{"categories":[{"label":"Artificial Intelligence","url":"https://skillfed.io/packages/category/scientific-engineering-artificial-intelligence/8"}],"enrichment":{"capability":"Integrates Oracle Database with LangChain to enable vector search, document loading, text splitting, and embedding generation for building retrieval-augmented generation (RAG) pipelines.","skillfed_tags":["vector-search","rag","oracle-integration"],"use_cases":["Build a RAG pipeline that stores document embeddings in Oracle and retrieves relevant chunks for LLM context","Load large documents from Oracle tables, split them into chunks, and index them for semantic search","Generate embeddings for text stored in Oracle Database and perform similarity searches within LangChain workflows","Integrate Oracle Autonomous Database as a vector store backend for LangChain applications","Process documents with Oracle's native text-splitting logic before ingesting into a vector store"],"what_it_does":"langchain-oracledb bridges LangChain and Oracle Database, providing components to store and retrieve vector embeddings, load documents from Oracle tables, split text using Oracle's native capabilities, and generate embeddings. It is designed for building RAG pipelines that leverage Oracle's AI Vector Search feature.\n\nThe package exposes OracleVS for vector storage with similarity search, OracleDocLoader for loading documents from Oracle tables or files, OracleTextSplitter for chunking text by characters, words, or sentences, and OracleEmbeddings for generating embeddings. It depends on oracledb for database connectivity, langchain-core for the integration framework, and standard data-science libraries (numpy, pydantic). Connection to an Oracle Database is required; the package supports both Thin mode (no Oracle Client needed) and Thick mode (with Oracle Client libraries).","worth_installing":"Yes, if you are building a LangChain RAG application on Oracle Database. The package is actively maintained, has low install friction, and no known vulnerabilities. The UPL-1.0 license is permissive but less standard\u2014verify it aligns with your project before committing. The integration is well-documented with examples and covers the full RAG workflow."},"id":"langchain-oracledb","links":{"html":"https://skillfed.io/packages/langchain-oracledb","md":"https://skillfed.io/packages/langchain-oracledb.md","pypi":"https://pypi.org/project/langchain-oracledb/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-06-03","license_spdx":"UPL-1.0","license_treatment":"unclear","name":"langchain-oracledb","python_support":"supports_current","summary":"An integration package connecting Oracle Database and LangChain"},"popularity":{"monthly_downloads":149307,"position":10997,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"1.5.0"}