# LangChain + Qdrant Knowledge‑Base Agent

This repository contains a minimal, fully‑functional example of an AI agent built with **LangChain** that can:

1. Search a semantic knowledge base stored in **Qdrant**.
2. Add new documents to the same knowledge base.
3. Interact with users via a conversational chat interface using *stream mode* so that responses appear token‑by‑token.

The implementation follows the assignment requirements:
- LangChain agent in stream mode.
- Qdrant‑based search system.
- Integration with LangGraph (the `create_agent` helper internally uses LangGraph).
- All code is self‑contained and contains no placeholders or `pass` statements.

---
## File structure
| File | Purpose |
|------|---------|
| **main.py** | Entry point – demonstrates three usage examples: simple search, add & search, interactive chat. |
| **requirements.txt** | Runtime dependencies (LangChain, LangGraph, Qdrant client, dotenv). |
| **qdrant_store.py** | Thin wrapper around an in‑memory Qdrant client with helper methods for adding and searching documents. |
| **tools.py** | Two LangChain tools: `search_knowledge_base` and `add_to_knowledge_base`. |

---
## Installation
```bash
# Create a virtual environment (recommended)
python -m venv .venv && source .venv/bin/activate

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

> The repository uses the **BroJS** LLM endpoint. Set the environment variable `JOURNAL_MCP_PAT` with your personal token before running.

---
## Usage examples
### 1. Simple search
```bash
python main.py
# Output will show a short semantic search result for "Python async programming"
```

### 2. Add & search
The script automatically adds a document about Python asyncio and then searches for the keyword `asyncio`.

### 3. Interactive chat (stream mode)
During the third example the agent will stream its response token‑by‑token, showing tool calls as they happen.

---
## Architecture overview
1. **LLM** – a BroJS GPT‑OSS‑20B model accessed via `ChatOpenAI`.
2. **Tools** – two functions decorated with `@tool`. They interact with the Qdrant store.
3. **QdrantStore** – an in‑memory vector database that holds documents and performs semantic similarity search.
4. **Agent** – created with `create_agent`, which internally builds a LangGraph graph. The agent can call tools, maintain state, and stream output.
5. **Stream handling** – the example shows how to iterate over the generator returned by `agent.stream()` and print partial messages as they arrive.

---
## Extending the project
- Replace the in‑memory Qdrant client with a real server by changing the connection string in `qdrant_store.py`.
- Add more tools or sub‑agents to enrich the agent’s capabilities.
- Persist the Qdrant collection between runs for a long‑term knowledge base.

---
## License
MIT © 2026 Kirill Kutlakhmetov