OracleAI vector search integrations

Oracle AI Database is built for AI workloads where you query data by meaning (semantics), not just keywords. It combines semantic search over unstructured content with relational filtering over business data in one system—so you can build AI retrieval workflows (like RAG) without introducing a separate vector database and fragmenting data across multiple platforms.

Why it matters

• One system for AI + business data: vectors and operational data live together.
• Less fragmentation: fewer moving parts than a separate vector store.
• Database-grade guarantees: apply security, transactions, scale, and availability to the same AI workload.

Prerequisites

Install langchain-oracledb. The python-oracledb driver will be installed automatically as a dependency.

pip install -qU langchain-oracledb

uv add langchain-oracledb

Document loaders

Please check the usage example.

from langchain_oracledb.document_loaders.oracleai import OracleDocLoader

Text splitter

Please check the usage example.

from langchain_oracledb.document_loaders.oracleai import OracleTextSplitter

Embeddings

Please check the usage example.

from langchain_oracledb.embeddings.oracleai import OracleEmbeddings

Summary

Please check the usage example.

from langchain_oracledb.utilities.oracleai import OracleSummary

Vector store

Please check the usage example.

from langchain_oracledb.vectorstores.oraclevs import OracleVS

End to end demo

Please check the Oracle AI Vector Search End-to-End Demo Guide.

Additional resources

• GitHub repository — the official oracle/langchain-oracle monorepo, which also ships langgraph-oracledb (LangGraph checkpointers and stores backed by Oracle Database) and @oracle/langchain-oracledb for LangChain.js
• Samples — a numbered learning path covering chat, agents, tool calling, structured output, embeddings, and RAG with Oracle datastores
• langchain-oracledb package documentation