Graph_RAG is an open-source framework that combines document retrieval, entity/relation extraction, and graph database storage to power retrieval-augmented generation workflows. Users can ingest text corpora, extract structured knowledge into Neo4j or other graph DBs, perform semantic graph queries, and seamlessly integrate results into LLM prompts. This enables explainable, context-rich answers by leveraging graph-based retrieval to augment LLM outputs.
AI Platforms & Frameworks
Tool Libraries
Data Analysis
#knowledge graph
#RAG-Framework
#document processing
#entity extraction
#relation extraction
#graph database integration
#semantic queries
#AI-Powered Analytics
#data ingestion
#NLP tools
#improve accuracy
#structured representation
#modular architecture
#explainable AI
#context retrieval
#automated extraction
#Application Scalability
#graph storage solutions
#Knowledge Management
#LLM Integration
...
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Graph_RAG
Graph_RAG is an open-source framework that combines document retrieval, entity/relation extraction, and graph database storage to power retrieval-augmented generation workflows. Users can ingest text corpora, extract structured knowledge into Neo4j or other graph DBs, perform semantic graph queries, and seamlessly integrate results into LLM prompts. This enables explainable, context-rich answers by leveraging graph-based retrieval to augment LLM outputs.
AI Platforms & Frameworks
Tool Libraries
Data Analysis
#knowledge graph
#RAG-Framework
#document processing
#entity extraction
#relation extraction
#graph database integration
#semantic queries
#AI-Powered Analytics
#data ingestion
#NLP tools
#improve accuracy
#structured representation
#modular architecture
#explainable AI
#context retrieval
#automated extraction
#Application Scalability
#graph storage solutions
#Knowledge Management
#LLM Integration
...
Featured
What is Graph_RAG?
Graph_RAG is a Python-based framework designed to build and query knowledge graphs for retrieval-augmented generation (RAG). It supports ingestion of unstructured documents, automated extraction of entities and relationships using LLMs or NLP tools, and storage in graph databases such as Neo4j. With Graph_RAG, developers can construct connected knowledge graphs, execute semantic graph queries to identify relevant nodes and paths, and feed the retrieved context into LLM prompts. The framework provides modular pipelines, configurable components, and integration examples to facilitate end-to-end RAG applications, improving answer accuracy and interpretability through structured knowledge representation.
Who will use Graph_RAG?
- Data scientists
- Machine learning engineers
- AI researchers
- Software developers
- Knowledge management specialists
How to use the Graph_RAG?
- Step1: Clone the Graph_RAG repository from GitHub.
- Step2: Install dependencies with pip install -r requirements.txt.
- Step3: Configure environment variables and set up a graph database (e.g., Neo4j).
- Step4: Prepare your document corpus and adjust ingestion settings.
- Step5: Run the ingestion pipeline to extract entities and relationships.
- Step6: Execute the graph construction pipeline to populate the graph database.
- Step7: Use the query module to perform semantic graph retrieval.
- Step8: Integrate retrieved context into LLM prompts for RAG outputs.
Platform
- mac
- windows
- linux
Graph_RAG's Core Features & Benefits
The Core Features
- Document ingestion
- Entity extraction
- Relation extraction
- Graph database storage
- Semantic graph query
- RAG pipeline integration
The Benefits
- Enhanced answer accuracy
- Explainable retrieval
- Structured knowledge representation
- Modular and extensible
- Scalable graph storage
Graph_RAG's Main Use Cases & Applications
- Building enterprise knowledge bases
- Q&A systems with contextual recall
- Academic research on linked data
- Customer support knowledge graphs
- Document intelligence and compliance
FAQs of Graph_RAG
Graph_RAG Company Information
Graph_RAG Reviews
5/5
Graph_RAG's Main Competitors and alternatives?
- LangChain
- LlamaIndex
- Haystack
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