Document QA
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Transforms unstructured documents into a searchable knowledge base, enabling AI-powered question answering.
About
This system provides a robust solution for document-based question answering. It seamlessly integrates with Google Drive to ingest text documents, processes them by splitting into chunks, and generates embeddings using OpenAI's models. These embeddings are then stored in Qdrant Cloud for efficient vector similarity search. When a user queries, the system retrieves relevant context from the stored documents and leverages OpenAI's GPT-4 to generate accurate, natural language answers, exposed via a flexible REST API. It also incorporates a modular MCP server for extensible tool handling.
Key Features
- Google Drive integration for document ingestion
- Qdrant Cloud for vector storage and similarity search
- 0 GitHub stars
- Modular MCP server with search and answer generation tools
- GPT-4 powered natural language question answering
- OpenAI embeddings for semantic search readiness
Use Cases
- Building an internal knowledge base for quick information retrieval
- Developing an AI assistant for answering document-specific queries
- Demonstrating a complete pipeline for semantic search and AI-driven QA
Sponsored