Skip to main contentOverview
Contextual AI lets you create and run specialized AI agents that are powered by your data. This demo creates a retrieval-augmented generation (RAG) agent for a financial use case. The agent will answer questions based on the documents provided, but avoid any forward looking statements (e.g., “Tell me about sales in 2048”)
Scope
This example can be completed in under 30 minutes and walks you through the core steps of building a simple RAG workflow:
- Create a Datastore: The home for your unstructured documents.
- Ingest Documents: We’ll upload a single PDF, though the platform scales to many formats—including HTML.
- Create a RAG Agent: All you need to begin is a well-crafted system prompt.
- Query the Agent: Get answers grounded entirely in your own data.
Prerequisites
- Contextual AI API Key
- Python 3.9+
Running The Demo
You can run the demo either locally or using Google Colab:
