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HTTP API Reference

A complete reference for RAGFlow’s RESTful API. Before proceeding, please ensure you have your RAGFlow API key ready for authentication.

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
- "language": 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:
- Permitted characters include:
- English letters (a-z, A-Z)
- Digits (0-9)
- “_” (underscore)
- Must begin with an English letter or underscore.
- Maximum 65,535 characters.
- Case-insensitive.
"avatar": (Body parameter), string
Base64 encoding of the avatar.
"description": (Body parameter), string
A brief description of the dataset to create.
"language": (Body parameter), string
The language setting of the dataset to create. Available options:
- "English" (default)
- "Chinese"
"embedding_model": (Body parameter), string
The name of the embedding model to use. For example: "BAAI/bge-zh-v1.5"
"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)
- "manual": Manual
- "qa": Q&A
- "table": Table
- "paper": Paper
- "book": Book
- "laws": Laws
- "presentation": Presentation
- "picture": Picture
- "one": One
- "knowledge_graph": Knowledge Graph
  Ensure your LLM is properly configured on the Settings page before selecting this. Please also note that Knowledge Graph consumes a large number of Tokens!
- "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 128.
- "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.
- If "chunk_method" is "knowledge_graph", the "parser_config" object contains the following attributes:
- "chunk_token_count": Defaults to 128.
- "delimiter": Defaults to "\n!?。；！？".
- "entity_types": Defaults to ["organization","person","location","event","time"]

Response

Success:

{
    "code": 0,
    "data": {
        "avatar": null,
        "chunk_count": 0,
        "chunk_method": "naive",
        "create_date": "Thu, 24 Oct 2024 09:14:07 GMT",
        "create_time": 1729761247434,
        "created_by": "69736c5e723611efb51b0242ac120007",
        "description": null,
        "document_count": 0,
        "embedding_model": "BAAI/bge-large-zh-v1.5",
        "id": "527fa74891e811ef9c650242ac120006",
        "language": "English",
        "name": "test_1",
        "parser_config": {
            "chunk_token_num": 128,
            "delimiter": "\\n!?;。；！？",
            "html4excel": false,
            "layout_recognize": true,
            "raptor": {
                "user_raptor": false
            }
        },
        "permission": "me",
        "similarity_threshold": 0.2,
        "status": "1",
        "tenant_id": "69736c5e723611efb51b0242ac120007",
        "token_num": 0,
        "update_date": "Thu, 24 Oct 2024 09:14:07 GMT",
        "update_time": 1729761247434,
        "vector_similarity_weight": 0.3
    }
}

Failure:

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

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]

Request example

curl --request DELETE \
     --url http://{address}/api/v1/datasets \
     --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 datasets to delete. If it is not specified, all 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
- "embedding_model": string
- "chunk_method": enum<string>

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.
"embedding_model": (Body parameter), string
The updated embedding model name.
- Ensure that "chunk_count" is 0 before updating "embedding_model".
"chunk_method": (Body parameter), enum<string>
The chunking method for the dataset. Available options:
- "naive": General
- "manual: Manual
- "qa": Q&A
- "table": Table
- "paper": Paper
- "book": Book
- "laws": Laws
- "presentation": Presentation
- "picture": Picture
- "one":One
- "email": Email
- "knowledge_graph": Knowledge Graph
  Ensure your LLM is properly configured on the Settings page before selecting this. Please also note that Knowledge Graph consumes a large number of Tokens!

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": "knowledge_graph",
            "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": {
                    "user_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
- "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
"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
- "knowledge_graph": Knowledge Graph
  Ensure your LLM is properly configured on the Settings page before selecting this. Please also note that Knowledge Graph consumes a large number of Tokens!
- "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 128.
- "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.
- If "chunk_method" is "knowledge_graph", the "parser_config" object contains the following attributes:
- "chunk_token_count": Defaults to 128.
- "delimiter": Defaults to "\n!?。；！？".
- "entity_types": Defaults to ["organization","person","location","event","time"]

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"
}

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.

Response

Success:

{
    "code": 0,
    "data": {
        "chunk": {
            "content": "ragflow content",
            "create_time": "2024-10-16 08:05:04",
            "create_timestamp": 1729065904.581025,
            "dataset_id": [
                "c7ee74067a2c11efb21c0242ac120006"
            ],
            "document_id": "5c5999ec7be811ef9cab0242ac120005",
            "id": "d78435d142bd5cf6704da62c778795c5",
            "important_keywords": []
        }
    }
}

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_ids: (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_int": 1,
                "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": {
                    "user_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 computaton. 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 0.2.
- "frequency penalty": float
  Similar to the presence penalty, this reduces the model’s tendency to repeat the same words frequently. Defaults to 0.7.
- "max_token": integer
  The maximum length of the model’s output, measured in the number of tokens (words or pieces of words). Defaults to 512. If disabled, you lift the maximum token limit, allowing the model to determine the number of tokens in its responses.
"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 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,
    "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,
            "max_tokens": 512,
            "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.
- "max_token": integer
  The maximum length of the model’s output, measured in the number of tokens (words or pieces of words). Defaults to 512. If disabled, you lift the maximum token limit, allowing the model to determine the number of tokens in its responses.
"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={dataset_name}&id={dataset_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={dataset_name}&id={dataset_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,
                "max_tokens": 512,
                "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

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.

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

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.

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}
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.

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"]
     }'

Request Parameters

chat_id: (Path parameter)
The ID of the associated chat assistant.
"ids": (Body Parameter), list[string]
The IDs of the sessions to delete. If it is not specified, all sessions associated with the specified chat assistant will be deleted.

Response

Success:

{
    "code": 0
}

Failure:

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

Converse with chat assistant

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

Asks a specified chat assistant a question to start an AI-powered conversation.

:::tip NOTE

In streaming mode, not all responses include a reference, as this depends on the system’s judgement.
In streaming mode, the last message is an empty message: json 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

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.

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
}

Failure:

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

Create session with agent

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

Creates a session with an agent.

Request

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

Request example

curl --request POST \
     --url http://{address}/api/v1/agents/{agent_id}/sessions \
     --header 'Content-Type: application/json' \
     --header 'Authorization: Bearer <YOUR_API_KEY>' \
     --data '{
     }'

Request parameters

agent_id: (Path parameter)
The ID of the associated agent.

Response

Success:

{
    "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"
    }
}

Failure:

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

Converse with agent

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

Asks a specified agent a question to start an AI-powered conversation.

:::tip NOTE

In streaming mode, not all responses include a reference, as this depends on the system’s judgement.
In streaming mode, the last message is an empty message: json 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
- other parameters: string
  
  Request example

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 '
     {
     }'

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"
     }'

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?"
     }'

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.

Other parameters: (Body Parameter)
The parameters in the begin component.

Response

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

data:{
    "code": 0,
    "message": "",
    "data": {
        "answer": "",
        "reference": [],
        "id": "7ed5c2e4-aa28-4397-bbed-59664a332aa0",
        "session_id": "ce1b4fa89c1811ef85720242ac120006"
    }
}
data:{
    "code": 0,
    "data": {
        "answer": "Hello",
        "reference": [],
        "id": "7ed5c2e4-aa28-4397-bbed-59664a332aa0",
        "session_id": "ce1b4fa89c1811ef85720242ac120006"
    }
}
data:{
    "code": 0,
    "data": {
        "answer": "Hello!",
        "reference": [],
        "id": "7ed5c2e4-aa28-4397-bbed-59664a332aa0",
        "session_id": "ce1b4fa89c1811ef85720242ac120006"
    }
}
data:{
    "code": 0,
    "data": {
        "answer": "Hello! How",
        "reference": [],
        "id": "7ed5c2e4-aa28-4397-bbed-59664a332aa0",
        "session_id": "ce1b4fa89c1811ef85720242ac120006"
    }
}
data:{
    "code": 0,
    "data": {
        "answer": "Hello! How can",
        "reference": [],
        "id": "7ed5c2e4-aa28-4397-bbed-59664a332aa0",
        "session_id": "ce1b4fa89c1811ef85720242ac120006"
    }
}
data:{
    "code": 0,
    "data": {
        "answer": "Hello! How can I",
        "reference": [],
        "id": "7ed5c2e4-aa28-4397-bbed-59664a332aa0",
        "session_id": "ce1b4fa89c1811ef85720242ac120006"
    }
}
data:{
    "code": 0,
    "data": {
        "answer": "Hello! How can I assist",
        "reference": [],
        "id": "7ed5c2e4-aa28-4397-bbed-59664a332aa0",
        "session_id": "ce1b4fa89c1811ef85720242ac120006"
    }
}
data:{
    "code": 0,
    "data": {
        "answer": "Hello! How can I assist you",
        "reference": [],
        "id": "7ed5c2e4-aa28-4397-bbed-59664a332aa0",
        "session_id": "ce1b4fa89c1811ef85720242ac120006"
    }
}
data:{
    "code": 0,
    "data": {
        "answer": "Hello! How can I assist you today",
        "reference": [],
        "id": "7ed5c2e4-aa28-4397-bbed-59664a332aa0",
        "session_id": "ce1b4fa89c1811ef85720242ac120006"
    }
}
data:{
    "code": 0,
    "data": {
        "answer": "Hello! How can I assist you today?",
        "reference": [],
        "id": "7ed5c2e4-aa28-4397-bbed-59664a332aa0",
        "session_id": "ce1b4fa89c1811ef85720242ac120006"
    }
}
data:{
    "code": 0,
    "data": {
        "answer": "Hello! How can I assist you today?",
        "reference": [],
        "id": "7ed5c2e4-aa28-4397-bbed-59664a332aa0",
        "session_id": "ce1b4fa89c1811ef85720242ac120006"
    }
}
data:{
    "code": 0,
    "data": true
}

Success with parameters 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
}

Failure:

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

List agent sessions

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

Lists sessions associated with a specified agent.

Request

Method: GET
URL: /api/v1/agents/{agent_id}/sessions?page={page}&page_size={page_size}&orderby={orderby}&desc={desc}&id={session_id}
Headers:
- 'Authorization: Bearer <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} \
     --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.

Response

Success:

{
    "code": 0,
    "data": {
        "agent_id": "e9e2b9c2b2f911ef801d0242ac120006",
        "dsl": {
            "answer": [],
            "components": {
                "Answer:OrangeTermsBurn": {
                    "downstream": [],
                    "obj": {
                        "component_name": "Answer",
                        "params": {}
                    },
                    "upstream": []
                },
                "Generate:SocialYearsRemain": {
                    "downstream": [],
                    "obj": {
                        "component_name": "Generate",
                        "params": {
                            "cite": true,
                            "frequency_penalty": 0.7,
                            "llm_id": "gpt-4o___OpenAI-API@OpenAI-API-Compatible",
                            "max_tokens": 256,
                            "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,
                                "max_tokens": 256,
                                "message_history_window_size": 12,
                                "parameters": [],
                                "presencePenaltyEnabled": true,
                                "presence_penalty": 0.4,
                                "prompt": "Please summarize the following paragraph. Pay attention to the numbers and do not make things up. The paragraph is as follows:\n{input}\nThis is what you need to summarize.",
                                "temperature": 0.1,
                                "temperatureEnabled": true,
                                "topPEnabled": true,
                                "top_p": 0.3
                            },
                            "label": "Generate",
                            "name": "Generate Answer_0"
                        },
                        "dragging": false,
                        "height": 105,
                        "id": "Generate:SocialYearsRemain",
                        "position": {
                            "x": 561.3457829707513,
                            "y": 178.7211182312641
                        },
                        "positionAbsolute": {
                            "x": 561.3457829707513,
                            "y": 178.7211182312641
                        },
                        "selected": true,
                        "sourcePosition": "right",
                        "targetPosition": "left",
                        "type": "generateNode",
                        "width": 200
                    },
                    {
                        "data": {
                            "form": {},
                            "label": "Answer",
                            "name": "Dialogue_0"
                        },
                        "height": 44,
                        "id": "Answer:OrangeTermsBurn",
                        "position": {
                            "x": 317.2368194777658,
                            "y": 218.30635555445093
                        },
                        "sourcePosition": "right",
                        "targetPosition": "left",
                        "type": "logicNode",
                        "width": 200
                    }
                ]
            },
            "history": [],
            "messages": [],
            "path": [],
            "reference": []
        },
        "id": "792dde22b2fa11ef97550242ac120006",
        "message": [
            {
                "content": "Hi! I'm your smart assistant. What can I do for you?",
                "role": "assistant"
            }
        ],
        "source": "agent",
        "user_id": ""
    }
}

Failure:

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

AGENT MANAGEMENT

List agents

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

Lists agents.

Request

Method: GET
URL: /api/v1/agents?page={page}&page_size={page_size}&orderby={orderby}&desc={desc}&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

Success:

{
    "code": 0,
    "data": [
        {
            "avatar": null,
            "canvas_type": null,
            "create_date": "Thu, 05 Dec 2024 19:10:36 GMT",
            "create_time": 1733397036424,
            "description": null,
            "dsl": {
                "answer": [],
                "components": {
                    "begin": {
                        "downstream": [],
                        "obj": {
                            "component_name": "Begin",
                            "params": {}
                        },
                        "upstream": []
                    }
                },
                "graph": {
                    "edges": [],
                    "nodes": [
                        {
                            "data": {
                                "label": "Begin",
                                "name": "begin"
                            },
                            "height": 44,
                            "id": "begin",
                            "position": {
                                "x": 50,
                                "y": 200
                            },
                            "sourcePosition": "left",
                            "targetPosition": "right",
                            "type": "beginNode",
                            "width": 200
                        }
                    ]
                },
                "history": [],
                "messages": [],
                "path": [],
                "reference": []
            },
            "id": "8d9ca0e2b2f911ef9ca20242ac120006",
            "title": "123465",
            "update_date": "Thu, 05 Dec 2024 19:10:56 GMT",
            "update_time": 1733397056801,
            "user_id": "69736c5e723611efb51b0242ac120007"
        }
    ]
}

Failure:

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

http_api_reference.md 80KB 原始文件 Blame 歷史記錄

slug: /http_api_reference

HTTP API Reference

DATASET MANAGEMENT

Create dataset

Request

Request example

Request parameters

Response

Delete datasets

Request

Request example

Request parameters

Response

Update dataset

Request

Request example

Request parameters

Response

List datasets

Request

Request example

Request parameters

Response

FILE MANAGEMENT WITHIN DATASET

Upload documents

Request

Request example

Request parameters

Response

Update document

Request

Request example

Request parameters

Response

Download document

Request

Request example

Request parameters

Response

List documents

Request

Request example

Request parameters

Response

Delete documents

Request

Request example

Request parameters

Response

Parse documents

Request

Request example

Request parameters

Response

Stop parsing documents

Request

Request example

Request parameters

Response

Add chunk

Request

Request example

Request parameters

Response

List chunks

Request

Request example

Request parameters

Response

Delete chunks

Request

Request example

Request parameters

Response

Update chunk

Request

Request example

Request parameters

Response

http_api_reference.md 80KB

原始文件 Blame 歷史記錄