Contextual AI RAG Agent

A Streamlit app that integrates Contextual AI's managed RAG platform. Create a datastore, ingest documents, spin up an agent, and chat grounded on your data.

## Features

- Document ingestion to Contextual AI datastores
- Agent creation bound to one or more datastores
- Response generation via Contextual’s Grounded Language Model (GLM) for faithful, retrieval-grounded answers
- Reranking of retrieved documents by query relevance and custom instructions (multilingual)
- Retrieval visualization (show attribution page image and metadata)
- LMUnit evaluation of answers using a custom rubric


## Prerequisites

- Contextual AI account and API key (Dashboard → API Keys)

### Generate an API key

1. Log in to your tenant at `app.contextual.ai`.
2. Click on "API Keys".
3. Click on "Create API Key".
4. Copy the key and paste it into the app sidebar when prompted.

## How to Run

1. Clone the repository and navigate to the app folder:
```bash
git clone https://github.com/Shubhamsaboo/awesome-llm-apps.git
cd awesome-llm-apps/rag_tutorials/contextualai_rag_agent
```

2. Create and activate a virtual environment.
3. Install dependencies:
```bash
pip install -r requirements.txt
```
4. Launch the app:
```bash
streamlit run contextualai_rag_agent.py
```

## Usage

1) In the sidebar, paste your Contextual AI API key. Optionally provide an existing Agent ID and/or Datastore ID if you already have them.

2) If needed, create a new datastore. Upload PDFs or text files to ingest. The app waits until documents finish processing.

3) Create a new agent (or use an existing one) linked to the datastore.

4) Ask questions in the chat input. Responses are generated by your Contextual AI agent.

5) Optional advanced features:
   - Agent Settings: Update the agent system prompt via the UI.
   - Debug & Evaluation: Toggle retrieval info to view attributions; run LMUnit evaluation on the last answer with a custom rubric.

## Configuration Notes

- If you're on a non-US cloud instance, set the Base URL in the sidebar (e.g., `http://api.contextual.ai/v1`). The app will use this base URL for all API calls, including readiness polling.
- Retrieval visualization uses `agents.query.retrieval_info` to fetch base64 page images and displays them directly.
- LMUnit evaluation uses `lmunit.create` to score the last answer against your rubric.