3395 lines
379 KiB
YAML
3395 lines
379 KiB
YAML
interactions:
|
||
- request:
|
||
headers:
|
||
accept:
|
||
- application/json
|
||
accept-encoding:
|
||
- gzip, deflate, zstd
|
||
connection:
|
||
- keep-alive
|
||
content-length:
|
||
- '96'
|
||
content-type:
|
||
- application/json
|
||
host:
|
||
- localhost:11434
|
||
method: POST
|
||
parsed_body:
|
||
encoding_format: base64
|
||
input:
|
||
- First document about cats.
|
||
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: 6
|
||
total_tokens: 6
|
||
status:
|
||
code: 200
|
||
message: OK
|
||
- request:
|
||
headers:
|
||
accept:
|
||
- application/json
|
||
accept-encoding:
|
||
- gzip, deflate, zstd
|
||
connection:
|
||
- keep-alive
|
||
content-length:
|
||
- '97'
|
||
content-type:
|
||
- application/json
|
||
host:
|
||
- localhost:11434
|
||
method: POST
|
||
parsed_body:
|
||
encoding_format: base64
|
||
input:
|
||
- Second document about dogs.
|
||
model: qwen3-embedding:4b
|
||
uri: http://localhost:11434/v1/embeddings
|
||
response:
|
||
headers:
|
||
content-type:
|
||
- application/json
|
||
transfer-encoding:
|
||
- chunked
|
||
parsed_body:
|
||
data:
|
||
- embedding: WYA3uV2j0Ty18KM65Tf2vOxiS7r7gCI90kYxPUtLPLzxw608LAWLu8oxubzLBK88TIi+Ox5Dmby5+G88AXmMvMS6Lj0gGly8LBaZvOgXlbvIkoC8h7BEPCNgRjw/1Hy81jNlPP6cTjqoPae8sjWavNSlVz38RX08CabOvFtC6LwekAQ94AExvKjZdDu2ksu72fvZOtW/nrt3AyU8apgOOYVHyDvdblG9f13jPCfR3DtYoDS9OaJivDL0YTt0yIw8+PvUvA3QDr2kUhY8FkyjO1TEprw/9F28JfhMPDogXLzEWmA98AA/u6K1J73sUAK9Ptifu6Tz27y5A5K7rqQmvG21Pbt81tG8vUt9O9bFzLwOvik8EmYlvdeC8bshUC09fsL8uyMjRTqTn+g8I/ywvDCW5rvnC/o8PTg3vELCGjzA4xM9DTeFPEsSEDwTZ4Y9EOvQPIz9ajsNcIs8fSGRPGMLj7v+qWQ8LntjPHV29TtzUU28EX6kPDW4mjuKBMG70M+4vHrGMLye4dS67/BtPFMBBDsQS8+7uJM/PHUodryjLOS70BzAvCgrB7zbx8e83yLfvBW1p7s8wNa7PFeGPH1VezyglxC8dWsHPJ0+qrvEbZU7maFHPQQeVDyG4sg8SwTwu3tkmjz/LmO8vF1BPHp7ZjwhIzY8b7mgvOdor7w/JWm80MPXPPz0Hz15jqe8eAdzPHbKQ7yTWHA8rqVKPE20s7twfI+72UPIO69+fzyfwwC8N9y/u9wrmDuNbvs8rjIIvSZrS718g/i7cZbdPEUsQzznEYS7qcW+PEzxAjwcqcg6oei0PJnVyDwtrsM8TqW8vE3RN7r8BrG7iWg8PDoChjvQIoY86WRzu8eBVDz/xqc8lLk6PMb9g7voNR66Fu6GvGTHfbzjMTC8VJWWu4YJmbzUN8O8sbGvvA6tP7uMNhe9cWKaPK/GpbqJloc79nQEvFQ6+rsKRta7iXWJuwNjeTyXSFo5TSBnuyqbgrotcDW7SAJVPEVouLxxsCM8If0tO1K0G7xyfP674n2Pu/BPtryMMje8BQy9vLq8zDy5ecE7O5xtvBNZCzzm/I68yhlcPBU3nLu23XS8QsKAvNPtHzyd7Zc5uE3HPHfHLTyNV6S8Kk0Jva8fEzz+GxS8snSlvNY5t7wzOsM8yLMlPZ91HDwexIE7B58QvFoWhDtKJ9K8nfYqO5C/izzTUQO7IyVBO0Q+x7zDZlo8YYuIvHMQJLuas4G8raP1OW6rMTzLWxE797kVPP53PDs2Uvm8NF0mvVq7ybuauJ+8gkoFPPbZ4zp50gS8xlu2u2mTN7xzP8O70V7rvCv/ErySfoY7vCjju3wDebzE/w28f29FOrdhtbwKDLO8CJvFO9fEED10Z8Y5ttS+vBHzAzytPo27sJwHuwYvmzzxULU7vWqGvCfSxTw+N8q814wKPUCRbzqXakA8kuEZPJ+fTT1EhgW8EaOnO8xKuLs0PQE80qYJPcLV5rxe5hU7OWrIvJtmbjxMFam6NFa2uxinADyJ6G88gXegvHqMLjxV3Ts84xMOvZgAhTyDCgy7ztwxvEkSvDzd6o08x4znvE9ciLwoLJa8qdeLvHw70ru/BX48vxKfPFEwyTxOB9U8PDeFPPLzjDwCy4o7+rnPvEGxwLwzsvS6nk39u5/58ju8sw49GxEIvSkBRLzYDwy8Vh0rvJ8zgbzqcRw7RS82vdmkYrwzb2k8BuuavI3FrTwYfYE8h7fePC2+VDySpXI9tK5iO4n2DT3idd68FvAgvP8NTLpO9rQ6ck34u2O1AT0g+o48UvaVPFyqDDse9aC7cC+aPHvOjrwLCWu9L6IVPNkR4DznkGG8AFp3vHVTDr01wBM6cYXSvAjOjbuIAOo7aSylPN/X+TxIuUy8JtKLPFX/Sz2UU9C7QQEZvL7mQbzeVJk7RfkkPYrWp7wcdNi7u7RAvCA0Mj1UeEK88togvArunDv2u7W8PRHnPEkF1buDKZG7E+Nyu9LCODyt0ey8buRfu7AvO7tEtFA8CXp+PCJ9h7w3Nbo8JOAKOx1Y+DuPfsi71Y5oPCzxlzxk/z08jUlIPCqFOLyZFnc7zJ4OPatqI72m2Vo8pjMrPBOgxjyKviQ92wEUu4Jua7zzD9E5856yvBJzYDtyWyI8118RvGecQLz0xYI9e2daPA+KIbsUwam61RkDPDHMuDtTDh29dLcZvMQu4jyls448kRLbvG/uwrzb/sI8FbRIO+nDTzrT+B86WqJUu7IbXzyRu0Y8Db1vvIivtTrKe6u8RMWOvB0tfLwtm8E8yPMvO+2B/jx2iaG7JsbBPBkUqrsSiqC8ohdnvD+pL72Hsb08008evBy97TxT4148d6PYvHsZIzx6EQc7S4p0u0Lkf7yAYkm9o4rMvKNgBzvm4xg7vbGnvI3AP73BhNc72M+jPHOY7ryRBvC8BK/YOa2OsL1liKK7XtVxvBwYuLyE7fq7Z8LmvEl6qbwFsDm8WYDoPGbqpjxLEEK9jcGvvNGYF70l3d08AWOxuh9iGzsFS0m87g4nPGz4ejyN3Ks8kocfPZ7E/ztgHsE822W8vFPj67uoMEs8QbdnPEh7hDyiJOc8S8GpujgXeTzW3tG82gSfuktB8ruAVts7lq0MPAoJrDtpVaC8pwZ6PFzHvrm/A+28QLIhPT/hhbx+JVq8RHn5vNltGj3ldzc8w9bXvAeMKLzI/w89hQaKPH4cTTzOqMI883UIvVdQ1DwMSPS674c9vZ1aRrxRCIO8t+O+vEiaH7yOC7q8pIEXO5DbB73635i851KwPKMH4zzZwfu8lLTlvH80/ju6Q2+8dAOAPE2ZkLqmslA7HBGvvFefWbvLMdA6+ZYuvGQ0AjuvpdQ8CZKAuz0MrbsVfQY8aoDAu6A9xDsu7IC7PfRZPRAIgDxkBAu8WZfTuyQGNDwdoP68xoi3O4vOPL0aKUM8XwPMPGxYy7zuh/a7RIPyuwdWYbwzCMO7UFa5u1Lw27uC/j08/Wkgu7We/rwSz9W8d8WPPGhw/LzS0KK8dP6rO7ZqGj3dFjM87Ee6OjprCb3mF+i7uunXPMuqwzrs9/u8QDyBPDl0lDzeaJg8974GPLACN7zkbhS8TnsRO230TrvJrcu7cAadPBr0+LsJGeS8rtTNOs/cL7wQAP68JuzxPLodabvfMPG7l8BFvR8CrDx6nfo6cJ8ePSdClLzOylS8a71suxlbELy8gEk8rURJPJXnFTxAMee8fE8GPW9S67whegG9Um2AvB+G1DyptLw86+yMvA1Z5bzE1hy9YyjrvPT4lrzrG0e822KIvK6BLrxntwW8DCYAvCOatb02qWS8KntRPIm4WzxY+ZW8yR2KvDAFRLxNKCo9fNldOjmn3jd8abq8K4IRvdVR5Dxb1gs8xy2ovNGvNT3YkQo8s0ZVPLXXiryQ6gI9YsKivHoHlLyxmy+9tiaXupQMrDx4bAU8l1hNvMTjO7yxiAy83DYuPEQYKjyX7Ck94FrTus0NkDtcqFA5XyyLu3vP07xNjKy7UIG5PKjuGDsCj6G6WgXAvDGRDr0HwSM8+MS7O+098DrAPBM9JBcpuzCj+zuqMqs7jPzMvB1FYzp4RMu7Ouy8O+NMFzwn/Z28Djx1O2zkbjwlGxy9diH1O079Ijw48gG8D/45vNTZMLyXsrQ8nJScO85gGryOcVi8njJxPMfokLnqQr48EFy3PLllIz2eVk+9YNsYveijAr2COy87lU6uOer6BLwnPTi78vPZPGAsWTl+0WY8uwCqPIsJBT0lX/I7KrrWvGbLtry9USO8RWkVvZkkgbxxq+m8n086vLHfnTu2d6m8m7ZMOuh0sruBDSe9lAfzu4xJx7xp6F47jiUZPZ+vHLzbf4c7YUxWPR6G2Duqjbs8lG6DvPUwvjz6nfG8hWHAPDGvVzwnNqw8zYluu4pYmTuFBRu8wGWVO34maDx6Hb48NdnFvIU5KjzZ+Pe8fDnxvDBkVjyM35E8tKORPKdm2boeN5U7NaJgvHEGqrysYqc8sCnlO0x2JbwpVig9pZHFu3l3j7twHhm88+/pOqHTmLuzor+8x0HGu+oVIzuEN9S8e3+yvLUvBT3TeYW8j59UPPdHPLzsBpm8mlNUPLdkvjwKHNm7V1N4vJGYBz3az8C6M06vOotK2ryYHbc8sJS3u6DBAb2UKPK7/TervD2jRbud3M4709FKve/K+TtmaQQ8YErtvIN6jrzGctw7PPutvLZn7TvN7oO8rolJvQ9xzjxGHgy8gvLOu6h3/jvFzro8W43Iu1fmWzzwoys8DNkqvAP6ibwOVKw8yDA2PHrxxLzZdcy8UAhjPILrebywowi9yFfGPMnq/bs+GTO8pAuUOwqFnzwpnIQ84HhPu1X8XrwvcUk817+4PO/JizwQ08a7M90PvO2poDz/Pgk96/00O5gEGTwnhgc8uUkkvEeW37z2czs8E58mvZZxDL2ro+O8oC3WOwJYFr1bixs8ixfSvDCcPrwqfhY7ZOPHvHgRXjre3hQ7RtW2O0wc5jw5piw9PXkePJsnA7skgsO7lImEPO7XIbcUysI57J/ePHe6crzYaNQ8VhSsvFqpk7yXE7+86VyJPAK2sTutl8S7N6C7u4eUjzzNgc28kB2JPC4H7TxPjy89Vb4evGaHbzzgbyI8B8BvPFYZ6bz0RRo8oiV5vCd5bzzm/rS7ZvzlPOUOvDuKrxi9onRhPPuVnztfQGu8XEZvvF6BXjyHt7Y7CYOcu5sZOL2liZO7y+u7vGJnyrt/LVA8tg0FvVkXbzwGqBK9cZFGvGlaiDwh/Ig7jSjqOtBV5jtqzcg87AaGvDuG8LzYw6E8OPwdvAkPpbyruBS8ElShPMATI7sanjK9Pb81vM/6CDsHbLI8J2+LvDt7Bb2TWQM9xsasvElKBL2Ez6W8huc6PcDfw7zG97i7VesGvGmWBL09/Mu8Anf3vKAXdLxhGQQ89KHnu3sAyTynMsQ8vLdoucTxhDsJCQI936vsuxI6cbxmE1u7QA9JPBT9vzvDKO+8oietPFcWYzwcTUo8JfCnOz1PXDyUAkG8ZV+AO4KaiDuJUo+8JHSgu5opcrzNAJC7gPzePIeqj7xVyMg7FpO2uynoXjy/78q8iRMUPL4F+DsP1ZO4xiOnu3qS9bxx2by8wq1/vIdZmrwOIOO8yxiiunkuGTsCS7C80vEJPcmyujs2SU48OLIDPZ6+PT1iZxO96nbEPG2BWjvTVh08fqoBvMHzvbz7CRk8sX6Fu2vskDyd4E68MLoePEjXFDyPdm48DjOCPAliITzOI5479e4FvWRQ2zzZnnc8TIT7PNb2M72V7tU8vs1fPDl6kblO7pQ8rciavBT8FT0w7U683w9SPZHoL7xB1yq8WQpmvG2YK7x4MSc8gcupuhW2X7y4t9w89F4jPbXkQL2YywA9tQ49vcCJhrySMY48ARzIPGYgqTz7kNG8PO/5PO6K8DwBScO8JOrDOzcwRzuPnAM8W45wvB7yVzxEY4C6am2ZO+qutLxV34A7JVqbPGsdCDw618W8vi/+u1eWibzaxpO7brIivK89WjyWkIo8xWvsOpupTr3P1wu9pn9VPHxvcjwKh2y8lXeQPNpGk7uvUSQ9I4uFOz5ZXLvTErc83XQ1PbuKgTx4DGu8QcyZu5UYDTuOkzi8PaHXO9SMg7zY+IK8B0aevDwdQjzGeSS9O02TugJk77uCugI8PbI5PKrJtTtWIic8frGmvOI5B71KKdi8U6kavU3d2LwYySa8rGkVO2yTYjzLPB+5rGagPAUBXLuUP3C7nHEOPYF6lDt9/gU8Yp20uxAOI7zTnB49s+QWvMq1EztDCVk8iPXtPBO/xLuIvtI8dAkmPLUZpryjFsQ7tF8DvTXhL7xyCyk8UIubvPOwGrtpghA9p8cFvbNzqrtDDwi8VUKyPMBVyLxTJdW7tqkrvHWBhLptxuC8tmTjvKH+zTuopeY758luOydYlDwi9Ke7RBHFO0pBSjxIeAG5YmMFvdyVPjz5SZq8Mum8PBbCtzrq4Vc8SvdgPWekhDxD5Iu7ga2JPKb5yDzWM7u68qMCvFs+aLpI98W8YJiPvIGWTDwFOek8g7Q5vC8Eijx7LXU8w8QZPb74E7w3CA08XenQO+gbvjt7j6O7kgxYvE5qoDvC2qI8W3WXPP4JwjwYxL+7nww5O+ZTrTw2+6Y8J6u6vF63yDx4S488C15CvMTcJTziUMK8qDyMvC3nAb1A2qg8LpkgOwHY+zv5ar08G21TPcHxojokgV08Ug1zvOH/zTsDfzM7kHpLPHECn7xP9iO8THR+PAoC1jycqny59E4GvErgvzsmpfU8E0fMvNN687sIXW28BTbHPIF7RbsB3Ge8WqqcvAKmPrsLvzG9ZSrVOhrxHL1vPaE8YEJNvNsbOztkd+I72CYiu33+BT3FPo88L5dnPVO7E7ytkRy8rnwyPOn6Cz0UIYe8AioHPCQFTTzwa9E66xF0PF3MeryyZLG7yL4BvOvCHj06tPm8dP18PGFO/bqTixU6c70XPS4C8TwfrFK61wh1PE7LtTwbrle8ELs5Pd3/ML3vcRG7dJgTvTYbSr1VOME8gWQiPdz4Wrw/6r27qEYlPGBzzzw5jD89y4HSOyuuF72Ktq67gfMePP7nVTvHkcq75pQVPA6ZwTvfyR88xoiVOtl2uDrTKMA86XCIvDGgnbsgjSc9IzOmvFSmm7yoxPy8DMtbOx9LP7teXbK8uIhUPPMKOjtAln+6+JguO/MVPz1s+wc9ByWlOwR2eTyCCri773QhvLyjnLzdt6y8KdgevBd3OrzJddU7mC7LPG5bhrxEAUo8UeuPvEDZAr2/+l+9NOR5PSfUmjuJ0Fe9LhKbPKeS07o6AQW9ePASva+Ug7y6OBG946QrOhAOB7w5w2a8Ee3HvFG+RLzedz+8nuOeO6xoV7wGdV660P0MvBTIIj3h3MU6la82vcPGu7sIOFk7ZEjcuiHjAr14CwO8uqIiPQBU0LvWILm8m02HvHH48LtI90S8gaKIO80my7yxI7e71EmVPMaysTzS0Mq8xBmmO0R2j7xTgiS8H3E3POfmILzQWEA8jgiJPFK82jw2uMG7Fu6uvLY0oTxM6t+8FFocPU1Hz7u87Km8Et8zuwPcnrxX3Zs8tqyOO5BDH7sEWgu9pN7OPNzckzxH9Qu89xHmPBEysjuY9BU7XRO0PHSpjDw6sc67/QLMOgQHLjxfzuo6g4zNuRR4FDxZQry8rlVAPOllPzuS3HG8uju6OYrc2Dyib+s8puJUOCLcPjqgc8i7dBUhPYG4x7tURGw6DX8jPB7GFrzcRK47mTNKPIK5CL1iCVe8CkHvvM8cILxXEBc8qtOgOlBOQT3yF9085MhKvPZvkDxCxhM91vmYPBDmorzs97C8hOsSve8T4zx3HFO8loJRvEtjzDy9oZG8OnhMOzAriLk7Kiw7KxRtPO4cwbz962s8kajrOziXKb3VgpI77W9wvAj8Br1csQi914DtO2prWT2i1E28gQcFvDILBrzpBqC8rW/WO8a6N72k2Xa8zI3PupVRLD1m5Au9GCCauzxXX7w9+Lg7b14GvEV+sjye/To8aR+5vHMRsbwuxoc8todDvKzupbvJK/e54EplO3RdwDwuR926xUSZPO6W4zxRyjw7DIx7vF3Vnrw2usg8NsCfuWVHS7wY6Z+8LjEmPBbdIry73vw8Qp1kvOD66rv4hUM8vXMBPVX2njqLs+Y8fl50O6N3DLs9Hjq84+yoPN/Fp7tS6Ow6J/6TPAOmCLraQko7mUQJPfQRhbwO6xS8ye4BvYt+FLykd9i8FODfvHvlcTxki0+8MAbQvAndvbyQuxo9ZM1gPEbwgTtYPUe8fI79u2AvCzvhMfw8zrDWOzHIjTzHgv+8tlYdO13+qby5kbe8QZDoPJnxSzxmDZI8pLRBvGmGprrWvkO8fNrYPGGENzyVceG8ubwbPGCcmDxeyv+8YLRQvH7huLq8lcC8YvVaPNo3CTiiTTG9J4u6PFFMmzp8f6M7PRDHNx7fhDw7yhe7MTduPKqUGj2TN5q8wYUsPH+i2join8U8SkyIvJA5kLwps5w8uuozvUvyzrwbw+M7mZKXO4JhBzybZKc8yKs3O4sEiTtIpYu7vPofPDWSrLyZMcs6G2WOvDFV4bz4SKE8VHyRvKbF2juPIXw8mvnAvNMCT7vB9ag8wIb/OitlsjuIZjw8NRcRPEVCKjxbHa88FqP9u8SyQjyctCi8MvkQvOlx+zyX3Km8Cyx3vCq9DT3yHZ08EUKHPO2LnrwB84y8EXZuvE6JFD2aqpU5Hnk2PCjmyzuUqYA8hQGmvCAddT2cXIG8NURvvJQaBr2d+Fs8cY/OPN0S+Dx4Lkg87OfxvAcRHbumx0I8ZocmvGRxbzxNRRI99UsovDtDwTu5cNI7UHEtO4b/H7z3NYe6wj+cPMSmOTudaKq8qfz/O5DXRjwO7x4730vtOt95L7z+XOm89JGXvOFXBTzDI3o8aNn6PJ56hLu8KQu8xo5Iu3iXnTxkqQm9SJBCvGaBFLvjVfS8LpAGvcAbRjwUdrM7o/Q2u+zNFjrUrmo82CLgO6lWAb3ZCVU8+XFmPJuxyDwTxSa843pNPcANKjq+8ge6dOK4uzpbjLtcwjg9xYrTvJo8Mjyv5Li8boylvNfwdDyeA5o8QJa/O6/Kkrzzh/e8R407O1Hcp7zeVSc9tRSCvDgVzLxDgfe87MHevI4Xq7okGIa9j8vrO5CVEr3OFtO7NefvPJlExTzji588XdrzPIh2hbz2osk8oeeJOWxGWjwbSOM8kS6YvO/MArz8+Jm8X0STPBl5x7xA/H68H4s8PSGdxjwUft68rEPGvAxfuryOkDE6D/q5O2o7ijzJ7ha8GEWHvLIXED045FM8Fy+BOqKFy7zh4a28s7nDPOyJ/rzWQ7W8otEBPJjUerwfIbK82H3aPE/QGzuCdl69sUzEO98lYjwDcAC6O9euu6znprwCQpe83dSOvFIVRD3+q4Y8KubvPBVWjLzCRKS7qvL3vD1/qjz07gq993SQvBVRyjyMWAg8hxMFPfSmLDwhgLe85T49vB+wBb3nBX285wjoOnMn4DymXZu8mCEDvZe+fDyt0MA82NzGvK8VwjszF2o8qxEnPOLIV7xu8EM8cvH7vNP4bToRaTq802mEPIaOxzyVOgA53UyKPAxUXDxCqZY8XS8cvF9hsjviSq68RQKWPHNvsLwvF028tarVu/NDnTu406Q6Jq+gvFZAEzyguJ28kdrcu44wszvciD26fXBOvO/3SLotjAg9QFQivCWxcrz6Vrs8jSTlO9pq4bwqijW9yKsQPZd/sDwFJ0e8eNg3PJaE9btbEGO8qNqZO5PFibt1w9y8gebGvNNSNrsvmYe8WTxpvDL0brz5tyG8uLe1vA9/LDsqBUE87NqnO63tvzys2Da7ljGrvARMtTzEftu5j1scvNsWj7wLm3M8q+bOvMFGkTyte3M7rrRxPOqmerwuhi85zhA4PRDK1rwvMN68DpEMu/cw07upw4K8hzbPO2A42Tzmjg+9epjvPJPxX7xTScU8DhwrO+u/0bmegC28qd9WO/lPpzwxRwG8OYASPBw8iruPrZG7PC8JvEsKdT0+OTg73ePqu7at0jojIvI6mX9Eu9IbjjtqfEk7g6mjvH5feLsI5rW8sh+LvENcBr1dxia7owZovWnP3bzNST08WHIJvdV8YzubWJI8qnUjOgCMJjtDEci8I/GVu3wzgzxzX4S8ehM2PILxG71Kqrg8IbOCOn6q2Dy6bBm6Wl6dvPDBcbsiZUa9g4trvPwY9Ly0KBo8GKIFvZ2foLsq4As8A2lmvHWO5br7k8+7gD9WOa8hOLxmg2m8reM4vMPpQDx3HNq7tiWpvBZ8P7zxiy88dCiFPLaMNbt34qC8pJMqOgoDvjshE9u8V4ofvDOslryunoq8Nh99PO1krLuD4tI4ipapPHQGZDxYPMA8d9i6PHi4oLtZXMS7b71IPRfxnzuZjE48w/PBPFDfYjwRx+A8uOX9Os9BgTzOe4w7mr+AvM/I7DziESo8sDAwOy3SNrvxZqw8kjUvvPs3ejwqIcG7jHI6PB3RGLzssI2840cNu5oAhrsfXja8N3lbOW5KFzyUqGO8uFeJvFGtwTxovee5wC+mvJFem7z5EPG8heshu73QAzySFDe8CKeROxv7+Lvp2ni8vY+yO3Jz9jzOFa48jcdgO3zDETzlsIc8qS6xPIKhpbvKd2Q8IpFLuz/rtTsK9xK9SnDSOfyMo7v0DIS8euN6u76FOD1piJM8MRQPPPS6sLqKIMq7ULjVPBwxATynpbM83s3HPPSlBr2tkt+6rH8yu4teAzy4cMq6usrrvFCVpbwYUUU8jEDGvCF4JL3swpm8ATOxu+ySQDxa0k67ztwgPIp5CDxIORA6JKU9vecZtbx7V2O8r9w0PIBxvjyEkWW8g4DSOwFy4ryotwE7nxrDvNJ+kjyVDf+8/JLTPOosBDydjoa87NS1uM76srvJQVm9FIqUPC+eFrq9Soy8UZSUu01ZQTyU0dy7KM9oPUz5arxth1Q82yzmPOIVqrw8XnK8cSefvFDFMrtMzXk8/zfkvLBmeDz8uwE97A3wuiD8GDy2uNU6wHeFPLNsaz3xav668wzFu1Ys9btlG7e65J+Hu5GmWDtMk647Vz92vIpPXLzdrRA8zGp1ufmeMr0O+qc76hAcvN8m4rv2HnO7j0RqvB3LHrzliYw7fkjPvL+mo7sMS147LVo+vfBK8zuOsM07KedxPE/qpjtlgoU7u6qAOuB6TbxD3mi7S/33PKrQ2LrCqRG9jDXhun0nAbzr3QG8fZb0uuMGR7uREuS77U0DPOgYuzxE68K7AVlxvJg3xrx4h9o7ekP4O3S1ZDyLDbE8JCytvAyijjwGbKC70orOvN3i4zyXHjo8gEREvMobDT2a3Dq94lZZPE0ElDxQ5Oq8ajEJPRoEFry8pse7Fly5upYbID3Vgs67eRMxvTfXlLx89PW8Nop4uykrvLzRb/M7J01HPCoelTy6HJ68LHKLvArkjTkFpMA7qJvevJCn8zxShPq87lQ2vAP6fLvAXdc747uRO6egAr33m5u6pBCnPN4Y/DsTGNk8T4EWPf0sUDt2BrU7dHTwOxWy7LuJk1G887b5OvgGyrtH2W872DCDPBMMObwbEMI7FRmKO5FOyjxwAYe8P6Y7vEJrXjuhzy07SlVevO9dBD3SWEs8ahnuPH3xtLyu4J86OGeZPPgtnjz4SDo8/K5QvN4+v7wLZ+A8z7/4u9gYdjxlh947lpMEvCjupruCI9K78zfiOR5OzLsh1OE8k6cBvZwF/ztFUcm7zVgjOWWvXjsDSOS8szPePMbCFDyZTAW8sFEVOy42yzwvI0I8rhjwPE4wlDwWvSK8UL6WPI6hsDylLIe7duBjvLD777xhJQ86I/noOig8t7yTIYI8zAG6uuH8tLyI2Mm57VJtvAgUCrxGQ6M7yfH6vMRCM7z8pBK8LjecO+NU4ryxSQw6pNqovIRfeTzilgS9ca3ZPEO/Nzt/3R06vxifvMogMbzV4eW8w6bOu0nObrt69k+6B/bvO/DaB73Y+U88zX9pvJDIwLzBwwe8wl2Ou41gRDxX3GE82QABvTMo/Lv781A6RELtvGnusDvZoPG7X7OFPIJbQDtlrq685h0svIQ0QL1LjfI8W4rsvFIkDTzzNpE7QC0KvIRXqzw18jS9oBRzO1I4BTyKJfa7C98YvLPW17yaZRg9rrdGuzsA9Dzg8cw7sBBlvKMUIzw65tK8QtA6PQ8CgjzZ2qA8gJm/vJXmYjx7NXk8/uG+OstXyTyyODs8ESMdPHb0FzhP/Yq8S6K/O/zkvLyoEbo8iGUCPSVca7ybbCs7B0+WPKzXPz3T0h28ZR/uu4dz6ryknrG80uOgO48XhTq7+tO7KlfyO8s26buBjs67ClW0vJDGBbs1Iyi6EOy5vBJRPbvI74s8pY1iu52VnTqkxBa8RY+hu/0d5DsPWak8MxIbOzQMSztBWei8NvPMOgMjX7uHnuC8VN3vu/4UvLwXpDa8IuFRvESY87yMJ628806iPMmnBj2mlJC8HmtNOnuRkrwHQU68v4t/PC2aBLwf03U8FIUEPbWOA7yoU/O8vqHFvOBv77qBJ6U78kvHPPcdb7z4Hpo8kbNjPM12xzxsCbI8dI0gvL3l/zsd4NI7kqCPvEwqOD0xSZC8JtP8u8rrcDzv3PY6UdalO0vBFj1Afj47kN0kvISwZbw42pK74CdePO5r/rtngMg8h0wVvHYqAr0tbQ+7mXuMvFQJlzxU1IM7veUCPMfhAbxI9ok8tuURPEaQqDwYj3m7jpPhvL9M3jyz/aA8mwrkvJqdSDxnQ4W8+amKvEe4hjqsVUS8Rv7PvO7CHjztSus7vFjivHDgRjx0bjS7q7xbvDAWODpkWjy8Aut9PMy8kLwBg4y7m1aOuwTpmLxnaye9QH+Zuoaumjvrzzg8a0umPHxQozxPv3u8x5qxvOWU3Dssazi9tYzPu88bsbszLgy9aca6u+4LHbxL8VK7mrtavGA/tbvhFCO8IkipPElO0rzb9ve81c1VujPBjDzolCm9oqcpPeeEa7tlSaS7doXtu6Y0qDyd7w89ovwHPNPegjwbEFw89djXt5wgnTxE3Hq8+mL4u5/Pt7s8bjU8bnGZvEpWLDpmp3s867mOvPREgbyqAT08/MW2PEOAAz0QxNI8IrXdO2BL2zv4dpW89KwCPYJWUDsMH/M71WaUvKwwhLwzmtw8YoOBvSQRLDwFg3i70sEkugOmETwbqFc7BDDEu1BHpztbV8S8E0J9PMap3zx5r7W7brNAPOwxe7r+YAy8k4qPvIe6TrxWGPk7lbbSvPBnUL0U7Ry7c9rxvFLmsjy63zU8yKxXvCvgP7y1TDE8rb4yPE4bBD1wsvu7kQq+O8OBurxwyeG8UsOKPMsmPzsYzTm7umhhPPAVnzwBa4Y7/uFCPeI1zrthF8w8fZlHPHaVqDzt1yO7Kr/HPAmulryc4QS785y3PHX5nLxOknw8OA0xPPTMjjzttEE8NcGzu78Ajbx2g4M8mOEmPRRjJLyM9r88XKf+OPj0lboNEIM8XF9euzC2VrzqCNa4Djz4uz3bfTv7s9m8N/IsPIMpxjzAE/68EbhLvIcBKj25p5w7qLIFPQCLHLyFiBM8ZllnPO0mzDlfDaC8pCQ2uUhGCTwgNc679WTcPMqclTu9q1g95ePgPPX9FjwGw4K8RpMDvCXFNrsZOnS8m3WCuxcZLTsPqYu6knOdu5WKxDxoWI+8FAtmvNvPWLx8+RK9rbEavPjj87y+7sM7nGBru5T22zxkv1k78wZEvO3qtDsN0SG7ShEXvXdrKbuW86c7x7eVO9lwdbu1iEC8qvE/PD/ghjwkrsg7eNkEPGYuzDtwiUE8k7M6vOUsGDuaKx47HTkVPH6jEjzlXO47+/IhvH5bwjqlH4e8uFK3vC3rojskO4Q8ti0ROR+7y7zzjqK8nCpTO4QuQLwfhCG81lWovBl8L7w7kJk8KPiCvL+BI7xF+rc76lrKvAU22DvLHrC7UqHCvEy+hTt96N+7yV8Juw==
|
||
index: 0
|
||
object: embedding
|
||
model: qwen3-embedding:4b
|
||
object: list
|
||
usage:
|
||
prompt_tokens: 6
|
||
total_tokens: 6
|
||
status:
|
||
code: 200
|
||
message: OK
|
||
- request:
|
||
headers:
|
||
accept:
|
||
- application/json
|
||
accept-encoding:
|
||
- gzip, deflate, zstd
|
||
connection:
|
||
- keep-alive
|
||
content-length:
|
||
- '97'
|
||
content-type:
|
||
- application/json
|
||
host:
|
||
- localhost:11434
|
||
method: POST
|
||
parsed_body:
|
||
encoding_format: base64
|
||
input:
|
||
- Third document about birds.
|
||
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: 6
|
||
total_tokens: 6
|
||
status:
|
||
code: 200
|
||
message: OK
|
||
- request:
|
||
headers:
|
||
accept:
|
||
- application/json
|
||
accept-encoding:
|
||
- gzip, deflate, zstd
|
||
connection:
|
||
- keep-alive
|
||
content-length:
|
||
- '8599'
|
||
content-type:
|
||
- application/json
|
||
host:
|
||
- localhost:11434
|
||
method: POST
|
||
parsed_body:
|
||
messages:
|
||
- content: |-
|
||
You are a focused execution agent. Complete the following task using the skills and instructions provided.
|
||
|
||
## Task
|
||
|
||
How many documents are in the database?
|
||
|
||
## Skill instructions
|
||
|
||
# Analysis
|
||
|
||
You answer questions over a document knowledge base. Most questions are answered directly with `search → cite → answer`. Reach for `execute_code` when a question requires computation, aggregation, or structural traversal that a single search cannot deliver.
|
||
|
||
## Tools
|
||
|
||
### execute_code
|
||
Execute Python code in a sandboxed interpreter. Variables persist between calls — you can build state incrementally. Use `print()` to output results.
|
||
|
||
Inside the code, these functions are available (use `await`):
|
||
- `await search(query, limit=10)` → list of dicts with keys: chunk_id, content, document_id, document_title, document_uri, score, page_numbers, headings, doc_item_refs, labels, picture_refs (subset of doc_item_refs labeled `picture`)
|
||
- `await list_documents()` → list of dicts with keys: id, title, uri, created_at
|
||
|
||
Available modules: `json`, `re`, `math`, `pathlib`
|
||
Not supported: class definitions, generators/yield, match statements, decorators, `with` statements
|
||
|
||
### search
|
||
Search the knowledge base directly (outside code execution). Each result has a `Type:` (paragraph, table, code, list_item, picture). When the Type is `picture`, the corresponding figure may also be attached to the tool response as an image alongside the text — use it directly to answer questions about figures, diagrams, charts, screenshots.
|
||
|
||
### cite
|
||
Register the chunk IDs that ground your answer. Call this BEFORE writing your final answer, with the `chunk_id` values from search results (from either the `search` tool or `await search(...)` inside `execute_code`) that support each claim. Every answer that uses search results must be backed by `cite`.
|
||
|
||
Use chunk_ids exactly as they appear in the search response — copy the full UUID verbatim. Do not abbreviate, paraphrase, or reconstruct chunk_ids from memory; the tool matches them as opaque strings.
|
||
|
||
## Document Filesystem (inside execute_code)
|
||
|
||
All documents are mounted as a virtual filesystem at `/documents/`:
|
||
|
||
```
|
||
/documents/{document_id}/
|
||
metadata.json # {"id", "title", "uri", "created_at"}
|
||
content.txt # Full document text
|
||
items.jsonl # Structured items (one JSON object per line)
|
||
toc.json # Section tree derived from heading_level
|
||
```
|
||
|
||
`{document_id}` is an internal identifier, not the user-facing `uri` (filename, URL, etc.). When you only know a document by its URI or title, use `await list_documents()` to enumerate ids, or read `metadata.json` from each directory and match against `uri` / `title`.
|
||
|
||
### Reading files
|
||
Always use `Path.read_text()` — do NOT use `open()` or `with` statements (they are not supported).
|
||
|
||
```python
|
||
from pathlib import Path
|
||
import json
|
||
|
||
# Discover documents
|
||
for doc_dir in Path('/documents').iterdir():
|
||
meta = json.loads((doc_dir / 'metadata.json').read_text())
|
||
print(meta['title'])
|
||
|
||
# Read full text
|
||
content = Path(f'/documents/{doc_id}/content.txt').read_text()
|
||
|
||
# Read and parse items
|
||
for line in Path(f'/documents/{doc_id}/items.jsonl').read_text().strip().split(chr(10)):
|
||
item = json.loads(line)
|
||
if item['label'] == 'table':
|
||
print(item['text'][:200])
|
||
```
|
||
|
||
### metadata.json
|
||
Document metadata: `id`, `title`, `uri`, `created_at`.
|
||
|
||
### content.txt
|
||
Full text content. Use for regex or keyword search across a whole document.
|
||
|
||
### items.jsonl
|
||
Structured document items. Each line is a JSON object with:
|
||
- `position`: sequential position in the document
|
||
- `self_ref`: item reference (e.g. "#/texts/5", "#/tables/0")
|
||
- `label`: item type — "section_header", "text", "table", "list_item", "caption", "formula", "picture", "code", "footnote"
|
||
- `text`: rendered content (tables are markdown with `|` columns)
|
||
- `page_numbers`: list of page numbers where the item appears
|
||
- `heading_level`: H-level (1–6) for `section_header` items, `0` otherwise. PDFs often collapse to `1` for every header.
|
||
- `tree_depth`: DOM nesting depth — varies meaningfully on HTML, near-uniform on PDFs.
|
||
|
||
### toc.json
|
||
Section tree derived from `heading_level`: `{"doc_id", "title", "tree": [...]}` where each node has `{self_ref, level, title, position, page_numbers, item_range: [start, end_exclusive], children}`. Slice `items.jsonl` by `item_range` to read a section's contents. PDFs typically produce a flat sibling list; HTML/markdown produce a real tree. `tree: []` for docs with no headers.
|
||
|
||
### Cross-referencing search results with items
|
||
Search results include `doc_item_refs` (e.g. `["#/texts/48", "#/tables/0"]`) that correspond to `self_ref` values in items.jsonl. Resolve each ref to a `position`, then walk `toc.json` to find the deepest node whose `item_range` contains it — that's the section the hit lives in.
|
||
|
||
## Strategy
|
||
|
||
1. Search first.
|
||
2. If the top results contain the answer, call `cite` with the supporting chunk_ids and write a concise answer.
|
||
3. Reach for `execute_code` when search results are insufficient or when the task requires computation, aggregation, traversal across documents, or section-scoped reading. From inside code you can search again with different terms, or read `items.jsonl` / `toc.json` / `content.txt` directly from the document filesystem.
|
||
4. Call `cite` with the chunk_ids that ground your answer before writing the final response.
|
||
|
||
You MUST call `cite` with at least one chunk ID before producing your final answer, **unless** you are refusing for lack of information. Answers without citations are considered ungrounded. In a refusal case do **not** call `cite` — there is nothing to cite.
|
||
|
||
## Important
|
||
|
||
- Variables persist between `execute_code` calls — you can search in one call and process results in the next
|
||
- Use `print()` to output results — the output is your only feedback
|
||
- When you write code, execute it — don't describe what code would do. But not every question needs code; simple lookups are best answered by `search → cite`.
|
||
- Use `await` for all async functions inside execute_code (search, list_documents)
|
||
- Use `Path.read_text()` to read files — do NOT use `open()`, `with` statements, or `collections` module
|
||
- Do NOT include chunk IDs or UUIDs in your answer text — your answer should read naturally. Use the `cite` tool separately to register citations.
|
||
|
||
## Guidelines
|
||
|
||
- Follow the skill instructions carefully
|
||
- Stay focused on the specific task described above
|
||
- Provide a clear, complete result
|
||
role: system
|
||
- content: How many documents are in the database?
|
||
role: user
|
||
model: gpt-oss
|
||
reasoning_effort: high
|
||
stream: true
|
||
stream_options:
|
||
include_usage: true
|
||
temperature: 0.3
|
||
tool_choice: auto
|
||
tools:
|
||
- function:
|
||
description: |-
|
||
Search the knowledge base using hybrid search (vector + full-text).
|
||
|
||
Returns ranked results with content and metadata. When picture
|
||
content is in the result set and the driving skill model is
|
||
vision-capable, picture bytes are attached as ``BinaryContent``
|
||
parts so the model sees figures alongside text.
|
||
name: search
|
||
parameters:
|
||
additionalProperties: false
|
||
properties:
|
||
limit:
|
||
anyOf:
|
||
- type: integer
|
||
- type: 'null'
|
||
default: null
|
||
description: Maximum number of results.
|
||
query:
|
||
description: The search query.
|
||
type: string
|
||
required:
|
||
- query
|
||
type: object
|
||
type: function
|
||
- function:
|
||
description: |-
|
||
Execute Python code in a sandboxed interpreter.
|
||
|
||
The code has access to search() and list_documents() functions
|
||
and a virtual filesystem at /documents/ with document content
|
||
and structure (metadata.json, content.txt, items.jsonl, toc.json
|
||
per document).
|
||
|
||
Use print() to output results. Variables persist between calls
|
||
within the same skill invocation.
|
||
name: execute_code
|
||
parameters:
|
||
additionalProperties: false
|
||
properties:
|
||
code:
|
||
description: Python code to execute.
|
||
type: string
|
||
required:
|
||
- code
|
||
type: object
|
||
type: function
|
||
- function:
|
||
description: |-
|
||
Register chunk IDs as citations for your answer.
|
||
|
||
Call this after searching, with the chunk_id values from search
|
||
results that support your answer.
|
||
name: cite
|
||
parameters:
|
||
additionalProperties: false
|
||
properties:
|
||
chunk_ids:
|
||
description: List of chunk_id values from search results.
|
||
items:
|
||
type: string
|
||
type: array
|
||
required:
|
||
- chunk_ids
|
||
type: object
|
||
type: function
|
||
uri: http://localhost:11434/v1/chat/completions
|
||
response:
|
||
body:
|
||
string: |+
|
||
data: {"id":"chatcmpl-109","object":"chat.completion.chunk","created":1779175626,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":"The"},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-109","object":"chat.completion.chunk","created":1779175626,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":" user"},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-109","object":"chat.completion.chunk","created":1779175626,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":" asks"},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-109","object":"chat.completion.chunk","created":1779175626,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":":"},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-109","object":"chat.completion.chunk","created":1779175626,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":" \""},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-109","object":"chat.completion.chunk","created":1779175627,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":"How"},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-109","object":"chat.completion.chunk","created":1779175627,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":" many"},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-109","object":"chat.completion.chunk","created":1779175627,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":" documents"},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-109","object":"chat.completion.chunk","created":1779175627,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":" are"},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-109","object":"chat.completion.chunk","created":1779175627,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":" in"},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-109","object":"chat.completion.chunk","created":1779175627,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":" the"},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-109","object":"chat.completion.chunk","created":1779175627,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":" database"},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-109","object":"chat.completion.chunk","created":1779175627,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":"?\""},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-109","object":"chat.completion.chunk","created":1779175627,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":" We"},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-109","object":"chat.completion.chunk","created":1779175627,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":" need"},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-109","object":"chat.completion.chunk","created":1779175627,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":" to"},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-109","object":"chat.completion.chunk","created":1779175627,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":" answer"},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-109","object":"chat.completion.chunk","created":1779175627,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":" how"},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-109","object":"chat.completion.chunk","created":1779175627,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":" many"},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-109","object":"chat.completion.chunk","created":1779175627,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":" documents"},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-109","object":"chat.completion.chunk","created":1779175627,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":" exist"},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-109","object":"chat.completion.chunk","created":1779175627,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":" in"},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-109","object":"chat.completion.chunk","created":1779175628,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":" the"},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-109","object":"chat.completion.chunk","created":1779175628,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":" knowledge"},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-109","object":"chat.completion.chunk","created":1779175628,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":" base"},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-109","object":"chat.completion.chunk","created":1779175628,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":"."},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-109","object":"chat.completion.chunk","created":1779175628,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":" We"},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-109","object":"chat.completion.chunk","created":1779175628,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":" need"},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-109","object":"chat.completion.chunk","created":1779175628,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":" to"},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-109","object":"chat.completion.chunk","created":1779175628,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":" provide"},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-109","object":"chat.completion.chunk","created":1779175628,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":" a"},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-109","object":"chat.completion.chunk","created":1779175628,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":" citation"},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-109","object":"chat.completion.chunk","created":1779175628,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":"."},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-109","object":"chat.completion.chunk","created":1779175628,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":" We"},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-109","object":"chat.completion.chunk","created":1779175628,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":" can"},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-109","object":"chat.completion.chunk","created":1779175628,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":" use"},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-109","object":"chat.completion.chunk","created":1779175628,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":" list"},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-109","object":"chat.completion.chunk","created":1779175628,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":"_documents"},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-109","object":"chat.completion.chunk","created":1779175628,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":"()"},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-109","object":"chat.completion.chunk","created":1779175628,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":" to"},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-109","object":"chat.completion.chunk","created":1779175628,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":" get"},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-109","object":"chat.completion.chunk","created":1779175628,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":" the"},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-109","object":"chat.completion.chunk","created":1779175628,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":" list"},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-109","object":"chat.completion.chunk","created":1779175628,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":" of"},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-109","object":"chat.completion.chunk","created":1779175628,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":" documents"},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-109","object":"chat.completion.chunk","created":1779175628,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":"."},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-109","object":"chat.completion.chunk","created":1779175628,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":" That"},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-109","object":"chat.completion.chunk","created":1779175628,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":" returns"},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-109","object":"chat.completion.chunk","created":1779175628,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":" a"},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-109","object":"chat.completion.chunk","created":1779175628,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":" list"},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-109","object":"chat.completion.chunk","created":1779175628,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":" of"},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-109","object":"chat.completion.chunk","created":1779175628,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":" dict"},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-109","object":"chat.completion.chunk","created":1779175628,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":"s"},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-109","object":"chat.completion.chunk","created":1779175628,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":" with"},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-109","object":"chat.completion.chunk","created":1779175628,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":" keys"},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-109","object":"chat.completion.chunk","created":1779175628,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":":"},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-109","object":"chat.completion.chunk","created":1779175628,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":" id"},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-109","object":"chat.completion.chunk","created":1779175628,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":","},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-109","object":"chat.completion.chunk","created":1779175628,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":" title"},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-109","object":"chat.completion.chunk","created":1779175628,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":","},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-109","object":"chat.completion.chunk","created":1779175628,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":" uri"},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-109","object":"chat.completion.chunk","created":1779175628,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":","},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-109","object":"chat.completion.chunk","created":1779175628,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":" created"},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-109","object":"chat.completion.chunk","created":1779175628,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":"_at"},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-109","object":"chat.completion.chunk","created":1779175628,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":"."},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-109","object":"chat.completion.chunk","created":1779175628,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":" We"},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-109","object":"chat.completion.chunk","created":1779175628,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":" can"},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-109","object":"chat.completion.chunk","created":1779175628,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":" count"},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-109","object":"chat.completion.chunk","created":1779175628,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":" them"},"finish_reason":null}]}
|
||
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
|
||
index: 0
|
||
object: embedding
|
||
model: qwen3-embedding:4b
|
||
object: list
|
||
usage:
|
||
prompt_tokens: 3
|
||
total_tokens: 3
|
||
status:
|
||
code: 200
|
||
message: OK
|
||
- request:
|
||
headers:
|
||
accept:
|
||
- application/json
|
||
accept-encoding:
|
||
- gzip, deflate, zstd
|
||
connection:
|
||
- keep-alive
|
||
content-length:
|
||
- '13001'
|
||
content-type:
|
||
- application/json
|
||
host:
|
||
- localhost:11434
|
||
method: POST
|
||
parsed_body:
|
||
messages:
|
||
- content: |-
|
||
You are a focused execution agent. Complete the following task using the skills and instructions provided.
|
||
|
||
## Task
|
||
|
||
How many documents are in the database?
|
||
|
||
## Skill instructions
|
||
|
||
# Analysis
|
||
|
||
You answer questions over a document knowledge base. Most questions are answered directly with `search → cite → answer`. Reach for `execute_code` when a question requires computation, aggregation, or structural traversal that a single search cannot deliver.
|
||
|
||
## Tools
|
||
|
||
### execute_code
|
||
Execute Python code in a sandboxed interpreter. Variables persist between calls — you can build state incrementally. Use `print()` to output results.
|
||
|
||
Inside the code, these functions are available (use `await`):
|
||
- `await search(query, limit=10)` → list of dicts with keys: chunk_id, content, document_id, document_title, document_uri, score, page_numbers, headings, doc_item_refs, labels, picture_refs (subset of doc_item_refs labeled `picture`)
|
||
- `await list_documents()` → list of dicts with keys: id, title, uri, created_at
|
||
|
||
Available modules: `json`, `re`, `math`, `pathlib`
|
||
Not supported: class definitions, generators/yield, match statements, decorators, `with` statements
|
||
|
||
### search
|
||
Search the knowledge base directly (outside code execution). Each result has a `Type:` (paragraph, table, code, list_item, picture). When the Type is `picture`, the corresponding figure may also be attached to the tool response as an image alongside the text — use it directly to answer questions about figures, diagrams, charts, screenshots.
|
||
|
||
### cite
|
||
Register the chunk IDs that ground your answer. Call this BEFORE writing your final answer, with the `chunk_id` values from search results (from either the `search` tool or `await search(...)` inside `execute_code`) that support each claim. Every answer that uses search results must be backed by `cite`.
|
||
|
||
Use chunk_ids exactly as they appear in the search response — copy the full UUID verbatim. Do not abbreviate, paraphrase, or reconstruct chunk_ids from memory; the tool matches them as opaque strings.
|
||
|
||
## Document Filesystem (inside execute_code)
|
||
|
||
All documents are mounted as a virtual filesystem at `/documents/`:
|
||
|
||
```
|
||
/documents/{document_id}/
|
||
metadata.json # {"id", "title", "uri", "created_at"}
|
||
content.txt # Full document text
|
||
items.jsonl # Structured items (one JSON object per line)
|
||
toc.json # Section tree derived from heading_level
|
||
```
|
||
|
||
`{document_id}` is an internal identifier, not the user-facing `uri` (filename, URL, etc.). When you only know a document by its URI or title, use `await list_documents()` to enumerate ids, or read `metadata.json` from each directory and match against `uri` / `title`.
|
||
|
||
### Reading files
|
||
Always use `Path.read_text()` — do NOT use `open()` or `with` statements (they are not supported).
|
||
|
||
```python
|
||
from pathlib import Path
|
||
import json
|
||
|
||
# Discover documents
|
||
for doc_dir in Path('/documents').iterdir():
|
||
meta = json.loads((doc_dir / 'metadata.json').read_text())
|
||
print(meta['title'])
|
||
|
||
# Read full text
|
||
content = Path(f'/documents/{doc_id}/content.txt').read_text()
|
||
|
||
# Read and parse items
|
||
for line in Path(f'/documents/{doc_id}/items.jsonl').read_text().strip().split(chr(10)):
|
||
item = json.loads(line)
|
||
if item['label'] == 'table':
|
||
print(item['text'][:200])
|
||
```
|
||
|
||
### metadata.json
|
||
Document metadata: `id`, `title`, `uri`, `created_at`.
|
||
|
||
### content.txt
|
||
Full text content. Use for regex or keyword search across a whole document.
|
||
|
||
### items.jsonl
|
||
Structured document items. Each line is a JSON object with:
|
||
- `position`: sequential position in the document
|
||
- `self_ref`: item reference (e.g. "#/texts/5", "#/tables/0")
|
||
- `label`: item type — "section_header", "text", "table", "list_item", "caption", "formula", "picture", "code", "footnote"
|
||
- `text`: rendered content (tables are markdown with `|` columns)
|
||
- `page_numbers`: list of page numbers where the item appears
|
||
- `heading_level`: H-level (1–6) for `section_header` items, `0` otherwise. PDFs often collapse to `1` for every header.
|
||
- `tree_depth`: DOM nesting depth — varies meaningfully on HTML, near-uniform on PDFs.
|
||
|
||
### toc.json
|
||
Section tree derived from `heading_level`: `{"doc_id", "title", "tree": [...]}` where each node has `{self_ref, level, title, position, page_numbers, item_range: [start, end_exclusive], children}`. Slice `items.jsonl` by `item_range` to read a section's contents. PDFs typically produce a flat sibling list; HTML/markdown produce a real tree. `tree: []` for docs with no headers.
|
||
|
||
### Cross-referencing search results with items
|
||
Search results include `doc_item_refs` (e.g. `["#/texts/48", "#/tables/0"]`) that correspond to `self_ref` values in items.jsonl. Resolve each ref to a `position`, then walk `toc.json` to find the deepest node whose `item_range` contains it — that's the section the hit lives in.
|
||
|
||
## Strategy
|
||
|
||
1. Search first.
|
||
2. If the top results contain the answer, call `cite` with the supporting chunk_ids and write a concise answer.
|
||
3. Reach for `execute_code` when search results are insufficient or when the task requires computation, aggregation, traversal across documents, or section-scoped reading. From inside code you can search again with different terms, or read `items.jsonl` / `toc.json` / `content.txt` directly from the document filesystem.
|
||
4. Call `cite` with the chunk_ids that ground your answer before writing the final response.
|
||
|
||
You MUST call `cite` with at least one chunk ID before producing your final answer, **unless** you are refusing for lack of information. Answers without citations are considered ungrounded. In a refusal case do **not** call `cite` — there is nothing to cite.
|
||
|
||
## Important
|
||
|
||
- Variables persist between `execute_code` calls — you can search in one call and process results in the next
|
||
- Use `print()` to output results — the output is your only feedback
|
||
- When you write code, execute it — don't describe what code would do. But not every question needs code; simple lookups are best answered by `search → cite`.
|
||
- Use `await` for all async functions inside execute_code (search, list_documents)
|
||
- Use `Path.read_text()` to read files — do NOT use `open()`, `with` statements, or `collections` module
|
||
- Do NOT include chunk IDs or UUIDs in your answer text — your answer should read naturally. Use the `cite` tool separately to register citations.
|
||
|
||
## Guidelines
|
||
|
||
- Follow the skill instructions carefully
|
||
- Stay focused on the specific task described above
|
||
- Provide a clear, complete result
|
||
role: system
|
||
- content: How many documents are in the database?
|
||
role: user
|
||
- content: null
|
||
reasoning: |-
|
||
The user asks: "How many documents are in the database?" We need to answer how many documents exist in the knowledge base. We need to provide a citation. We can use list_documents() to get the list of documents. That returns a list of dicts with keys: id, title, uri, created_at. We can count them. We need to call execute_code to run Python code that calls await list_documents() and prints the count. Then we need to cite the chunk IDs that support the answer. But list_documents() returns a list of documents, but not chunk IDs. The search results have chunk IDs. But the question is about the number of documents in the database. The answer is a count. We can compute that. But we need to cite something. We can cite the list_documents() result? The guidelines: "Use cite to register chunk IDs that ground your answer." But list_documents() returns no chunk IDs. We need to search for a chunk that says something like "There are X documents" or "The database contains X documents". But likely there is no such chunk. We can compute the number. But we still need to provide a citation. We could search for "number of documents" or "documents in the database" to find a chunk that states the number. But it's unlikely to exist. We could search for "documents" and see if any chunk mentions the count. But maybe the database is small. We can search for "documents" and see if any chunk mentions the count. But we need to provide a citation. The guidelines: "You MUST call cite with at least one chunk ID before producing your final answer, unless you are refusing for lack of information." So we need to find at least one chunk ID that supports the answer. We can search for "documents" or "document count" or "database" or "list_documents" or something. But the search tool returns chunk IDs. We can search for "documents" and see if any chunk mentions the count. But we might not find a chunk that says the count. But we can still compute the count. But we need a citation. We could search for "list_documents" or "list_documents()" or "list_documents" maybe there's a chunk that references the function. But that might not mention the count. We could search for "documents" and see if any chunk contains the phrase "There are". But we need to find a chunk that states the number. If we can't find such a chunk, we might have to refuse? But the guidelines say we must cite. But we can compute the count. But we still need a citation. We could search for "documents" and find a chunk that says "The database contains X documents." If we can't find that, maybe we can search for "documents" and find a chunk that lists the documents. But that might not mention the count. But we can still compute the count. But we need a citation. We could cite the chunk that lists the documents. But that chunk might not mention the count. But we can still cite it as a source that we used to compute the count. But the guidelines: "Use cite to register chunk IDs that ground your answer." The answer is the count. The chunk that lists the documents can be considered a source. But we need to provide the chunk ID. But we can find a chunk that lists the documents. But we need to search for something like "document list" or "list of documents" or "documents" and then find a chunk that lists them. But we can also search for "list_documents" maybe there's a chunk that says "list_documents" and returns the list. But that might not mention the count. But we can still cite that chunk. But we need to provide the chunk ID. But we can search for "list_documents" and see if any chunk contains that. But we need to find a chunk ID. Let's do that.
|
||
|
||
We need to use the search tool. Let's search for "list_documents".
|
||
role: assistant
|
||
tool_calls:
|
||
- function:
|
||
arguments: '{"query":"list_documents","limit":10}'
|
||
name: search
|
||
id: call_tfecv1h0
|
||
type: function
|
||
- content: |-
|
||
[b7d4d3a9-d3b2-4fa0-8705-e930b6183e89] [rank 1 of 3]
|
||
Source: "Doc 2"
|
||
Type: text
|
||
Content:
|
||
Second document about dogs.
|
||
|
||
---
|
||
|
||
[bbf13a07-db63-418e-8043-cba8ba9e1d95] [rank 2 of 3]
|
||
Source: "Doc 1"
|
||
Type: text
|
||
Content:
|
||
First document about cats.
|
||
|
||
---
|
||
|
||
[bdcf4476-6add-43ee-88ea-6a687260cb4a] [rank 3 of 3]
|
||
Source: "Doc 3"
|
||
Type: text
|
||
Content:
|
||
Third document about birds.
|
||
role: tool
|
||
tool_call_id: call_tfecv1h0
|
||
model: gpt-oss
|
||
reasoning_effort: high
|
||
stream: true
|
||
stream_options:
|
||
include_usage: true
|
||
temperature: 0.3
|
||
tool_choice: auto
|
||
tools:
|
||
- function:
|
||
description: |-
|
||
Search the knowledge base using hybrid search (vector + full-text).
|
||
|
||
Returns ranked results with content and metadata. When picture
|
||
content is in the result set and the driving skill model is
|
||
vision-capable, picture bytes are attached as ``BinaryContent``
|
||
parts so the model sees figures alongside text.
|
||
name: search
|
||
parameters:
|
||
additionalProperties: false
|
||
properties:
|
||
limit:
|
||
anyOf:
|
||
- type: integer
|
||
- type: 'null'
|
||
default: null
|
||
description: Maximum number of results.
|
||
query:
|
||
description: The search query.
|
||
type: string
|
||
required:
|
||
- query
|
||
type: object
|
||
type: function
|
||
- function:
|
||
description: |-
|
||
Execute Python code in a sandboxed interpreter.
|
||
|
||
The code has access to search() and list_documents() functions
|
||
and a virtual filesystem at /documents/ with document content
|
||
and structure (metadata.json, content.txt, items.jsonl, toc.json
|
||
per document).
|
||
|
||
Use print() to output results. Variables persist between calls
|
||
within the same skill invocation.
|
||
name: execute_code
|
||
parameters:
|
||
additionalProperties: false
|
||
properties:
|
||
code:
|
||
description: Python code to execute.
|
||
type: string
|
||
required:
|
||
- code
|
||
type: object
|
||
type: function
|
||
- function:
|
||
description: |-
|
||
Register chunk IDs as citations for your answer.
|
||
|
||
Call this after searching, with the chunk_id values from search
|
||
results that support your answer.
|
||
name: cite
|
||
parameters:
|
||
additionalProperties: false
|
||
properties:
|
||
chunk_ids:
|
||
description: List of chunk_id values from search results.
|
||
items:
|
||
type: string
|
||
type: array
|
||
required:
|
||
- chunk_ids
|
||
type: object
|
||
type: function
|
||
uri: http://localhost:11434/v1/chat/completions
|
||
response:
|
||
body:
|
||
string: |+
|
||
data: {"id":"chatcmpl-108","object":"chat.completion.chunk","created":1779176084,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":"The"},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-108","object":"chat.completion.chunk","created":1779176084,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":" search"},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-108","object":"chat.completion.chunk","created":1779176084,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":" returned"},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-108","object":"chat.completion.chunk","created":1779176084,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":" "},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-108","object":"chat.completion.chunk","created":1779176084,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":"3"},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-108","object":"chat.completion.chunk","created":1779176084,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":" results"},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-108","object":"chat.completion.chunk","created":1779176084,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":":"},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-108","object":"chat.completion.chunk","created":1779176084,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":" Doc"},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-108","object":"chat.completion.chunk","created":1779176084,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":" "},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-108","object":"chat.completion.chunk","created":1779176084,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":"2"},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-108","object":"chat.completion.chunk","created":1779176084,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":","},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-108","object":"chat.completion.chunk","created":1779176084,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":" Doc"},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-108","object":"chat.completion.chunk","created":1779176084,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":" "},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-108","object":"chat.completion.chunk","created":1779176084,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":"1"},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-108","object":"chat.completion.chunk","created":1779176084,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":","},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-108","object":"chat.completion.chunk","created":1779176084,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":" Doc"},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-108","object":"chat.completion.chunk","created":1779176084,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":" "},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-108","object":"chat.completion.chunk","created":1779176084,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":"3"},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-108","object":"chat.completion.chunk","created":1779176084,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":"."},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-108","object":"chat.completion.chunk","created":1779176084,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":" The"},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-108","object":"chat.completion.chunk","created":1779176084,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":" content"},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-108","object":"chat.completion.chunk","created":1779176084,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":" is"},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-108","object":"chat.completion.chunk","created":1779176084,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":" short"},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-108","object":"chat.completion.chunk","created":1779176084,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":"."},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-108","object":"chat.completion.chunk","created":1779176084,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":" But"},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-108","object":"chat.completion.chunk","created":1779176084,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":" we"},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-108","object":"chat.completion.chunk","created":1779176084,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":" need"},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-108","object":"chat.completion.chunk","created":1779176084,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":" the"},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-108","object":"chat.completion.chunk","created":1779176084,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":" number"},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-108","object":"chat.completion.chunk","created":1779176084,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":" of"},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-108","object":"chat.completion.chunk","created":1779176084,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":" documents"},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-108","object":"chat.completion.chunk","created":1779176084,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":"."},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-108","object":"chat.completion.chunk","created":1779176084,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":" We"},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-108","object":"chat.completion.chunk","created":1779176084,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":" can"},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-108","object":"chat.completion.chunk","created":1779176084,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":" count"},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-108","object":"chat.completion.chunk","created":1779176084,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":" the"},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-108","object":"chat.completion.chunk","created":1779176084,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":" results"},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-108","object":"chat.completion.chunk","created":1779176084,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":":"},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-108","object":"chat.completion.chunk","created":1779176084,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":" "},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-108","object":"chat.completion.chunk","created":1779176084,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":"3"},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-108","object":"chat.completion.chunk","created":1779176084,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":"."},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-108","object":"chat.completion.chunk","created":1779176084,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":" But"},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-108","object":"chat.completion.chunk","created":1779176084,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":" is"},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-108","object":"chat.completion.chunk","created":1779176084,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":" that"},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-108","object":"chat.completion.chunk","created":1779176084,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":" the"},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-108","object":"chat.completion.chunk","created":1779176084,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":" number"},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-108","object":"chat.completion.chunk","created":1779176084,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":" of"},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-108","object":"chat.completion.chunk","created":1779176084,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":" documents"},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-108","object":"chat.completion.chunk","created":1779176084,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":"?"},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-108","object":"chat.completion.chunk","created":1779176084,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":" Possibly"},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-108","object":"chat.completion.chunk","created":1779176084,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":"."},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-108","object":"chat.completion.chunk","created":1779176084,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":" But"},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-108","object":"chat.completion.chunk","created":1779176085,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":" we"},"finish_reason":null}]}
|
||
|
||
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method: POST
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parsed_body:
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messages:
|
||
- content: |-
|
||
You are a focused execution agent. Complete the following task using the skills and instructions provided.
|
||
|
||
## Task
|
||
|
||
How many documents are in the database?
|
||
|
||
## Skill instructions
|
||
|
||
# Analysis
|
||
|
||
You answer questions over a document knowledge base. Most questions are answered directly with `search → cite → answer`. Reach for `execute_code` when a question requires computation, aggregation, or structural traversal that a single search cannot deliver.
|
||
|
||
## Tools
|
||
|
||
### execute_code
|
||
Execute Python code in a sandboxed interpreter. Variables persist between calls — you can build state incrementally. Use `print()` to output results.
|
||
|
||
Inside the code, these functions are available (use `await`):
|
||
- `await search(query, limit=10)` → list of dicts with keys: chunk_id, content, document_id, document_title, document_uri, score, page_numbers, headings, doc_item_refs, labels, picture_refs (subset of doc_item_refs labeled `picture`)
|
||
- `await list_documents()` → list of dicts with keys: id, title, uri, created_at
|
||
|
||
Available modules: `json`, `re`, `math`, `pathlib`
|
||
Not supported: class definitions, generators/yield, match statements, decorators, `with` statements
|
||
|
||
### search
|
||
Search the knowledge base directly (outside code execution). Each result has a `Type:` (paragraph, table, code, list_item, picture). When the Type is `picture`, the corresponding figure may also be attached to the tool response as an image alongside the text — use it directly to answer questions about figures, diagrams, charts, screenshots.
|
||
|
||
### cite
|
||
Register the chunk IDs that ground your answer. Call this BEFORE writing your final answer, with the `chunk_id` values from search results (from either the `search` tool or `await search(...)` inside `execute_code`) that support each claim. Every answer that uses search results must be backed by `cite`.
|
||
|
||
Use chunk_ids exactly as they appear in the search response — copy the full UUID verbatim. Do not abbreviate, paraphrase, or reconstruct chunk_ids from memory; the tool matches them as opaque strings.
|
||
|
||
## Document Filesystem (inside execute_code)
|
||
|
||
All documents are mounted as a virtual filesystem at `/documents/`:
|
||
|
||
```
|
||
/documents/{document_id}/
|
||
metadata.json # {"id", "title", "uri", "created_at"}
|
||
content.txt # Full document text
|
||
items.jsonl # Structured items (one JSON object per line)
|
||
toc.json # Section tree derived from heading_level
|
||
```
|
||
|
||
`{document_id}` is an internal identifier, not the user-facing `uri` (filename, URL, etc.). When you only know a document by its URI or title, use `await list_documents()` to enumerate ids, or read `metadata.json` from each directory and match against `uri` / `title`.
|
||
|
||
### Reading files
|
||
Always use `Path.read_text()` — do NOT use `open()` or `with` statements (they are not supported).
|
||
|
||
```python
|
||
from pathlib import Path
|
||
import json
|
||
|
||
# Discover documents
|
||
for doc_dir in Path('/documents').iterdir():
|
||
meta = json.loads((doc_dir / 'metadata.json').read_text())
|
||
print(meta['title'])
|
||
|
||
# Read full text
|
||
content = Path(f'/documents/{doc_id}/content.txt').read_text()
|
||
|
||
# Read and parse items
|
||
for line in Path(f'/documents/{doc_id}/items.jsonl').read_text().strip().split(chr(10)):
|
||
item = json.loads(line)
|
||
if item['label'] == 'table':
|
||
print(item['text'][:200])
|
||
```
|
||
|
||
### metadata.json
|
||
Document metadata: `id`, `title`, `uri`, `created_at`.
|
||
|
||
### content.txt
|
||
Full text content. Use for regex or keyword search across a whole document.
|
||
|
||
### items.jsonl
|
||
Structured document items. Each line is a JSON object with:
|
||
- `position`: sequential position in the document
|
||
- `self_ref`: item reference (e.g. "#/texts/5", "#/tables/0")
|
||
- `label`: item type — "section_header", "text", "table", "list_item", "caption", "formula", "picture", "code", "footnote"
|
||
- `text`: rendered content (tables are markdown with `|` columns)
|
||
- `page_numbers`: list of page numbers where the item appears
|
||
- `heading_level`: H-level (1–6) for `section_header` items, `0` otherwise. PDFs often collapse to `1` for every header.
|
||
- `tree_depth`: DOM nesting depth — varies meaningfully on HTML, near-uniform on PDFs.
|
||
|
||
### toc.json
|
||
Section tree derived from `heading_level`: `{"doc_id", "title", "tree": [...]}` where each node has `{self_ref, level, title, position, page_numbers, item_range: [start, end_exclusive], children}`. Slice `items.jsonl` by `item_range` to read a section's contents. PDFs typically produce a flat sibling list; HTML/markdown produce a real tree. `tree: []` for docs with no headers.
|
||
|
||
### Cross-referencing search results with items
|
||
Search results include `doc_item_refs` (e.g. `["#/texts/48", "#/tables/0"]`) that correspond to `self_ref` values in items.jsonl. Resolve each ref to a `position`, then walk `toc.json` to find the deepest node whose `item_range` contains it — that's the section the hit lives in.
|
||
|
||
## Strategy
|
||
|
||
1. Search first.
|
||
2. If the top results contain the answer, call `cite` with the supporting chunk_ids and write a concise answer.
|
||
3. Reach for `execute_code` when search results are insufficient or when the task requires computation, aggregation, traversal across documents, or section-scoped reading. From inside code you can search again with different terms, or read `items.jsonl` / `toc.json` / `content.txt` directly from the document filesystem.
|
||
4. Call `cite` with the chunk_ids that ground your answer before writing the final response.
|
||
|
||
You MUST call `cite` with at least one chunk ID before producing your final answer, **unless** you are refusing for lack of information. Answers without citations are considered ungrounded. In a refusal case do **not** call `cite` — there is nothing to cite.
|
||
|
||
## Important
|
||
|
||
- Variables persist between `execute_code` calls — you can search in one call and process results in the next
|
||
- Use `print()` to output results — the output is your only feedback
|
||
- When you write code, execute it — don't describe what code would do. But not every question needs code; simple lookups are best answered by `search → cite`.
|
||
- Use `await` for all async functions inside execute_code (search, list_documents)
|
||
- Use `Path.read_text()` to read files — do NOT use `open()`, `with` statements, or `collections` module
|
||
- Do NOT include chunk IDs or UUIDs in your answer text — your answer should read naturally. Use the `cite` tool separately to register citations.
|
||
|
||
## Guidelines
|
||
|
||
- Follow the skill instructions carefully
|
||
- Stay focused on the specific task described above
|
||
- Provide a clear, complete result
|
||
role: system
|
||
- content: How many documents are in the database?
|
||
role: user
|
||
- content: null
|
||
reasoning: |-
|
||
The user asks: "How many documents are in the database?" We need to answer how many documents exist in the knowledge base. We need to provide a citation. We can use list_documents() to get the list of documents. That returns a list of dicts with keys: id, title, uri, created_at. We can count them. We need to call execute_code to run Python code that calls await list_documents() and prints the count. Then we need to cite the chunk IDs that support the answer. But list_documents() returns a list of documents, but not chunk IDs. The search results have chunk IDs. But the question is about the number of documents in the database. The answer is a count. We can compute that. But we need to cite something. We can cite the list_documents() result? The guidelines: "Use cite to register chunk IDs that ground your answer." But list_documents() returns no chunk IDs. We need to search for a chunk that says something like "There are X documents" or "The database contains X documents". But likely there is no such chunk. We can compute the number. But we still need to provide a citation. We could search for "number of documents" or "documents in the database" to find a chunk that states the number. But it's unlikely to exist. We could search for "documents" and see if any chunk mentions the count. But maybe the database is small. We can search for "documents" and see if any chunk mentions the count. But we need to provide a citation. The guidelines: "You MUST call cite with at least one chunk ID before producing your final answer, unless you are refusing for lack of information." So we need to find at least one chunk ID that supports the answer. We can search for "documents" or "document count" or "database" or "list_documents" or something. But the search tool returns chunk IDs. We can search for "documents" and see if any chunk mentions the count. But we might not find a chunk that says the count. But we can still compute the count. But we need a citation. We could search for "list_documents" or "list_documents()" or "list_documents" maybe there's a chunk that references the function. But that might not mention the count. We could search for "documents" and see if any chunk contains the phrase "There are". But we need to find a chunk that states the number. If we can't find such a chunk, we might have to refuse? But the guidelines say we must cite. But we can compute the count. But we still need a citation. We could search for "documents" and find a chunk that says "The database contains X documents." If we can't find that, maybe we can search for "documents" and find a chunk that lists the documents. But that might not mention the count. But we can still compute the count. But we need a citation. We could cite the chunk that lists the documents. But that chunk might not mention the count. But we can still cite it as a source that we used to compute the count. But the guidelines: "Use cite to register chunk IDs that ground your answer." The answer is the count. The chunk that lists the documents can be considered a source. But we need to provide the chunk ID. But we can find a chunk that lists the documents. But we need to search for something like "document list" or "list of documents" or "documents" and then find a chunk that lists them. But we can also search for "list_documents" maybe there's a chunk that says "list_documents" and returns the list. But that might not mention the count. But we can still cite that chunk. But we need to provide the chunk ID. But we can search for "list_documents" and see if any chunk contains that. But we need to find a chunk ID. Let's do that.
|
||
|
||
We need to use the search tool. Let's search for "list_documents".
|
||
role: assistant
|
||
tool_calls:
|
||
- function:
|
||
arguments: '{"query":"list_documents","limit":10}'
|
||
name: search
|
||
id: call_tfecv1h0
|
||
type: function
|
||
- content: |-
|
||
[b7d4d3a9-d3b2-4fa0-8705-e930b6183e89] [rank 1 of 3]
|
||
Source: "Doc 2"
|
||
Type: text
|
||
Content:
|
||
Second document about dogs.
|
||
|
||
---
|
||
|
||
[bbf13a07-db63-418e-8043-cba8ba9e1d95] [rank 2 of 3]
|
||
Source: "Doc 1"
|
||
Type: text
|
||
Content:
|
||
First document about cats.
|
||
|
||
---
|
||
|
||
[bdcf4476-6add-43ee-88ea-6a687260cb4a] [rank 3 of 3]
|
||
Source: "Doc 3"
|
||
Type: text
|
||
Content:
|
||
Third document about birds.
|
||
role: tool
|
||
tool_call_id: call_tfecv1h0
|
||
- content: null
|
||
reasoning: 'The search returned 3 results: Doc 2, Doc 1, Doc 3. The content is short. But we need the number of documents.
|
||
We can count the results: 3. But is that the number of documents? Possibly. But we need to confirm. We can also
|
||
use list_documents() to get the list. Let''s run execute_code to call await list_documents() and count.'
|
||
role: assistant
|
||
tool_calls:
|
||
- function:
|
||
arguments: '{"code":"import asyncio\nimport json\n\nasync def main():\n docs = await list_documents()\n print(len(docs))\n\nasyncio.run(main())"}'
|
||
name: execute_code
|
||
id: call_jo79tsue
|
||
type: function
|
||
- content: |
|
||
3
|
||
role: tool
|
||
tool_call_id: call_jo79tsue
|
||
model: gpt-oss
|
||
reasoning_effort: high
|
||
stream: true
|
||
stream_options:
|
||
include_usage: true
|
||
temperature: 0.3
|
||
tool_choice: auto
|
||
tools:
|
||
- function:
|
||
description: |-
|
||
Search the knowledge base using hybrid search (vector + full-text).
|
||
|
||
Returns ranked results with content and metadata. When picture
|
||
content is in the result set and the driving skill model is
|
||
vision-capable, picture bytes are attached as ``BinaryContent``
|
||
parts so the model sees figures alongside text.
|
||
name: search
|
||
parameters:
|
||
additionalProperties: false
|
||
properties:
|
||
limit:
|
||
anyOf:
|
||
- type: integer
|
||
- type: 'null'
|
||
default: null
|
||
description: Maximum number of results.
|
||
query:
|
||
description: The search query.
|
||
type: string
|
||
required:
|
||
- query
|
||
type: object
|
||
type: function
|
||
- function:
|
||
description: |-
|
||
Execute Python code in a sandboxed interpreter.
|
||
|
||
The code has access to search() and list_documents() functions
|
||
and a virtual filesystem at /documents/ with document content
|
||
and structure (metadata.json, content.txt, items.jsonl, toc.json
|
||
per document).
|
||
|
||
Use print() to output results. Variables persist between calls
|
||
within the same skill invocation.
|
||
name: execute_code
|
||
parameters:
|
||
additionalProperties: false
|
||
properties:
|
||
code:
|
||
description: Python code to execute.
|
||
type: string
|
||
required:
|
||
- code
|
||
type: object
|
||
type: function
|
||
- function:
|
||
description: |-
|
||
Register chunk IDs as citations for your answer.
|
||
|
||
Call this after searching, with the chunk_id values from search
|
||
results that support your answer.
|
||
name: cite
|
||
parameters:
|
||
additionalProperties: false
|
||
properties:
|
||
chunk_ids:
|
||
description: List of chunk_id values from search results.
|
||
items:
|
||
type: string
|
||
type: array
|
||
required:
|
||
- chunk_ids
|
||
type: object
|
||
type: function
|
||
uri: http://localhost:11434/v1/chat/completions
|
||
response:
|
||
body:
|
||
string: |+
|
||
data: {"id":"chatcmpl-98","object":"chat.completion.chunk","created":1779176159,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":"The"},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-98","object":"chat.completion.chunk","created":1779176159,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":" output"},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-98","object":"chat.completion.chunk","created":1779176159,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":" is"},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-98","object":"chat.completion.chunk","created":1779176159,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":" "},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-98","object":"chat.completion.chunk","created":1779176159,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":"3"},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-98","object":"chat.completion.chunk","created":1779176159,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":"."},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-98","object":"chat.completion.chunk","created":1779176159,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":" So"},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-98","object":"chat.completion.chunk","created":1779176159,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":" there"},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-98","object":"chat.completion.chunk","created":1779176159,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":" are"},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-98","object":"chat.completion.chunk","created":1779176159,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":" "},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-98","object":"chat.completion.chunk","created":1779176159,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":"3"},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-98","object":"chat.completion.chunk","created":1779176159,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":" documents"},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-98","object":"chat.completion.chunk","created":1779176159,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":"."},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-98","object":"chat.completion.chunk","created":1779176159,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":" We"},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-98","object":"chat.completion.chunk","created":1779176159,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":" need"},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-98","object":"chat.completion.chunk","created":1779176160,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":" to"},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-98","object":"chat.completion.chunk","created":1779176160,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":" cite"},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-98","object":"chat.completion.chunk","created":1779176160,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":"."},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-98","object":"chat.completion.chunk","created":1779176160,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":" We"},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-98","object":"chat.completion.chunk","created":1779176160,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":" can"},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-98","object":"chat.completion.chunk","created":1779176160,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":" cite"},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-98","object":"chat.completion.chunk","created":1779176160,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":" the"},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-98","object":"chat.completion.chunk","created":1779176160,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":" chunk"},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-98","object":"chat.completion.chunk","created":1779176160,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":" IDs"},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-98","object":"chat.completion.chunk","created":1779176160,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":" from"},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-98","object":"chat.completion.chunk","created":1779176160,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":" the"},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-98","object":"chat.completion.chunk","created":1779176160,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":" search"},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-98","object":"chat.completion.chunk","created":1779176160,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":" results"},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-98","object":"chat.completion.chunk","created":1779176160,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":"."},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-98","object":"chat.completion.chunk","created":1779176160,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":" But"},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-98","object":"chat.completion.chunk","created":1779176160,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":" we"},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-98","object":"chat.completion.chunk","created":1779176160,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":" might"},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-98","object":"chat.completion.chunk","created":1779176160,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":" also"},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-98","object":"chat.completion.chunk","created":1779176160,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":" cite"},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-98","object":"chat.completion.chunk","created":1779176160,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":" the"},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-98","object":"chat.completion.chunk","created":1779176160,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":" result"},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-98","object":"chat.completion.chunk","created":1779176160,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":" from"},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-98","object":"chat.completion.chunk","created":1779176160,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":" list"},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-98","object":"chat.completion.chunk","created":1779176160,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":"_documents"},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-98","object":"chat.completion.chunk","created":1779176160,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":"()"},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-98","object":"chat.completion.chunk","created":1779176160,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":" but"},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-98","object":"chat.completion.chunk","created":1779176160,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":" that"},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-98","object":"chat.completion.chunk","created":1779176160,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":" doesn't"},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-98","object":"chat.completion.chunk","created":1779176160,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":" have"},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-98","object":"chat.completion.chunk","created":1779176160,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":" chunk"},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-98","object":"chat.completion.chunk","created":1779176160,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":" IDs"},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-98","object":"chat.completion.chunk","created":1779176160,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":"."},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-98","object":"chat.completion.chunk","created":1779176160,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":" We"},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-98","object":"chat.completion.chunk","created":1779176160,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":" need"},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-98","object":"chat.completion.chunk","created":1779176160,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":" at"},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-98","object":"chat.completion.chunk","created":1779176160,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":" least"},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-98","object":"chat.completion.chunk","created":1779176160,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":" one"},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-98","object":"chat.completion.chunk","created":1779176160,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":" chunk"},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-98","object":"chat.completion.chunk","created":1779176160,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":" ID"},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-98","object":"chat.completion.chunk","created":1779176160,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":"."},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-98","object":"chat.completion.chunk","created":1779176160,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":" The"},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-98","object":"chat.completion.chunk","created":1779176160,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":" search"},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-98","object":"chat.completion.chunk","created":1779176160,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":" results"},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-98","object":"chat.completion.chunk","created":1779176160,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":" gave"},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-98","object":"chat.completion.chunk","created":1779176160,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":" chunk"},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-98","object":"chat.completion.chunk","created":1779176160,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":" IDs"},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-98","object":"chat.completion.chunk","created":1779176160,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":":"},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-98","object":"chat.completion.chunk","created":1779176160,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":" b"},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-98","object":"chat.completion.chunk","created":1779176160,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":"7"},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-98","object":"chat.completion.chunk","created":1779176160,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":"d"},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-98","object":"chat.completion.chunk","created":1779176161,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":"4"},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-98","object":"chat.completion.chunk","created":1779176161,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":"d"},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-98","object":"chat.completion.chunk","created":1779176161,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":"3"},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-98","object":"chat.completion.chunk","created":1779176161,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":"a"},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-98","object":"chat.completion.chunk","created":1779176161,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":"9"},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-98","object":"chat.completion.chunk","created":1779176161,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":"-d"},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-98","object":"chat.completion.chunk","created":1779176161,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":"3"},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-98","object":"chat.completion.chunk","created":1779176161,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":"b"},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-98","object":"chat.completion.chunk","created":1779176161,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":"2"},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-98","object":"chat.completion.chunk","created":1779176161,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":"-"},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-98","object":"chat.completion.chunk","created":1779176161,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":"4"},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-98","object":"chat.completion.chunk","created":1779176161,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":"fa"},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-98","object":"chat.completion.chunk","created":1779176161,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":"0"},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-98","object":"chat.completion.chunk","created":1779176161,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":"-"},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-98","object":"chat.completion.chunk","created":1779176161,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":"870"},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-98","object":"chat.completion.chunk","created":1779176161,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":"5"},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-98","object":"chat.completion.chunk","created":1779176161,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":"-e"},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-98","object":"chat.completion.chunk","created":1779176161,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":"930"},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-98","object":"chat.completion.chunk","created":1779176161,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":"b"},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-98","object":"chat.completion.chunk","created":1779176161,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":"618"},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-98","object":"chat.completion.chunk","created":1779176161,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":"3"},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-98","object":"chat.completion.chunk","created":1779176161,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":"e"},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-98","object":"chat.completion.chunk","created":1779176161,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":"89"},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-98","object":"chat.completion.chunk","created":1779176161,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":","},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-98","object":"chat.completion.chunk","created":1779176161,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":" b"},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-98","object":"chat.completion.chunk","created":1779176161,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":"bf"},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-98","object":"chat.completion.chunk","created":1779176161,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning":"13"},"finish_reason":null}]}
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|
||
- content: |-
|
||
You are a focused execution agent. Complete the following task using the skills and instructions provided.
|
||
|
||
## Task
|
||
|
||
How many documents are in the database?
|
||
|
||
## Skill instructions
|
||
|
||
# Analysis
|
||
|
||
You answer questions over a document knowledge base. Most questions are answered directly with `search → cite → answer`. Reach for `execute_code` when a question requires computation, aggregation, or structural traversal that a single search cannot deliver.
|
||
|
||
## Tools
|
||
|
||
### execute_code
|
||
Execute Python code in a sandboxed interpreter. Variables persist between calls — you can build state incrementally. Use `print()` to output results.
|
||
|
||
Inside the code, these functions are available (use `await`):
|
||
- `await search(query, limit=10)` → list of dicts with keys: chunk_id, content, document_id, document_title, document_uri, score, page_numbers, headings, doc_item_refs, labels, picture_refs (subset of doc_item_refs labeled `picture`)
|
||
- `await list_documents()` → list of dicts with keys: id, title, uri, created_at
|
||
|
||
Available modules: `json`, `re`, `math`, `pathlib`
|
||
Not supported: class definitions, generators/yield, match statements, decorators, `with` statements
|
||
|
||
### search
|
||
Search the knowledge base directly (outside code execution). Each result has a `Type:` (paragraph, table, code, list_item, picture). When the Type is `picture`, the corresponding figure may also be attached to the tool response as an image alongside the text — use it directly to answer questions about figures, diagrams, charts, screenshots.
|
||
|
||
### cite
|
||
Register the chunk IDs that ground your answer. Call this BEFORE writing your final answer, with the `chunk_id` values from search results (from either the `search` tool or `await search(...)` inside `execute_code`) that support each claim. Every answer that uses search results must be backed by `cite`.
|
||
|
||
Use chunk_ids exactly as they appear in the search response — copy the full UUID verbatim. Do not abbreviate, paraphrase, or reconstruct chunk_ids from memory; the tool matches them as opaque strings.
|
||
|
||
## Document Filesystem (inside execute_code)
|
||
|
||
All documents are mounted as a virtual filesystem at `/documents/`:
|
||
|
||
```
|
||
/documents/{document_id}/
|
||
metadata.json # {"id", "title", "uri", "created_at"}
|
||
content.txt # Full document text
|
||
items.jsonl # Structured items (one JSON object per line)
|
||
toc.json # Section tree derived from heading_level
|
||
```
|
||
|
||
`{document_id}` is an internal identifier, not the user-facing `uri` (filename, URL, etc.). When you only know a document by its URI or title, use `await list_documents()` to enumerate ids, or read `metadata.json` from each directory and match against `uri` / `title`.
|
||
|
||
### Reading files
|
||
Always use `Path.read_text()` — do NOT use `open()` or `with` statements (they are not supported).
|
||
|
||
```python
|
||
from pathlib import Path
|
||
import json
|
||
|
||
# Discover documents
|
||
for doc_dir in Path('/documents').iterdir():
|
||
meta = json.loads((doc_dir / 'metadata.json').read_text())
|
||
print(meta['title'])
|
||
|
||
# Read full text
|
||
content = Path(f'/documents/{doc_id}/content.txt').read_text()
|
||
|
||
# Read and parse items
|
||
for line in Path(f'/documents/{doc_id}/items.jsonl').read_text().strip().split(chr(10)):
|
||
item = json.loads(line)
|
||
if item['label'] == 'table':
|
||
print(item['text'][:200])
|
||
```
|
||
|
||
### metadata.json
|
||
Document metadata: `id`, `title`, `uri`, `created_at`.
|
||
|
||
### content.txt
|
||
Full text content. Use for regex or keyword search across a whole document.
|
||
|
||
### items.jsonl
|
||
Structured document items. Each line is a JSON object with:
|
||
- `position`: sequential position in the document
|
||
- `self_ref`: item reference (e.g. "#/texts/5", "#/tables/0")
|
||
- `label`: item type — "section_header", "text", "table", "list_item", "caption", "formula", "picture", "code", "footnote"
|
||
- `text`: rendered content (tables are markdown with `|` columns)
|
||
- `page_numbers`: list of page numbers where the item appears
|
||
- `heading_level`: H-level (1–6) for `section_header` items, `0` otherwise. PDFs often collapse to `1` for every header.
|
||
- `tree_depth`: DOM nesting depth — varies meaningfully on HTML, near-uniform on PDFs.
|
||
|
||
### toc.json
|
||
Section tree derived from `heading_level`: `{"doc_id", "title", "tree": [...]}` where each node has `{self_ref, level, title, position, page_numbers, item_range: [start, end_exclusive], children}`. Slice `items.jsonl` by `item_range` to read a section's contents. PDFs typically produce a flat sibling list; HTML/markdown produce a real tree. `tree: []` for docs with no headers.
|
||
|
||
### Cross-referencing search results with items
|
||
Search results include `doc_item_refs` (e.g. `["#/texts/48", "#/tables/0"]`) that correspond to `self_ref` values in items.jsonl. Resolve each ref to a `position`, then walk `toc.json` to find the deepest node whose `item_range` contains it — that's the section the hit lives in.
|
||
|
||
## Strategy
|
||
|
||
1. Search first.
|
||
2. If the top results contain the answer, call `cite` with the supporting chunk_ids and write a concise answer.
|
||
3. Reach for `execute_code` when search results are insufficient or when the task requires computation, aggregation, traversal across documents, or section-scoped reading. From inside code you can search again with different terms, or read `items.jsonl` / `toc.json` / `content.txt` directly from the document filesystem.
|
||
4. Call `cite` with the chunk_ids that ground your answer before writing the final response.
|
||
|
||
You MUST call `cite` with at least one chunk ID before producing your final answer, **unless** you are refusing for lack of information. Answers without citations are considered ungrounded. In a refusal case do **not** call `cite` — there is nothing to cite.
|
||
|
||
## Important
|
||
|
||
- Variables persist between `execute_code` calls — you can search in one call and process results in the next
|
||
- Use `print()` to output results — the output is your only feedback
|
||
- When you write code, execute it — don't describe what code would do. But not every question needs code; simple lookups are best answered by `search → cite`.
|
||
- Use `await` for all async functions inside execute_code (search, list_documents)
|
||
- Use `Path.read_text()` to read files — do NOT use `open()`, `with` statements, or `collections` module
|
||
- Do NOT include chunk IDs or UUIDs in your answer text — your answer should read naturally. Use the `cite` tool separately to register citations.
|
||
|
||
## Guidelines
|
||
|
||
- Follow the skill instructions carefully
|
||
- Stay focused on the specific task described above
|
||
- Provide a clear, complete result
|
||
role: system
|
||
- content: How many documents are in the database?
|
||
role: user
|
||
- content: null
|
||
reasoning: |-
|
||
The user asks: "How many documents are in the database?" We need to answer how many documents exist in the knowledge base. We need to provide a citation. We can use list_documents() to get the list of documents. That returns a list of dicts with keys: id, title, uri, created_at. We can count them. We need to call execute_code to run Python code that calls await list_documents() and prints the count. Then we need to cite the chunk IDs that support the answer. But list_documents() returns a list of documents, but not chunk IDs. The search results have chunk IDs. But the question is about the number of documents in the database. The answer is a count. We can compute that. But we need to cite something. We can cite the list_documents() result? The guidelines: "Use cite to register chunk IDs that ground your answer." But list_documents() returns no chunk IDs. We need to search for a chunk that says something like "There are X documents" or "The database contains X documents". But likely there is no such chunk. We can compute the number. But we still need to provide a citation. We could search for "number of documents" or "documents in the database" to find a chunk that states the number. But it's unlikely to exist. We could search for "documents" and see if any chunk mentions the count. But maybe the database is small. We can search for "documents" and see if any chunk mentions the count. But we need to provide a citation. The guidelines: "You MUST call cite with at least one chunk ID before producing your final answer, unless you are refusing for lack of information." So we need to find at least one chunk ID that supports the answer. We can search for "documents" or "document count" or "database" or "list_documents" or something. But the search tool returns chunk IDs. We can search for "documents" and see if any chunk mentions the count. But we might not find a chunk that says the count. But we can still compute the count. But we need a citation. We could search for "list_documents" or "list_documents()" or "list_documents" maybe there's a chunk that references the function. But that might not mention the count. We could search for "documents" and see if any chunk contains the phrase "There are". But we need to find a chunk that states the number. If we can't find such a chunk, we might have to refuse? But the guidelines say we must cite. But we can compute the count. But we still need a citation. We could search for "documents" and find a chunk that says "The database contains X documents." If we can't find that, maybe we can search for "documents" and find a chunk that lists the documents. But that might not mention the count. But we can still compute the count. But we need a citation. We could cite the chunk that lists the documents. But that chunk might not mention the count. But we can still cite it as a source that we used to compute the count. But the guidelines: "Use cite to register chunk IDs that ground your answer." The answer is the count. The chunk that lists the documents can be considered a source. But we need to provide the chunk ID. But we can find a chunk that lists the documents. But we need to search for something like "document list" or "list of documents" or "documents" and then find a chunk that lists them. But we can also search for "list_documents" maybe there's a chunk that says "list_documents" and returns the list. But that might not mention the count. But we can still cite that chunk. But we need to provide the chunk ID. But we can search for "list_documents" and see if any chunk contains that. But we need to find a chunk ID. Let's do that.
|
||
|
||
We need to use the search tool. Let's search for "list_documents".
|
||
role: assistant
|
||
tool_calls:
|
||
- function:
|
||
arguments: '{"query":"list_documents","limit":10}'
|
||
name: search
|
||
id: call_tfecv1h0
|
||
type: function
|
||
- content: |-
|
||
[b7d4d3a9-d3b2-4fa0-8705-e930b6183e89] [rank 1 of 3]
|
||
Source: "Doc 2"
|
||
Type: text
|
||
Content:
|
||
Second document about dogs.
|
||
|
||
---
|
||
|
||
[bbf13a07-db63-418e-8043-cba8ba9e1d95] [rank 2 of 3]
|
||
Source: "Doc 1"
|
||
Type: text
|
||
Content:
|
||
First document about cats.
|
||
|
||
---
|
||
|
||
[bdcf4476-6add-43ee-88ea-6a687260cb4a] [rank 3 of 3]
|
||
Source: "Doc 3"
|
||
Type: text
|
||
Content:
|
||
Third document about birds.
|
||
role: tool
|
||
tool_call_id: call_tfecv1h0
|
||
- content: null
|
||
reasoning: 'The search returned 3 results: Doc 2, Doc 1, Doc 3. The content is short. But we need the number of documents.
|
||
We can count the results: 3. But is that the number of documents? Possibly. But we need to confirm. We can also
|
||
use list_documents() to get the list. Let''s run execute_code to call await list_documents() and count.'
|
||
role: assistant
|
||
tool_calls:
|
||
- function:
|
||
arguments: '{"code":"import asyncio\nimport json\n\nasync def main():\n docs = await list_documents()\n print(len(docs))\n\nasyncio.run(main())"}'
|
||
name: execute_code
|
||
id: call_jo79tsue
|
||
type: function
|
||
- content: |
|
||
3
|
||
role: tool
|
||
tool_call_id: call_jo79tsue
|
||
- content: null
|
||
reasoning: 'The output is 3. So there are 3 documents. We need to cite. We can cite the chunk IDs from the search
|
||
results. But we might also cite the result from list_documents() but that doesn''t have chunk IDs. We need at least
|
||
one chunk ID. The search results gave chunk IDs: b7d4d3a9-d3b2-4fa0-8705-e930b6183e89, bbf13a07-db63-418e-8043-cba8ba9e1d95,
|
||
bdcf4476-6add-43ee-88ea-6a687260cb4a. We can cite these. But we need to register citations. We can call cite with
|
||
these chunk_ids. Then answer: "There are 3 documents in the database." That is the answer. Let''s do that.'
|
||
role: assistant
|
||
tool_calls:
|
||
- function:
|
||
arguments: '{"chunk_ids":["b7d4d3a9-d3b2-4fa0-8705-e930b6183e89","bbf13a07-db63-418e-8043-cba8ba9e1d95","bdcf4476-6add-43ee-88ea-6a687260cb4a"]}'
|
||
name: cite
|
||
id: call_634vzbrd
|
||
type: function
|
||
- content: Registered 3 citation(s).
|
||
role: tool
|
||
tool_call_id: call_634vzbrd
|
||
model: gpt-oss
|
||
reasoning_effort: high
|
||
stream: true
|
||
stream_options:
|
||
include_usage: true
|
||
temperature: 0.3
|
||
tool_choice: auto
|
||
tools:
|
||
- function:
|
||
description: |-
|
||
Search the knowledge base using hybrid search (vector + full-text).
|
||
|
||
Returns ranked results with content and metadata. When picture
|
||
content is in the result set and the driving skill model is
|
||
vision-capable, picture bytes are attached as ``BinaryContent``
|
||
parts so the model sees figures alongside text.
|
||
name: search
|
||
parameters:
|
||
additionalProperties: false
|
||
properties:
|
||
limit:
|
||
anyOf:
|
||
- type: integer
|
||
- type: 'null'
|
||
default: null
|
||
description: Maximum number of results.
|
||
query:
|
||
description: The search query.
|
||
type: string
|
||
required:
|
||
- query
|
||
type: object
|
||
type: function
|
||
- function:
|
||
description: |-
|
||
Execute Python code in a sandboxed interpreter.
|
||
|
||
The code has access to search() and list_documents() functions
|
||
and a virtual filesystem at /documents/ with document content
|
||
and structure (metadata.json, content.txt, items.jsonl, toc.json
|
||
per document).
|
||
|
||
Use print() to output results. Variables persist between calls
|
||
within the same skill invocation.
|
||
name: execute_code
|
||
parameters:
|
||
additionalProperties: false
|
||
properties:
|
||
code:
|
||
description: Python code to execute.
|
||
type: string
|
||
required:
|
||
- code
|
||
type: object
|
||
type: function
|
||
- function:
|
||
description: |-
|
||
Register chunk IDs as citations for your answer.
|
||
|
||
Call this after searching, with the chunk_id values from search
|
||
results that support your answer.
|
||
name: cite
|
||
parameters:
|
||
additionalProperties: false
|
||
properties:
|
||
chunk_ids:
|
||
description: List of chunk_id values from search results.
|
||
items:
|
||
type: string
|
||
type: array
|
||
required:
|
||
- chunk_ids
|
||
type: object
|
||
type: function
|
||
uri: http://localhost:11434/v1/chat/completions
|
||
response:
|
||
body:
|
||
string: |+
|
||
data: {"id":"chatcmpl-843","object":"chat.completion.chunk","created":1779176238,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"There"},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-843","object":"chat.completion.chunk","created":1779176238,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":" are"},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-843","object":"chat.completion.chunk","created":1779176238,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":" **"},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-843","object":"chat.completion.chunk","created":1779176238,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"3"},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-843","object":"chat.completion.chunk","created":1779176238,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":" documents"},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-843","object":"chat.completion.chunk","created":1779176238,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"**"},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-843","object":"chat.completion.chunk","created":1779176238,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":" in"},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-843","object":"chat.completion.chunk","created":1779176238,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":" the"},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-843","object":"chat.completion.chunk","created":1779176238,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":" database"},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-843","object":"chat.completion.chunk","created":1779176238,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":"."},"finish_reason":null}]}
|
||
|
||
data: {"id":"chatcmpl-843","object":"chat.completion.chunk","created":1779176238,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[{"index":0,"delta":{"role":"assistant","content":""},"finish_reason":"stop"}]}
|
||
|
||
data: {"id":"chatcmpl-843","object":"chat.completion.chunk","created":1779176238,"model":"gpt-oss","system_fingerprint":"fp_ollama","choices":[],"usage":{"prompt_tokens":3299,"completion_tokens":14,"total_tokens":3313}}
|
||
|
||
data: [DONE]
|
||
|
||
headers:
|
||
content-type:
|
||
- text/event-stream
|
||
transfer-encoding:
|
||
- chunked
|
||
status:
|
||
code: 200
|
||
message: OK
|
||
version: 1
|
||
...
|