Automated Private Document QA System with Llama3, Postgres, Qdrant & Google Driv

Overview

This workflow establishes a secure, automated question-answering (QA) system for private documents by integrating advanced AI models, vector databases, and cloud storage. It leverages Llama3 via Ollama, Postgres for chat memory, Qdrant for vector storage, and Google Drive for document management.

Key Features

• AI-Powered Chat: Utilizes Llama3 through Ollama for natural language understanding and response generation.
• Document Ingestion: Monitors Google Drive for new or updated files, automatically processing them for QA.
• Vector Search: Embeds document content using Ollama and stores vectors in Qdrant for efficient semantic search.
• Persistent Memory: Maintains chat context and history in Postgres, enabling contextual, multi-turn conversations.
• Automated Triggers: Initiates workflows on chat messages or document changes, ensuring real-time updates.

Benefits

• Enhanced Productivity: Automates document-based Q&A, reducing manual search and response time.
• Data Security: Keeps sensitive documents private while enabling advanced AI capabilities.
• Scalable Integration: Easily adapts to growing document repositories and user queries.

Use Cases

• Internal knowledge bases for enterprises
• Automated support for HR, legal, or technical documentation
• Secure, AI-driven document search for teams

Integrations

• Google Drive, Ollama (Llama3), Postgres, Qdrant, LangChain