--- title: "RAGFlow Vector Stores" url: "/docs/providers/ragflow_vector_store" canonical_url: "https://docs.litellm.ai/docs/providers/ragflow_vector_store" type: "docs" last_updated: "2026-10-06" summary: "Litellm support creation and management of datasets for document processing and knowledge base management in Ragflow." related: - "/docs/providers/ragflow" - "/docs/providers/recraft" --- # RAGFlow Vector Stores > Index of all LiteLLM docs: https://docs.litellm.ai/llms.txt Litellm support creation and management of datasets for document processing and knowledge base management in Ragflow. | Property | Details | |----------|---------| | Description | RAGFlow datasets enable document processing, chunking, and knowledge base management for RAG applications. | | Provider Route on LiteLLM | `ragflow` in the litellm vector_store_registry | | Provider Doc | [RAGFlow API Documentation ↗](https://ragflow.io/docs) | | Supported Operations | Dataset Management (Create, List, Update, Delete) | | Search/Retrieval | ❌ Not supported (management only) | ## Quick Start ### LiteLLM Python SDK ```python showLineNumbers title="Example using LiteLLM Python SDK" import os import litellm # Set RAGFlow credentials os.environ["RAGFLOW_API_KEY"] = "your-ragflow-api-key" os.environ["RAGFLOW_API_BASE"] = "http://localhost:9380" # Optional, defaults to localhost:9380 # Create a RAGFlow dataset response = litellm.vector_stores.create( name="my-dataset", custom_llm_provider="ragflow", metadata={ "description": "My knowledge base dataset", "embedding_model": "BAAI/bge-large-zh-v1.5@BAAI", "chunk_method": "naive" } ) print(f"Created dataset ID: {response.id}") print(f"Dataset name: {response.name}") ``` ### LiteLLM Proxy #### 1. Configure your vector_store_registry **config.yaml** ```yaml model_list: - model_name: gpt-5.6-luna litellm_params: model: gpt-5.6-luna api_key: os.environ/OPENAI_API_KEY vector_store_registry: - vector_store_name: "ragflow-knowledge-base" litellm_params: vector_store_id: "your-dataset-id" custom_llm_provider: "ragflow" api_key: os.environ/RAGFLOW_API_KEY api_base: os.environ/RAGFLOW_API_BASE # Optional vector_store_description: "RAGFlow dataset for knowledge base" vector_store_metadata: source: "Company documentation" ``` **LiteLLM UI** On the LiteLLM UI, Navigate to Experimental > Vector Stores > Create Vector Store. On this page you can create a vector store with a name, vector store id and credentials. #### 2. Create a dataset via Proxy **Curl** ```bash curl http://localhost:4000/v1/vector_stores \ -H "Content-Type: application/json" \ -H "Authorization: Bearer $LITELLM_API_KEY" \ -d '{ "name": "my-ragflow-dataset", "custom_llm_provider": "ragflow", "metadata": { "description": "Test dataset", "chunk_method": "naive" } }' ``` **OpenAI Python SDK** ```python from openai import OpenAI # Initialize client with your LiteLLM proxy URL client = OpenAI( base_url="http://localhost:4000", api_key="your-litellm-api-key" ) # Create a RAGFlow dataset response = client.vector_stores.create( name="my-ragflow-dataset", custom_llm_provider="ragflow", metadata={ "description": "Test dataset", "chunk_method": "naive" } ) print(f"Created dataset: {response.id}") ``` ## Configuration ### Environment Variables RAGFlow vector stores support configuration via environment variables: - `RAGFLOW_API_KEY` - Your RAGFlow API key (required) - `RAGFLOW_API_BASE` - RAGFlow API base URL (optional, defaults to `http://localhost:9380`) ### Parameters You can also pass these via `litellm_params`: - `api_key` - RAGFlow API key (overrides `RAGFLOW_API_KEY` env var) - `api_base` - RAGFlow API base URL (overrides `RAGFLOW_API_BASE` env var) ## Dataset Creation Options ### Basic Dataset Creation ```python response = litellm.vector_stores.create( name="basic-dataset", custom_llm_provider="ragflow" ) ``` ### Dataset with Chunk Method RAGFlow supports various chunk methods for different document types: **Naive (General)** ```python response = litellm.vector_stores.create( name="general-dataset", custom_llm_provider="ragflow", metadata={ "chunk_method": "naive", "parser_config": { "chunk_token_num": 512, "delimiter": "\n", "html4excel": False, "layout_recognize": "DeepDOC" } } ) ``` **Book** ```python response = litellm.vector_stores.create( name="book-dataset", custom_llm_provider="ragflow", metadata={ "chunk_method": "book", "parser_config": { "raptor": { "use_raptor": False } } } ) ``` **Q&A** ```python response = litellm.vector_stores.create( name="qa-dataset", custom_llm_provider="ragflow", metadata={ "chunk_method": "qa", "parser_config": { "raptor": { "use_raptor": False } } } ) ``` **Paper** ```python response = litellm.vector_stores.create( name="paper-dataset", custom_llm_provider="ragflow", metadata={ "chunk_method": "paper", "parser_config": { "raptor": { "use_raptor": False } } } ) ``` ### Dataset with Ingestion Pipeline Instead of using a chunk method, you can use an ingestion pipeline: ```python response = litellm.vector_stores.create( name="pipeline-dataset", custom_llm_provider="ragflow", metadata={ "parse_type": 2, # Number of parsers in your pipeline "pipeline_id": "d0bebe30ae2211f0970942010a8e0005" # 32-character hex ID } ) ``` **Note**: `chunk_method` and `pipeline_id` are mutually exclusive. Use one or the other. ### Advanced Parser Configuration ```python response = litellm.vector_stores.create( name="advanced-dataset", custom_llm_provider="ragflow", metadata={ "chunk_method": "naive", "description": "Advanced dataset with custom parser config", "embedding_model": "BAAI/bge-large-zh-v1.5@BAAI", "permission": "me", # or "team" "parser_config": { "chunk_token_num": 1024, "delimiter": "\n!?;。；！？", "html4excel": True, "layout_recognize": "DeepDOC", "auto_keywords": 5, "auto_questions": 3, "task_page_size": 12, "raptor": { "use_raptor": True }, "graphrag": { "use_graphrag": False } } } ) ``` ## Supported Chunk Methods RAGFlow supports the following chunk methods: - `naive` - General purpose (default) - `book` - For book documents - `email` - For email documents - `laws` - For legal documents - `manual` - Manual chunking - `one` - Single chunk - `paper` - For academic papers - `picture` - For image documents - `presentation` - For presentation documents - `qa` - Q&A format - `table` - For table documents - `tag` - Tag-based chunking ## RAGFlow-Specific Parameters All RAGFlow-specific parameters should be passed via the `metadata` field: | Parameter | Type | Description | |-----------|------|-------------| | `avatar` | string | Base64 encoding of the avatar (max 65535 chars) | | `description` | string | Brief description of the dataset (max 65535 chars) | | `embedding_model` | string | Embedding model name (e.g., "BAAI/bge-large-zh-v1.5@BAAI") | | `permission` | string | Access permission: "me" (default) or "team" | | `chunk_method` | string | Chunking method (see supported methods above) | | `parser_config` | object | Parser configuration (varies by chunk_method) | | `parse_type` | int | Number of parsers in pipeline (required with pipeline_id) | | `pipeline_id` | string | 32-character hex pipeline ID (required with parse_type) | ## Error Handling RAGFlow returns error responses in the following format: ```json { "code": 101, "message": "Dataset name 'my-dataset' already exists" } ``` LiteLLM automatically maps these to appropriate exceptions: - `code != 0` → Raises exception with the error message - Missing required fields → Raises `ValueError` - Mutually exclusive parameters → Raises `ValueError` ## Limitations - **Search/Retrieval**: RAGFlow vector stores support dataset management only. Search operations are not supported and will raise `NotImplementedError`. - **List/Update/Delete**: These operations are not yet implemented through the standard vector store API. Use RAGFlow's native API endpoints directly. ## Further Reading Vector Stores: - [Vector Store Creation](../vector_stores/create.md) - [Using Vector Stores with Completions](../completion/knowledgebase.md) - [Vector Store Registry](../completion/knowledgebase.md#vectorstoreregistry) ## Related pages - [RAGFlow](https://docs.litellm.ai/docs/providers/ragflow.md) - [Recraft](https://docs.litellm.ai/docs/providers/recraft.md)