# RAG Agent with Qdrant & Ollama

This project implements a simple **RAG (Retrieval‑Augmented Generation) agent** that uses:

* **Qdrant** – a vector database for storing embeddings.
* **Ollama** – local LLM and embedding model (`llama3` and `nomic‑embed‑text`).
* **LangChain** – framework for building the agent and tools.

The agent can:

* **Add** documents to the knowledge base.
* **Search** the knowledge base for relevant chunks.
* Answer user queries using the stored knowledge.

## Project structure

```
workspace/
├── src/
│   ├── __init__.py
│   ├── vector_store.py   # Qdrant wrapper
│   ├── tools.py          # LangChain tools
│   ├── agent.py          # Agent definition
│   ├── loader.py         # Load all .txt files from a directory
│   └── cli.py            # Interactive command‑line client
├── requirements.txt
└── README.md
```

## Installation

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

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

## Usage

### 1. Load documents into the knowledge base

```bash
python -m src.loader /path/to/text/files
```

All `.txt` files in the directory (recursively) are added to the vector store.

### 2. Start the interactive CLI

```bash
python -m src.cli
```

Once started you can use the following commands:

| Command | Description |
|---------|-------------|
| `/add <title> <file_path>` | Add a single file to the knowledge base. |
| `/search <query>` | Search the knowledge base and display top results. |
| `/quit` | Exit the program. |
| `/help` | Show help. |
| Any other text | Sent to the agent as a user query. |

### 3. Example session

```
RAG Agent CLI. Type /help for commands.
> /add example docs/example.txt
Document 'example' added.
> /search quantum
Results:
1. [example - chunk 0] Quantum mechanics is the branch of physics that deals with...
> Tell me more about quantum.
Sure! Here is what I found in the knowledge base: ...
> /quit
Goodbye.
```

## How it works

* **Vector Store** – `KnowledgeBase` wraps a `QdrantVectorStore`.  It creates the collection only if it does not exist, preventing accidental data loss.
* **Chunking** – Documents are split into 500‑character chunks with 50‑character overlap using `RecursiveCharacterTextSplitter`.
* **Tools** – Two LangChain tools are exposed:
  * `search_knowledge_base(query, max_results)` – returns a list of relevant chunks.
  * `add_to_knowledge_base(content, title)` – adds a document.
* **Agent** – Built with `create_agent` from `langchain.agents`.  It uses the local `ChatOllama` model (`llama3`).

## Extending

* Replace the embedding model by editing `KnowledgeBase.__init__`.
* Add more tools (e.g., delete from knowledge base) following the same pattern.
* Deploy the agent as a web service by wrapping `run_query` in a FastAPI endpoint.

## License

MIT License.