cgc

Context Graph Connector (CGC)

Programmatic data access layer for LLM agents — navigate context, don't memorize it.

CGC connects AI agents to your databases, files, and vector stores. Instead of stuffing everything into the context window, your agent explores data on-demand: discovering schemas, sampling rows, chunking documents, extracting knowledge graphs, and querying across sources.

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Features

• Connect to PostgreSQL, MySQL, SQLite, filesystems, Qdrant, Pinecone, pgvector, MongoDB
• Discover schemas and structure automatically
• Sample data to understand what's in each table or file
• Chunk large documents into LLM-friendly pieces
• Search across files and databases with pattern matching
• Extract knowledge graphs from text and structured data (GLiNER2 unified model + 17 industry packs)
• Store extracted triplets in Neo4j, PostgreSQL AGE, or KuzuDB
• Query graph sinks with Cypher
• MCP server for Claude Desktop / Claude Code / Cursor / Windsurf
• HTTP API for integration with any tool or platform
• CLI for quick tasks and scripting

Quick Start

Prerequisites

• Python 3.10+ must be installed on your system

Install

pip install context-graph-connector

With graph extraction (GLiNER2 — requires ML models):

pip install context-graph-connector[extraction]

With legacy v1 extraction (GliNER + GLiREL + spaCy):

pip install context-graph-connector[extraction-v1]

With everything:

pip install context-graph-connector[all]

CLI

# Discover what's in a database
cgc discover sqlite ./mydata.db

# Sample rows from a table
cgc sample sqlite ./mydata.db users --n 10

# Run a SQL query
cgc sql sqlite ./mydata.db "SELECT * FROM orders WHERE total > 100"

# Extract knowledge graph from text
cgc extract "Steve Jobs co-founded Apple with Steve Wozniak in 1976"

# Extract from a file and store in a graph database
cgc extract-file ./report.pdf --sink kuzudb://./my_graph

# Chunk a large PDF for processing
cgc chunk filesystem ./docs report.pdf --strategy tokens:2000

# Start the HTTP API server
cgc serve

# Start the MCP server (for Claude integration)
cgc mcp

MCP Integration (use CGC with AI assistants)

Claude Code (VS Code / CLI):

claude mcp add cgc -s global -- python -m cgc.mcp.server

Claude Desktop / Cursor / Windsurf — add to your config file (locations):

{
  "mcpServers": {
    "cgc": {
      "command": "python",
      "args": ["-m", "cgc.mcp.server"]
    }
  }
}

See the MCP Reference for step-by-step setup instructions for each editor.

Python API

from cgc import Connector

connector = Connector()

# Add a data source
from cgc.adapters.sql import SqlAdapter
connector.add_source(SqlAdapter("mydb", "sqlite:///data.db"))

# Discover schema
schema = await connector.discover("mydb")

# Sample data
rows = await connector.sample("mydb", "users", 5)

# Extract triplets
triplets = connector.extract_triplets("Elon Musk founded SpaceX in 2002")

Optional Dependencies

CGC has a minimal core with optional extras for specific integrations:

Extra	What it adds
extraction	GLiNER2 — unified NER + relation extraction (default, recommended)
extraction-v1	GliNER v1, GLiREL, spaCy, sentence-transformers (legacy pipeline)
postgres	asyncpg, pgvector (PostgreSQL support)
mysql	aiomysql (MySQL support)
vector	qdrant-client, pinecone-client, pymongo, motor (vector DB support)
graph	kuzu (embedded graph database)
all	Everything above
dev	pytest, ruff, mypy (development)

Architecture

cgc/
├── connector.py          # Main interface — Connector class
├── core/                 # Types: Schema, Query, Chunk, Triplet, Graph
├── adapters/
│   ├── sql.py            # PostgreSQL, MySQL, SQLite
│   ├── filesystem.py     # Local files (PDF, DOCX, CSV, etc.)
│   ├── vector/           # Qdrant, Pinecone, pgvector, MongoDB
│   └── graph/            # Neo4j, PostgreSQL AGE, KuzuDB (sinks)
├── discovery/            # Schema inference, relationship detection
│   ├── extractor.py      # Triplet extraction orchestrator (v1/v2 pipeline)
│   ├── gliner2.py        # GLiNER2 unified NER + relation extraction (v2, default)
│   ├── gliner.py         # GliNER v1 NER integration (legacy)
│   ├── glirel.py         # GLiREL relation extraction (legacy)
│   ├── router.py         # Industry pack routing (E5 embeddings, v1 only)
│   ├── industry_packs.py # 17 domain-specific extraction configs
│   └── structured.py     # Hub-and-spoke structured data extraction
├── cli/                  # Typer CLI
├── api/                  # FastAPI HTTP server
├── mcp/                  # Model Context Protocol server
├── session/              # Session tracking
└── security/             # API key auth, rate limiting

Documentation

Document	Description
API Reference	HTTP API endpoints
CLI Reference	Command-line interface
MCP Reference	Model Context Protocol for Claude
Security Guide	API keys, rate limiting, data protection
Technical Details	Architecture and internals

Contributing

Contributions are welcome but this project is maintained on a best-effort basis. PRs may not be reviewed immediately. See CONTRIBUTING.md for guidelines.

License

Apache 2.0 — see LICENSE for details.