{"categories":[{"label":"Database","url":"https://skillfed.io/packages/category/database/3"}],"enrichment":{"capability":"Integrates PostgreSQL with pgvector extension as a vector store backend for LlamaIndex, supporting multiple query modes including similarity search, hybrid retrieval, BM25 sparse search, full-text search, and maximal marginal relevance.","skillfed_tags":["vector-search","embeddings","postgres-integration"],"use_cases":["Store and retrieve embeddings from PostgreSQL when you already have Postgres infrastructure and want to avoid a separate vector database.","Implement hybrid search combining dense vector similarity with sparse BM25 text retrieval on the same dataset.","Use MMR queries to retrieve diverse, relevant results\u2014useful for summarization or multi-faceted information retrieval.","Build full-text search on embeddings using PostgreSQL's native text search capabilities alongside vector operations.","Integrate vector search into existing LlamaIndex applications that already use Postgres for other data."],"what_it_does":"This package bridges LlamaIndex and PostgreSQL by providing a vector store adapter that leverages the pgvector extension for storing and querying embeddings. It wraps the underlying database operations (via sqlalchemy, psycopg2-binary, and asyncpg) into a LlamaIndex-compatible interface, letting you use PostgreSQL as your embedding backend instead of a specialized vector database.\n\nThe integration supports five query modes: DEFAULT (standard similarity), HYBRID (dense + sparse), SPARSE (BM25 text), TEXT_SEARCH (full-text), and MMR (maximal marginal relevance for diverse results). You configure it by pointing to a PostgreSQL database and table, specifying embedding dimension, and then instantiate a PGVectorStore object that integrates directly with LlamaIndex's index and query engine APIs.","worth_installing":"Yes, if you are building a LlamaIndex application and have PostgreSQL with pgvector already available. The package is actively maintained, has no known vulnerabilities, and low install friction. It is most valuable when you want to avoid adding a separate vector database and can leverage existing Postgres infrastructure. Not necessary if you prefer a dedicated vector store or do not use LlamaIndex."},"id":"llama-index-vector-stores-postgres","links":{"html":"https://skillfed.io/packages/llama-index-vector-stores-postgres","md":"https://skillfed.io/packages/llama-index-vector-stores-postgres.md","pypi":"https://pypi.org/project/llama-index-vector-stores-postgres/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-03-13","license_spdx":"MIT","license_treatment":"permissive","name":"llama-index-vector-stores-postgres","python_support":"supports_current","summary":"llama-index vector_stores postgres integration"},"popularity":{"monthly_downloads":527763,"position":6169,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"0.8.1"}