errors and try again.
-        role: user
-      model: gpt-oss
-      reasoning_effort: high
-      stream: false
-      tool_choice: auto
-      tools:
-      - function:
-          description: |-
-            Search the knowledge base for relevant documents.
-
-            Returns results with chunk IDs and relevance scores.
-            Reference results by their chunk_id in cited_chunks.
-          name: search_documents
-          parameters:
-            additionalProperties: false
-            properties:
-              limit:
-                anyOf:
-                - type: integer
-                - type: 'null'
-                default: null
-              query:
-                type: string
-            required:
-            - query
-            type: object
-        type: function
-      - function:
-          description: Answer to a search query with chunk references.
-          name: final_result
-          parameters:
-            additionalProperties: false
-            properties:
-              answer:
-                description: The answer to the question
-                type: string
-              cited_chunks:
-                description: IDs of chunks used to form the answer
-                items:
-                  type: string
-                type: array
-              confidence:
-                default: 1.0
-                description: Confidence score for this answer (0-1)
-                maximum: 1.0
-                minimum: 0.0
-                type: number
-              query:
-                description: The question that was answered
-                type: string
-            required:
-            - query
-            - answer
-            type: object
-        type: function
-    uri: http://localhost:11434/v1/chat/completions
-  response:
-    headers:
-      content-length:
-      - '1332'
+      - '1666'
       content-type:
       - application/json
     parsed_body:
@@ -1057,39 +456,29 @@ interactions:
         message:
           content: ''
           reasoning: |-
-            The response was not in a tool call. We need to use the final_result tool to provide the answer.
+            We have the chunk from source: "Jakarta Election Campaigns Heat Up: Here's How to Understand the System". It mentions "Bintang's interactive app for gathering real-time feedback from citizens about daily commute challenges was applauded as an innovative form of civic engagement". That appears to be the answer: Bintang introduced an interactive app for gathering real-time feedback from citizens about daily commute challenges. The question: "What innovative form of civic engagement did Bintang introduce for gathering feedback from citizens during Jakarta's election?" The answer: an interactive app for collecting real-time feedback about daily commute challenges.
 
-            We can call final_result. Let's produce final_result with answer, cited_chunks, confidence, query.
-
-            We'll include answer: "Bintang introduced an interactive mobile app for real-time feedback from citizens about daily commute challenges."
-
-            Cited_chunks: ["33635a00-3e8a-4d08-a76e-833cf37878e5"].
-
-            Confidence: 0.95.
-
-            Also include query that was answered.
-
-            Thus we need to produce a final_result tool call.
+            We need to format answer with cited_chunks: chunk id e6d3622c-0d09-4caa-a7ab-ef01949233c8. The answer: Bintang introduced an interactive app. Provide answer concise. Use final_result.
           role: assistant
           tool_calls:
           - function:
-              arguments: '{"answer":"Bintang introduced an interactive mobile app that lets citizens submit real‑time feedback
-                on daily commute challenges during Jakarta’s election cycle.","cited_chunks":["33635a00-3e8a-4d08-a76e-833cf37878e5"],"confidence":0.95,"query":"What
+              arguments: '{"answer":"Bintang introduced an interactive app that allowed voters to give real‑time feedback
+                on daily commute challenges during the Jakarta election.","cited_chunks":["e6d3622c-0d09-4caa-a7ab-ef01949233c8"],"confidence":0.93,"query":"What
                 innovative form of civic engagement did Bintang introduce for gathering feedback from citizens during Jakarta''s
                 election?"}'
               name: final_result
-            id: call_tesqa24k
+            id: call_2tcii5ad
             index: 0
             type: function
-      created: 1768996031
-      id: chatcmpl-73
+      created: 1769001382
+      id: chatcmpl-355
       model: gpt-oss
       object: chat.completion
       system_fingerprint: fp_ollama
       usage:
-        completion_tokens: 225
-        prompt_tokens: 2847
-        total_tokens: 3072
+        completion_tokens: 278
+        prompt_tokens: 1657
+        total_tokens: 1935
     status:
       code: 200
       message: OK
@@ -1102,7 +491,7 @@ interactions:
       connection:
       - keep-alive
       content-length:
-      - '1834'
+      - '1823'
       content-type:
       - application/json
       host:
@@ -1136,7 +525,7 @@ interactions:
       - content: |-
           QUESTION: What innovative form of civic engagement did Bintang introduce for gathering feedback from citizens during Jakarta's election?
 
-          GENERATED ANSWER: Bintang introduced an interactive mobile app that lets citizens submit real‑time feedback on daily commute challenges during Jakarta’s election cycle.
+          GENERATED ANSWER: Bintang introduced an interactive app that allowed voters to give real‑time feedback on daily commute challenges during the Jakarta election.
 
           EXPECTED ANSWER: Bintang introduced an interactive app for real-time feedback on daily commute challenges.
         role: user
@@ -1162,7 +551,7 @@ interactions:
   response:
     headers:
       content-length:
-      - '705'
+      - '571'
       content-type:
       - application/json
     parsed_body:
@@ -1171,26 +560,25 @@ interactions:
         index: 0
         message:
           content: ''
-          reasoning: 'We compare. Generated says interactive mobile app that lets citizens submit real-time feedback on daily
-            commute challenges during Jakarta’s election cycle. Expected: interactive app for real-time feedback on daily
-            commute challenges. Same core info. Equivalent.'
+          reasoning: Need to evaluate equivalence. Both mention interactive app for real-time feedback on daily commute challenges.
+            So yes equivalent.
           role: assistant
           tool_calls:
           - function:
               arguments: '{"equivalent":true}'
               name: final_result
-            id: call_bqvqea3x
+            id: call_hvd0sams
             index: 0
             type: function
-      created: 1768996033
-      id: chatcmpl-493
+      created: 1769001384
+      id: chatcmpl-355
       model: gpt-oss
       object: chat.completion
       system_fingerprint: fp_ollama
       usage:
-        completion_tokens: 69
-        prompt_tokens: 388
-        total_tokens: 457
+        completion_tokens: 48
+        prompt_tokens: 387
+        total_tokens: 435
     status:
       code: 200
       message: OK
diff --git a/tests/cassettes/test_qa/test_qa_openai.yaml b/tests/cassettes/test_qa/test_qa_openai.yaml
index 2318e2d5..4d3e7630 100644
--- a/tests/cassettes/test_qa/test_qa_openai.yaml
+++ b/tests/cassettes/test_qa/test_qa_openai.yaml
@@ -86,7 +86,7 @@ interactions:
       connection:
       - keep-alive
       content-length:
-      - '2762'
+      - '2760'
       content-type:
       - application/json
       host:
@@ -144,7 +144,7 @@ interactions:
           description: |-
             Search the knowledge base for relevant documents.
 
-            Returns results with chunk IDs and relevance scores.
+            Returns results with chunk IDs and rank positions.
             Reference results by their chunk_id in cited_chunks.
           name: search_documents
           parameters:
@@ -199,13 +199,13 @@ interactions:
       connection:
       - keep-alive
       content-length:
-      - '1148'
+      - '1157'
       content-type:
       - application/json
       openai-organization:
       - enfold-systems
       openai-processing-ms:
-      - '824'
+      - '854'
       openai-project:
       - proj_XYweeUJpJHbbRSNsezto5Wfh
       openai-version:
@@ -226,18 +226,18 @@ interactions:
           role: assistant
           tool_calls:
           - function:
-              arguments: '{"query":"Bintang innovative civic engagement feedback Jakarta election"}'
+              arguments: '{"query":"Bintang innovative civic engagement feedback citizens Jakarta election"}'
               name: search_documents
-            id: call_pBK3fkvRJH9zWFCzRwNvqLlW
+            id: call_KJ0lkxmRRzDYnJw7mFWHlwQ9
             type: function
-      created: 1768996037
-      id: chatcmpl-D0Qy5rGZkDYno9mPz2k0oplxrpE4F
+      created: 1769001388
+      id: chatcmpl-D0SMOLCHuJI24tOe3j2GdQXAVwWax
       model: gpt-4o-mini-2024-07-18
       object: chat.completion
       service_tier: default
-      system_fingerprint: fp_29330a9688
+      system_fingerprint: fp_c4585b5b9c
       usage:
-        completion_tokens: 21
+        completion_tokens: 22
         completion_tokens_details:
           accepted_prediction_tokens: 0
           audio_tokens: 0
@@ -247,7 +247,7 @@ interactions:
         prompt_tokens_details:
           audio_tokens: 0
           cached_tokens: 0
-        total_tokens: 519
+        total_tokens: 520
     status:
       code: 200
       message: OK
@@ -260,7 +260,7 @@ interactions:
       connection:
       - keep-alive
       content-length:
-      - '131'
+      - '140'
       content-type:
       - application/json
       host:
@@ -269,7 +269,7 @@ interactions:
     parsed_body:
       encoding_format: base64
       input:
-      - Bintang innovative civic engagement feedback Jakarta election
+      - Bintang innovative civic engagement feedback citizens Jakarta election
       model: qwen3-embedding:4b
     uri: http://localhost:11434/v1/embeddings
   response:
@@ -280,14 +280,14 @@ interactions:
       - chunked
     parsed_body:
       data:
-      - embedding: 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
         index: 0
         object: embedding
       model: qwen3-embedding:4b
       object: list
       usage:
-        prompt_tokens: 10
-        total_tokens: 10
+        prompt_tokens: 11
+        total_tokens: 11
     status:
       code: 200
       message: OK
@@ -300,12 +300,12 @@ interactions:
       connection:
       - keep-alive
       content-length:
-      - '7970'
+      - '7977'
       content-type:
       - application/json
       cookie:
-      - __cf_bm=zPRjPdR2vAuTIgGZxLAsLIk_ASzLwNT8lTm81DzkI_w-1768996038-1.0.1.1-Yb55H59XZju2zkw3tE6Xo0q_.6fIbxJIxn3LvuBeDwAQPGCs.vaiBHLfYd_orCyV7sEq2aFLCQjr8rcWm3WYWrLaMkgOa0j1LELACwFQPPI;
-        _cfuvid=QAkRIxDYvoKU5Yx_a14V3COK2g9MAuO_Ymft2EmSe0Y-1768996038788-0.0.1.1-604800000
+      - __cf_bm=108bG22zp7CfRbsoTon0Y7gdKTNzGRDyQlxDt1UckcM-1769001389-1.0.1.1-iOVL5b63uCO82cfii245ExHhy0wsHN5qYGfAQ25xlnfsSjS_QjetPxu1LMlpVNoAbPPn_9MyRRiCyWvXn3HsuPc20rni7HkA9rGwYQCXEd0;
+        _cfuvid=FOK8eKwc4R_UPlI1U1m2RwZMQH.KdP3vAmUQ_AJrWWY-1769001389377-0.0.1.1-604800000
       host:
       - api.openai.com
     method: POST
@@ -357,12 +357,12 @@ interactions:
         role: assistant
         tool_calls:
         - function:
-            arguments: '{"query":"Bintang innovative civic engagement feedback Jakarta election"}'
+            arguments: '{"query":"Bintang innovative civic engagement feedback citizens Jakarta election"}'
             name: search_documents
-          id: call_pBK3fkvRJH9zWFCzRwNvqLlW
+          id: call_KJ0lkxmRRzDYnJw7mFWHlwQ9
           type: function
       - content: |-
-          [5d743cd9-3ec4-4499-ba6f-ad69ed85f271] [rank 1 of 1]
+          [6429d519-77af-4097-b2d6-b2060d67ab25] [rank 1 of 1]
           Type: text
           Content:
           Jakarta Election Campaigns Heat Up: Here's How to Understand the System
@@ -407,7 +407,7 @@ interactions:
 
           Jakarta's vibrant election campaign offers an insight into its flourishing democracy. As the city looks ahead to an exciting new chapter in its political history, electoral processes demonstrate the significance of people-power in shaping our collective futures.
         role: tool
-        tool_call_id: call_pBK3fkvRJH9zWFCzRwNvqLlW
+        tool_call_id: call_KJ0lkxmRRzDYnJw7mFWHlwQ9
       model: gpt-4o-mini
       stream: false
       tool_choice: required
@@ -416,7 +416,7 @@ interactions:
           description: |-
             Search the knowledge base for relevant documents.
 
-            Returns results with chunk IDs and relevance scores.
+            Returns results with chunk IDs and rank positions.
             Reference results by their chunk_id in cited_chunks.
           name: search_documents
           parameters:
@@ -471,13 +471,13 @@ interactions:
       connection:
       - keep-alive
       content-length:
-      - '1516'
+      - '1513'
       content-type:
       - application/json
       openai-organization:
       - enfold-systems
       openai-processing-ms:
-      - '1881'
+      - '2534'
       openai-project:
       - proj_XYweeUJpJHbbRSNsezto5Wfh
       openai-version:
@@ -499,30 +499,30 @@ interactions:
           tool_calls:
           - function:
               arguments: '{"query":"What innovative form of civic engagement did Bintang introduce for gathering feedback
-                from citizens during Jakarta''s election?","answer":"Amira Bintang introduced an interactive app for gathering
-                real-time feedback from citizens about daily commute challenges, which was praised as an innovative form of
-                civic engagement during Jakarta''s election.","cited_chunks":["5d743cd9-3ec4-4499-ba6f-ad69ed85f271"],"confidence":1.0}'
+                from citizens during Jakarta''s election?","answer":"Bintang introduced an interactive app for gathering real-time
+                feedback from citizens about daily commute challenges, which was recognized as an innovative form of civic
+                engagement during Jakarta''s election.","cited_chunks":["6429d519-77af-4097-b2d6-b2060d67ab25"],"confidence":1.0}'
               name: final_result
-            id: call_6XCiKTGWcCe5l1Vsp0xYdlCG
+            id: call_xdN8TNZZHFQSL0tRWTaIHf4G
             type: function
-      created: 1768996040
-      id: chatcmpl-D0Qy8diPzSydOeie7hTBi2LQX99yz
+      created: 1769001390
+      id: chatcmpl-D0SMQ7hzSkBJXPCVsxaDtcnMXBClQ
       model: gpt-4o-mini-2024-07-18
       object: chat.completion
       service_tier: default
-      system_fingerprint: fp_c4585b5b9c
+      system_fingerprint: fp_29330a9688
       usage:
-        completion_tokens: 103
+        completion_tokens: 101
         completion_tokens_details:
           accepted_prediction_tokens: 0
           audio_tokens: 0
           reasoning_tokens: 0
           rejected_prediction_tokens: 0
-        prompt_tokens: 1404
+        prompt_tokens: 1405
         prompt_tokens_details:
           audio_tokens: 0
           cached_tokens: 0
-        total_tokens: 1507
+        total_tokens: 1506
     status:
       code: 200
       message: OK
@@ -535,7 +535,7 @@ interactions:
       connection:
       - keep-alive
       content-length:
-      - '1889'
+      - '1886'
       content-type:
       - application/json
       host:
@@ -569,7 +569,7 @@ interactions:
       - content: |-
           QUESTION: What innovative form of civic engagement did Bintang introduce for gathering feedback from citizens during Jakarta's election?
 
-          GENERATED ANSWER: Amira Bintang introduced an interactive app for gathering real-time feedback from citizens about daily commute challenges, which was praised as an innovative form of civic engagement during Jakarta's election.
+          GENERATED ANSWER: Bintang introduced an interactive app for gathering real-time feedback from citizens about daily commute challenges, which was recognized as an innovative form of civic engagement during Jakarta's election.
 
           EXPECTED ANSWER: Bintang introduced an interactive app for real-time feedback on daily commute challenges.
         role: user
@@ -595,7 +595,7 @@ interactions:
   response:
     headers:
       content-length:
-      - '704'
+      - '845'
       content-type:
       - application/json
     parsed_body:
@@ -604,26 +604,27 @@ interactions:
         index: 0
         message:
           content: ''
-          reasoning: 'We compare: both mention interactive app real-time feedback on daily commute challenges. Both mention
-            innovation civic engagement. The first says "praised as innovative form of civic engagement during Jakarta''s
-            election." That''s same gist. They are equivalent.'
+          reasoning: 'We need to compare. Both say interactive app for real-time feedback on daily commute challenges. The
+            first says during Jakarta''s election and recognized as innovative. The question: "What innovative form of civic
+            engagement did Bintang introduce for gathering feedback from citizens during Jakarta''s election?" The expected
+            answer: the app. Both answers mention same core factual info. Yes equivalent.'
           role: assistant
           tool_calls:
           - function:
               arguments: '{"equivalent":true}'
               name: final_result
-            id: call_p70niqa3
+            id: call_u9kj2xm2
             index: 0
             type: function
-      created: 1768996048
-      id: chatcmpl-250
+      created: 1769001395
+      id: chatcmpl-845
       model: gpt-oss
       object: chat.completion
       system_fingerprint: fp_ollama
       usage:
-        completion_tokens: 71
-        prompt_tokens: 398
-        total_tokens: 469
+        completion_tokens: 97
+        prompt_tokens: 396
+        total_tokens: 493
     status:
       code: 200
       message: OK