Create a LangChain Agentic RAG system using the IBM Granite-3.0-8B-Instruct model
Anna's tutorial on agentic retrieval augmented generation (RAG) explains setting up AI models, creating knowledge bases, and using tools like LangChain and Chroma DB for enhanced information retrieval.
MAIN POINTS FROM TRANSCRIPT
- Agentic RAG involves using AI models as reasoning engines to decide actions.
- The tutorial uses IBM's granite 3.08b instruct model but allows flexibility in model choice.
- Knowledge bases are created by listing URLs and processing content with LangChain's web loader.
- Chroma DB is used as a vector store for storing and retrieving embedded documents.
TAKEAWAYS
- Setting up credentials via a .env file is crucial for secure API access.
- The LLM's ability to answer questions depends on the training data's recency.
- Text splitters help process large documents into manageable chunks for AI models.
- Structured chat prompts enable AI agents to utilize multiple tools effectively.