ragflow_api.md 146 KB
HTTP API A complete reference for RAGFlow's RESTful API. Before proceeding, please ensure you have your RAGFlow API key ready for authentication.
ERROR CODES Code Message Description 400 Bad Request Invalid request parameters 401 Unauthorized Unauthorized access 403 Forbidden Access denied 404 Not Found Resource not found 500 Internal Server Error Server internal error 1001 Invalid Chunk ID Invalid Chunk ID 1002 Chunk Update Failed Chunk update failed OpenAI-Compatible API Create chat completion POST /api/v1/chats_openai/{chat_id}/chat/completions
Creates a model response for a given chat conversation.
This API follows the same request and response format as OpenAI's API. It allows you to interact with the model in a manner similar to how you would with OpenAI's API.
Request Method: POST URL: /api/v1/chats_openai/{chat_id}/chat/completions Headers: 'content-Type: application/json' 'Authorization: Bearer ' Body: "model": string "messages": object list "stream": boolean "extra_body": object (optional) Request example curl --request POST
--url http://{address}/api/v1/chats_openai/{chat_id}/chat/completions \
--header 'Content-Type: application/json' \
--header 'Authorization: Bearer <YOUR_API_KEY>' \
--data '{
"model": "model",
"messages": [{"role": "user", "content": "Say this is a test!"}],
"stream": true,
"extra_body": {
"reference": true,
"metadata_condition": {
"logic": "and",
"conditions": [
{
"name": "author",
"comparison_operator": "is",
"value": "bob"
}
]
}
}
}'
Request Parameters model (Body parameter) string, Required The model used to generate the response. The server will parse this automatically, so you can set it to any value for now.
messages (Body parameter) list[object], Required A list of historical chat messages used to generate the response. This must contain at least one message with the user role.
stream (Body parameter) boolean Whether to receive the response as a stream. Set this to false explicitly if you prefer to receive the entire response in one go instead of as a stream.
extra_body (Body parameter) object Extra request parameters:
reference: boolean - include reference in the final chunk (stream) or in the final message (non-stream). metadata_condition: object - metadata filter conditions applied to retrieval results. Response Stream:
data:{
"id": "chatcmpl-3b0397f277f511f0b47f729e3aa55728",
"choices": [
{
"delta": {
"content": "Hello! It seems like you're just greeting me. If you have a specific",
"role": "assistant",
"function_call": null,
"tool_calls": null,
"reasoning_content": null
},
"finish_reason": null,
"index": 0,
"logprobs": null
}
],
"created": 1755084508,
"model": "model",
"object": "chat.completion.chunk",
"system_fingerprint": "",
"usage": null
}
data:{"id": "chatcmpl-3b0397f277", "choices": [{"delta": {"content": " question or need information, feel free to ask, and I'll do my best", "role": "assistant", "function_call": null, "tool_calls": null, "reasoning_content": null}, "finish_reason": null, "index": 0, "logprobs": null}], "created": 1755084508, "model": "model", "object": "chat.completion.chunk", "system_fingerprint": "", "usage": null}
data:{"id": "chatcmpl-3b0397f277", "choices": [{"delta": {"content": " to assist you based on the knowledge base provided.", "role": "assistant", "function_call": null, "tool_calls": null, "reasoning_content": null}, "finish_reason": null, "index": 0, "logprobs": null}], "created": 1755084508, "model": "model", "object": "chat.completion.chunk", "system_fingerprint": "", "usage": null}
data:{"id": "chatcmpl-3b0397f277", "choices": [{"delta": {"content": null, "role": "assistant", "function_call": null, "tool_calls": null, "reasoning_content": null}, "finish_reason": "stop", "index": 0, "logprobs": null}], "created": 1755084508, "model": "model", "object": "chat.completion.chunk", "system_fingerprint": "", "usage": {"prompt_tokens": 5, "completion_tokens": 188, "total_tokens": 193}}
data:[DONE] Non-stream:
{
"choices": [
{
"finish_reason": "stop",
"index": 0,
"logprobs": null,
"message": {
"content": "Hello! I'm your smart assistant. What can I do for you?",
"role": "assistant"
}
}
],
"created": 1755084403,
"id": "chatcmpl-3b0397f277f511f0b47f729e3aa55728",
"model": "model",
"object": "chat.completion",
"usage": {
"completion_tokens": 55,
"completion_tokens_details": {
"accepted_prediction_tokens": 55,
"reasoning_tokens": 5,
"rejected_prediction_tokens": 0
},
"prompt_tokens": 5,
"total_tokens": 60
}
} Failure:
{ "code": 102, "message": "The last content of this conversation is not from user." } Create agent completion POST /api/v1/agents_openai/{agent_id}/chat/completions
Creates a model response for a given chat conversation.
This API follows the same request and response format as OpenAI's API. It allows you to interact with the model in a manner similar to how you would with OpenAI's API.
Request Method: POST URL: /api/v1/agents_openai/{agent_id}/chat/completions Headers: 'content-Type: application/json' 'Authorization: Bearer ' Body: "model": string "messages": object list "stream": boolean Request example curl --request POST
--url http://{address}/api/v1/agents_openai/{agent_id}/chat/completions \
--header 'Content-Type: application/json' \
--header 'Authorization: Bearer <YOUR_API_KEY>' \
--data '{
"model": "model",
"messages": [{"role": "user", "content": "Say this is a test!"}],
"stream": true
}'
Request Parameters model (Body parameter) string, Required The model used to generate the response. The server will parse this automatically, so you can set it to any value for now.
messages (Body parameter) list[object], Required A list of historical chat messages used to generate the response. This must contain at least one message with the user role.
stream (Body parameter) boolean Whether to receive the response as a stream. Set this to false explicitly if you prefer to receive the entire response in one go instead of as a stream.
session_id (Body parameter) string Agent session id.
Response Stream:
...
data: {
"id": "c39f6f9c83d911f0858253708ecb6573",
"object": "chat.completion.chunk",
"model": "d1f79142831f11f09cc51795b9eb07c0",
"choices": [
{
"delta": {
"content": " terminal"
},
"finish_reason": null,
"index": 0
}
]
}
data: {
"id": "c39f6f9c83d911f0858253708ecb6573",
"object": "chat.completion.chunk",
"model": "d1f79142831f11f09cc51795b9eb07c0",
"choices": [
{
"delta": {
"content": "."
},
"finish_reason": null,
"index": 0
}
]
}
data: {
"id": "c39f6f9c83d911f0858253708ecb6573",
"object": "chat.completion.chunk",
"model": "d1f79142831f11f09cc51795b9eb07c0",
"choices": [
{
"delta": {
"content": "",
"reference": {
"chunks": {
"20": {
"id": "4b8935ac0a22deb1",
"content": "```cd /usr/ports/editors/neovim/ && make install```## Android[Termux](https://github.com/termux/termux-app) offers a Neovim package.",
"document_id": "4bdd2ff65e1511f0907f09f583941b45",
"document_name": "INSTALL22.md",
"dataset_id": "456ce60c5e1511f0907f09f583941b45",
"image_id": "",
"positions": [
[
12,
11,
11,
11,
11
]
],
"url": null,
"similarity": 0.5697155305154673,
"vector_similarity": 0.7323851005515574,
"term_similarity": 0.5000000005,
"doc_type": ""
}
},
"doc_aggs": {
"INSTALL22.md": {
"doc_name": "INSTALL22.md",
"doc_id": "4bdd2ff65e1511f0907f09f583941b45",
"count": 3
},
"INSTALL.md": {
"doc_name": "INSTALL.md",
"doc_id": "4bd7fdd85e1511f0907f09f583941b45",
"count": 2
},
"INSTALL(1).md": {
"doc_name": "INSTALL(1).md",
"doc_id": "4bdfb42e5e1511f0907f09f583941b45",
"count": 2
},
"INSTALL3.md": {
"doc_name": "INSTALL3.md",
"doc_id": "4bdab5825e1511f0907f09f583941b45",
"count": 1
}
}
}
},
"finish_reason": null,
"index": 0
}
]
}
data: [DONE] Non-stream:
{
"choices": [
{
"finish_reason": "stop",
"index": 0,
"logprobs": null,
"message": {
"content": "\nTo install Neovim, the process varies depending on your operating system:\n\n### For Windows:\n1. **Download from GitHub**: \n - Visit the [Neovim releases page](https://github.com/neovim/neovim/releases)\n - Download the latest Windows installer (nvim-win64.msi)\n - Run the installer and follow the prompts\n\n2. **Using winget** (Windows Package Manager):\n...",
"reference": {
"chunks": {
"20": {
"content": "```cd /usr/ports/editors/neovim/ && make install```## Android[Termux](https://github.com/termux/termux-app) offers a Neovim package.",
"dataset_id": "456ce60c5e1511f0907f09f583941b45",
"doc_type": "",
"document_id": "4bdd2ff65e1511f0907f09f583941b45",
"document_name": "INSTALL22.md",
"id": "4b8935ac0a22deb1",
"image_id": "",
"positions": [
[
12,
11,
11,
11,
11
]
],
"similarity": 0.5697155305154673,
"term_similarity": 0.5000000005,
"url": null,
"vector_similarity": 0.7323851005515574
}
},
"doc_aggs": {
"INSTALL(1).md": {
"count": 2,
"doc_id": "4bdfb42e5e1511f0907f09f583941b45",
"doc_name": "INSTALL(1).md"
},
"INSTALL.md": {
"count": 2,
"doc_id": "4bd7fdd85e1511f0907f09f583941b45",
"doc_name": "INSTALL.md"
},
"INSTALL22.md": {
"count": 3,
"doc_id": "4bdd2ff65e1511f0907f09f583941b45",
"doc_name": "INSTALL22.md"
},
"INSTALL3.md": {
"count": 1,
"doc_id": "4bdab5825e1511f0907f09f583941b45",
"doc_name": "INSTALL3.md"
}
}
},
"role": "assistant"
}
}
],
"created": null,
"id": "c39f6f9c83d911f0858253708ecb6573",
"model": "d1f79142831f11f09cc51795b9eb07c0",
"object": "chat.completion",
"param": null,
"usage": {
"completion_tokens": 415,
"completion_tokens_details": {
"accepted_prediction_tokens": 0,
"reasoning_tokens": 0,
"rejected_prediction_tokens": 0
},
"prompt_tokens": 6,
"total_tokens": 421
}
} Failure:
{ "code": 102, "message": "The last content of this conversation is not from user." } DATASET MANAGEMENT Create dataset POST /api/v1/datasets
Creates a dataset.
Request Method: POST URL: /api/v1/datasets Headers: 'content-Type: application/json' 'Authorization: Bearer ' Body: "name": string "avatar": string "description": string "embedding_model": string "permission": string "chunk_method": string "parser_config": object "parse_type": int "pipeline_id": string A basic request example curl --request POST
--url http://{address}/api/v1/datasets \
--header 'Content-Type: application/json' \
--header 'Authorization: Bearer <YOUR_API_KEY>' \
--data '{
"name": "test_1"
}'
A request example specifying ingestion pipeline :::caution WARNING You must not include "chunk_method" or "parser_config" when specifying an ingestion pipeline. :::
curl --request POST \ --url http://{address}/api/v1/datasets \ --header 'Content-Type: application/json' \ --header 'Authorization: Bearer ' \ --data '{ "name": "test-sdk", "parse_type": , "pipeline_id": "" }' Request parameters "name": (Body parameter), string, Required The unique name of the dataset to create. It must adhere to the following requirements:
Basic Multilingual Plane (BMP) only Maximum 128 characters Case-insensitive "avatar": (Body parameter), string Base64 encoding of the avatar.
Maximum 65535 characters "description": (Body parameter), string A brief description of the dataset to create.
Maximum 65535 characters "embedding_model": (Body parameter), string The name of the embedding model to use. For example: "BAAI/bge-large-zh-v1.5@BAAI"
Maximum 255 characters Must follow model_name@model_factory format "permission": (Body parameter), string Specifies who can access the dataset to create. Available options:
"me": (Default) Only you can manage the dataset. "team": All team members can manage the dataset. "chunk_method": (Body parameter), enum The default chunk method of the dataset to create. Mutually exclusive with "parse_type" and "pipeline_id". If you set "chunk_method", do not include "parse_type" or "pipeline_id". Available options:
"naive": General (default) "book": Book "email": Email "laws": Laws "manual": Manual "one": One "paper": Paper "picture": Picture "presentation": Presentation "qa": Q&A "table": Table "tag": Tag "parser_config": (Body parameter), object The configuration settings for the dataset parser. The attributes in this JSON object vary with the selected "chunk_method":
If "chunk_method" is "naive", the "parser_config" object contains the following attributes: "auto_keywords": int Defaults to 0 Minimum: 0 Maximum: 32 "auto_questions": int Defaults to 0 Minimum: 0 Maximum: 10 "chunk_token_num": int Defaults to 512 Minimum: 1 Maximum: 2048 "delimiter": string Defaults to "\n". "html4excel": bool Whether to convert Excel documents into HTML format. Defaults to false "layout_recognize": string Defaults to DeepDOC "tag_kb_ids": array IDs of datasets to be parsed using the Tag chunk method. Before setting this, ensure a tag set is created and properly configured. For details, see Use tag set. "task_page_size": int For PDFs only. Defaults to 12 Minimum: 1 "raptor": object RAPTOR-specific settings. Defaults to: {"use_raptor": false} "graphrag": object GRAPHRAG-specific settings. Defaults to: {"use_graphrag": false} If "chunk_method" is "qa", "manuel", "paper", "book", "laws", or "presentation", the "parser_config" object contains the following attribute: "raptor": object RAPTOR-specific settings. Defaults to: {"use_raptor": false}. If "chunk_method" is "table", "picture", "one", or "email", "parser_config" is an empty JSON object. "parse_type": (Body parameter), int The ingestion pipeline parse type identifier, i.e., the number of parsers in your Parser component.
Required (along with "pipeline_id") if specifying an ingestion pipeline. Must not be included when "chunk_method" is specified. "pipeline_id": (Body parameter), string The ingestion pipeline ID. Can be found in the corresponding URL in the RAGFlow UI.
Required (along with "parse_type") if specifying an ingestion pipeline.
Must be a 32-character lowercase hexadecimal string, e.g., "d0bebe30ae".
Must not be included when "chunk_method" is specified.
:::caution WARNING You can choose either of the following ingestion options when creating a dataset, but not both:
Use a built-in chunk method -- specify "chunk_method" (optionally with "parser_config"). Use an ingestion pipeline -- specify both "parse_type" and "pipeline_id". If none of "chunk_method", "parse_type", or "pipeline_id" are provided, the system defaults to chunk_method = "naive". :::
Response Success:
{
"code": 0,
"data": {
"avatar": null,
"chunk_count": 0,
"chunk_method": "naive",
"create_date": "Mon, 28 Apr 2025 18:40:41 GMT",
"create_time": 1745836841611,
"created_by": "3af81804241d11f0a6a79f24fc270c7f",
"description": null,
"document_count": 0,
"embedding_model": "BAAI/bge-large-zh-v1.5@BAAI",
"id": "3b4de7d4241d11f0a6a79f24fc270c7f",
"language": "English",
"name": "RAGFlow example",
"pagerank": 0,
"parser_config": {
"chunk_token_num": 128,
"delimiter": "\\n!?;。；！？",
"html4excel": false,
"layout_recognize": "DeepDOC",
"raptor": {
"use_raptor": false
}
},
"permission": "me",
"similarity_threshold": 0.2,
"status": "1",
"tenant_id": "3af81804241d11f0a6a79f24fc270c7f",
"token_num": 0,
"update_date": "Mon, 28 Apr 2025 18:40:41 GMT",
"update_time": 1745836841611,
"vector_similarity_weight": 0.3,
},
} Failure:
{
"code": 101,
"message": "Dataset name 'RAGFlow example' already exists"
} Delete datasets DELETE /api/v1/datasets
Deletes datasets by ID.
Request Method: DELETE URL: /api/v1/datasets Headers: 'content-Type: application/json' 'Authorization: Bearer ' Body: "ids": list[string] or null Request example curl --request DELETE
--url http://{address}/api/v1/datasets \
--header 'Content-Type: application/json' \
--header 'Authorization: Bearer <YOUR_API_KEY>' \
--data '{
"ids": ["d94a8dc02c9711f0930f7fbc369eab6d", "e94a8dc02c9711f0930f7fbc369eab6e"]
}'
Request parameters "ids": (Body parameter), list[string] or null, Required Specifies the datasets to delete: If null, all datasets will be deleted. If an array of IDs, only the specified datasets will be deleted. If an empty array, no datasets will be deleted. Response Success:
{
"code": 0
} Failure:
{
"code": 102,
"message": "You don't own the dataset."
} Update dataset PUT /api/v1/datasets/{dataset_id}
Updates configurations for a specified dataset.
Request Method: PUT URL: /api/v1/datasets/{dataset_id} Headers: 'content-Type: application/json' 'Authorization: Bearer ' Body: "name": string "avatar": string "description": string "embedding_model": string "permission": string "chunk_method": string "pagerank": int "parser_config": object Request example curl --request PUT
--url http://{address}/api/v1/datasets/{dataset_id} \
--header 'Content-Type: application/json' \
--header 'Authorization: Bearer <YOUR_API_KEY>' \
--data '
{
"name": "updated_dataset"
}'
Request parameters dataset_id: (Path parameter) The ID of the dataset to update. "name": (Body parameter), string The revised name of the dataset. Basic Multilingual Plane (BMP) only Maximum 128 characters Case-insensitive "avatar": (Body parameter), string The updated base64 encoding of the avatar. Maximum 65535 characters "embedding_model": (Body parameter), string The updated embedding model name. Ensure that "chunk_count" is 0 before updating "embedding_model". Maximum 255 characters Must follow model_name@model_factory format "permission": (Body parameter), string The updated dataset permission. Available options: "me": (Default) Only you can manage the dataset. "team": All team members can manage the dataset. "pagerank": (Body parameter), int refer to Set page rank Default: 0 Minimum: 0 Maximum: 100 "chunk_method": (Body parameter), enum The chunking method for the dataset. Available options: "naive": General (default) "book": Book "email": Email "laws": Laws "manual": Manual "one": One "paper": Paper "picture": Picture "presentation": Presentation "qa": Q&A "table": Table "tag": Tag "parser_config": (Body parameter), object The configuration settings for the dataset parser. The attributes in this JSON object vary with the selected "chunk_method": If "chunk_method" is "naive", the "parser_config" object contains the following attributes: "auto_keywords": int Defaults to 0 Minimum: 0 Maximum: 32 "auto_questions": int Defaults to 0 Minimum: 0 Maximum: 10 "chunk_token_num": int Defaults to 512 Minimum: 1 Maximum: 2048 "delimiter": string Defaults to "\n". "html4excel": bool Indicates whether to convert Excel documents into HTML format. Defaults to false "layout_recognize": string Defaults to DeepDOC "tag_kb_ids": array refer to Use tag set Must include a list of dataset IDs, where each dataset is parsed using the Tag Chunking Method "task_page_size": int For PDF only. Defaults to 12 Minimum: 1 "raptor": object RAPTOR-specific settings. Defaults to: {"use_raptor": false} "graphrag": object GRAPHRAG-specific settings. Defaults to: {"use_graphrag": false} If "chunk_method" is "qa", "manuel", "paper", "book", "laws", or "presentation", the "parser_config" object contains the following attribute: "raptor": object RAPTOR-specific settings. Defaults to: {"use_raptor": false}. If "chunk_method" is "table", "picture", "one", or "email", "parser_config" is an empty JSON object. Response Success:
{
"code": 0
} Failure:
{
"code": 102,
"message": "Can't change tenant_id."
} List datasets GET /api/v1/datasets?page={page}&page_size={page_size}&orderby={orderby}&desc={desc}&name={dataset_name}&id={dataset_id}
Lists datasets.
Request Method: GET URL: /api/v1/datasets?page={page}&page_size={page_size}&orderby={orderby}&desc={desc}&name={dataset_name}&id={dataset_id} Headers: 'Authorization: Bearer ' Request example curl --request GET
--url http://{address}/api/v1/datasets?page={page}&page_size={page_size}&orderby={orderby}&desc={desc}&name={dataset_name}&id={dataset_id} \
--header 'Authorization: Bearer <YOUR_API_KEY>'
Request parameters page: (Filter parameter) Specifies the page on which the datasets will be displayed. Defaults to 1. page_size: (Filter parameter) The number of datasets on each page. Defaults to 30. orderby: (Filter parameter) The field by which datasets should be sorted. Available options: create_time (default) update_time desc: (Filter parameter) Indicates whether the retrieved datasets should be sorted in descending order. Defaults to true. name: (Filter parameter) The name of the dataset to retrieve. id: (Filter parameter) The ID of the dataset to retrieve. Response Success:
{
"code": 0,
"data": [
{
"avatar": "",
"chunk_count": 59,
"create_date": "Sat, 14 Sep 2024 01:12:37 GMT",
"create_time": 1726276357324,
"created_by": "69736c5e723611efb51b0242ac120007",
"description": null,
"document_count": 1,
"embedding_model": "BAAI/bge-large-zh-v1.5",
"id": "6e211ee0723611efa10a0242ac120007",
"language": "English",
"name": "mysql",
"chunk_method": "naive",
"parser_config": {
"chunk_token_num": 8192,
"delimiter": "\\n",
"entity_types": [
"organization",
"person",
"location",
"event",
"time"
]
},
"permission": "me",
"similarity_threshold": 0.2,
"status": "1",
"tenant_id": "69736c5e723611efb51b0242ac120007",
"token_num": 12744,
"update_date": "Thu, 10 Oct 2024 04:07:23 GMT",
"update_time": 1728533243536,
"vector_similarity_weight": 0.3
}
],
"total": 1
} Failure:
{
"code": 102,
"message": "The dataset doesn't exist"
} Get knowledge graph GET /api/v1/datasets/{dataset_id}/knowledge_graph
Retrieves the knowledge graph of a specified dataset.
Request Method: GET URL: /api/v1/datasets/{dataset_id}/knowledge_graph Headers: 'Authorization: Bearer ' Request example curl --request GET
--url http://{address}/api/v1/datasets/{dataset_id}/knowledge_graph \
--header 'Authorization: Bearer <YOUR_API_KEY>'
Request parameters dataset_id: (Path parameter) The ID of the target dataset. Response Success:
{
"code": 0,
"data": {
"graph": {
"directed": false,
"edges": [
{
"description": "The notice is a document issued to convey risk warnings and operational alerts.<SEP>The notice is a specific instance of a notification document issued under the risk warning framework.",
"keywords": ["9", "8"],
"source": "notice",
"source_id": ["8a46cdfe4b5c11f0a5281a58e595aa1c"],
"src_id": "xxx",
"target": "xxx",
"tgt_id": "xxx",
"weight": 17.0
}
],
"graph": {
"source_id": ["8a46cdfe4b5c11f0a5281a58e595aa1c", "8a7eb6424b5c11f0a5281a58e595aa1c"]
},
"multigraph": false,
"nodes": [
{
"description": "xxx",
"entity_name": "xxx",
"entity_type": "ORGANIZATION",
"id": "xxx",
"pagerank": 0.10804906590624092,
"rank": 3,
"source_id": ["8a7eb6424b5c11f0a5281a58e595aa1c"]
}
]
},
"mind_map": {}
}
} Failure:
{
"code": 102,
"message": "The dataset doesn't exist"
} Delete knowledge graph DELETE /api/v1/datasets/{dataset_id}/knowledge_graph
Removes the knowledge graph of a specified dataset.
Request Method: DELETE URL: /api/v1/datasets/{dataset_id}/knowledge_graph Headers: 'Authorization: Bearer ' Request example curl --request DELETE
--url http://{address}/api/v1/datasets/{dataset_id}/knowledge_graph \
--header 'Authorization: Bearer <YOUR_API_KEY>'
Request parameters dataset_id: (Path parameter) The ID of the target dataset. Response Success:
{
"code": 0,
"data": true
} Failure:
{
"code": 102,
"message": "The dataset doesn't exist"
} Construct knowledge graph POST /api/v1/datasets/{dataset_id}/run_graphrag
Constructs a knowledge graph from a specified dataset.
Request Method: POST URL: /api/v1/datasets/{dataset_id}/run_graphrag Headers: 'Authorization: Bearer ' Request example curl --request POST
--url http://{address}/api/v1/datasets/{dataset_id}/run_graphrag \
--header 'Authorization: Bearer <YOUR_API_KEY>'
Request parameters dataset_id: (Path parameter) The ID of the target dataset. Response Success:
{
"code":0,
"data":{
"graphrag_task_id":"e498de54bfbb11f0ba028f704583b57b"
}
} Failure:
{
"code": 102,
"message": "Invalid Dataset ID"
} Get knowledge graph construction status GET /api/v1/datasets/{dataset_id}/trace_graphrag
Retrieves the knowledge graph construction status for a specified dataset.
Request Method: GET URL: /api/v1/datasets/{dataset_id}/trace_graphrag Headers: 'Authorization: Bearer ' Request example curl --request GET
--url http://{address}/api/v1/datasets/{dataset_id}/trace_graphrag \
--header 'Authorization: Bearer <YOUR_API_KEY>'
Request parameters dataset_id: (Path parameter) The ID of the target dataset. Response Success:
{
"code":0,
"data":{
"begin_at":"Wed, 12 Nov 2025 19:36:56 GMT",
"chunk_ids":"",
"create_date":"Wed, 12 Nov 2025 19:36:56 GMT",
"create_time":1762947416350,
"digest":"39e43572e3dcd84f",
"doc_id":"44661c10bde211f0bc93c164a47ffc40",
"from_page":100000000,
"id":"e498de54bfbb11f0ba028f704583b57b",
"priority":0,
"process_duration":2.45419,
"progress":1.0,
"progress_msg":"19:36:56 created task graphrag\n19:36:57 Task has been received.\n19:36:58 [GraphRAG] doc:083661febe2411f0bc79456921e5745f has no available chunks, skip generation.\n19:36:58 [GraphRAG] build_subgraph doc:44661c10bde211f0bc93c164a47ffc40 start (chunks=1, timeout=10000000000s)\n19:36:58 Graph already contains 44661c10bde211f0bc93c164a47ffc40\n19:36:58 [GraphRAG] build_subgraph doc:44661c10bde211f0bc93c164a47ffc40 empty\n19:36:58 [GraphRAG] kb:33137ed0bde211f0bc93c164a47ffc40 no subgraphs generated successfully, end.\n19:36:58 Knowledge Graph done (0.72s)","retry_count":1,
"task_type":"graphrag",
"to_page":100000000,
"update_date":"Wed, 12 Nov 2025 19:36:58 GMT",
"update_time":1762947418454
}
} Failure:
{
"code": 102,
"message": "Invalid Dataset ID"
} Construct RAPTOR POST /api/v1/datasets/{dataset_id}/run_raptor
Construct a RAPTOR from a specified dataset.
Request Method: POST URL: /api/v1/datasets/{dataset_id}/run_raptor Headers: 'Authorization: Bearer ' Request example curl --request POST
--url http://{address}/api/v1/datasets/{dataset_id}/run_raptor \
--header 'Authorization: Bearer <YOUR_API_KEY>'
Request parameters dataset_id: (Path parameter) The ID of the target dataset. Response Success:
{
"code":0,
"data":{
"raptor_task_id":"50d3c31cbfbd11f0ba028f704583b57b"
}
} Failure:
{
"code": 102,
"message": "Invalid Dataset ID"
} Get RAPTOR construction status GET /api/v1/datasets/{dataset_id}/trace_raptor
Retrieves the RAPTOR construction status for a specified dataset.
Request Method: GET URL: /api/v1/datasets/{dataset_id}/trace_raptor Headers: 'Authorization: Bearer ' Request example curl --request GET
--url http://{address}/api/v1/datasets/{dataset_id}/trace_raptor \
--header 'Authorization: Bearer <YOUR_API_KEY>'
Request parameters dataset_id: (Path parameter) The ID of the target dataset. Response Success:
{
"code":0,
"data":{
"begin_at":"Wed, 12 Nov 2025 19:47:07 GMT",
"chunk_ids":"",
"create_date":"Wed, 12 Nov 2025 19:47:07 GMT",
"create_time":1762948027427,
"digest":"8b279a6248cb8fc6",
"doc_id":"44661c10bde211f0bc93c164a47ffc40",
"from_page":100000000,
"id":"50d3c31cbfbd11f0ba028f704583b57b",
"priority":0,
"process_duration":0.948244,
"progress":1.0,
"progress_msg":"19:47:07 created task raptor\n19:47:07 Task has been received.\n19:47:07 Processing...\n19:47:07 Processing...\n19:47:07 Indexing done (0.01s).\n19:47:07 Task done (0.29s)",
"retry_count":1,
"task_type":"raptor",
"to_page":100000000,
"update_date":"Wed, 12 Nov 2025 19:47:07 GMT",
"update_time":1762948027948
}
} Failure:
{
"code": 102,
"message": "Invalid Dataset ID"
} FILE MANAGEMENT WITHIN DATASET Upload documents POST /api/v1/datasets/{dataset_id}/documents
Uploads documents to a specified dataset.
Request Method: POST URL: /api/v1/datasets/{dataset_id}/documents Headers: 'Content-Type: multipart/form-data' 'Authorization: Bearer ' Form: 'file=@{FILE_PATH}' Request example curl --request POST
--url http://{address}/api/v1/datasets/{dataset_id}/documents \
--header 'Content-Type: multipart/form-data' \
--header 'Authorization: Bearer <YOUR_API_KEY>' \
--form 'file=@./test1.txt' \
--form 'file=@./test2.pdf'
Request parameters dataset_id: (Path parameter) The ID of the dataset to which the documents will be uploaded. 'file': (Body parameter) A document to upload. Response Success:
{
"code": 0,
"data": [
{
"chunk_method": "naive",
"created_by": "69736c5e723611efb51b0242ac120007",
"dataset_id": "527fa74891e811ef9c650242ac120006",
"id": "b330ec2e91ec11efbc510242ac120004",
"location": "1.txt",
"name": "1.txt",
"parser_config": {
"chunk_token_num": 128,
"delimiter": "\\n",
"html4excel": false,
"layout_recognize": true,
"raptor": {
"use_raptor": false
}
},
"run": "UNSTART",
"size": 17966,
"thumbnail": "",
"type": "doc"
}
]
} Failure:
{
"code": 101,
"message": "No file part!"
} Update document PUT /api/v1/datasets/{dataset_id}/documents/{document_id}
Updates configurations for a specified document.
Request Method: PUT URL: /api/v1/datasets/{dataset_id}/documents/{document_id} Headers: 'content-Type: application/json' 'Authorization: Bearer ' Body: "name":string "meta_fields":object "chunk_method":string "parser_config":object Request example curl --request PUT
--url http://{address}/api/v1/datasets/{dataset_id}/documents/{document_id} \
--header 'Authorization: Bearer <YOUR_API_KEY>' \
--header 'Content-Type: application/json' \
--data '
{
"name": "manual.txt",
"chunk_method": "manual",
"parser_config": {"chunk_token_num": 128}
}'
Request parameters dataset_id: (Path parameter) The ID of the associated dataset. document_id: (Path parameter) The ID of the document to update. "name": (Body parameter), string "meta_fields": (Body parameter), dict[str, Any] The meta fields of the document. "chunk_method": (Body parameter), string The parsing method to apply to the document: "naive": General "manual: Manual "qa": Q&A "table": Table "paper": Paper "book": Book "laws": Laws "presentation": Presentation "picture": Picture "one": One "email": Email "parser_config": (Body parameter), object The configuration settings for the dataset parser. The attributes in this JSON object vary with the selected "chunk_method": If "chunk_method" is "naive", the "parser_config" object contains the following attributes: "chunk_token_num": Defaults to 256. "layout_recognize": Defaults to true. "html4excel": Indicates whether to convert Excel documents into HTML format. Defaults to false. "delimiter": Defaults to "\n". "task_page_size": Defaults to 12. For PDF only. "raptor": RAPTOR-specific settings. Defaults to: {"use_raptor": false}. If "chunk_method" is "qa", "manuel", "paper", "book", "laws", or "presentation", the "parser_config" object contains the following attribute: "raptor": RAPTOR-specific settings. Defaults to: {"use_raptor": false}. If "chunk_method" is "table", "picture", "one", or "email", "parser_config" is an empty JSON object. "enabled": (Body parameter), integer Whether the document should be available in the knowledge base. 1 → （available） 0 → （unavailable） Response Success:
{ "code": 0, "data": {
"id": "cd38dd72d4a611f0af9c71de94a988ef",
"name": "large.md",
"type": "doc",
"suffix": "md",
"size": 2306906,
"location": "large.md",
"source_type": "local",
"status": "1",
"run": "DONE",
"dataset_id": "5f546a1ad4a611f0af9c71de94a988ef",
"chunk_method": "naive",
"chunk_count": 2,
"token_count": 8126,
"created_by": "eab7f446cb5a11f0ab334fbc3aa38f35",
"create_date": "Tue, 09 Dec 2025 10:28:52 GMT",
"create_time": 1765247332122,
"update_date": "Wed, 17 Dec 2025 10:51:16 GMT",
"update_time": 1765939876819,
"process_begin_at": "Wed, 17 Dec 2025 10:33:55 GMT",
"process_duration": 14.8615,
"progress": 1.0,
"progress_msg": [
"10:33:58 Task has been received.",
"10:33:59 Page(1~100000001): Start to parse.",
"10:33:59 Page(1~100000001): Finish parsing.",
"10:34:07 Page(1~100000001): Generate 2 chunks",
"10:34:09 Page(1~100000001): Embedding chunks (2.13s)",
"10:34:09 Page(1~100000001): Indexing done (0.31s).",
"10:34:09 Page(1~100000001): Task done (11.68s)"
],
"parser_config": {
"chunk_token_num": 512,
"delimiter": "\n",
"auto_keywords": 0,
"auto_questions": 0,
"topn_tags": 3,
"layout_recognize": "DeepDOC",
"html4excel": false,
"image_context_size": 0,
"table_context_size": 0,
"graphrag": {
"use_graphrag": true,
"method": "light",
"entity_types": [
"organization",
"person",
"geo",
"event",
"category"
]
},
"raptor": {
"use_raptor": true,
"max_cluster": 64,
"max_token": 256,
"threshold": 0.1,
"random_seed": 0,
"prompt": "Please summarize the following paragraphs. Be careful with the numbers, do not make things up. Paragraphs as following:\n {cluster_content}\nThe above is the content you need to summarize."
}
},
"meta_fields": {},
"pipeline_id": "",
"thumbnail": ""
} }
Failure:
{
"code": 102,
"message": "The dataset does not have the document."
} Download document GET /api/v1/datasets/{dataset_id}/documents/{document_id}
Downloads a document from a specified dataset.
Request Method: GET URL: /api/v1/datasets/{dataset_id}/documents/{document_id} Headers: 'Authorization: Bearer ' Output: '{PATH_TO_THE_FILE}' Request example curl --request GET
--url http://{address}/api/v1/datasets/{dataset_id}/documents/{document_id} \
--header 'Authorization: Bearer <YOUR_API_KEY>' \
--output ./ragflow.txt
Request parameters dataset_id: (Path parameter) The associated dataset ID. documents_id: (Path parameter) The ID of the document to download. Response Success:
This is a test to verify the file download feature. Failure:
{
"code": 102,
"message": "You do not own the dataset 7898da028a0511efbf750242ac1220005."
} List documents GET /api/v1/datasets/{dataset_id}/documents?page={page}&page_size={page_size}&orderby={orderby}&desc={desc}&keywords={keywords}&id={document_id}&name={document_name}&create_time_from={timestamp}&create_time_to={timestamp}&suffix={file_suffix}&run={run_status}&metadata_condition={json}
Lists documents in a specified dataset.
Request Method: GET URL: /api/v1/datasets/{dataset_id}/documents?page={page}&page_size={page_size}&orderby={orderby}&desc={desc}&keywords={keywords}&id={document_id}&name={document_name}&create_time_from={timestamp}&create_time_to={timestamp}&suffix={file_suffix}&run={run_status} Headers: 'content-Type: application/json' 'Authorization: Bearer ' Request examples A basic request with pagination:
curl --request GET
--url http://{address}/api/v1/datasets/{dataset_id}/documents?page=1&page_size=10 \
--header 'Authorization: Bearer <YOUR_API_KEY>'
Request parameters dataset_id: (Path parameter) The associated dataset ID. keywords: (Filter parameter), string The keywords used to match document titles. page: (Filter parameter), integer Specifies the page on which the documents will be displayed. Defaults to 1. page_size: (Filter parameter), integer The maximum number of documents on each page. Defaults to 30. orderby: (Filter parameter), string The field by which documents should be sorted. Available options: create_time (default) update_time desc: (Filter parameter), boolean Indicates whether the retrieved documents should be sorted in descending order. Defaults to true. id: (Filter parameter), string The ID of the document to retrieve. create_time_from: (Filter parameter), integer Unix timestamp for filtering documents created after this time. 0 means no filter. Defaults to 0. create_time_to: (Filter parameter), integer Unix timestamp for filtering documents created before this time. 0 means no filter. Defaults to 0. suffix: (Filter parameter), array[string] Filter by file suffix. Supports multiple values, e.g., pdf, txt, and docx. Defaults to all suffixes. run: (Filter parameter), array[string] Filter by document processing status. Supports numeric, text, and mixed formats: Numeric format: ["0", "1", "2", "3", "4"] Text format: [UNSTART, RUNNING, CANCEL, DONE, FAIL] Mixed format: UNSTART, 1, DONE Status mapping: 0 / UNSTART: Document not yet processed 1 / RUNNING: Document is currently being processed 2 / CANCEL: Document processing was cancelled 3 / DONE: Document processing completed successfully 4 / FAIL: Document processing failed Defaults to all statuses. metadata_condition: (Filter parameter), object (JSON in query) Optional metadata filter applied to documents when document_ids is not provided. Uses the same structure as retrieval: logic: "and" (default) or "or" conditions: array of { "name": string, "comparison_operator": string, "value": string } comparison_operator supports: is, not is, contains, not contains, in, not in, start with, end with, >, <, ≥, ≤, empty, not empty Usage examples A request with multiple filtering parameters
curl --request GET
--url 'http://{address}/api/v1/datasets/{dataset_id}/documents?suffix=pdf&run=DONE&page=1&page_size=10' \
--header 'Authorization: Bearer <YOUR_API_KEY>'
Filter by metadata (query JSON):
curl -G \ --url "http://localhost:9222/api/v1/datasets/{{KB_ID}}/documents" \ --header 'Authorization: Bearer ' \ --data-urlencode 'metadata_condition={"logic":"and","conditions":[{"name":"tags","comparison_operator":"is","value":"bar"},{"name":"author","comparison_operator":"is","value":"alice"}]}' Response Success:
{
"code": 0,
"data": {
"docs": [
{
"chunk_count": 0,
"create_date": "Mon, 14 Oct 2024 09:11:01 GMT",
"create_time": 1728897061948,
"created_by": "69736c5e723611efb51b0242ac120007",
"id": "3bcfbf8a8a0c11ef8aba0242ac120006",
"knowledgebase_id": "7898da028a0511efbf750242ac120005",
"location": "Test_2.txt",
"name": "Test_2.txt",
"parser_config": {
"chunk_token_count": 128,
"delimiter": "\n",
"layout_recognize": true,
"task_page_size": 12
},
"chunk_method": "naive",
"process_begin_at": null,
"process_duration": 0.0,
"progress": 0.0,
"progress_msg": "",
"run": "UNSTART",
"size": 7,
"source_type": "local",
"status": "1",
"thumbnail": null,
"token_count": 0,
"type": "doc",
"update_date": "Mon, 14 Oct 2024 09:11:01 GMT",
"update_time": 1728897061948
}
],
"total_datasets": 1
}
} Failure:
{
"code": 102,
"message": "You don't own the dataset 7898da028a0511efbf750242ac1220005. "
} Delete documents DELETE /api/v1/datasets/{dataset_id}/documents
Deletes documents by ID.
Request Method: DELETE URL: /api/v1/datasets/{dataset_id}/documents Headers: 'Content-Type: application/json' 'Authorization: Bearer ' Body: "ids": list[string] Request example curl --request DELETE
--url http://{address}/api/v1/datasets/{dataset_id}/documents \
--header 'Content-Type: application/json' \
--header 'Authorization: Bearer <YOUR_API_KEY>' \
--data '
{
"ids": ["id_1","id_2"]
}'
Request parameters dataset_id: (Path parameter) The associated dataset ID. "ids": (Body parameter), list[string] The IDs of the documents to delete. If it is not specified, all documents in the specified dataset will be deleted. Response Success:
{
"code": 0
}. Failure:
{
"code": 102,
"message": "You do not own the dataset 7898da028a0511efbf750242ac1220005."
} Parse documents POST /api/v1/datasets/{dataset_id}/chunks
Parses documents in a specified dataset.
Request Method: POST URL: /api/v1/datasets/{dataset_id}/chunks Headers: 'content-Type: application/json' 'Authorization: Bearer ' Body: "document_ids": list[string] Request example curl --request POST
--url http://{address}/api/v1/datasets/{dataset_id}/chunks \
--header 'Content-Type: application/json' \
--header 'Authorization: Bearer <YOUR_API_KEY>' \
--data '
{
"document_ids": ["97a5f1c2759811efaa500242ac120004","97ad64b6759811ef9fc30242ac120004"]
}'
Request parameters dataset_id: (Path parameter) The dataset ID. "document_ids": (Body parameter), list[string], Required The IDs of the documents to parse. Response Success:
{
"code": 0
} Failure:
{
"code": 102,
"message": "`document_ids` is required"
} Stop parsing documents DELETE /api/v1/datasets/{dataset_id}/chunks
Stops parsing specified documents.
Request Method: DELETE URL: /api/v1/datasets/{dataset_id}/chunks Headers: 'content-Type: application/json' 'Authorization: Bearer ' Body: "document_ids": list[string] Request example curl --request DELETE
--url http://{address}/api/v1/datasets/{dataset_id}/chunks \
--header 'Content-Type: application/json' \
--header 'Authorization: Bearer <YOUR_API_KEY>' \
--data '
{
"document_ids": ["97a5f1c2759811efaa500242ac120004","97ad64b6759811ef9fc30242ac120004"]
}'
Request parameters dataset_id: (Path parameter) The associated dataset ID. "document_ids": (Body parameter), list[string], Required The IDs of the documents for which the parsing should be stopped. Response Success:
{
"code": 0
} Failure:
{
"code": 102,
"message": "`document_ids` is required"
} CHUNK MANAGEMENT WITHIN DATASET Add chunk POST /api/v1/datasets/{dataset_id}/documents/{document_id}/chunks
Adds a chunk to a specified document in a specified dataset.
Request Method: POST URL: /api/v1/datasets/{dataset_id}/documents/{document_id}/chunks Headers: 'content-Type: application/json' 'Authorization: Bearer ' Body: "content": string "important_keywords": list[string] Request example curl --request POST
--url http://{address}/api/v1/datasets/{dataset_id}/documents/{document_id}/chunks \
--header 'Content-Type: application/json' \
--header 'Authorization: Bearer <YOUR_API_KEY>' \
--data '
{
"content": "<CHUNK_CONTENT_HERE>"
}'
Request parameters dataset_id: (Path parameter) The associated dataset ID. document_ids: (Path parameter) The associated document ID. "content": (Body parameter), string, Required The text content of the chunk. "important_keywords(Body parameter), list[string] The key terms or phrases to tag with the chunk. "questions"(Body parameter), list[string] If there is a given question, the embedded chunks will be based on them Response Success:
{
"code": 0,
"data": {
"chunk": {
"content": "who are you",
"create_time": "2024-12-30 16:59:55",
"create_timestamp": 1735549195.969164,
"dataset_id": "72f36e1ebdf411efb7250242ac120006",
"document_id": "61d68474be0111ef98dd0242ac120006",
"id": "12ccdc56e59837e5",
"important_keywords": [],
"questions": []
}
}
} Failure:
{
"code": 102,
"message": "`content` is required"
} List chunks GET /api/v1/datasets/{dataset_id}/documents/{document_id}/chunks?keywords={keywords}&page={page}&page_size={page_size}&id={id}
Lists chunks in a specified document.
Request Method: GET URL: /api/v1/datasets/{dataset_id}/documents/{document_id}/chunks?keywords={keywords}&page={page}&page_size={page_size}&id={chunk_id} Headers: 'Authorization: Bearer ' Request example curl --request GET
--url http://{address}/api/v1/datasets/{dataset_id}/documents/{document_id}/chunks?keywords={keywords}&page={page}&page_size={page_size}&id={chunk_id} \
--header 'Authorization: Bearer <YOUR_API_KEY>'
Request parameters dataset_id: (Path parameter) The associated dataset ID. document_id: (Path parameter) The associated document ID. keywords(Filter parameter), string The keywords used to match chunk content. page(Filter parameter), integer Specifies the page on which the chunks will be displayed. Defaults to 1. page_size(Filter parameter), integer The maximum number of chunks on each page. Defaults to 1024. id(Filter parameter), string The ID of the chunk to retrieve. Response Success:
{
"code": 0,
"data": {
"chunks": [
{
"available": true,
"content": "This is a test content.",
"docnm_kwd": "1.txt",
"document_id": "b330ec2e91ec11efbc510242ac120004",
"id": "b48c170e90f70af998485c1065490726",
"image_id": "",
"important_keywords": "",
"positions": [
""
]
}
],
"doc": {
"chunk_count": 1,
"chunk_method": "naive",
"create_date": "Thu, 24 Oct 2024 09:45:27 GMT",
"create_time": 1729763127646,
"created_by": "69736c5e723611efb51b0242ac120007",
"dataset_id": "527fa74891e811ef9c650242ac120006",
"id": "b330ec2e91ec11efbc510242ac120004",
"location": "1.txt",
"name": "1.txt",
"parser_config": {
"chunk_token_num": 128,
"delimiter": "\\n",
"html4excel": false,
"layout_recognize": true,
"raptor": {
"use_raptor": false
}
},
"process_begin_at": "Thu, 24 Oct 2024 09:56:44 GMT",
"process_duration": 0.54213,
"progress": 0.0,
"progress_msg": "Task dispatched...",
"run": "2",
"size": 17966,
"source_type": "local",
"status": "1",
"thumbnail": "",
"token_count": 8,
"type": "doc",
"update_date": "Thu, 24 Oct 2024 11:03:15 GMT",
"update_time": 1729767795721
},
"total": 1
}
} Failure:
{
"code": 102,
"message": "You don't own the document 5c5999ec7be811ef9cab0242ac12000e5."
} Delete chunks DELETE /api/v1/datasets/{dataset_id}/documents/{document_id}/chunks
Deletes chunks by ID.
Request Method: DELETE URL: /api/v1/datasets/{dataset_id}/documents/{document_id}/chunks Headers: 'content-Type: application/json' 'Authorization: Bearer ' Body: "chunk_ids": list[string] Request example curl --request DELETE
--url http://{address}/api/v1/datasets/{dataset_id}/documents/{document_id}/chunks \
--header 'Content-Type: application/json' \
--header 'Authorization: Bearer <YOUR_API_KEY>' \
--data '
{
"chunk_ids": ["test_1", "test_2"]
}'
Request parameters dataset_id: (Path parameter) The associated dataset ID. document_ids: (Path parameter) The associated document ID. "chunk_ids": (Body parameter), list[string] The IDs of the chunks to delete. If it is not specified, all chunks of the specified document will be deleted. Response Success:
{
"code": 0
} Failure:
{
"code": 102,
"message": "`chunk_ids` is required"
} Update chunk PUT /api/v1/datasets/{dataset_id}/documents/{document_id}/chunks/{chunk_id}
Updates content or configurations for a specified chunk.
Request Method: PUT URL: /api/v1/datasets/{dataset_id}/documents/{document_id}/chunks/{chunk_id} Headers: 'content-Type: application/json' 'Authorization: Bearer ' Body: "content": string "important_keywords": list[string] "available": boolean Request example curl --request PUT
--url http://{address}/api/v1/datasets/{dataset_id}/documents/{document_id}/chunks/{chunk_id} \
--header 'Content-Type: application/json' \
--header 'Authorization: Bearer <YOUR_API_KEY>' \
--data '
{
"content": "ragflow123",
"important_keywords": []
}'
Request parameters dataset_id: (Path parameter) The associated dataset ID. document_ids: (Path parameter) The associated document ID. chunk_id: (Path parameter) The ID of the chunk to update. "content": (Body parameter), string The text content of the chunk. "important_keywords": (Body parameter), list[string] A list of key terms or phrases to tag with the chunk. "available": (Body parameter) boolean The chunk's availability status in the dataset. Value options: true: Available (default) false: Unavailable Response Success:
{
"code": 0
} Failure:
{
"code": 102,
"message": "Can't find this chunk 29a2d9987e16ba331fb4d7d30d99b71d2"
} Retrieve a metadata summary from a dataset GET /api/v1/datasets/{dataset_id}/metadata/summary
Aggregates metadata values across all documents in a dataset.
Request Method: GET URL: /api/v1/datasets/{dataset_id}/metadata/summary Headers: 'Authorization: Bearer ' Response Success:
{ "code": 0, "data": {
"summary": {
"tags": [["bar", 2], ["foo", 1], ["baz", 1]],
"author": [["alice", 2], ["bob", 1]]
}
} } Update or delete metadata POST /api/v1/datasets/{dataset_id}/metadata/update
Batch update or delete document-level metadata within a specified dataset. If both document_ids and metadata_condition are omitted, all documents within that dataset are selected. When both are provided, the intersection is used.
Request Method: POST URL: /api/v1/datasets/{dataset_id}/metadata/update Headers: 'content-Type: application/json' 'Authorization: Bearer ' Body: selector: object updates: list[object] deletes: list[object] Request parameters dataset_id: (Path parameter) The associated dataset ID. "selector": (Body parameter), object, optional A document selector: "document_ids": list[string] optional The associated document ID. "metadata_condition": object, optional "logic": Defines the logic relation between conditions if multiple conditions are provided. Options: "and" (default) "or" "conditions": list[object] optional Each object: { "name": string, "comparison_operator": string, "value": string } "name": string The key name to search by. "comparison_operator": string Available options: "is" "not is" "contains" "not contains" "in" "not in" "start with" "end with" ">" "<" "≥" "≤" "empty" "not empty" "value": string The key value to search by. "updates": (Body parameter), list[object], optional Replaces metadata of the retrieved documents. Each object: { "key": string, "match": string, "value": string }. "key": string The name of the key to update. "match": string optional The current value of the key to update. When omitted, the corresponding keys are updated to "value" regardless of their current values. "value": string The new value to set for the specified keys. "deletes: (Body parameter), list[ojbect], optional Deletes metadata of the retrieved documents. Each object: { "key": string, "value": string }. "key": string The name of the key to delete. "value": string Optional The value of the key to delete. When provided, only keys with a matching value are deleted. When omitted, all specified keys are deleted. Request example curl --request POST
--url http://{address}/api/v1/datasets/{dataset_id}/metadata/update \
--header 'Content-Type: application/json' \
--header 'Authorization: Bearer <YOUR_API_KEY>' \
--data '{
"selector": {
"metadata_condition": {
"logic": "and",
"conditions": [
{"name": "author", "comparison_operator": "is", "value": "alice"}
]
}
},
"updates": [
{"key": "tags", "match": "foo", "value": "foo_new"}
],
"deletes": [
{"key": "obsolete_key"},
{"key": "author", "value": "alice"}
]
}'
Response Success:
{ "code": 0, "data": {
"updated": 1,
"matched_docs": 2
} } Retrieve chunks POST /api/v1/retrieval
Retrieves chunks from specified datasets.
Request Method: POST URL: /api/v1/retrieval Headers: 'content-Type: application/json' 'Authorization: Bearer ' Body: "question": string "dataset_ids": list[string] "document_ids": list[string] "page": integer "page_size": integer "similarity_threshold": float "vector_similarity_weight": float "top_k": integer "rerank_id": string "keyword": boolean "highlight": boolean "cross_languages": list[string] "metadata_condition": object "use_kg": boolean "toc_enhance": boolean Request example curl --request POST
--url http://{address}/api/v1/retrieval \
--header 'Content-Type: application/json' \
--header 'Authorization: Bearer <YOUR_API_KEY>' \
--data '
{
"question": "What is advantage of ragflow?",
"dataset_ids": ["b2a62730759d11ef987d0242ac120004"],
"document_ids": ["77df9ef4759a11ef8bdd0242ac120004"],
"metadata_condition": {
"logic": "and",
"conditions": [
{
"name": "author",
"comparison_operator": "=",
"value": "Toby"
},
{
"name": "url",
"comparison_operator": "not contains",
"value": "amd"
}
]
}
}'
Request parameter "question": (Body parameter), string, Required The user query or query keywords. "dataset_ids": (Body parameter) list[string] The IDs of the datasets to search. If you do not set this argument, ensure that you set "document_ids". "document_ids": (Body parameter), list[string] The IDs of the documents to search. Ensure that all selected documents use the same embedding model. Otherwise, an error will occur. If you do not set this argument, ensure that you set "dataset_ids". "page": (Body parameter), integer Specifies the page on which the chunks will be displayed. Defaults to 1. "page_size": (Body parameter) The maximum number of chunks on each page. Defaults to 30. "similarity_threshold": (Body parameter) The minimum similarity score. Defaults to 0.2. "vector_similarity_weight": (Body parameter), float The weight of vector cosine similarity. Defaults to 0.3. If x represents the weight of vector cosine similarity, then (1 - x) is the term similarity weight. "top_k": (Body parameter), integer The number of chunks engaged in vector cosine computation. Defaults to 1024. "use_kg": (Body parameter), boolean Whether to search chunks related to the generated knowledge graph for multi-hop queries. Defaults to False. Before enabling this, ensure you have successfully constructed a knowledge graph for the specified datasets. See here for details. "toc_enhance": (Body parameter), boolean Whether to search chunks with extracted table of content. Defaults to False. Before enabling this, ensure you have enabled TOC_Enhance and successfully extracted table of contents for the specified datasets. See here for details. "rerank_id": (Body parameter), integer The ID of the rerank model. "keyword": (Body parameter), boolean Indicates whether to enable keyword-based matching: true: Enable keyword-based matching. false: Disable keyword-based matching (default). "highlight": (Body parameter), boolean Specifies whether to enable highlighting of matched terms in the results: true: Enable highlighting of matched terms. false: Disable highlighting of matched terms (default). "cross_languages": (Body parameter) list[string] The languages that should be translated into, in order to achieve keywords retrievals in different languages. "metadata_condition": (Body parameter), object The metadata condition used for filtering chunks: "logic": (Body parameter), string "and": Return only results that satisfy every condition (default). "or": Return results that satisfy any condition. "conditions": (Body parameter), array A list of metadata filter conditions. "name": string - The metadata field name to filter by, e.g., "author", "company", "url". Ensure this parameter before use. See Set metadata for details. comparison_operator: string - The comparison operator. Can be one of: "contains" "not contains" "start with" "empty" "not empty" "=" "≠" ">" "<" "≥" "≤" "value": string - The value to compare. Response Success:
{
"code": 0,
"data": {
"chunks": [
{
"content": "ragflow content",
"content_ltks": "ragflow content",
"document_id": "5c5999ec7be811ef9cab0242ac120005",
"document_keyword": "1.txt",
"highlight": "<em>ragflow</em> content",
"id": "d78435d142bd5cf6704da62c778795c5",
"image_id": "",
"important_keywords": [
""
],
"kb_id": "c7ee74067a2c11efb21c0242ac120006",
"positions": [
""
],
"similarity": 0.9669436601210759,
"term_similarity": 1.0,
"vector_similarity": 0.8898122004035864
}
],
"doc_aggs": [
{
"count": 1,
"doc_id": "5c5999ec7be811ef9cab0242ac120005",
"doc_name": "1.txt"
}
],
"total": 1
}
} Failure:
{
"code": 102,
"message": "`datasets` is required."
} CHAT ASSISTANT MANAGEMENT Create chat assistant POST /api/v1/chats
Creates a chat assistant.
Request Method: POST URL: /api/v1/chats Headers: 'content-Type: application/json' 'Authorization: Bearer ' Body: "name": string "avatar": string "dataset_ids": list[string] "llm": object "prompt": object Request example curl --request POST
--url http://{address}/api/v1/chats \
--header 'Content-Type: application/json' \
--header 'Authorization: Bearer <YOUR_API_KEY>' \
--data '{
"dataset_ids": ["0b2cbc8c877f11ef89070242ac120005"],
"name":"new_chat_1"
}' Request parameters "name": (Body parameter), string, Required The name of the chat assistant.
"avatar": (Body parameter), string Base64 encoding of the avatar.
"dataset_ids": (Body parameter), list[string] The IDs of the associated datasets.
"llm": (Body parameter), object The LLM settings for the chat assistant to create. If it is not explicitly set, a JSON object with the following values will be generated as the default. An llm JSON object contains the following attributes:
"model_name", string The chat model name. If not set, the user's default chat model will be used. :::caution WARNING model_type is an internal parameter, serving solely as a temporary workaround for the current model-configuration design limitations.
Its main purpose is to let multimodal models (stored in the database as "image2text") pass backend validation/dispatching. Be mindful that:
Do not treat it as a stable public API.
It is subject to change or removal in future releases. :::
"model_type": string A model type specifier. Only "chat" and "image2text" are recognized; any other inputs, or when omitted, are treated as "chat".
"model_name", string
"temperature": float Controls the randomness of the model's predictions. A lower temperature results in more conservative responses, while a higher temperature yields more creative and diverse responses. Defaults to 0.1.
"top_p": float Also known as “nucleus sampling”, this parameter sets a threshold to select a smaller set of words to sample from. It focuses on the most likely words, cutting off the less probable ones. Defaults to 0.3
"presence_penalty": float This discourages the model from repeating the same information by penalizing words that have already appeared in the conversation. Defaults to 0.4.
"frequency penalty": float Similar to the presence penalty, this reduces the model’s tendency to repeat the same words frequently. Defaults to 0.7.
"prompt": (Body parameter), object Instructions for the LLM to follow. If it is not explicitly set, a JSON object with the following values will be generated as the default. A prompt JSON object contains the following attributes:
"similarity_threshold": float RAGFlow employs either a combination of weighted keyword similarity and weighted vector cosine similarity, or a combination of weighted keyword similarity and weighted reranking score during retrieval. This argument sets the threshold for similarities between the user query and chunks. If a similarity score falls below this threshold, the corresponding chunk will be excluded from the results. The default value is 0.2. "keywords_similarity_weight": float This argument sets the weight of keyword similarity in the hybrid similarity score with vector cosine similarity or reranking model similarity. By adjusting this weight, you can control the influence of keyword similarity in relation to other similarity measures. The default value is 0.7. "top_n": int This argument specifies the number of top chunks with similarity scores above the similarity_threshold that are fed to the LLM. The LLM will only access these 'top N' chunks. The default value is 6. "variables": object[] This argument lists the variables to use in the 'System' field of Chat Configurations. Note that: "knowledge" is a reserved variable, which represents the retrieved chunks. All the variables in 'System' should be curly bracketed. The default value is [{"key": "knowledge", "optional": true}]. "rerank_model": string If it is not specified, vector cosine similarity will be used; otherwise, reranking score will be used. top_k: int Refers to the process of reordering or selecting the top-k items from a list or set based on a specific ranking criterion. Default to 1024. "empty_response": string If nothing is retrieved in the dataset for the user's question, this will be used as the response. To allow the LLM to improvise when nothing is found, leave this blank. "opener": string The opening greeting for the user. Defaults to "Hi! I am your assistant, can I help you?". "show_quote: boolean Indicates whether the source of text should be displayed. Defaults to true. "prompt": string The prompt content. Response Success:
{
"code": 0,
"data": {
"avatar": "",
"create_date": "Thu, 24 Oct 2024 11:18:29 GMT",
"create_time": 1729768709023,
"dataset_ids": [
"527fa74891e811ef9c650242ac120006"
],
"description": "A helpful Assistant",
"do_refer": "1",
"id": "b1f2f15691f911ef81180242ac120003",
"language": "English",
"llm": {
"frequency_penalty": 0.7,
"model_name": "qwen-plus@Tongyi-Qianwen",
"presence_penalty": 0.4,
"temperature": 0.1,
"top_p": 0.3
},
"name": "12234",
"prompt": {
"empty_response": "Sorry! No relevant content was found in the knowledge base!",
"keywords_similarity_weight": 0.3,
"opener": "Hi! I'm your assistant. What can I do for you?",
"prompt": "You are an intelligent assistant. Please summarize the content of the knowledge base to answer the question. Please list the data in the knowledge base and answer in detail. When all knowledge base content is irrelevant to the question, your answer must include the sentence \"The answer you are looking for is not found in the knowledge base!\" Answers need to consider chat history.\n ",
"rerank_model": "",
"similarity_threshold": 0.2,
"top_n": 6,
"variables": [
{
"key": "knowledge",
"optional": false
}
]
},
"prompt_type": "simple",
"status": "1",
"tenant_id": "69736c5e723611efb51b0242ac120007",
"top_k": 1024,
"update_date": "Thu, 24 Oct 2024 11:18:29 GMT",
"update_time": 1729768709023
}
} Failure:
{
"code": 102,
"message": "Duplicated chat name in creating dataset."
} Update chat assistant PUT /api/v1/chats/{chat_id}
Updates configurations for a specified chat assistant.
Request Method: PUT URL: /api/v1/chats/{chat_id} Headers: 'content-Type: application/json' 'Authorization: Bearer ' Body: "name": string "avatar": string "dataset_ids": list[string] "llm": object "prompt": object Request example curl --request PUT
--url http://{address}/api/v1/chats/{chat_id} \
--header 'Content-Type: application/json' \
--header 'Authorization: Bearer <YOUR_API_KEY>' \
--data '
{
"name":"Test"
}'
Parameters chat_id: (Path parameter) The ID of the chat assistant to update. "name": (Body parameter), string, Required The revised name of the chat assistant. "avatar": (Body parameter), string Base64 encoding of the avatar. "dataset_ids": (Body parameter), list[string] The IDs of the associated datasets. "llm": (Body parameter), object The LLM settings for the chat assistant to create. If it is not explicitly set, a dictionary with the following values will be generated as the default. An llm object contains the following attributes: "model_name", string The chat model name. If not set, the user's default chat model will be used. "temperature": float Controls the randomness of the model's predictions. A lower temperature results in more conservative responses, while a higher temperature yields more creative and diverse responses. Defaults to 0.1. "top_p": float Also known as “nucleus sampling”, this parameter sets a threshold to select a smaller set of words to sample from. It focuses on the most likely words, cutting off the less probable ones. Defaults to 0.3 "presence_penalty": float This discourages the model from repeating the same information by penalizing words that have already appeared in the conversation. Defaults to 0.2. "frequency penalty": float Similar to the presence penalty, this reduces the model’s tendency to repeat the same words frequently. Defaults to 0.7. "prompt": (Body parameter), object Instructions for the LLM to follow. A prompt object contains the following attributes: "similarity_threshold": float RAGFlow employs either a combination of weighted keyword similarity and weighted vector cosine similarity, or a combination of weighted keyword similarity and weighted rerank score during retrieval. This argument sets the threshold for similarities between the user query and chunks. If a similarity score falls below this threshold, the corresponding chunk will be excluded from the results. The default value is 0.2. "keywords_similarity_weight": float This argument sets the weight of keyword similarity in the hybrid similarity score with vector cosine similarity or reranking model similarity. By adjusting this weight, you can control the influence of keyword similarity in relation to other similarity measures. The default value is 0.7. "top_n": int This argument specifies the number of top chunks with similarity scores above the similarity_threshold that are fed to the LLM. The LLM will only access these 'top N' chunks. The default value is 8. "variables": object[] This argument lists the variables to use in the 'System' field of Chat Configurations. Note that: "knowledge" is a reserved variable, which represents the retrieved chunks. All the variables in 'System' should be curly bracketed. The default value is [{"key": "knowledge", "optional": true}] "rerank_model": string If it is not specified, vector cosine similarity will be used; otherwise, reranking score will be used. "empty_response": string If nothing is retrieved in the dataset for the user's question, this will be used as the response. To allow the LLM to improvise when nothing is found, leave this blank. "opener": string The opening greeting for the user. Defaults to "Hi! I am your assistant, can I help you?". "show_quote: boolean Indicates whether the source of text should be displayed. Defaults to true. "prompt": string The prompt content. Response Success:
{
"code": 0
} Failure:
{
"code": 102,
"message": "Duplicated chat name in updating dataset."
} Delete chat assistants DELETE /api/v1/chats
Deletes chat assistants by ID.
Request Method: DELETE URL: /api/v1/chats Headers: 'content-Type: application/json' 'Authorization: Bearer ' Body: "ids": list[string] Request example curl --request DELETE
--url http://{address}/api/v1/chats \
--header 'Content-Type: application/json' \
--header 'Authorization: Bearer <YOUR_API_KEY>' \
--data '
{
"ids": ["test_1", "test_2"]
}'
Request parameters "ids": (Body parameter), list[string] The IDs of the chat assistants to delete. If it is not specified, all chat assistants in the system will be deleted. Response Success:
{
"code": 0
} Failure:
{
"code": 102,
"message": "ids are required"
} List chat assistants GET /api/v1/chats?page={page}&page_size={page_size}&orderby={orderby}&desc={desc}&name={chat_name}&id={chat_id}
Lists chat assistants.
Request Method: GET URL: /api/v1/chats?page={page}&page_size={page_size}&orderby={orderby}&desc={desc}&name={chat_name}&id={chat_id} Headers: 'Authorization: Bearer ' Request example curl --request GET
--url http://{address}/api/v1/chats?page={page}&page_size={page_size}&orderby={orderby}&desc={desc}&name={chat_name}&id={chat_id} \
--header 'Authorization: Bearer <YOUR_API_KEY>'
Request parameters page: (Filter parameter), integer Specifies the page on which the chat assistants will be displayed. Defaults to 1. page_size: (Filter parameter), integer The number of chat assistants on each page. Defaults to 30. orderby: (Filter parameter), string The attribute by which the results are sorted. Available options: create_time (default) update_time desc: (Filter parameter), boolean Indicates whether the retrieved chat assistants should be sorted in descending order. Defaults to true. id: (Filter parameter), string The ID of the chat assistant to retrieve. name: (Filter parameter), string The name of the chat assistant to retrieve. Response Success:
{
"code": 0,
"data": [
{
"avatar": "",
"create_date": "Fri, 18 Oct 2024 06:20:06 GMT",
"create_time": 1729232406637,
"description": "A helpful Assistant",
"do_refer": "1",
"id": "04d0d8e28d1911efa3630242ac120006",
"dataset_ids": ["527fa74891e811ef9c650242ac120006"],
"language": "English",
"llm": {
"frequency_penalty": 0.7,
"model_name": "qwen-plus@Tongyi-Qianwen",
"presence_penalty": 0.4,
"temperature": 0.1,
"top_p": 0.3
},
"name": "13243",
"prompt": {
"empty_response": "Sorry! No relevant content was found in the knowledge base!",
"keywords_similarity_weight": 0.3,
"opener": "Hi! I'm your assistant. What can I do for you?",
"prompt": "You are an intelligent assistant. Please summarize the content of the knowledge base to answer the question. Please list the data in the knowledge base and answer in detail. When all knowledge base content is irrelevant to the question, your answer must include the sentence \"The answer you are looking for is not found in the knowledge base!\" Answers need to consider chat history.\n",
"rerank_model": "",
"similarity_threshold": 0.2,
"top_n": 6,
"variables": [
{
"key": "knowledge",
"optional": false
}
]
},
"prompt_type": "simple",
"status": "1",
"tenant_id": "69736c5e723611efb51b0242ac120007",
"top_k": 1024,
"update_date": "Fri, 18 Oct 2024 06:20:06 GMT",
"update_time": 1729232406638
}
]
} Failure:
{
"code": 102,
"message": "The chat doesn't exist"
} SESSION MANAGEMENT Create session with chat assistant POST /api/v1/chats/{chat_id}/sessions
Creates a session with a chat assistant.
Request Method: POST URL: /api/v1/chats/{chat_id}/sessions Headers: 'content-Type: application/json' 'Authorization: Bearer ' Body: "name": string "user_id": string (optional) Request example curl --request POST
--url http://{address}/api/v1/chats/{chat_id}/sessions \
--header 'Content-Type: application/json' \
--header 'Authorization: Bearer <YOUR_API_KEY>' \
--data '
{
"name": "new session"
}'
Request parameters chat_id: (Path parameter) The ID of the associated chat assistant. "name": (Body parameter), string The name of the chat session to create. "user_id": (Body parameter), string Optional user-defined ID. Response Success:
{
"code": 0,
"data": {
"chat_id": "2ca4b22e878011ef88fe0242ac120005",
"create_date": "Fri, 11 Oct 2024 08:46:14 GMT",
"create_time": 1728636374571,
"id": "4606b4ec87ad11efbc4f0242ac120006",
"messages": [
{
"content": "Hi! I am your assistant, can I help you?",
"role": "assistant"
}
],
"name": "new session",
"update_date": "Fri, 11 Oct 2024 08:46:14 GMT",
"update_time": 1728636374571
}
} Failure:
{
"code": 102,
"message": "Name cannot be empty."
} Update chat assistant's session PUT /api/v1/chats/{chat_id}/sessions/{session_id}
Updates a session of a specified chat assistant.
Request Method: PUT URL: /api/v1/chats/{chat_id}/sessions/{session_id} Headers: 'content-Type: application/json' 'Authorization: Bearer ' Body: "name: string "user_id: string (optional) Request example curl --request PUT
--url http://{address}/api/v1/chats/{chat_id}/sessions/{session_id} \
--header 'Content-Type: application/json' \
--header 'Authorization: Bearer <YOUR_API_KEY>' \
--data '
{
"name": "<REVISED_SESSION_NAME_HERE>"
}'
Request Parameter chat_id: (Path parameter) The ID of the associated chat assistant. session_id: (Path parameter) The ID of the session to update. "name": (Body Parameter), string The revised name of the session. "user_id": (Body parameter), string Optional user-defined ID. Response Success:
{
"code": 0
} Failure:
{
"code": 102,
"message": "Name cannot be empty."
} List chat assistant's sessions GET /api/v1/chats/{chat_id}/sessions?page={page}&page_size={page_size}&orderby={orderby}&desc={desc}&name={session_name}&id={session_id}
Lists sessions associated with a specified chat assistant.
Request Method: GET URL: /api/v1/chats/{chat_id}/sessions?page={page}&page_size={page_size}&orderby={orderby}&desc={desc}&name={session_name}&id={session_id}&user_id={user_id} Headers: 'Authorization: Bearer ' Request example curl --request GET
--url http://{address}/api/v1/chats/{chat_id}/sessions?page={page}&page_size={page_size}&orderby={orderby}&desc={desc}&name={session_name}&id={session_id} \
--header 'Authorization: Bearer <YOUR_API_KEY>'
Request Parameters chat_id: (Path parameter) The ID of the associated chat assistant. page: (Filter parameter), integer Specifies the page on which the sessions will be displayed. Defaults to 1. page_size: (Filter parameter), integer The number of sessions on each page. Defaults to 30. orderby: (Filter parameter), string The field by which sessions should be sorted. Available options: create_time (default) update_time desc: (Filter parameter), boolean Indicates whether the retrieved sessions should be sorted in descending order. Defaults to true. name: (Filter parameter) string The name of the chat session to retrieve. id: (Filter parameter), string The ID of the chat session to retrieve. user_id: (Filter parameter), string The optional user-defined ID passed in when creating session. Response Success:
{
"code": 0,
"data": [
{
"chat": "2ca4b22e878011ef88fe0242ac120005",
"create_date": "Fri, 11 Oct 2024 08:46:43 GMT",
"create_time": 1728636403974,
"id": "578d541e87ad11ef96b90242ac120006",
"messages": [
{
"content": "Hi! I am your assistant, can I help you?",
"role": "assistant"
}
],
"name": "new session",
"update_date": "Fri, 11 Oct 2024 08:46:43 GMT",
"update_time": 1728636403974
}
]
} Failure:
{
"code": 102,
"message": "The session doesn't exist"
} Delete chat assistant's sessions DELETE /api/v1/chats/{chat_id}/sessions
Deletes sessions of a chat assistant by ID.
Request Method: DELETE URL: /api/v1/chats/{chat_id}/sessions Headers: 'content-Type: application/json' 'Authorization: Bearer ' Body: "ids": list[string] Request example curl --request DELETE
--url http://{address}/api/v1/chats/{chat_id}/sessions \
--header 'Content-Type: application/json' \
--header 'Authorization: Bearer <YOUR_API_KEY>' \
--data '
{
"ids": ["test_1", "test_2"]
}'
Request Parameters chat_id: (Path parameter) The ID of the associated chat assistant. "ids": (Body Parameter), list[string] The IDs of the sessions to delete. If it is not specified, all sessions associated with the specified chat assistant will be deleted. Response Success:
{
"code": 0
} Failure:
{
"code": 102,
"message": "The chat doesn't own the session"
} Converse with chat assistant POST /api/v1/chats/{chat_id}/completions
Asks a specified chat assistant a question to start an AI-powered conversation.
:::tip NOTE
In streaming mode, not all responses include a reference, as this depends on the system's judgement.
In streaming mode, the last message is an empty message:
data: { "code": 0, "data": true } :::
Request Method: POST URL: /api/v1/chats/{chat_id}/completions Headers: 'content-Type: application/json' 'Authorization: Bearer ' Body: "question": string "stream": boolean "session_id": string (optional) "user_id: string (optional) "metadata_condition": object (optional) Request example curl --request POST
--url http://{address}/api/v1/chats/{chat_id}/completions \
--header 'Content-Type: application/json' \
--header 'Authorization: Bearer <YOUR_API_KEY>' \
--data-binary '
{
}'
curl --request POST
--url http://{address}/api/v1/chats/{chat_id}/completions \
--header 'Content-Type: application/json' \
--header 'Authorization: Bearer <YOUR_API_KEY>' \
--data-binary '
{
"question": "Who are you",
"stream": true,
"session_id":"9fa7691cb85c11ef9c5f0242ac120005",
"metadata_condition": {
"logic": "and",
"conditions": [
{
"name": "author",
"comparison_operator": "is",
"value": "bob"
}
]
}
}'
Request Parameters chat_id: (Path parameter) The ID of the associated chat assistant. "question": (Body Parameter), string, Required The question to start an AI-powered conversation. "stream": (Body Parameter), boolean Indicates whether to output responses in a streaming way: true: Enable streaming (default). false: Disable streaming. "session_id": (Body Parameter) The ID of session. If it is not provided, a new session will be generated. "user_id": (Body parameter), string The optional user-defined ID. Valid only when no session_id is provided. "metadata_condition": (Body parameter), object Optional metadata filter conditions applied to retrieval results. logic: string, one of and / or conditions: list[object] where each condition contains: name: string metadata key comparison_operator: string (e.g. is, not is, contains, not contains, start with, end with, empty, not empty, >, <, ≥, ≤) value: string|number|boolean (optional for empty/not empty) Response Success without session_id:
data:{
"code": 0,
"message": "",
"data": {
"answer": "Hi! I'm your assistant. What can I do for you?",
"reference": {},
"audio_binary": null,
"id": null,
"session_id": "b01eed84b85611efa0e90242ac120005"
}
} data:{
"code": 0,
"message": "",
"data": true
} Success with session_id:
data:{
"code": 0,
"data": {
"answer": "I am an intelligent assistant designed to help answer questions by summarizing content from a",
"reference": {},
"audio_binary": null,
"id": "a84c5dd4-97b4-4624-8c3b-974012c8000d",
"session_id": "82b0ab2a9c1911ef9d870242ac120006"
}
} data:{
"code": 0,
"data": {
"answer": "I am an intelligent assistant designed to help answer questions by summarizing content from a knowledge base. My responses are based on the information available in the knowledge base and",
"reference": {},
"audio_binary": null,
"id": "a84c5dd4-97b4-4624-8c3b-974012c8000d",
"session_id": "82b0ab2a9c1911ef9d870242ac120006"
}
} data:{
"code": 0,
"data": {
"answer": "I am an intelligent assistant designed to help answer questions by summarizing content from a knowledge base. My responses are based on the information available in the knowledge base and any relevant chat history.",
"reference": {},
"audio_binary": null,
"id": "a84c5dd4-97b4-4624-8c3b-974012c8000d",
"session_id": "82b0ab2a9c1911ef9d870242ac120006"
}
} data:{
"code": 0,
"data": {
"answer": "I am an intelligent assistant designed to help answer questions by summarizing content from a knowledge base ##0$$. My responses are based on the information available in the knowledge base and any relevant chat history.",
"reference": {
"total": 1,
"chunks": [
{
"id": "faf26c791128f2d5e821f822671063bd",
"content": "xxxxxxxx",
"document_id": "dd58f58e888511ef89c90242ac120006",
"document_name": "1.txt",
"dataset_id": "8e83e57a884611ef9d760242ac120006",
"image_id": "",
"url": null,
"similarity": 0.7,
"vector_similarity": 0.0,
"term_similarity": 1.0,
"doc_type": [],
"positions": [
""
]
}
],
"doc_aggs": [
{
"doc_name": "1.txt",
"doc_id": "dd58f58e888511ef89c90242ac120006",
"count": 1
}
]
},
"prompt": "xxxxxxxxxxx",
"created_at": 1755055623.6401553,
"id": "a84c5dd4-97b4-4624-8c3b-974012c8000d",
"session_id": "82b0ab2a9c1911ef9d870242ac120006"
}
} data:{
"code": 0,
"data": true
} Failure:
{
"code": 102,
"message": "Please input your question."
} Create session with agent :::danger DEPRECATED This method is deprecated and not recommended. You can still call it but be mindful that calling Converse with agent will automatically generate a session ID for the associated agent. :::
POST /api/v1/agents/{agent_id}/sessions
Creates a session with an agent.
Request Method: POST URL: /api/v1/agents/{agent_id}/sessions?user_id={user_id} Headers: `'content-Type: application/json' 'Authorization: Bearer ' Body: the required parameters:str other parameters: The variables specified in the Begin component. Request example If the Begin component in your agent does not take required parameters:
curl --request POST
--url http://{address}/api/v1/agents/{agent_id}/sessions \
--header 'Content-Type: application/json' \
--header 'Authorization: Bearer <YOUR_API_KEY>' \
--data '{
}'
Request parameters agent_id: (Path parameter) The ID of the associated agent. user_id: (Filter parameter) The optional user-defined ID for parsing docs (especially images) when creating a session while uploading files. Response Success:
{
"code": 0,
"data": {
"agent_id": "dbb4ed366e8611f09690a55a6daec4ef",
"dsl": {
"components": {
"Message:EightyJobsAsk": {
"downstream": [],
"obj": {
"component_name": "Message",
"params": {
"content": [
"{begin@var1}{begin@var2}"
],
"debug_inputs": {},
"delay_after_error": 2.0,
"description": "",
"exception_default_value": null,
"exception_goto": null,
"exception_method": null,
"inputs": {},
"max_retries": 0,
"message_history_window_size": 22,
"outputs": {
"content": {
"type": "str",
"value": null
}
},
"stream": true
}
},
"upstream": [
"begin"
]
},
"begin": {
"downstream": [
"Message:EightyJobsAsk"
],
"obj": {
"component_name": "Begin",
"params": {
"debug_inputs": {},
"delay_after_error": 2.0,
"description": "",
"enablePrologue": true,
"enable_tips": true,
"exception_default_value": null,
"exception_goto": null,
"exception_method": null,
"inputs": {
"var1": {
"name": "var1",
"optional": false,
"options": [],
"type": "line",
"value": null
},
"var2": {
"name": "var2",
"optional": false,
"options": [],
"type": "line",
"value": null
}
},
"max_retries": 0,
"message_history_window_size": 22,
"mode": "conversational",
"outputs": {},
"prologue": "Hi! I'm your assistant. What can I do for you?",
"tips": "Please fill in the form"
}
},
"upstream": []
}
},
"globals": {
"sys.conversation_turns": 0,
"sys.files": [],
"sys.query": "",
"sys.user_id": ""
},
"graph": {
"edges": [
{
"data": {
"isHovered": false
},
"id": "xy-edge__beginstart-Message:EightyJobsAskend",
"markerEnd": "logo",
"source": "begin",
"sourceHandle": "start",
"style": {
"stroke": "rgba(151, 154, 171, 1)",
"strokeWidth": 1
},
"target": "Message:EightyJobsAsk",
"targetHandle": "end",
"type": "buttonEdge",
"zIndex": 1001
}
],
"nodes": [
{
"data": {
"form": {
"enablePrologue": true,
"inputs": {
"var1": {
"name": "var1",
"optional": false,
"options": [],
"type": "line"
},
"var2": {
"name": "var2",
"optional": false,
"options": [],
"type": "line"
}
},
"mode": "conversational",
"prologue": "Hi! I'm your assistant. What can I do for you?"
},
"label": "Begin",
"name": "begin"
},
"dragging": false,
"id": "begin",
"measured": {
"height": 112,
"width": 200
},
"position": {
"x": 270.64098070942583,
"y": -56.320928437811176
},
"selected": false,
"sourcePosition": "left",
"targetPosition": "right",
"type": "beginNode"
},
{
"data": {
"form": {
"content": [
"{begin@var1}{begin@var2}"
]
},
"label": "Message",
"name": "Message_0"
},
"dragging": false,
"id": "Message:EightyJobsAsk",
"measured": {
"height": 57,
"width": 200
},
"position": {
"x": 279.5,
"y": 190
},
"selected": true,
"sourcePosition": "right",
"targetPosition": "left",
"type": "messageNode"
}
]
},
"history": [],
"memory": [],
"messages": [],
"path": [],
"retrieval": [],
"task_id": "dbb4ed366e8611f09690a55a6daec4ef"
},
"id": "0b02fe80780e11f084adcfdc3ed1d902",
"message": [
{
"content": "Hi! I'm your assistant. What can I do for you?",
"role": "assistant"
}
],
"source": "agent",
"user_id": "c3fb861af27a11efa69751e139332ced"
}
} Failure:
{
"code": 102,
"message": "Agent not found."
} Converse with agent POST /api/v1/agents/{agent_id}/completions
Asks a specified agent a question to start an AI-powered conversation.
:::tip NOTE
In streaming mode, not all responses include a reference, as this depends on the system's judgement.
In streaming mode, the last message is an empty message:
[DONE] You can optionally return step-by-step trace logs (see return_trace below).
:::
Request Method: POST URL: /api/v1/agents/{agent_id}/completions Headers: 'content-Type: application/json' 'Authorization: Bearer ' Body: "question": string "stream": boolean "session_id": string (optional) "inputs": object (optional) "user_id": string (optional) "return_trace": boolean (optional, default false) — include execution trace logs. Streaming events to handle When stream=true, the server sends Server-Sent Events (SSE). Clients should handle these event types:
message: streaming content from Message components. message_end: end of a Message component; may include reference/attachment. node_finished: a component finishes; data.inputs/outputs/error/elapsed_time describe the node result. If return_trace=true, the trace is attached inside the same node_finished event (data.trace). The stream terminates with [DONE].
:::info IMPORTANT You can include custom parameters in the request body, but first ensure they are defined in the Begin component. :::
Request example If the Begin component does not take parameters: curl --request POST
--url http://{address}/api/v1/agents/{agent_id}/completions \
--header 'Content-Type: application/json' \
--header 'Authorization: Bearer <YOUR_API_KEY>' \
--data-binary '
{
"question": "Hello",
"stream": false,
}'
If the Begin component takes parameters, include their values in the body of "inputs" as follows: curl --request POST
--url http://{address}/api/v1/agents/{agent_id}/completions \
--header 'Content-Type: application/json' \
--header 'Authorization: Bearer <YOUR_API_KEY>' \
--data-binary '
{
"question": "Hello",
"stream": false,
"inputs": {
"line_var": {
"type": "line",
"value": "I am line_var"
},
"int_var": {
"type": "integer",
"value": 1
},
"paragraph_var": {
"type": "paragraph",
"value": "a\nb\nc"
},
"option_var": {
"type": "options",
"value": "option 2"
},
"boolean_var": {
"type": "boolean",
"value": true
}
}
}'
The following code will execute the completion process
curl --request POST
--url http://{address}/api/v1/agents/{agent_id}/completions \
--header 'Content-Type: application/json' \
--header 'Authorization: Bearer <YOUR_API_KEY>' \
--data-binary '
{
"question": "Hello",
"stream": true,
"session_id": "cb2f385cb86211efa36e0242ac120005"
}'
Request Parameters agent_id: (Path parameter), string The ID of the associated agent. "question": (Body Parameter), string, Required The question to start an AI-powered conversation. "stream": (Body Parameter), boolean Indicates whether to output responses in a streaming way: true: Enable streaming (default). false: Disable streaming. "session_id": (Body Parameter) The ID of the session. If it is not provided, a new session will be generated. "inputs": (Body Parameter) Variables specified in the Begin component. "user_id": (Body parameter), string The optional user-defined ID. Valid only when no session_id is provided. :::tip NOTE For now, this method does not support a file type input/variable. As a workaround, use the following to upload a file to an agent: http://{address}/v1/canvas/upload/{agent_id} You will get a corresponding file ID from its response body. :::
Response success without session_id provided and with no variables specified in the Begin component:
Stream:
...
data: {
"event": "message",
"message_id": "cecdcb0e83dc11f0858253708ecb6573",
"created_at": 1756364483,
"task_id": "d1f79142831f11f09cc51795b9eb07c0",
"data": {
"content": " themes"
},
"session_id": "cd097ca083dc11f0858253708ecb6573"
}
data: {
"event": "message",
"message_id": "cecdcb0e83dc11f0858253708ecb6573",
"created_at": 1756364483,
"task_id": "d1f79142831f11f09cc51795b9eb07c0",
"data": {
"content": "."
},
"session_id": "cd097ca083dc11f0858253708ecb6573"
}
data: {
"event": "message_end",
"message_id": "cecdcb0e83dc11f0858253708ecb6573",
"created_at": 1756364483,
"task_id": "d1f79142831f11f09cc51795b9eb07c0",
"data": {
"reference": {
"chunks": {
"20": {
"id": "4b8935ac0a22deb1",
"content": "```cd /usr/ports/editors/neovim/ && make install```## Android[Termux](https://github.com/termux/termux-app) offers a Neovim package.",
"document_id": "4bdd2ff65e1511f0907f09f583941b45",
"document_name": "INSTALL22.md",
"dataset_id": "456ce60c5e1511f0907f09f583941b45",
"image_id": "",
"positions": [
[
12,
11,
11,
11,
11
]
],
"url": null,
"similarity": 0.5705525104787287,
"vector_similarity": 0.7351750337624289,
"term_similarity": 0.5000000005,
"doc_type": ""
}
},
"doc_aggs": {
"INSTALL22.md": {
"doc_name": "INSTALL22.md",
"doc_id": "4bdd2ff65e1511f0907f09f583941b45",
"count": 3
},
"INSTALL.md": {
"doc_name": "INSTALL.md",
"doc_id": "4bd7fdd85e1511f0907f09f583941b45",
"count": 2
},
"INSTALL(1).md": {
"doc_name": "INSTALL(1).md",
"doc_id": "4bdfb42e5e1511f0907f09f583941b45",
"count": 2
},
"INSTALL3.md": {
"doc_name": "INSTALL3.md",
"doc_id": "4bdab5825e1511f0907f09f583941b45",
"count": 1
}
}
}
},
"session_id": "cd097ca083dc11f0858253708ecb6573"
}
data: {
"event": "node_finished",
"message_id": "cecdcb0e83dc11f0858253708ecb6573",
"created_at": 1756364483,
"task_id": "d1f79142831f11f09cc51795b9eb07c0",
"data": {
"inputs": {
"sys.query": "how to install neovim?"
},
"outputs": {
"content": "xxxxxxx",
"_created_time": 15294.0382,
"_elapsed_time": 0.00017
},
"component_id": "Agent:EveryHairsChew",
"component_name": "Agent_1",
"component_type": "Agent",
"error": null,
"elapsed_time": 11.2091,
"created_at": 15294.0382,
"trace": [
{
"component_id": "begin",
"trace": [
{
"inputs": {},
"outputs": {
"_created_time": 15257.7949,
"_elapsed_time": 0.00070
},
"component_id": "begin",
"component_name": "begin",
"component_type": "Begin",
"error": null,
"elapsed_time": 0.00085,
"created_at": 15257.7949
}
]
},
{
"component_id": "Agent:WeakDragonsRead",
"trace": [
{
"inputs": {
"sys.query": "how to install neovim?"
},
"outputs": {
"content": "xxxxxxx",
"_created_time": 15257.7982,
"_elapsed_time": 36.2382
},
"component_id": "Agent:WeakDragonsRead",
"component_name": "Agent_0",
"component_type": "Agent",
"error": null,
"elapsed_time": 36.2385,
"created_at": 15257.7982
}
]
},
{
"component_id": "Agent:EveryHairsChew",
"trace": [
{
"inputs": {
"sys.query": "how to install neovim?"
},
"outputs": {
"content": "xxxxxxxxxxxxxxxxx",
"_created_time": 15294.0382,
"_elapsed_time": 0.00017
},
"component_id": "Agent:EveryHairsChew",
"component_name": "Agent_1",
"component_type": "Agent",
"error": null,
"elapsed_time": 11.2091,
"created_at": 15294.0382
}
]
}
]
},
"session_id": "cd097ca083dc11f0858253708ecb6573"
}
data:[DONE] Non-stream:
{
"code": 0,
"data": {
"created_at": 1756363177,
"data": {
"content": "\nTo install Neovim, the process varies depending on your operating system:\n\n### For macOS:\nUsing Homebrew:\n```bash\nbrew install neovim\n```\n\n### For Linux (Debian/Ubuntu):\n```bash\nsudo apt update\nsudo apt install neovim\n```\n\nFor other Linux distributions, you can use their respective package managers or build from source.\n\n### For Windows:\n1. Download the latest Windows installer from the official Neovim GitHub releases page\n2. Run the installer and follow the prompts\n3. Add Neovim to your PATH if not done automatically\n\n### From source (Unix-like systems):\n```bash\ngit clone https://github.com/neovim/neovim.git\ncd neovim\nmake CMAKE_BUILD_TYPE=Release\nsudo make install\n```\n\nAfter installation, you can verify it by running `nvim --version` in your terminal.",
"created_at": 18129.044975627,
"elapsed_time": 10.0157331670016,
"inputs": {
"var1": {
"value": "I am var1"
},
"var2": {
"value": "I am var2"
}
},
"outputs": {
"_created_time": 18129.502422278,
"_elapsed_time": 0.00013378599760471843,
"content": "\nTo install Neovim, the process varies depending on your operating system:\n\n### For macOS:\nUsing Homebrew:\n```bash\nbrew install neovim\n```\n\n### For Linux (Debian/Ubuntu):\n```bash\nsudo apt update\nsudo apt install neovim\n```\n\nFor other Linux distributions, you can use their respective package managers or build from source.\n\n### For Windows:\n1. Download the latest Windows installer from the official Neovim GitHub releases page\n2. Run the installer and follow the prompts\n3. Add Neovim to your PATH if not done automatically\n\n### From source (Unix-like systems):\n```bash\ngit clone https://github.com/neovim/neovim.git\ncd neovim\nmake CMAKE_BUILD_TYPE=Release\nsudo make install\n```\n\nAfter installation, you can verify it by running `nvim --version` in your terminal."
},
"reference": {
"chunks": {
"20": {
"content": "```cd /usr/ports/editors/neovim/ && make install```## Android[Termux](https://github.com/termux/termux-app) offers a Neovim package.",
"dataset_id": "456ce60c5e1511f0907f09f583941b45",
"doc_type": "",
"document_id": "4bdd2ff65e1511f0907f09f583941b45",
"document_name": "INSTALL22.md",
"id": "4b8935ac0a22deb1",
"image_id": "",
"positions": [
[
12,
11,
11,
11,
11
]
],
"similarity": 0.5705525104787287,
"term_similarity": 0.5000000005,
"url": null,
"vector_similarity": 0.7351750337624289
}
},
"doc_aggs": {
"INSTALL(1).md": {
"count": 2,
"doc_id": "4bdfb42e5e1511f0907f09f583941b45",
"doc_name": "INSTALL(1).md"
},
"INSTALL.md": {
"count": 2,
"doc_id": "4bd7fdd85e1511f0907f09f583941b45",
"doc_name": "INSTALL.md"
},
"INSTALL22.md": {
"count": 3,
"doc_id": "4bdd2ff65e1511f0907f09f583941b45",
"doc_name": "INSTALL22.md"
},
"INSTALL3.md": {
"count": 1,
"doc_id": "4bdab5825e1511f0907f09f583941b45",
"doc_name": "INSTALL3.md"
}
}
},
"trace": [
{
"component_id": "begin",
"trace": [
{
"component_id": "begin",
"component_name": "begin",
"component_type": "Begin",
"created_at": 15926.567517862,
"elapsed_time": 0.0008189299987861887,
"error": null,
"inputs": {},
"outputs": {
"_created_time": 15926.567517862,
"_elapsed_time": 0.0006958619997021742
}
}
]
},
{
"component_id": "Agent:WeakDragonsRead",
"trace": [
{
"component_id": "Agent:WeakDragonsRead",
"component_name": "Agent_0",
"component_type": "Agent",
"created_at": 15926.569121755,
"elapsed_time": 53.49016142000073,
"error": null,
"inputs": {
"sys.query": "how to install neovim?"
},
"outputs": {
"_created_time": 15926.569121755,
"_elapsed_time": 53.489981256001556,
"content": "xxxxxxxxxxxxxx",
"use_tools": [
{
"arguments": {
"query": "xxxx"
},
"name": "search_my_dateset",
"results": "xxxxxxxxxxx"
}
]
}
}
]
},
{
"component_id": "Agent:EveryHairsChew",
"trace": [
{
"component_id": "Agent:EveryHairsChew",
"component_name": "Agent_1",
"component_type": "Agent",
"created_at": 15980.060569101,
"elapsed_time": 23.61718057500002,
"error": null,
"inputs": {
"sys.query": "how to install neovim?"
},
"outputs": {
"_created_time": 15980.060569101,
"_elapsed_time": 0.0003451630000199657,
"content": "xxxxxxxxxxxx"
}
}
]
},
{
"component_id": "Message:SlickDingosHappen",
"trace": [
{
"component_id": "Message:SlickDingosHappen",
"component_name": "Message_0",
"component_type": "Message",
"created_at": 15980.061302513,
"elapsed_time": 23.61655923699982,
"error": null,
"inputs": {
"Agent:EveryHairsChew@content": "xxxxxxxxx",
"Agent:WeakDragonsRead@content": "xxxxxxxxxxx"
},
"outputs": {
"_created_time": 15980.061302513,
"_elapsed_time": 0.0006695749998471001,
"content": "xxxxxxxxxxx"
}
}
]
}
]
},
"event": "workflow_finished",
"message_id": "c4692a2683d911f0858253708ecb6573",
"session_id": "c39f6f9c83d911f0858253708ecb6573",
"task_id": "d1f79142831f11f09cc51795b9eb07c0"
}
} Success without session_id provided and with variables specified in the Begin component:
Stream:
data:{
"event": "message",
"message_id": "0e273472783711f0806e1a6272e682d8",
"created_at": 1755083830,
"task_id": "99ee29d6783511f09c921a6272e682d8",
"data": {
"content": "Hello"
},
"session_id": "0e0d1542783711f0806e1a6272e682d8"
}
data:{
"event": "message",
"message_id": "0e273472783711f0806e1a6272e682d8",
"created_at": 1755083830,
"task_id": "99ee29d6783511f09c921a6272e682d8",
"data": {
"content": "!"
},
"session_id": "0e0d1542783711f0806e1a6272e682d8"
}
data:{
"event": "message",
"message_id": "0e273472783711f0806e1a6272e682d8",
"created_at": 1755083830,
"task_id": "99ee29d6783511f09c921a6272e682d8",
"data": {
"content": " How"
},
"session_id": "0e0d1542783711f0806e1a6272e682d8"
}
...
data:[DONE] Non-stream:
{
"code": 0,
"data": {
"created_at": 1755083779,
"data": {
"created_at": 547400.868004651,
"elapsed_time": 3.5037803899031132,
"inputs": {
"boolean_var": {
"type": "boolean",
"value": true
},
"int_var": {
"type": "integer",
"value": 1
},
"line_var": {
"type": "line",
"value": "I am line_var"
},
"option_var": {
"type": "options",
"value": "option 2"
},
"paragraph_var": {
"type": "paragraph",
"value": "a\nb\nc"
}
},
"outputs": {
"_created_time": 547400.869271305,
"_elapsed_time": 0.0001251999055966735,
"content": "Hello there! How can I assist you today?"
}
},
"event": "workflow_finished",
"message_id": "effdad8c783611f089261a6272e682d8",
"session_id": "efe523b6783611f089261a6272e682d8",
"task_id": "99ee29d6783511f09c921a6272e682d8"
}
} Success with variables specified in the Begin component:
Stream:
data:{
"event": "message",
"message_id": "5b62e790783711f0bc531a6272e682d8",
"created_at": 1755083960,
"task_id": "99ee29d6783511f09c921a6272e682d8",
"data": {
"content": "Hello"
},
"session_id": "979e450c781d11f095cb729e3aa55728"
}
data:{
"event": "message",
"message_id": "5b62e790783711f0bc531a6272e682d8",
"created_at": 1755083960,
"task_id": "99ee29d6783511f09c921a6272e682d8",
"data": {
"content": "!"
},
"session_id": "979e450c781d11f095cb729e3aa55728"
}
data:{
"event": "message",
"message_id": "5b62e790783711f0bc531a6272e682d8",
"created_at": 1755083960,
"task_id": "99ee29d6783511f09c921a6272e682d8",
"data": {
"content": " You"
},
"session_id": "979e450c781d11f095cb729e3aa55728"
}
...
data:[DONE] Non-stream:
{
"code": 0,
"data": {
"created_at": 1755084029,
"data": {
"created_at": 547650.750818867,
"elapsed_time": 1.6227330720284954,
"inputs": {},
"outputs": {
"_created_time": 547650.752800839,
"_elapsed_time": 9.628792759031057e-05,
"content": "Hello! It appears you've sent another \"Hello\" without additional context. I'm here and ready to respond to any requests or questions you may have. Is there something specific you'd like to discuss or learn about?"
}
},
"event": "workflow_finished",
"message_id": "84eec534783711f08db41a6272e682d8",
"session_id": "979e450c781d11f095cb729e3aa55728",
"task_id": "99ee29d6783511f09c921a6272e682d8"
}
} Failure:
{
"code": 102,
"message": "`question` is required."
} List agent sessions GET /api/v1/agents/{agent_id}/sessions?page={page}&page_size={page_size}&orderby={orderby}&desc={desc}&id={session_id}&user_id={user_id}&dsl={dsl}
Lists sessions associated with a specified agent.
Request Method: GET URL: /api/v1/agents/{agent_id}/sessions?page={page}&page_size={page_size}&orderby={orderby}&desc={desc}&id={session_id} Headers: 'Authorization: Bearer ' Request example curl --request GET
--url http://{address}/api/v1/agents/{agent_id}/sessions?page={page}&page_size={page_size}&orderby={orderby}&desc={desc}&id={session_id}&user_id={user_id} \
--header 'Authorization: Bearer <YOUR_API_KEY>'
Request Parameters agent_id: (Path parameter) The ID of the associated agent. page: (Filter parameter), integer Specifies the page on which the sessions will be displayed. Defaults to 1. page_size: (Filter parameter), integer The number of sessions on each page. Defaults to 30. orderby: (Filter parameter), string The field by which sessions should be sorted. Available options: create_time (default) update_time desc: (Filter parameter), boolean Indicates whether the retrieved sessions should be sorted in descending order. Defaults to true. id: (Filter parameter), string The ID of the agent session to retrieve. user_id: (Filter parameter), string The optional user-defined ID passed in when creating session. dsl: (Filter parameter), boolean Indicates whether to include the dsl field of the sessions in the response. Defaults to true. Response Success:
{
"code": 0,
"data": [{
"agent_id": "e9e2b9c2b2f911ef801d0242ac120006",
"dsl": {
"answer": [],
"components": {
"Answer:OrangeTermsBurn": {
"downstream": [],
"obj": {
"component_name": "Answer",
"params": {}
},
"upstream": []
},
"Generate:SocialYearsRemain": {
"downstream": [],
"obj": {
"component_name": "Generate",
"params": {
"cite": true,
"frequency_penalty": 0.7,
"llm_id": "gpt-4o___OpenAI-API@OpenAI-API-Compatible",
"message_history_window_size": 12,
"parameters": [],
"presence_penalty": 0.4,
"prompt": "Please summarize the following paragraph. Pay attention to the numbers and do not make things up. The paragraph is as follows:\n{input}\nThis is what you need to summarize.",
"temperature": 0.1,
"top_p": 0.3
}
},
"upstream": []
},
"begin": {
"downstream": [],
"obj": {
"component_name": "Begin",
"params": {}
},
"upstream": []
}
},
"graph": {
"edges": [],
"nodes": [
{
"data": {
"label": "Begin",
"name": "begin"
},
"height": 44,
"id": "begin",
"position": {
"x": 50,
"y": 200
},
"sourcePosition": "left",
"targetPosition": "right",
"type": "beginNode",
"width": 200
},
{
"data": {
"form": {
"cite": true,
"frequencyPenaltyEnabled": true,
"frequency_penalty": 0.7,
"llm_id": "gpt-4o___OpenAI-API@OpenAI-API-Compatible",
"maxTokensEnabled": true,
"message_history_window_size": 12,
"parameters": [],
"presencePenaltyEnabled": true,
"presence_penalty": 0.4,
"prompt": "Please summarize the following paragraph. Pay attention to the numbers and do not make things up. The paragraph is as follows:\n{input}\nThis is what you need to summarize.",
"temperature": 0.1,
"temperatureEnabled": true,
"topPEnabled": true,
"top_p": 0.3
},
"label": "Generate",
"name": "Generate Answer_0"
},
"dragging": false,
"height": 105,
"id": "Generate:SocialYearsRemain",
"position": {
"x": 561.3457829707513,
"y": 178.7211182312641
},
"positionAbsolute": {
"x": 561.3457829707513,
"y": 178.7211182312641
},
"selected": true,
"sourcePosition": "right",
"targetPosition": "left",
"type": "generateNode",
"width": 200
},
{
"data": {
"form": {},
"label": "Answer",
"name": "Dialogue_0"
},
"height": 44,
"id": "Answer:OrangeTermsBurn",
"position": {
"x": 317.2368194777658,
"y": 218.30635555445093
},
"sourcePosition": "right",
"targetPosition": "left",
"type": "logicNode",
"width": 200
}
]
},
"history": [],
"messages": [],
"path": [],
"reference": []
},
"id": "792dde22b2fa11ef97550242ac120006",
"message": [
{
"content": "Hi! I'm your smart assistant. What can I do for you?",
"role": "assistant"
}
],
"source": "agent",
"user_id": ""
}]
} Failure:
{
"code": 102,
"message": "You don't own the agent ccd2f856b12311ef94ca0242ac1200052."
} Delete agent's sessions DELETE /api/v1/agents/{agent_id}/sessions
Deletes sessions of an agent by ID.
Request Method: DELETE URL: /api/v1/agents/{agent_id}/sessions Headers: 'content-Type: application/json' 'Authorization: Bearer ' Body: "ids": list[string] Request example curl --request DELETE
--url http://{address}/api/v1/agents/{agent_id}/sessions \
--header 'Content-Type: application/json' \
--header 'Authorization: Bearer <YOUR_API_KEY>' \
--data '
{
"ids": ["test_1", "test_2"]
}'
Request Parameters agent_id: (Path parameter) The ID of the associated agent. "ids": (Body Parameter), list[string] The IDs of the sessions to delete. If it is not specified, all sessions associated with the specified agent will be deleted. Response Success:
{
"code": 0
} Failure:
{
"code": 102,
"message": "The agent doesn't own the session cbd31e52f73911ef93b232903b842af6"
} Generate related questions POST /api/v1/sessions/related_questions
Generates five to ten alternative question strings from the user's original query to retrieve more relevant search results.
This operation requires a Bearer Login Token, which typically expires with in 24 hours. You can find it in the Request Headers in your browser easily as shown below:
Image
:::tip NOTE The chat model autonomously determines the number of questions to generate based on the instruction, typically between five and ten. :::
Request Method: POST URL: /api/v1/sessions/related_questions Headers: 'content-Type: application/json' 'Authorization: Bearer ' Body: "question": string "industry": string Request example curl --request POST
--url http://{address}/api/v1/sessions/related_questions \
--header 'Content-Type: application/json' \
--header 'Authorization: Bearer <YOUR_LOGIN_TOKEN>' \
--data '
{
"question": "What are the key advantages of Neovim over Vim?",
"industry": "software_development"
}'
Request Parameters "question": (Body Parameter), string The original user question. "industry": (Body Parameter), string Industry of the question. Response Success:
{
"code": 0,
"data": [
"What makes Neovim superior to Vim in terms of features?",
"How do the benefits of Neovim compare to those of Vim?",
"What advantages does Neovim offer that are not present in Vim?",
"In what ways does Neovim outperform Vim in functionality?",
"What are the most significant improvements in Neovim compared to Vim?",
"What unique advantages does Neovim bring to the table over Vim?",
"How does the user experience in Neovim differ from Vim in terms of benefits?",
"What are the top reasons to switch from Vim to Neovim?",
"What features of Neovim are considered more advanced than those in Vim?"
],
"message": "success"
} Failure:
{
"code": 401,
"data": null,
"message": "<Unauthorized '401: Unauthorized'>"
} AGENT MANAGEMENT List agents GET /api/v1/agents?page={page}&page_size={page_size}&orderby={orderby}&desc={desc}&name={agent_name}&id={agent_id}
Lists agents.
Request Method: GET URL: /api/v1/agents?page={page}&page_size={page_size}&orderby={orderby}&desc={desc}&title={agent_name}&id={agent_id} Headers: 'Authorization: Bearer ' Request example curl --request GET
--url http://{address}/api/v1/agents?page={page}&page_size={page_size}&orderby={orderby}&desc={desc}&title={agent_name}&id={agent_id} \
--header 'Authorization: Bearer <YOUR_API_KEY>'
Request parameters page: (Filter parameter), integer Specifies the page on which the agents will be displayed. Defaults to 1. page_size: (Filter parameter), integer The number of agents on each page. Defaults to 30. orderby: (Filter parameter), string The attribute by which the results are sorted. Available options: create_time (default) update_time desc: (Filter parameter), boolean Indicates whether the retrieved agents should be sorted in descending order. Defaults to true. id: (Filter parameter), string The ID of the agent to retrieve. title: (Filter parameter), string The name of the agent to retrieve. Response Success:
{
"code": 0,
"data": [
{
"avatar": null,
"canvas_type": null,
"create_date": "Thu, 05 Dec 2024 19:10:36 GMT",
"create_time": 1733397036424,
"description": null,
"dsl": {
"answer": [],
"components": {
"begin": {
"downstream": [],
"obj": {
"component_name": "Begin",
"params": {}
},
"upstream": []
}
},
"graph": {
"edges": [],
"nodes": [
{
"data": {
"label": "Begin",
"name": "begin"
},
"height": 44,
"id": "begin",
"position": {
"x": 50,
"y": 200
},
"sourcePosition": "left",
"targetPosition": "right",
"type": "beginNode",
"width": 200
}
]
},
"history": [],
"messages": [],
"path": [],
"reference": []
},
"id": "8d9ca0e2b2f911ef9ca20242ac120006",
"title": "123465",
"update_date": "Thu, 05 Dec 2024 19:10:56 GMT",
"update_time": 1733397056801,
"user_id": "69736c5e723611efb51b0242ac120007"
}
]
} Failure:
{
"code": 102,
"message": "The agent doesn't exist."
} Create agent POST /api/v1/agents
Create an agent.
Request Method: POST URL: /api/v1/agents Headers: 'Content-Type: application/json 'Authorization: Bearer ' Body: "title": string "description": string "dsl": object Request example curl --request POST
--url http://{address}/api/v1/agents \
--header 'Content-Type: application/json' \
--header 'Authorization: Bearer <YOUR_API_KEY>' \
--data '{
"title": "Test Agent",
"description": "A test agent",
"dsl": {
// ... Canvas DSL here ...
}
}'
Request parameters title: (Body parameter), string, Required The title of the agent. description: (Body parameter), string The description of the agent. Defaults to None. dsl: (Body parameter), object, Required The canvas DSL object of the agent. Response Success:
{
"code": 0,
"data": true,
"message": "success"
} Failure:
{
"code": 102,
"message": "Agent with title test already exists."
} Update agent PUT /api/v1/agents/{agent_id}
Update an agent by id.
Request Method: PUT URL: /api/v1/agents/{agent_id} Headers: 'Content-Type: application/json 'Authorization: Bearer ' Body: "title": string "description": string "dsl": object Request example curl --request PUT
--url http://{address}/api/v1/agents/58af890a2a8911f0a71a11b922ed82d6 \
--header 'Content-Type: application/json' \
--header 'Authorization: Bearer <YOUR_API_KEY>' \
--data '{
"title": "Test Agent",
"description": "A test agent",
"dsl": {
// ... Canvas DSL here ...
}
}'
Request parameters agent_id: (Path parameter), string The id of the agent to be updated. title: (Body parameter), string The title of the agent. description: (Body parameter), string The description of the agent. dsl: (Body parameter), object The canvas DSL object of the agent. Only specify the parameter you want to change in the request body. If a parameter does not exist or is None, it won't be updated.
Response Success:
{
"code": 0,
"data": true,
"message": "success"
} Failure:
{
"code": 103,
"message": "Only owner of canvas authorized for this operation."
} Delete agent DELETE /api/v1/agents/{agent_id}
Delete an agent by id.
Request Method: DELETE URL: /api/v1/agents/{agent_id} Headers: 'Content-Type: application/json 'Authorization: Bearer ' Request example curl --request DELETE
--url http://{address}/api/v1/agents/58af890a2a8911f0a71a11b922ed82d6 \
--header 'Content-Type: application/json' \
--header 'Authorization: Bearer <YOUR_API_KEY>' \
--data '{}'
Request parameters agent_id: (Path parameter), string The id of the agent to be deleted. Response Success:
{
"code": 0,
"data": true,
"message": "success"
} Failure:
{
"code": 103,
"message": "Only owner of canvas authorized for this operation."
} System Check system health GET /v1/system/healthz
Check the health status of RAGFlow’s dependencies (database, Redis, document engine, object storage).
Request Method: GET URL: /v1/system/healthz Headers: 'Content-Type: application/json' (no Authorization required) Request example curl --request GET
--url http://{address}/v1/system/healthz
--header 'Content-Type: application/json'
Request parameters address: (Path parameter), string The host and port of the backend service (e.g., localhost:7897). Responses 200 OK – All services healthy HTTP/1.1 200 OK Content-Type: application/json
{ "db": "ok", "redis": "ok", "doc_engine": "ok", "storage": "ok", "status": "ok" } 500 Internal Server Error – At least one service unhealthy HTTP/1.1 500 INTERNAL SERVER ERROR Content-Type: application/json
{ "db": "ok", "redis": "nok", "doc_engine": "ok", "storage": "ok", "status": "nok", "_meta": {
"redis": {
"elapsed": "5.2",
"error": "Lost connection!"
}
} } Explanation:
Each service is reported as "ok" or "nok". The top-level status reflects overall health. If any service is "nok", detailed error info appears in _meta. FILE MANAGEMENT Upload file POST /api/v1/file/upload
Uploads one or multiple files to the system.
Request Method: POST URL: /api/v1/file/upload Headers: 'Content-Type: multipart/form-data' 'Authorization: Bearer ' Form: 'file=@{FILE_PATH}' 'parent_id': string (optional) Request example curl --request POST
--url http://{address}/api/v1/file/upload \
--header 'Content-Type: multipart/form-data' \
--header 'Authorization: Bearer <YOUR_API_KEY>' \
--form 'file=@./test1.txt' \
--form 'file=@./test2.pdf' \
--form 'parent_id={folder_id}'
Request parameters 'file': (Form parameter), file, Required The file(s) to upload. Multiple files can be uploaded in a single request. 'parent_id': (Form parameter), string The parent folder ID where the file will be uploaded. If not specified, files will be uploaded to the root folder. Response Success:
{
"code": 0,
"data": [
{
"id": "b330ec2e91ec11efbc510242ac120004",
"name": "test1.txt",
"size": 17966,
"type": "doc",
"parent_id": "527fa74891e811ef9c650242ac120006",
"location": "test1.txt",
"create_time": 1729763127646
}
]
} Failure:
{
"code": 400,
"message": "No file part!"
} Create file or folder POST /api/v1/file/create
Creates a new file or folder in the system.
Request Method: POST URL: /api/v1/file/create Headers: 'Content-Type: application/json' 'Authorization: Bearer ' Body: "name": string "parent_id": string (optional) "type": string Request example curl --request POST
--url http://{address}/api/v1/file/create \
--header 'Content-Type: application/json' \
--header 'Authorization: Bearer <YOUR_API_KEY>' \
--data '{
"name": "New Folder",
"type": "FOLDER",
"parent_id": "{folder_id}"
}'
Request parameters "name": (Body parameter), string, Required The name of the file or folder to create. "parent_id": (Body parameter), string The parent folder ID. If not specified, the file/folder will be created in the root folder. "type": (Body parameter), string The type of the file to create. Available options: "FOLDER": Create a folder "VIRTUAL": Create a virtual file Response Success:
{
"code": 0,
"data": {
"id": "b330ec2e91ec11efbc510242ac120004",
"name": "New Folder",
"type": "FOLDER",
"parent_id": "527fa74891e811ef9c650242ac120006",
"size": 0,
"create_time": 1729763127646
}
} Failure:
{
"code": 409,
"message": "Duplicated folder name in the same folder."
} List files GET /api/v1/file/list?parent_id={parent_id}&keywords={keywords}&page={page}&page_size={page_size}&orderby={orderby}&desc={desc}
Lists files and folders under a specific folder.
Request Method: GET URL: /api/v1/file/list?parent_id={parent_id}&keywords={keywords}&page={page}&page_size={page_size}&orderby={orderby}&desc={desc} Headers: 'Authorization: Bearer ' Request example curl --request GET
--url 'http://{address}/api/v1/file/list?parent_id={folder_id}&page=1&page_size=15' \
--header 'Authorization: Bearer <YOUR_API_KEY>'
Request parameters parent_id: (Filter parameter), string The folder ID to list files from. If not specified, the root folder is used by default. keywords: (Filter parameter), string Search keyword to filter files by name. page: (Filter parameter), integer Specifies the page on which the files will be displayed. Defaults to 1. page_size: (Filter parameter), integer The number of files on each page. Defaults to 15. orderby: (Filter parameter), string The field by which files should be sorted. Available options: create_time (default) desc: (Filter parameter), boolean Indicates whether the retrieved files should be sorted in descending order. Defaults to true. Response Success:
{
"code": 0,
"data": {
"total": 10,
"files": [
{
"id": "b330ec2e91ec11efbc510242ac120004",
"name": "test1.txt",
"type": "doc",
"size": 17966,
"parent_id": "527fa74891e811ef9c650242ac120006",
"create_time": 1729763127646
}
],
"parent_folder": {
"id": "527fa74891e811ef9c650242ac120006",
"name": "Parent Folder"
}
}
} Failure:
{
"code": 404,
"message": "Folder not found!"
} Get root folder GET /api/v1/file/root_folder
Retrieves the user's root folder information.
Request Method: GET URL: /api/v1/file/root_folder Headers: 'Authorization: Bearer ' Request example curl --request GET
--url http://{address}/api/v1/file/root_folder \
--header 'Authorization: Bearer <YOUR_API_KEY>'
Request parameters No parameters required.
Response Success:
{
"code": 0,
"data": {
"root_folder": {
"id": "527fa74891e811ef9c650242ac120006",
"name": "root",
"type": "FOLDER"
}
}
} Get parent folder GET /api/v1/file/parent_folder?file_id={file_id}
Retrieves the immediate parent folder information of a specified file.
Request Method: GET URL: /api/v1/file/parent_folder?file_id={file_id} Headers: 'Authorization: Bearer ' Request example curl --request GET
--url 'http://{address}/api/v1/file/parent_folder?file_id={file_id}' \
--header 'Authorization: Bearer <YOUR_API_KEY>'
Request parameters file_id: (Filter parameter), string, Required The ID of the file whose immediate parent folder to retrieve. Response Success:
{
"code": 0,
"data": {
"parent_folder": {
"id": "527fa74891e811ef9c650242ac120006",
"name": "Parent Folder"
}
}
} Failure:
{
"code": 404,
"message": "Folder not found!"
} Get all parent folders GET /api/v1/file/all_parent_folder?file_id={file_id}
Retrieves all parent folders of a specified file in the folder hierarchy.
Request Method: GET URL: /api/v1/file/all_parent_folder?file_id={file_id} Headers: 'Authorization: Bearer ' Request example curl --request GET
--url 'http://{address}/api/v1/file/all_parent_folder?file_id={file_id}' \
--header 'Authorization: Bearer <YOUR_API_KEY>'
Request parameters file_id: (Filter parameter), string, Required The ID of the file whose parent folders to retrieve. Response Success:
{
"code": 0,
"data": {
"parent_folders": [
{
"id": "527fa74891e811ef9c650242ac120006",
"name": "Parent Folder 1"
},
{
"id": "627fa74891e811ef9c650242ac120007",
"name": "Parent Folder 2"
}
]
}
} Failure:
{
"code": 404,
"message": "Folder not found!"
} Delete files POST /api/v1/file/rm
Deletes one or multiple files or folders.
Request Method: POST URL: /api/v1/file/rm Headers: 'Content-Type: application/json' 'Authorization: Bearer ' Body: "file_ids": list[string] Request example curl --request POST
--url http://{address}/api/v1/file/rm \
--header 'Content-Type: application/json' \
--header 'Authorization: Bearer <YOUR_API_KEY>' \
--data '{
"file_ids": ["file_id_1", "file_id_2"]
}'
Request parameters "file_ids": (Body parameter), list[string], Required The IDs of the files or folders to delete. Response Success:
{
"code": 0,
"data": true
} Failure:
{
"code": 404,
"message": "File or Folder not found!"
} Rename file POST /api/v1/file/rename
Renames a file or folder.
Request Method: POST URL: /api/v1/file/rename Headers: 'Content-Type: application/json' 'Authorization: Bearer ' Body: "file_id": string "name": string Request example curl --request POST
--url http://{address}/api/v1/file/rename \
--header 'Content-Type: application/json' \
--header 'Authorization: Bearer <YOUR_API_KEY>' \
--data '{
"file_id": "{file_id}",
"name": "new_name.txt"
}'
Request parameters "file_id": (Body parameter), string, Required The ID of the file or folder to rename. "name": (Body parameter), string, Required The new name for the file or folder. Note: Changing file extensions is not supported. Response Success:
{
"code": 0,
"data": true
} Failure:
{
"code": 400,
"message": "The extension of file can't be changed"
} or
{
"code": 409,
"message": "Duplicated file name in the same folder."
} Download file GET /api/v1/file/get/{file_id}
Downloads a file from the system.
Request Method: GET URL: /api/v1/file/get/{file_id} Headers: 'Authorization: Bearer ' Request example curl --request GET
--url http://{address}/api/v1/file/get/{file_id} \
--header 'Authorization: Bearer <YOUR_API_KEY>' \
--output ./downloaded_file.txt
Request parameters file_id: (Path parameter), string, Required The ID of the file to download. Response Success:
Returns the file content as a binary stream with appropriate Content-Type headers.
Failure:
{
"code": 404,
"message": "Document not found!"
} Move files POST /api/v1/file/mv
Moves one or multiple files or folders to a specified folder.
Request Method: POST URL: /api/v1/file/mv Headers: 'Content-Type: application/json' 'Authorization: Bearer ' Body: "src_file_ids": list[string] "dest_file_id": string Request example curl --request POST
--url http://{address}/api/v1/file/mv \
--header 'Content-Type: application/json' \
--header 'Authorization: Bearer <YOUR_API_KEY>' \
--data '{
"src_file_ids": ["file_id_1", "file_id_2"],
"dest_file_id": "{destination_folder_id}"
}'
Request parameters "src_file_ids": (Body parameter), list[string], Required The IDs of the files or folders to move. "dest_file_id": (Body parameter), string, Required The ID of the destination folder. Response Success:
{
"code": 0,
"data": true
} Failure:
{
"code": 404,
"message": "File or Folder not found!"
} or
{
"code": 404,
"message": "Parent Folder not found!"
} Convert files to documents and link them to datasets POST /api/v1/file/convert
Converts files to documents and links them to specified datasets.
Request Method: POST URL: /api/v1/file/convert Headers: 'Content-Type: application/json' 'Authorization: Bearer ' Body: "file_ids": list[string] "kb_ids": list[string] Request example curl --request POST
--url http://{address}/api/v1/file/convert \
--header 'Content-Type: application/json' \
--header 'Authorization: Bearer <YOUR_API_KEY>' \
--data '{
"file_ids": ["file_id_1", "file_id_2"],
"kb_ids": ["dataset_id_1", "dataset_id_2"]
}'
Request parameters "file_ids": (Body parameter), list[string], Required The IDs of the files to convert. If a folder ID is provided, all files within that folder will be converted. "kb_ids": (Body parameter), list[string], Required The IDs of the target datasets. Response Success:
{
"code": 0,
"data": [
{
"id": "file2doc_id_1",
"file_id": "file_id_1",
"document_id": "document_id_1"
}
]
} Failure:
{
"code": 404,
"message": "File not found!"
} or
{
"code": 404,
"message": "Can't find this dataset!"
}