{"categories":[{"label":"Artificial Intelligence","url":"https://skillfed.io/packages/category/scientific-engineering-artificial-intelligence/5"}],"enrichment":{"capability":"Builds graph retrieval-augmented generation (GraphRAG) applications by integrating Neo4j graph databases with LLM-powered knowledge extraction and retrieval pipelines.","skillfed_tags":["knowledge-graphs","rag","neo4j"],"use_cases":["Build a knowledge graph from unstructured documents, then use it to answer domain-specific questions with LLM-grounded retrieval.","Implement hybrid search combining vector similarity with graph traversal to find contextually relevant entities and their relationships.","Extract structured facts (entities, relationships, patterns) from PDFs or text using LLM-guided pipelines without manual annotation.","Create a text-to-Cypher retriever that translates natural language queries into graph database queries for precise, schema-aware retrieval.","Augment LLM responses with real-time graph data to reduce hallucination and ground answers in a curated knowledge base."],"what_it_does":"Neo4j GraphRAG is a first-party Python library from Neo4j that bridges language models and graph databases to build retrieval-augmented generation systems. It provides two main workflows: constructing knowledge graphs from text or PDFs using LLM-driven entity and relationship extraction, and retrieving relevant graph data to augment LLM prompts for question-answering and reasoning tasks.\n\nThe package wraps Neo4j's graph database with high-level abstractions\u2014SimpleKGPipeline for streamlined knowledge graph building, Pipeline for advanced customization, and multiple retriever strategies (vector search, text-to-Cypher, hybrid traversal). It integrates with LLM providers (OpenAI, Anthropic, Cohere, Bedrock, etc.) and optional embeddings backends (sentence-transformers, Weaviate, Pinecone, Qdrant). Core dependencies include pydantic for schema validation, tenacity for retry logic, and utilities like pypdf and json-repair for data handling.","worth_installing":"Yes, if you have a Neo4j instance and need to build RAG applications grounded in structured knowledge graphs. The package is actively maintained, has no known vulnerabilities, and offers a permissive license. Install with caution if you rely on spaCy-based NLP features on Python 3.14 (currently unsupported upstream); otherwise, low friction and well-suited for production use."},"id":"neo4j-graphrag","links":{"html":"https://skillfed.io/packages/neo4j-graphrag","md":"https://skillfed.io/packages/neo4j-graphrag.md","pypi":"https://pypi.org/project/neo4j-graphrag/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-06-24","license_spdx":null,"license_treatment":"permissive","name":"neo4j-graphrag","python_support":"supports_current","summary":"Python package to allow easy integration to Neo4j's GraphRAG features"},"popularity":{"monthly_downloads":440023,"position":6649,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"1.18.0"}