feat: Add Contextual AI RAG agent

rag_tutorials/contextualai_rag_agent/README.md

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+# 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.