GraphRAG-SDK

Build intelligent GraphRAG applications with FalkorDB and LLMs

  • Automatically converts natural language questions into high-quality Cypher queries.
  • Automatically generates contextually relevant answers from knowledge graph data.
  • Supports streaming responses and conversational sessions.
  • Integrates with multiple language model providers (OpenAI, Gemini, Groq, etc.).

Quick Start

Install & Setup 

# Start FalkorDB
docker run -p 6379:6379 -p 3000:3000 -it --rm falkordb/falkordb:edge

# Install SDK
pip install graphrag-sdk

Basic Usage 

from falkordb import FalkorDB
from graphrag_sdk import KnowledgeGraph
from graphrag_sdk.ontology import Ontology
from graphrag_sdk.models.litellm import LiteModel
from graphrag_sdk.model_config import KnowledgeGraphModelConfig

# Connect to your knowledge graph
db = FalkorDB()
graph = db.select_graph("my_existing_graph")

# Extract ontology from existing knowledge graph
ontology = Ontology.from_kg_graph(graph, sample_size=100)

# Configure model and create GraphRAG instance
model = LiteModel()
model_config = KnowledgeGraphModelConfig.with_model(model)
kg = KnowledgeGraph(graph, model_config, ontology)

# Start chat session
chat = kg.start_chat()

# Ask questions
response = chat.send_message("What products are available?")
print(response["response"])

# Ask follow-up questions
response = chat.send_message("Tell me about user relationships")
print(response["response"])

Key Features

  • Ontology Extraction: Automatically extract schema and attributes from existing knowledge graphs
  • Smart Query Generation: Convert natural language questions to optimized Cypher queries
  • Conversational Context: Maintain chat history for contextual follow-up questions
  • Streaming Support: Real-time response chunks for better user experience
  • Flexible Sources: Create ontologies from JSON, existing graphs, or data sources
  • Schema Management: Build ontologies from graph schemas or knowledge graphs with sampling

How it works

1️⃣ Extract and Build Ontologies from Multiple Sources

  • From Existing Graphs: Automatically extract schema and attributes from knowledge graphs using Ontology.from_kg_graph()
  • From Data Sources: Generate ontologies from diverse formats (PDF, CSV, HTML, TXT, JSON, URLs) using AI
  • From Schema Graphs: Import ontologies directly from graph schemas with Ontology.from_schema_graph()
  • From JSON: Load pre-defined ontologies from JSON configurations

2️⃣ Intelligent Query Generation and Execution

  • Natural Language Processing: Convert user questions into optimized Cypher queries using LLMs
  • Context-Aware Generation: Leverage ontology schema to ensure accurate and relevant queries
  • Multi-Step Pipeline: Execute graph queries and synthesize natural language responses

3️⃣ Interactive Chat Sessions with Graph Context

  • Conversational Interface: Maintain chat history for contextual follow-up questions
  • Streaming Responses: Real-time response chunks for better user experience
  • Flexible Model Support: Compatible with multiple LLM providers (OpenAI, Gemini, Groq)

📓 Understanding Ontologies and Knowledge Graphs