N8N Tools - GraphRAG
Actions9
Overview
The "Analysis & Insights" resource with the "Analyze" operation in this node provides advanced AI-powered analysis and insight generation on input documents or data. It leverages a GraphRAG system that combines document processing, knowledge graphs, vector search, and optionally large language model (LLM) enhancements to deliver intelligent analysis results.
This node is beneficial when you want to extract meaningful insights, summaries, or answers from unstructured text or complex datasets by querying and analyzing them through a hybrid AI approach. For example, it can be used to analyze customer feedback documents, research papers, or business reports to generate actionable insights or answer specific questions posed via queries.
Properties
| Name | Meaning |
|---|---|
| Input Data | The document text or raw data to be processed and analyzed. |
| Query | A question or query string to guide the analysis or information retrieval from the GraphRAG system. |
| Additional Options | Collection of optional parameters to customize processing: - Language: Processing language (English, Portuguese, Spanish, French). - Result Limit: Max number of results to return. - Chunk Size: Size of text chunks for processing. - Chunk Overlap: Overlap size between text chunks. |
| Database Configuration | Settings to configure external vector and graph databases: - Vector Database: Choose provider (Local FAISS/ChromaDB, Pinecone, Weaviate, Qdrant, Milvus). - Vector DB Connection URL and API Key for external vector DBs. - Graph Database: Choose provider (Local NetworkX, Neo4j, ArangoDB, Amazon Neptune). - Graph DB Connection URL, Username, Password, Database Name. - AWS credentials and region for Amazon Neptune. |
| 🤖 LLM Enhancement | Configure Large Language Model enhancement for deeper analysis: - Enable or disable LLM-powered analysis. - Select LLM provider (N8N Tools internal, OpenAI GPT models, Anthropic Claude models). - Choose specific LLM model depending on provider. |
Output
The node outputs a JSON object containing the analysis results returned by the GraphRAG API. This typically includes insights, answers, or structured data derived from the input document and query. The exact structure depends on the backend response but generally represents processed knowledge graph data, vector search results, or LLM-generated insights.
No binary data output is indicated.
Dependencies
- Requires an API key credential for the N8N Tools GraphRAG service.
- Optional configuration of external vector databases (e.g., Pinecone, Weaviate) and graph databases (e.g., Neo4j, Amazon Neptune) may require connection URLs, authentication credentials, and AWS keys.
- If LLM enhancement is enabled, access to the selected LLM provider's API (internal N8N Tools, OpenAI, or Anthropic) is required.
- Proper network connectivity to the GraphRAG API endpoint (
https://graphrag.n8ntools.io/api/v1/graphrag) is necessary.
Troubleshooting
- Authentication errors: Ensure the API key credential for the GraphRAG service is correctly configured and valid.
- Connection issues: Verify that any external database URLs and credentials are correct and accessible from the n8n environment.
- Invalid input data: Confirm that the input document text or data is properly formatted and not empty.
- Query problems: If no relevant results are returned, check the query string for clarity and relevance to the input data.
- LLM configuration errors: When enabling LLM enhancement, ensure the selected provider and model are supported and that any required API keys or tokens are set up.
- API errors: The node throws errors prefixed with "GraphRAG Tool error:" which include the underlying message. Review these messages for hints on misconfiguration or service issues.
- Use the "Continue On Fail" option to handle errors gracefully during workflow execution.
Links and References
- GraphRAG API Documentation (hypothetical link based on API URL)
- n8n Documentation on Custom Nodes
- Vector Databases:
- Graph Databases:
- Large Language Models: