api/http_api.md

DRAFT! HTTP API Reference

THE API REFERENCES BELOW ARE STILL UNDER DEVELOPMENT.

---

:::tip NOTE Dataset Management :::

---

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:
  • "name": string
  • "avatar": 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

# "name": name is required and can't be duplicated.
# "embedding_model": embedding_model must not be provided.
# "naive" 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

• "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.

• "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 access 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 ID.

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) The IDs of the dataset 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 configurations for a specified dataset.

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. 
# "naive" 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": "naive"
}'

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}

Lists all datasets?????

Retrieves a list of 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"
}

---

:::tip API GROUPING File Management within Dataset :::

---

Upload documents

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

Uploads documents to a specified 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!"
}

---

Update document

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

Updates configurations for a specified document.

Request

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

Request example

curl --request PUT \
  --url http://{address}/api/v1/dataset/{dataset_id}/info/{document_id} \
  --header 'Authorization: Bearer {YOUR_ACCESS TOKEN}' \
  --header 'Content-Type: application/json' \
  --data '{
  "name": "manual.txt", 
  "parser_method": "manual", 
  "parser_config": {"chunk_token_count": 128, "delimiter": "\n!?。；！？", "layout_recognize": true, "task_page_size": 12}
  }'

Request parameters

• "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.

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

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."
}

---

Download document

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

Downloads a document from a specified dataset.

Request

• Method: GET
• URL: http://{address}/api/v1/dataset/{dataset_id}/document/{document_id}
• Headers:
  • 'Authorization: Bearer {YOUR_ACCESS_TOKEN}'
• Output:
  • '{FILE_NAME}'

Request example

curl --request GET \
  --url http://{address}/api/v1/dataset/{dataset_id}/document/{document_id} \
  --header 'Authorization: Bearer {YOUR_ACCESS_TOKEN}' \
  --output ./ragflow.txt

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 text 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 documents

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

Retrieves a list of documents from a specified 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. "
}

---

Delete documents

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

Deletes documents by ID.

Request

• Method: DELETE
• URL: http://{address}/api/v1/dataset/{dataset_id}/document
• Headers:
  • 'Content-Type: application/json'
  • 'Authorization: Bearer {YOUR_ACCESS_TOKEN}'
• Body:
  • ids:list[str]

Request example

curl --request DELETE \
  --url http://{address}/api/v1/dataset/{dataset_id}/document \
  --header 'Content-Type: application/json' \
  --header 'Authorization: {YOUR ACCESS TOKEN}' \
  --data '{
  "ids": ["id_1","id_2"]
  }'

Request parameters

• "ids": (Body parameter) The ids of teh documents 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 do not own the dataset 7898da028a0511efbf750242ac1220005."
}

---

Parse documents

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

Parses documents in a specified dataset.

Request

• Method: POST
• URL: http://{address}/api/v1/dataset/{dataset_id}/chunk
• Headers:
  • content-Type: application/json
  • 'Authorization: Bearer {YOUR_ACCESS_TOKEN}'
• Body:
  • document_ids:List[str]

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}' \
    --data '{"document_ids": ["97a5f1c2759811efaa500242ac120004","97ad64b6759811ef9fc30242ac120004"]}'

Request parameters

• "dataset_id": (Path parameter)
• "document_ids":(Body parameter)
  The ids of the documents to be parsed

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": "`document_ids` is required"
}

---

Stop parsing documents

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

Stops parsing specified documents.

Request

• Method: DELETE
• URL: http://{address}/api/v1/dataset/{dataset_id}/chunk
• Headers:
  • content-Type: application/json
  • 'Authorization: Bearer {YOUR_ACCESS_TOKEN}'
• Body:
  • document_ids:List[str]

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}' \
   --data '{"document_ids": ["97a5f1c2759811efaa500242ac120004","97ad64b6759811ef9fc30242ac120004"]}'

Request parameters

• "dataset_id": (Path parameter)
• "document_ids":(Body parameter)
  The ids of the documents to be parsed

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": "`document_ids` is required"
}

---

Add chunks

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

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

Request

• Method: POST
• URL: http://{address}/api/v1/dataset/{dataset_id}/document/{document_id}/chunk
• Headers:
  • content-Type: application/json
  • 'Authorization: Bearer {YOUR_ACCESS_TOKEN}'
• Body:
  • content: string
  • important_keywords: list[string]

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}' \
  --data '{
    "content": "ragflow content"
}'

Request parameters

• content:(Body parameter)
  Contains the main text or information of the chunk.
• important_keywords(Body parameter)
  list the key terms or phrases that are significant or central to the chunk's content.

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": []
        }
    }
}

Error

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

---

List chunks

GET /api/v1/dataset/{dataset_id}/document/{document_id}/chunk?keywords={keywords}&offset={offset}&limit={limit}&id={id}

Retrieves a list of chunks from a specified document in a specified dataset.

Request

• Method: GET
• URL: http://{address}/api/v1/dataset/{dataset_id}/document/{document_id}/chunk?keywords={keywords}&offset={offset}&limit={limit}&id={id}
• Headers:
  • 'Authorization: Bearer {YOUR_ACCESS_TOKEN}'

Request example

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

Request parameters

• "dataset_id": (Path parameter)
• "document_id": (Path parameter)
• "offset"(Filter parameter)
  The beginning number of records for paging.
• "keywords"(Filter parameter)
  List chunks whose name has the given keywords
• "limit"(Filter parameter)
  Records number to return
• "id"(Filter parameter)
  The ID of chunk to retrieve.

Response

The successful response includes a JSON object like the following:

{
    "code": 0,
    "data": {
        "chunks": [],
        "doc": {
            "chunk_num": 0,
            "create_date": "Sun, 29 Sep 2024 03:47:29 GMT",
            "create_time": 1727581649216,
            "created_by": "69736c5e723611efb51b0242ac120007",
            "id": "8cb781ec7e1511ef98ac0242ac120006",
            "kb_id": "c7ee74067a2c11efb21c0242ac120006",
            "location": "sunny_tomorrow.txt",
            "name": "sunny_tomorrow.txt",
            "parser_config": {
                "pages": [
                    [
                        1,
                        1000000
                    ]
                ]
            },
            "parser_id": "naive",
            "process_begin_at": "Tue, 15 Oct 2024 10:23:51 GMT",
            "process_duation": 1435.37,
            "progress": 0.0370833,
            "progress_msg": "\nTask has been received.",
            "run": "1",
            "size": 24,
            "source_type": "local",
            "status": "1",
            "thumbnail": null,
            "token_num": 0,
            "type": "doc",
            "update_date": "Tue, 15 Oct 2024 10:47:46 GMT",
            "update_time": 1728989266371
        },
        "total": 0
    }
}

The error response includes a JSON object like the following:

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

---

Delete chunks

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

Deletes chunks by ID.

Request

• Method: DELETE
• URL: http://{address}/api/v1/dataset/{dataset_id}/document/{document_id}/chunk
• Headers:
  • content-Type: application/json
  • 'Authorization: Bearer {YOUR_ACCESS_TOKEN}'
• Body:
  • chunk_ids: list[string]

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}' \
  --data '{
  "chunk_ids": ["test_1", "test_2"]
  }'

Request parameters

• "chunk_ids":(Body parameter) The chunks of the document to be deleted

Response

Success

{
    "code": 0
}

Error

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

---

Update chunk

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

Updates content or configurations for a specified chunk.

Request

• Method: PUT
• URL: http://{address}/api/v1/dataset/{dataset_id}/document/{document_id}/chunk/{chunk_id}
• Headers:
  • content-Type: application/json
  • 'Authorization: Bearer {YOUR_ACCESS_TOKEN}'
• Body:
  • content:str
  • important_keywords:str
  • available:int

Request example

curl --request PUT \
  --url http://{address}/api/v1/dataset/{dataset_id}/document/{document_id}/chunk/{chunk_id} \
  --header 'Content-Type: application/json' \
  --header 'Authorization: {YOUR_ACCESS_TOKEN}' \
  --data '{   
    "content": "ragflow123",  
    "important_keywords": [],   
}'

Request parameters

• "content":(Body parameter) Contains the main text or information of the chunk.
• "important_keywords":(Body parameter) list the key terms or phrases that are significant or central to the chunk's content.
• "available":(Body parameter) Indicating the availability status, 0 means unavailable and 1 means available.

Response

Success

{
    "code": 0
}

Error

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

---

Dataset retrieval test

GET /api/v1/retrieval

Retrieval test of a dataset

Request

• Method: POST
• URL: http://{address}/api/v1/retrieval
• Headers:
  • content-Type: application/json
  • 'Authorization: Bearer {YOUR_ACCESS_TOKEN}'
• Body:
  • question: str
  • datasets: List[str]
  • documents: List[str]
  • offset: int
  • limit: int
  • similarity_threshold: float
  • vector_similarity_weight: float
  • top_k: int
  • rerank_id: string
  • keyword: bool
  • highlight: bool

Request example

curl --request POST \
  --url http://{address}/api/v1/retrieval \
  --header 'Content-Type: application/json' \
  --header 'Authorization: {YOUR_ACCESS_TOKEN}' \
  --data '{
    "question": "What is advantage of ragflow?",
    "datasets": [
        "b2a62730759d11ef987d0242ac120004"
    ],
    "documents": [
        "77df9ef4759a11ef8bdd0242ac120004"
    ]
}'

Request parameter

• "question": (Body parameter)
  User's question, search keywords
  ""

• "datasets": (Body parameter)
  The scope of datasets
  None

• "documents": (Body parameter)
  The scope of document. None means no limitation
  None

• "offset": (Body parameter)
  The beginning point of retrieved records
  1

• "limit": (Body parameter)
  The maximum number of records needed to return
  30

• "similarity_threshold": (Body parameter)
  The minimum similarity score
  0.2

• "vector_similarity_weight": (Body parameter)
  The weight of vector cosine similarity, 1 - x is the term similarity weight
  0.3

• "top_k": (Body parameter)
  Number of records engaged in vector cosine computation
  1024

• "rerank_id": (Body parameter)
  ID of the rerank model
  None

• "keyword": (Body parameter)
  Whether keyword-based matching is enabled
  False

• "highlight": (Body parameter)
  Whether to enable highlighting of matched terms in the results
  False

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",
                "img_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
    }
}

Error

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

---

:::tip API GROUPING Chat Assistant Management :::

---

Create chat assistant

POST /api/v1/chat

Creates a chat assistant.

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 assistant

PUT /api/v1/chat/{chat_id}

Updates configurations for a specified chat assistant.

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 chat assistants

DELETE /api/v1/chat

Deletes chat assistants by ID.

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 (INCONSISTENT WITH THE PYTHON API)

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

Retrieves a list of chat assistants.

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."
}

---

:::tip API GROUPING Chat Session APIs :::

---

=========MISSING CREATE SESSION API!==============

---

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."
}

---

List sessions

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

Lists sessions associated with a specified????????????? chat assistant.

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 sessions

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

Deletes sessions by ID.

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"
}

---

Chat with a chat session???

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

Asks a question to start a conversation.

Request

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

Request example

curl --request POST \
  --url http://{address} /api/v1/chat/{chat_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
• session_id: (Body Parameter)
  The id of session.If not provided, a new session will be generated.

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。",
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                    "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",
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                    "similarity": 0.38337178633282265,
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                    "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": [],
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                    "similarity": 0.2788204323926715,
                    "vector_similarity": 0.35489427679953667,
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            "doc_aggs": [
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                    "doc_name": "1.txt",
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        "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"
    }
}

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Error

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