RAG Agent with ChromaDB and Web Search

This project implements a Retrieval-Augmented Generation (RAG) agent that:

• Stores and retrieves embeddings from ChromaDB.
• Performs web search using DuckDuckGo to fetch additional context.
• Generates answers with an Ollama language model.

Prerequisites

• Node.js v20 or newer
• ChromaDB server running locally (default URL: chromadb://localhost:8000)
• Ollama server running locally (default URL: http://localhost:11434)

Setup

1. Clone the repository

   git clone https://git.brojs.ru/kuzakhmetovartur/ekzamen-rag-agent-s-chromadb-i-veb-poisk.git
   cd ekzamen-rag-agent-s-chromadb-i-veb-poisk
   

2. Install dependencies

   npm install
   

3. Configure environment variables

   Create a .env file in the project root (or modify the existing one):

   CHROMA_URL=chromadb://localhost:8000
   CHROMA_COLLECTION=rag_collection
   OLLAMA_HOST=http://localhost:11434
   OLLAMA_MODEL=llama3
   OLLAMA_EMBEDDING_MODEL=nomic-embed-text
   ADD_SAMPLE_DOCS=true
   

   • CHROMA_URL: URL of your ChromaDB instance.
   • CHROMA_COLLECTION: Name of the collection to use.
   • OLLAMA_HOST: URL of your Ollama server.
   • OLLAMA_MODEL: Ollama model for generation.
   • OLLAMA_EMBEDDING_MODEL: Ollama model for embeddings.
   • ADD_SAMPLE_DOCS: Set to true to automatically add a few sample documents on startup.

4. Run the agent

   npm start -- "Your question here"
   

   Example:

   npm start -- "What is LangChain?"
   

   The agent will:

   • Search the local ChromaDB collection.
   • Perform a DuckDuckGo web search.
   • Combine the results and generate an answer using Ollama.

Project Structure

.
├── src
│   ├── agent.js        # Agent logic (retrieval + generation)
│   ├── index.js        # CLI entry point
│   ├── vectorStore.js  # ChromaDB wrapper
│   └── webSearch.js    # DuckDuckGo search helper
├── .env                # Environment configuration
├── package.json        # Dependencies and scripts
└── README.md           # Documentation

Notes

• The agent uses LangChain 1.x APIs.
• No Qdrant references are present; only ChromaDB is used.
• The web search is performed via DuckDuckGo’s public JSON API (no API key required).
• The Ollama LLM is used for both embeddings and generation.

Feel free to extend the agent with additional retrievers or custom prompts as needed.