# 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**

   ```bash
   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**

   ```bash
   npm install
   ```

3. **Configure environment variables**

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

   ```dotenv
   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**

   ```bash
   npm start -- "Your question here"
   ```

   Example:

   ```bash
   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.