# RAG‑Agent with ChromaDB and Web Search

A lightweight AI agent that can answer user questions by searching a local knowledge base stored in **ChromaDB** and the web via **Tavily**.  
The agent automatically decides which source to use, making it ideal for exam‑style assignments or quick prototyping.

---

## Table of Contents
- [Features](#features)
- [Prerequisites](#prerequisites)
- [Installation](#installation)
- [Project Structure](#project-structure)
- [Running the Agent](#running-the-agent)
  - `vectorstore.py`
  - `main.py`
- [Example Usage](#example-usage)
- [License](#license)

---

## Features
| Feature | Description |
|---------|-------------|
| **Local RAG** | Stores documents in a persistent ChromaDB collection. |
| **Web Search** | Uses Tavily to fetch up‑to‑date information from the internet. |
| **LLM & Embeddings** | Powered by Ollama (`llama3` for generation, `nomic-embed-text` for embeddings). |
| **Agent** | LangChain agent that chooses between local and web sources automatically. |
| **Easy Setup** | One‑liner install script and minimal configuration. |

---

## Prerequisites

| Requirement | Command / Note |
|-------------|----------------|
| Python | `>=3.10` (recommended 3.11+) |
| Ollama | Install from <https://ollama.ai> |
| Tavily API Key | Sign up at <https://tavily.com> and set `TAVILY_API_KEY` in `.env`. |

---

## Installation

```bash
# Pull required models into Ollama
ollama pull llama3
ollama pull nomic-embed-text

# Install Python dependencies
pip install langchain langchain-chroma langchain-tavily langchain-ollama tavily-python chromadb python-dotenv
```

Create a `.env` file in the project root:

```dotenv
TAVILY_API_KEY=your_tavily_api_key_here
```

---

## Project Structure

```
.
├── vectorstore.py   # Helpers for creating/loading ChromaDB and adding docs
├── main.py          # Agent entry point
└── .env             # Tavily API key (not committed)
```

- **`vectorstore.py`**  
  * `create_vectorstore(persist_directory)` – returns a ready‑to‑use Chroma collection.  
  * `load_documents(directory, vectorstore)` – reads `.txt/.md`, splits into chunks, and adds them to the store.

- **`main.py`**  
  Sets up the LangChain agent with two tools:  
  - `search_local_kb(query, top_k)` – semantic search in Chroma.  
  - `web_search(query)` – Tavily web search.  
  The agent decides which tool to invoke based on the query.

---

## Running the Agent

### 1. Prepare the Knowledge Base

```bash
# Place your .txt or .md files into a folder, e.g., ./docs
mkdir docs
echo "Hello world!" > docs/hello.txt

# Load them into ChromaDB
python -c "
from vectorstore import create_vectorstore, load_documents
vs = create_vectorstore()
load_documents('docs', vs)
print('Documents loaded')
"
```

### 2. Start the Agent

```bash
python main.py
```

You will see a prompt:

```
> What would you like to know?
```

Type any question; the agent will answer using either the local KB or Tavily.

---

## Example Usage

```text
> Who is the current President of France?

Agent: The current President of France is Emmanuel Macron. (Source: web_search)

> Summarize the contents of hello.txt

Agent: The file contains a simple greeting: "Hello world!". (Source: search_local_kb)
```

The agent automatically selects the most relevant source.

---

## License

MIT © 2026 – feel free to adapt and extend.