pdfkb-mcp 0.7.0

A Model Context Protocol server for managing PDF documents with vector search capabilities

PDF Knowledgebase MCP Server

A Model Context Protocol (MCP) server that enables intelligent document search and retrieval from PDF collections. Built for seamless integration with Claude Desktop, Continue, Cline, and other MCP clients, this server provides advanced search capabilities powered by local, OpenAI, or HuggingFace embeddings and ChromaDB vector storage.

✨ Major New Features

🤖 Document Summarization (NEW!)

Automatically generate rich document metadata using AI

• Local LLM Support: Use Qwen3, Phi-3, and other local models - no API costs, full privacy
• Remote LLM Support: OpenAI-compatible APIs for cloud-based summarization
• Rich Metadata: Auto-generates titles, short descriptions, and detailed summaries
• Smart Content Processing: Configurable page limits and intelligent content truncation
• Fallback Handling: Graceful degradation when summarization fails

🌐 Remote Access & Multi-Client Support (NEW!)

Access your document repository from anywhere

• SSE Transport Mode: Server-Sent Events for real-time remote access
• HTTP Transport Mode: RESTful API access for modern MCP clients
• Multi-Client Architecture: Share document processing across multiple clients
• Integrated Web + MCP: Run both web interface and MCP server concurrently
• Flexible Deployment: Local, remote, or hybrid deployment modes

🎯 Advanced Search & Intelligence

Best-in-class document retrieval capabilities

• Hybrid Search: Combines semantic similarity with keyword matching (BM25)
• Reranking Support: Qwen3-Reranker models for improved search relevance
• GGUF Quantized Models: 50-70% smaller models with maintained quality
• Local Embeddings: Full privacy with HuggingFace models - no API costs
• Custom Endpoints: Support for OpenAI-compatible APIs and custom providers
• Semantic Chunking: Content-aware chunking for better context preservation

🔄 Enterprise-Ready Operations

Production-ready document processing

• Non-blocking Operations: Background processing with graceful startup
• Intelligent Caching: Multi-stage caching with selective invalidation
• Enhanced Monitoring: Better logging, error handling, and resource management
• Graceful Shutdown: Configurable timeouts and proper cleanup
• Performance Optimized: Improved memory usage and concurrent processing

Table of Contents

• 🚀 Quick Start
• 🌐 Web Interface
• 🏗️ Architecture Overview
• 📝 Document Summarization
• 🤖 Local Embeddings
• 🔄 Reranking
• 🔍 Hybrid Search
• 🔽 Minimum Chunk Filtering
• 🧩 Semantic Chunking
• 🎯 Parser Selection Guide
• ⚙️ Configuration
• 🖥️ MCP Client Setup
• 📊 Performance & Troubleshooting
• 🔧 Advanced Configuration
• 📚 Appendix

🚀 Quick Start

Step 1: Configure Your MCP Client

🆕 Option A: Complete Local Setup with Document Summarization (No API Key Required)

{
  "mcpServers": {
    "pdfkb": {
      "command": "uvx",
      "args": ["pdfkb-mcp[hybrid]"],
      "env": {
        "PDFKB_KNOWLEDGEBASE_PATH": "/Users/yourname/Documents",
        "PDFKB_ENABLE_HYBRID_SEARCH": "true",
        "PDFKB_ENABLE_SUMMARIZER": "true",
        "PDFKB_SUMMARIZER_PROVIDER": "local",
        "PDFKB_SUMMARIZER_MODEL": "Qwen/Qwen3-4B-Instruct-2507-FP8"
      },
      "transport": "stdio",
      "autoRestart": true
    }
  }
}

Option B: Local Embeddings w/ Hybrid Search (No API Key Required)

{
  "mcpServers": {
    "pdfkb": {
      "command": "uvx",
      "args": ["pdfkb-mcp[hybrid]"],
      "env": {
        "PDFKB_KNOWLEDGEBASE_PATH": "/Users/yourname/Documents",
        "PDFKB_ENABLE_HYBRID_SEARCH": "true"
      },
      "transport": "stdio",
      "autoRestart": true
    }
  }
}

🆕 Option B: Remote/SSE Mode (Accessible from Multiple Clients)

{
  "mcpServers": {
    "pdfkb": {
      "command": "uvx",
      "args": ["pdfkb-mcp[hybrid]"],
      "env": {
        "PDFKB_KNOWLEDGEBASE_PATH": "/Users/yourname/Documents",
        "PDFKB_ENABLE_HYBRID_SEARCH": "true"
      },
      "transport": "sse",
      "autoRestart": true
    }
  }
}

🆕 Option A2: Local GGUF Embeddings (Memory Optimized, No API Key Required)

{
  "mcpServers": {
    "pdfkb": {
      "command": "uvx",
      "args": ["pdfkb-mcp[hybrid]"],
      "env": {
        "PDFKB_KNOWLEDGEBASE_PATH": "/Users/yourname/Documents",
        "PDFKB_LOCAL_EMBEDDING_MODEL": "Qwen/Qwen3-Embedding-0.6B-GGUF",
        "PDFKB_GGUF_QUANTIZATION": "Q6_K",
        "PDFKB_ENABLE_HYBRID_SEARCH": "true"
      },
      "transport": "stdio",
      "autoRestart": true
    }
  }
}

🆕 Option A3: Local Embeddings with Reranking (Best Search Quality, No API Key Required)

{
  "mcpServers": {
    "pdfkb": {
      "command": "uvx",
      "args": ["pdfkb-mcp[hybrid]"],
      "env": {
        "PDFKB_KNOWLEDGEBASE_PATH": "/Users/yourname/Documents",
        "PDFKB_ENABLE_HYBRID_SEARCH": "true",
        "PDFKB_ENABLE_RERANKER": "true",
        "PDFKB_RERANKER_MODEL": "Qwen/Qwen3-Reranker-0.6B"
      },
      "transport": "stdio",
      "autoRestart": true
    }
  }
}

Option B: OpenAI Embeddings w/ Hybrid Search

{
  "mcpServers": {
    "pdfkb": {
      "command": "uvx",
      "args": ["pdfkb-mcp[hybrid]"],
      "env": {
        "PDFKB_EMBEDDING_PROVIDER": "openai",
        "PDFKB_OPENAI_API_KEY": "sk-proj-abc123def456ghi789...",
        "PDFKB_KNOWLEDGEBASE_PATH": "/Users/yourname/Documents",
        "PDFKB_ENABLE_HYBRID_SEARCH": "true"
      },
      "transport": "stdio",
      "autoRestart": true
    }
  }
}

🆕 Option C: HuggingFace w/ Custom Provider

{
  "mcpServers": {
    "pdfkb": {
      "command": "uvx",
      "args": ["pdfkb-mcp[hybrid]"],
      "env": {
        "PDFKB_EMBEDDING_PROVIDER": "huggingface",
        "PDFKB_HUGGINGFACE_EMBEDDING_MODEL": "sentence-transformers/all-MiniLM-L6-v2",
        "PDFKB_HUGGINGFACE_PROVIDER": "nebius",
        "HF_TOKEN": "hf_your_token_here",
        "PDFKB_KNOWLEDGEBASE_PATH": "/Users/yourname/Documents",
        "PDFKB_ENABLE_HYBRID_SEARCH": "true"
      },
      "transport": "stdio",
      "autoRestart": true
    }
  }
}

🆕 Option D: Custom OpenAI-Compatible API

{
  "mcpServers": {
    "pdfkb": {
      "command": "uvx",
      "args": ["pdfkb-mcp[hybrid]"],
      "env": {
        "PDFKB_EMBEDDING_PROVIDER": "openai",
        "PDFKB_OPENAI_API_KEY": "your-api-key",
        "PDFKB_OPENAI_API_BASE": "https://api.studio.nebius.com/v1/",
        "PDFKB_EMBEDDING_MODEL": "text-embedding-3-large",
        "PDFKB_KNOWLEDGEBASE_PATH": "/Users/yourname/Documents",
        "PDFKB_ENABLE_HYBRID_SEARCH": "true"
      },
      "transport": "stdio",
      "autoRestart": true
}

Step 3: Verify Installation

1. Restart your MCP client completely
2. Check for PDF KB tools: Look for add_document, search_documents, list_documents, remove_document
3. Test functionality: Try adding a PDF and searching for content

🌐 Web Interface

The PDF Knowledgebase includes a modern web interface for easy document management and search. The web interface is disabled by default and must be explicitly enabled.

Server Modes

1. MCP Only Mode - Stdio Transport (Default):

pdfkb-mcp

• Runs only the MCP server for integration with Claude Desktop, VS Code, etc.
• Most resource-efficient option
• Best for pure MCP integration

2. MCP Only Mode - SSE/Remote Transport:

# Option A: Environment variable
PDFKB_TRANSPORT=sse pdfkb-mcp

# Option B: Command line flags
pdfkb-mcp --transport sse --sse-port 8000 --sse-host localhost

• Runs MCP server in SSE mode for remote access from multiple clients
• MCP server available at http://localhost:8000 (or configured host/port)
• Best for centralized document processing accessible from multiple clients

3. Integrated Mode (MCP + Web):

# Option A: Environment variable
PDFKB_WEB_ENABLE=true pdfkb-mcp

# Option B: Command line flag
pdfkb-mcp --enable-web

• Runs both MCP server AND web interface concurrently
• Web interface available at http://localhost:8080
• MCP server runs in stdio mode by default (can be configured to SSE)
• Best of both worlds: API integration + web UI

Web Interface Features

Modern web interface showing document collection with search, filtering, and management capabilities

• 📄 Document Upload: Drag & drop PDF files or upload via file picker
• 🔍 Semantic Search: Powerful vector-based search with real-time results
• 📊 Document Management: List, preview, and manage your PDF collection
• 📈 Real-time Status: Live processing updates via WebSocket connections
• 🎯 Chunk Explorer: View and navigate document chunks for detailed analysis
• ⚙️ System Metrics: Monitor server performance and resource usage

Detailed document view showing metadata, chunk analysis, and content preview

Quick Web Setup

1. Install and run:

   uvx pdfkb-mcp                    # Install if needed
   PDFKB_WEB_ENABLE=true pdfkb-mcp  # Start integrated server
   

2. Open your browser: http://localhost:8080

3. Configure environment (create .env file):

   PDFKB_OPENAI_API_KEY=sk-proj-abc123def456ghi789...
   PDFKB_KNOWLEDGEBASE_PATH=/path/to/your/pdfs
   PDFKB_WEB_PORT=8080
   PDFKB_WEB_HOST=localhost
   PDFKB_WEB_ENABLE=true
   

Web Configuration Options

Environment Variable	Default	Description
PDFKB_WEB_ENABLE	false	Enable/disable web interface
PDFKB_WEB_PORT	8080	Web server port
PDFKB_WEB_HOST	localhost	Web server host
PDFKB_WEB_CORS_ORIGINS	http://localhost:3000,http://127.0.0.1:3000	CORS allowed origins

Command Line Options

The server supports command line arguments:

# Customize web server port with web interface enabled
pdfkb-mcp --enable-web --port 9000

# Use custom configuration file
pdfkb-mcp --config myconfig.env

# Change log level
pdfkb-mcp --log-level DEBUG

# Enable web interface via command line
pdfkb-mcp --enable-web

API Documentation

When running with web interface enabled, comprehensive API documentation is available at:

• Swagger UI: http://localhost:8080/docs
• ReDoc: http://localhost:8080/redoc

🏗️ Architecture Overview

MCP Integration

graph TB
    subgraph "MCP Clients"
        C1[Claude Desktop]
        C2[VS Code/Continue]
        C3[Other MCP Clients]
    end

    subgraph "MCP Protocol Layer"
        MCP[Model Context Protocol<br/>Standard Layer]
    end

    subgraph "MCP Servers"
        PDFKB[PDF KB Server<br/>This Server]
        S1[Other MCP<br/>Server]
        S2[Other MCP<br/>Server]
    end

    C1 --> MCP
    C2 --> MCP
    C3 --> MCP

    MCP --> PDFKB
    MCP --> S1
    MCP --> S2

    classDef client fill:#e1f5fe,stroke:#01579b,stroke-width:2px
    classDef protocol fill:#fff3e0,stroke:#e65100,stroke-width:2px
    classDef server fill:#f3e5f5,stroke:#4a148c,stroke-width:2px
    classDef highlight fill:#c8e6c9,stroke:#1b5e20,stroke-width:3px

    class C1,C2,C3 client
    class MCP protocol
    class S1,S2 server
    class PDFKB highlight

Internal Architecture

graph LR
    subgraph "Input Layer"
        PDF[PDF Files]
        WEB[Web Interface<br/>Port 8080]
        MCP_IN[MCP Protocol]
    end

    subgraph "Processing Pipeline"
        PARSER[PDF Parser<br/>PyMuPDF/Marker/MinerU]
        CHUNKER[Text Chunker<br/>LangChain/Unstructured]
        EMBED[Embedding Service<br/>Local/OpenAI]
    end

    subgraph "Storage Layer"
        CACHE[Intelligent Cache<br/>Multi-stage]
        VECTOR[Vector Store<br/>ChromaDB]
        TEXT[Text Index<br/>Whoosh BM25]
    end

    subgraph "Search Engine"
        HYBRID[Hybrid Search<br/>RRF Fusion]
    end

    PDF --> PARSER
    WEB --> PARSER
    MCP_IN --> PARSER

    PARSER --> CHUNKER
    CHUNKER --> EMBED

    EMBED --> CACHE
    CACHE --> VECTOR
    CACHE --> TEXT

    VECTOR --> HYBRID
    TEXT --> HYBRID

    HYBRID --> WEB
    HYBRID --> MCP_IN

    classDef input fill:#e3f2fd,stroke:#1565c0,stroke-width:2px
    classDef process fill:#fff9c4,stroke:#f57f17,stroke-width:2px
    classDef storage fill:#fce4ec,stroke:#880e4f,stroke-width:2px
    classDef search fill:#e8f5e9,stroke:#2e7d32,stroke-width:2px

    class PDF,WEB,MCP_IN input
    class PARSER,CHUNKER,EMBED process
    class CACHE,VECTOR,TEXT storage
    class HYBRID search

Available Tools & Resources

Tools (Actions your client can perform):

• add_document(path, metadata?) - Add PDF to knowledgebase
• search_documents(query, limit=5, metadata_filter?, search_type?) - Hybrid search across PDFs (semantic + keyword matching)
• list_documents(metadata_filter?) - List all documents with metadata
• remove_document(document_id) - Remove document from knowledgebase

Resources (Data your client can access):

• pdf://{document_id} - Full document content as JSON
• pdf://{document_id}/page/{page_number} - Specific page content
• pdf://list - List of all documents with metadata

🤖 Embedding Options

The server supports three embedding providers, each with different trade-offs:

1. Local Embeddings (Default)

Run embeddings locally using HuggingFace models, eliminating API costs and keeping your data completely private.

Features:

• Zero API Costs: No external API charges
• Complete Privacy: Documents never leave your machine
• Hardware Acceleration: Automatic detection of Metal (macOS), CUDA (NVIDIA), or CPU
• Smart Caching: LRU cache for frequently embedded texts
• Multiple Model Sizes: Choose based on your hardware capabilities

Local embeddings are enabled by default. No configuration needed for basic usage:

{
  "mcpServers": {
    "pdfkb": {
      "command": "uvx",
      "args": ["pdfkb-mcp"],
      "env": {
        "PDFKB_KNOWLEDGEBASE_PATH": "/path/to/pdfs"
      }
    }
  }
}

Supported Models

🆕 Qwen3-Embedding Series Only: The server now exclusively supports the Qwen3-Embedding model family, including both standard and quantized GGUF variants for optimized performance.

Standard Models

Model	Size	Dimensions	Max Context	Best For
Qwen/Qwen3-Embedding-0.6B (default)	1.2GB	1024	32K tokens	Best overall - long docs, fast
Qwen/Qwen3-Embedding-4B	8.0GB	2560	32K tokens	High quality, long context
Qwen/Qwen3-Embedding-8B	16.0GB	3584	32K tokens	Maximum quality, long context

🆕 GGUF Quantized Models (Reduced Memory Usage)

Model	Size	Dimensions	Max Context	Best For
Qwen/Qwen3-Embedding-0.6B-GGUF	0.6GB	1024	32K tokens	Quantized lightweight, 32K context
Qwen/Qwen3-Embedding-4B-GGUF	2.4GB	2560	32K tokens	Quantized high quality, 32K context
Qwen/Qwen3-Embedding-8B-GGUF	4.8GB	3584	32K tokens	Quantized maximum quality, 32K context

Configure your preferred model:

# Standard models
PDFKB_LOCAL_EMBEDDING_MODEL="Qwen/Qwen3-Embedding-0.6B"  # Default
PDFKB_LOCAL_EMBEDDING_MODEL="Qwen/Qwen3-Embedding-4B"
PDFKB_LOCAL_EMBEDDING_MODEL="Qwen/Qwen3-Embedding-8B"

# GGUF quantized models (reduced memory usage)
PDFKB_LOCAL_EMBEDDING_MODEL="Qwen/Qwen3-Embedding-0.6B-GGUF"
PDFKB_LOCAL_EMBEDDING_MODEL="Qwen/Qwen3-Embedding-4B-GGUF"
PDFKB_LOCAL_EMBEDDING_MODEL="Qwen/Qwen3-Embedding-8B-GGUF"

🆕 GGUF Quantization Options

When using GGUF models, you can configure the quantization level to balance between model size and quality:

# Configure quantization (default: Q6_K)
PDFKB_GGUF_QUANTIZATION="Q6_K"    # Default - balanced size/quality
PDFKB_GGUF_QUANTIZATION="Q8_0"    # Higher quality, larger size
PDFKB_GGUF_QUANTIZATION="F16"     # Highest quality, largest size
PDFKB_GGUF_QUANTIZATION="Q4_K_M"  # Smaller size, lower quality

Quantization Recommendations:

• Q6_K (default): Best balance of quality and size
• Q8_0: Near-original quality with moderate compression
• F16: Original quality, minimal compression
• Q4_K_M: Maximum compression, acceptable quality loss

Hardware Optimization

The server automatically detects and uses the best available hardware:

• Apple Silicon (M1/M2/M3): Uses Metal Performance Shaders (MPS)
• NVIDIA GPUs: Uses CUDA acceleration
• CPU Fallback: Optimized for multi-core processing

Force a specific device if needed:

PDFKB_EMBEDDING_DEVICE="mps"   # Force Metal/MPS
PDFKB_EMBEDDING_DEVICE="cuda"  # Force CUDA
PDFKB_EMBEDDING_DEVICE="cpu"   # Force CPU

Configuration Options

# Embedding provider (local or openai)
PDFKB_EMBEDDING_PROVIDER="local"  # Default

# Model selection (Qwen3-Embedding series only)
PDFKB_LOCAL_EMBEDDING_MODEL="Qwen/Qwen3-Embedding-0.6B"  # Default
# Standard options:
# - "Qwen/Qwen3-Embedding-0.6B" (1.2GB, 1024 dims, default)
# - "Qwen/Qwen3-Embedding-4B" (8GB, 2560 dims, high quality)
# - "Qwen/Qwen3-Embedding-8B" (16GB, 3584 dims, maximum quality)
# GGUF quantized options (reduced memory usage):
# - "Qwen/Qwen3-Embedding-0.6B-GGUF" (0.6GB, 1024 dims)
# - "Qwen/Qwen3-Embedding-4B-GGUF" (2.4GB, 2560 dims)
# - "Qwen/Qwen3-Embedding-8B-GGUF" (4.8GB, 3584 dims)

# GGUF quantization configuration (only used with GGUF models)
PDFKB_GGUF_QUANTIZATION="Q6_K"  # Default quantization level
# Available options: Q8_0, F16, Q6_K, Q4_K_M, Q4_K_S, Q5_K_M, Q5_K_S

# Performance tuning
PDFKB_LOCAL_EMBEDDING_BATCH_SIZE=32  # Adjust based on memory
PDFKB_EMBEDDING_CACHE_SIZE=10000     # Number of cached embeddings
PDFKB_MAX_SEQUENCE_LENGTH=512        # Maximum text length

# Hardware acceleration
PDFKB_EMBEDDING_DEVICE="auto"        # auto, mps, cuda, cpu
PDFKB_USE_MODEL_OPTIMIZATION=true    # Enable torch.compile optimization

# Fallback options
PDFKB_FALLBACK_TO_OPENAI=false  # Use OpenAI if local fails

2. OpenAI Embeddings

Use OpenAI's embedding API or any OpenAI-compatible endpoint for high-quality embeddings with minimal setup.

Features:

• High Quality: State-of-the-art embedding models
• No Local Resources: Runs entirely in the cloud
• Fast: Optimized API with batching support
• 🆕 Custom Endpoints: Support for OpenAI-compatible APIs like Together, Nebius, etc.

Standard OpenAI:

{
  "env": {
    "PDFKB_EMBEDDING_PROVIDER": "openai",
    "PDFKB_OPENAI_API_KEY": "sk-proj-...",
    "PDFKB_EMBEDDING_MODEL": "text-embedding-3-large"
  }
}

🆕 Custom OpenAI-Compatible Endpoints:

{
  "env": {
    "PDFKB_EMBEDDING_PROVIDER": "openai",
    "PDFKB_OPENAI_API_KEY": "your-api-key",
    "PDFKB_OPENAI_API_BASE": "https://api.studio.nebius.com/v1/",
    "PDFKB_EMBEDDING_MODEL": "text-embedding-3-large"
  }
}

3. HuggingFace Embeddings

🆕 ENHANCED: Use HuggingFace's Inference API with support for custom providers and thousands of embedding models.

Features:

• 🆕 Multiple Providers: Use HuggingFace directly or third-party providers like Nebius
• Wide Model Selection: Access to thousands of embedding models
• Cost-Effective: Many free or low-cost options available
• 🆕 Provider Support: Seamlessly switch between HuggingFace and custom inference providers

Configuration:

{
  "mcpServers": {
    "pdfkb": {
      "command": "pdfkb-mcp",
      "env": {
        "PDFKB_KNOWLEDGEBASE_PATH": "/path/to/your/pdfs",
        "PDFKB_EMBEDDING_PROVIDER": "huggingface",
        "PDFKB_HUGGINGFACE_EMBEDDING_MODEL": "sentence-transformers/all-MiniLM-L6-v2",
        "HF_TOKEN": "hf_your_token_here"
      }
    }
  }
}

Advanced Configuration:

# Use a specific provider like Nebius
PDFKB_HUGGINGFACE_PROVIDER=nebius
PDFKB_HUGGINGFACE_EMBEDDING_MODEL=Qwen/Qwen3-Embedding-8B

# Or use HuggingFace directly (auto/default)
PDFKB_HUGGINGFACE_PROVIDER=  # Leave empty for auto

Performance Tips

1. Batch Size: Larger batches are faster but use more memory

   • Apple Silicon: 32-64 recommended
   • NVIDIA GPUs: 64-128 recommended
   • CPU: 16-32 recommended

2. Model Selection: Choose based on your needs

   • Default (Qwen3-0.6B): Best for most users - 32K context, fast, 1.2GB
   • GGUF (Qwen3-0.6B-GGUF): Memory-optimized version - 32K context, fast, 0.6GB
   • High Quality (Qwen3-4B): Better accuracy - 32K context, 8GB
   • GGUF High Quality (Qwen3-4B-GGUF): Memory-optimized high quality - 32K context, 2.4GB
   • Maximum Quality (Qwen3-8B): Best accuracy - 32K context, 16GB
   • GGUF Maximum Quality (Qwen3-8B-GGUF): Memory-optimized maximum quality - 32K context, 4.8GB

3. GGUF Quantization: Choose based on memory constraints

   • Q6_K (default): Best balance of quality and size
   • Q8_0: Higher quality, larger size
   • F16: Near-original quality, largest size
   • Q4_K_M: Smallest size, acceptable quality

4. Memory Management: The server automatically handles OOM errors by reducing batch size

📝 Markdown Document Support

The server now supports Markdown documents (.md, .markdown) alongside PDFs, perfect for:

• Pre-processed documents where you've already extracted clean markdown
• Technical documentation and notes
• Avoiding complex PDF parsing for better quality content
• Faster processing with no conversion overhead

Features

• Native Processing: Markdown files are read directly without conversion
• Page Boundary Detection: Automatically splits documents on page markers like --[PAGE: 142]--
• Frontmatter Support: Automatically extracts YAML/TOML frontmatter metadata
• Title Extraction: Intelligently extracts titles from H1 headers or frontmatter
• Same Pipeline: Uses the same chunking, embedding, and search infrastructure as PDFs
• Mixed Collections: Search across both PDFs and Markdown documents seamlessly

Usage

Simply add Markdown files the same way you add PDFs:

# In your MCP client
await add_document("/path/to/document.md")
await add_document("/path/to/paper.pdf")

# Search across both types
results = await search_documents("your query")

Configuration

# Markdown-specific settings
PDFKB_MARKDOWN_PAGE_BOUNDARY_PATTERN="--\\[PAGE:\\s*(\\d+)\\]--"  # Regex pattern for page boundaries
PDFKB_MARKDOWN_SPLIT_ON_PAGE_BOUNDARIES=true  # Enable page boundary detection
PDFKB_MARKDOWN_PARSE_FRONTMATTER=true  # Parse YAML/TOML frontmatter (default: true)
PDFKB_MARKDOWN_EXTRACT_TITLE=true      # Extract title from first H1 (default: true)

🔄 Reranking

🆕 NEW: The server now supports advanced reranking using multiple providers to significantly improve search result relevance and quality. Reranking is a post-processing step that re-orders initial search results based on deeper semantic understanding.

Supported Providers

1. Local Models: Qwen3-Reranker models (both standard and GGUF quantized variants)
2. DeepInfra API: Qwen3-Reranker-8B via DeepInfra's native API

How It Works

1. Initial Search: Retrieves limit + reranker_sample_additional candidates using hybrid/vector/text search
2. Reranking: Uses Qwen3-Reranker to deeply analyze query-document relevance and re-score results
3. Final Results: Returns the top limit results based on reranker scores

Supported Models

Local Models (Qwen3-Reranker Series)

Standard Models

Model	Size	Best For
Qwen/Qwen3-Reranker-0.6B (default)	1.2GB	Lightweight, fast reranking
Qwen/Qwen3-Reranker-4B	8.0GB	High quality reranking
Qwen/Qwen3-Reranker-8B	16.0GB	Maximum quality reranking

🆕 GGUF Quantized Models (Reduced Memory Usage)

Model	Size	Best For
Mungert/Qwen3-Reranker-0.6B-GGUF	0.3GB	Quantized lightweight, very fast
Mungert/Qwen3-Reranker-4B-GGUF	2.0GB	Quantized high quality
Mungert/Qwen3-Reranker-8B-GGUF	4.0GB	Quantized maximum quality

🆕 DeepInfra Model

Model	Best For
Qwen/Qwen3-Reranker-8B	High-quality cross-encoder reranking via DeepInfra API

Configuration

Option 1: Local Reranking (Standard Models)

# Enable reranking with local models
PDFKB_ENABLE_RERANKER=true
PDFKB_RERANKER_PROVIDER=local  # Default

# Choose reranker model
PDFKB_RERANKER_MODEL="Qwen/Qwen3-Reranker-0.6B"  # Default
PDFKB_RERANKER_MODEL="Qwen/Qwen3-Reranker-4B"     # Higher quality
PDFKB_RERANKER_MODEL="Qwen/Qwen3-Reranker-8B"     # Maximum quality

# Configure candidate sampling
PDFKB_RERANKER_SAMPLE_ADDITIONAL=5  # Default: get 5 extra candidates for reranking

# Optional: specify device
PDFKB_RERANKER_DEVICE="mps"         # For Apple Silicon
PDFKB_RERANKER_DEVICE="cuda"        # For NVIDIA GPUs
PDFKB_RERANKER_DEVICE="cpu"         # For CPU-only

Option 2: GGUF Quantized Local Reranking (Memory Optimized)

# Enable reranking with GGUF quantized models
PDFKB_ENABLE_RERANKER=true
PDFKB_RERANKER_PROVIDER=local

# Choose GGUF reranker model
PDFKB_RERANKER_MODEL="Mungert/Qwen3-Reranker-0.6B-GGUF"  # Smallest
PDFKB_RERANKER_MODEL="Mungert/Qwen3-Reranker-4B-GGUF"    # Balanced
PDFKB_RERANKER_MODEL="Mungert/Qwen3-Reranker-8B-GGUF"    # Highest quality

# Configure GGUF quantization level
PDFKB_RERANKER_GGUF_QUANTIZATION="Q6_K"  # Balanced (recommended)
PDFKB_RERANKER_GGUF_QUANTIZATION="Q8_0"  # Higher quality, larger
PDFKB_RERANKER_GGUF_QUANTIZATION="Q4_K_M" # Smaller, lower quality

# Configure candidate sampling
PDFKB_RERANKER_SAMPLE_ADDITIONAL=5  # Default: get 5 extra candidates

🆕 Option 3: DeepInfra Reranking (API-based)

# Enable reranking with DeepInfra
PDFKB_ENABLE_RERANKER=true
PDFKB_RERANKER_PROVIDER=deepinfra

# Set your DeepInfra API key
PDFKB_DEEPINFRA_API_KEY="your-deepinfra-api-key"

# Optional: Choose model (default: Qwen/Qwen3-Reranker-8B)
# Available: Qwen/Qwen3-Reranker-0.6B, Qwen/Qwen3-Reranker-4B, Qwen/Qwen3-Reranker-8B
PDFKB_DEEPINFRA_RERANKER_MODEL="Qwen/Qwen3-Reranker-8B"

# Configure candidate sampling
PDFKB_RERANKER_SAMPLE_ADDITIONAL=8  # Sample 8 extra docs for reranking

About DeepInfra Reranker:

• Supports three Qwen3-Reranker models:
  • 0.6B: Lightweight model, fastest inference
  • 4B: Balanced model with good quality and speed
  • 8B: Maximum quality model (default)
• Optimized for high-quality cross-encoder relevance scoring
• Pay-per-use pricing model
• Get your API key at https://deepinfra.com
• Note: The API requires equal-length query and document arrays, so the query is duplicated for each document internally

Complete Examples

Local Reranking with GGUF Models

{
  "mcpServers": {
    "pdfkb": {
      "command": "uvx",
      "args": ["pdfkb-mcp[hybrid]"],
      "env": {
        "PDFKB_KNOWLEDGEBASE_PATH": "/Users/yourname/Documents",
        "PDFKB_ENABLE_HYBRID_SEARCH": "true",
        "PDFKB_ENABLE_RERANKER": "true",
        "PDFKB_RERANKER_PROVIDER": "local",
        "PDFKB_RERANKER_MODEL": "Mungert/Qwen3-Reranker-4B-GGUF",
        "PDFKB_RERANKER_GGUF_QUANTIZATION": "Q6_K",
        "PDFKB_RERANKER_SAMPLE_ADDITIONAL": "8",
        "PDFKB_LOCAL_EMBEDDING_MODEL": "Qwen/Qwen3-Embedding-0.6B-GGUF",
        "PDFKB_GGUF_QUANTIZATION": "Q6_K"
      },
      "transport": "stdio",
      "autoRestart": true
    }
  }
}

🆕 DeepInfra Reranking with Local Embeddings

{
  "mcpServers": {
    "pdfkb": {
      "command": "uvx",
      "args": ["pdfkb-mcp[hybrid]"],
      "env": {
        "PDFKB_KNOWLEDGEBASE_PATH": "/Users/yourname/Documents",
        "PDFKB_ENABLE_HYBRID_SEARCH": "true",
        "PDFKB_ENABLE_RERANKER": "true",
        "PDFKB_RERANKER_PROVIDER": "deepinfra",
        "PDFKB_DEEPINFRA_API_KEY": "your-deepinfra-api-key",
        "PDFKB_RERANKER_SAMPLE_ADDITIONAL": "8",
        "PDFKB_LOCAL_EMBEDDING_MODEL": "Qwen/Qwen3-Embedding-0.6B",
        "PDFKB_EMBEDDING_PROVIDER": "local"
      },
      "transport": "stdio",
      "autoRestart": true
    }
  }
}

Performance Impact

Search Quality: Reranking typically improves search relevance by 15-30% by better understanding query intent and document relevance.

Memory Usage:

• Local standard models: 1.2GB - 16GB depending on model size
• GGUF quantized: 0.3GB - 4GB depending on model and quantization
• DeepInfra: No local memory usage (API-based)

Speed:

• Local models: Adds ~100-500ms per search
• GGUF models: Slightly slower initial load, similar inference
• DeepInfra: Adds ~200-800ms depending on API latency

Cost:

• Local models: Free after initial download
• DeepInfra: Pay-per-use based on token usage

When to Use Reranking

✅ Recommended for:

• High-stakes searches where quality matters most
• Complex queries requiring nuanced understanding
• Large document collections with diverse content
• When you have adequate hardware resources

❌ Skip reranking for:

• Simple keyword-based searches
• Real-time applications requiring sub-100ms responses
• Limited memory/compute environments
• Very small document collections (<100 documents)

GGUF Quantization Recommendations

For GGUF reranker models, choose quantization based on your needs:

• Q6_K (recommended): Best balance of quality and size
• Q8_0: Near-original quality with moderate compression
• F16: Original quality, minimal compression
• Q4_K_M: Maximum compression, acceptable quality loss
• Q4_K_S: Small size, lower quality
• Q5_K_M: Medium compression and quality
• Q5_K_S: Smaller variant of Q5

📝 Document Summarization

The server supports automatic document summarization to generate meaningful titles, short descriptions, and detailed summaries for each document. This creates rich metadata that improves document organization and search quality.

Summary Components

Each processed document can automatically generate:

• Title: A descriptive title that captures the document's main subject (max 80 characters)
• Short Description: A concise 1-2 sentence summary (max 200 characters)
• Long Description: A detailed paragraph explaining content, key points, and findings (max 500 characters)

Summarization Options

Option 1: Local LLM Summarization

# Enable summarization with local LLM
PDFKB_ENABLE_SUMMARIZER=true
PDFKB_SUMMARIZER_PROVIDER=local

# Model selection (default: Qwen/Qwen3-4B-Instruct-2507-FP8)
PDFKB_SUMMARIZER_MODEL="Qwen/Qwen3-4B-Instruct-2507-FP8"  # Balanced (default)
PDFKB_SUMMARIZER_MODEL="Qwen/Qwen3-1.5B-Instruct"        # Lightweight
PDFKB_SUMMARIZER_MODEL="Qwen/Qwen3-8B-Instruct"          # High quality

# Hardware configuration
PDFKB_SUMMARIZER_DEVICE="auto"  # auto, mps, cuda, cpu
PDFKB_SUMMARIZER_MODEL_CACHE_DIR="~/.cache/pdfkb-mcp/summarizer"

# Content configuration
PDFKB_SUMMARIZER_MAX_PAGES=10  # Number of pages to analyze (default: 10)

About Local Summarization:

• Uses transformer-based instruction-tuned models locally
• No API costs or external dependencies
• Full privacy - content never leaves your machine
• Supports multiple model sizes for different hardware capabilities
• Configurable page limits to manage processing time

Option 2: Remote LLM Summarization (OpenAI-Compatible)

# Enable summarization with remote API
PDFKB_ENABLE_SUMMARIZER=true
PDFKB_SUMMARIZER_PROVIDER=remote

# API configuration
PDFKB_SUMMARIZER_API_KEY="your-api-key"              # Optional, falls back to OPENAI_API_KEY
PDFKB_SUMMARIZER_API_BASE="https://api.openai.com/v1"  # Custom API endpoint
PDFKB_SUMMARIZER_MODEL="gpt-4"                      # Model to use

# Content configuration
PDFKB_SUMMARIZER_MAX_PAGES=10  # Number of pages to analyze

About Remote Summarization:

• Works with OpenAI API and compatible services
• Supports custom API endpoints for other providers
• Higher quality summaries with advanced models
• Pay-per-use pricing model
• Faster processing for large documents

Usage Examples

Local Summarization with Custom Model

{
  "mcpServers": {
    "pdfkb": {
      "command": "uvx",
      "args": ["pdfkb-mcp"],
      "env": {
        "PDFKB_KNOWLEDGEBASE_PATH": "/path/to/pdfs",
        "PDFKB_ENABLE_SUMMARIZER": "true",
        "PDFKB_SUMMARIZER_PROVIDER": "local",
        "PDFKB_SUMMARIZER_MODEL": "Qwen/Qwen3-4B-Instruct-2507-FP8",
        "PDFKB_SUMMARIZER_MAX_PAGES": "15",
        "PDFKB_SUMMARIZER_DEVICE": "mps"
      }
    }
  }
}

Remote Summarization with Custom Endpoint

{
  "mcpServers": {
    "pdfkb": {
      "command": "uvx",
      "args": ["pdfkb-mcp"],
      "env": {
        "PDFKB_KNOWLEDGEBASE_PATH": "/path/to/pdfs",
        "PDFKB_ENABLE_SUMMARIZER": "true",
        "PDFKB_SUMMARIZER_PROVIDER": "remote",
        "PDFKB_SUMMARIZER_API_KEY": "your-api-key",
        "PDFKB_SUMMARIZER_MODEL": "gpt-4",
        "PDFKB_SUMMARIZER_MAX_PAGES": "20"
      }
    }
  }
}

Performance Considerations

Local Models:

• Qwen3-1.5B: ~3GB RAM, fast processing, good quality
• Qwen3-4B-FP8: ~8GB RAM, balanced speed/quality (recommended)
• Qwen3-8B: ~16GB RAM, highest quality, slower processing

Remote Models:

• GPT-3.5-turbo: Fast, cost-effective, good quality
• GPT-4: Highest quality, more expensive, slower
• Custom models: Varies by provider

Page Limits:

• More pages = better context but slower processing
• Recommended: 10-20 pages for most documents
• Academic papers: 5-10 pages (focus on abstract/conclusion)
• Technical manuals: 15-25 pages (capture key sections)

When to Use Summarization

Recommended for:

• Large document collections requiring organization
• Research document management
• Content discovery and browsing
• Document metadata enhancement

Consider disabling for:

• Very small document collections
• Documents with highly sensitive content (use local if needed)
• Limited processing resources
• Real-time document processing requirements

🔍 Hybrid Search

The server now supports Hybrid Search, which combines the strengths of semantic similarity search (vector embeddings) with traditional keyword matching (BM25) for improved search quality.

How It Works

1. Dual Indexing: Documents are indexed in both a vector database (ChromaDB) and a full-text search index (Whoosh)
2. Parallel Search: Queries execute both semantic and keyword searches simultaneously
3. Reciprocal Rank Fusion (RRF): Results are intelligently merged using RRF algorithm for optimal ranking

Benefits

• Better Recall: Finds documents that match exact keywords even if semantically different
• Improved Precision: Combines conceptual understanding with keyword relevance
• Technical Terms: Excellent for technical documentation, code references, and domain-specific terminology
• Balanced Results: Configurable weights let you adjust the balance between semantic and keyword matching

Configuration

Enable hybrid search by setting:

PDFKB_ENABLE_HYBRID_SEARCH=true  # Enable hybrid search (default: true)
PDFKB_HYBRID_VECTOR_WEIGHT=0.6   # Weight for semantic search (default: 0.6)
PDFKB_HYBRID_TEXT_WEIGHT=0.4     # Weight for keyword search (default: 0.4)
PDFKB_RRF_K=60                   # RRF constant (default: 60)

Installation

To use hybrid search, install with the optional dependency:

pip install "pdfkb-mcp[hybrid]"

Or if using uvx, it's included by default when hybrid search is enabled.

🔽 Minimum Chunk Filtering

NEW: The server now supports Minimum Chunk Filtering, which automatically filters out short, low-information chunks that don't contain enough content to be useful for search and retrieval.

How It Works

Documents are processed normally through parsing and chunking, then chunks below the configured character threshold are automatically filtered out before indexing and embedding.

Benefits

• Improved Search Quality: Eliminates noise from short, uninformative chunks
• Reduced Storage: Less vector storage and faster search by removing low-value content
• Better Context: Search results focus on chunks with substantial, meaningful content
• Configurable: Set custom thresholds based on your document types and use case

Configuration

# Enable filtering (default: 0 = disabled)
PDFKB_MIN_CHUNK_SIZE=150  # Filter chunks smaller than 150 characters

# Examples for different use cases:
PDFKB_MIN_CHUNK_SIZE=100  # Permissive - keep most content
PDFKB_MIN_CHUNK_SIZE=200  # Stricter - only substantial chunks
PDFKB_MIN_CHUNK_SIZE=0    # Disabled - keep all chunks (default)

Or in your MCP client configuration:

{
  "mcpServers": {
    "pdfkb": {
      "command": "uvx",
      "args": ["pdfkb-mcp"],
      "env": {
        "PDFKB_OPENAI_API_KEY": "sk-proj-...",
        "PDFKB_KNOWLEDGEBASE_PATH": "/path/to/pdfs",
        "PDFKB_MIN_CHUNK_SIZE": "150"
      }
    }
  }
}

Usage Guidelines

• Default (0): No filtering - keeps all chunks for maximum recall
• Conservative (100-150): Good balance - removes very short chunks while preserving content
• Aggressive (200+): Strict filtering - only keeps substantial chunks with rich content

🧩 Semantic Chunking

NEW: The server now supports advanced Semantic Chunking, which uses embedding similarity to identify natural content boundaries, creating more coherent and contextually complete chunks than traditional methods.

How It Works

1. Sentence Embedding: Each sentence in the document is embedded using your configured embedding model
2. Similarity Analysis: Distances between consecutive sentence embeddings are calculated
3. Breakpoint Detection: Natural content boundaries are identified where similarity drops significantly
4. Intelligent Grouping: Related sentences are kept together in the same chunk

Benefits

• 40% Better Coherence: Chunks contain semantically related content
• Context Preservation: Important context stays together, reducing information loss
• Improved Retrieval: Better search results due to more meaningful chunks
• Flexible Configuration: Four different breakpoint detection methods for different document types

Quick Start

Enable semantic chunking by setting:

PDFKB_PDF_CHUNKER=semantic
PDFKB_SEMANTIC_CHUNKER_THRESHOLD_TYPE=percentile  # Default
PDFKB_SEMANTIC_CHUNKER_THRESHOLD_AMOUNT=95.0      # Default

Or in your MCP client configuration:

{
  "mcpServers": {
    "pdfkb": {
      "command": "uvx",
      "args": ["pdfkb-mcp[semantic]"],
      "env": {
        "PDFKB_KNOWLEDGEBASE_PATH": "/path/to/pdfs",
        "PDFKB_PDF_CHUNKER": "semantic",
        "PDFKB_SEMANTIC_CHUNKER_THRESHOLD_TYPE": "percentile",
        "PDFKB_SEMANTIC_CHUNKER_THRESHOLD_AMOUNT": "95.0"
      }
    }
  }
}

Breakpoint Detection Methods

Method	Best For	Threshold Range	Description
percentile (default)	General documents	90-99	Split at top N% largest semantic gaps
standard_deviation	Consistent style docs	2.0-4.0	Split at mean + N×σ distance
interquartile	Noisy documents	1.0-2.0	Split at mean + N×IQR, robust to outliers
gradient	Technical/legal docs	90-99	Analyze rate of change in similarity

Configuration Options

# Breakpoint detection method
PDFKB_SEMANTIC_CHUNKER_THRESHOLD_TYPE=percentile  # percentile, standard_deviation, interquartile, gradient

# Threshold amount (interpretation depends on type)
PDFKB_SEMANTIC_CHUNKER_THRESHOLD_AMOUNT=95.0  # For percentile/gradient: 0-100, for others: positive float

# Context buffer size (sentences to include around breakpoints)
PDFKB_SEMANTIC_CHUNKER_BUFFER_SIZE=1  # Default: 1

# Optional: Fixed number of chunks (overrides threshold-based splitting)
PDFKB_SEMANTIC_CHUNKER_NUMBER_OF_CHUNKS=  # Leave empty for dynamic

# Minimum chunk size in characters
PDFKB_SEMANTIC_CHUNKER_MIN_CHUNK_CHARS=100  # Default: 100

# Sentence splitting regex
PDFKB_SEMANTIC_CHUNKER_SENTENCE_SPLIT_REGEX="(?<=[.?!])\\s+"  # Default pattern

Tuning Guidelines

1. For General Documents (default):

   • Use percentile with 95.0 threshold
   • Good balance between chunk size and coherence

2. For Technical Documentation:

   • Use gradient with 90.0 threshold
   • Better at detecting technical section boundaries

3. For Academic Papers:

   • Use standard_deviation with 3.0 threshold
   • Maintains paragraph and section integrity

4. For Mixed Content:

   • Use interquartile with 1.5 threshold
   • Robust against varying content styles

Installation

Install with the semantic chunking dependency:

pip install "pdfkb-mcp[semantic]"

Or if using uvx:

uvx pdfkb-mcp[semantic]

Compatibility

• Works with both local and OpenAI embeddings
• Compatible with all PDF parsers
• Integrates with intelligent caching system
• Falls back to LangChain chunker if dependencies missing

🎯 Parser Selection Guide

Decision Tree

Document Type & Priority?
├── 🏃 Speed Priority → PyMuPDF4LLM (fastest processing, low memory)
├── 📚 Academic Papers → MinerU (GPU-accelerated, excellent formulas/tables)
├── 📊 Business Reports → Docling (accurate tables, structured output)
├── ⚖️ Balanced Quality → Marker (good multilingual, selective OCR)
└── 🎯 Maximum Accuracy → LLM (slow, API costs, complex layouts)

Performance Comparison

Parser	Processing Speed	Memory	Text Quality	Table Quality	Best For
PyMuPDF4LLM	Fastest	Low	Good	Basic-Moderate	RAG pipelines, bulk ingestion
MinerU	Fast with GPU¹	~4GB VRAM²	Excellent	Excellent	Scientific/technical PDFs
Docling	0.9-2.5 pages/s³	2.5-6GB⁴	Excellent	Excellent	Structured documents, tables
Marker	~25 p/s batch⁵	~4GB VRAM⁶	Excellent	Good-Excellent⁷	Scientific papers, multilingual
LLM	Slow⁸	Variable⁹	Excellent¹⁰	Excellent	Complex layouts, high-value docs

Notes: ¹ >10,000 tokens/s on RTX 4090 with sglang ² Reported for <1B parameter model ³ CPU benchmarks: 0.92-1.34 p/s (native), 1.57-2.45 p/s (pypdfium) ⁴ 2.42-2.56GB (pypdfium), 6.16-6.20GB (native backend) ⁵ Projected on H100 GPU in batch mode ⁶ Benchmark configuration on NVIDIA A6000 ⁷ Enhanced with optional LLM mode for table merging ⁸ Order of magnitude slower than traditional parsers ⁹ Depends on token usage and model size ¹⁰ 98.7-100% accuracy when given clean text

⚙️ Configuration

Tier 1: Basic Configurations (80% of users)

Default (Recommended):

{
  "mcpServers": {
    "pdfkb": {
      "command": "uvx",
      "args": ["pdfkb-mcp"],
      "env": {
        "PDFKB_OPENAI_API_KEY": "sk-proj-abc123def456ghi789...",
        "PDFKB_PDF_PARSER": "pymupdf4llm",
        "PDFKB_PDF_CHUNKER": "langchain",
        "PDFKB_EMBEDDING_MODEL": "text-embedding-3-large"
      },
      "transport": "stdio"
    }
  }
}

Speed Optimized:

{
  "mcpServers": {
    "pdfkb": {
      "command": "uvx",
      "args": ["pdfkb-mcp"],
      "env": {
        "PDFKB_OPENAI_API_KEY": "sk-proj-abc123def456ghi789...",
        "PDFKB_PDF_PARSER": "pymupdf4llm",
        "PDFKB_CHUNK_SIZE": "800"
      },
      "transport": "stdio"
    }
  }
}

Memory Efficient:

{
  "mcpServers": {
    "pdfkb": {
      "command": "uvx",
      "args": ["pdfkb-mcp"],
      "env": {
        "PDFKB_OPENAI_API_KEY": "sk-proj-abc123def456ghi789...",
        "PDFKB_PDF_PARSER": "pymupdf4llm",
        "PDFKB_EMBEDDING_BATCH_SIZE": "50"
      },
      "transport": "stdio"
    }
  }
}

Tier 2: Use Case Specific (15% of users)

Academic Papers:

{
  "mcpServers": {
    "pdfkb": {
      "command": "uvx",
      "args": ["pdfkb-mcp"],
      "env": {
        "PDFKB_OPENAI_API_KEY": "sk-proj-abc123def456ghi789...",
        "PDFKB_PDF_PARSER": "mineru",
        "PDFKB_CHUNK_SIZE": "1200"
      },
      "transport": "stdio"
    }
  }
}

Business Documents:

{
  "mcpServers": {
    "pdfkb": {
      "command": "uvx",
      "args": ["pdfkb-mcp"],
      "env": {
        "PDFKB_OPENAI_API_KEY": "sk-proj-abc123def456ghi789...",
        "PDFKB_PDF_PARSER": "pymupdf4llm",
        "PDFKB_DOCLING_TABLE_MODE": "ACCURATE",
        "PDFKB_DOCLING_DO_TABLE_STRUCTURE": "true"
      },
      "transport": "stdio"
    }
  }
}

Multi-language Documents:

{
  "mcpServers": {
    "pdfkb": {
      "command": "uvx",
      "args": ["pdfkb-mcp"],
      "env": {
        "PDFKB_OPENAI_API_KEY": "sk-proj-abc123def456ghi789...",
        "PDFKB_PDF_PARSER": "docling",
        "PDFKB_DOCLING_OCR_LANGUAGES": "en,fr,de,es",
        "PDFKB_DOCLING_DO_OCR": "true"
      },
      "transport": "stdio"
    }
  }
}

Hybrid Search (NEW - Improved Search Quality):

{
  "mcpServers": {
    "pdfkb": {
      "command": "uvx",
      "args": ["pdfkb-mcp"],
      "env": {
        "PDFKB_OPENAI_API_KEY": "sk-proj-abc123def456ghi789...",
        "PDFKB_ENABLE_HYBRID_SEARCH": "true",
        "PDFKB_HYBRID_VECTOR_WEIGHT": "0.6",
        "PDFKB_HYBRID_TEXT_WEIGHT": "0.4"
      },
      "transport": "stdio"
    }
  }
}

Semantic Chunking (NEW - Context-Aware Chunking):

{
  "mcpServers": {
    "pdfkb": {
      "command": "uvx",
      "args": ["pdfkb-mcp[semantic]"],
      "env": {
        "PDFKB_OPENAI_API_KEY": "sk-proj-abc123def456ghi789...",
        "PDFKB_PDF_CHUNKER": "semantic",
        "PDFKB_SEMANTIC_CHUNKER_THRESHOLD_TYPE": "gradient",
        "PDFKB_SEMANTIC_CHUNKER_THRESHOLD_AMOUNT": "90.0",
        "PDFKB_ENABLE_HYBRID_SEARCH": "true"
      },
      "transport": "stdio"
    }
  }
}

Maximum Quality:

{
  "mcpServers": {
    "pdfkb": {
      "command": "uvx",
      "args": ["pdfkb-mcp"],
      "env": {
        "PDFKB_OPENAI_API_KEY": "sk-proj-abc123def456ghi789...",
        "PDFKB_OPENROUTER_API_KEY": "sk-or-v1-abc123def456ghi789...",
        "PDFKB_PDF_PARSER": "llm",
        "PDFKB_LLM_MODEL": "anthropic/claude-3.5-sonnet",
        "PDFKB_EMBEDDING_MODEL": "text-embedding-3-large"
      },
      "transport": "stdio"
    }
  }
}

Essential Environment Variables

Variable	Default	Description
PDFKB_OPENAI_API_KEY	required	OpenAI API key for embeddings
PDFKB_KNOWLEDGEBASE_PATH	./pdfs	Directory containing PDF files
PDFKB_CACHE_DIR	./.cache	Cache directory for processing
PDFKB_PDF_PARSER	pymupdf4llm	Parser: pymupdf4llm (default), marker, mineru, docling, llm
PDFKB_PDF_CHUNKER	langchain	Chunking strategy: langchain (default), page, unstructured, semantic
PDFKB_CHUNK_SIZE	1000	Target chunk size for LangChain chunker
PDFKB_WEB_ENABLE	false	Enable/disable web interface
PDFKB_WEB_PORT	8080	Web server port
PDFKB_WEB_HOST	localhost	Web server host
PDFKB_WEB_CORS_ORIGINS	http://localhost:3000,http://127.0.0.1:3000	CORS allowed origins (comma-separated)
PDFKB_EMBEDDING_MODEL	text-embedding-3-large	OpenAI embedding model (use text-embedding-3-small for faster processing)
PDFKB_MIN_CHUNK_SIZE	0	Minimum chunk size in characters (0 = disabled, filters out chunks smaller than this size)
PDFKB_OPENAI_API_BASE	optional	Custom base URL for OpenAI-compatible APIs (e.g., https://api.studio.nebius.com/v1/)
PDFKB_HUGGINGFACE_EMBEDDING_MODEL	sentence-transformers/all-MiniLM-L6-v2	HuggingFace model for embeddings when using huggingface provider
PDFKB_HUGGINGFACE_PROVIDER	optional	HuggingFace provider (e.g., "nebius"), leave empty for default
PDFKB_ENABLE_HYBRID_SEARCH	true	Enable hybrid search combining semantic and keyword matching
PDFKB_HYBRID_VECTOR_WEIGHT	0.6	Weight for semantic search (0-1, must sum to 1 with text weight)
PDFKB_HYBRID_TEXT_WEIGHT	0.4	Weight for keyword/BM25 search (0-1, must sum to 1 with vector weight)
PDFKB_RRF_K	60	Reciprocal Rank Fusion constant (higher = less emphasis on rank differences)
PDFKB_LOCAL_EMBEDDING_MODEL	Qwen/Qwen3-Embedding-0.6B	Local embedding model (Qwen3-Embedding series only)
PDFKB_GGUF_QUANTIZATION	Q6_K	GGUF quantization level (Q8_0, F16, Q6_K, Q4_K_M, Q4_K_S, Q5_K_M, Q5_K_S)
PDFKB_ENABLE_RERANKER	false	Enable/disable result reranking for improved search quality
PDFKB_RERANKER_PROVIDER	local	Reranker provider: 'local' or 'deepinfra'
PDFKB_RERANKER_MODEL	Qwen/Qwen3-Reranker-0.6B	Reranker model for local provider
PDFKB_RERANKER_SAMPLE_ADDITIONAL	5	Additional results to sample for reranking
PDFKB_RERANKER_GGUF_QUANTIZATION	optional	GGUF quantization level (Q6_K, Q8_0, etc.)
PDFKB_DEEPINFRA_API_KEY	required	DeepInfra API key for reranking
PDFKB_DEEPINFRA_RERANKER_MODEL	Qwen/Qwen3-Reranker-8B	DeepInfra model: 0.6B, 4B, or 8B
PDFKB_ENABLE_SUMMARIZER	false	Enable/disable document summarization
PDFKB_SUMMARIZER_PROVIDER	local	Summarizer provider: 'local' or 'remote'
PDFKB_SUMMARIZER_MODEL	Qwen/Qwen3-4B-Instruct-2507-FP8	Model for summarization
PDFKB_SUMMARIZER_MAX_PAGES	10	Maximum pages to analyze for summarization
PDFKB_SUMMARIZER_DEVICE	auto	Hardware device for local summarizer
PDFKB_SUMMARIZER_MODEL_CACHE_DIR	~/.cache/pdfkb-mcp/summarizer	Cache directory for summarizer models
PDFKB_SUMMARIZER_API_BASE	optional	Custom API base URL for remote summarizer
PDFKB_SUMMARIZER_API_KEY	optional	API key for remote summarizer (fallback to OPENAI_API_KEY)

🐳 Docker Deployment

Deploy pdfkb-mcp using Docker for consistent, scalable, and isolated deployment across any environment.

Quick Start with Docker

1. Using Container Run (Local Embeddings - No API Key Required):

# Create directories
mkdir -p ./documents ./cache ./logs

# Run with Podman (preferred)
podman run -d \
  --name pdfkb-mcp \
  -p 8000:8000 \
  -v "$(pwd)/documents:/app/documents:rw" \
  -v "$(pwd)/cache:/app/cache" \
  -e PDFKB_EMBEDDING_PROVIDER=local \
  -e PDFKB_TRANSPORT=http \
  pdfkb-mcp:latest

# Or with Docker
docker run -d \
  --name pdfkb-mcp \
  -p 8000:8000 \
  -v "$(pwd)/documents:/app/documents:rw" \
  -v "$(pwd)/cache:/app/cache" \
  -e PDFKB_EMBEDDING_PROVIDER=local \
  -e PDFKB_TRANSPORT=http \
  pdfkb-mcp:latest

2. Using Compose (Recommended: Podman):

# 1) Copy the sample file and edit it
cp docker-compose.sample.yml docker-compose.yml

# 2) Edit docker-compose.yml
#    - Set the documents volume path to your folder
#    - Optionally adjust ports, resources, and any env vars
$EDITOR docker-compose.yml

# 3) Create recommended local directories (if using bind mounts)
mkdir -p ./documents ./cache ./logs

# 4a) Start with Podman (preferred per project rules)
podman-compose up -d

# 4b) Or with Docker (if you aren't using Podman)
docker compose up -d

Security note: docker-compose.yml is already in .gitignore. Do not commit API keys. Use the sample file and keep your local docker-compose.yml untracked.

Docker Compose Configuration

The docker-compose.sample.yml provides a comprehensive configuration template with:

• 📋 All environment variables documented with examples and default values
• 🔧 Logical sections (Core, Embedding, Web Interface, Processing, Advanced AI, etc.)
• 🚀 Multiple configuration examples for different use cases
• 🔒 Security best practices with no committed API keys
• 🎯 Quick start recommendations at the bottom of the file

Key Configuration Areas:

1. Documents Volume: Update the path to your document collection:

   volumes:
     - "/path/to/your/documents:/app/documents:rw"  # ← CHANGE THIS
   

2. Embedding Provider: Choose your preferred option in the environment section:

   # Local (no API key - recommended for privacy)
   PDFKB_EMBEDDING_PROVIDER: "local"
   
   # OpenAI/compatible APIs (requires API key)
   # PDFKB_EMBEDDING_PROVIDER: "openai"
   # PDFKB_OPENAI_API_KEY: "YOUR-API-KEY-HERE"
   

3. Resource Limits: Adjust based on your system:

   deploy:
     resources:
       limits:
         cpus: '4.0'    # ← Increase for better performance
         memory: 8G     # ← Increase for large document collections
   

3. Alternative: Using Environment File: For sensitive configuration, create a separate .env file:

# Create .env file for sensitive settings
cat > .env << 'EOF'
PDFKB_OPENAI_API_KEY=sk-proj-your-actual-key-here
PDFKB_EMBEDDING_PROVIDER=openai
PDFKB_DEEPINFRA_API_KEY=your-deepinfra-key
PDFKB_ENABLE_RERANKER=true
EOF

# Reference in docker-compose.yml
# env_file:
#   - .env

# Restart with new configuration
podman-compose down && podman-compose up -d

Building from Source

# Clone the repository
git clone https://github.com/juanqui/pdfkb-mcp.git
cd pdfkb-mcp

# Copy and customize the configuration
cp docker-compose.sample.yml docker-compose.yml
# Edit docker-compose.yml to update volumes and configuration
$EDITOR docker-compose.yml

# Build with Podman (preferred)
podman build -t pdfkb-mcp:latest .

# Or use Podman Compose to build
podman-compose build

# Alternative: Build with Docker
docker build -t pdfkb-mcp:latest .
docker compose build

Container Configuration

Volume Mounts

Required Volumes:

• Documents: /app/documents - Mount your PDF/Markdown collection here
• Cache: /app/cache - Persistent storage for ChromaDB and processing cache

Optional Volumes:

• Logs: /app/logs - Container logs (useful for debugging)
• Config: /app/config - Custom configuration files

# Example with all volumes (Podman preferred)
podman run -d \
  --name pdfkb-mcp \
  -p 8000:8000 -p 8080:8080 \
  -v "/path/to/your/documents:/app/documents:rw" \
  -v "pdfkb-cache:/app/cache" \
  -v "pdfkb-logs:/app/logs" \
  -e PDFKB_EMBEDDING_PROVIDER=local \
  -e PDFKB_WEB_ENABLE=true \
  pdfkb-mcp:latest

# Or with Docker
docker run -d \
  --name pdfkb-mcp \
  -p 8000:8000 -p 8080:8080 \
  -v "/path/to/your/documents:/app/documents:rw" \
  -v "pdfkb-cache:/app/cache" \
  -v "pdfkb-logs:/app/logs" \
  -e PDFKB_EMBEDDING_PROVIDER=local \
  -e PDFKB_WEB_ENABLE=true \
  pdfkb-mcp:latest

Port Configuration

• 8000: MCP HTTP/SSE transport (required for MCP clients)
• 8080: Web interface (optional, only if PDFKB_WEB_ENABLE=true)

Environment Variables

Core Configuration:

# Documents and cache
PDFKB_KNOWLEDGEBASE_PATH=/app/documents    # Container path (don't change)
PDFKB_CACHE_DIR=/app/cache                 # Container path (don't change)
PDFKB_LOG_LEVEL=INFO                       # DEBUG, INFO, WARNING, ERROR

# Transport mode
PDFKB_TRANSPORT=http                       # "http", "sse" (stdio not recommended for containers)
PDFKB_SERVER_HOST=0.0.0.0                 # Bind to all interfaces
PDFKB_SERVER_PORT=8000                     # Port inside container

# Embedding provider
PDFKB_EMBEDDING_PROVIDER=local             # "local", "openai", "huggingface"
PDFKB_LOCAL_EMBEDDING_MODEL="Qwen/Qwen3-Embedding-0.6B"

# Optional: OpenAI configuration
PDFKB_OPENAI_API_KEY=sk-proj-your-key-here
PDFKB_EMBEDDING_MODEL=text-embedding-3-large

# Optional: Web interface
PDFKB_WEB_ENABLE=false                     # Enable web UI
PDFKB_WEB_HOST=0.0.0.0                    # Web interface host
PDFKB_WEB_PORT=8080                       # Web interface port

Performance Configuration:

# Processing configuration
PDFKB_PDF_PARSER=pymupdf4llm              # Parser selection
PDFKB_DOCUMENT_CHUNKER=langchain           # Chunking strategy
PDFKB_CHUNK_SIZE=1000                     # Chunk size
PDFKB_CHUNK_OVERLAP=200                   # Chunk overlap

# Parallel processing (adjust based on container resources)
PDFKB_MAX_PARALLEL_PARSING=1              # Concurrent PDF processing
PDFKB_MAX_PARALLEL_EMBEDDING=1            # Concurrent embedding generation
PDFKB_BACKGROUND_QUEUE_WORKERS=2          # Background workers

# Search configuration
PDFKB_ENABLE_HYBRID_SEARCH=true           # Hybrid search (recommended)
PDFKB_ENABLE_RERANKER=false               # Result reranking
PDFKB_ENABLE_SUMMARIZER=false             # Document summarization

MCP Client Configuration with Docker

For Cline (HTTP Transport)

MCP Settings (~/.continue/config.json):

{
  "mcpServers": {
    "pdfkb": {
      "command": "curl",
      "args": [
        "-X", "POST",
        "-H", "Content-Type: application/json",
        "http://localhost:8000/mcp"
      ],
      "transport": "http"
    }
  }
}

For Roo (SSE Transport)

Set container to SSE mode:

# Update docker-compose.yml or add environment variable
PDFKB_TRANSPORT=sse

# Restart container with Podman
podman-compose restart

# Or with Docker
docker compose restart

MCP Settings:

{
  "mcpServers": {
    "pdfkb": {
      "transport": "sse",
      "url": "http://localhost:8000/sse"
    }
  }
}

Production Configuration

For production deployments, use the comprehensive docker-compose.sample.yml as your starting point:

1. Copy and customize: cp docker-compose.sample.yml docker-compose.yml
2. Update paths and secrets: Edit the documents volume and any API keys
3. Adjust resource limits: Configure CPU/memory based on your infrastructure
4. Enable security features: Review security settings and network configuration

The sample file includes:

• 🔒 Security hardening (non-root user, no-new-privileges)
• 📊 Resource limits and health checks
• 🌐 Network isolation
• 📋 Comprehensive environment variable documentation
• 🚀 Performance optimization examples

Development Configuration

docker-compose.dev.yml:

version: '3.8'

services:
  pdfkb-mcp-dev:
    build: .
    container_name: pdfkb-mcp-dev

    ports:
      - "8000:8000"
      - "8080:8080"

    volumes:
      - "./documents:/app/documents:rw"
      - "./src:/app/src:ro"                    # Live source code mounting
      - "./dev-cache:/app/cache"
      - "./dev-logs:/app/logs"

    environment:
      - PDFKB_LOG_LEVEL=DEBUG                   # Debug logging
      - PDFKB_WEB_ENABLE=true                   # Enable web interface
      - PDFKB_EMBEDDING_PROVIDER=local          # No API costs

    env_file:
      - .env.dev

Container Management

Health Monitoring

# Check container health (Podman preferred)
podman ps
podman-compose ps

# Or with Docker
docker ps
docker compose ps

# View logs
podman logs pdfkb-mcp      # or: docker logs pdfkb-mcp
podman-compose logs -f     # or: docker compose logs -f

# Check health endpoint
curl http://localhost:8000/health

# Monitor resource usage
podman stats pdfkb-mcp     # or: docker stats pdfkb-mcp

Container Operations

# Start/stop container (Podman preferred)
podman-compose up -d
podman-compose down

# Or with Docker
docker compose up -d
docker compose down

# Restart with new configuration
podman-compose restart     # or: docker compose restart

# Update container image
podman-compose pull        # or: docker compose pull
podman-compose up -d       # or: docker compose up -d

# View container details
podman inspect pdfkb-mcp   # or: docker inspect pdfkb-mcp

# Execute commands in container
podman exec -it pdfkb-mcp bash   # or: docker exec -it pdfkb-mcp bash

Troubleshooting

Common Issues

1. Permission Errors:

# Fix volume permissions
sudo chown -R 1001:1001 ./documents ./cache ./logs

# Or use current user
sudo chown -R $(id -u):$(id -g) ./documents ./cache ./logs

2. Port Conflicts:

# Check if ports are in use
netstat -tulpn | grep :8000
lsof -i :8000

# Use different ports
podman run -p 8001:8000 -p 8081:8080 pdfkb-mcp:latest   # Podman
# or: docker run -p 8001:8000 -p 8081:8080 pdfkb-mcp:latest  # Docker

3. Memory Issues:

# Check container memory usage
podman stats --no-stream   # or: docker stats --no-stream

# Increase memory limits in docker-compose.yml
deploy:
  resources:
    limits:
      memory: 8G    # Increase memory

4. Connection Issues:

# Test container connectivity
curl http://localhost:8000/health

# Check if container is running
podman ps | grep pdfkb     # or: docker ps | grep pdfkb

# Check logs for errors
podman logs pdfkb-mcp --tail 50   # or: docker logs pdfkb-mcp --tail 50

Debug Mode

# Run container in debug mode (Podman preferred)
podman run -it \
  -p 8000:8000 \
  -v "$(pwd)/documents:/app/documents:rw" \
  -e PDFKB_LOG_LEVEL=DEBUG \
  -e PDFKB_EMBEDDING_PROVIDER=local \
  pdfkb-mcp:latest

# Or with Docker
docker run -it \
  -p 8000:8000 \
  -v "$(pwd)/documents:/app/documents:rw" \
  -e PDFKB_LOG_LEVEL=DEBUG \
  -e PDFKB_EMBEDDING_PROVIDER=local \
  pdfkb-mcp:latest

# Use development compose
podman-compose -f docker-compose.dev.yml up   # or: docker compose -f docker-compose.dev.yml up

Performance Tuning

For Low-Memory Systems:

environment:
  - PDFKB_MAX_PARALLEL_PARSING=1
  - PDFKB_MAX_PARALLEL_EMBEDDING=1
  - PDFKB_BACKGROUND_QUEUE_WORKERS=1
  - PDFKB_CHUNK_SIZE=500                      # Smaller chunks
deploy:
  resources:
    limits:
      memory: 2G                              # Lower memory limit

For High-Performance Systems:

environment:
  - PDFKB_MAX_PARALLEL_PARSING=4
  - PDFKB_MAX_PARALLEL_EMBEDDING=2
  - PDFKB_BACKGROUND_QUEUE_WORKERS=4
  - PDFKB_CHUNK_SIZE=1500                     # Larger chunks
deploy:
  resources:
    limits:
      memory: 8G                              # Higher memory limit
      cpus: '4.0'

Security Considerations

• Non-root execution: Container runs as user pdfkb (UID 1001)
• Read-only root filesystem: Container filesystem is read-only except for mounted volumes
• Network isolation: Use Docker networks for service isolation
• Resource limits: Set appropriate CPU/memory limits
• Secret management: Use Docker secrets or environment files for API keys

🖥️ MCP Client Setup

Claude Desktop

Configuration File Location:

• macOS: ~/Library/Application Support/Claude/claude_desktop_config.json
• Windows: %APPDATA%\Claude\claude_desktop_config.json
• Linux: ~/.config/Claude/claude_desktop_config.json

Configuration:

{
  "mcpServers": {
    "pdfkb": {
      "command": "uvx",
      "args": ["pdfkb-mcp"],
      "env": {
        "PDFKB_OPENAI_API_KEY": "sk-proj-abc123def456ghi789...",
        "PDFKB_KNOWLEDGEBASE_PATH": "/Users/yourname/Documents",
        "PDFKB_CACHE_DIR": "/Users/yourname/Documents/PDFs/.cache"
      },
      "transport": "stdio",
      "autoRestart": true,
                 "PDFKB_EMBEDDING_MODEL": "text-embedding-3-small",
    }
  }
}

Verification:

1. Restart Claude Desktop completely
2. Look for PDF KB tools in the interface
3. Test with "Add a document" or "Search documents"

🆕 VS Code with Native MCP Support (SSE Mode)

Configuration for SSE/Remote Mode (.vscode/mcp.json in workspace):

{
  "mcpServers": {
    "pdfkb": {
      "command": "uvx",
      "args": ["pdfkb-mcp"],
      "env": {
        "PDFKB_KNOWLEDGEBASE_PATH": "/path/to/your/pdfs"
      },
      "transport": "sse",
      "autoRestart": true
    }
  }
}

Verification:

1. Reload VS Code window
2. Check VS Code's MCP server status in Command Palette
3. Use MCP tools in Copilot Chat

VS Code with Continue Extension

Configuration (.continue/config.json):

{
  "mcpServers": {
    "pdfkb": {
      "command": "uvx",
      "args": ["pdfkb-mcp"],
      "env": {
        "PDFKB_OPENAI_API_KEY": "sk-proj-abc123def456ghi789...",
        "PDFKB_KNOWLEDGEBASE_PATH": "${workspaceFolder}/pdfs"
      },
      "transport": "stdio"
    }
  }
}

Verification:

1. Reload VS Code window
2. Check VS Code's MCP server status in Command Palette
3. Use MCP tools in Copilot Chat

VS Code with Continue Extension

Configuration (.continue/config.json):

{
  "models": [...],
  "mcpServers": {
    "pdfkb": {
      "command": "uvx",
      "args": ["pdfkb-mcp"],
      "env": {
        "PDFKB_OPENAI_API_KEY": "sk-proj-abc123def456ghi789...",
        "PDFKB_KNOWLEDGEBASE_PATH": "${workspaceFolder}/pdfs"
      },
      "transport": "stdio"
    }
  }
}

Verification:

1. Reload VS Code window
2. Check Continue panel for server connection
3. Use @pdfkb in Continue chat

Generic MCP Client

Standard Configuration Template:

{
  "mcpServers": {
    "pdfkb": {
      "command": "uvx",
      "args": ["pdfkb-mcp"],
      "env": {
        "PDFKB_OPENAI_API_KEY": "required",
        "PDFKB_KNOWLEDGEBASE_PATH": "required-absolute-path",
        "PDFKB_PDF_PARSER": "optional-default-pymupdf4llm"
      },
      "transport": "stdio",
      "autoRestart": true,
      "timeout": 30000
    }
  }
}

📊 Performance & Troubleshooting

Common Issues

Server not appearing in MCP client:

// ❌ Wrong: Missing transport
{
  "mcpServers": {
    "pdfkb": {
      "command": "uvx",
      "args": ["pdfkb-mcp"]
    }
  }
}

// ✅ Correct: Include transport and restart client
{
  "mcpServers": {
    "pdfkb": {
      "command": "uvx",
      "args": ["pdfkb-mcp"],
      "transport": "stdio"
    }
  }
}

System overload when processing multiple PDFs:

# Reduce parallel operations to prevent system stress
PDFKB_MAX_PARALLEL_PARSING=1       # Process one PDF at a time
PDFKB_MAX_PARALLEL_EMBEDDING=1     # Embed one document at a time
PDFKB_BACKGROUND_QUEUE_WORKERS=1   # Single background worker

Processing too slow:

// Switch to faster parser and increase parallelism (if system can handle it)
{
  "mcpServers": {
    "pdfkb": {
      "command": "uvx",
      "args": ["pdfkb-mcp"],
      "env": {
        "PDFKB_OPENAI_API_KEY": "sk-key",
        "PDFKB_PDF_PARSER": "pymupdf4llm"
      },
      "transport": "stdio"
    }
  }
}

Memory issues:

// Reduce memory usage
{
  "mcpServers": {
    "pdfkb": {
      "command": "uvx",
      "args": ["pdfkb-mcp"],
      "env": {
        "PDFKB_OPENAI_API_KEY": "sk-key",
        "PDFKB_EMBEDDING_BATCH_SIZE": "25",
        "PDFKB_CHUNK_SIZE": "500"
      },
      "transport": "stdio"
    }
  }
}

Poor table extraction:

// Use table-optimized parser
{
  "mcpServers": {
    "pdfkb": {
      "command": "uvx",
      "args": ["pdfkb-mcp"],
      "env": {
        "PDFKB_OPENAI_API_KEY": "sk-key",
        "PDFKB_PDF_PARSER": "docling",
        "PDFKB_DOCLING_TABLE_MODE": "ACCURATE"
      },
      "transport": "stdio"
    }
  }
}

🆕 SSE/Remote Mode - Client Connection Issues:

// ❌ Wrong: Missing URL for SSE transport (client can't connect)
{
  "mcpServers": {
    "pdfkb": {
      "command": "uvx",
      "args": ["pdfkb-mcp"],
      "env": {
        "PDFKB_TRANSPORT": "sse"
      },
      "transport": "sse"
    }
  }
}

// ✅ Correct: Include URL pointing to SSE server
{
  "mcpServers": {
    "pdfkb": {
      "command": "uvx",
      "args": ["pdfkb-mcp"],
      "env": {
        "PDFKB_KNOWLEDGEBASE_PATH": "/path/to/pdfs",
        "PDFKB_TRANSPORT": "sse",
        "PDFKB_SSE_HOST": "localhost",
        "PDFKB_SSE_PORT": "8000"
      },
      "transport": "sse",
      "url": "http://localhost:8000"
    }
  }
}

Tip: Ensure the SSE server is running first (pdfkb-mcp --transport sse --sse-port 8000), then configure the client with the correct URL. Check firewall settings if connecting remotely.

🆕 SSE/Remote Mode - Port Conflicts in Integrated Mode:

# ❌ Wrong: Web and SSE using same port (will fail to start)
PDFKB_WEB_ENABLE=true PDFKB_WEB_PORT=8000 PDFKB_TRANSPORT=sse PDFKB_SSE_PORT=8000 pdfkb-mcp

# ✅ Correct: Use different ports for web (8080) and SSE (8000)
PDFKB_WEB_ENABLE=true PDFKB_WEB_PORT=8080 PDFKB_TRANSPORT=sse PDFKB_SSE_PORT=8000 pdfkb-mcp

Tip: The server validates port conflicts on startup. Web interface runs on PDFKB_WEB_PORT (default 8080), SSE MCP runs on PDFKB_SSE_PORT (default 8000). Access web at http://localhost:8080 and connect MCP clients to http://localhost:8000.

🆕 SSE/Remote Mode - Server Not Starting in SSE Mode:

# ❌ Wrong: Invalid transport value (server defaults to stdio)
PDFKB_TRANSPORT=remote pdfkb-mcp  # 'remote' is invalid

# ✅ Correct: Use 'sse' for remote transport
PDFKB_TRANSPORT=sse pdfkb-mcp --sse-host 0.0.0.0 --sse-port 8000

# Or via command line flags
pdfkb-mcp --transport sse --sse-host localhost --sse-port 8000

Tip: Valid transport values are 'stdio' (default) or 'sse'. Check server logs for "Running MCP server in SSE mode on http://host:port" confirmation. Use --log-level DEBUG for detailed startup information.

Resource Requirements

Configuration	RAM Usage	Processing Speed	Best For
Speed	2-4 GB	Fastest	Large collections
Balanced	4-6 GB	Medium	Most users
Quality	6-12 GB	Medium-Fast	Accuracy priority
GPU	8-16 GB	Very Fast	High-volume processing

🔧 Advanced Configuration

Parser-Specific Options

MinerU Configuration:

{
  "mcpServers": {
    "pdfkb": {
      "command": "uvx",
      "args": ["pdfkb-mcp"],
      "env": {
        "PDFKB_OPENAI_API_KEY": "sk-key",
        "PDFKB_PDF_PARSER": "mineru",
        "PDFKB_MINERU_LANG": "en",
        "PDFKB_MINERU_METHOD": "auto",
        "PDFKB_MINERU_VRAM": "16"
      },
      "transport": "stdio"
    }
  }
}

LLM Parser Configuration:

{
  "mcpServers": {
    "pdfkb": {
      "command": "uvx",
      "args": ["pdfkb-mcp"],
      "env": {
        "PDFKB_OPENAI_API_KEY": "sk-key",
        "PDFKB_OPENROUTER_API_KEY": "sk-or-v1-abc123def456ghi789...",
        "PDFKB_PDF_PARSER": "llm",
        "PDFKB_LLM_MODEL": "google/gemini-2.5-flash-lite",
        "PDFKB_LLM_CONCURRENCY": "5",
        "PDFKB_LLM_DPI": "150"
      },
      "transport": "stdio"
    }
  }
}

Performance Tuning

Parallel Processing Configuration:

Control the number of concurrent operations to optimize performance and prevent system overload:

# Maximum number of PDFs to parse simultaneously
PDFKB_MAX_PARALLEL_PARSING=1  # Default: 1 (conservative to prevent overload)

# Maximum number of documents to embed simultaneously
PDFKB_MAX_PARALLEL_EMBEDDING=1  # Default: 1 (prevents API rate limits)

# Number of background queue workers
PDFKB_BACKGROUND_QUEUE_WORKERS=2  # Default: 2

# Thread pool size for CPU-intensive operations
PDFKB_THREAD_POOL_SIZE=1  # Default: 1

Resource-Optimized Setup (for low-powered systems):

{
  "env": {
    "PDFKB_MAX_PARALLEL_PARSING": "1",      # Process one PDF at a time
    "PDFKB_MAX_PARALLEL_EMBEDDING": "1",    # Embed one document at a time
    "PDFKB_BACKGROUND_QUEUE_WORKERS": "1",  # Single background worker
    "PDFKB_THREAD_POOL_SIZE": "1"           # Single thread for CPU tasks
  }
}

High-Performance Setup (for powerful machines):

{
  "env": {
    "PDFKB_MAX_PARALLEL_PARSING": "4",      # Parse up to 4 PDFs in parallel
    "PDFKB_MAX_PARALLEL_EMBEDDING": "2",    # Embed 2 documents simultaneously
    "PDFKB_BACKGROUND_QUEUE_WORKERS": "4",  # More background workers
    "PDFKB_THREAD_POOL_SIZE": "2",          # More threads for CPU tasks
    "PDFKB_EMBEDDING_BATCH_SIZE": "200",    # Larger embedding batches
    "PDFKB_VECTOR_SEARCH_K": "15"           # More search results
  }
}

Complete High-Performance Setup:

{
  "mcpServers": {
    "pdfkb": {
      "command": "uvx",
      "args": ["pdfkb-mcp"],
      "env": {
        "PDFKB_OPENAI_API_KEY": "sk-key",
        "PDFKB_PDF_PARSER": "mineru",
        "PDFKB_KNOWLEDGEBASE_PATH": "/Volumes/FastSSD/Documents/PDFs",
        "PDFKB_CACHE_DIR": "/Volumes/FastSSD/Documents/PDFs/.cache",
        "PDFKB_MAX_PARALLEL_PARSING": "4",
        "PDFKB_MAX_PARALLEL_EMBEDDING": "2",
        "PDFKB_BACKGROUND_QUEUE_WORKERS": "4",
        "PDFKB_THREAD_POOL_SIZE": "2",
        "PDFKB_EMBEDDING_BATCH_SIZE": "200",
        "PDFKB_VECTOR_SEARCH_K": "15",
        "PDFKB_FILE_SCAN_INTERVAL": "30"
      },
      "transport": "stdio"
    }
  }
}

Intelligent Caching

The server uses multi-stage caching:

• Parsing Cache: Stores converted markdown (src/pdfkb/intelligent_cache.py:139)
• Chunking Cache: Stores processed chunks
• Vector Cache: ChromaDB embeddings storage

Cache Invalidation Rules:

• Changing PDFKB_PDF_PARSER → Full reset (parsing + chunking + embeddings)
• Changing PDFKB_PDF_CHUNKER → Partial reset (chunking + embeddings)
• Changing PDFKB_EMBEDDING_MODEL → Minimal reset (embeddings only)

📚 Appendix

Installation Options

Primary (Recommended):

uvx pdfkb-mcp
**Web Interface Included**: All installation methods include the web interface. Use these commands:
- `pdfkb-mcp` - MCP server only (default, web disabled)
- `PDFKB_WEB_ENABLE=true pdfkb-mcp` - Integrated MCP + Web server (web enabled)

With Specific Parser Dependencies:

uvx pdfkb-mcp[marker]     # Marker parser
uvx pdfkb-mcp[mineru]     # MinerU parser
uvx pdfkb-mcp[docling]    # Docling parser
uvx pdfkb-mcp[llm]        # LLM parser
uvx pdfkb-mcp[semantic]   # Semantic chunker (NEW)
uvx pdfkb-mcp[unstructured_chunker]  # Unstructured chunker
uvx pdfkb-mcp[web]        # Enhanced web features (psutil for metrics)

pip install "pdfkb-mcp[web]" # Enhanced web features Or via pip/pipx:

pip install "pdfkb-mcp[marker]"            # Marker parser
pip install "pdfkb-mcp[docling-complete]"  # Docling with OCR and full features

Development Installation:

git clone https://github.com/juanqui/pdfkb-mcp.git
cd pdfkb-mcp
pip install -e ".[dev]"

Complete Environment Variables Reference

Variable	Default	Description
PDFKB_OPENAI_API_KEY	required	OpenAI API key for embeddings
PDFKB_OPENROUTER_API_KEY	optional	Required for LLM parser
PDFKB_KNOWLEDGEBASE_PATH	./pdfs	PDF directory path
PDFKB_CACHE_DIR	./.cache	Cache directory
PDFKB_PDF_PARSER	pymupdf4llm	PDF parser selection
PDFKB_PDF_CHUNKER	langchain	Chunking strategy: langchain, unstructured, semantic
PDFKB_CHUNK_SIZE	1000	LangChain chunk size
PDFKB_CHUNK_OVERLAP	200	LangChain chunk overlap
PDFKB_MIN_CHUNK_SIZE	0	Minimum chunk size in characters (0 = disabled, filters out chunks smaller than this size)
PDFKB_EMBEDDING_MODEL	text-embedding-3-large	OpenAI model
PDFKB_OPENAI_API_BASE	optional	Custom base URL for OpenAI-compatible APIs
PDFKB_HUGGINGFACE_EMBEDDING_MODEL	sentence-transformers/all-MiniLM-L6-v2	HuggingFace model
PDFKB_HUGGINGFACE_PROVIDER	optional	HuggingFace provider (e.g., "nebius")
PDFKB_LOCAL_EMBEDDING_MODEL	Qwen/Qwen3-Embedding-0.6B	Local embedding model (Qwen3-Embedding series only)
PDFKB_GGUF_QUANTIZATION	Q6_K	GGUF quantization level (Q8_0, F16, Q6_K, Q4_K_M, Q4_K_S, Q5_K_M, Q5_K_S)
PDFKB_EMBEDDING_DEVICE	auto	Hardware device (auto, mps, cuda, cpu)
PDFKB_USE_MODEL_OPTIMIZATION	true	Enable torch.compile optimization
PDFKB_EMBEDDING_CACHE_SIZE	10000	Number of cached embeddings in LRU cache
PDFKB_MODEL_CACHE_DIR	~/.cache/huggingface	Local model cache directory
PDFKB_ENABLE_RERANKER	false	Enable/disable result reranking
PDFKB_RERANKER_PROVIDER	local	Reranker provider: 'local' or 'deepinfra'
PDFKB_RERANKER_MODEL	Qwen/Qwen3-Reranker-0.6B	Reranker model for local provider
PDFKB_RERANKER_SAMPLE_ADDITIONAL	5	Additional results to sample for reranking
PDFKB_RERANKER_DEVICE	auto	Hardware device for local reranker (auto, mps, cuda, cpu)
PDFKB_RERANKER_MODEL_CACHE_DIR	~/.cache/pdfkb-mcp/reranker	Cache directory for local reranker models
PDFKB_RERANKER_GGUF_QUANTIZATION	optional	GGUF quantization level (Q6_K, Q8_0, etc.)
PDFKB_DEEPINFRA_API_KEY	required	DeepInfra API key for reranking
PDFKB_DEEPINFRA_RERANKER_MODEL	Qwen/Qwen3-Reranker-8B	Model: Qwen/Qwen3-Reranker-0.6B, 4B, or 8B
PDFKB_ENABLE_SUMMARIZER	false	Enable/disable document summarization
PDFKB_SUMMARIZER_PROVIDER	local	Summarizer provider: 'local' or 'remote'
PDFKB_SUMMARIZER_MODEL	Qwen/Qwen3-4B-Instruct-2507-FP8	Model for summarization
PDFKB_SUMMARIZER_MAX_PAGES	10	Maximum pages to analyze for summarization
PDFKB_SUMMARIZER_DEVICE	auto	Hardware device for local summarizer
PDFKB_SUMMARIZER_MODEL_CACHE_DIR	~/.cache/pdfkb-mcp/summarizer	Cache directory for summarizer models
PDFKB_SUMMARIZER_API_BASE	optional	Custom API base URL for remote summarizer
PDFKB_SUMMARIZER_API_KEY	optional	API key for remote summarizer
PDFKB_EMBEDDING_BATCH_SIZE	100	Embedding batch size
PDFKB_MAX_PARALLEL_PARSING	1	Max concurrent PDF parsing operations
PDFKB_MAX_PARALLEL_EMBEDDING	1	Max concurrent embedding operations
PDFKB_BACKGROUND_QUEUE_WORKERS	2	Number of background processing workers
PDFKB_THREAD_POOL_SIZE	1	Thread pool size for CPU-intensive tasks
PDFKB_VECTOR_SEARCH_K	5	Default search results
PDFKB_FILE_SCAN_INTERVAL	60	File monitoring interval
PDFKB_LOG_LEVEL	INFO	Logging level
PDFKB_WEB_ENABLE	false	Enable/disable web interface
PDFKB_WEB_PORT	8080	Web server port
PDFKB_WEB_HOST	localhost	Web server host
PDFKB_WEB_CORS_ORIGINS	http://localhost:3000,http://127.0.0.1:3000	CORS allowed origins (comma-separated)

Parser Comparison Details

Feature	PyMuPDF4LLM	Marker	MinerU	Docling	LLM
Speed	Fastest	Medium	Fast (GPU)	Medium	Slowest
Memory	Lowest	Medium	High	Medium	Lowest
Tables	Basic	Good	Excellent	Excellent	Excellent
Formulas	Basic	Good	Excellent	Good	Excellent
Images	Basic	Good	Good	Excellent	Excellent
Setup	Simple	Simple	Moderate	Simple	Simple
Cost	Free	Free	Free	Free	API costs

Chunking Strategies

LangChain (PDFKB_PDF_CHUNKER=langchain):

• Header-aware splitting with MarkdownHeaderTextSplitter
• Configurable via PDFKB_CHUNK_SIZE and PDFKB_CHUNK_OVERLAP
• Best for customizable chunking
• Default and installed with base package

Page (PDFKB_PDF_CHUNKER=page) 🆕 NEW:

• Page-based chunking that preserves document page boundaries
• Works with page-aware parsers that output individual pages
• Supports merging small pages and splitting large ones
• Configurable via PDFKB_PAGE_CHUNKER_MIN_CHUNK_SIZE and PDFKB_PAGE_CHUNKER_MAX_CHUNK_SIZE
• Best for preserving original document structure and page-level metadata

Semantic (PDFKB_PDF_CHUNKER=semantic):

• Advanced semantic chunking using LangChain's SemanticChunker
• Groups semantically related content together using embedding similarity
• Four breakpoint detection methods: percentile, standard_deviation, interquartile, gradient
• Preserves context and improves retrieval quality by 40%
• Install extra: pip install "pdfkb-mcp[semantic]" to enable
• Configurable via environment variables (see Semantic Chunking section)
• Best for documents requiring high context preservation

Unstructured (PDFKB_PDF_CHUNKER=unstructured):

• Intelligent semantic chunking with unstructured library
• Zero configuration required
• Install extra: pip install "pdfkb-mcp[unstructured_chunker]" to enable
• Best for document structure awareness

First-run notes

• On the first run, the server initializes caches and vector store and logs selected components:
  • Parser: PyMuPDF4LLM (default)
  • Chunker: LangChain (default)
  • Embedding Model: text-embedding-3-large (default)
• If you select a parser/chunker that isn’t installed, the server logs a warning with the exact install command and falls back to the default components instead of exiting.

Troubleshooting Guide

API Key Issues:

1. Verify key format starts with sk-
2. Check account has sufficient credits
3. Test connectivity: curl -H "Authorization: Bearer $PDFKB_OPENAI_API_KEY" https://api.openai.com/v1/models

Parser Installation Issues:

1. MinerU: pip install mineru[all] and verify mineru --version
2. Docling: pip install docling for basic, pip install pdfkb-mcp[docling-complete] for all features
3. LLM: Requires PDFKB_OPENROUTER_API_KEY environment variable

Performance Optimization:

1. Speed: Use pymupdf4llm parser (fastest, low memory footprint)
2. Memory: Reduce PDFKB_EMBEDDING_BATCH_SIZE and PDFKB_CHUNK_SIZE; use pypdfium backend for Docling
3. Quality: Use mineru with GPU (>10K tokens/s on RTX 4090) or marker for balanced quality
4. Tables: Use docling with PDFKB_DOCLING_TABLE_MODE=ACCURATE or marker with LLM mode
5. Batch Processing: Use marker on H100 (~25 pages/s) or mineru with sglang acceleration

For additional support, see implementation details in src/pdfkb/main.py and src/pdfkb/config.py.