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3645181d0e
Added `similarity_search_with_score_by_vector()` function to the `QdrantVectorStore` class. It is required when we want to query multiple time with the same embeddings. It was present in the now deprecated original `Qdrant` vectorstore implementation, but was absent from the new one. It is also implemented in a number of others `VectorStore` implementations I have added tests for this new function Note that I also argued in this discussion that it should be part of the general `VectorStore` https://github.com/langchain-ai/langchain/discussions/29638 Co-authored-by: Erick Friis <erick@langchain.dev> |
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| langchain_qdrant | ||
| scripts | ||
| tests | ||
| .gitignore | ||
| LICENSE | ||
| Makefile | ||
| pyproject.toml | ||
| README.md | ||
| uv.lock | ||
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",
)