# RAG Agent with Qdrant and Ollama

## Project Overview
This repository contains a minimal yet complete implementation of an AI agent that can **search** and **add** information to a local knowledge base powered by **Qdrant** (vector database) and **Ollama** (local LLM & embeddings). The agent is built using the LangChain framework.

The main components are:
- **Vector store** – Qdrant client with an initialized collection.
- **Text splitter** – RecursiveCharacterTextSplitter for chunking documents.
- **Embedding model** – OllamaEmbeddings (`nomic-embed-text`).
- **LLM** – ChatOllama (`llama3`).
- **Tools** – `search_knowledge_base` and `add_to_knowledge_base`.
- **Agent** – created with `create_agent` from LangChain.
- **CLI client** – simple interactive loop to demonstrate adding documents and searching the knowledge base.

## Directory Structure
```
├── README.md
├── requirements.txt
├── main.py          # CLI entry point
├── agent.py         # Agent creation logic
├── tools.py         # LangChain tool definitions
├── utils.py         # Qdrant client, splitter, and helper functions
└── docs/            # Directory with text files to load initially (optional)
```

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

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

## Usage
1. **Load documents** – Place any `.txt` files in the `docs/` directory.
2. **Run the CLI**:
   ```bash
   python main.py
   ```
3. In the interactive prompt you can use:
   - `/add <file_path>` – Add a new document to the knowledge base.
   - `/search <query>` – Search the knowledge base and display results.
   - `/quit` – Exit the program.

## Example
```text
> /search python data structures
1. Python lists are ordered collections...
2. Tuples are immutable sequences...
```

## Architecture
- The **agent** is a LangChain agent that uses two tools: `search_knowledge_base` and `add_to_knowledge_base`. It receives user messages, decides which tool to call, and returns the result.
- The **vector store** is wrapped by `QdrantVectorStore`, which handles embedding generation via OllamaEmbeddings. Documents are split into chunks before insertion.
- The **CLI** orchestrates loading documents at startup and provides a simple REPL for demonstration purposes.

## License
MIT © 2026