Hamuna RAG API

HTTP API

ERROR CODES

Code	Message	Description
400	Bad Request	Invalid request parameters
401	Unauthorized	Unauthorized access
403	Forbidden	Access denied
404	Not Found	Resource not found
500	Internal Server Error	Server internal error
1001	Invalid Chunk ID	Invalid Chunk ID
1002	Chunk Update Failed	Chunk update failed

OpenAI-Compatible API

Create chat completion

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

Creates a model response for a given chat conversation.

This API follows the same request and response format as OpenAI's API. It allows you to interact with the model in a manner similar to how you would with OpenAI's API.

Request

• Method: POST
• URL: /api/v1/chats_openai/{chat_id}/chat/completions
• Headers:
  
  • 'content-Type: application/json'
  • 'Authorization: Bearer <YOUR_API_KEY>'
• Body:
  
  • "model": string
  • "messages": object list
  • "stream": boolean

Request example

curl --request POST \
     --url http://{address}/api/v1/chats_openai/{chat_id}/chat/completions \
     --header 'Content-Type: application/json' \
     --header 'Authorization: Bearer <YOUR_API_KEY>' \
     --data '{
        "model": "model",
        "messages": [{"role": "user", "content": "Say this is a test!"}],
        "stream": true
      }'

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
    }
}

Non-stream:

{
    "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
    }
}

Failure:

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

Create agent completion

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

Creates a model response for a given chat conversation.

This API follows the same request and response format as OpenAI's API. It allows you to interact with the model in a manner similar to how you would with OpenAI's API.

Request

• Method: POST
• URL: /api/v1/agents_openai/{agent_id}/chat/completions
• Headers:
  
  • 'content-Type: application/json'
  • 'Authorization: Bearer <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
    }
}

Non-stream:

{
    "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
    }
}

Failure:

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

DATASET MANAGEMENT

Create dataset

POST /api/v1/datasets

Creates a dataset.

Request

• Method: POST
• URL: /api/v1/datasets
• Headers:
  
  • 'content-Type: application/json'
  • 'Authorization: Bearer <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 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.

• "pagerank": (Body parameter), int
  refer to Set page rank

  • Default: 0
  • Minimum: 0
  • Maximum: 100

• "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 128
    • 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

Success:

{
    "code": 0,
    "data": {
        "avatar": null,
        "chunk_count": 0,
        "chunk_method": "naive",
        "create_date": "Mon, 28 Apr 2025 18:40:41 GMT",
        "create_time": 1745836841611,
        "created_by": "3af81804241d11f0a6a79f24fc270c7f",
        "description": null,
        "document_count": 0,
        "embedding_model": "BAAI/bge-large-zh-v1.5@BAAI",
        "id": "3b4de7d4241d11f0a6a79f24fc270c7f",
        "language": "English",
        "name": "RAGFlow example",
        "pagerank": 0,
        "parser_config": {
            "chunk_token_num": 128, 
            "delimiter": "\\n!?;。；！？", 
            "html4excel": false, 
            "layout_recognize": "DeepDOC", 
            "raptor": {
                "use_raptor": false
                }
            },
        "permission": "me",
        "similarity_threshold": 0.2,
        "status": "1",
        "tenant_id": "3af81804241d11f0a6a79f24fc270c7f",
        "token_num": 0,
        "update_date": "Mon, 28 Apr 2025 18:40:41 GMT",
        "update_time": 1745836841611,
        "vector_similarity_weight": 0.3,
    },
}

Failure:

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

Delete datasets

DELETE /api/v1/datasets

Deletes datasets by ID.

Request

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

Success:

{
    "code": 0 
}

Failure:

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

Update dataset

PUT /api/v1/datasets/{dataset_id}

Updates configurations for a specified dataset.

Request

• Method: PUT
• URL: /api/v1/datasets/{dataset_id}
• Headers:
  
  • 'content-Type: application/json'
  • 'Authorization: Bearer <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 128
    • 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

Success:

{
    "code": 0 
}

Failure:

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

List datasets

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

Lists datasets.

Request

• Method: GET
• URL: /api/v1/datasets?page={page}&page_size={page_size}&orderby={orderby}&desc={desc}&name={dataset_name}&id={dataset_id}
• Headers:
  
  • 'Authorization: Bearer <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

Success:

{
    "code": 0,
    "data": [
        {
            "avatar": "",
            "chunk_count": 59,
            "create_date": "Sat, 14 Sep 2024 01:12:37 GMT",
            "create_time": 1726276357324,
            "created_by": "69736c5e723611efb51b0242ac120007",
            "description": null,
            "document_count": 1,
            "embedding_model": "BAAI/bge-large-zh-v1.5",
            "id": "6e211ee0723611efa10a0242ac120007",
            "language": "English",
            "name": "mysql",
            "chunk_method": "naive",
            "parser_config": {
                "chunk_token_num": 8192,
                "delimiter": "\\n",
                "entity_types": [
                    "organization",
                    "person",
                    "location",
                    "event",
                    "time"
                ]
            },
            "permission": "me",
            "similarity_threshold": 0.2,
            "status": "1",
            "tenant_id": "69736c5e723611efb51b0242ac120007",
            "token_num": 12744,
            "update_date": "Thu, 10 Oct 2024 04:07:23 GMT",
            "update_time": 1728533243536,
            "vector_similarity_weight": 0.3
        }
    ]
}

Failure:

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

FILE MANAGEMENT WITHIN DATASET

Upload documents

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

Uploads documents to a specified dataset.

Request

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

Success:

{
    "code": 0,
    "data": [
        {
            "chunk_method": "naive",
            "created_by": "69736c5e723611efb51b0242ac120007",
            "dataset_id": "527fa74891e811ef9c650242ac120006",
            "id": "b330ec2e91ec11efbc510242ac120004",
            "location": "1.txt",
            "name": "1.txt",
            "parser_config": {
                "chunk_token_num": 128,
                "delimiter": "\\n",
                "html4excel": false,
                "layout_recognize": true,
                "raptor": {
                    "use_raptor": false
                }
            },
            "run": "UNSTART",
            "size": 17966,
            "thumbnail": "",
            "type": "doc"
        }
    ]
}

Failure:

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

Update document

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

Updates configurations for a specified document.

Request

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

Success:

{
    "code": 0
}

Failure:

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

Download document

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

Downloads a document from a specified dataset.

Request

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

Success:

This is a test to verify the file download feature.

Failure:

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

List documents

GET /api/v1/datasets/{dataset_id}/documents?page={page}&page_size={page_size}&orderby={orderby}&desc={desc}&keywords={keywords}&id={document_id}&name={document_name}

Lists documents in a specified dataset.

Request

• Method: GET
• URL: /api/v1/datasets/{dataset_id}/documents?page={page}&page_size={page_size}&orderby={orderby}&desc={desc}&keywords={keywords}&id={document_id}&name={document_name}
• 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} \
     --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.

Response

Success:

{
    "code": 0,
    "data": {
        "docs": [
            {
                "chunk_count": 0,
                "create_date": "Mon, 14 Oct 2024 09:11:01 GMT",
                "create_time": 1728897061948,
                "created_by": "69736c5e723611efb51b0242ac120007",
                "id": "3bcfbf8a8a0c11ef8aba0242ac120006",
                "knowledgebase_id": "7898da028a0511efbf750242ac120005",
                "location": "Test_2.txt",
                "name": "Test_2.txt",
                "parser_config": {
                    "chunk_token_count": 128,
                    "delimiter": "\n",
                    "layout_recognize": true,
                    "task_page_size": 12
                },
                "chunk_method": "naive",
                "process_begin_at": null,
                "process_duation": 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
    }
}

Failure:

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

Delete documents

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

Deletes documents by ID.

Request

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

Success:

{
    "code": 0
}.

Failure:

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

Parse documents

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

Parses documents in a specified dataset.

Request

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

Success:

{
    "code": 0
}

Failure:

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

Stop parsing documents

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

Stops parsing specified documents.

Request

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

Success:

{
    "code": 0
}

Failure:

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

CHUNK MANAGEMENT WITHIN DATASET

Add chunk

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

Adds a chunk to a specified document in a specified dataset.

Request

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

Success:

{
    "code": 0,
    "data": {
        "chunk": {
            "content": "who are you",
            "create_time": "2024-12-30 16:59:55",
            "create_timestamp": 1735549195.969164,
            "dataset_id": "72f36e1ebdf411efb7250242ac120006",
            "document_id": "61d68474be0111ef98dd0242ac120006",
            "id": "12ccdc56e59837e5",
            "important_keywords": [],
            "questions": []
        }
    }
}

Failure:

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

List chunks

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

Lists chunks in a specified document.

Request

• Method: GET
• URL: /api/v1/datasets/{dataset_id}/documents/{document_id}/chunks?keywords={keywords}&page={page}&page_size={page_size}&id={chunk_id}
• Headers:
  
  • 'Authorization: Bearer <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

Success:

{
    "code": 0,
    "data": {
        "chunks": [
            {
                "available": true,
                "content": "This is a test content.",
                "docnm_kwd": "1.txt",
                "document_id": "b330ec2e91ec11efbc510242ac120004",
                "id": "b48c170e90f70af998485c1065490726",
                "image_id": "",
                "important_keywords": "",
                "positions": [
                    ""
                ]
            }
        ],
        "doc": {
            "chunk_count": 1,
            "chunk_method": "naive",
            "create_date": "Thu, 24 Oct 2024 09:45:27 GMT",
            "create_time": 1729763127646,
            "created_by": "69736c5e723611efb51b0242ac120007",
            "dataset_id": "527fa74891e811ef9c650242ac120006",
            "id": "b330ec2e91ec11efbc510242ac120004",
            "location": "1.txt",
            "name": "1.txt",
            "parser_config": {
                "chunk_token_num": 128,
                "delimiter": "\\n",
                "html4excel": false,
                "layout_recognize": true,
                "raptor": {
                    "use_raptor": false
                }
            },
            "process_begin_at": "Thu, 24 Oct 2024 09:56:44 GMT",
            "process_duation": 0.54213,
            "progress": 0.0,
            "progress_msg": "Task dispatched...",
            "run": "2",
            "size": 17966,
            "source_type": "local",
            "status": "1",
            "thumbnail": "",
            "token_count": 8,
            "type": "doc",
            "update_date": "Thu, 24 Oct 2024 11:03:15 GMT",
            "update_time": 1729767795721
        },
        "total": 1
    }
}

Failure:

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

Delete chunks

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

Deletes chunks by ID.

Request

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

Success:

{
    "code": 0
}

Failure:

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

Update chunk

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

Updates content or configurations for a specified chunk.

Request

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

Success:

{
    "code": 0
}

Failure:

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

Retrieve chunks

POST /api/v1/retrieval

Retrieves chunks from specified datasets.

Request

• Method: POST
• URL: /api/v1/retrieval
• Headers:
  
  • 'content-Type: application/json'
  • 'Authorization: Bearer <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

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).

Response

Success:

{
    "code": 0,
    "data": {
        "chunks": [
            {
                "content": "ragflow content",
                "content_ltks": "ragflow content",
                "document_id": "5c5999ec7be811ef9cab0242ac120005",
                "document_keyword": "1.txt",
                "highlight": "<em>ragflow</em> content",
                "id": "d78435d142bd5cf6704da62c778795c5",
                "image_id": "",
                "important_keywords": [
                    ""
                ],
                "kb_id": "c7ee74067a2c11efb21c0242ac120006",
                "positions": [
                    ""
                ],
                "similarity": 0.9669436601210759,
                "term_similarity": 1.0,
                "vector_similarity": 0.8898122004035864
            }
        ],
        "doc_aggs": [
            {
                "count": 1,
                "doc_id": "5c5999ec7be811ef9cab0242ac120005",
                "doc_name": "1.txt"
            }
        ],
        "total": 1
    }
}

Failure:

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

CHAT ASSISTANT MANAGEMENT

Create chat assistant

POST /api/v1/chats

Creates a chat assistant.

Request

• Method: POST
• URL: /api/v1/chats
• Headers:
  
  • 'content-Type: application/json'
  • 'Authorization: Bearer <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

Success:

{
    "code": 0,
    "data": {
        "avatar": "",
        "create_date": "Thu, 24 Oct 2024 11:18:29 GMT",
        "create_time": 1729768709023,
        "dataset_ids": [
            "527fa74891e811ef9c650242ac120006"
        ],
        "description": "A helpful Assistant",
        "do_refer": "1",
        "id": "b1f2f15691f911ef81180242ac120003",
        "language": "English",
        "llm": {
            "frequency_penalty": 0.7,
            "model_name": "qwen-plus@Tongyi-Qianwen",
            "presence_penalty": 0.4,
            "temperature": 0.1,
            "top_p": 0.3
        },
        "name": "12234",
        "prompt": {
            "empty_response": "Sorry! No relevant content was found in the knowledge base!",
            "keywords_similarity_weight": 0.3,
            "opener": "Hi! I'm your assistant, what can I do for you?",
            "prompt": "You are an intelligent assistant. Please summarize the content of the knowledge base to answer the question. Please list the data in the knowledge base and answer in detail. When all knowledge base content is irrelevant to the question, your answer must include the sentence \"The answer you are looking for is not found in the knowledge base!\" Answers need to consider chat history.\n ",
            "rerank_model": "",
            "similarity_threshold": 0.2,
            "top_n": 6,
            "variables": [
                {
                    "key": "knowledge",
                    "optional": false
                }
            ]
        },
        "prompt_type": "simple",
        "status": "1",
        "tenant_id": "69736c5e723611efb51b0242ac120007",
        "top_k": 1024,
        "update_date": "Thu, 24 Oct 2024 11:18:29 GMT",
        "update_time": 1729768709023
    }
}

Failure:

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

Update chat assistant

PUT /api/v1/chats/{chat_id}

Updates configurations for a specified chat assistant.

Request

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

Success:

{
    "code": 0
}

Failure:

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

Delete chat assistants

DELETE /api/v1/chats

Deletes chat assistants by ID.

Request

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

Success:

{
    "code": 0
}

Failure:

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

List chat assistants

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

Lists chat assistants.

Request

• Method: GET
• URL: /api/v1/chats?page={page}&page_size={page_size}&orderby={orderby}&desc={desc}&name={chat_name}&id={chat_id}
• Headers:
  
  • 'Authorization: Bearer <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

Success:

{
    "code": 0,
    "data": [
        {
            "avatar": "",
            "create_date": "Fri, 18 Oct 2024 06:20:06 GMT",
            "create_time": 1729232406637,
            "description": "A helpful Assistant",
            "do_refer": "1",
            "id": "04d0d8e28d1911efa3630242ac120006",
            "dataset_ids": ["527fa74891e811ef9c650242ac120006"],
            "language": "English",
            "llm": {
                "frequency_penalty": 0.7,
                "model_name": "qwen-plus@Tongyi-Qianwen",
                "presence_penalty": 0.4,
                "temperature": 0.1,
                "top_p": 0.3
            },
            "name": "13243",
            "prompt": {
                "empty_response": "Sorry! No relevant content was found in the knowledge base!",
                "keywords_similarity_weight": 0.3,
                "opener": "Hi! I'm your assistant, what can I do for you?",
                "prompt": "You are an intelligent assistant. Please summarize the content of the knowledge base to answer the question. Please list the data in the knowledge base and answer in detail. When all knowledge base content is irrelevant to the question, your answer must include the sentence \"The answer you are looking for is not found in the knowledge base!\" Answers need to consider chat history.\n",
                "rerank_model": "",
                "similarity_threshold": 0.2,
                "top_n": 6,
                "variables": [
                    {
                        "key": "knowledge",
                        "optional": false
                    }
                ]
            },
            "prompt_type": "simple",
            "status": "1",
            "tenant_id": "69736c5e723611efb51b0242ac120007",
            "top_k": 1024,
            "update_date": "Fri, 18 Oct 2024 06:20:06 GMT",
            "update_time": 1729232406638
        }
    ]
}

Failure:

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

SESSION MANAGEMENT

Create session with chat assistant

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

Creates a session with a chat assistant.

Request

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

Success:

{
    "code": 0,
    "data": {
        "chat_id": "2ca4b22e878011ef88fe0242ac120005",
        "create_date": "Fri, 11 Oct 2024 08:46:14 GMT",
        "create_time": 1728636374571,
        "id": "4606b4ec87ad11efbc4f0242ac120006",
        "messages": [
            {
                "content": "Hi! I am your assistant，can I help you?",
                "role": "assistant"
            }
        ],
        "name": "new session",
        "update_date": "Fri, 11 Oct 2024 08:46:14 GMT",
        "update_time": 1728636374571
    }
}

Failure:

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

Update chat assistant's session

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

Updates a session of a specified chat assistant.

Request

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

Success:

{
    "code": 0
}

Failure:

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

List chat assistant's sessions

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

Lists sessions associated with a specified chat assistant.

Request

• Method: GET
• URL: /api/v1/chats/{chat_id}/sessions?page={page}&page_size={page_size}&orderby={orderby}&desc={desc}&name={session_name}&id={session_id}&user_id={user_id}
• Headers:
  
  • 'Authorization: Bearer <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

Success:

{
    "code": 0,
    "data": [
        {
            "chat": "2ca4b22e878011ef88fe0242ac120005",
            "create_date": "Fri, 11 Oct 2024 08:46:43 GMT",
            "create_time": 1728636403974,
            "id": "578d541e87ad11ef96b90242ac120006",
            "messages": [
                {
                    "content": "Hi! I am your assistant，can I help you?",
                    "role": "assistant"
                }
            ],
            "name": "new session",
            "update_date": "Fri, 11 Oct 2024 08:46:43 GMT",
            "update_time": 1728636403974
        }
    ]
}

Failure:

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

Delete chat assistant's sessions

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

Deletes sessions of a chat assistant by ID.

Request

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