neo4j-graphrag-python and llm-graph-builder
These are complements: llm-graph-builder constructs knowledge graphs from unstructured data using LLMs, while graphrag-python provides RAG retrieval and generation capabilities over existing graphs, so they work together in a pipeline where one builds the graph and the other queries it.
About neo4j-graphrag-python
neo4j/neo4j-graphrag-python
Neo4j GraphRAG for Python
This package helps Python developers build applications that can answer complex questions using a knowledge graph. It takes unstructured text, like documents or articles, and transforms it into a structured knowledge graph within a Neo4j database. This allows the application to retrieve precise information and generate more accurate, context-rich answers, making it useful for developers creating AI-powered question-answering systems.
About llm-graph-builder
neo4j-labs/llm-graph-builder
Neo4j graph construction from unstructured data using LLMs
This tool helps researchers, analysts, and knowledge managers transform disorganized information like PDFs, web pages, or video transcripts into a structured, interconnected knowledge graph. You feed it unstructured documents from various sources, and it outputs an organized Neo4j knowledge graph, making it easier to visualize connections and ask complex questions about your data.
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