Insights Knowledge Base

A free, plug-and-play knowledge base with over 10,000 built-in insight reports and support for parsing private documents.

🍭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. ⚠️⚠️
- 🍾 Zero configuration required, designed forplug-and-playusage.
- 🚀 Built-inQwen3-Embedding-0.6Bembedding model, related reports can be retrieved throughvector search.📢 Report details can also be searched viakeyword retrieval.
- 🍥 over 100 insights reports from well-known consulting firms such as McKinsey, PwC, and BAIN have been collected, including 6,000+ report pages, covering 70+ topics.
- 💎Real-timeonline browsing of full reports in MCP Client.
- 🎉Ultra-fastresponse: All Function_call returns typically <1 second, keyword-based queries <150ms.
- 🎨 Pasteprivate local documentsinto the library_files folder (create it manually if absent; name must match). Configure VLM models/parameters in .env (e.g., VLM_MODEL_NAME=qwen2.5-vl-72b-instruct) for local document extraction, parsing, and recognition.
- 🦉 Permanentlyfree—nowasted effort collecting reports. Share reliable, copyright-compliant resources via issues.
- 🔔 Commit toweekly report updates; bug fixes depend on personal whim (I'm not an engineer 🤭).
- Continuous report updates.
- Prompt engineering optimization.

{ "statistics": { "total_files": 174, "total_pages": 9320, "unique_publishers": 9, "unique_topics": 93, "last_updated": "2025-06-30T10:08:35.928329" }, "details": { "publishers": [ "", "Accenture", "BAIN", "BCG", "CBS", "Deloite", "McKinsey", "PWC", "亿欧" ], "topics": [ "", "AI", "AI Agent", "Africa", "Aftermarket", "Asian American", "Auto", "Aviation", "Beauty", "Business", "Chemical industry", "Chemicals", "Chinese banking", "Chinese securities", "Consumer Goods", "Decarbonation", "Decarbonization", "Digital", "ESG", "Economy", "Economy and Trade", "Education", "Electric two wheelers", "Employment", "Energy", "Europe", "FMCG", "Fashion", "Finance", "Financial Technology", "Financial service", "Fintech", "Food-meatless", "Gen Z", "Global banking", "Global energy", "Global insurance", "Global macroeconomic", "Global materials", "Global private market", "Global private markets", "Global trade", "Grocery", "Grocery retail", "Health", "Healthcare", "Human capital", "Hydrogen", "Insurance", "Investing", "Investment management", "Labor market", "Latinos", "Low-altitude Economy", "Luxury Goods", "Luxury goods", "M&A", "Maritime", "Media", "Medical Health", "Medtech", "Net zero", "New Energy Vehicle", "New era", "Packing", "Payments", "Pet Food", "Population", "Power", "Private Equity", "Private market", "Productivity", "Quantum", "Real estate", "Retail", "Retail Digitalization", "Retailers", "Risk", "Small business", "Smart Home", "Smart hospital", "Sporting goods", "Sustainability", "Sustainable", "Tax-free", "Technology", "Travel", "Truck", "United Kingdom", "VSOC", "Wealth management", "Workplace", "连锁经营" ] } }

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

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

uv venv .venv # Create dedicated virtual environment # Activate environment # Windows: .\.venv\Scripts\activate # Mac/Linux: source .venv/bin/activate

uv install . # Note the trailing dot indicating current directory

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

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

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

Adding Private Documents to ikb_mcp_server

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

# Navigate to the project root directory # Activate the virtual environment uv run main.py (InsightsLibrary) PS D:\Projects\mcp\InsightsLibrary> uv run main.py [INFO] extractor: PDF extraction initialized | Files directory: library_files | Pages directory: library_pages [INFO] extractor: Starting scan of directory: library_files [INFO] extractor: Found 69 PDF files [INFO] extractor: Scan completed | Total files: 69 | Processed: 0 | Failed: 0 [INFO] recognizer: No pages to process. # Data has been updated to the database ============================================================ Confirm if you need to create text vector embeddings ⚠️ This process may take approximately 20 minutes ============================================================ Create embeddings? (Enter Y or N): # Y: create text vector embeddings # N: Skip text vector embeddings and exit program

This project is licensed under the MIT License. See theLICENSEfile for details.
- 💡Optimizedmodels.py: Improved data query efficiency by 1,000%
- 💡Optimizedextractor.py: Slightly enhanced PDF extraction efficiency
- 💡Optimizedrecognizer.py: Boosted image comprehension efficiency by 50%
- 💡Optimizedikb_mcp_server.py:

Deterministic O(1) memory for AI agents — local-first, MCP-native, with multi-agent speaker attribution and millisecond recall.