Document QA System

Example Use Cases

• 1

  Implement document question-answering systems using vector similarity search.

• 2

  Index and query large document repositories with contextual embeddings.

• 3

  Effective tools for context-aware document retrieval and analysis.

Description

This MCP server utilizes DuckDB-VSS and Python to create a document question-answering system. It employs the plamo-embedding-1b model and Mean Contextualized Pooling (MCP) to efficiently index and search documents. The system is designed for querying documents with context-aware embeddings, enhancing the precision of search results.