feat(langchain-oracle): Add Oracle Document Loader and Oracle Vector Search

[hackerdave]

Description

This PR integrates Oracle Document Loader and Oracle Vector Search with LangChain.js. The integration enables the use of Oracle's advanced vector search capabilities within the LangChain.js framework.

Key Features

• Adds support for Oracle Document Loader in LangChain.js.
  • loading documents either from the file system or a table
  • generating embeddings either via an ONNX model loaded into the database or a third-party REST call
  • summarizing documents either via Oracle Text or a third-party REST call
• Adds support for Oracle Vector Search in LangChain.js.
  • includes filter support
• Source is under libs/langchain-oracle
• Includes doc with example usage and tests for the integration.

Example usage

Text Splitter

import {OracleTextSplitter} from "@langchain/oracle";

const splitter = new OracleTextSplitter(conn, splitter_params);
let chunks = await splitter.splitText(doc.pageContent);

Embeddings

import {OracleEmbeddings} from "@langchain/oracle";

const embedder = new OracleEmbeddings(conn, embedder_params, proxy);
const embed = await embedder.embedQuery(chunk);

Testing

• Verified integration with unit tests.
• Added relevant test cases to ensure proper functionality.

[vercel]

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Project	Deployment	Preview	Comments	Updated (UTC)
langchainjs-docs	Error			Aug 21, 2025 7:22pm

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Project	Deployment	Preview	Comments	Updated (UTC)
langchainjs-api-refs	Ignored			Aug 21, 2025 7:22pm