Knowledge base lookup

[Agent-E11]

There are ways to train an LLM using documents. We might be able to do this with documents in the club's public resources repo

[Agent-E11]

I just found this randomly:

For developers, the process of adding RAG features to your app is basically: EMBEDDINGS

• choose your vector database; pg_vector, chromadb, several others
• chunk your document into paragraphs or sentences
• send each chunk through an embedding model locally or use a service like OpenAI
• store the embeddings and chunks in your db CHAT
• a user creates a prompt
• you generate an embedding for this prompt
• you do a cosine similarity search to find the most relevant documents to your prompt embeddings
• you receive the top results in a response and send the original prompt + the relavent documents to the model
• the model returns with a response with additional context you provided

RAG stands for Retrieval-Augmented Generation