RAG_TOOLS

[srilaasya]

📥 Pull Request

[RAG_TOOL] ChromaDB vector store with OpenAIEmbeddings

📘 Description
RAG (Retrieval-Augmented Generation) tool that integrates ChromaDB vector store with OpenAI embeddings for document storage and semantic search retrievals.

Features

• Vector Store Management: Create and manage ChromaDB collections with OpenAI's text-embedding-ada-002 model
• Document Ingestion: Add documents with metadata (content + URL) to the vector store
• Semantic Search: Query documents using natural language, with relevance scoring
• Persistent Storage: Collections are stored on disk for reuse across sessions

Core Functions

1. create_collection(): Initialize a new ChromaDB collection with OpenAI embeddings
2. add_documents(): Ingest documents with their metadata into the collection
3. query_collection(): Perform semantic search with relevance scoring

#273