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
Turn PDFs into structured, queryable memory—built for LLMs.
Large Language Models struggle with memory. PdfToMem makes it effortless.
By combining reasoning-powered ingestion, structured retrieval, and a multi-agent architecture, it transforms unstructured PDFs into rich memory representations.
It exposes a powerful MCP Server to coordinate ingestion, reasoning, storage, and querying—using state-of-the-art tools like LlamaIndex and LangGraph.
🎥 Video Walkthrough
🧠 MCP Server — Memory Control Plane
The MCP Server is the brain of PdfToMem. It leverages tool-based reasoning to:
- 🏗️ Design ingestion pipelines
- 🧩 Choose the right memory representation
- 📊 Index and structure PDF content for retrieval
Use tools like determine_memory_architecture to automatically infer the optimal memory structure using LlamaIndex abstractions:
VectorIndexQueryEngineLLMSelector
🛠️ Tool-Driven Parsing — Powered by LangGraph
PdfToMem employs modular tools to extract structured data from PDFs:
- 📄 Text Extraction
- 📊 Table Detection
- 🧾 OCR for Scanned Docs
- 🖼️ Image Extraction
- 📸 PDF Screenshots
These tools feed into the multi-agent system, enabling intelligent, context-aware processing.
🤖 Multi-Agent Architecture
PdfToMem uses a LangGraph-based orchestrator to coordinate specialized agents. Each agent performs a distinct task:
- 🕵️ Extractor Agent – pulls raw content
- 🧠 Semantic Agent – applies embedding & understanding
- ✂️ Segmenter Agent – splits content intelligently
- 🕸️ Relationship Agent – builds semantic links
The orchestrator decides when and how to invoke these agents based on the content type and reasoning goals.
🧭 Memory Planning — Reasoning Over Representation
Once the data is structured, a planning model (currently o3-mini) determines the best memory format using LlamaIndex components:
- 🧱
SimpleSentenceIndex - 🌐
AutoMergingIndex - 🧮 Custom tool-enhanced indices
This reasoning-driven approach ensures optimal retrieval performance tailored to the document.
💾 Storage — Flexible, Dynamic, Queryable
Based on the selected memory representation, a FastAPI-powered Storage Service builds a tailored query engine for each PDF.
- 🔍 Built-in search and retrieval
- 🧠 Vector and hybrid index support
- 🧪 Modular for experimentation
The storage is designed to support scalable memory systems across domains.
🖥️ MCP Client — Full Control with a Friendly UI
The MCP Client is a React-based interface for controlling the full lifecycle:
- ⚙️ Configure agents, tools, memory plans
- 📂 Upload and ingest PDFs
- 🔎 Preview structured chunks
- 🧪 Query memory and view responses
Everything is interactive, inspectable, and customizable.
🚀 Why PdfToMem?
- ✅ LLM-optimized ingestion & memory
- 🧩 Modular tools & agents
- 🧠 Reasoning-based memory planning
- 💬 Queryable representations via LlamaIndex
- 🌐 UI + API for full pipeline control
🛠️ Contributions welcome! Help us build the future of intelligent memory systems.
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