api/http_api_reference.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: /api/v1/dataset
• Headers:
  • 'content-Type: application/json'
  • 'Authorization: Bearer {YOUR_API_KEY}'
• Body:
  • "name": string
  • "avatar": string
  • "description": string
  • "language": string
  • "embedding_model": string
  • "permission": string
  • "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_API_KEY}' \
  --data '{
  "name": "test",
  "chunk_method": "naive"
}'

Request parameters

• "name": (Body parameter), string, Required
  The unique name of the dataset to create. It must adhere to the following requirements:

  • Permitted characters include:
    • English letters (a-z, A-Z)
    • Digits (0-9)
    • "_" (underscore)
  • Must begin with an English letter or underscore.
  • Maximum 65,535 characters.
  • Case-insensitive.

• "avatar": (Body parameter), string
  Base64 encoding of the avatar. Defaults to "".

• "description": (Body parameter), string
  A brief description of the dataset to create. Defaults to "".

• "language": (Body parameter), string
  The language setting of the dataset to create. Available options:

  • "English" (Default)
  • "Chinese"

• "embedding_model": (Body parameter), string
  The name of the embedding model to use. For example: "BAAI/bge-zh-v1.5"

• "permission": (Body parameter), string
  Specifies who can access the dataset to create. You can set it only to "me" for now.

• "chunk_method": (Body parameter), enum<string>
  The chunking method of the dataset to create. Available options:

  • "naive": General (default)
  • "manual: Manual
  • "qa": Q&A
  • "table": Table
  • "paper": Paper
  • "book": Book
  • "laws": Laws
  • "presentation": Presentation
  • "picture": Picture
  • "one":One
  • "knowledge_graph": Knowledge Graph
  • "email": Email

• "parser_config": (Body parameter)
  The configuration settings for the dataset parser. A ParserConfig object contains the following attributes:

  • "chunk_token_count": Defaults to 128.
  • "layout_recognize": Defaults to True.
  • "delimiter": Defaults to "\n!?。；！？".
  • "task_page_size": Defaults to 12.

Response

Success:

{
    "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
    }
}

Failure:

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

---

Delete datasets

DELETE /api/v1/dataset

Deletes datasets by ID.

Request

• Method: DELETE
• URL: /api/v1/dataset
• Headers:
  • 'content-Type: application/json'
  • 'Authorization: Bearer {YOUR_API_KEY}'
  • 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_API_KEY}' \
  --data '{
  "ids": ["test_1", "test_2"]
  }'

Request parameters

• "ids": (Body parameter) The IDs of the datasets to delete. Defaults to "". If not specified, all datasets in the system will be deleted.

Response

Success:

{
    "code": 0 
}

Failure:

{
    "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: /api/v1/dataset/{dataset_id}
• Headers:
  • 'content-Type: application/json'
  • 'Authorization: Bearer {YOUR_API_KEY}'
  • Body:
    • "name": string
    • "embedding_model": string
    • "chunk_method": enum<string>

Request example

# "id":  id is required.
# "name": If you update name, it can't be duplicated.
# "embedding_model": If you update embedding_model, 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_API_KEY}' \
  --data '{
  "name": "test",
  "embedding_model": "BAAI/bge-zh-v1.5",
  "parse_method": "naive"
}'

Request parameters

• "name": string The name of the dataset to update.
• "embedding_model": string The embedding model name to update.
  • Ensure that "chunk_count" is 0 before updating "embedding_model".
• "chunk_method": enum<string> The chunking method for the dataset. Available options:
  • "naive": General
  • "manual: Manual
  • "qa": Q&A
  • "table": Table
  • "paper": Paper
  • "book": Book
  • "laws": Laws
  • "presentation": Presentation
  • "picture": Picture
  • "one":One
  • "knowledge_graph": Knowledge Graph
  • "email": Email

Response

Success:

{
    "code": 0 
}

Failure:

{
    "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 datasets.

Request

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

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_API_KEY}'

Request parameters

• "page": (Path parameter)
  Specifies the page on which the datasets will be displayed. Defaults to 1.
• "page_size": (Path parameter)
  The number of datasets on each page. Defaults to 1024.
• "orderby": (Path parameter)
  The field by which datasets should be sorted. Available options:
  • "create_time" (default)
  • "update_time"
• "desc": (Path parameter)
  Indicates whether the retrieved datasets should be sorted in descending order. Defaults to True.
• "id": (Path parameter)
  The ID of the dataset to retrieve.
• "name": (Path parameter)
  The name of the dataset to retrieve.

Response

Success:

{
    "code": 0,
    "data": [
        {
            "avatar": "",
            "chunk_count": 59,
            "create_date": "Sat, 14 Sep 2024 01:12:37 GMT",
            "create_time": 1726276357324,
            "created_by": "69736c5e723611efb51b0242ac120007",
            "description": null,
            "document_count": 1,
            "embedding_model": "BAAI/bge-large-zh-v1.5",
            "id": "6e211ee0723611efa10a0242ac120007",
            "language": "English",
            "name": "mysql",
            "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
        }
    ]
}

Failure:

{
    "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_API_KEY}'
• 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_API_KEY}' \     
     --form 'file=@./test1.txt' \
     --form 'file=@./test2.pdf'

Request parameters

• "dataset_id": (Path parameter)
  The ID of the dataset to which the documents will be uploaded.
• "file": (Body parameter)
  The document to upload.

Response

Success:

{
    "code": 0 
}

Failure:

{
    "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: /api/v1/dataset/{dataset_id}/document/{document_id}
• Headers:
  • 'content-Type: application/json'
  • 'Authorization: Bearer {YOUR_API_KEY}'
• Body:
  • "name":string
  • "chunk_method":string
  • "parser_config":object

Request example

curl --request PUT \
  --url http://{address}/api/v1/dataset/{dataset_id}/info/{document_id} \
  --header 'Authorization: Bearer {YOUR_API_KEY}' \
  --header 'Content-Type: application/json' \
  --data '{
  "name": "manual.txt", 
  "chunk_method": "manual", 
  "parser_config": {"chunk_token_count": 128}
  }'

Request parameters

• "name": (Body parameter), string
• "chunk_method": (Body parameter), string
  The parsing method to apply to the document:
  • "naive": General
  • "manual: Manual
  • "qa": Q&A
  • "table": Table
  • "paper": Paper
  • "book": Book
  • "laws": Laws
  • "presentation": Presentation
  • "picture": Picture
  • "one": One
  • "knowledge_graph": Knowledge Graph
  • "email": Email
• "parser_config": (Body parameter), object The parsing configuration for the document:
  • "chunk_token_count": Defaults to 128.
  • "layout_recognize": Defaults to True.
  • "delimiter": Defaults to "\n!?。；！？".
  • "task_page_size": Defaults to 12.

Response

Success:

{
    "code": 0
}

Failure:

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

---

Download document

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

Downloads a document from a specified dataset.

Request

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

Request example

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

Request parameters

• "dataset_id": (Path parameter) The dataset ID.
• "documents_id": (Path parameter)
  The ID of the document to download.

Response

A successful response includes a text object like the following:

test_2.
```????????????????

Failure:

```json
{
    "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}

Lists documents in 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_API_KEY}'

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_API_KEY}'

Request parameters

• "dataset_id": (Path parameter)
  The dataset ID.
• "keywords": (Filter parameter), string
  The keywords used to match document titles.
• "offset": (Filter parameter), integer
  The starting index for the documents to retrieve. Typically used in conjunction with limit. Defaults to 1.
• "limit": (Filter parameter), integer
  The maximum number of documents to retrieve. Defaults to 1024.
• "orderby": (Filter parameter), string
  The field by which documents should be sorted. Available options:
  • "create_time" (default)
  • "update_time"
• "desc": (Filter parameter), boolean
  Indicates whether the retrieved documents should be sorted in descending order. Defaults to True.
• "document_id": (Filter parameter)
  The ID of the document to retrieve.

Response

Success:

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

Failure:

{
    "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: /api/v1/dataset/{dataset_id}/document
• Headers:
  • 'Content-Type: application/json'
  • 'Authorization: Bearer {YOUR_API_KEY}'
• Body:
  • "ids": list[string]

Request example

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

Request parameters

• "ids": (Body parameter), list[string] The IDs of the documents to delete. If not specified, all documents in the dataset will be deleted.

Response

Success:

{
    "code": 0
}.

Failure:

{
    "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: /api/v1/dataset/{dataset_id}/chunk
• Headers:
  • 'content-Type: application/json'
  • 'Authorization: Bearer {YOUR_API_KEY}'
• Body:
  • "document_ids": list[string]

Request example

curl --request POST \
    --url http://{address}/api/v1/dataset/{dataset_id}/chunk \
    --header 'Content-Type: application/json' \
    --header 'Authorization: Bearer {YOUR_API_KEY}' \
    --data '{"document_ids": ["97a5f1c2759811efaa500242ac120004","97ad64b6759811ef9fc30242ac120004"]}'

Request parameters

• "dataset_id": (Path parameter)
  The dataset ID.
• "document_ids": (Body parameter), list[string]
  The IDs of the documents to parse.

Response

Success:

{
    "code": 0
}

Failure:

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

---

Stop parsing documents

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

Stops parsing specified documents.

Request

• Method: DELETE
• URL: /api/v1/dataset/{dataset_id}/chunk
• Headers:
  • 'content-Type: application/json'
  • 'Authorization: Bearer {YOUR_API_KEY}'
• Body:
  • "document_ids": list[string]

Request example

curl --request DELETE \
   --url http://{address}/api/v1/dataset/{dataset_id}/chunk \
   --header 'Content-Type: application/json' \
   --header 'Authorization: Bearer {YOUR_API_KEY}' \
   --data '{"document_ids": ["97a5f1c2759811efaa500242ac120004","97ad64b6759811ef9fc30242ac120004"]}'

Request parameters

• "dataset_id": (Path parameter)
  The dataset ID
• "document_ids": (Body parameter)
  The IDs of the documents for which the parsing should be stopped.

Response

Success:

{
    "code": 0
}

Failure:

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

---

Add 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: /api/v1/dataset/{dataset_id}/document/{document_id}/chunk
• Headers:
  • 'content-Type: application/json'
  • 'Authorization: Bearer {YOUR_API_KEY}'
• Body:
  • "content": string
  • "important_keywords": list[string]

Request example

curl --request POST \
  --url http://{address}/api/v1/dataset/{dataset_id}/document/{document_id}/chunk \
  --header 'Content-Type: application/json' \
  --header 'Authorization: Bearer {YOUR_API_KEY}' \
  --data '{
    "content": "<SOME_CHUNK_CONTENT_HERE>"
}'

Request parameters

• "content": (Body parameter), string, Required
  The text content of the chunk.
• "important_keywords(Body parameter)
  The key terms or phrases to tag with the chunk.

Response

Success:

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

Failure:

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

---

List chunks

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

Lists chunks in a specified document.

Request

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

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_API_KEY}' 

Request parameters

• "dataset_id": (Path parameter)
  The dataset ID.
• "document_id": (Path parameter)
  The document ID.
• "keywords"(Filter parameter), string
  The keywords used to match chunk content. Defaults to None
• "offset"(Filter parameter), string
  The starting index for the chunks to retrieve. Defaults to 1.
• "limit"(Filter parameter), integer
  The maximum number of chunks to retrieve. Default: 1024
• "id"(Filter parameter), string
  The ID of the chunk to retrieve. Default: None

Response

Success:

{
    "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
    }
}

Failure:

{
    "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: /api/v1/dataset/{dataset_id}/document/{document_id}/chunk
• Headers:
  • 'content-Type: application/json'
  • 'Authorization: Bearer {YOUR_API_KEY}'
• Body:
  • "chunk_ids": list[string]

Request example

curl --request DELETE \
  --url http://{address}/api/v1/dataset/{dataset_id}/document/{document_id}/chunk \
  --header 'Content-Type: application/json' \
  --header 'Authorization: Bearer {YOUR_API_KEY}' \
  --data '{
  "chunk_ids": ["test_1", "test_2"]
  }'

Request parameters

• "chunk_ids": (Body parameter)
  The IDs of the chunks to delete. If not specified, all chunks of the current document will be deleted.

Response

Success:

{
    "code": 0
}

Failure:

{
    "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: /api/v1/dataset/{dataset_id}/document/{document_id}/chunk/{chunk_id}
• Headers:
  • 'content-Type: application/json'
  • 'Authorization: Bearer {YOUR_API_KEY}'
• Body:
  • "content": string
  • "important_keywords": string
  • "available": integer

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_API_KEY}' \
  --data '{   
    "content": "ragflow123",  
    "important_keywords": [],   
}'

Request parameters

• "content": (Body parameter), string
  The text content of the chunk.
• "important_keywords": (Body parameter), list[string]
  A list of key terms or phrases to tag with the chunk.
• "available": (Body parameter) boolean
  The chunk's availability status in the dataset. Value options:
  • False: Unavailable
  • True: Available

Response

Success:

{
    "code": 0
}

Failure:

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

---

Retrieve chunks

GET /api/v1/retrieval

Retrieves chunks from specified datasets.

Request

• Method: POST
• URL: /api/v1/retrieval
• Headers:
  • 'content-Type: application/json'
  • 'Authorization: Bearer {YOUR_API_KEY}'
• Body:
  • "question": string
  • "datasets": list[string]
  • "documents": list[string]
  • "offset": integer
  • "limit": integer
  • "similarity_threshold": float
  • "vector_similarity_weight": float
  • "top_k": integer
  • "rerank_id": string
  • "keyword": boolean
  • "highlight": boolean

Request example

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

Request parameter

• "question": (Body parameter), string, Required
  The user query or query keywords. Defaults to "".
• "datasets": (Body parameter) list[string], Required
  The IDs of the datasets to search from.
• "documents": (Body parameter), list[string]
  The IDs of the documents to search from.
• "offset": (Body parameter), integer
  The starting index for the documents to retrieve. Defaults to 1.
• "limit": (Body parameter)
  The maximum number of chunks to retrieve. Defaults to 1024.
• "similarity_threshold": (Body parameter)
  The minimum similarity score. Defaults to 0.2.
• "vector_similarity_weight": (Body parameter)
  The weight of vector cosine similarity. Defaults to 0.3. If x represents the vector cosine similarity, then (1 - x) is the term similarity weight.
• "top_k": (Body parameter)
  The number of chunks engaged in vector cosine computaton. Defaults to 1024.
• "rerank_id": (Body parameter)
  The ID of the rerank model.
• "keyword": (Body parameter), boolean
  Indicates whether to enable keyword-based matching:
  • True: Enable keyword-based matching.
  • False: Disable keyword-based matching (default).
• "highlight": (Body parameter), boolean
  Specifies whether to enable highlighting of matched terms in the results:
  • True: Enable highlighting of matched terms.
  • False: Disable highlighting of matched terms (default).

Response

Success:

{
    "code": 0,
    "data": {
        "chunks": [
            {
                "content": "ragflow content",
                "content_ltks": "ragflow content",
                "document_id": "5c5999ec7be811ef9cab0242ac120005",
                "document_keyword": "1.txt",
                "highlight": "<em>ragflow</em> content",
                "id": "d78435d142bd5cf6704da62c778795c5",
                "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
    }
}

Failure:

{
    "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: /api/v1/chat
• Headers:
  • 'content-Type: application/json'
  • 'Authorization: Bearer {YOUR_API_KEY}'
• Body:
  • "name": string
  • "avatar": string
  • "knowledgebases": list[string]
  • "llm": object
  • "prompt": object

Request example

curl --request POST \
     --url http://{address}/api/v1/chat \
     --header 'Content-Type: application/json' \
     --header 'Authorization: Bearer {YOUR_API_KEY}'
     --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), string, Required
  The name of the chat assistant.
• "avatar": (Body parameter)
  Base64 encoding of the avatar. Defaults to "".
• "knowledgebases": (Body parameter)
  The IDs of the associated datasets. Defaults to [""].
• "llm": (Body parameter), object
  The LLM settings for the chat assistant to create. When the value is None, a dictionary with the following values will be generated as the default. An llm object contains the following attributes:
  • "model_name", string
    The chat model name. If it is None, the user's default chat model will be returned.
  • "temperature": float
    Controls the randomness of the model's predictions. A lower temperature increases the model's confidence in its responses; a higher temperature increases creativity and diversity. Defaults to 0.1.
  • "top_p": float
    Also known as “nucleus sampling”, this parameter sets a threshold to select a smaller set of words to sample from. It focuses on the most likely words, cutting off the less probable ones. Defaults to 0.3
  • "presence_penalty": float
    This discourages the model from repeating the same information by penalizing words that have already appeared in the conversation. Defaults to 0.2.
  • "frequency penalty": float
    Similar to the presence penalty, this reduces the model’s tendency to repeat the same words frequently. Defaults to 0.7.
  • "max_token": integer
    The maximum length of the model’s output, measured in the number of tokens (words or pieces of words). Defaults to 512.
• "prompt": (Body parameter), object
  Instructions for the LLM to follow. A prompt object contains the following attributes:
  • "similarity_threshold": float RAGFlow uses a hybrid of weighted keyword similarity and vector cosine similarity during retrieval. This argument sets the threshold for similarities between the user query and chunks. If a similarity score falls below this threshold, the corresponding chunk will be excluded from the results. The default value is 0.2.
  • "keywords_similarity_weight": float This argument sets the weight of keyword similarity in the hybrid similarity score with vector cosine similarity or reranking model similarity. By adjusting this weight, you can control the influence of keyword similarity in relation to other similarity measures. The default value is 0.7.
  • "top_n": int This argument specifies the number of top chunks with similarity scores above the similarity_threshold that are fed to the LLM. The LLM will only access these 'top N' chunks. The default value is 8.
  • "variables": object[] This argument lists the variables to use in the 'System' field of Chat Configurations. Note that:
    • "knowledge" is a reserved variable, which will be replaced with the retrieved chunks.
    • All the variables in 'System' should be curly bracketed.
    • The default value is [{"key": "knowledge", "optional": True}]
  • "rerank_model": string If it is not specified, vector cosine similarity will be used; otherwise, reranking score will be used. Defaults to "".
  • "empty_response": string If nothing is retrieved in the dataset for the user's question, this will be used as the response. To allow the LLM to improvise when nothing is found, leave this blank.
  • "opener": string The opening greeting for the user. Defaults to "Hi! I am your assistant, can I help you?".
  • "show_quote: boolean Indicates whether the source of text should be displayed. Defaults to True.
  • "prompt": string The prompt content. Defaults to You are an intelligent assistant. Please summarize the content of the dataset 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
    }
}

Failure:

{
    "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: /api/v1/chat/{chat_id}
• Headers:
  • 'content-Type: application/json'
  • 'Authorization: Bearer {YOUR_API_KEY}'
• Body:
  • "name": string
  • "avatar": string
  • "knowledgebases": list[string]
  • "llm": object
  • "prompt": object

Request example

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

Parameters

• "name": (Body parameter), string, Required
  The name of the chat assistant.
• "avatar": (Body parameter)
  Base64 encoding of the avatar. Defaults to "".
• "knowledgebases": (Body parameter)
  The IDs of the associated datasets. Defaults to [""].
• "llm": (Body parameter), object
  The LLM settings for the chat assistant to create. When the value is None, a dictionary with the following values will be generated as the default. An llm object contains the following attributes:
  • "model_name", string
    The chat model name. If it is None, the user's default chat model will be returned.
  • "temperature": float
    Controls the randomness of the model's predictions. A lower temperature increases the model's confidence in its responses; a higher temperature increases creativity and diversity. Defaults to 0.1.
  • "top_p": float
    Also known as “nucleus sampling”, this parameter sets a threshold to select a smaller set of words to sample from. It focuses on the most likely words, cutting off the less probable ones. Defaults to 0.3
  • "presence_penalty": float
    This discourages the model from repeating the same information by penalizing words that have already appeared in the conversation. Defaults to 0.2.
  • "frequency penalty": float
    Similar to the presence penalty, this reduces the model’s tendency to repeat the same words frequently. Defaults to 0.7.
  • "max_token": integer
    The maximum length of the model’s output, measured in the number of tokens (words or pieces of words). Defaults to 512.
• "prompt": (Body parameter), object
  Instructions for the LLM to follow. A prompt object contains the following attributes:
  • "similarity_threshold": float RAGFlow uses a hybrid of weighted keyword similarity and vector cosine similarity during retrieval. This argument sets the threshold for similarities between the user query and chunks. If a similarity score falls below this threshold, the corresponding chunk will be excluded from the results. The default value is 0.2.
  • "keywords_similarity_weight": float This argument sets the weight of keyword similarity in the hybrid similarity score with vector cosine similarity or reranking model similarity. By adjusting this weight, you can control the influence of keyword similarity in relation to other similarity measures. The default value is 0.7.
  • "top_n": int This argument specifies the number of top chunks with similarity scores above the similarity_threshold that are fed to the LLM. The LLM will only access these 'top N' chunks. The default value is 8.
  • "variables": object[] This argument lists the variables to use in the 'System' field of Chat Configurations. Note that:
    • "knowledge" is a reserved variable, which will be replaced with the retrieved chunks.
    • All the variables in 'System' should be curly bracketed.
    • The default value is [{"key": "knowledge", "optional": True}]
  • "rerank_model": string If it is not specified, vector cosine similarity will be used; otherwise, reranking score will be used. Defaults to "".
  • "empty_response": string If nothing is retrieved in the dataset for the user's question, this will be used as the response. To allow the LLM to improvise when nothing is found, leave this blank.
  • "opener": string The opening greeting for the user. Defaults to "Hi! I am your assistant, can I help you?".
  • "show_quote: boolean Indicates whether the source of text should be displayed. Defaults to True.
  • "prompt": string The prompt content. Defaults to You are an intelligent assistant. Please summarize the content of the dataset 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
}

Failure:

{
    "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: /api/v1/chat
• Headers:
  • 'content-Type: application/json'
  • 'Authorization: Bearer {YOUR_API_KEY}'
• 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_API_KEY}' \
  --data '{
  "ids": ["test_1", "test_2"]
  }'
}'

Request parameters

• "ids": (Body parameter), list[string]
  The IDs of the chat assistants to delete. If not specified, all chat assistants in the system will be deleted.

Response

Success:

{
    "code": 0
}

Failure:

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

---

List chats

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

Lists chat assistants.

Request

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

Request example

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

Request parameters

• "page": (Path parameter), integer
  Specifies the page on which the chat assistants will be displayed. Defaults to 1.
• "page_size": (Path parameter), integer
  The number of chat assistants on each page. Defaults to 1024.
• "orderby": (Path parameter), string
  The attribute by which the results are sorted. Available options:
  • "create_time" (default)
  • "update_time"
• "desc": (Path parameter), boolean
  Indicates whether the retrieved chat assistants should be sorted in descending order. Defaults to True.
• "id": (Path parameter), string
  The ID of the chat assistant to retrieve.
• "name": (Path parameter), string
  The name of the chat assistant to retrieve.

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
        }
    ]
}

Failure:

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

Create session

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

Creates a chat session.

Request

• Method: POST
• URL: /api/v1/chat/{chat_id}/session
• Headers:
  • 'content-Type: application/json'
  • 'Authorization: Bearer {YOUR_API_KEY}'
• 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_API_KEY}' \
  --data '{
    "name": "new session"
  }'

Request parameters

• "name": (Body parameter), string
  The name of the chat session to create.

Response

Success:

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

Failure:

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

---

Update session

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

Update a chat session

Request

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

Request example

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

Request Parameter

• "name: (*Body Parameter), string
  The name of the session to update.

Response

Success:

{
    "code": 0
}

Failure:

{
    "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={session_name}&id={session_id}

Lists sessions associated with a specified chat assistant.

Request

• Method: GET
• URL: /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_API_KEY}'

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_API_KEY}'

Request Parameters

• "page": (Path parameter), integer
  Specifies the page on which the sessions will be displayed. Defaults to 1.
• "page_size": (Path parameter), integer
  The number of sessions on each page. Defaults to 1024.
• "orderby": (Path parameter), string
  The field by which sessions should be sorted. Available options:
  • "create_time" (default)
  • "update_time"
• "desc": (Path parameter), boolean
  Indicates whether the retrieved sessions should be sorted in descending order. Defaults to True.
• "id": (Path parameter), string
  The ID of the chat session to retrieve.
• "name": (Path parameter) string
  The name of the chat session to retrieve.

Response

Success:

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

Failure:

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

---

Delete sessions

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

Deletes sessions by ID.

Request

• Method: DELETE
• URL: /api/v1/chat/{chat_id}/session
• Headers:
  • 'content-Type: application/json'
  • 'Authorization: Bearer {YOUR_API_KEY}'
• 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_API_KEY}' \
  --data '{
  "ids": ["test_1", "test_2"]
  }'

Request Parameters

• "ids": (Body Parameter), list[string]
  The IDs of the sessions to delete. If not specified, all sessions associated with the current chat assistant will be deleted.

Response

Success:

{
    "code": 0
}

Failure:

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

---

Chat

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

Asks a question to start a conversation.

Request

• Method: POST
• URL: /api/v1/chat/{chat_id}/completion
• Headers:
  • 'content-Type: application/json'
  • 'Authorization: Bearer {YOUR_API_KEY}'
• Body:
  • "question": string
  • "stream": boolean
  • "session_id": string

Request example

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

Request Parameters

• "question": (Body Parameter), string Required
  The question to start an AI chat.
• "stream": (Body Parameter), string
  Indicates whether to output responses in a streaming way:
  • True: Enable streaming.
  • False: (Default) Disable streaming.
• "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。",
        "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
}

Failure:

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