Graph-based-RAG.md

ScrapeGraphAI is a web scraping python library that uses LLM and direct graph logic to create scraping pipelines for websites, documents and XML files. Just say which information you want to extract and the library will do it for you!

• Building LLM-Powered Coding Assitant for GitHub: RAG with Gemini and Redis

Description:

1. G-Indexing (graph-based indexing): This is the initial stage of the GraphRAG process, which aims to identify or build a graph database G that is aligned with downstream tasks and build an index on it. The graph database can be derived from a public knowledge graph, graph data, or built based on proprietary data sources such as text or other forms of data. The indexing process includes mapping the properties of nodes and edges, establishing pointers between connected nodes, and organizing data to support fast traversal and retrieval operations.

2. G-Retrieval: Following graph indexing, the graph retrieval phase focuses on extracting relevant information from the graph database based on user queries or inputs. Given a user query expressed in natural language, the goal of the retrieval phase is to extract the most relevant elements (e.g., entities, triples, paths, subgraphs) from the knowledge graph.

3. G-Generation (Graph Augmentation Generation): The generation phase of graph augmentation involves synthesizing meaningful output or responses based on the retrieved graph data. This may include answering user queries, generating reports, etc. In this phase, the generator takes the query, retrieved graph elements, and an optional prompt as input to generate a response.

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EXAMPLES

GraphRAG

Microsoft GraphRAG with Ollama Locally

knowledge_graph_agent

llamaindex-KG-notebooks

Knowledge-Graph

GraphRAG vs VectorRAG

Knowledge Graph Creation and Hybrid Search by LLM + RAG

Graph-RAG-LLM-Research-Consultant

Knowledge Graph-Langchain

GraphRAG from scratch

Entity Linking and Relationship Extraction With Relik in LlamaIndex

LlamaIndex-GraphRAG-Neo4J

graphfleet

graphiti

How Knowledge Graph RAG Boosts LLM Results

webify MindSearch QuerywithWebArticle camel_roleplaying_scraper

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[WEB SCRAPING]

custom_websearch_agent

LLMScrape

Create a LangChain RAG system for web data in Python using Llama 3.1-405b in watsonx.ai Building Smart AI Agents with LangChain

How to Ask Questions to Any Website by Building a RAG App with LangChain

AI-Web-Scraper

crew4AI-webscraping

pipet

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Resources The RAG Stack: Featuring Knowledge Graphs

Code to Visualize the Knowledge Graph

Web scrap : LlamaFactory-Ollama-Langchain large model training-deployment one-stop service

/~https://github.com/Aseer-Ahmad/RAG-with-Ray-Langchain/tree/main

Groq API + LangChain & LangGraph + Tavily

Crawl4AI

Agentic RAG with Llama 3

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Graph RAG:

• /~https://github.com/architecturebytes/graphrag/tree/main/tools/GraphQueryTool
• GraphRAG Analysis, Part 2: Graph Creation and Retrieval vs Vector Database Retrieval

Notebook:

• CAMEL's Role-Playing Scraper