From Documents to Knowledge Graphs: How to Use Unstructured2Graph RAG Tool
Blog post from Memgraph
Unstructured2Graph, a tool within the Memgraph AI Toolkit, enables the transformation of unstructured documents such as PDFs, DOCX, and HTML into a structured knowledge graph that can be queried and reasoned over by language models. It utilizes Unstructured IO for the extraction and cleaning of text, while LightRAG handles entity recognition and relationship mapping, resulting in a graph with nodes, edges, and embeddings ready for retrieval. Users can quickly set up a project in Memgraph Cloud, ingest documents, and build entity graphs without extensive local setup. The tool also allows for the optimization of graphs through embeddings and vector indexing for semantic search, and it supports the integration of structured data for a comprehensive knowledge graph. The process involves several steps, from initializing the environment and ingesting documents to optimizing and visualizing the graph, all of which are facilitated by a few Python commands.
| Trend | Post Mentions | Total Month Mentions | Posts | Companies | MoM |
|---|---|---|---|---|---|
| Vector Search | 13 | 1,541 | 318 | 153 | -17% |
| LLM | 6 | 5,048 | 855 | 225 | +5% |
| RAG | 3 | 1,167 | 195 | 86 | +2% |
| MCP | 1 | 5,085 | 420 | 153 | -2% |
| Multi-agent systems | 1 | 338 | 121 | 62 | +27% |
| Real-time | 1 | 5,379 | 1,225 | 279 | -24% |
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