AI Assistant with RAG (Retrieval Augmented Generation) and Agent

Check out live at ask.ankurpatel.dev

This is a proof of concept project for an LLM application with RAG and AI Agents.

1. The Chat_UI folder contains a chatbot UI built with React and Tailwind.

2. The endpoint can be deployed as a lambda function on Amazon with the following components:

1. Agent Augmentation with LlamaIndex.

   • TypeScript is used for agent and tool calling.
   • Inference is done via GPT 3.5.

2. Vector Storage with Qdrant Vector Database.

   • A free tier cluster is used via Qdrant.
   • Points/Vectors are stored in a separate collection on a cluster.
   • OpenAI Embeddings are used for vector generation.

3. Tools/Scripts Information

   • /src/Tools/calendar.tool.ts - Used to fetch availability from Google Calendar. It also includes a tool for meeting creation on Google Calendar (NOTE: Domain-wide Delegation is needed for the service account to add attendees to Google Calendar events.)
   • src/Tools/pdfreader.tools.ts - This tool can be used to create embeddings and then indexes for documents stored in the /data folder.
   • src/Tools/qdrant_vector_store.tool.ts - This is how the vector store can be made available as a tool.
   • src/vector-store/qdrant.ingestion.ts - This is used to create vectors from documents stored in the /data folder and store them in the vector database.