{"categories":[{"label":"Artificial Intelligence","url":"https://skillfed.io/packages/category/scientific-engineering-artificial-intelligence/6"}],"enrichment":{"capability":"Integrates Elasticsearch with LangChain to provide vector storage, retrieval, embeddings, chat history, and LLM caching backed by Elasticsearch.","skillfed_tags":["langchain-integration","vector-database","embeddings-cache"],"use_cases":["Store and retrieve document embeddings in Elasticsearch for semantic search within a LangChain application.","Implement custom retrieval logic using ElasticsearchRetriever with fuzzy matching or other advanced Elasticsearch queries.","Cache LLM responses in Elasticsearch to reduce API costs and latency for repeated queries.","Persist chat conversation history across sessions using Elasticsearch as the backend store.","Generate embeddings on-demand using models deployed within an Elasticsearch cluster.","Cache embedding computations to avoid recomputing vectors for identical inputs."],"what_it_does":"langchain-elasticsearch is a LangChain integration package that bridges Elasticsearch and LangChain applications. It provides multiple components: ElasticsearchStore for vector storage and similarity search, ElasticsearchRetriever for custom query logic, ElasticsearchEmbeddings for generating embeddings using Elasticsearch-deployed models, ElasticsearchChatMessageHistory for persisting conversation state, and caching layers (ElasticsearchCache and ElasticsearchEmbeddingsCache) for reducing LLM and embedding costs.\n\nThe package depends on elasticsearch and langchain-core as its runtime dependencies. It supports Python 3.10 through 3.12 and is designed for developers building LangChain applications that need persistent, searchable storage and caching backed by Elasticsearch. Setup requires either an Elastic Cloud deployment or a self-hosted Elasticsearch instance with appropriate credentials.","worth_installing":"Yes. The package has low install friction, active maintenance, permissive MIT licensing, no known vulnerabilities, and fills a clear integration gap for LangChain users who need Elasticsearch-backed storage and caching. Install it if you are building a LangChain application that requires persistent vector storage, retrieval, or LLM/embedding caching with Elasticsearch."},"id":"langchain-elasticsearch","links":{"html":"https://skillfed.io/packages/langchain-elasticsearch","md":"https://skillfed.io/packages/langchain-elasticsearch.md","pypi":"https://pypi.org/project/langchain-elasticsearch/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2025-12-16","license_spdx":null,"license_treatment":"permissive","name":"langchain-elasticsearch","python_support":"supports_current","summary":"An integration package connecting Elasticsearch and LangChain"},"popularity":{"monthly_downloads":352711,"position":7311,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"1.0.0"}