From 821b7361e98ca29f8baf2f1b7c0314e0813da57b Mon Sep 17 00:00:00 2001 From: Yiorgis Gozadinos Date: Fri, 24 Apr 2026 16:07:07 +0300 Subject: [PATCH] Push document-id filter into chunk search query --- CHANGELOG.md | 1 + .../haiku/rag/store/repositories/chunk.py | 32 +- ...search_with_filter_returns_full_limit.yaml | 534 ++++++++++++++++++ tests/test_filter.py | 42 ++ 4 files changed, 588 insertions(+), 21 deletions(-) create mode 100644 tests/cassettes/test_filter/test_search_with_filter_returns_full_limit.yaml diff --git a/CHANGELOG.md b/CHANGELOG.md index b8c6e301..3e15c489 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -11,6 +11,7 @@ ### Fixed - **Chat TUI now renders citations again.** After the 0.42.1 flattening of skill state `citations` to `list[str]`, the TUI still indexed `citations[-1]` and iterated the resulting chunk-id string character-by-character, so no citations resolved through `citation_index` and the citation panel stayed empty. Fixed by iterating `state.citations` directly. +- **`search(..., filter=...)` no longer silently under-returns.** The filter path used to materialize LanceDB's top-N window, filter to matching `document_id`s in pandas, and `head(limit)`. When matching chunks lived outside that top-N window (selective filters, broad queries), the caller got fewer than `limit` results even though plenty of matching chunks existed in the index. The document filter is now pushed down into the chunk query as `document_id IN (...)` so `.limit(limit)` applies to matching chunks directly. Behavior change: searches that previously under-returned will start returning the requested count. ## [0.42.1] - 2026-04-22 diff --git a/haiku_rag_slim/haiku/rag/store/repositories/chunk.py b/haiku_rag_slim/haiku/rag/store/repositories/chunk.py index 5fde340d..ed6286ca 100644 --- a/haiku_rag_slim/haiku/rag/store/repositories/chunk.py +++ b/haiku_rag_slim/haiku/rag/store/repositories/chunk.py @@ -236,24 +236,25 @@ class ChunkRepository: """ if not query.strip(): return [] - filtered_doc_ids = None + + chunk_filter: str | None = None if filter: - # We perform filtering as a two-step process, first filtering documents, then - # filtering chunks based on those document IDs. - # This is because LanceDB does not support joins directly in search queries. + # Translate the document-level filter into a chunk-level + # document_id IN (...) clause so LanceDB can combine it with + # limit. The previous two-step pattern (materialize top-N, + # filter in pandas, head(limit)) silently under-returned + # whenever the top-N window lacked `limit` matching chunks. docs_df = await ( self.store.documents_table.query() .select(["id"]) .where(filter) .to_pandas() ) - # Early exit if no documents match the filter if docs_df.empty: return [] - # Keep as pandas Series for efficient vectorized operations - filtered_doc_ids = docs_df["id"] + id_list = ", ".join(f"'{d}'" for d in docs_df["id"]) + chunk_filter = f"document_id IN ({id_list})" - # Prepare search query based on search type if search_type == "vector": query_embedding = await self.embedder.embed_query(query) results = ( @@ -262,17 +263,13 @@ class ChunkRepository: .column("vector") .refine_factor(self.store._config.search.vector_refine_factor) ) - elif search_type == "fts": results = self.store.chunks_table.query().nearest_to_text( query, columns="content_fts" ) - else: # hybrid (default) query_embedding = await self.embedder.embed_query(query) - # Create RRF reranker reranker = RRFReranker() - # Perform native hybrid search with RRF reranking results = ( self.store.chunks_table.query() .nearest_to(query_embedding) @@ -282,15 +279,8 @@ class ChunkRepository: .rerank(reranker) ) - # Apply filtering if needed (common for all search types) - if filtered_doc_ids is not None: - chunks_df = await results.to_pandas() - filtered_chunks_df = chunks_df.loc[ - chunks_df["document_id"].isin(filtered_doc_ids) - ].head(limit) - return await self._process_search_results(filtered_chunks_df) - - # No filtering needed, apply limit and return + if chunk_filter is not None: + results = results.where(chunk_filter) results = results.limit(limit) return await self._process_search_results(results) diff --git a/tests/cassettes/test_filter/test_search_with_filter_returns_full_limit.yaml b/tests/cassettes/test_filter/test_search_with_filter_returns_full_limit.yaml new file mode 100644 index 00000000..da94b8f6 --- /dev/null +++ b/tests/cassettes/test_filter/test_search_with_filter_returns_full_limit.yaml @@ -0,0 +1,534 @@ +interactions: +- request: + headers: + accept: + - application/json + accept-encoding: + - gzip, deflate, zstd + connection: + - keep-alive + content-length: + - '225' + content-type: + - application/json + host: + - localhost:11434 + method: POST + parsed_body: + encoding_format: base64 + input: + - machine learning neural network deep learning model machine learning neural network deep learning model machine learning + neural network deep learning model + 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: 22 + total_tokens: 22 + status: + code: 200 + message: OK +- request: + headers: + accept: + - application/json + accept-encoding: + - gzip, deflate, zstd + connection: + - keep-alive + content-length: + - '225' + content-type: + - application/json + host: + - localhost:11434 + method: POST + parsed_body: + encoding_format: base64 + input: + - machine learning neural network deep learning model machine learning neural network deep learning model machine learning + neural network deep learning model + model: qwen3-embedding:4b + uri: http://localhost:11434/v1/embeddings + response: + headers: + content-type: + - application/json + transfer-encoding: + - chunked + parsed_body: + data: + - embedding: WF7nuAPsoDz2hog8VP/zPLL5H7rxgbY9TXhjPWt9Kr2Vibc8L/itvIqVaz16QHK8HNfRuLgEFLzATwm86DQdusBWGz0d1wC9lOQePBWuBryKrkK8wi0MPTnkxLzNcBA9wKmMu/AOlbwc1rq8oqBKvVz3jbpj8c48pyuHPBnqy7z5nlE9wzaHOF+DkTsNJPy8NQiZvF0zNbyd0p889ICrvNLFbzy7uuW84ZweO4MJnzzLCo488ICHvNOe9TsEtJe7+fpzvOl9gbvwESA8ELoUPNW4pDxVYRC9fBIkPSKbqzzub8Y80c0lvJQTJDsAiqA7/BHcuw3rlrw8L8+8CoC/vFvs5Lths5i8sx+MPGXfDL2xi2U88+Z5PKehL7yG0qw8f9z7vLOUVzyPQcE8+BTWvKFCw7vIqIc8/pCTO98QUztmTwi8s7Mhu7E3EDz92uw8CveMPL0ciDzZzUy6vdFPu1BitbzSPmw8bwqUu3ThjbtNhcq7RpTyvM03zjtrfoU8PMqou7N0irx78Iy8qpqWO46hPLw3vW681tDYu/Xp2bv/5sg8tZYYvXXip7wbT6065WCnOx+K1LqcUkw7HZmIO0M0kTzTPIQ7NOagvOmYjrzFj5K8hh0ZPR+LnjwnygM6AkTNux2Tejw7orm7ivBJOdsjKzyXCoU7nhaiOkCDxrtSHfy7ASnXum72sjyJUD+8IY9svGed/LwSHeW7BM1aPDOBg7vIHtO77RvjuwsXejzFENi661uCPIKlLzm6zFU8ha1NPFQBR73pEcE7PBXtuxIlLDyT5iC8TB3AOxTOD7xttV47sUm8PO6jIzw0j4E8ob60vKtDdzxjfU0700XaPBKZHjyojFw8/T1XvAILvjwfgpO6IUjTPDCpF7xs5QE84UHAu0npXLwjJwu8EQq1vK+LYLy99GW7cppzvIsxDLs6DbK7+4uavInOBbwl8GC8JuQiultpijzgEZi7xy3JuQgaszwHXAg6fQo6Owi2G7zWiE88iGMPvM5GeDhfiLw7TgKAvAE56zpn3zO8C1xjvFo3QTsTt468MjLDPP0w2TzaFoM8Hwaxu+wL3jtbxDY7A05tPHx+1ruG9ZI72ayIvCNDFzzYHqu8UZGgO7kCsbzNwGK88ZnvvLJbrTtkcKk8xv9RvPggmLza9Lc8AbdoPA8gaDruney6RckQvIwiE7ypedq8bmF6O7iUJ7poBzK69FsUvNOZcroJwlc8ZPQpPGU/mrvmrf+6ndy7u4/hajyGFXO8gOFLO9bH6Ttnm/W7O8SCvJxOjrwV83A7QlY5OQ4ENzzZnhW9N2y9OvAhyrz3eLG7PbwZvFpewzqE4MA7jBIVPDlIKLwEJ947rbaKvPYOqLiHhTa9YpzMusRBDbxQTx0817sEPW6vmLw79+O7WXZSvE9UyjwB6lU6m0b8vLU4PDsF9fC6JZWIPN2TfbyHoEW7jhenO+ax1DvABgO91SWPusLnOzwULqI857fuPENnJryfoL487eDDOuImrTzIzCC8a+lZPKGzeD0jnt66pT7lvB/iiDtSXYw88BHKvKzHIDxCyyy7HFDNvPlEzLpAh5M8Y3mIvCRxm7vgk+C8021bvMRh9bulPqq8rczyu3OIa7x4fWQ8/HPHO6/FOLyF+L68Swt0OvghWrso1nW8OPY1PAyxzDzG2Ko7dSVDvTx04Tt+2ei7rbzqvLzu6byllYK7RllSvWvFnry+6xO9D0/zvGJVdbtRTwQ9VOraPBSQBz3QlBK9RwwzPAK3mDx7ROe8UdCFvEOKqbqFEoK81MR1PD4+Hj07u16811dtPFMi57u7puI7QO7GO+5Zo7z81QS9XDJNPNdCkDw/tUi8vMFMvA6shbxYtPm7UNdpvEPKHTzADDs8oYmFuzJHhzxanYu74R9ivCwE0Dy+2IU7T+3EvBeKBLw/m708HJMSPE9YvrznClO8qBuDvFnKyjxIzmG6zAlaO2XV5zvAFAo71k6APGOMPDzkPIC7FfthvJH9Sjycu5C74OiLvP9zzbtxmck80r6yPCHfkrxITLg8rsOhvD9EHLx7mrm7CfwEPCQMc7qiFCQ8e9w/u6WFKT2EHzM6XMCXvG2MCb3Mb6o8w1z3O4RYCT0ff9Q55gQPvWed1LvzGpa8t821vPQaFL0NyT289tImvHuZ27szkh89LH62O4kqnDq1siy8Yf4LPMx0xjxS2re8RkYbPEUfNbxGxCY8Sr8eva7r3DwGmMs7Tl1YvKFxHb2gpCA77jSoOsNyPTtFtMk81pwcvD+9Hj33kFG8LbLCvFaxWzyZU7c78FDnPPB1xjy9AHg8wkCcPLcDv7zQUZe88PPlvGbtEb3IQF083/cYvMj6cjwgJM48nJhhvWkZqrw/8zm7UqWtO8j6ljxD2ma8G1wrvK+YvjoDf7A7PIq7uuxTsbyoBRg9EWjOO7KHpTzxp9q7lMDfO0DKzb2s1uw50mtwPKZBm7y2iW68xT8qvf0X/rwjafM7r8gzvJTG6TvuUjq9P8gVvBrVUjwchJc82DdUu08sizymtjM8aMBJPO90fTpkma28BR1AuwKrsDxjceo8XOLzOwwRk7jMS808p9Wluz8tAT1IIYe7DuAxvLpdMjx7rpC8kW3Ku6K/XbzBH368WGhBPLxyvjz1+ui8NaiFu4cZP7qlhfq7P0m0vHAb8TxnGkw7QZhAvG+MsznwKX674rfvOnnSw7zfkrM8g7gFumqQjjtqoJI61SgkvSqY8zt0+DW7csVpOwcB2Lt7DBM9RA4guzQqkLzvnCm9QUnpPNMNgLksy927lVclPf1z/TyQGii93sAJvf6dFTy1m+i8e11ZvJGktbwG2VA8W/XkvMphMbv1WxM7+NRDPHEiaDvVkYm8HnbsuXek/DsZDKo8U5oCvMC/KTyni8G8wfXavAWwWrw3t8+8iwqVPFCKHTwZ/3W8hNbBOzLEebxc5qe8vB/FPFLB2TuG4Bo9OYoEPQ6m7TqCZcy64DvSvO40kzyPbyq80fZ6O+Z/JLy5gUG8BvCsPN7IS7zFTCA8KOlTvIyAQTy4LsI8jpI8PG5Iyzy7tyM8C+qtvHwxyLzSsPO8qpiAvAkNYjyMRsE8pVDFPMp397xrO9E8x8sQPNI+tTxWBbW68pSDvBJ4mLw7C5e7BSElvM0ivLp3JyA874LLO3yyBr3xs7A82PCiO57/mbxavb47aRO2PCu/e7q0ON87gQfOPE50zbx9qxg74aL1O7Y8sjxh4CW9mo9LPPOPRTzXE/i720jyuZMXrzwrHAo8R9FCvCVtrrz0wpm8PMPbPEzSirswUtK8SkIcvasFELzVOha9tu4OPWOm87w52dy8A3u1vHEKa7xUGpq8dwyoOiutjLt5gtU8V0l/PPmH0ryQtpe83x35vM9ZwDz5jwK85X2WvCgzX7xLnfA7aGGMuwxe8LzEtss8OjALvP5RO70+g1i6y9mdujBKGTwBC4Y8pMJFPccFO72L7Ao7e8vsOu+RPrwiVJI8uoWFPKM0vTwHOoM6LSuEPFY7e7wdXQe8xEfqPMSAiTtsrpa706oFvYu5J7t3fAM99FXMvK+ihbw4+cw8fer1PKh5djtEZIi8aPUgvMyQozxGZmw8oKq2PE06lbyg55s8IZiyO6RXtLs19yy8jmS1vF4gxDvi3QO9dy6BO+E47Tx01qI8qSYjuwekUTpYE1A8DsVHPa6CnzyoFyY8X0lwvBRrGzyBzIa8RBj2vO3HILy3wh48Xni8uimzxrvG+qe8Cm1APHRCizyT79Y7MRcCPb7K7TpT43y7ejS8vMKbmb0sRYs8rH3dvJM11jzybg69hcH4u0AZqLz4/5W8H3ZavJxTAjsUmWi9CGEivD+znDuQYLg8asNXvN6G/Lv+wq28ESVjPTYSlztVzLq8RlBpPDV1CTqGSSa8zp58u8NSKLy3PcM8JT3fO+3BhLxim8A8p1TnvOLpEjzWey47Uu/lO+y4Mjx/Cka8XILlvLMIgDwkOOK6nKPEvPt1qzthWjs8IK3Yu6y5c7rGHsk7ymTkO3lS6Lnm18U8YmMMu3u1/bzNJbG8s4G9Org1yrx1mSA6TMaTvIOAKz1Xdgk6xOJLvOys5Dqrkzi8t+vXPAYEhbuSfqu87d/hPFiPDrwO9Ne8i6LCOl2owTz8A6a7l4d2ueEJwbwKhfq7e7QlPJVe+junrLy8r3QDvRfXnry2F7s7LShRvLJhJjsNN3U8Oif1vE8tszoLJyW8lm+lvO/bnTyTB6Y8b5XxvMJVqbw/3LE7KF28vATIVLytIH09hjuHvJcjFT0GxUU8JdduuREIbTwF1Y08BZmtPEdFhLzgcI28OV84PBvoJzq1VSe83nsBPZm7Cr0pxQk9crC0O/ssFTy15d88JXkGPOPBojsoe4e2aws7PNu7rDzjnUu8Ipm3PH12DDvn6Q08+wjqPDhtvLxAsCs765tPvIyzxbxR+iI9Adgivcsn8bwS24C6WT9MO/W9QLuTEw894qqPvBZiUzrYQbU7GyWJvIk/yjyHlGW8GumCvPkL1TyxdZI9LtSuPHZtKj3rec48Jcsuu/06Tj0xbp88azStOzxug7yIHpQ8Z/EYvfc0eLyW/yu9ovTeO1hzj7tVrDI7Y9cBvS8W+zuKAhy9EvP3PLCwgDyyDqg8mcqNPGco+Twa6M679iSEO92oirxTT2a8O9+iPF5NvDx51cW65haePFVIcTyYGZc7/OyxPKYDTLwT0LK7Fw6xO2tGrrv/7v28lMgJPTEI3LuoapU86K8lPGbsnzqCjbs7ydfOvG2XtDzD/hW77iJBPHFSfzuoKVu8ixfDPBWaBT2M64i8vy3guwKFMbzkEUA8WjKVu4kW07yI8QK9nWY0PNIg2Dz+rxm9G46Xux8BMDwxqcE8GL9QvL7dDzwi2+E7qAChvL9uXbxX4QG8uIraPDjx2rqSzA+9Yv6XPCZtYL3tItE72JSFvFF6tjzHVo68MtY1POrGNbwgGnu8nB7bu7ev3rwgGIM8KHiVutui1bzZjBa8WPevu0pMjD30oJc8N8ZWPNb0jTwxYzQ8Bj8SPPgrnjoTMPW7OGznvPVUBr2PX7a7f+swu2V/6bz8vM87rLIsPKDTwTm1HBU92vRuPOh/E7xb95i7zp29O1c8EzzviyA8X804O/FXPrzdxXu7Kf/su5w2H73LGje8tws7O+e/77yBso48fsOAugpyG7z88+I8KnDZPABbAjxI23O8GmHtOxfDwjxjStS7bshguvmB/rzbg6k7sbRvuo3wLDzhWjO7WVHYPI+udzwbKi+8yibPO5WAm7sLfxc9kVacOfWkEj0ThhY8uJ3oO3h7CbwVXnc8fuuqPKQHmbzvfpi6/J9lvIqwCj0cm/i7PXxeOfGQx7x8G++7oT2NurRNqDuYM6I8q1kJvW3257tG7jC8G+jcufRxqbxZsYU85EcdPLiXFT3Cw+K77+UcvJsuvzyi5IS7Hl2OO0LZyzwccbG8OwO6PN6qFzxHIqo724Y6vSLkBjykxlY7e06QusQjkTu4yrm8gKJ+PP5UpLxnC6+8FY3eO1zcGzyBqxK8q/9lvMBVT7uaHim7C3iJO+FSBbzAqsk8dSpPPBcU6zwL38Y6HGK8PP4h17yWsQ46hXwSPGGSljyq7hs8y+RWPObwRTyWXEY8h8v4O3w1WLxe0pS77xX8vKqyGzxsEIi8cn61vJIZ2jy/UmO9BvqCPKigsLwLvQs8RY6jPIiCTb06+y28MitZvLYwULyo9Oq6Y5jjvIuByTf/zya7U95+vGitUzz3rXK8BTIrPNr7Pjyw+Zm76XkQvCfYFzzLmzg87RacO28fyzm8SPs8GLaXvI46s7xNVbw7yuWqvJdUKL01lMy896Y6vGcRSTqRhGC7eG2jvP+AxLyLoXO7gz0cu5GIrDxznGI8Sq/Bu3Idgjuzumc7S/gRPSggKrygTUa8K08HvI8OWLw5ZwG9krC7vPY8rjyVRBA8FiaOvIv7gLwazxe8eHORvD0tRzxDNlU8u43xvAWa5jvJYmW8q2YGPajVqTznYcm7kJwePTaHHbxGXIG7yuVzPOn7ojyR5Iu6tAuHvKUYprz4vTK8icjSOgpazTsYOJk8Za0CuyDasDx66dC6nmRaPJHpm7yc88q8iV6XPETCKT3Zuyw8Y9SuPDQxqDty+tc8vmAGPD4drzt1kOW73zM/PCkn87vyY/M8yVz2vMgePDynC0+8F4UkvGX7G7wb6re8t3mOvC/VK70hpui8J93NPGb7dDvgVoE8wyMYPJgc6ToA4TI7wSfYuxxQ/jwX5Zy87XgXPaDLSjwH+rG8YyHvPDAiKruoZI07OCHnu6skRTzJets8kNY9OKA0tDzUCyW9h+MNPRSEPbxaBxq73nsIvbscAL3EADi9glbPu+OEIL3VukG8tRV+vEl/LDvljIk89uEIvTKIfbybjLc7IuIbPd5kMLxgvaM7lzVvvAmvrzwPxkW8teORvGUuBz0vLru7M2q8O29AO71uoJC8CJRYu+NJmjo8QoC8N5PXvGuWm7zRDAG8Dqz3OxBQp7w0TRS8Eo8FPOYEvjxfneM8tiIaPW8LILwRJLo8b9qEvIUkeTocMIs8GAqtOutHtrzy7/o7HcIRPAjxwjulUIc9hXdYPEPlE7xiaVo8F6rhu/u47bvURyK9Fs1vul8xJrylzdC8/nuBPFUWKTwDMhQ8FJeSu6AGYLyAoT890kajup2qAbx1dGk7wOW7vHLDszzZ75c7JzdbvFaGS7x0R7K78Py/O01qND3CwhQ8wtz6u4ZGTjwhcqy8BVtXPHQZsLzzLoC8HvX7PDPQobwmtk08TvPLu6EDEzzrXx48AiljvAlLn7yq0n68lNRYPTZF/DqOsJS8PWPHPO6cCDyDxD+95Pk8vGFfGLzrrK28VvTjO/tSMLzizq48JgIFvG3hiLzKGcw830NaPMWyTTxOfqq7gvLJvBbUHD1peGO70hdOvFzDqjzTuJI8Xcf/vGzvobzjWti5ieEIPV0BYryjjjC7n8DcvIB5FL1/aFa8U7lWPKcV1bue3Ng8rvLrPBQlpjwykri7aPOYO3Q6mbwwk528vdbYu4nRETtVF7A8S2csPOtF9zyNojS8Ym5kvMhHAz2nlWA8xMrnPKtwN7y+Dc28P5oovWNv5bwaG6A8lYfNOxmEvDoUrAm8DSMFPWwIAj2nPd+81SA0PLPhEzyC0LU7di08PKUImjyyej28cEKzO8wwJTtCZVK7wbV/u7pYrbzZMm68HsyVu5dvlTyjjJG7bGtYvNAALTz5W6s8nrZ4u94bXzzfT7o8DncLPciUsLwMCxu8aa8RPRlSEjsLFZu78bJuPKh8azvGrM23c87ku5w1fbxptEU8RAuUPANtlzy1pEg9frHbvDGTqjxh3Zi755E5PahYMDw9+dW8Rqm4vJhitDyEYjk7I8EdvRjC7Dxr/sa7LlZTvNHlkzz1ZcM8sx68PEpWoLt7Nq88gAYdu+Bm0LuEEQY9t36MPBo4ibzDATW8fgptvNznPz3vJ2263yC/u68tTbwyoSO7Ibzzu8cIw7wkPq+6QtELvARQlDx9rom8mDFmPHKo4Lstse87Y4L+vD/cqDyEOQA7rGgVu+jX4rx9WaU7Zr8BPAEmjryiog28QzgIPBKBBT1ScYo7BSIEOrnOGz00DRW79MGkvIcuN7w4VjY9HeHzuvr0yTn7al68yYfCOjIYAzxuXGY8CPgJPb5c67x7jFm8Obc4PWWpb7tBE1Y80sL7uoP3y7w9meO8DFzaPNJgrDwvsj+7gnRwPMZ2wrxSJ+k7ytfTPAzvJr0GaJ28cacCvHsIKjyFIgM8f0gSvdaRdTzodzq8AFz2PNt5/bxk6jM8UWwCPV8Ks7zzc448G3EFvVaETTwoIZA81yaUPIvV9jxrzA29RG0IPI//hrw/Vfe8Wg2YPKjXWjyJsv87Py6DvIAfyzx3Q4C74fUbPBW7GL3ME6e8nBDjvCI2Frxuxzu9BH5IPNykAbwvOr47jtYMuxH7drzJb9m6S55wPGjilzvD5SW8bLR7u05hzbzUJPW8Yq7OPBXLC7ztiwQ8iRUsPK0HZTyOWFQ70iXyuzGwYLwl+tc7v8V5PN5U3zcq3Zq85Ma7PMa377pG9588qoudPKZnybwd4wI8ss+UPI73tzvfRoW7F1aLvI6W7DvjhL87E9T9Oz5BBTxgt3o8M82MO5weRzyYDgG7by0BPBw5zDugQuG8sY9RvFTgv7ozugI8+/HOu6kr2zwV5iK81XOju3qR9DwRFec6LwMYvflPGD1VrjQ9oevfOwkQojm6H187zN2WuphqJj2fT5W8+fSFPF5ksbxRJ5W8BOHjumi6MTxO7hy8gRELvAITrrx5PsI7iOLcO7togjxFlrC74fk8PEqZKjztVRE8EJQ3u/Mf9TyEHpw8WHCsO+ggSTuCQMK7M6GsPM0ZELyXdPC7duWyO95J0bxedVm8tf49PIsIHLx+Nl86F0IbPaGNzjtL0ji8sR2TvEyBtjymhOY8lM1VPGQFjzyVXl68T4OxO2clFD37r+286YB9vB+MUTx/pCK9H71CvDBuxjq2Xk47Cpq/PA1mS7ycrGW7v/U3POlViryLU+m789LDPHIpGb1nGlU9RndtPLumfDxwkCe9Rd0YvYMvs7uKNHE9sDP+urBgyrr/MMY8dRtuO9bTBz3xZSY71UzUvEYIrbz3PT29uyuKu8x+e7zMpMw8cwOCPDEdzbyTK1y64feMvMDMsjyl6Um9PtehvC/Bp7zfogI9hMoTPSLeNzw5vas8bEcZPWL4TLuHZ8Q7umWEO5WCdTzztRY7HjTKu4K3ejxhmlI7zIXcPIv9AL2C4cS89xL9PH3uWLpqZD29q+E4vP0cZ7zPedG8DEoJveEpzLzfzo68PRsOvVWjBD0IarU8UTaVvKNu27t25MO87jBNPAACZ7wHdhK8zD78O2Gr6rw04N28lgvqPBULhbtDRY26dhuPvOrdLDwAnUg8PI/lPOUQ2jqHDG08cDQIu26W8TyXZOQ7UOHbuwInjLwn1PO8sfONOwhGYDywET+80oZgPK4KpzoiVU+8wOu5u/OQM7zMeCe8V4wSvXlUSbvU37E7FubzuwCNxjwi4YG8SY6YvGRKizuaSnI7KBbVvBnO6zzVN6I8JzLLO25P3DuGyO473217vMlZNTukRSA9M9QMPIBLBz3fUv87SdffPE7CyjxT+oA8ocjivAJ+oDzOdqO8zL+dPJN7xbwgxkS8JVExvOuSALstoiE73sGBvPXKozwfGiC9GvvevBBNJbsRf8m7QvOduwrCqDxOVMk8fFeBvOcmgjxjK+A8lLaFvFywwDx/buC8++hlPPk+HbyeYa+8o7J6PH/CrzxrTAo7+jFNvbzyW7yIY/u8wA9Fu0JpKDw3rB+9nk0VPA4kVTsFTVu8WMvcvEteWT3jZjo7vAkPPeY42LvNFkG6+Om0PCzu0jxLUFK7O7v4vLrNd7srE4W8z6gcvR7HqjuWz5k8jWA9PGaMDzy8QtS7t/NZPJY/bbsuaK+8yK9KPA7B2zzRjCm8r6aGPPNHoTzg5pW8ZhwZPNAh3rvXHww9ZNRBPAc6C73pQVG8azqpPAmd2zwC4y49TyWsOfwOYbxhdDm8oR8mvNYfhDs0u3Q7vl2hPK4FBrwt+EC8jgmduk7FDb0qh+08tCrquwUeFL2MgIO8PtYiuTRN4LwDREE6aVaqvO5AKrxLF268hSoiO93+QzwG57M54Y4VvYuNOjwfrlm9D7dpvTZTmzx5jIM7dJTGvMJfC71UxKk8ARHAvObSsTyjUfE74F+AvKbPIjzAhL+7nVKyvKEDB7tCcjA7GC3CukZEIzwXtxM7XlSAvL5k27yuno+7zy7cOyMxQDxXMB06B3g0PCS1rrzyT4K74qS/uvl0N700mo+8A58dPOoUTbw0EMu7WcQEu/ipkLwjfDK9jAbdO8yAcbrn7C687mKXu+7BEb27/ke8yZOZPIDpFjzYjZs8ihpOvBPA0zvhoDc88zlnPC73/zmasfw8Keu4PGBSxzwIqIM8i2TfPCWNKjyF3w+9lGH+vMSYjDwRL/I8eeuOPCrQeryB0GY8Kvr4Ow48MTt5I4G8i5AuPTK20rwaAKC8M0tNvdwQhjx1g648v0aIPJkb47ta/dK8ADaXO6OKhTynsNU8GGNnuzQ2/jwl1Oy6Q9QwvK80WT0Xps88QWHzO7csoDvOP7O69SY1vJ1XhDx+IRs9p2rXO1/h8TtyOYU8uhxWvIHLIr2maRa6ScSwPEkhzbuhQoI6dzKYPFzM87vDeey5Ze4IOwS/DbxHLT48adDyPD76qbzJat27kXdHPHgo+LwVHA48AULEu0liCb1R0Qk8MEsTPFfqGD34Qlu7aqigPOJVHr2S+mu8DuYZPT8sEjyuJio7oPOMPAZPK72ai6U84ks/PYargjxnrB88Yo8avYNa5bwY9zq81jx6PMj7Vj3ZsRe9ZeIMPWLnLLzixdW7fyebvBOAL7xahi28gNi5PCoRoTwS/WC8dzoZPJiua7yKpXK9zU7IPA+bS7y+Xdi8G5RwPGjAUbqZtt28ubASPTq7Hr2KHfg6nOlYPYLPHbndp4c7/myju5SmFjwwPSU8iohtvEioljzm/ao8ubt7OwTjYbsvzy68RY45PF4PbTxmchk880zRuPPs4ryOVEC9p520u7henryxG3m8tcUnO9xzHb38ARI8mIK7O1jV6LwNDse7qYvlvHYDSzvozzu8paiqvJN7uzy6Kpm8Sf5guqb1GDzH7s687FIjO1sQpzym7OS7hBsCPc0qxLscG6a8wgNdPA20YryEQxu5YKv5PJj6N7w9RDG9PG6QvDmBGz1F9IW6GcSkvOuxvjwAhEg8IwzIPJBagrrUOYU8RRnkupW/x7zrnK48E0mSPKyDsbrqxnA83QjZvLB/7DuCI0g6WlxmvG6kOzyn00o61RYCvOOz4jxd5za9CH+5PPN3gjzqUQq92teGvIJYhLybJhg8PygjPVIuuzsCpaq8AdHmvMd4dbxvqTa9uDUyvD2rezsFm1c8DtHBO9Gv1jsjWUu8I/MBvXhExbvGfyq9mPMjO1TJ4TxRLMe8dOnVvK+FMTyhKtS7eXsQPEdJJ72WTq687fgOPUczPbujheC7noWfPGzf3DzSlpi7g/g7vGYzDrtNve+8f5WEPP6kybxOwTK83MeRvBNVqDttHvc6pNeqPOC3dTxfmuY71dFPuoIxN7x0twe8PClWvGaNj7zHkaM7J586PFS6YrzI5ey8X2XjO4TDAD1eaoQ8krSmOsgQpDx3ghE9DzqSvNTip7ycFqS8slKDvNd057qY8ZO8pRbMO15/AzwfAR48mhX5O6wX2To4Sg69NVn0PFk+Jjyqb8m8pAhcO9qb57vJcTe8XazzOtAI+ztA9tO5Q/L1O+MRIz2sUxo8vseiOz1+27pu6rs86JwoPTiO27xywpO8kBU7PCnLJL0uDA89Wjqiu2Fh7brBtb28XTnbPHbAZDypRqi7llASPMmjKTwCObe8TPgTPOt3Cb3LVTm8bcYcve7Tx7skRhi9ADotvKfKGbzodgQ8san0vNnj9jtu9gw87DZcvMPAZrz1yQM8iWR1uti9Nbz0Rac7tyrIvIfP2rpDmaW8agyYPICkMj0kj048IqEzPOVgA7yTTxo9Upn6u2YL3DuB3wQ7sHGlPPtlrrzIWW66TZvKPKyoyLz9VH48Qv0Wvdf4sztaW7u8cxYqvJN75Du1chO9Q4C2Ox9vMTwPZ7y8yDYpPITqML1Uhzw9TOYhvS26HD10wWC8vmcdvQq66Lvy8Za71YFJPdh2LTzCCow8NsQrO6Ci+Ly0ap88S4a+PMg/ITwsp406wCCbPNGOsjsV4w28XnCnu+Ux+Lxa5xo94G5RPLfWubyuCRI8f2yYvLGioTycDfo6KxesvMA+ALwvteG66tC8u73RPjs74gK8vpxcPJfG9rrIRAo7U/jnu4FfiTzk6sG83wzHu08mxzxqk1k8WgYfvWttTTx+TWM8eb5GvMMpljzDeou8bABzPEwuZjyvH2+8hNuLO2mvHbvFTB69DHaXO7m7Q7zMDoG8zNUWPB3YArw6ESk7WLq6O5p6Hjy82zm8QB8zPUeqPjwDDPi6uCjyPKsOFjwVw1y8nw32uoIPtbwiYpa71/+1vGdZ4LscF3c7gW7HPEtFATqa9dI81YbqOxE4zDwcHXI82HPjupGbaTxys1e8/9P5u81b8DzveoE8ygbauucFlLvhJT68AqSTPJzQOz1Gp568rJjBvApHRbxX6Yy8gXUrvMZ/rjzjp+K70EHlvOvWBL1SPia7OWfmO9mZrjx+1ho9zMzCvPzr57zA+is8cPn0O1uHMzsovFA6R7rmvGtm5TvswB681A7SvKdCLzvdvZa8YKDbvKqdYrz0j5q7cbGavCTsVD2db/Q7ve6JO2V8Jj078ZE8OBCdPPZrrTwtgD28cwHGOybZ4zxpngq8aTipuk22SDuAEwu99GgVO6f4SDxn50a8895aPNIUgzygA2S8y4XLvDD5nzuYFMi7w2idPDjJq7ssk9q74ctbPEmooDuJnQU8eBoDvdZCMjsi0lu8JDLKPGxtmruRwVM76GMCPHGrDjtdm2i8HjEePaobkrx/cKU84arlPEP7/7t5PQi8czOPPC3LsTxlty48z2vFOp+yITx8G4086dGDvAheCDuspaq8NQ4kvDNmtTyo2Rs9nWA3PIxJ5bx0Hi89zS4PPaoX2LsvUee7NPyZvGQd2jvbNJU8PAHpPN+s/Lz1rLo852elvCXDpztr2VW82bdDvF2DjDwLHR67XXAwO7QpWLwiOgq86ixjuxroRDxO/BC8XPXQO66STTzeTKc8NusdO17FoLtJFNu6oTIPuycyFjz//6o8qra0vAgTOL27Lb6854ZtvGySB73Ir+e87UEbvC+onbzRtc48ahKIvAO4/zytANs8lPEYPMWa0rw+0hQ7hOePPE7fzTsdkEW7TAFBPbZT6jwwwoE7lUtIPBC7Wzyv/jc8lMGaPMwJSj0QFqk8jMI1On/2bDz/vm68EnqvvFEYRLxpqjS638utu0pMHz00c1e8suAivMSriDupca27ihpVO0QYzjyt+MU8dCUxPH0gTLsZ9NC7I0dEvDPD7Lt1PQ+7OMlUvNgrVDxltH+7kBhJPOZNyrtisJu8iwCWu56APTvBpTG8OeO9OWsqV7z1dsq86jesvF9zGzx6WqA8snm/u0IKR7xeK9W8DGgxOwtlfbyQxcQ8EgcPvPYbdbzdbIA8Fahhu/kksbzDOFK7O1SNPCgbjLzfeDg8r2hsvAQjXbw6oyM5F+6Bu6RfILvDB6O8CEL7OwU4wryTitu8LU/HPLonNLqKSj0962K+vIs9Qjyb8GU8W502vDFRwboqFS+8bSMjvNiXcLzqUeq7EnpdumfXPTxbe7U8r/wUvAQkm7vBA7U7IoeuvJ6aBbzA1DA8t155u464jLyqrTq8yq/wO6NpH7ysMgG7teXRu6q5i7mx/sG7R8s0PPrqhjvX6pe8KoGkusUKeDyYG/S8omcivCXbpDv47Im5rBe6PGu9ALtK9SG8qSCiPPA0IDuAU1U8SoEqu14dG7y+YZy7gYNcuw== + index: 0 + object: embedding + model: qwen3-embedding:4b + object: list + usage: + prompt_tokens: 22 + total_tokens: 22 + status: + code: 200 + message: OK +- request: + headers: + accept: + - application/json + accept-encoding: + - gzip, deflate, zstd + connection: + - keep-alive + content-length: + - '225' + content-type: + - application/json + host: + - localhost:11434 + method: POST + parsed_body: + encoding_format: base64 + input: + - machine learning neural network deep learning model machine learning neural network deep learning model machine learning + neural network deep learning model + 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: 22 + total_tokens: 22 + status: + code: 200 + message: OK +- request: + headers: + accept: + - application/json + accept-encoding: + - gzip, deflate, zstd + connection: + - keep-alive + content-length: + - '225' + content-type: + - application/json + host: + - localhost:11434 + method: POST + parsed_body: + encoding_format: base64 + input: + - machine learning neural network deep learning model machine learning neural network deep learning model machine learning + neural network deep learning model + 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: 22 + total_tokens: 22 + status: + code: 200 + message: OK +- request: + headers: + accept: + - application/json + accept-encoding: + - gzip, deflate, zstd + connection: + - keep-alive + content-length: + - '225' + content-type: + - application/json + host: + - localhost:11434 + method: POST + parsed_body: + encoding_format: base64 + input: + - machine learning neural network deep learning model machine learning neural network deep learning model machine learning + neural network deep learning model + 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: 22 + total_tokens: 22 + status: + code: 200 + message: OK +- request: + headers: + accept: + - application/json + accept-encoding: + - gzip, deflate, zstd + connection: + - keep-alive + content-length: + - '225' + content-type: + - application/json + host: + - localhost:11434 + method: POST + parsed_body: + encoding_format: base64 + input: + - machine learning neural network deep learning model machine learning neural network deep learning model machine learning + neural network deep learning model + 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: 22 + total_tokens: 22 + status: + code: 200 + message: OK +- request: + headers: + accept: + - application/json + accept-encoding: + - gzip, deflate, zstd + connection: + - keep-alive + content-length: + - '225' + content-type: + - application/json + host: + - localhost:11434 + method: POST + parsed_body: + encoding_format: base64 + input: + - machine learning neural network deep learning model machine learning neural network deep learning model machine learning + neural network deep learning model + 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: 22 + total_tokens: 22 + status: + code: 200 + message: OK +- request: + headers: + accept: + - application/json + accept-encoding: + - gzip, deflate, zstd + connection: + - keep-alive + content-length: + - '225' + content-type: + - application/json + host: + - localhost:11434 + method: POST + parsed_body: + encoding_format: base64 + input: + - machine learning neural network deep learning model machine learning neural network deep learning model machine learning + neural network deep learning model + model: qwen3-embedding:4b + uri: http://localhost:11434/v1/embeddings + response: + headers: + content-type: + - application/json + transfer-encoding: + - chunked + parsed_body: + data: + - embedding: WF7nuAPsoDz2hog8VP/zPLL5H7rxgbY9TXhjPWt9Kr2Vibc8L/itvIqVaz16QHK8HNfRuLgEFLzATwm86DQdusBWGz0d1wC9lOQePBWuBryKrkK8wi0MPTnkxLzNcBA9wKmMu/AOlbwc1rq8oqBKvVz3jbpj8c48pyuHPBnqy7z5nlE9wzaHOF+DkTsNJPy8NQiZvF0zNbyd0p889ICrvNLFbzy7uuW84ZweO4MJnzzLCo488ICHvNOe9TsEtJe7+fpzvOl9gbvwESA8ELoUPNW4pDxVYRC9fBIkPSKbqzzub8Y80c0lvJQTJDsAiqA7/BHcuw3rlrw8L8+8CoC/vFvs5Lths5i8sx+MPGXfDL2xi2U88+Z5PKehL7yG0qw8f9z7vLOUVzyPQcE8+BTWvKFCw7vIqIc8/pCTO98QUztmTwi8s7Mhu7E3EDz92uw8CveMPL0ciDzZzUy6vdFPu1BitbzSPmw8bwqUu3ThjbtNhcq7RpTyvM03zjtrfoU8PMqou7N0irx78Iy8qpqWO46hPLw3vW681tDYu/Xp2bv/5sg8tZYYvXXip7wbT6065WCnOx+K1LqcUkw7HZmIO0M0kTzTPIQ7NOagvOmYjrzFj5K8hh0ZPR+LnjwnygM6AkTNux2Tejw7orm7ivBJOdsjKzyXCoU7nhaiOkCDxrtSHfy7ASnXum72sjyJUD+8IY9svGed/LwSHeW7BM1aPDOBg7vIHtO77RvjuwsXejzFENi661uCPIKlLzm6zFU8ha1NPFQBR73pEcE7PBXtuxIlLDyT5iC8TB3AOxTOD7xttV47sUm8PO6jIzw0j4E8ob60vKtDdzxjfU0700XaPBKZHjyojFw8/T1XvAILvjwfgpO6IUjTPDCpF7xs5QE84UHAu0npXLwjJwu8EQq1vK+LYLy99GW7cppzvIsxDLs6DbK7+4uavInOBbwl8GC8JuQiultpijzgEZi7xy3JuQgaszwHXAg6fQo6Owi2G7zWiE88iGMPvM5GeDhfiLw7TgKAvAE56zpn3zO8C1xjvFo3QTsTt468MjLDPP0w2TzaFoM8Hwaxu+wL3jtbxDY7A05tPHx+1ruG9ZI72ayIvCNDFzzYHqu8UZGgO7kCsbzNwGK88ZnvvLJbrTtkcKk8xv9RvPggmLza9Lc8AbdoPA8gaDruney6RckQvIwiE7ypedq8bmF6O7iUJ7poBzK69FsUvNOZcroJwlc8ZPQpPGU/mrvmrf+6ndy7u4/hajyGFXO8gOFLO9bH6Ttnm/W7O8SCvJxOjrwV83A7QlY5OQ4ENzzZnhW9N2y9OvAhyrz3eLG7PbwZvFpewzqE4MA7jBIVPDlIKLwEJ947rbaKvPYOqLiHhTa9YpzMusRBDbxQTx0817sEPW6vmLw79+O7WXZSvE9UyjwB6lU6m0b8vLU4PDsF9fC6JZWIPN2TfbyHoEW7jhenO+ax1DvABgO91SWPusLnOzwULqI857fuPENnJryfoL487eDDOuImrTzIzCC8a+lZPKGzeD0jnt66pT7lvB/iiDtSXYw88BHKvKzHIDxCyyy7HFDNvPlEzLpAh5M8Y3mIvCRxm7vgk+C8021bvMRh9bulPqq8rczyu3OIa7x4fWQ8/HPHO6/FOLyF+L68Swt0OvghWrso1nW8OPY1PAyxzDzG2Ko7dSVDvTx04Tt+2ei7rbzqvLzu6byllYK7RllSvWvFnry+6xO9D0/zvGJVdbtRTwQ9VOraPBSQBz3QlBK9RwwzPAK3mDx7ROe8UdCFvEOKqbqFEoK81MR1PD4+Hj07u16811dtPFMi57u7puI7QO7GO+5Zo7z81QS9XDJNPNdCkDw/tUi8vMFMvA6shbxYtPm7UNdpvEPKHTzADDs8oYmFuzJHhzxanYu74R9ivCwE0Dy+2IU7T+3EvBeKBLw/m708HJMSPE9YvrznClO8qBuDvFnKyjxIzmG6zAlaO2XV5zvAFAo71k6APGOMPDzkPIC7FfthvJH9Sjycu5C74OiLvP9zzbtxmck80r6yPCHfkrxITLg8rsOhvD9EHLx7mrm7CfwEPCQMc7qiFCQ8e9w/u6WFKT2EHzM6XMCXvG2MCb3Mb6o8w1z3O4RYCT0ff9Q55gQPvWed1LvzGpa8t821vPQaFL0NyT289tImvHuZ27szkh89LH62O4kqnDq1siy8Yf4LPMx0xjxS2re8RkYbPEUfNbxGxCY8Sr8eva7r3DwGmMs7Tl1YvKFxHb2gpCA77jSoOsNyPTtFtMk81pwcvD+9Hj33kFG8LbLCvFaxWzyZU7c78FDnPPB1xjy9AHg8wkCcPLcDv7zQUZe88PPlvGbtEb3IQF083/cYvMj6cjwgJM48nJhhvWkZqrw/8zm7UqWtO8j6ljxD2ma8G1wrvK+YvjoDf7A7PIq7uuxTsbyoBRg9EWjOO7KHpTzxp9q7lMDfO0DKzb2s1uw50mtwPKZBm7y2iW68xT8qvf0X/rwjafM7r8gzvJTG6TvuUjq9P8gVvBrVUjwchJc82DdUu08sizymtjM8aMBJPO90fTpkma28BR1AuwKrsDxjceo8XOLzOwwRk7jMS808p9Wluz8tAT1IIYe7DuAxvLpdMjx7rpC8kW3Ku6K/XbzBH368WGhBPLxyvjz1+ui8NaiFu4cZP7qlhfq7P0m0vHAb8TxnGkw7QZhAvG+MsznwKX674rfvOnnSw7zfkrM8g7gFumqQjjtqoJI61SgkvSqY8zt0+DW7csVpOwcB2Lt7DBM9RA4guzQqkLzvnCm9QUnpPNMNgLksy927lVclPf1z/TyQGii93sAJvf6dFTy1m+i8e11ZvJGktbwG2VA8W/XkvMphMbv1WxM7+NRDPHEiaDvVkYm8HnbsuXek/DsZDKo8U5oCvMC/KTyni8G8wfXavAWwWrw3t8+8iwqVPFCKHTwZ/3W8hNbBOzLEebxc5qe8vB/FPFLB2TuG4Bo9OYoEPQ6m7TqCZcy64DvSvO40kzyPbyq80fZ6O+Z/JLy5gUG8BvCsPN7IS7zFTCA8KOlTvIyAQTy4LsI8jpI8PG5Iyzy7tyM8C+qtvHwxyLzSsPO8qpiAvAkNYjyMRsE8pVDFPMp397xrO9E8x8sQPNI+tTxWBbW68pSDvBJ4mLw7C5e7BSElvM0ivLp3JyA874LLO3yyBr3xs7A82PCiO57/mbxavb47aRO2PCu/e7q0ON87gQfOPE50zbx9qxg74aL1O7Y8sjxh4CW9mo9LPPOPRTzXE/i720jyuZMXrzwrHAo8R9FCvCVtrrz0wpm8PMPbPEzSirswUtK8SkIcvasFELzVOha9tu4OPWOm87w52dy8A3u1vHEKa7xUGpq8dwyoOiutjLt5gtU8V0l/PPmH0ryQtpe83x35vM9ZwDz5jwK85X2WvCgzX7xLnfA7aGGMuwxe8LzEtss8OjALvP5RO70+g1i6y9mdujBKGTwBC4Y8pMJFPccFO72L7Ao7e8vsOu+RPrwiVJI8uoWFPKM0vTwHOoM6LSuEPFY7e7wdXQe8xEfqPMSAiTtsrpa706oFvYu5J7t3fAM99FXMvK+ihbw4+cw8fer1PKh5djtEZIi8aPUgvMyQozxGZmw8oKq2PE06lbyg55s8IZiyO6RXtLs19yy8jmS1vF4gxDvi3QO9dy6BO+E47Tx01qI8qSYjuwekUTpYE1A8DsVHPa6CnzyoFyY8X0lwvBRrGzyBzIa8RBj2vO3HILy3wh48Xni8uimzxrvG+qe8Cm1APHRCizyT79Y7MRcCPb7K7TpT43y7ejS8vMKbmb0sRYs8rH3dvJM11jzybg69hcH4u0AZqLz4/5W8H3ZavJxTAjsUmWi9CGEivD+znDuQYLg8asNXvN6G/Lv+wq28ESVjPTYSlztVzLq8RlBpPDV1CTqGSSa8zp58u8NSKLy3PcM8JT3fO+3BhLxim8A8p1TnvOLpEjzWey47Uu/lO+y4Mjx/Cka8XILlvLMIgDwkOOK6nKPEvPt1qzthWjs8IK3Yu6y5c7rGHsk7ymTkO3lS6Lnm18U8YmMMu3u1/bzNJbG8s4G9Org1yrx1mSA6TMaTvIOAKz1Xdgk6xOJLvOys5Dqrkzi8t+vXPAYEhbuSfqu87d/hPFiPDrwO9Ne8i6LCOl2owTz8A6a7l4d2ueEJwbwKhfq7e7QlPJVe+junrLy8r3QDvRfXnry2F7s7LShRvLJhJjsNN3U8Oif1vE8tszoLJyW8lm+lvO/bnTyTB6Y8b5XxvMJVqbw/3LE7KF28vATIVLytIH09hjuHvJcjFT0GxUU8JdduuREIbTwF1Y08BZmtPEdFhLzgcI28OV84PBvoJzq1VSe83nsBPZm7Cr0pxQk9crC0O/ssFTy15d88JXkGPOPBojsoe4e2aws7PNu7rDzjnUu8Ipm3PH12DDvn6Q08+wjqPDhtvLxAsCs765tPvIyzxbxR+iI9Adgivcsn8bwS24C6WT9MO/W9QLuTEw894qqPvBZiUzrYQbU7GyWJvIk/yjyHlGW8GumCvPkL1TyxdZI9LtSuPHZtKj3rec48Jcsuu/06Tj0xbp88azStOzxug7yIHpQ8Z/EYvfc0eLyW/yu9ovTeO1hzj7tVrDI7Y9cBvS8W+zuKAhy9EvP3PLCwgDyyDqg8mcqNPGco+Twa6M679iSEO92oirxTT2a8O9+iPF5NvDx51cW65haePFVIcTyYGZc7/OyxPKYDTLwT0LK7Fw6xO2tGrrv/7v28lMgJPTEI3LuoapU86K8lPGbsnzqCjbs7ydfOvG2XtDzD/hW77iJBPHFSfzuoKVu8ixfDPBWaBT2M64i8vy3guwKFMbzkEUA8WjKVu4kW07yI8QK9nWY0PNIg2Dz+rxm9G46Xux8BMDwxqcE8GL9QvL7dDzwi2+E7qAChvL9uXbxX4QG8uIraPDjx2rqSzA+9Yv6XPCZtYL3tItE72JSFvFF6tjzHVo68MtY1POrGNbwgGnu8nB7bu7ev3rwgGIM8KHiVutui1bzZjBa8WPevu0pMjD30oJc8N8ZWPNb0jTwxYzQ8Bj8SPPgrnjoTMPW7OGznvPVUBr2PX7a7f+swu2V/6bz8vM87rLIsPKDTwTm1HBU92vRuPOh/E7xb95i7zp29O1c8EzzviyA8X804O/FXPrzdxXu7Kf/su5w2H73LGje8tws7O+e/77yBso48fsOAugpyG7z88+I8KnDZPABbAjxI23O8GmHtOxfDwjxjStS7bshguvmB/rzbg6k7sbRvuo3wLDzhWjO7WVHYPI+udzwbKi+8yibPO5WAm7sLfxc9kVacOfWkEj0ThhY8uJ3oO3h7CbwVXnc8fuuqPKQHmbzvfpi6/J9lvIqwCj0cm/i7PXxeOfGQx7x8G++7oT2NurRNqDuYM6I8q1kJvW3257tG7jC8G+jcufRxqbxZsYU85EcdPLiXFT3Cw+K77+UcvJsuvzyi5IS7Hl2OO0LZyzwccbG8OwO6PN6qFzxHIqo724Y6vSLkBjykxlY7e06QusQjkTu4yrm8gKJ+PP5UpLxnC6+8FY3eO1zcGzyBqxK8q/9lvMBVT7uaHim7C3iJO+FSBbzAqsk8dSpPPBcU6zwL38Y6HGK8PP4h17yWsQ46hXwSPGGSljyq7hs8y+RWPObwRTyWXEY8h8v4O3w1WLxe0pS77xX8vKqyGzxsEIi8cn61vJIZ2jy/UmO9BvqCPKigsLwLvQs8RY6jPIiCTb06+y28MitZvLYwULyo9Oq6Y5jjvIuByTf/zya7U95+vGitUzz3rXK8BTIrPNr7Pjyw+Zm76XkQvCfYFzzLmzg87RacO28fyzm8SPs8GLaXvI46s7xNVbw7yuWqvJdUKL01lMy896Y6vGcRSTqRhGC7eG2jvP+AxLyLoXO7gz0cu5GIrDxznGI8Sq/Bu3Idgjuzumc7S/gRPSggKrygTUa8K08HvI8OWLw5ZwG9krC7vPY8rjyVRBA8FiaOvIv7gLwazxe8eHORvD0tRzxDNlU8u43xvAWa5jvJYmW8q2YGPajVqTznYcm7kJwePTaHHbxGXIG7yuVzPOn7ojyR5Iu6tAuHvKUYprz4vTK8icjSOgpazTsYOJk8Za0CuyDasDx66dC6nmRaPJHpm7yc88q8iV6XPETCKT3Zuyw8Y9SuPDQxqDty+tc8vmAGPD4drzt1kOW73zM/PCkn87vyY/M8yVz2vMgePDynC0+8F4UkvGX7G7wb6re8t3mOvC/VK70hpui8J93NPGb7dDvgVoE8wyMYPJgc6ToA4TI7wSfYuxxQ/jwX5Zy87XgXPaDLSjwH+rG8YyHvPDAiKruoZI07OCHnu6skRTzJets8kNY9OKA0tDzUCyW9h+MNPRSEPbxaBxq73nsIvbscAL3EADi9glbPu+OEIL3VukG8tRV+vEl/LDvljIk89uEIvTKIfbybjLc7IuIbPd5kMLxgvaM7lzVvvAmvrzwPxkW8teORvGUuBz0vLru7M2q8O29AO71uoJC8CJRYu+NJmjo8QoC8N5PXvGuWm7zRDAG8Dqz3OxBQp7w0TRS8Eo8FPOYEvjxfneM8tiIaPW8LILwRJLo8b9qEvIUkeTocMIs8GAqtOutHtrzy7/o7HcIRPAjxwjulUIc9hXdYPEPlE7xiaVo8F6rhu/u47bvURyK9Fs1vul8xJrylzdC8/nuBPFUWKTwDMhQ8FJeSu6AGYLyAoT890kajup2qAbx1dGk7wOW7vHLDszzZ75c7JzdbvFaGS7x0R7K78Py/O01qND3CwhQ8wtz6u4ZGTjwhcqy8BVtXPHQZsLzzLoC8HvX7PDPQobwmtk08TvPLu6EDEzzrXx48AiljvAlLn7yq0n68lNRYPTZF/DqOsJS8PWPHPO6cCDyDxD+95Pk8vGFfGLzrrK28VvTjO/tSMLzizq48JgIFvG3hiLzKGcw830NaPMWyTTxOfqq7gvLJvBbUHD1peGO70hdOvFzDqjzTuJI8Xcf/vGzvobzjWti5ieEIPV0BYryjjjC7n8DcvIB5FL1/aFa8U7lWPKcV1bue3Ng8rvLrPBQlpjwykri7aPOYO3Q6mbwwk528vdbYu4nRETtVF7A8S2csPOtF9zyNojS8Ym5kvMhHAz2nlWA8xMrnPKtwN7y+Dc28P5oovWNv5bwaG6A8lYfNOxmEvDoUrAm8DSMFPWwIAj2nPd+81SA0PLPhEzyC0LU7di08PKUImjyyej28cEKzO8wwJTtCZVK7wbV/u7pYrbzZMm68HsyVu5dvlTyjjJG7bGtYvNAALTz5W6s8nrZ4u94bXzzfT7o8DncLPciUsLwMCxu8aa8RPRlSEjsLFZu78bJuPKh8azvGrM23c87ku5w1fbxptEU8RAuUPANtlzy1pEg9frHbvDGTqjxh3Zi755E5PahYMDw9+dW8Rqm4vJhitDyEYjk7I8EdvRjC7Dxr/sa7LlZTvNHlkzz1ZcM8sx68PEpWoLt7Nq88gAYdu+Bm0LuEEQY9t36MPBo4ibzDATW8fgptvNznPz3vJ2263yC/u68tTbwyoSO7Ibzzu8cIw7wkPq+6QtELvARQlDx9rom8mDFmPHKo4Lstse87Y4L+vD/cqDyEOQA7rGgVu+jX4rx9WaU7Zr8BPAEmjryiog28QzgIPBKBBT1ScYo7BSIEOrnOGz00DRW79MGkvIcuN7w4VjY9HeHzuvr0yTn7al68yYfCOjIYAzxuXGY8CPgJPb5c67x7jFm8Obc4PWWpb7tBE1Y80sL7uoP3y7w9meO8DFzaPNJgrDwvsj+7gnRwPMZ2wrxSJ+k7ytfTPAzvJr0GaJ28cacCvHsIKjyFIgM8f0gSvdaRdTzodzq8AFz2PNt5/bxk6jM8UWwCPV8Ks7zzc448G3EFvVaETTwoIZA81yaUPIvV9jxrzA29RG0IPI//hrw/Vfe8Wg2YPKjXWjyJsv87Py6DvIAfyzx3Q4C74fUbPBW7GL3ME6e8nBDjvCI2Frxuxzu9BH5IPNykAbwvOr47jtYMuxH7drzJb9m6S55wPGjilzvD5SW8bLR7u05hzbzUJPW8Yq7OPBXLC7ztiwQ8iRUsPK0HZTyOWFQ70iXyuzGwYLwl+tc7v8V5PN5U3zcq3Zq85Ma7PMa377pG9588qoudPKZnybwd4wI8ss+UPI73tzvfRoW7F1aLvI6W7DvjhL87E9T9Oz5BBTxgt3o8M82MO5weRzyYDgG7by0BPBw5zDugQuG8sY9RvFTgv7ozugI8+/HOu6kr2zwV5iK81XOju3qR9DwRFec6LwMYvflPGD1VrjQ9oevfOwkQojm6H187zN2WuphqJj2fT5W8+fSFPF5ksbxRJ5W8BOHjumi6MTxO7hy8gRELvAITrrx5PsI7iOLcO7togjxFlrC74fk8PEqZKjztVRE8EJQ3u/Mf9TyEHpw8WHCsO+ggSTuCQMK7M6GsPM0ZELyXdPC7duWyO95J0bxedVm8tf49PIsIHLx+Nl86F0IbPaGNzjtL0ji8sR2TvEyBtjymhOY8lM1VPGQFjzyVXl68T4OxO2clFD37r+286YB9vB+MUTx/pCK9H71CvDBuxjq2Xk47Cpq/PA1mS7ycrGW7v/U3POlViryLU+m789LDPHIpGb1nGlU9RndtPLumfDxwkCe9Rd0YvYMvs7uKNHE9sDP+urBgyrr/MMY8dRtuO9bTBz3xZSY71UzUvEYIrbz3PT29uyuKu8x+e7zMpMw8cwOCPDEdzbyTK1y64feMvMDMsjyl6Um9PtehvC/Bp7zfogI9hMoTPSLeNzw5vas8bEcZPWL4TLuHZ8Q7umWEO5WCdTzztRY7HjTKu4K3ejxhmlI7zIXcPIv9AL2C4cS89xL9PH3uWLpqZD29q+E4vP0cZ7zPedG8DEoJveEpzLzfzo68PRsOvVWjBD0IarU8UTaVvKNu27t25MO87jBNPAACZ7wHdhK8zD78O2Gr6rw04N28lgvqPBULhbtDRY26dhuPvOrdLDwAnUg8PI/lPOUQ2jqHDG08cDQIu26W8TyXZOQ7UOHbuwInjLwn1PO8sfONOwhGYDywET+80oZgPK4KpzoiVU+8wOu5u/OQM7zMeCe8V4wSvXlUSbvU37E7FubzuwCNxjwi4YG8SY6YvGRKizuaSnI7KBbVvBnO6zzVN6I8JzLLO25P3DuGyO473217vMlZNTukRSA9M9QMPIBLBz3fUv87SdffPE7CyjxT+oA8ocjivAJ+oDzOdqO8zL+dPJN7xbwgxkS8JVExvOuSALstoiE73sGBvPXKozwfGiC9GvvevBBNJbsRf8m7QvOduwrCqDxOVMk8fFeBvOcmgjxjK+A8lLaFvFywwDx/buC8++hlPPk+HbyeYa+8o7J6PH/CrzxrTAo7+jFNvbzyW7yIY/u8wA9Fu0JpKDw3rB+9nk0VPA4kVTsFTVu8WMvcvEteWT3jZjo7vAkPPeY42LvNFkG6+Om0PCzu0jxLUFK7O7v4vLrNd7srE4W8z6gcvR7HqjuWz5k8jWA9PGaMDzy8QtS7t/NZPJY/bbsuaK+8yK9KPA7B2zzRjCm8r6aGPPNHoTzg5pW8ZhwZPNAh3rvXHww9ZNRBPAc6C73pQVG8azqpPAmd2zwC4y49TyWsOfwOYbxhdDm8oR8mvNYfhDs0u3Q7vl2hPK4FBrwt+EC8jgmduk7FDb0qh+08tCrquwUeFL2MgIO8PtYiuTRN4LwDREE6aVaqvO5AKrxLF268hSoiO93+QzwG57M54Y4VvYuNOjwfrlm9D7dpvTZTmzx5jIM7dJTGvMJfC71UxKk8ARHAvObSsTyjUfE74F+AvKbPIjzAhL+7nVKyvKEDB7tCcjA7GC3CukZEIzwXtxM7XlSAvL5k27yuno+7zy7cOyMxQDxXMB06B3g0PCS1rrzyT4K74qS/uvl0N700mo+8A58dPOoUTbw0EMu7WcQEu/ipkLwjfDK9jAbdO8yAcbrn7C687mKXu+7BEb27/ke8yZOZPIDpFjzYjZs8ihpOvBPA0zvhoDc88zlnPC73/zmasfw8Keu4PGBSxzwIqIM8i2TfPCWNKjyF3w+9lGH+vMSYjDwRL/I8eeuOPCrQeryB0GY8Kvr4Ow48MTt5I4G8i5AuPTK20rwaAKC8M0tNvdwQhjx1g648v0aIPJkb47ta/dK8ADaXO6OKhTynsNU8GGNnuzQ2/jwl1Oy6Q9QwvK80WT0Xps88QWHzO7csoDvOP7O69SY1vJ1XhDx+IRs9p2rXO1/h8TtyOYU8uhxWvIHLIr2maRa6ScSwPEkhzbuhQoI6dzKYPFzM87vDeey5Ze4IOwS/DbxHLT48adDyPD76qbzJat27kXdHPHgo+LwVHA48AULEu0liCb1R0Qk8MEsTPFfqGD34Qlu7aqigPOJVHr2S+mu8DuYZPT8sEjyuJio7oPOMPAZPK72ai6U84ks/PYargjxnrB88Yo8avYNa5bwY9zq81jx6PMj7Vj3ZsRe9ZeIMPWLnLLzixdW7fyebvBOAL7xahi28gNi5PCoRoTwS/WC8dzoZPJiua7yKpXK9zU7IPA+bS7y+Xdi8G5RwPGjAUbqZtt28ubASPTq7Hr2KHfg6nOlYPYLPHbndp4c7/myju5SmFjwwPSU8iohtvEioljzm/ao8ubt7OwTjYbsvzy68RY45PF4PbTxmchk880zRuPPs4ryOVEC9p520u7henryxG3m8tcUnO9xzHb38ARI8mIK7O1jV6LwNDse7qYvlvHYDSzvozzu8paiqvJN7uzy6Kpm8Sf5guqb1GDzH7s687FIjO1sQpzym7OS7hBsCPc0qxLscG6a8wgNdPA20YryEQxu5YKv5PJj6N7w9RDG9PG6QvDmBGz1F9IW6GcSkvOuxvjwAhEg8IwzIPJBagrrUOYU8RRnkupW/x7zrnK48E0mSPKyDsbrqxnA83QjZvLB/7DuCI0g6WlxmvG6kOzyn00o61RYCvOOz4jxd5za9CH+5PPN3gjzqUQq92teGvIJYhLybJhg8PygjPVIuuzsCpaq8AdHmvMd4dbxvqTa9uDUyvD2rezsFm1c8DtHBO9Gv1jsjWUu8I/MBvXhExbvGfyq9mPMjO1TJ4TxRLMe8dOnVvK+FMTyhKtS7eXsQPEdJJ72WTq687fgOPUczPbujheC7noWfPGzf3DzSlpi7g/g7vGYzDrtNve+8f5WEPP6kybxOwTK83MeRvBNVqDttHvc6pNeqPOC3dTxfmuY71dFPuoIxN7x0twe8PClWvGaNj7zHkaM7J586PFS6YrzI5ey8X2XjO4TDAD1eaoQ8krSmOsgQpDx3ghE9DzqSvNTip7ycFqS8slKDvNd057qY8ZO8pRbMO15/AzwfAR48mhX5O6wX2To4Sg69NVn0PFk+Jjyqb8m8pAhcO9qb57vJcTe8XazzOtAI+ztA9tO5Q/L1O+MRIz2sUxo8vseiOz1+27pu6rs86JwoPTiO27xywpO8kBU7PCnLJL0uDA89Wjqiu2Fh7brBtb28XTnbPHbAZDypRqi7llASPMmjKTwCObe8TPgTPOt3Cb3LVTm8bcYcve7Tx7skRhi9ADotvKfKGbzodgQ8san0vNnj9jtu9gw87DZcvMPAZrz1yQM8iWR1uti9Nbz0Rac7tyrIvIfP2rpDmaW8agyYPICkMj0kj048IqEzPOVgA7yTTxo9Upn6u2YL3DuB3wQ7sHGlPPtlrrzIWW66TZvKPKyoyLz9VH48Qv0Wvdf4sztaW7u8cxYqvJN75Du1chO9Q4C2Ox9vMTwPZ7y8yDYpPITqML1Uhzw9TOYhvS26HD10wWC8vmcdvQq66Lvy8Za71YFJPdh2LTzCCow8NsQrO6Ci+Ly0ap88S4a+PMg/ITwsp406wCCbPNGOsjsV4w28XnCnu+Ux+Lxa5xo94G5RPLfWubyuCRI8f2yYvLGioTycDfo6KxesvMA+ALwvteG66tC8u73RPjs74gK8vpxcPJfG9rrIRAo7U/jnu4FfiTzk6sG83wzHu08mxzxqk1k8WgYfvWttTTx+TWM8eb5GvMMpljzDeou8bABzPEwuZjyvH2+8hNuLO2mvHbvFTB69DHaXO7m7Q7zMDoG8zNUWPB3YArw6ESk7WLq6O5p6Hjy82zm8QB8zPUeqPjwDDPi6uCjyPKsOFjwVw1y8nw32uoIPtbwiYpa71/+1vGdZ4LscF3c7gW7HPEtFATqa9dI81YbqOxE4zDwcHXI82HPjupGbaTxys1e8/9P5u81b8DzveoE8ygbauucFlLvhJT68AqSTPJzQOz1Gp568rJjBvApHRbxX6Yy8gXUrvMZ/rjzjp+K70EHlvOvWBL1SPia7OWfmO9mZrjx+1ho9zMzCvPzr57zA+is8cPn0O1uHMzsovFA6R7rmvGtm5TvswB681A7SvKdCLzvdvZa8YKDbvKqdYrz0j5q7cbGavCTsVD2db/Q7ve6JO2V8Jj078ZE8OBCdPPZrrTwtgD28cwHGOybZ4zxpngq8aTipuk22SDuAEwu99GgVO6f4SDxn50a8895aPNIUgzygA2S8y4XLvDD5nzuYFMi7w2idPDjJq7ssk9q74ctbPEmooDuJnQU8eBoDvdZCMjsi0lu8JDLKPGxtmruRwVM76GMCPHGrDjtdm2i8HjEePaobkrx/cKU84arlPEP7/7t5PQi8czOPPC3LsTxlty48z2vFOp+yITx8G4086dGDvAheCDuspaq8NQ4kvDNmtTyo2Rs9nWA3PIxJ5bx0Hi89zS4PPaoX2LsvUee7NPyZvGQd2jvbNJU8PAHpPN+s/Lz1rLo852elvCXDpztr2VW82bdDvF2DjDwLHR67XXAwO7QpWLwiOgq86ixjuxroRDxO/BC8XPXQO66STTzeTKc8NusdO17FoLtJFNu6oTIPuycyFjz//6o8qra0vAgTOL27Lb6854ZtvGySB73Ir+e87UEbvC+onbzRtc48ahKIvAO4/zytANs8lPEYPMWa0rw+0hQ7hOePPE7fzTsdkEW7TAFBPbZT6jwwwoE7lUtIPBC7Wzyv/jc8lMGaPMwJSj0QFqk8jMI1On/2bDz/vm68EnqvvFEYRLxpqjS638utu0pMHz00c1e8suAivMSriDupca27ihpVO0QYzjyt+MU8dCUxPH0gTLsZ9NC7I0dEvDPD7Lt1PQ+7OMlUvNgrVDxltH+7kBhJPOZNyrtisJu8iwCWu56APTvBpTG8OeO9OWsqV7z1dsq86jesvF9zGzx6WqA8snm/u0IKR7xeK9W8DGgxOwtlfbyQxcQ8EgcPvPYbdbzdbIA8Fahhu/kksbzDOFK7O1SNPCgbjLzfeDg8r2hsvAQjXbw6oyM5F+6Bu6RfILvDB6O8CEL7OwU4wryTitu8LU/HPLonNLqKSj0962K+vIs9Qjyb8GU8W502vDFRwboqFS+8bSMjvNiXcLzqUeq7EnpdumfXPTxbe7U8r/wUvAQkm7vBA7U7IoeuvJ6aBbzA1DA8t155u464jLyqrTq8yq/wO6NpH7ysMgG7teXRu6q5i7mx/sG7R8s0PPrqhjvX6pe8KoGkusUKeDyYG/S8omcivCXbpDv47Im5rBe6PGu9ALtK9SG8qSCiPPA0IDuAU1U8SoEqu14dG7y+YZy7gYNcuw== + index: 0 + object: embedding + model: qwen3-embedding:4b + object: list + usage: + prompt_tokens: 22 + total_tokens: 22 + status: + code: 200 + message: OK +- request: + headers: + accept: + - application/json + accept-encoding: + - gzip, deflate, zstd + connection: + - keep-alive + content-length: + - '225' + content-type: + - application/json + host: + - localhost:11434 + method: POST + parsed_body: + encoding_format: base64 + input: + - machine learning neural network deep learning model machine learning neural network deep learning model machine learning + neural network deep learning model + 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: 22 + total_tokens: 22 + status: + code: 200 + message: OK +- request: + headers: + accept: + - application/json + accept-encoding: + - gzip, deflate, zstd + connection: + - keep-alive + content-length: + - '225' + content-type: + - application/json + host: + - localhost:11434 + method: POST + parsed_body: + encoding_format: base64 + input: + - machine learning neural network deep learning model machine learning neural network deep learning model machine learning + neural network deep learning model + 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: 22 + total_tokens: 22 + status: + code: 200 + message: OK +- request: + headers: + accept: + - application/json + accept-encoding: + - gzip, deflate, zstd + connection: + - keep-alive + content-length: + - '225' + content-type: + - application/json + host: + - localhost:11434 + method: POST + parsed_body: + encoding_format: base64 + input: + - machine learning neural network deep learning model machine learning neural network deep learning model machine learning + neural network deep learning model + 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: 22 + total_tokens: 22 + status: + code: 200 + message: OK +- request: + headers: + accept: + - application/json + accept-encoding: + - gzip, deflate, zstd + connection: + - keep-alive + content-length: + - '225' + content-type: + - application/json + host: + - localhost:11434 + method: POST + parsed_body: + encoding_format: base64 + input: + - machine learning neural network deep learning model machine learning neural network deep learning model machine learning + neural network deep learning model + 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: 22 + total_tokens: 22 + status: + code: 200 + message: OK +- request: + headers: + accept: + - application/json + accept-encoding: + - gzip, deflate, zstd + connection: + - keep-alive + content-length: + - '114' + content-type: + - application/json + host: + - localhost:11434 + method: POST + parsed_body: + encoding_format: base64 + input: + - one passing mention of machine learning here + 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: 8 + total_tokens: 8 + status: + code: 200 + message: OK +version: 1 diff --git a/tests/test_filter.py b/tests/test_filter.py index 1c54ad69..4fd96fbb 100644 --- a/tests/test_filter.py +++ b/tests/test_filter.py @@ -179,3 +179,45 @@ async def test_search_filter_with_all_search_types(temp_db_path): for result in results: assert result.document_uri is not None assert "other.com" in result.document_uri + + +@pytest.mark.vcr() +async def test_search_with_filter_returns_full_limit(temp_db_path): + """Regression: filter + limit must return up to `limit` matching chunks + even when non-matching chunks would dominate the top-N window. + + Previously the filter path materialized LanceDB's default top-N window + (~10), filtered to matching document_ids in pandas, then took `head(limit)`. + If the top-N window was dominated by non-matching chunks, the caller got + silently fewer results than requested — even when plenty of matching + chunks existed further down the ranking. This test puts the target + document behind many distractor documents and asserts we still get the + requested count back. + """ + async with HaikuRAG(db_path=temp_db_path, create=True) as client: + for i in range(12): + await client.create_document( + content=( + "machine learning neural network deep learning model " + "machine learning neural network deep learning model " + "machine learning neural network deep learning model" + ), + uri=f"https://distractor.com/doc{i}.html", + title=f"Distractor {i}", + ) + + await client.create_document( + content="one passing mention of machine learning here", + uri="https://target.com/one.html", + title="Target One", + ) + + results = await client.search( + "machine learning", + limit=5, + search_type="fts", + filter="uri LIKE '%target.com%'", + ) + + assert len(results) == 1 + assert results[0].document_uri == "https://target.com/one.html"