neo4j-document-import-skill

仓库创建 2026年1月20日最近提交 7 天前SkillHot 收录 20 天前
▸ 精选理由

方便把非结构化内容转成知识图谱与向量索引,适合知识库构建。

▸ 风险提示

导入与 LLM 抽取过程可能泄露敏感文档内容或调用外部模型。

这个 Skill 做什么

把 PDF/HTML/Markdown 等文档切块并导入 Neo4j,生成实体与嵌入。

把 PDF、HTML、Markdown 等非结构化文档切块后导入 Neo4j,建立 Document→Chunk→Entity 的知识图谱并为 chunk 生成嵌入。常用于做 RAG 管道的素材入库、用 LLM 抽实体关系或把 LangChain/LlamaIndex 的 loader 接入 Neo4j。特色是把文档粒度化并把实体和嵌入都存到图里,方便后续检索与推理。

▸ 展开 SKILL.md 英文原文

Ingests unstructured and semi-structured documents into Neo4j as a knowledge graph. Use when chunking PDFs, HTML, plain text, or Markdown; extracting entities and relationships from text with an LLM (SimpleKGPipeline, neo4j-graphrag); loading JSON via apoc.load.json; building Document→Chunk→Entity graph structures; or connecting LangChain/LlamaIndex document loaders to Neo4j. Covers neo4j-graphrag SimpleKGPipeline, LLM Graph Builder web UI, entity resolution, chunking strategies, and graph schema design for RAG pipelines. Does NOT handle structured CSV/relational import — use neo4j-import-skill. Does NOT handle GraphRAG retrieval after ingestion — use neo4j-graphrag-skill. Does NOT handle vector index creation — use neo4j-vector-search-skill.

数据与抓取文档导入分块嵌入实体抽取通用
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安装 / 使用
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# Neo4j Document Import Skill

## When to Use

- Ingesting PDFs, HTML, plain text, Markdown into Neo4j as a knowledge graph
- Chunking documents and storing `:Chunk` nodes with embeddings
- Extracting entities and relationships from text with an LLM
- Using `SimpleKGPipeline` (neo4j-graphrag) programmatically
- Using Neo4j LLM Graph Builder (no-code web UI)
- Loading semi-structured JSON via `apoc.load.json`
- Connecting LangChain or LlamaIndex document loaders to Neo4j

## When NOT to Use

- **Structured CSV / relational data** → `neo4j-import-skill`
- **GraphRAG retrieval after ingestion** → `neo4j-graphrag-skill`
- **Vector index creation** → `neo4j-vector-search-skill`
- **Cypher query w
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