graph TD
    subgraph Ingestion ["1. Multi-Source Ingestion & ETL"]
        F1["📄 Documents (PDF, Word, Markdown)"]
        F2["📊 Tabular (Excel, CSV)"]
        F3["🗄️ Notes & Text Snippets"]
        F1 & F2 & F3 --> PII["🛡️ PII Masker (TCKN, IBAN, Cards)"]
        PII --> Chunk["✂️ Semantic & Parent-Child Chunker"]
    end

    subgraph Storage ["2. Storage & Hybrid Search Engine"]
        Chunk --> V["V-Store: Cosine Dense Embeddings"]
        Chunk --> B["BM25: Sparse Keyword Index"]
        V & B --> RRF["🎯 Reciprocal Rank Fusion (RRF)"]
    end

    subgraph Governance ["3. Security & Governance"]
        RRF --> RBAC["👥 RBAC & Clearance Filter"]
        RBAC --> Val["🔍 Citation & Grounding Validator"]
    end

    subgraph Interfaces ["4. LLM & Agent Gateways"]
        Val --> MCP["⚡ MCP Server (Claude Desktop / Cursor)"]
        Val --> API["🌐 FastAPI REST API (/api/v1/context)"]
        Val --> UI["🖥️ Modern Web Dashboard (/dashboard)"]
        Val --> SDK["💼 LangChain / LangGraph Adapter"]
    end

git clone https://github.com/your-username/enterprise-knowledge-integrator.git
cd enterprise-knowledge-integrator
pip install -r requirements.txt

python -m knowledge_integrator.interfaces.api.app

python -m knowledge_integrator.interfaces.cli.main ingest-text \
  --title "2025 Travel Policy" \
  --content "Daily travel allowance is 2,500 TL. Stays above 5,000 TL require CFO approval." \
  --category "policy"

python -m knowledge_integrator.interfaces.cli.main ingest ./company_docs/ --category "finance"

python -m knowledge_integrator.interfaces.cli.main query "What is the travel budget limit?"

python -m knowledge_integrator.interfaces.cli.main list

{
  "mcpServers": {
    "company-knowledge": {
      "command": "python",
      "args": ["-m", "knowledge_integrator.interfaces.cli.main", "serve-mcp"]
    }
  }
}

from knowledge_integrator import KnowledgeEngine
from knowledge_integrator.agentic_cfo_adapter import AgenticCFOKnowledgeAdapter

# 1. Initialize engine
engine = KnowledgeEngine()

# 2. Ingest document
engine.ingest_file("budget_2025.xlsx", category="finance")

# 3. Retrieve LLM context
ctx = engine.get_context_for_llm("What was the Q3 software budget?")
print(ctx.context_text)

# 4. Use as a LangChain / LangGraph Tool for AI Agents
adapter = AgenticCFOKnowledgeAdapter(engine)
agent_tool = adapter.as_langchain_tool()

docker-compose up -d

python -m pytest knowledge_integrator/tests/ -v