Insights Knowledge Base(IKB) MCP Server

>>>中文版

🍭A free, plug-and-play knowledge base. Built-in with 10,000+ high-quality insights reports, packaged as MCP Server, and secure local data storage.

⚠️⚠️ All collected reports in this project come from free resources on official research report websites. ⚠️⚠️

Features

1. 🍾 No configuration needed, truly plug-and-play. For private document parsing, configure VLM models and parameters in .env (e.g., VLM_MODEL_NAME=qwen2.5-vl-72b-instruct).
2. 🦉 Permanently free - no need to waste effort collecting report resources. Welcome to share reliable, copyright-free report sources via issues.
3. 📢 Committed to weekly report updates, but bug fixes depend on my mood (Actually I'm not a engineer 🤭).

Optimization Updates as of June 17th

1. 💡Optimized models.py: Improved data query efficiency by 1,000%
2. 💡Optimized extractor.py: Slightly enhanced PDF extraction efficiency
3. 💡Optimized recognizer.py: Boosted image comprehension efficiency by 50%
4. 💡Optimized ikb_mcp_server.py:
   • Added pagination functionality
   • Displayed local paths of referenced files
5. Add MIT License(https://github.com/v587d/InsightsLibrary/pull/1#issuecomment-2969226661)
6. 📦 Overall compressed project package size reduced by approximately 50%
7. 💡Streamline Private Document Handling
8. 💡Fixed other identified bugs

Future Work Directions

1. Embedding Model Integration
   • Implement sentence-transformers for document embeddings
   • Create Function Tools endpoints for vector search
2. Reporting System Enhancement
   • Daily automated report generation
   • Real-time update notifications

Newest Files Profile

{
    "statistics": {
        "total_files": 61,
        "total_pages": 3031,
        "unique_publishers": 7,
        "unique_topics": 45,
        "last_updated": "2025-06-17T10:36:52.437453"
    },
    "details": {
        "publishers": [
            "Accenture",
            "BAIN",
            "BCG",
            "CBS",
            "McKinsey",
            "PWC",
            "亿欧"
        ],
        "topics": [
            "AI",
            "AI Agent",
            "Asian American",
            "Aviation",
            "Business",
            "Chemicals",
            "Consumer Goods",
            "Decarbonation",
            "Decarbonization",
            "Digital",
            "Economy and Trade",
            "Education",
            "Employment",
            "Fashion",
            "Financial Technology",
            "Fintech",
            "Food-meatless",
            "Gen Z",
            "Global banking",
            "Global energy",
            "Global insurance",
            "Global macroeconomic",
            "Global materials",
            "Global private market",
            "Global trade",
            "Health",
            "Human capital",
            "Insurance",
            "Low-altitude Economy",
            "Luxury Goods",
            "Maritime",
            "Media",
            "Medical Health",
            "Net zero",
            "New Energy Vehicle",
            "Pet Food",
            "Population",
            "Private Equity",
            "Real estate",
            "Retail Digitalization",
            "Small business",
            "Smart Home",
            "Sustainability",
            "Technology",
            "Travel"
        ]
    }
}

Installation (Beginner-Friendly)

💡Pro tip: Stuck? Drag this page to an LLM client (like DeepSeek) for step-by-step guidance. Actually, these instructions were written by DeepSeek too...

Prerequisites: Python 3.12+ (Download from official website and ADD ENVIRONMENT PATH)

Install UV:

pip install uv

1. Clone the project

git clone https://github.com/v587d/InsightsLibrary.git
cd InsightsLibrary

2. Create virtual environment

uv venv .venv  # Create dedicated virtual environment

# Activate environment
# Windows:
.\.venv\Scripts\activate
# Mac/Linux:
source .venv/bin/activate

3. Install core dependencies

uv pip install -e .  # Note the trailing dot indicating current directory

4. Create environment variables (for future needs)

notepad .env  # Windows
# Or
nano .env     # Mac/Linux

5. Configure MCP Server

• VSCode.Cline

Note: Replace <Your Project Root Directory!!!> with actual root directory.

{
  "mcpServers": {
    "ikb-mcp-server": {
      "command": "uv",
      "args": [
        "--directory",
        "<Your Project Root Directory!!!>", 
        "run",
        "ikb_mcp_server.py"
      ]
    }
  }
}

• Cherry Studio
  • Command: uv
  • Arguments:

--directory
<Your Project Root Directory!!!>
run
ikb_mcp_server.py

Adding Private Documents to ikb_mcp_server

1. Configure VLM models and parameters in .env:

   VLM_API_KEY=<API Key>
   VLM_BASE_URL=<Base URL> # https://openrouter.ai/api/v1
   VLM_MODEL_NAME=<Model Name> # qwen/qwen2.5-vl-72b-instruct:free
   

2. Upload the PDF document to the library_files folder under the project root directory.
3. Manually run main.py.

# Navigate to the project root directory
# Activate the virtual environment
uv run main.py
# [INFO]PDF extraction initialized | Files directory: library_files | Pages directory: library_pages
# ... Please waiting for a while
# [INFO]Processing completed. Success: xxx pages, Failed: 0 pages.
# Data has been updated to the database

License

This project is licensed under the MIT License. See the LICENSE file for details.