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RAGFlowConnector

Retrieve knowledge from or store files into RAGFlow knowledge bases using the RAGFlow API.

About RAGFlow

RAGFlow is an open-source RAG (Retrieval-Augmented Generation) engine based on deep document understanding. It provides truthful question-answering capabilities with well-founded citations from various complex formatted data.

Features

  • Retrieve knowledge chunks from RAGFlow datasets/knowledge bases
  • Upload and ingest files into RAGFlow datasets with automatic parsing
  • Support for multiple datasets in a single query
  • Configurable similarity thresholds and vector weights
  • Hybrid search combining keyword and vector similarity
  • Auto-trigger GraphRAG knowledge graph construction after ingestion
  • Auto-trigger RAPTOR hierarchical summarization after ingestion
  • Dataset ID validation on knowledge base creation
  • Returns results with rich metadata including term and vector similarity scores

Configuration

This plugin requires the following configuration parameters:

Required Parameters (Creation Settings)

  • api_base_url: Base URL for RAGFlow API
    • For local deployment: http://localhost:9380 (default)
    • For remote server: Your server URL (e.g., http://your-domain.com:9380)
  • api_key: Your RAGFlow API key from your RAGFlow instance
  • dataset_ids: Comma-separated dataset IDs to search
    • Format: "dataset_id1,dataset_id2,dataset_id3"
    • Example: "b2a62730759d11ef987d0242ac120004,a3b52830859d11ef887d0242ac120005"

Optional Parameters (Creation Settings)

  • auto_graphrag (default: false): Automatically trigger GraphRAG knowledge graph construction after file ingestion
  • auto_raptor (default: false): Automatically trigger RAPTOR hierarchical summarization after file ingestion

Optional Parameters (Retrieval Settings)

  • top_k (default: 1024): Maximum number of retrieved results
  • similarity_threshold (default: 0.2): Minimum similarity score (0-1)
  • vector_similarity_weight (default: 0.3): Weight for vector similarity in hybrid search (0-1)
  • page_size (default: 30): Number of results per page
  • keyword (default: false): Use LLM to extract keywords from query to enhance retrieval
  • rerank_id: Rerank model ID configured in RAGFlow (e.g., BAAI/bge-reranker-v2-m3)
  • use_kg (default: false): Enable knowledge graph retrieval

How to Get Configuration Values

Getting your RAGFlow API Key

  1. Access your RAGFlow instance (e.g., http://localhost:9380)
  2. Navigate to User Settings > API section
  3. Generate or copy your API key (format: ragflow-xxxxx)

Getting your Dataset IDs

  1. In RAGFlow, go to your knowledge base/dataset list
  2. Click on a dataset to view its details
  3. The dataset ID is typically shown in the URL or dataset details
  4. For multiple datasets, collect all IDs and join them with commas

API Reference

This plugin uses the following RAGFlow APIs:

  • Retrieval: POST /api/v1/retrieval
  • Upload documents: POST /api/v1/datasets/{dataset_id}/documents
  • Parse documents: POST /api/v1/datasets/{dataset_id}/chunks
  • Delete documents: DELETE /api/v1/datasets/{dataset_id}/documents
  • GraphRAG construction: POST /api/v1/datasets/{dataset_id}/run_graphrag
  • RAPTOR construction: POST /api/v1/datasets/{dataset_id}/run_raptor
  • List datasets (validation): GET /api/v1/datasets
  • Documentation: https://ragflow.io/docs/dev/http_api_reference

Retrieval Method

RAGFlow employs a hybrid retrieval approach:

  • Keyword Similarity: Traditional keyword-based matching
  • Vector Similarity: Semantic similarity using embeddings
  • Weighted Combination: Combines both methods with configurable weights
  • Knowledge Graph: Optional graph-based retrieval for relationship-aware answers
  • Reranking: Optional reranking model for improved result quality

The vector_similarity_weight parameter controls the balance between keyword and vector methods.

Runtime placement

This version remains dedicated. SDK 0.7.4 shared singleton placement requires a signed stateless-v1 component model, not just shared-runtime-v1. The existing per-installation mutation state and cancellation model have not passed that contract. See the audit.