Enumerator of the Distance strategies for calculating distances between vectors.
Elasticsearch embedding models.
This class provides an interface to generate embeddings using a model deployed in an Elasticsearch cluster. It requires an Elasticsearch connection and the model_id
Adapter for LangChain Embeddings to support the EmbeddingService interface from elasticsearch.helpers.vectorstore.
Elasticsearch chat message history.
Stores chat message history in Elasticsearch for persistence across sessions.
Elasticsearch vector store.
Elasticsearch retriever.
Setup:
Install langchain_elasticsearch and start Elasticsearch locally using
the start-local script.
pip install -qU langchai
Elasticsearch LLM cache.
Caches LLM responses in Elasticsearch to avoid repeated calls for identical prompts.
Elasticsearch embeddings cache.
Caches embeddings in Elasticsearch to avoid repeated embedding computations.
Elasticsearch embedding models.
This class provides an interface to generate embeddings using a model deployed in an Elasticsearch cluster. It requires an Elasticsearch connection and the model_id
Adapter for LangChain Embeddings to support the EmbeddingService interface from elasticsearch.helpers.vectorstore.
Elasticsearch chat message history.
Stores chat message history in Elasticsearch for persistence across sessions.
Elasticsearch vector store.
Elasticsearch retriever.
Setup:
Install langchain_elasticsearch and start Elasticsearch locally using
the start-local script.
pip install -qU langchai
Elasticsearch LLM cache.
Caches LLM responses in Elasticsearch to avoid repeated calls for identical prompts.
Elasticsearch embeddings cache.
Caches embeddings in Elasticsearch to avoid repeated embedding computations.
Base class for Elasticsearch retrieval strategies.
Approximate retrieval strategy using the HNSW algorithm.
Exact retrieval strategy using the script_score query.
Sparse retrieval strategy using the text_expansion processor.
Retrieval strategy using the native BM25 algorithm of Elasticsearch.