{"categories":[{"label":"Database","url":"https://skillfed.io/packages/category/database/2"}],"enrichment":{"capability":"Provides LangChain abstractions for vector storage, chat history, and document management backed by PostgreSQL with support for both synchronous and asynchronous operations.","skillfed_tags":["langchain-integration","vector-database","rag"],"use_cases":["Build a RAG (retrieval-augmented generation) application that stores document embeddings in Postgres and retrieves relevant context for LLM prompts.","Persist multi-turn chat conversations in a database so users can resume sessions or audit interaction history.","Implement hybrid search combining semantic similarity and keyword matching to improve retrieval relevance.","Deploy LangChain applications on cloud-hosted Postgres (AlloyDB, Cloud SQL) with simplified connection management.","Extend the provided abstractions to add custom metadata filtering or schema changes for domain-specific requirements."],"what_it_does":"langchain-postgres bridges LangChain and PostgreSQL, providing implementations of core LangChain abstractions (vector stores, chat message history, document storage) that persist data in a Postgres database. It supports both synchronous and asynchronous workflows and offers two driver options: asyncpg and psycopg3.\n\nThe package's main components are PGVectorStore for semantic search with optional hybrid search (combining vector and keyword matching), and PostgresChatMessageHistory for persisting conversation state across sessions. Both are designed to be used directly or extended for custom applications. The package depends on sqlalchemy for ORM operations, pgvector for vector operations, and numpy for numerical work.","worth_installing":"Yes. The package is actively maintained, has low install friction, carries no known vulnerabilities, and is licensed permissively. It fills a clear role for developers building LangChain applications that need persistent vector storage or chat history in PostgreSQL. Install it if you are already using LangChain and need a Postgres-backed vector store or session manager; skip it if you do not use LangChain or prefer a different database backend."},"id":"langchain-postgres","links":{"html":"https://skillfed.io/packages/langchain-postgres","md":"https://skillfed.io/packages/langchain-postgres.md","pypi":"https://pypi.org/project/langchain-postgres/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-02-17","license_spdx":"MIT","license_treatment":"permissive","name":"langchain-postgres","python_support":"supports_current","summary":"An integration package connecting Postgres and LangChain"},"popularity":{"monthly_downloads":1578491,"position":3743,"tier":"top_5000"},"security":{"n_vulnerabilities":0},"version":"0.0.17"}