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PDF Dashboard With MCP

<p align="center"> <a href="https://streamlit.io/"><img src="https://img.shields.io/badge/UI-Streamlit-FF4B4B?logo=streamlit&logoColor=white" alt="Streamlit" /></a> <a href="https://www.langchain.com/"><img src="https://img.shields.io/badge/LangChain-1C3C3C?logo=langchain&logoColor=white" alt="LangChain" /></a> <a href="https://pymupdf.readthedocs.io/"><img src="https://img.shields.io/badge/PDF-PyMuPDF4LLM-094D8E" alt="PyMuPDF4LLM" /></a> <a href="https://ollama.com/"><img src="https://img.shields.io/badge/LLM-Ollama-black?logo=ollama&logoColor=white" alt="Ollama" /></a> <a href="https://www.trychroma.com/"><img src="https://img.shields.io/badge/vectorstore-Chroma-FF6B35" alt="Chroma" /></a> <a href="https://docs.astral.sh/uv/"><img src="https://img.shields.io/badge/package%20manager-uv-DE5FE9" alt="uv" /></a> <a href="https://modelcontextprotocol.io/"><img src="https://img.shields.io/badge/MCP-mcp%5Bcli%5D-black" alt="MCP" /></a> <img src="https://img.shields.io/badge/python-3.11%2B-blue.svg" alt="Python 3.11+" /> </p>

Upload PDFs, extract text with PyMuPDF or GLM-OCR (Ollama), and ask questions against the document with a local Ollama model. No API keys.

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Features

• PDF extraction: PyMuPDF for text layers; GLM-OCR when the PDF is scanned or image-only
• Per-document RAG: each upload gets its own Chroma collection
• Local chat: LangChain agent with inline citations; choose any installed Ollama model from the dropdown
• Markdown viewer: read extracted text, preview chunks, download markdown

Prerequisites

• Python 3.11+
• uv
• Ollama

Setup

1. Clone the repository

git clone https://github.com/dakshp26/PDFDashboardWithMCP.git
cd PDFDashboardWithMCP

2. Install dependencies

uv sync

3. Pull Ollama models

ollama pull qwen2.5:3b       # chat (or another chat model)
ollama pull nomic-embed-text # embeddings
ollama pull glm-ocr          # OCR for scanned PDFs

4. Run the app

uv run streamlit run app/main.py

Open http://localhost:8501 in your browser.

Usage

1. Upload PDF: open Upload PDF, select a file, wait for extraction to finish
2. Chat: open Chat, pick the PDF and an Ollama model, ask questions

Project Structure

app/
├── main.py                       # Entry point, page navigation
├── app_pages/
│   ├── landing.py                # Home page
│   ├── process_pdf_upload.py     # Upload + pipeline UI
│   ├── pdf_library.py            # Browse uploaded PDFs (read-only viewer)
│   └── process_pdf.py            # Viewer + chat UI
└── process_pdf/
    ├── extract.py                 # PDF → Markdown (pymupdf4llm + GLM-OCR)
    ├── pipeline.py                # Extraction pipeline with live progress
    ├── rag.py                     # Chunking, embeddings, Chroma persistence
    └── agent.py                   # LangChain agent with retriever tool
mcp_server/
└── server.py                     # MCP server (list_documents, get_document)
data/                             # Runtime data (gitignored)
├── process_pdf/                  # Saved PDFs and extracted markdown
└── process_chroma/               # Chroma vector collections (one per PDF)

[!NOTE] File-by-file breakdown, execution order, and data flow: APP_STRUCTURE.md.

Pages

Page	What it does
Home	Links and setup summary
Upload PDF	Run extraction (text layer, OCR fallback, chunking, embedding); download markdown
PDF Library	Open past uploads; view markdown and chunk previews without re-running extraction
Chat	Query an indexed PDF with citations

Extraction progress shows in an st.status block. After processing, the Chroma collection lives in data/process_chroma/ and loads on the next run without re-extracting.

MCP Server

Two tools for MCP clients (Claude Desktop, Cursor, Claude Code):

• list_documents: indexed document collections
• get_document(document, query): semantic search over a collection

<details> <summary>Claude Desktop</summary>

Add to claude_desktop_config.json (Windows: %APPDATA%\Claude\claude_desktop_config.json) or use .mcp.json in the project root:

{
  "mcpServers": {
    "PDFDashboardWithMCP": {
      "command": "uv",
      "args": ["run", "--directory", "/absolute/path/to/PDFDashboardWithMCP", "mcp_server/server.py"]
    }
  }
}

</details>

<details> <summary>Cursor</summary>

Add to .cursor/mcp.json in the project root or global ~/.cursor/mcp.json:

{
  "mcpServers": {
    "PDFDashboardWithMCP": {
      "command": "uv",
      "args": ["run", "--directory", "/absolute/path/to/PDFDashboardWithMCP", "mcp_server/server.py"]
    }
  }
}

</details>

<details> <summary>Claude Code</summary>

Project-scoped .mcp.json in the repo root keeps the server tied to this repo:

{
  "mcpServers": {
    "PDFDashboardWithMCP": {
      "command": "uv",
      "args": ["run", "--directory", "/absolute/path/to/PDFDashboardWithMCP", "mcp_server/server.py"]
    }
  }
}

Claude Code reads .mcp.json when you open the project.

</details>

Replace /absolute/path/to/PDFDashboardWithMCP with your clone path.

Ollama must be running with nomic-embed-text pulled before the MCP server can load collections.

Tech Stack

Component	Library
UI	Streamlit
PDF extraction	langchain-pymupdf4llm, PyMuPDF
OCR fallback	Ollama glm-ocr
Embeddings	Ollama nomic-embed-text
Vector store	Chroma (langchain-chroma)
LLM / agent	Ollama chat model (e.g. qwen2.5:3b), LangChain
Package manager	uv
MCP server	mcp[cli]