/AI-Assistant-Agent-RAG

AI Assistant (Agent ) with Llamaindex, Qdrant and OpenAI

Primary LanguageTypeScript

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.

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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.