Rag System Builder

What it does

The rag-system-builder skill allows you to construct Retrieval Augmented Generation (RAG) systems. It facilitates defining data sources, chunking strategies, embedding models, and vectorstores for building AI agents capable of accessing and reasoning over external knowledge. This enables your agent to provide more informed and contextually relevant responses based on provided documents or datasets.

When to use it

• Knowledge-intensive tasks: Build an agent that answers questions about a specific company's internal documentation.
• Content summarization: Create an agent capable of summarizing long articles or research papers by retrieving key information.
• Chatbots with external data: Power a chatbot with access to a product catalog, FAQs, or other relevant knowledge bases.
• Research assistance: Develop an agent that can quickly find and synthesize information from multiple scientific publications.

Key capabilities

• Data source definition
• Chunking strategy selection
• Embedding model integration
• Vectorstore configuration

Example prompts

• "Create a RAG system using my company's PDF documents as the data source."
• "Build a chatbot that can answer questions about this product catalog, chunking it into 500-token segments."
• "Set up a RAG pipeline with OpenAI embeddings and Pinecone vectorstore."

Tips & gotchas

The skill requires access to appropriate API keys for embedding models and vectorstores. Ensure these are configured correctly before attempting to build your RAG system.