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
...
Promote this Tool
Update this Tool
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
You may also like:
Gobii lets teams create 24/7 autonomous digital workers to automate web research and routine tasks.
Salesloft is an AI-driven platform enhancing sales engagement and workflow automation.
Autogpt is a Rust library for building autonomous AI agents that interact with the OpenAI API to complete multi-step tasks
Angular is a web development framework for building modern, scalable applications.
An AI-driven RAG pipeline builder that ingests documents, generates embeddings, and provides real-time Q&A through customizable chat interfaces.
BrandCrowd offers customizable logos, business cards, and social media designs with thousands of templates.
Refly.AI empowers non-technical creators to automate workflows using natural language and a visual canvas.
Skywork AI is an innovative tool to enhance productivity using AI.
Five9 AI Agents enhance customer interactions with intelligent automation.
Mosaic AI Agent Framework enhances AI capabilities with data retrieval and advanced generation techniques.
Windsurf AI Agent helps optimize windsurfing conditions and gear recommendations.
Glean is an AI assistant platform for enterprise search and knowledge discovery.
NVIDIA Cosmos empowers AI developers with advanced tools for data processing and model training.
AI-driven customer service platform offering efficient communication solutions.
A framework that dynamically routes requests across multiple LLMs and uses GraphQL to handle composite prompts efficiently.
Flowith is a canvas-based agentic workspace which offers free 🍌Nano Banana Pro and other effective models...
AI Library is a developer platform for building and deploying customizable AI agents using modular chains and tools.
A Python-based framework implementing flocking algorithms for multi-agent simulation, enabling AI agents to coordinate and navigate dynamically.
An open-source framework enabling autonomous LLM agents with retrieval-augmented generation, vector database support, tool integration, and customizable workflows.
An AI agent template showing automated task planning, memory management, and tool execution via OpenAI API.
Pipe Pilot is a Python framework that orchestrates LLM-driven agent pipelines, enabling complex multi-step AI workflows with ease.
Open-source repository providing practical code recipes to build AI agents leveraging Google Gemini's reasoning and tool usage capabilities.
RModel is an open-source AI agent framework orchestrating LLMs, tool integration, and memory for advanced conversational and task-driven applications.
An open-source framework implementing cooperative multi-agent reinforcement learning for autonomous driving coordination in simulation.
Python library with Flet-based interactive chat UI for building LLM agents, featuring tool execution and memory support.
Agentic Workflow is a Python framework to design, orchestrate, and manage multi-agent AI workflows for complex automated tasks.
Clone, Design, and Create Expressive AI Voices in Seconds, with Perfect Sound Effects and Music.
A GitHub demo showcasing SmolAgents, a lightweight Python framework for orchestrating LLM-powered multi-agent workflows with tool integration.
A Python framework for easily defining and executing AI agent workflows declaratively using YAML-like specifications.
OpenSpiel provides a library of environments and algorithms for research in reinforcement learning and game theoretic planning.
A Pythonic framework implementing the Model Context Protocol to build and run AI agent servers with custom tools.
pyafai is a Python modular framework to build, train, and run autonomous AI agents with plug-in memory and tool support.
LangGraph enables Python developers to construct and orchestrate custom AI agent workflows using modular graph-based pipelines.
A Python wrapper enabling seamless Anthropic Claude API calls through existing OpenAI Python SDK interfaces.
Agent Adapters provides pluggable middleware to integrate LLM-based agents with various external frameworks and tools seamlessly.
Java-Action-Storage is a LightJason module that logs, stores, and retrieves agent actions for distributed multi-agent applications.
LinkAgent orchestrates multiple language models, retrieval systems, and external tools to automate complex AI-driven workflows.
All-in-one AI video creation studio that turns any text and images into full videos up to 30 minutes.
Refly.AI empowers non-technical creators to automate workflows using natural language and a visual canvas.
GenSphere is an AI agent that automates data analysis and provides insights for informed decision-making.
Crux is an AI agent that enhances productivity through intelligent task automation and decision support.
Track your entire crypto portfolio in one place with Recap.
TiDB Cloud is a fully-managed DBaaS delivering scalable, MySQL-compatible distributed SQL database solutions.
AI Agent that gathers and summarizes company financials, funding, leadership, and market insights into concise reports.
Synergetics AI automates complex decision-making processes using advanced algorithms.
The World Cup Archives is a comprehensive resource for football World Cup history.
An AI assistant boosting team productivity through task automation and code execution.
TextQL is an AI agent that accelerates data retrieval and analysis through natural language queries.
Faraday provides predictive analytics for personalized customer experiences.
AI-driven toolkit automating data quality checks, anomaly detection, and exploratory data analysis using GPT models.
Spacum: AI-powered search engine that pays users through ad revenue sharing.
Qoder is an agentic coding platform for real software, Free to use the best model in preview.