alinvdu/PdfToMem
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PdfToMem is a system that transforms PDFs into structured, queryable memory representations optimized for Large Language Models (LLMs).
PdfToMem addresses the challenge of memory in Large Language Models by converting unstructured PDF content into structured, queryable memory. It utilizes a combination of reasoning-powered ingestion, structured retrieval, and a multi-agent architecture to achieve this transformation. The core of PdfToMem is the MCP Server, which coordinates the ingestion, reasoning, storage, and querying processes using advanced tools like LlamaIndex and LangGraph. The system is designed to optimize memory representation and retrieval performance, making it highly suitable for LLMs. PdfToMem also features a user-friendly MCP Client interface for managing the entire lifecycle of PDF ingestion and memory querying.
Features
- Designs ingestion pipelines using tool-based reasoning.
- Employs modular tools for structured data extraction from PDFs.
- Utilizes a multi-agent architecture for intelligent processing.
- Offers reasoning-driven memory planning for optimal retrieval.
- Provides a React-based interface for full lifecycle control.
Tools
determine_memory_architecture
Automatically infers the optimal memory structure using LlamaIndex abstractions.