Knowledge Fabric

Vendor-neutral, governance-first evidence retrieval for AI systems.
Native PostgreSQL + pgvector • Hybrid RRF Fusion • Cross-Encoder Reranking • Dual-Mode Multi-Tenancy • MCP-Native

⚡ 30-Second Quickstart

1. Instant Cloud Sandbox (Zero Local Setup)

Click to launch a fully configured browser VS Code workspace with PostgreSQL + pgvector and Tika running automatically:

2. Connect to Claude Desktop or Cursor (MCP)

Give Claude Desktop or Cursor private, local long-term memory over your enterprise codebase and documents. Add this to your claude_desktop_config.json:

{
  "mcpServers": {
    "knowledge-fabric": {
      "command": "docker",
      "args": [
        "run", "-i", "--rm",
        "-e", "DATABASE_URL=postgresql://knowledge_fabric:knowledge_fabric@host.docker.internal:5432/knowledge_fabric",
        "-e", "KF_DEFAULT_TENANT=default",
        "ghcr.io/invarcore/knowledge-fabric:0.1.4",
        "knowledge-fabric-mcp"
      ]
    }
  }
}

3. Run Locally with Docker Compose (60 Seconds)

git clone https://github.com/invarcore/knowledge-fabric.git && cd knowledge-fabric
docker compose up -d

# Run the interactive hybrid RRF demonstration
python examples/quickstart_interactive.py

Why Knowledge Fabric?

The $100k/Year Dedicated Vector DB Trap vs. The PostgreSQL Reality

Most enterprise AI initiatives stall not because of model capability, but because of operational sprawl, data leakage, and ungrounded retrieval. Traditional architectures force engineering teams to introduce dedicated vector databases (Pinecone, Qdrant, Weaviate), creating a second source of truth, new vendor contracts, complex VPC peering, and $50k–$100k/year in recurring cloud spend.

Knowledge Fabric eliminates this entire infrastructure tier by running directly on your existing PostgreSQL database with pgvector HNSW indexes and native full-text search (tsvector), combined with Reciprocal Rank Fusion (RRF):

... View full README on GitHub