4 Commits (9b3a025f9c806a6f8a00030c7058c689536ae5a0)

Author	SHA1	Message	Date
Joydeep Banik Roy	3796672c67	community, milvus, pinecone, qdrant, mongo: Broadcast operation failure while using simsimd beyond v3.7.7 (#22271) ... - [ ] **Packages affected**: - community: fix `cosine_similarity` to support simsimd beyond 3.7.7 - partners/milvus: fix `cosine_similarity` to support simsimd beyond 3.7.7 - partners/mongodb: fix `cosine_similarity` to support simsimd beyond 3.7.7 - partners/pinecone: fix `cosine_similarity` to support simsimd beyond 3.7.7 - partners/qdrant: fix `cosine_similarity` to support simsimd beyond 3.7.7 - [ ] **Broadcast operation failure while using simsimd beyond v3.7.7**: - **Description:** I was using simsimd 4.3.1 and the unsupported operand type issue popped up. When I checked out the repo and ran the tests, they failed as well (have attached a screenshot for that). Looks like it is a variant of https://github.com/langchain-ai/langchain/issues/18022 . Prior to 3.7.7, simd.cdist returned an ndarray but now it returns simsimd.DistancesTensor which is ineligible for a broadcast operation with numpy. With this change, it also remove the need to explicitly cast `Z` to numpy array - **Issue:** #19905 - **Dependencies:** No - **Twitter handle:** https://x.com/GetzJoydeep <img width="1622" alt="Screenshot 2024-05-29 at 2 50 00 PM" src="https://github.com/langchain-ai/langchain/assets/31132555/fb27b383-a9ae-4a6f-b355-6d503b72db56"> - [ ] **Considerations**: 1. I started with community but since similar changes were there in Milvus, MongoDB, Pinecone, and QDrant so I modified their files as well. If touching multiple packages in one PR is not the norm, then I can remove them from this PR and raise separate ones 2. I have run and verified that the tests work. Since, only MongoDB had tests, I ran theirs and verified it works as well. Screenshots attached : <img width="1573" alt="Screenshot 2024-05-29 at 2 52 13 PM" src="https://github.com/langchain-ai/langchain/assets/31132555/ce87d1ea-19b6-4900-9384-61fbc1a30de9"> <img width="1614" alt="Screenshot 2024-05-29 at 3 33 51 PM" src="https://github.com/langchain-ai/langchain/assets/31132555/6ce1d679-db4c-4291-8453-01028ab2dca5"> I have added a test for simsimd. I feel it may not go well with the CI/CD setup as installing simsimd is not a dependency requirement. I have just imported simsimd to ensure simsimd cosine similarity is invoked. However, its not a good approach. Suggestions are welcome and I can make the required changes on the PR. Please provide guidance on the same as I am new to the community. --------- Co-authored-by: Bagatur <baskaryan@gmail.com> Co-authored-by: Bagatur <22008038+baskaryan@users.noreply.github.com>	4 months ago
William FH	b7b62e29fb	community[patch], mongodb[patch]: Stop spamming SIMD import warnings (#19531) ... If you use an embedding dist function in an eval loop, you get warned every time. Would prefer to just check once and forget about it. --------- Co-authored-by: Bagatur <baskaryan@gmail.com>	6 months ago
Ash Vardanian	d01bad5169	core[patch]: Convert SimSIMD back to NumPy (#19473) ... This patch fixes the #18022 issue, converting the SimSIMD internal zero-copy outputs to NumPy. I've also noticed, that oftentimes `dtype=np.float32` conversion is used before passing to SimSIMD. Which numeric types do LangChain users generally care about? We support `float64`, `float32`, `float16`, and `int8` for cosine distances and `float16` seems reasonable for practically any kind of embeddings and any modern piece of hardware, so we can change that part as well 🤗	6 months ago
Jib	72bfc1d3db	mongodb[minor]: MongoDB Partner Package -- Porting MongoDBAtlasVectorSearch (#17652) ... This PR migrates the existing MongoDBAtlasVectorSearch abstraction from the `langchain_community` section to the partners package section of the codebase. - [x] Run the partner package script as advised in the partner-packages documentation. - [x] Add Unit Tests - [x] Migrate Integration Tests - [x] Refactor `MongoDBAtlasVectorStore` (autogenerated) to `MongoDBAtlasVectorSearch` - [x] ~Remove~ deprecate the old `langchain_community` VectorStore references. ## Additional Callouts - Implemented the `delete` method - Included any missing async function implementations - `amax_marginal_relevance_search_by_vector` - `adelete` - Added new Unit Tests that test for functionality of `MongoDBVectorSearch` methods - Removed [`del res[self._embedding_key]`](e0c81e1cb0/libs/community/langchain_community/vectorstores/mongodb_atlas.py (L218)) in `_similarity_search_with_score` function as it would make the `maximal_marginal_relevance` function fail otherwise. The `Document` needs to store the embedding key in metadata to work. Checklist: - [x] PR title: Please title your PR "package: description", where "package" is whichever of langchain, community, core, experimental, etc. is being modified. Use "docs: ..." for purely docs changes, "templates: ..." for template changes, "infra: ..." for CI changes. - Example: "community: add foobar LLM" - [x] PR message - [x] Pass lint and test: Run `make format`, `make lint` and `make test` from the root of the package(s) you've modified to check that you're passing lint and testing. See contribution guidelines for more information on how to write/run tests, lint, etc: https://python.langchain.com/docs/contributing/ - [x] Add tests and docs: If you're adding a new integration, please include 1. Existing tests supplied in docs/docs do not change. Updated docstrings for new functions like `delete` 2. an example notebook showing its use. It lives in `docs/docs/integrations` directory. (This already exists) If no one reviews your PR within a few days, please @-mention one of baskaryan, efriis, eyurtsev, hwchase17. --------- Co-authored-by: Steven Silvester <steven.silvester@ieee.org> Co-authored-by: Erick Friis <erick@langchain.dev>	7 months ago