Add One-to-Many Batch Similarity Search Against Custom Entity Lists #36090
Description
Checked other resources
- This is a feature request, not a bug report or usage question.
- I added a clear and descriptive title that summarizes the feature request.
- I used the GitHub search to find a similar feature request and didn't find it.
- I checked the LangChain documentation and API reference to see if this feature already exists.
- This is not related to the langchain-community package.
Package (Required)
- langchain
- langchain-openai
- langchain-anthropic
- langchain-classic
- langchain-core
- langchain-model-profiles
- langchain-tests
- langchain-text-splitters
- langchain-chroma
- langchain-deepseek
- langchain-exa
- langchain-fireworks
- langchain-groq
- langchain-huggingface
- langchain-mistralai
- langchain-nomic
- langchain-ollama
- langchain-openrouter
- langchain-perplexity
- langchain-qdrant
- langchain-xai
- Other / not sure / general
Feature Description
Currently, LangChain supports similarity search for a single query embedding against all vectors in a vector store (top-k search), and some vector stores support multi-query (many queries at once). However, there is no native, efficient API for the following use case:
Given one query embedding and a custom list of entity embeddings (not the entire database), efficiently compute the similarity between the query and each entity in the list, ideally in a single call.
Use Case
- Usage of different embedding strategies in multi-step ranking. One embedding for retrieval and another for re-ranking
- Efficient enrichment of previously retrieved documents with similarity scores for re-ranking or hybrid retrieval workflows
Two-stage retrieval is a common pattern in Search, Recommender Systems and RAG applications.
Proposed Solution
- Add a method to the VectorStore interface, e.g., batch_similarity(query_embedding, entity_ids) or similarity_to_entities(query_embedding, entity_embeddings)
- The method should efficiently compute similarity scores between the query and the provided list of entities, leveraging backend capabilities where possible (e.g., SQL IN clause, vector store filtering)
- Return a list of (entity_id, similarity_score) pairs, sorted by similarity
Alternatives Considered
No response
Additional Context
No response