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- [ ] **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> |
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| langchain_qdrant | ||
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| tests | ||
| .gitignore | ||
| LICENSE | ||
| Makefile | ||
| poetry.lock | ||
| pyproject.toml | ||
| README.md | ||
langchain-qdrant
This package contains the LangChain integration with Qdrant.
Installation
pip install -U langchain-qdrant
Usage
The Qdrant class exposes the connection to the Qdrant vector store.
from langchain_qdrant import Qdrant
embeddings = ... # use a LangChain Embeddings class
vectorstore = Qdrant.from_existing_collection(
embeddings=embeddings,
collection_name="<COLLECTION_NAME>",
url="http://localhost:6333",
)