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DRAFT! HTTP API Reference

THE API REFERENCES BELOW ARE STILL UNDER DEVELOPMENT.

Create dataset

POST /api/v1/dataset

Creates a dataset.

Request

  • Method: POST
  • URL: http://{address}/api/v1/dataset
  • Headers:
    • content-Type: application/json
    • 'Authorization: Bearer {YOUR_ACCESS_TOKEN}'
  • Body:
    • "id": string
    • "name": string
    • "avatar": string
    • "tenant_id": string
    • "description": string
    • "language": string
    • "embedding_model": string
    • "permission": string
    • "document_count": integer
    • "chunk_count": integer
    • "parse_method": string
    • "parser_config": Dataset.ParserConfig

Request example 

# "id": id must not be provided.
# "name": name is required and can't be duplicated.
# "tenant_id": tenant_id must not be provided.
# "embedding_model": embedding_model must not be provided.
# "navie" means general.
curl --request POST \
  --url http://{address}/api/v1/dataset \
  --header 'Content-Type: application/json' \
  --header 'Authorization: Bearer {YOUR_ACCESS_TOKEN}' \
  --data '{
  "name": "test",
  "chunk_count": 0,
  "document_count": 0,
  "parse_method": "naive"
}'

Request parameters

  • "id": (Body parameter)
    The ID of the created dataset used to uniquely identify different datasets.

    • If creating a dataset, id must not be provided.

  • "name": (Body parameter)
    The name of the dataset, which must adhere to the following requirements:

    • Required when creating a dataset and must be unique.
    • If updating a dataset, name must still be unique.

  • "avatar": (Body parameter)
    Base64 encoding of the avatar.

  • "tenant_id": (Body parameter)
    The ID of the tenant associated with the dataset, used to link it with specific users.

    • If creating a dataset, tenant_id must not be provided.
    • If updating a dataset, tenant_id cannot be changed.

  • "description": (Body parameter)
    The description of the dataset.

  • "language": (Body parameter)
    The language setting for the dataset.

  • "embedding_model": (Body parameter)
    Embedding model used in the dataset to generate vector embeddings.

    • If creating a dataset, embedding_model must not be provided.
    • If updating a dataset, embedding_model cannot be changed.

  • "permission": (Body parameter)
    Specifies who can manipulate the dataset.

  • "document_count": (Body parameter)
    Document count of the dataset.

    • If updating a dataset, document_count cannot be changed.

  • "chunk_count": (Body parameter)
    Chunk count of the dataset.

    • If updating a dataset, chunk_count cannot be changed.

  • "parse_method": (Body parameter)
    Parsing method of the dataset.

    • If updating parse_method, chunk_count must be greater than 0.

  • "parser_config": (Body parameter)
    The configuration settings for the dataset parser.

Response

The successful response includes a JSON object like the following:

{
    "code": 0,
    "data": {
        "avatar": null,
        "chunk_count": 0,
        "create_date": "Thu, 10 Oct 2024 05:57:37 GMT",
        "create_time": 1728539857641,
        "created_by": "69736c5e723611efb51b0242ac120007",
        "description": null,
        "document_count": 0,
        "embedding_model": "BAAI/bge-large-zh-v1.5",
        "id": "8d73076886cc11ef8c270242ac120006",
        "language": "English",
        "name": "test_1",
        "parse_method": "naive",
        "parser_config": {
            "pages": [
                [
                    1,
                    1000000
                ]
            ]
        },
        "permission": "me",
        "similarity_threshold": 0.2,
        "status": "1",
        "tenant_id": "69736c5e723611efb51b0242ac120007",
        "token_num": 0,
        "update_date": "Thu, 10 Oct 2024 05:57:37 GMT",
        "update_time": 1728539857641,
        "vector_similarity_weight": 0.3
    }
}
  • "error_code": integer
    0: The operation succeeds.

The error response includes a JSON object like the following:

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

Delete datasets

DELETE /api/v1/dataset

Deletes datasets by ids.

Request

  • Method: DELETE
  • URL: http://{address}/api/v1/dataset
  • Headers:
    • content-Type: application/json
    • 'Authorization: Bearer {YOUR_ACCESS_TOKEN}'
    • Body:
      • "ids": List[string]

Request example 

# Either id or name must be provided, but not both.
curl --request DELETE \
  --url http://{address}/api/v1/dataset \
  --header 'Content-Type: application/json' \
  --header 'Authorization: Bearer {YOUR_ACCESS_TOKEN}' \
  --data '{
  "ids": ["test_1", "test_2"]
  }'

Request parameters

  • "ids": (Body parameter) Dataset IDs to delete.

Response

The successful response includes a JSON object like the following:

{
    "code": 0 
}
  • "error_code": integer
    0: The operation succeeds.

The error response includes a JSON object like the following:

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

Update dataset

PUT /api/v1/dataset/{dataset_id}

Updates a dataset by its id.

Request

  • Method: PUT
  • URL: http://{address}/api/v1/dataset/{dataset_id}
  • Headers:
    • content-Type: application/json
    • 'Authorization: Bearer {YOUR_ACCESS_TOKEN}'
    • Body: (Refer to the "Create Dataset" for the complete structure of the request body.)

Request example 

# "id":  id is required.
# "name": If you update name, it can't be duplicated.
# "tenant_id": If you update tenant_id, it can't be changed
# "embedding_model": If you update embedding_model, it can't be changed.
# "chunk_count": If you update chunk_count, it can't be changed.
# "document_count": If you update document_count, it can't be changed.
# "parse_method": If you update parse_method, chunk_count must be 0. 
# "navie" means general.
curl --request PUT \
  --url http://{address}/api/v1/dataset/{dataset_id} \
  --header 'Content-Type: application/json' \
  --header 'Authorization: Bearer {YOUR_ACCESS_TOKEN}' \
  --data '{
  "name": "test",
  "tenant_id": "4fb0cd625f9311efba4a0242ac120006",
  "embedding_model": "BAAI/bge-zh-v1.5",
  "chunk_count": 0,
  "document_count": 0,
  "parse_method": "navie"
}'

Request parameters

(Refer to the "Create Dataset" for the complete structure of the request parameters.)

Response

The successful response includes a JSON object like the following:

{
    "code": 0 
}
  • "error_code": integer
    0: The operation succeeds.

The error response includes a JSON object like the following:

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

List datasets

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

List all datasets

Request

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

Request example 

# If no page parameter is passed, the default is 1
# If no page_size parameter is passed, the default is 1024
# If no order_by parameter is passed, the default is "create_time"
# If no desc parameter is passed, the default is True
curl --request GET \
  --url http://{address}/api/v1/dataset?page={page}&page_size={page_size}&orderby={orderby}&desc={desc}&name={dataset_name}&id={dataset_id} \
  --header 'Authorization: Bearer {YOUR_ACCESS_TOKEN}'

Request parameters

  • path: (Path parameter) The current page number to retrieve from the paginated data. This parameter determines which set of records will be fetched.
  • path_size: (Path parameter) The number of records to retrieve per page. This controls how many records will be included in each page.
  • orderby: (Path parameter) The field by which the records should be sorted. This specifies the attribute or column used to order the results.
  • desc: (Path parameter) A boolean flag indicating whether the sorting should be in descending order.
  • name: (Path parameter) Dataset name
  • "id": (Path parameter)
    The ID of the dataset to be retrieved.
  • "name": (Path parameter)
    The name of the dataset to be retrieved.

Response

The successful response includes a JSON object like the following:

{
    "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",
            "parse_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
        }
    ]
}

The error response includes a JSON object like the following:

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

Upload files to a dataset

POST /api/v1/dataset/{dataset_id}/document

Uploads files to a dataset.

Request

  • Method: POST
  • URL: /api/v1/dataset/{dataset_id}/document
  • Headers:
    • 'Content-Type: multipart/form-data'
    • 'Authorization: Bearer {YOUR_ACCESS_TOKEN}'
  • Form:
    • 'file=@{FILE_PATH}'

Request example 

curl --request POST \
     --url http://{address}/api/v1/dataset/{dataset_id}/document \
     --header 'Content-Type: multipart/form-data' \
     --header 'Authorization: Bearer {YOUR_ACCESS_TOKEN}' \     
     --form 'file=@./test.txt'

Request parameters

  • "dataset_id": (Path parameter) The dataset id
  • "file": (Body parameter)
    The file to upload

Response

The successful response includes a JSON object like the following:

{
    "code": 0 
}
  • "error_code": integer
    0: The operation succeeds.

The error response includes a JSON object like the following:

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

Download a file from a dataset

GET /api/v1/dataset/{dataset_id}/document/{document_id}

Downloads files from a dataset.

Request

  • Method: GET
  • URL: /api/v1/dataset/{dataset_id}/document/{document_id}
  • Headers:
    • content-Type: application/json
    • 'Authorization: Bearer {YOUR_ACCESS_TOKEN}'
  • Output:
    • '{FILE_NAME}'

Request example 

curl --request GET \
     --url http://{address}/api/v1/dataset/{dataset_id}/document/{documents_id} \
     --header 'Content-Type: application/json' \
     --header 'Authorization: Bearer {YOUR_ACCESS_TOKEN}'
     --output '{FILE_NAME}'

Request parameters

  • "dataset_id": (PATH parameter) The dataset id
  • "documents_id": (PATH parameter)
    The document id of the file.

Response

The successful response includes a JSON object like the following:

test_2.
  • "error_code": integer
    0: The operation succeeds.

The error response includes a JSON object like the following:

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

List files of a dataset

GET /api/v1/dataset/{dataset_id}/info?offset={offset}&limit={limit}&orderby={orderby}&desc={desc}&keywords={keywords}&id={document_id}

List files to a dataset.

Request

  • Method: GET
  • URL: /api/v1/dataset/{dataset_id}/info?keywords={keyword}&page={page}&page_size={limit}&orderby={orderby}&desc={desc}&name={name
  • Headers:
    • content-Type: application/json
    • 'Authorization: Bearer {YOUR_ACCESS_TOKEN}'

Request example 

curl --request GET \
  --url http://{address}/api/v1/dataset/{dataset_id}/info?offset={offset}&limit={limit}&orderby={orderby}&desc={desc}&keywords={keywords}&id={document_id} \
  --header 'Authorization: Bearer {YOUR_ACCESS_TOKEN}'

Request parameters

  • "dataset_id": (PATH parameter) The dataset id
  • offset: (Filter parameter) The beginning number of records for paging.
  • keywords: (Filter parameter) The keywords matches the search key workds;
  • limit: (Filter parameter) Records number to return.
  • orderby: (Filter parameter) The field by which the records should be sorted. This specifies the attribute or column used to order the results.
  • desc: (Filter parameter) A boolean flag indicating whether the sorting should be in descending order.
  • id: (Filter parameter) The id of the document to be got.

Response

The successful response includes a JSON object like the following:

{
    "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
                },
                "parser_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
    }
}
  • "error_code": integer
    0: The operation succeeds.

The error response includes a JSON object like the following:

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

Update a file information in dataset

PUT /api/v1/dataset/{dataset_id}/info/{document_id}

Update a file in a dataset

Request

  • Method: PUT
  • URL: http://{address}/api/v1/dataset/{dataset_id}/document/{document_id}
  • Headers:
    • content-Type: application/json
    • 'Authorization: Bearer {YOUR_ACCESS_TOKEN}'

Request example 

curl --request PUT \
  --url http://{address}/api/v1/dataset/{dataset_id}/document/{document_id} \
  --header 'Authorization: Bearer {YOUR_ACCESS TOKEN}' \
  --header 'Content-Type: application/json' \
  --data '{
  "name": "manual.txt", 
  "thumbnail": null, 
  "knowledgebase_id": "779333c0758611ef910f0242ac120004", 
  "parser_method": "manual", 
  "parser_config": {"chunk_token_count": 128, "delimiter": "\n!?。；！？", "layout_recognize": true, "task_page_size": 12}, 
  "source_type": "local", "type": "doc", 
  "created_by": "134408906b6811efbcd20242ac120005", 
  "size": 0, "token_count": 0, "chunk_count": 0, 
  "progress": 0.0, 
  "progress_msg": "", 
  "process_begin_at": null, 
  "process_duration": 0.0
  }'

Request parameters

  • "thumbnail": (Body parameter)
    Thumbnail image of the document.

    • ""

  • "knowledgebase_id": (Body parameter)
    Knowledge base ID related to the document.

    • ""

  • "parser_method": (Body parameter)
    Method used to parse the document.

    • ""

  • "parser_config": (Body parameter)
    Configuration object for the parser.

    • If the value is None, a dictionary with default values will be generated.

  • "source_type": (Body parameter)
    Source type of the document.

    • ""

  • "type": (Body parameter)
    Type or category of the document.

    • ""

  • "created_by": (Body parameter)
    Creator of the document.

    • ""

  • "name": (Body parameter)
    Name or title of the document.

    • ""

  • "size": (Body parameter)
    Size of the document in bytes or some other unit.

    • 0

  • "token_count": (Body parameter)
    Number of tokens in the document.

    • 0

  • "chunk_count": (Body parameter)
    Number of chunks the document is split into.

    • 0

  • "progress": (Body parameter)
    Current processing progress as a percentage.

    • 0.0

  • "progress_msg": (Body parameter)
    Message indicating current progress status.

    • ""

  • "process_begin_at": (Body parameter)
    Start time of the document processing.

    • None

  • "process_duration": (Body parameter)
    Duration of the processing in seconds or minutes.

    • 0.0

Response

The successful response includes a JSON object like the following:

{
    "code": 0
}

The error response includes a JSON object like the following:

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

Parse files in dataset

POST /api/v1/dataset/{dataset_id}/chunk

Parse files into chunks in a dataset

Request

  • Method: POST
  • URL: /api/v1/dataset/{dataset_id}/chunk
  • Headers:
    • content-Type: application/json
    • 'Authorization: Bearer {YOUR_ACCESS_TOKEN}'

Request example 

curl --request POST \
     --url http://{address}/api/v1/dataset/{dataset_id}/chunk \
     --header 'Content-Type: application/json' \
     --header 'Authorization: Bearer {YOUR_ACCESS_TOKEN}'
     --raw '{
         "documents": ["f6b170ac758811efa0660242ac120004", "97ad64b6759811ef9fc30242ac120004"]
     }'

Request parameters

  • "dataset_id": (Path parameter)
  • "documents": (Body parameter)
    • Documents to parse

Response

The successful response includes a JSON object like the following:

{
    "code": 0
}

The error response includes a JSON object like the following:

{
    "code": 3016,
    "message": "Can't connect database"
}

Stop file parsing

DELETE /api/v1/dataset/{dataset_id}/chunk

Stop file parsing

Request

  • Method: POST
  • URL: /api/v1/dataset/{dataset_id}/chunk
  • Headers:
    • content-Type: application/json
    • 'Authorization: Bearer {YOUR_ACCESS_TOKEN}'

Request example 

curl --request DELETE \
     --url http://{address}/api/v1/dataset/{dataset_id}/chunk \
     --header 'Content-Type: application/json' \
     --header 'Authorization: Bearer {YOUR_ACCESS_TOKEN}'
     --raw '{
         "documents": ["f6b170ac758811efa0660242ac120004", "97ad64b6759811ef9fc30242ac120004"]
     }'

Request parameters

  • "dataset_id": (Path parameter)
  • "documents": (Body parameter)
    • Documents to stop parsing

Response

The successful response includes a JSON object like the following:

{
    "code": 0
}

The error response includes a JSON object like the following:

{
    "code": 3016,
    "message": "Can't connect database"
}

Get document chunk list

GET /api/v1/dataset/{dataset_id}/document/{document_id}/chunk

Get document chunk list

Request

  • Method: GET
  • URL: /api/v1/dataset/{dataset_id}/document/{document_id}/chunk
  • Headers:
    • content-Type: application/json
    • 'Authorization: Bearer {YOUR_ACCESS_TOKEN}'

Request example 

curl --request GET \
     --url http://{address}/api/v1/dataset/{dataset_id}/document/{document_id}/chunk \
     --header 'Content-Type: application/json' \
     --header 'Authorization: Bearer {YOUR_ACCESS_TOKEN}'

Request parameters

  • "dataset_id": (Path parameter)
  • "document_id": (Path parameter)

Response

The successful response includes a JSON object like the following:

{
    "code": 0
    "data": {
        "chunks": [
            {
                "available_int": 1,
                "content": "<em>advantag</em>of ragflow increas accuraci and relev:by incorpor retriev inform , ragflow can gener respons that are more accur",
                "document_keyword": "ragflow_test.txt",
                "document_id": "77df9ef4759a11ef8bdd0242ac120004",
                "id": "4ab8c77cfac1a829c8d5ed022a0808c0",
                "image_id": "",
                "important_keywords": [],
                "positions": [
                    ""
                ]
            }
        ],
        "doc": {
            "chunk_count": 5,
            "create_date": "Wed, 18 Sep 2024 08:46:16 GMT",
            "create_time": 1726649176833,
            "created_by": "134408906b6811efbcd20242ac120005",
            "id": "77df9ef4759a11ef8bdd0242ac120004",
            "knowledgebase_id": "77d9d24e759a11ef880c0242ac120004",
            "location": "ragflow_test.txt",
            "name": "ragflow_test.txt",
            "parser_config": {
                "chunk_token_count": 128,
                "delimiter": "\n!?。；！？",
                "layout_recognize": true,
                "task_page_size": 12
            },
            "parser_method": "naive",
            "process_begin_at": "Wed, 18 Sep 2024 08:46:16 GMT",
            "process_duation": 7.3213,
            "progress": 1.0,
            "progress_msg": "\nTask has been received.\nStart to parse.\nFinish parsing.\nFinished slicing files(5). Start to embedding the content.\nFinished embedding(6.16)! Start to build index!\nDone!",
            "run": "3",
            "size": 4209,
            "source_type": "local",
            "status": "1",
            "thumbnail": null,
            "token_count": 746,
            "type": "doc",
            "update_date": "Wed, 18 Sep 2024 08:46:23 GMT",
            "update_time": 1726649183321
        },
        "total": 1
    },
}

The error response includes a JSON object like the following:

{
    "code": 3016,
    "message": "Can't connect database"
}

Delete document chunks

DELETE /api/v1/dataset/{dataset_id}/document/{document_id}/chunk

Delete document chunks

Request

  • Method: DELETE
  • URL: /api/v1/dataset/{dataset_id}/document/{document_id}/chunk
  • Headers:
    • content-Type: application/json
    • 'Authorization: Bearer {YOUR_ACCESS_TOKEN}'

Request example 

curl --request DELETE \
     --url http://{address}/api/v1/dataset/{dataset_id}/document/{document_id}/chunk \
     --header 'Content-Type: application/json' \
     --header 'Authorization: Bearer {YOUR_ACCESS_TOKEN}'
     --raw '{
         "chunks": ["f6b170ac758811efa0660242ac120004", "97ad64b6759811ef9fc30242ac120004"]
     }'

Update document chunk

PUT /api/v1/dataset/{dataset_id}/document/{document_id}/chunk

Update document chunk

Request

  • Method: PUT
  • URL: /api/v1/dataset/{dataset_id}/document/{document_id}/chunk
  • Headers:
    • content-Type: application/json
    • 'Authorization: Bearer {YOUR_ACCESS_TOKEN}'

Request example 

curl --request PUT \
     --url http://{address}/api/v1/dataset/{dataset_id}/document/{document_id}/chunk \
     --header 'Content-Type: application/json' \
     --header 'Authorization: Bearer {YOUR_ACCESS_TOKEN}'
     --raw '{
        "chunk_id": "d87fb0b7212c15c18d0831677552d7de",  
        "knowledgebase_id": null,  
        "name": "",  
        "content": "ragflow123",  
        "important_keywords": [],   
        "document_id": "e6bbba92759511efaa900242ac120004",  
        "status": "1" 
     }'

Insert document chunks

POST /api/v1/dataset/{dataset_id}/document/{document_id}/chunk

Insert document chunks

Request

  • Method: POST
  • URL: /api/v1/dataset/{dataset_id}/document/{document_id}/chunk
  • Headers:
    • content-Type: application/json
    • 'Authorization: Bearer {YOUR_ACCESS_TOKEN}'

Request example 

curl --request POST \
     --url http://{address}/api/v1/dataset/{dataset_id}/document/{document_id}/chunk \
     --header 'Content-Type: application/json' \
     --header 'Authorization: Bearer {YOUR_ACCESS_TOKEN}'
     --raw '{
         "document_id": "97ad64b6759811ef9fc30242ac120004",
         "content": ["ragflow content", "ragflow content"]
     }'

Dataset retrieval test

GET /api/v1/dataset/{dataset_id}/retrieval

Retrieval test of a dataset

Request

  • Method: GET
  • URL: /api/v1/dataset/{dataset_id}/retrieval
  • Headers:
    • content-Type: application/json
    • 'Authorization: Bearer {YOUR_ACCESS_TOKEN}'

Request example 

curl --request GET \
     --url http://{address}/api/v1/dataset/{dataset_id}/retrieval \
     --header 'Content-Type: application/json' \
     --header 'Authorization: Bearer {YOUR_ACCESS_TOKEN}'
     --raw '{
         "query_text": "This is a cat."
     }'

Create chat

POST /api/v1/chat

Create a chat

Request

  • Method: POST
  • URL: http://{address}/api/v1/chat
  • Headers:
    • content-Type: application/json
    • 'Authorization: Bearer {YOUR_ACCESS_TOKEN}'
  • Body:
    • "name": string
    • "avatar": string
    • "knowledgebases": List[DataSet]
    • "id": string
    • "llm": LLM
    • "prompt": Prompt

Request example 

curl --request POST \
     --url http://{address}/api/v1/chat \
     --header 'Content-Type: application/json' \
     --header 'Authorization: Bearer {YOUR_ACCESS_TOKEN}'
     --data-binary '{
   "knowledgebases": [
    {
      "avatar": null,
      "chunk_count": 0,
      "description": null,
      "document_count": 0,
      "embedding_model": "",
      "id": "0b2cbc8c877f11ef89070242ac120005",
      "language": "English",
      "name": "Test_assistant",
      "parse_method": "naive",
      "parser_config": {
        "pages": [
          [
            1,
            1000000
          ]
        ]
      },
      "permission": "me",
      "tenant_id": "4fb0cd625f9311efba4a0242ac120006"
    }
  ],
    "name":"new_chat_1"
}'

Request parameters

  • "name": (Body parameter)
    The name of the created chat.

    • "assistant"

  • "avatar": (Body parameter)
    The icon of the created chat.

    • "path"

  • "knowledgebases": (Body parameter)
    Select knowledgebases associated.

    • ["kb1"]

  • "id": (Body parameter)
    The id of the created chat.

    • ""

  • "llm": (Body parameter)
    The LLM of the created chat.

    • If the value is None, a dictionary with default values will be generated.

  • "prompt": (Body parameter)
    The prompt of the created chat.

    • If the value is None, a dictionary with default values will be generated.
Chat.LLM parameters:

  • "model_name": (Body parameter)
    Large language chat model.

    • If it is None, it will return the user's default model.

  • "temperature": (Body parameter)
    Controls the randomness of predictions by the model. A lower temperature makes the model more confident, while a higher temperature makes it more creative and diverse.

    • 0.1

  • "top_p": (Body parameter)
    Also known as "nucleus sampling," it focuses on the most likely words, cutting off the less probable ones.

    • 0.3

  • "presence_penalty": (Body parameter)
    Discourages the model from repeating the same information by penalizing repeated content.

    • 0.4

  • "frequency_penalty": (Body parameter)
    Reduces the model’s tendency to repeat words frequently.

    • 0.7

  • "max_tokens": (Body parameter)
    Sets the maximum length of the model’s output, measured in tokens (words or pieces of words).

    • 512
Chat.Prompt parameters:

  • "similarity_threshold": (Body parameter)
    Filters out chunks with similarity below this threshold.

    • 0.2

  • "keywords_similarity_weight": (Body parameter)
    Weighted keywords similarity and vector cosine similarity; the sum of weights is 1.0.

    • 0.7

  • "top_n": (Body parameter)
    Only the top N chunks above the similarity threshold will be fed to LLMs.

    • 8

  • "variables": (Body parameter)
    Variables help with different chat strategies by filling in the 'System' part of the prompt.

    • [{"key": "knowledge", "optional": True}]

  • "rerank_model": (Body parameter)
    If empty, it uses vector cosine similarity; otherwise, it uses rerank score.

    • ""

  • "empty_response": (Body parameter)
    If nothing is retrieved, this will be used as the response. Leave blank if LLM should provide its own opinion.

    • None

  • "opener": (Body parameter)
    The welcome message for clients.

    • "Hi! I'm your assistant, what can I do for you?"

  • "show_quote": (Body parameter)
    Indicates whether the source of the original text should be displayed.

    • True

  • "prompt": (Body parameter)
    Instructions for LLM to follow when answering questions, such as character design or answer length.

    • "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. Here is the knowledge base: {knowledge} The above is the knowledge base."

Response

Success:

{
    "code": 0,
    "data": {
        "avatar": "",
        "create_date": "Fri, 11 Oct 2024 03:23:24 GMT",
        "create_time": 1728617004635,
        "description": "A helpful Assistant",
        "do_refer": "1",
        "id": "2ca4b22e878011ef88fe0242ac120005",
        "knowledgebases": [
            {
                "avatar": null,
                "chunk_count": 0,
                "description": null,
                "document_count": 0,
                "embedding_model": "",
                "id": "0b2cbc8c877f11ef89070242ac120005",
                "language": "English",
                "name": "Test_assistant",
                "parse_method": "naive",
                "parser_config": {
                    "pages": [
                        [
                            1,
                            1000000
                        ]
                    ]
                },
                "permission": "me",
                "tenant_id": "4fb0cd625f9311efba4a0242ac120006"
            }
        ],
        "language": "English",
        "llm": {
            "frequency_penalty": 0.7,
            "max_tokens": 512,
            "model_name": "deepseek-chat___OpenAI-API@OpenAI-API-Compatible",
            "presence_penalty": 0.4,
            "temperature": 0.1,
            "top_p": 0.3
        },
        "name": "new_chat_1",
        "prompt": {
            "empty_response": "Sorry! 知识库中未找到相关内容！",
            "keywords_similarity_weight": 0.3,
            "opener": "您好，我是您的助手小樱，长得可爱又善良，can I help you?",
            "prompt": "你是一个智能助手，请总结知识库的内容来回答问题，请列举知识库中的数据详细回答。当所有知识库内容都与问题无关时，你的回答必须包括“知识库中未找到您要的答案！”这句话。回答需要考虑聊天历史。\n            以下是知识库：\n            {knowledge}\n            以上是知识库。",
            "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, 11 Oct 2024 03:23:24 GMT",
        "update_time": 1728617004635
    }
}

Error:

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

Update chat

PUT /api/v1/chat/{chat_id}

Update a chat

Request

  • Method: PUT
  • URL: http://{address}/api/v1/chat/{chat_id}
  • Headers:
    • content-Type: application/json
    • 'Authorization: Bearer {YOUR_ACCESS_TOKEN}'
  • Body: (Refer to the "Create chat" for the complete structure of the request body.)

Request example 

curl --request PUT \
  --url http://{address}/api/v1/chat/{chat_id} \
  --header 'Content-Type: application/json' \
  --header 'Authorization: Bearer {YOUR_ACCESS_TOKEN}' \
  --data '{
    "name":"Test"
}'

Parameters

(Refer to the "Create chat" for the complete structure of the request parameters.)

Response

Success

{
    "code": 0
}

Error

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

Delete chats

DELETE /api/v1/chat

Delete chats

Request

  • Method: DELETE
  • URL: http://{address}/api/v1/chat
  • Headers:
    • content-Type: application/json
    • 'Authorization: Bearer {YOUR_ACCESS_TOKEN}'
  • Body:
    • ids: List[string]

Request example 

# Either id or name must be provided, but not both.
curl --request DELETE \
  --url http://{address}/api/v1/chat \
  --header 'Content-Type: application/json' \
  --header 'Authorization: Bearer {YOUR_ACCESS_TOKEN}' \
  --data '{
  "ids": ["test_1", "test_2"]
  }'
}'

Request parameters:

  • "ids": (Body parameter)
    IDs of the chats to be deleted.
    • None

Response

Success

{
    "code": 0
}

Error

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

List chats

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

List chats based on filter criteria.

Request

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

Request example 

curl --request GET \
  --url http://{address}/api/v1/chat?page={page}&page_size={page_size}&orderby={orderby}&desc={desc}&name={dataset_name}&id={dataset_id} \
  --header 'Authorization: Bearer {YOUR_ACCESS_TOKEN}'

Request parameters

  • "page": (Path parameter)
    The current page number to retrieve from the paginated data. This parameter determines which set of records will be fetched.

    • 1

  • "page_size": (Path parameter)
    The number of records to retrieve per page. This controls how many records will be included in each page.

    • 1024

  • "orderby": (Path parameter)
    The field by which the records should be sorted. This specifies the attribute or column used to order the results.

    • "create_time"

  • "desc": (Path parameter)
    A boolean flag indicating whether the sorting should be in descending order.

    • True

  • "id": (Path parameter)
    The ID of the chat to be retrieved.

    • None

  • "name": (Path parameter)
    The name of the chat to be retrieved.

    • None

Response

Success

{
    "code": 0,
    "data": [
        {
            "avatar": "",
            "create_date": "Fri, 11 Oct 2024 03:23:24 GMT",
            "create_time": 1728617004635,
            "description": "A helpful Assistant",
            "do_refer": "1",
            "id": "2ca4b22e878011ef88fe0242ac120005",
            "knowledgebases": [
                {
                    "avatar": "",
                    "chunk_num": 0,
                    "create_date": "Fri, 11 Oct 2024 03:15:18 GMT",
                    "create_time": 1728616518986,
                    "created_by": "69736c5e723611efb51b0242ac120007",
                    "description": "",
                    "doc_num": 0,
                    "embd_id": "BAAI/bge-large-zh-v1.5",
                    "id": "0b2cbc8c877f11ef89070242ac120005",
                    "language": "English",
                    "name": "test_delete_chat",
                    "parser_config": {
                        "chunk_token_count": 128,
                        "delimiter": "\n!?。；！？",
                        "layout_recognize": true,
                        "task_page_size": 12
                    },
                    "parser_id": "naive",
                    "permission": "me",
                    "similarity_threshold": 0.2,
                    "status": "1",
                    "tenant_id": "69736c5e723611efb51b0242ac120007",
                    "token_num": 0,
                    "update_date": "Fri, 11 Oct 2024 04:01:31 GMT",
                    "update_time": 1728619291228,
                    "vector_similarity_weight": 0.3
                }
            ],
            "language": "English",
            "llm": {
                "frequency_penalty": 0.7,
                "max_tokens": 512,
                "model_name": "deepseek-chat___OpenAI-API@OpenAI-API-Compatible",
                "presence_penalty": 0.4,
                "temperature": 0.1,
                "top_p": 0.3
            },
            "name": "Test",
            "prompt": {
                "empty_response": "Sorry! 知识库中未找到相关内容！",
                "keywords_similarity_weight": 0.3,
                "opener": "您好，我是您的助手小樱，长得可爱又善良，can I help you?",
                "prompt": "你是一个智能助手，请总结知识库的内容来回答问题，请列举知识库中的数据详细回答。当所有知识库内容都与问题无关时，你的回答必须包括“知识库中未找到您要的答案！”这句话。回答需要考虑聊天历史。\n            以下是知识库：\n            {knowledge}\n            以上是知识库。",
                "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, 11 Oct 2024 03:47:58 GMT",
            "update_time": 1728618478392
        }
    ]
}

Error

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

Create a chat session

POST /api/v1/chat/{chat_id}/session

Create a chat session

Request

  • Method: POST
  • URL: http://{address}/api/v1/chat/{chat_id}/session
  • Headers:
    • content-Type: application/json
    • 'Authorization: Bearer {YOUR_ACCESS_TOKEN}'
  • Body:
    • name: string

Request example 

curl --request POST \
  --url http://{address}/api/v1/chat/{chat_id}/session \
  --header 'Content-Type: application/json' \
  --header 'Authorization: Bearer {YOUR_ACCESS_TOKEN}' \
  --data '{
    "name": "new session"
  }'

Request parameters

  • "id": (Body parameter)
    The ID of the created session used to identify different sessions.

    • None
    • id cannot be provided when creating.

  • "name": (Body parameter)
    The name of the created session.

    • "New session"

  • "messages": (Body parameter)
    The messages of the created session.

    • [{"role": "assistant", "content": "Hi! I am your assistant, can I help you?"}]
    • messages cannot be provided when creating.

  • "chat_id": (Path parameter)
    The ID of the associated chat.

    • ""
    • chat_id cannot be changed.

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
    }
}

Error

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

List the sessions of a chat

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

List all sessions under the chat based on the filtering criteria.

Request

  • Method: GET
  • URL: http://{address}/api/v1/chat/{chat_id}/session?page={page}&page_size={page_size}&orderby={orderby}&desc={desc}&name={dataset_name}&id={dataset_id}
  • Headers:
    • 'Authorization: Bearer {YOUR_ACCESS_TOKEN}'

Request example 

curl --request GET \
  --url http://{address}/api/v1/chat/{chat_id}/session?page={page}&page_size={page_size}&orderby={orderby}&desc={desc}&name={dataset_name}&id={dataset_id} \
  --header 'Authorization: Bearer {YOUR_ACCESS_TOKEN}'

Request Parameters

  • "page": (Path parameter)
    The current page number to retrieve from the paginated data. This parameter determines which set of records will be fetched.

    • 1

  • "page_size": (Path parameter)
    The number of records to retrieve per page. This controls how many records will be included in each page.

    • 1024

  • "orderby": (Path parameter)
    The field by which the records should be sorted. This specifies the attribute or column used to order the results.

    • "create_time"

  • "desc": (Path parameter)
    A boolean flag indicating whether the sorting should be in descending order.

    • True

  • "id": (Path parameter)
    The ID of the session to be retrieved.

    • None

  • "name": (Path parameter)
    The name of the session to be retrieved.

    • None

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
        }
    ]
}

Error

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

Delete chat sessions

DELETE /api/v1/chat/{chat_id}/session

Delete chat sessions

Request

  • Method: DELETE
  • URL: http://{address}/api/v1/chat/{chat_id}/session
  • Headers:
    • content-Type: application/json
    • 'Authorization: Bearer {YOUR_ACCESS_TOKEN}'
  • Body:
    • ids: List[string]

Request example 

# Either id or name must be provided, but not both.
curl --request DELETE \
--url http://{address}/api/v1/chat/{chat_id}/session \
--header 'Content-Type: application/json' \
--header 'Authorization: Bear {YOUR_ACCESS_TOKEN}' \
  --data '{
  "ids": ["test_1", "test_2"]
  }'

Request Parameters

  • ids: (Body Parameter)
    IDs of the sessions to be deleted.
    • None

Response

Success

{
    "code": 0
}

Error

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

Update a chat session

PUT /api/v1/chat/{chat_id}/session/{session_id}

Update a chat session

Request

  • Method: PUT
  • URL: http://{address}/api/v1/chat/{chat_id}/session/{session_id}
  • Headers:
    • content-Type: application/json
    • 'Authorization: Bearer {YOUR_ACCESS_TOKEN}'
  • Body:
    • name: string

Request example 

curl --request PUT \
  --url http://{address}/api/v1/chat/{chat_id}/session/{session_id} \
  --header 'Content-Type: application/json' \
  --header 'Authorization: Bearer {YOUR_ACCESS_TOKEN}' \
  --data '{
    "name": "Updated session"
  }'

Request Parameter

  • name:(*Body Parameter)
    The name of the created session.
    • None

Response

Success

{
    "code": 0
}

Error

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

Chat with a chat session

POST /api/v1/chat/{chat_id}/session/{session_id}/completion

Chat with a chat session

Request

  • Method: POST
  • URL: http://{address} /api/v1/chat/{chat_id}/session/{session_id}/completion
  • Headers:
    • content-Type: application/json
    • 'Authorization: Bearer {YOUR_ACCESS_TOKEN}'
  • Body:
    • question: string
    • stream: bool

Request example 

curl --request POST \
  --url http://{address} /api/v1/chat/{chat_id}/session/{session_id}/completion \
  --header 'Content-Type: application/json' \
  --header 'Authorization: Bearer {YOUR_ACCESS_TOKEN}' \
  --data-binary '{
    "question":  "你好!",
    "stream": true
  }'

Request Parameters

  • question:(Body Parameter)
    The question you want to ask.
    • question is required. None
  • stream: (Body Parameter)
    The approach of streaming text generation. False

Response

Success

data: {
    "code": 0,
    "data": {
        "answer": "您好！有什么具体的问题或者需要的帮助",
        "reference": {},
        "audio_binary": null,
        "id": "31153052-7bac-4741-a513-ed07d853f29e"
    }
}

data: {
    "code": 0,
    "data": {
        "answer": "您好！有什么具体的问题或者需要的帮助可以告诉我吗？我在这里是为了帮助",
        "reference": {},
        "audio_binary": null,
        "id": "31153052-7bac-4741-a513-ed07d853f29e"
    }
}

data: {
    "code": 0,
    "data": {
        "answer": "您好！有什么具体的问题或者需要的帮助可以告诉我吗？我在这里是为了帮助您的。如果您有任何疑问或是需要获取",
        "reference": {},
        "audio_binary": null,
        "id": "31153052-7bac-4741-a513-ed07d853f29e"
    }
}

data: {
    "code": 0,
    "data": {
        "answer": "您好！有什么具体的问题或者需要的帮助可以告诉我吗？我在这里是为了帮助您的。如果您有任何疑问或是需要获取某些信息，请随时提出。",
        "reference": {},
        "audio_binary": null,
        "id": "31153052-7bac-4741-a513-ed07d853f29e"
    }
}

data: {
    "code": 0,
    "data": {
        "answer": "您好！有什么具体的问题或者需要的帮助可以告诉我吗 ##0$$？我在这里是为了帮助您的。如果您有任何疑问或是需要获取某些信息，请随时提出。",
        "reference": {
            "total": 19,
            "chunks": [
                {
                    "chunk_id": "9d87f9d70a0d8a7565694a81fd4c5d5f",
                    "content_ltks": "当所有知识库内容都与问题无关时 ,你的回答必须包括“知识库中未找到您要的答案!”这句话。回答需要考虑聊天历史。\r\n以下是知识库:\r\n{knowledg}\r\n以上是知识库\r\n\"\"\"\r\n 1\r\n 2\r\n 3\r\n 4\r\n 5\r\n 6\r\n总结\r\n通过上面的介绍,可以对开源的 ragflow有了一个大致的了解,与前面的有道qanyth整体流程还是比较类似的。 ",
                    "content_with_weight": "当所有知识库内容都与问题无关时，你的回答必须包括“知识库中未找到您要的答案！”这句话。回答需要考虑聊天历史。\r\n    以下是知识库：\r\n    {knowledge}\r\n    以上是知识库\r\n\"\"\"\r\n1\r\n2\r\n3\r\n4\r\n5\r\n6\r\n总结\r\n通过上面的介绍，可以对开源的 RagFlow 有了一个大致的了解，与前面的 有道 QAnything 整体流程还是比较类似的。",
                    "doc_id": "5c5999ec7be811ef9cab0242ac120005",
                    "docnm_kwd": "1.txt",
                    "kb_id": "c7ee74067a2c11efb21c0242ac120006",
                    "important_kwd": [],
                    "img_id": "",
                    "similarity": 0.38337178633282265,
                    "vector_similarity": 0.3321336754679629,
                    "term_similarity": 0.4053309767034769,
                    "positions": [
                        ""
                    ]
                },
                {
                    "chunk_id": "895d34de762e674b43e8613c6fb54c6d",
                    "content_ltks": "\r\n\r\n实际内容可能会超过大模型的输入token数量,因此在调用大模型前会调用api/db/servic/dialog_service.py文件中 messag_fit_in ()根据大模型可用的 token数量进行过滤。这部分与有道的 qanyth的实现大同小异,就不额外展开了。\r\n\r\n将检索的内容,历史聊天记录以及问题构造为 prompt ,即可作为大模型的输入了 ,默认的英文prompt如下所示:\r\n\r\n\"\"\"\r\nyou are an intellig assistant. pleas summar the content of the knowledg base to answer the question. pleas list thedata in the knowledg base and answer in detail. when all knowledg base content is irrelev to the question , your answer must includ the sentenc\"the answer you are lookfor isnot found in the knowledg base!\" answer needto consid chat history.\r\n here is the knowledg base:\r\n{ knowledg}\r\nthe abov is the knowledg base.\r\n\"\"\"\r\n1\r\n 2\r\n 3\r\n 4\r\n 5\r\n 6\r\n对应的中文prompt如下所示:\r\n\r\n\"\"\"\r\n你是一个智能助手,请总结知识库的内容来回答问题,请列举知识库中的数据详细回答。 ",
                    "content_with_weight": "\r\n\r\n实际内容可能会超过大模型的输入 token 数量，因此在调用大模型前会调用 api/db/services/dialog_service.py 文件中 message_fit_in() 根据大模型可用的 token 数量进行过滤。这部分与有道的 QAnything 的实现大同小异，就不额外展开了。\r\n\r\n将检索的内容，历史聊天记录以及问题构造为 prompt，即可作为大模型的输入了，默认的英文 prompt 如下所示：\r\n\r\n\"\"\"\r\nYou 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.\r\n      Here is the knowledge base:\r\n      {knowledge}\r\n      The above is the knowledge base.\r\n\"\"\"\r\n1\r\n2\r\n3\r\n4\r\n5\r\n6\r\n对应的中文 prompt 如下所示：\r\n\r\n\"\"\"\r\n你是一个智能助手，请总结知识库的内容来回答问题，请列举知识库中的数据详细回答。",
                    "doc_id": "5c5999ec7be811ef9cab0242ac120005",
                    "docnm_kwd": "1.txt",
                    "kb_id": "c7ee74067a2c11efb21c0242ac120006",
                    "important_kwd": [],
                    "img_id": "",
                    "similarity": 0.2788204323926715,
                    "vector_similarity": 0.35489427679953667,
                    "term_similarity": 0.2462173562183008,
                    "positions": [
                        ""
                    ]
                }
            ],
            "doc_aggs": [
                {
                    "doc_name": "1.txt",
                    "doc_id": "5c5999ec7be811ef9cab0242ac120005",
                    "count": 2
                }
            ]
        },
        "prompt": "你是一个智能助手，请总结知识库的内容来回答问题，请列举知识库中的数据详细回答。当所有知识库内容都与问题无关时，你的回答必须包括“知识库中未找到您要的答案！”这句话。回答需要考虑聊天历史。\n            以下是知识库：\n            当所有知识库内容都与问题无关时，你的回答必须包括“知识库中未找到您要的答案！”这句话。回答需要考虑聊天历史。\r\n    以下是知识库：\r\n    {knowledge}\r\n    以上是知识库\r\n\"\"\"\r\n1\r\n2\r\n3\r\n4\r\n5\r\n6\r\n总结\r\n通过上面的介绍，可以对开源的 RagFlow 有了一个大致的了解，与前面的 有道 QAnything 整体流程还是比较类似的。\n\n------\n\n\r\n\r\n实际内容可能会超过大模型的输入 token 数量，因此在调用大模型前会调用 api/db/services/dialog_service.py 文件中 message_fit_in() 根据大模型可用的 token 数量进行过滤。这部分与有道的 QAnything 的实现大同小异，就不额外展开了。\r\n\r\n将检索的内容，历史聊天记录以及问题构造为 prompt，即可作为大模型的输入了，默认的英文 prompt 如下所示：\r\n\r\n\"\"\"\r\nYou 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.\r\n      Here is the knowledge base:\r\n      {knowledge}\r\n      The above is the knowledge base.\r\n\"\"\"\r\n1\r\n2\r\n3\r\n4\r\n5\r\n6\r\n对应的中文 prompt 如下所示：\r\n\r\n\"\"\"\r\n你是一个智能助手，请总结知识库的内容来回答问题，请列举知识库中的数据详细回答。\n            以上是知识库。\n\n### Query:\n你好，请问有什么问题需要我帮忙解答吗？\n\n### Elapsed\n  - Retrieval: 9131.1 ms\n  - LLM: 12802.6 ms",
        "id": "31153052-7bac-4741-a513-ed07d853f29e"
    }
}

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

Error

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