Skip to main content

Documentation Index

Fetch the complete documentation index at: https://docs.langchain.com/llms.txt

Use this file to discover all available pages before exploring further.

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

​
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.
Connect these docs to Claude, VSCode, and more via MCP for real-time answers.
Edit this page on GitHub or file an issue.