knowledgetree-rag 0.1.0

Knowledge-graph-powered RAG sidecar for infrastructure querying

kt-rag

Knowledge-graph-powered RAG sidecar for infrastructure querying

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Turn your infrastructure graph into a searchable knowledge base. kt-rag feeds discovered infrastructure data — services, databases, Kubernetes clusters, DNS zones, CI/CD pipelines — into a LightRAG knowledge graph, then lets you ask questions in plain English.

What you can do:

• "Which services depend on Postgres?" — get a structured answer with confidence scoring
• "What's the blast radius if us-east-1 goes down?" — trace dependencies across providers
• "Show me all EC2 instances tagged production in eu-west-1" — filter by metadata
• "Generate a runbook for the auth service" — auto-document from live graph data
• "What changed in my infrastructure since last week?" — incremental sync awareness

How It Works

┌─────────────┐    ┌──────────┐    ┌──────────┐    ┌──────┐
│ Discovery   │───▶│ kt-rag   │───▶│ LightRAG │───▶│ LLM  │
│ Plugins     │    │ seed/sync│    │ KG Store │    │      │
└─────────────┘    └──────────┘    └──────────┘    └──────┘
                        │                                
                   ┌────┴────┐                    ┌─────┴──────┐
                   │   API   │                    │  Query CLI  │
                   │  Server │                    │  / Library  │
                   └─────────┘                    └─────────────┘

1. Plugins discover your infrastructure (AWS, GitHub, K8s, etc.)
2. kt-rag seeds entities and relationships into LightRAG's graph store
3. LightRAG indexes everything for hybrid search (graph + vector)
4. Your question triggers retrieval-augmented generation through an LLM

Quick Start

Install

pip install knowledgetree-rag

Or from source:

git clone https://github.com/knowledgetree-dev/kt-rag.git
cd kt-rag
pip install -e .

Configure

Copy the template and set your LLM API key:

cp .env.example .env
# Edit .env with your LLM provider and API key

Minimal .env:

LLM_BINDING_API_KEY=sk-...

Seed the graph

Populate LightRAG from your infrastructure data:

kt-rag-seed

Query

kt-rag-query "which services depend on postgres?"

Example output:

Response: The following services depend on Postgres:
  - auth-service (production, us-east-1)
  - user-api (staging, eu-west-1)
  - analytics-backend (production, us-west-2)
  - docs-service (production, us-east-1)

Confidence: 0.82

Start the API server

kt-rag-server
# Listening on http://0.0.0.0:8085

curl -s http://localhost:8085/api/v1/rag/health

CLI Commands

Command	Description
kt-rag-seed	Full seed from Knowledge Tree into LightRAG
kt-rag-query "..."	Natural-language query (supports --filter, --min-score, --profile)
kt-rag-server	REST API on port 8085

API

Query

POST /api/v1/rag/query
{
  "question": "which services depend on postgres?",
  "mode": "hybrid",
  "filter": {"provider": "aws", "region": "us-east-1"},
  "profile_name": "claude"
}

Response:

{
  "response": "The following services depend on Postgres...",
  "confidence": 0.82,
  "metadata": {
    "mode": "hybrid",
    "profile": "claude",
    "tokens_used": 1247
  }
}

Profile Management

# List profiles
GET /api/v1/rag/profiles

# Create profile
POST /api/v1/rag/profiles
{
  "name": "claude",
  "provider": "anthropic",
  "api_key": "sk-ant-...",
  "model": "claude-sonnet-4-20250514"
}

# Delete profile
DELETE /api/v1/rag/profiles/{name}

Multi-LLM Profiles

Switch between LLM providers at query time:

# Named profiles (persisted)
kt-rag-query "what's my blast radius?" --profile claude

# Ad-hoc via headers (server mode)
curl -H "X-KT-LLM-Provider: anthropic" \
     -H "X-KT-LLM-Api-Key: sk-ant-..." \
     -d '{"question": "..."}' \
     http://localhost:8085/api/v1/rag/query

Configuration

All config via environment variables or .env:

Variable	Default	Description
LLM_BINDING	openai	LLM provider (openai, anthropic, ollama, azure)
LLM_BINDING_API_KEY	—	API key
LLM_BINDING_MODEL	gpt-4o	Model name
EMBEDDING_BINDING	openai	Embedding provider
EMBEDDING_BINDING_MODEL	text-embedding-3-small	Embedding model
LIGHTRAG_DIR	./output	LightRAG working directory
LIGHTRAG_PORT	8085	API server port
KT_API_URL	http://localhost:8080	Knowledge Tree API URL
KT_API_TOKEN	—	Knowledge Tree auth token
PG_URI	postgresql://localhost:5432/knowledgetree	Database (read-only seed)
SYNC_INTERVAL	300	Sync interval in seconds

Confidence Scoring

Every query response includes a 0.0–1.0 confidence score based on:

• Retrieval quality (30%) — how well the query matched stored entities
• Context coverage (30%) — how completely the retrieved data covers the question
• Factual support (40%) — whether the LLM's answer is grounded in retrieved facts

Set a minimum threshold:

kt-rag-query "what repos are production?" --min-score 0.6

Contributing

PRs are welcome. For feature requests or major changes, open an issue first.

# Dev setup
python -m venv .venv && source .venv/bin/activate
pip install -e ".[dev]"

# Run tests
pytest tests/ -v --tb=short

# Type check
mypy .

Project Structure

├── seed.py              # Full graph seed
├── sync.py              # Incremental sync
├── query.py             # CLI query entry point
├── server.py            # FastAPI server
├── config.py            # Environment config loader
├── profile_store.py     # LLM provider profile persistence
├── scorer.py            # Confidence scoring
├── kt_client.py         # Knowledge Tree API client
├── tests/               # Test suite
└── .env.example         # Config template

License

MIT