# Agent with RAG Memory

This repository contains a simple **Retrieval‑Augmented Generation (RAG)** agent
implemented with LangChain, FAISS for vector storage, and OpenAI embeddings
and LLM.  It also provides an `auto_check_graph` function that verifies the
generated answer against a ground‑truth mapping and returns a `verdict_row`.

> **Important**  
> The auto‑check graph must return a `verdict_row`.  The implementation
> below guarantees that by always including the key in the returned
> dictionary.

## Features

- **RAG Agent** – Load documents, embed them, store in FAISS, and answer queries.
- **Auto‑Check Graph** – Run a query, generate an answer, compare it to a
  ground‑truth answer, and return a verdict (`PASS`, `FAIL`, or `UNKNOWN`).
- **Unit Tests** – Verify that the agent and auto‑check graph work as
  expected.

## Installation

```bash
# Create a virtual environment (recommended)
python -m venv .venv
source .venv/bin/activate   # On Windows: .venv\Scripts\activate

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

`requirements.txt` contains:

```
langchain
openai
faiss-cpu
pytest
```

> **OpenAI API Key**  
> If you want to use real embeddings and LLM, set the environment variable
> `OPENAI_API_KEY`:

```bash
export OPENAI_API_KEY="sk-..."
```

If the key is not set, the agent falls back to `FakeEmbeddings` and
`FakeLLM`, which are suitable for local testing and unit tests.

## Usage

```python
from src.index import RAGAgent, auto_check_graph

# Create agent
agent = RAGAgent()

# Add documents (e.g., from a directory)
agent.add_documents([
    "The capital of France is Paris.",
    "William Shakespeare wrote Hamlet."
])

# Define ground truth mapping
ground_truth = {
    "What is the capital of France?": "Paris",
    "Who wrote Hamlet?": "William Shakespeare",
}

# Run auto‑check graph
result = auto_check_graph(
    "What is the capital of France?",
    agent,
    ground_truth
)

print(result)
# Output:
# {
#   "verdict_row": "PASS",
#   "answer": "Paris",
#   "expected": "Paris"
# }
```

## Running Tests

```bash
pytest
```

The tests cover:

- Adding documents and querying.
- Auto‑check graph returning `PASS`, `FAIL`, and `UNKNOWN` verdicts.
- Handling of empty queries and missing ground‑truth.

## Project Structure

```
src/
├── index.py          # Main implementation
tests/
├── test_agent.py     # Unit tests
README.md
requirements.txt
```

## License

MIT License