# RAG‑Agent with ChromaDB and Web Search

A lightweight RAG (Retrieval‑Augmented Generation) agent that uses a local **ChromaDB** vector store for knowledge retrieval and **Tavily** for live web search.  
The agent automatically decides whether to answer from the local knowledge base or to fetch fresh information from the web.

> **Prerequisites**  
> • Python 3.10+  
> • Ollama (LLM & embeddings)  
> • Tavily API key

---

## 📦 Project Structure

```
.
├── vectorstore.py          # Vector store creation & document ingestion
├── agent.py                # RAG agent implementation (not shown in the prompt)
├── .env                    # Tavily API key
├── requirements.txt        # Dependencies
└── README.md
```

---

## 🚀 Installation

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

# 2. Install Python dependencies
pip install -r requirements.txt
```

`requirements.txt`

```text
langchain
langchain-chroma
langchain-tavily
langchain-ollama
tavily-python
chromadb
python-dotenv
```

> **Note**:  
> *If you use a different LLM or embeddings provider, adjust the `create_vectorstore` function accordingly.*

---

## ⚙️ Configuration

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

```dotenv
TAVILY_API_KEY=your_tavily_api_key_here
```

The key is used by the Tavily client for web search.

---

## 📚 Using the Vector Store

### 1. Create the store

```python
from vectorstore import create_vectorstore

vectorstore = create_vectorstore("./chroma_db")
```

### 2. Load documents into the store

```python
from vectorstore import load_documents

# Directory containing .txt or .md files
load_documents("./knowledge_base", vectorstore)
```

The function will:

1. Read all `.txt` and `.md` files in the given directory.
2. Split them into chunks using `RecursiveCharacterTextSplitter`.
3. Add the chunks to the Chroma collection.

---

## 🧩 Running the Agent

> **Assumption**: `agent.py` contains the main RAG agent logic that imports `vectorstore.py`.  
> The agent automatically chooses between the local vector store and Tavily search.

```bash
python agent.py
```

The agent will:

1. Prompt the user for a question.
2. Query the vector store for relevant chunks.
3. If the answer is insufficient, perform a web search via Tavily.
4. Generate a final answer with the chosen source.

---

## 🔧 Example Workflow

```bash
$ python agent.py
Enter your question (or 'exit' to quit): What is the capital of France?

Answer: The capital of France is Paris.
Source: Local knowledge base (retrieved from chroma_db)
```

If the question is about a very recent event:

```bash
$ python agent.py
Enter your question (or 'exit' to quit): Who won the 2024 World Series?

Answer: The 2024 World Series was won by the Texas Rangers.
Source: Web search (Tavily)
```

---

## 📦 Adding New Documents

1. Drop your `.txt` or `.md` files into the `knowledge_base/` directory.
2. Run:

```bash
python -c "from vectorstore import load_documents, create_vectorstore; load_documents('knowledge_base', create_vectorstore())"
```

The new documents will be indexed automatically.

---

## 🛠️ Troubleshooting

| Symptom | Likely Cause | Fix |
|---------|--------------|-----|
| `ModuleNotFoundError: No module named 'langchain_ollama'` | Missing dependency | `pip install langchain-ollama` |
| `Ollama` not running | Ollama daemon stopped | `ollama serve` |
| Tavily errors | Invalid API key | Verify `.env` and restart |

---

## 📄 License

MIT License – feel free to adapt and extend.

---