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HTTP API

RAGFlow RESTful API 的完整参考。在继续之前，请确保您已准备好 RAGFlow API 密钥进行身份验证。

错误代码​

代码消息描述
400Bad Request无效的请求参数
401Unauthorized未授权访问
403Forbidden访问被拒绝
404Not Found资源未找到
500Internal Server Error服务器内部错误
1001Invalid Chunk ID无效的块 ID
1002Chunk Update Failed块更新失败

OpenAI 兼容 API​

创建聊天完成​

POST /api/v1/chats_openai/{chat_id}/chat/completions

为给定的聊天对话创建模型响应。

此 API 遵循与 OpenAI API 相同的请求和响应格式。它允许您以类似于使用 OpenAI API 的方式与模型交互。

请求​

  • 方法：POST
  • URL：/api/v1/chats_openai/{chat_id}/chat/completions
  • 请求头：
    • 'content-Type: application/json'
    • 'Authorization: Bearer <YOUR_API_KEY>'
  • 请求体：
    • "model"：string
    • "messages"：object list
    • "stream"：boolean
请求示例​
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
      }'
请求参数​

  • model（Body 参数）string，必填
    用于生成响应的模型。服务器将自动解析此参数，因此您现在可以将其设置为任何值。

  • messages（Body 参数）list[object]，必填
    用于生成响应的历史聊天消息列表。这必须包含至少一条具有 user 角色的消息。

  • stream（Body 参数）boolean
    是否以流的形式接收响应。如果您希望一次性接收完整响应而不是流，请明确设置为 false。

响应​

流式：

{
    "id": "chatcmpl-3a9c3572f29311efa69751e139332ced",
    "choices": [
        {
            "delta": {
                "content": "This is a test. If you have any specific questions or need information, feel",
                "role": "assistant",
                "function_call": null,
                "tool_calls": null
            },
            "finish_reason": null,
            "index": 0,
            "logprobs": null
        }
    ],
    "created": 1740543996,
    "model": "model",
    "object": "chat.completion.chunk",
    "system_fingerprint": "",
    "usage": null
}
// omit duplicated information
{"choices":[{"delta":{"content":" free to ask, and I will do my best to provide an answer based on","role":"assistant"}}]}
{"choices":[{"delta":{"content":" the knowledge I have. If your question is unrelated to the provided knowledge base,","role":"assistant"}}]}
{"choices":[{"delta":{"content":" I will let you know.","role":"assistant"}}]}
// the last chunk
{
    "id": "chatcmpl-3a9c3572f29311efa69751e139332ced",
    "choices": [
        {
            "delta": {
                "content": null,
                "role": "assistant",
                "function_call": null,
                "tool_calls": null
            },
            "finish_reason": "stop",
            "index": 0,
            "logprobs": null
        }
    ],
    "created": 1740543996,
    "model": "model",
    "object": "chat.completion.chunk",
    "system_fingerprint": "",
    "usage": {
        "prompt_tokens": 18,
        "completion_tokens": 225,
        "total_tokens": 243
    }
}

非流式：

{
    "choices":[
        {
            "finish_reason":"stop",
            "index":0,
            "logprobs":null,
            "message":{
                "content":"This is a test. If you have any specific questions or need information, feel free to ask, and I will do my best to provide an answer based on the knowledge I have. If your question is unrelated to the provided knowledge base, I will let you know.",
                "role":"assistant"
            }
        }
    ],
    "created":1740543499,
    "id":"chatcmpl-3a9c3572f29311efa69751e139332ced",
    "model":"model",
    "object":"chat.completion",
    "usage":{
        "completion_tokens":246,
        "completion_tokens_details":{
            "accepted_prediction_tokens":246,
            "reasoning_tokens":18,
            "rejected_prediction_tokens":0
        },
        "prompt_tokens":18,
        "total_tokens":264
    }
}

失败：

{
  "code": 102,
  "message": "The last content of this conversation is not from user."
}

创建智能体完成​

POST /api/v1/agents_openai/{agent_id}/chat/completions

为给定的聊天对话创建模型响应。

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 <YOUR_API_KEY>'
  • 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.

Response​

Stream:

{
    "id": "chatcmpl-3a9c3572f29311efa69751e139332ced",
    "choices": [
        {
            "delta": {
                "content": "This is a test. If you have any specific questions or need information, feel",
                "role": "assistant",
                "function_call": null,
                "tool_calls": null
            },
            "finish_reason": null,
            "index": 0,
            "logprobs": null
        }
    ],
    "created": 1740543996,
    "model": "model",
    "object": "chat.completion.chunk",
    "system_fingerprint": "",
    "usage": null
}
// omit duplicated information
{"choices":[{"delta":{"content":" free to ask, and I will do my best to provide an answer based on","role":"assistant"}}]}
{"choices":[{"delta":{"content":" the knowledge I have. If your question is unrelated to the provided knowledge base,","role":"assistant"}}]}
{"choices":[{"delta":{"content":" I will let you know.","role":"assistant"}}]}
// the last chunk
{
    "id": "chatcmpl-3a9c3572f29311efa69751e139332ced",
    "choices": [
        {
            "delta": {
                "content": null,
                "role": "assistant",
                "function_call": null,
                "tool_calls": null
            },
            "finish_reason": "stop",
            "index": 0,
            "logprobs": null
        }
    ],
    "created": 1740543996,
    "model": "model",
    "object": "chat.completion.chunk",
    "system_fingerprint": "",
    "usage": {
        "prompt_tokens": 18,
        "completion_tokens": 225,
        "total_tokens": 243
    }
}

非流式：

{
    "choices":[
        {
            "finish_reason":"stop",
            "index":0,
            "logprobs":null,
            "message":{
                "content":"This is a test. If you have any specific questions or need information, feel free to ask, and I will do my best to provide an answer based on the knowledge I have. If your question is unrelated to the provided knowledge base, I will let you know.",
                "role":"assistant"
            }
        }
    ],
    "created":1740543499,
    "id":"chatcmpl-3a9c3572f29311efa69751e139332ced",
    "model":"model",
    "object":"chat.completion",
    "usage":{
        "completion_tokens":246,
        "completion_tokens_details":{
            "accepted_prediction_tokens":246,
            "reasoning_tokens":18,
            "rejected_prediction_tokens":0
        },
        "prompt_tokens":18,
        "total_tokens":264
    }
}

失败：

{
  "code": 102,
  "message": "The last content of this conversation is not from user."
}

数据集管理​

创建数据集​

POST /api/v1/datasets

创建数据集。

Request​

  • Method: POST
  • URL: /api/v1/datasets
  • Headers:
    • 'content-Type: application/json'
    • 'Authorization: Bearer <YOUR_API_KEY>'
  • Body:
    • "name": string
    • "avatar": string
    • "description": string
    • "embedding_model": string
    • "permission": string
    • "chunk_method": string
    • "parser_config": object
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"
      }'
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<string>
    The chunking method of the dataset to create. 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<string> refer to Use tag set
        • Must include a list of dataset IDs, where each dataset is parsed using the ​​Tag Chunk 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​

成功：

{
    "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,
    },
}

失败：

{
    "code": 101,
    "message": "Dataset name 'RAGFlow example' already exists"
}

删除数据集​

DELETE /api/v1/datasets

按 ID 删除数据集。

Request​

  • Method: DELETE
  • URL: /api/v1/datasets
  • Headers:
    • 'content-Type: application/json'
    • 'Authorization: Bearer <YOUR_API_KEY>'
    • 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​

成功：

{
    "code": 0 
}

失败：

{
    "code": 102,
    "message": "You don't own the dataset."
}

更新数据集​

PUT /api/v1/datasets/{dataset_id}

更新指定数据集的配置。

Request​

  • Method: PUT
  • URL: /api/v1/datasets/{dataset_id}
  • Headers:
    • 'content-Type: application/json'
    • 'Authorization: Bearer <YOUR_API_KEY>'
  • 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<string>
    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<string> refer to Use tag set
        • Must include a list of dataset IDs, where each dataset is parsed using the ​​Tag Chunk 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​

成功：

{
    "code": 0 
}

失败：

{
    "code": 102,
    "message": "Can't change tenant_id."
}

列出数据集​

GET /api/v1/datasets?page={page}&page_size={page_size}&orderby={orderby}&desc={desc}&name={dataset_name}&id={dataset_id}

列出数据集。

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 <YOUR_API_KEY>'
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​

成功：

{
    "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
        }
    ]
}

失败：

{
    "code": 102,
    "message": "The dataset doesn't exist"
}

获取数据集的知识图谱​

GET /api/v1/datasets/{dataset_id}/knowledge_graph

检索指定数据集的知识图谱。

Request​

  • Method: GET
  • URL: /api/v1/datasets/{dataset_id}/knowledge_graph
  • Headers:
    • 'Authorization: Bearer <YOUR_API_KEY>'
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​

成功：

{
    "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": {}
    }
}

失败：

{
    "code": 102,
    "message": "The dataset doesn't exist"
}

删除数据集的知识图谱​

DELETE /api/v1/datasets/{dataset_id}/knowledge_graph

删除指定数据集的知识图谱。

Request​

  • Method: DELETE
  • URL: /api/v1/datasets/{dataset_id}/knowledge_graph
  • Headers:
    • 'Authorization: Bearer <YOUR_API_KEY>'
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​

成功：

{
    "code": 0,
    "data": true
}

失败：

{
    "code": 102,
    "message": "The dataset doesn't exist"
}

数据集内的文件管理​

上传文档​

POST /api/v1/datasets/{dataset_id}/documents

向指定数据集上传文档。

Request​

  • Method: POST
  • URL: /api/v1/datasets/{dataset_id}/documents
  • Headers:
    • 'Content-Type: multipart/form-data'
    • 'Authorization: Bearer <YOUR_API_KEY>'
  • 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​

成功：

{
    "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"
        }
    ]
}

失败：

{
    "code": 101,
    "message": "No file part!"
}

更新文档​

PUT /api/v1/datasets/{dataset_id}/documents/{document_id}

更新指定文档的配置。

Request​

  • Method: PUT
  • URL: /api/v1/datasets/{dataset_id}/documents/{document_id}
  • Headers:
    • 'content-Type: application/json'
    • 'Authorization: Bearer <YOUR_API_KEY>'
  • 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}/info/{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.

Response​

成功：

{
    "code": 0
}

失败：

{
    "code": 102,
    "message": "The dataset does not have the document."
}

下载文档​

GET /api/v1/datasets/{dataset_id}/documents/{document_id}

从指定数据集下载文档。

Request​

  • Method: GET
  • URL: /api/v1/datasets/{dataset_id}/documents/{document_id}
  • Headers:
    • 'Authorization: Bearer <YOUR_API_KEY>'
  • 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​

成功：

This is a test to verify the file download feature.

失败：

{
    "code": 102,
    "message": "You do not own the dataset 7898da028a0511efbf750242ac1220005."
}

列出文档​

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}

列出指定数据集中的文档。

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}
  • Headers:
    • 'content-Type: application/json'
    • 'Authorization: Bearer <YOUR_API_KEY>'
Request example​
curl --request GET \
     --url http://{address}/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} \
     --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.

Response​

成功：

{
    "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": "0",
                "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": 1
    }
}

失败：

{
    "code": 102,
    "message": "You don't own the dataset 7898da028a0511efbf750242ac1220005. "
}

删除文档​

DELETE /api/v1/datasets/{dataset_id}/documents

按 ID 删除文档。

Request​

  • Method: DELETE
  • URL: /api/v1/datasets/{dataset_id}/documents
  • Headers:
    • 'Content-Type: application/json'
    • 'Authorization: Bearer <YOUR_API_KEY>'
  • 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​

成功：

{
    "code": 0
}.

失败：

{
    "code": 102,
    "message": "You do not own the dataset 7898da028a0511efbf750242ac1220005."
}

解析文档​

POST /api/v1/datasets/{dataset_id}/chunks

解析指定数据集中的文档。

Request​

  • Method: POST
  • URL: /api/v1/datasets/{dataset_id}/chunks
  • Headers:
    • 'content-Type: application/json'
    • 'Authorization: Bearer <YOUR_API_KEY>'
  • 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​

成功：

{
    "code": 0
}

失败：

{
    "code": 102,
    "message": "`document_ids` is required"
}

停止解析文档​

DELETE /api/v1/datasets/{dataset_id}/chunks

停止解析指定文档。

Request​

  • Method: DELETE
  • URL: /api/v1/datasets/{dataset_id}/chunks
  • Headers:
    • 'content-Type: application/json'
    • 'Authorization: Bearer <YOUR_API_KEY>'
  • 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​

成功：

{
    "code": 0
}

失败：

{
    "code": 102,
    "message": "`document_ids` is required"
}

数据集内的块管理​

添加块​

POST /api/v1/datasets/{dataset_id}/documents/{document_id}/chunks

向指定数据集的指定文档添加块。

Request​

  • Method: POST
  • URL: /api/v1/datasets/{dataset_id}/documents/{document_id}/chunks
  • Headers:
    • 'content-Type: application/json'
    • 'Authorization: Bearer <YOUR_API_KEY>'
  • 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​

成功：

{
    "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": []
        }
    }
}

失败：

{
    "code": 102,
    "message": "`content` is required"
}

列出块​

GET /api/v1/datasets/{dataset_id}/documents/{document_id}/chunks?keywords={keywords}&page={page}&page_size={page_size}&id={id}

列出指定文档中的块。

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 <YOUR_API_KEY>'
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​

成功：

{
    "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
    }
}

失败：

{
    "code": 102,
    "message": "You don't own the document 5c5999ec7be811ef9cab0242ac12000e5."
}

删除块​

DELETE /api/v1/datasets/{dataset_id}/documents/{document_id}/chunks

按 ID 删除块。

Request​

  • Method: DELETE
  • URL: /api/v1/datasets/{dataset_id}/documents/{document_id}/chunks
  • Headers:
    • 'content-Type: application/json'
    • 'Authorization: Bearer <YOUR_API_KEY>'
  • 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​

成功：

{
    "code": 0
}

失败：

{
    "code": 102,
    "message": "`chunk_ids` is required"
}

更新块​

PUT /api/v1/datasets/{dataset_id}/documents/{document_id}/chunks/{chunk_id}

更新指定块的内容或配置。

Request​

  • Method: PUT
  • URL: /api/v1/datasets/{dataset_id}/documents/{document_id}/chunks/{chunk_id}
  • Headers:
    • 'content-Type: application/json'
    • 'Authorization: Bearer <YOUR_API_KEY>'
  • 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​

成功：

{
    "code": 0
}

失败：

{
    "code": 102,
    "message": "Can't find this chunk 29a2d9987e16ba331fb4d7d30d99b71d2"
}

检索块​

POST /api/v1/retrieval

从指定数据集检索块。

Request​

  • Method: POST
  • URL: /api/v1/retrieval
  • Headers:
    • 'content-Type: application/json'
    • 'Authorization: Bearer <YOUR_API_KEY>'
  • 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]
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"]
     }'
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.
  • "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.

Response​

成功：

{
    "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
    }
}

失败：

{
    "code": 102,
    "message": "`datasets` is required."
}

聊天助手管理​

创建聊天助手​

POST /api/v1/chats

创建聊天助手。

Request​

  • Method: POST
  • URL: /api/v1/chats
  • Headers:
    • 'content-Type: application/json'
    • 'Authorization: Bearer <YOUR_API_KEY>'
  • 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.
    • "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​

成功：

{
    "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
    }
}

失败：

{
    "code": 102,
    "message": "Duplicated chat name in creating dataset."
}

更新聊天助手​

PUT /api/v1/chats/{chat_id}

更新指定聊天助手的配置。

Request​

  • Method: PUT
  • URL: /api/v1/chats/{chat_id}
  • Headers:
    • 'content-Type: application/json'
    • 'Authorization: Bearer <YOUR_API_KEY>'
  • 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​

成功：

{
    "code": 0
}

失败：

{
    "code": 102,
    "message": "Duplicated chat name in updating dataset."
}

删除聊天助手​

DELETE /api/v1/chats

按 ID 删除聊天助手。

Request​

  • Method: DELETE
  • URL: /api/v1/chats
  • Headers:
    • 'content-Type: application/json'
    • 'Authorization: Bearer <YOUR_API_KEY>'
  • 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​

成功：

{
    "code": 0
}

失败：

{
    "code": 102,
    "message": "ids are required"
}

列出聊天助手​

GET /api/v1/chats?page={page}&page_size={page_size}&orderby={orderby}&desc={desc}&name={chat_name}&id={chat_id}

列出聊天助手。

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 <YOUR_API_KEY>'
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​

成功：

{
    "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
        }
    ]
}

失败：

{
    "code": 102,
    "message": "The chat doesn't exist"
}

会话管理​

与聊天助手创建会话​

POST /api/v1/chats/{chat_id}/sessions

与聊天助手创建会话。

Request​

  • Method: POST
  • URL: /api/v1/chats/{chat_id}/sessions
  • Headers:
    • 'content-Type: application/json'
    • 'Authorization: Bearer <YOUR_API_KEY>'
  • 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​

成功：

{
    "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
    }
}

失败：

{
    "code": 102,
    "message": "Name cannot be empty."
}

更新聊天助手的会话​

PUT /api/v1/chats/{chat_id}/sessions/{session_id}

更新指定聊天助手的会话。

Request​

  • Method: PUT
  • URL: /api/v1/chats/{chat_id}/sessions/{session_id}
  • Headers:
    • 'content-Type: application/json'
    • 'Authorization: Bearer <YOUR_API_KEY>'
  • 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​

成功：

{
    "code": 0
}

失败：

{
    "code": 102,
    "message": "Name cannot be empty."
}

列出聊天助手的会话​

GET /api/v1/chats/{chat_id}/sessions?page={page}&page_size={page_size}&orderby={orderby}&desc={desc}&name={session_name}&id={session_id}

列出与指定聊天助手关联的会话。

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 <YOUR_API_KEY>'
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​

成功：

{
    "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
        }
    ]
}

失败：

{
    "code": 102,
    "message": "The session doesn't exist"
}

删除聊天助手的会话​

DELETE /api/v1/chats/{chat_id}/sessions

按 ID 删除聊天助手的会话。

Request​

  • Method: DELETE
  • URL: /api/v1/chats/{chat_id}/sessions
  • Headers:
    • 'content-Type: application/json'
    • 'Authorization: Bearer <YOUR_API_KEY>'
  • 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​

成功：

{
    "code": 0
}

失败：

{
    "code": 102,
    "message": "The chat doesn't own the session"
}

与聊天助手对话​

POST /api/v1/chats/{chat_id}/completions

向指定的聊天助手提问以开始AI驱动的对话。

注意

  • 在流式模式下，并非所有响应都包含引用，这取决于系统的判断。

  • 在流式模式下，最后一条消息是空消息：

    data:
    {
      "code": 0,
      "data": true
    }

Request​

  • Method: POST
  • URL: /api/v1/chats/{chat_id}/completions
  • Headers:
    • 'content-Type: application/json'
    • 'Authorization: Bearer <YOUR_API_KEY>'
  • Body:
    • "question": string
    • "stream": boolean
    • "session_id": string (optional)
    • "user_id: string (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"
     }'
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.

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": "",
                    "similarity": 0.7,
                    "vector_similarity": 0.0,
                    "term_similarity": 1.0,
                    "positions": [
                        ""
                    ]
                }
            ],
            "doc_aggs": [
                {
                    "doc_name": "1.txt",
                    "doc_id": "dd58f58e888511ef89c90242ac120006",
                    "count": 1
                }
            ]
        },
        "prompt": "xxxxxxxxxxx",
        "id": "a84c5dd4-97b4-4624-8c3b-974012c8000d",
        "session_id": "82b0ab2a9c1911ef9d870242ac120006"
    }
}
data:{
    "code": 0,
    "data": true
}

失败：

{
    "code": 102,
    "message": "Please input your question."
}

与智能体创建会话​

POST /api/v1/agents/{agent_id}/sessions

与智能体创建会话。

Request​

  • Method: POST
  • URL: /api/v1/agents/{agent_id}/sessions?user_id={user_id}
  • Headers:
    • 'content-Type: application/json' or 'multipart/form-data'
    • 'Authorization: Bearer <YOUR_API_KEY>'
  • Body:
    • the required parameters:str
    • other parameters: The parameters 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 '{
     }'

If the Begin component in your agent takes 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 '{
            "lang":"Japanese",
            "file":"Who are you"
     }'

If the Begin component in your agent takes required file parameters:

curl --request POST \
     --url http://{address}/api/v1/agents/{agent_id}/sessions?user_id={user_id} \
     --header 'Content-Type: multipart/form-data' \
     --header 'Authorization: Bearer <YOUR_API_KEY>' \
     --form '<FILE_KEY>=@./test1.png'
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​

成功：

{
    "code": 0,
    "data": {
        "agent_id": "b4a39922b76611efaa1a0242ac120006",
        "dsl": {
            "answer": [],
            "components": {
                "Answer:GreenReadersDrum": {
                    "downstream": [],
                    "obj": {
                        "component_name": "Answer",
                        "inputs": [],
                        "output": null,
                        "params": {}
                    },
                    "upstream": []
                },
                "begin": {
                    "downstream": [],
                    "obj": {
                        "component_name": "Begin",
                        "inputs": [],
                        "output": {},
                        "params": {}
                    },
                    "upstream": []
                }
            },
            "embed_id": "",
            "graph": {
                "edges": [],
                "nodes": [
                    {
                        "data": {
                            "label": "Begin",
                            "name": "begin"
                        },
                        "dragging": false,
                        "height": 44,
                        "id": "begin",
                        "position": {
                            "x": 53.25688640427177,
                            "y": 198.37155679786412
                        },
                        "positionAbsolute": {
                            "x": 53.25688640427177,
                            "y": 198.37155679786412
                        },
                        "selected": false,
                        "sourcePosition": "left",
                        "targetPosition": "right",
                        "type": "beginNode",
                        "width": 200
                    },
                    {
                        "data": {
                            "form": {},
                            "label": "Answer",
                            "name": "dialog_0"
                        },
                        "dragging": false,
                        "height": 44,
                        "id": "Answer:GreenReadersDrum",
                        "position": {
                            "x": 360.43473114516974,
                            "y": 207.29298425089348
                        },
                        "positionAbsolute": {
                            "x": 360.43473114516974,
                            "y": 207.29298425089348
                        },
                        "selected": false,
                        "sourcePosition": "right",
                        "targetPosition": "left",
                        "type": "logicNode",
                        "width": 200
                    }
                ]
            },
            "history": [],
            "messages": [],
            "path": [
                [
                    "begin"
                ],
                []
            ],
            "reference": []
        },
        "id": "2581031eb7a311efb5200242ac120005",
        "message": [
            {
                "content": "Hi! I'm your smart assistant. What can I do for you?",
                "role": "assistant"
            }
        ],
        "source": "agent",
        "user_id": "69736c5e723611efb51b0242ac120007"
    }
}

失败：

{
    "code": 102,
    "message": "Agent not found."
}

与智能体对话​

POST /api/v1/agents/{agent_id}/completions

向指定智能体提问以开始AI驱动的对话。

注意

  • 在流式模式下，并非所有响应都包含引用，这取决于系统的判断。

  • 在流式模式下，最后一条消息是空消息：

    data:
    {
      "code": 0,
      "data": true
    }

Request​

  • Method: POST
  • URL: /api/v1/agents/{agent_id}/completions
  • Headers:
    • 'content-Type: application/json'
    • 'Authorization: Bearer <YOUR_API_KEY>'
  • Body:
    • "question": string
    • "stream": boolean
    • "session_id": string
    • "user_id": string(optional)
    • "sync_dsl": boolean (optional)
    • other parameters: string
IMPORTANT

You can include custom parameters in the request body, but first ensure they are defined in the Begin agent component.

Request example​
  • If the Begin component does not take parameters, the following code will create a session.
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 '
     {
     }'
  • If the Begin component takes parameters, the following code will create a session.
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 '
     {
          "lang":"English",
          "file":"How is the weather tomorrow?"
     }'

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.
  • "user_id": (Body parameter), string
    The optional user-defined ID. Valid only when no session_id is provided.
  • "sync_dsl": (Body parameter), boolean Whether to synchronize the changes to existing sessions when an agent is modified, defaults to false.
  • Other parameters: (Body Parameter)
    Parameters specified in the Begin component.

Response​

success without session_id provided and with no parameters specified in the Begin component:

data:{
    "code": 0,
    "message": "",
    "data": {
        "answer": "Hi! I'm your smart assistant. What can I do for you?",
        "reference": {},
        "id": "31e6091d-88d4-441b-ac65-eae1c055be7b",
        "session_id": "2987ad3eb85f11efb2a70242ac120005"
    }
}
data:{
    "code": 0,
    "message": "",
    "data": true
}

Success without session_id provided and with parameters specified in the Begin component:

data:{
    "code": 0,
    "message": "",
    "data": {
        "session_id": "eacb36a0bdff11ef97120242ac120006",
        "answer": "",
        "reference": [],
        "param": [
            {
                "key": "lang",
                "name": "Target Language",
                "optional": false,
                "type": "line",
                "value": "English"
            },
            {
                "key": "file",
                "name": "Files",
                "optional": false,
                "type": "file",
                "value": "How is the weather tomorrow?"
            },
            {
                "key": "hhyt",
                "name": "hhty",
                "optional": true,
                "type": "line"
            }
        ]
    }
}
data:

Success with parameters specified in the Begin component:

data:{
    "code": 0,
    "message": "",
    "data": {
        "answer": "How",
        "reference": {},
        "id": "0379ac4c-b26b-4a44-8b77-99cebf313fdf",
        "session_id": "4399c7d0b86311efac5b0242ac120005"
    }
}
data:{
    "code": 0,
    "message": "",
    "data": {
        "answer": "How is",
        "reference": {},
        "id": "0379ac4c-b26b-4a44-8b77-99cebf313fdf",
        "session_id": "4399c7d0b86311efac5b0242ac120005"
    }
}
data:{
    "code": 0,
    "message": "",
    "data": {
        "answer": "How is the",
        "reference": {},
        "id": "0379ac4c-b26b-4a44-8b77-99cebf313fdf",
        "session_id": "4399c7d0b86311efac5b0242ac120005"
    }
}
data:{
    "code": 0,
    "message": "",
    "data": {
        "answer": "How is the weather",
        "reference": {},
        "id": "0379ac4c-b26b-4a44-8b77-99cebf313fdf",
        "session_id": "4399c7d0b86311efac5b0242ac120005"
    }
}
data:{
    "code": 0,
    "message": "",
    "data": {
        "answer": "How is the weather tomorrow",
        "reference": {},
        "id": "0379ac4c-b26b-4a44-8b77-99cebf313fdf",
        "session_id": "4399c7d0b86311efac5b0242ac120005"
    }
}
data:{
    "code": 0,
    "message": "",
    "data": {
        "answer": "How is the weather tomorrow?",
        "reference": {},
        "id": "0379ac4c-b26b-4a44-8b77-99cebf313fdf",
        "session_id": "4399c7d0b86311efac5b0242ac120005"
    }
}
data:{
    "code": 0,
    "message": "",
    "data": {
        "answer": "How is the weather tomorrow?",
        "reference": {},
        "id": "0379ac4c-b26b-4a44-8b77-99cebf313fdf",
        "session_id": "4399c7d0b86311efac5b0242ac120005"
    }
}
data:{
    "code": 0,
    "message": "",
    "data": true
}

失败：

{
    "code": 102,
    "message": "`question` is required."
}

列出智能体会话​

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}

列出与指定智能体关联的会话。

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 <YOUR_API_KEY>'
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​

成功：

{
    "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": ""
    }]
}

失败：

{
    "code": 102,
    "message": "You don't own the agent ccd2f856b12311ef94ca0242ac1200052."
}

删除智能体的会话​

DELETE /api/v1/agents/{agent_id}/sessions

按 ID 删除智能体的会话。

Request​

  • Method: DELETE
  • URL: /api/v1/agents/{agent_id}/sessions
  • Headers:
    • 'content-Type: application/json'
    • 'Authorization: Bearer <YOUR_API_KEY>'
  • 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​

成功：

{
    "code": 0
}

失败：

{
    "code": 102,
    "message": "The agent doesn't own the session cbd31e52f73911ef93b232903b842af6"
}

生成相关问题​

POST /v1/sessions/related_questions

从用户的原始查询生成五到十个替代问题字符串，以检索更相关的搜索结果。

This operation requires a Bearer Login Token, which typically expires with in 24 hours. You can find the it in the Request Headers in your browser easily as shown below:

Image

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: /v1/sessions/related_questions
  • Headers:
    • 'content-Type: application/json'
    • 'Authorization: Bearer <YOUR_LOGIN_TOKEN>'
  • Body:
    • "question": string
    • "industry": string
Request example​
curl --request POST \
     --url http://{address}/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​

成功：

{
    "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"
}

失败：

{
    "code": 401,
    "data": null,
    "message": "<Unauthorized '401: Unauthorized'>"
}

智能体管理​

列出智能体​

GET /api/v1/agents?page={page}&page_size={page_size}&orderby={orderby}&desc={desc}&name={agent_name}&id={agent_id}

列出智能体。

Request​

  • Method: GET
  • URL: /api/v1/agents?page={page}&page_size={page_size}&orderby={orderby}&desc={desc}&name={agent_name}&id={agent_id}
  • Headers:
    • 'Authorization: Bearer <YOUR_API_KEY>'
Request example​
curl --request GET \
     --url http://{address}/api/v1/agents?page={page}&page_size={page_size}&orderby={orderby}&desc={desc}&name={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.
  • name: (Filter parameter), string
    The name of the agent to retrieve.

Response​

成功：

{
    "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"
        }
    ]
}

失败：

{
    "code": 102,
    "message": "The agent doesn't exist."
}

创建智能体​

POST /api/v1/agents

创建智能体。

Request​

  • Method: POST
  • URL: /api/v1/agents
  • Headers:
    • 'Content-Type: application/json
    • 'Authorization: Bearer <YOUR_API_KEY>'
  • 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​

成功：

{
    "code": 0,
    "data": true,
    "message": "success"
}

失败：

{
    "code": 102,
    "message": "Agent with title test already exists."
}

更新智能体​

PUT /api/v1/agents/{agent_id}

按 ID 更新智能体。

Request​

  • Method: PUT
  • URL: /api/v1/agents/{agent_id}
  • Headers:
    • 'Content-Type: application/json
    • 'Authorization: Bearer <YOUR_API_KEY>'
  • 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​

成功：

{
    "code": 0,
    "data": true,
    "message": "success"
}

失败：

{
    "code": 103,
    "message": "Only owner of canvas authorized for this operation."
}

删除智能体​

DELETE /api/v1/agents/{agent_id}

按 ID 删除智能体。

Request​

  • Method: DELETE
  • URL: /api/v1/agents/{agent_id}
  • Headers:
    • 'Content-Type: application/json
    • 'Authorization: Bearer <YOUR_API_KEY>'
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​

成功：

{
    "code": 0,
    "data": true,
    "message": "success"
}

失败：

{
    "code": 103,
    "message": "Only owner of canvas authorized for this operation."
}