HTTP API Reference
A complete reference for RAGFlow's RESTful API. Before proceeding, please ensure you have your RAGFlow API key ready for authentication.
Dataset Management
Create dataset
POST /api/v1/datasets
Creates a dataset.
Request
- Method: POST
- URL:
/api/v1/datasets
- Headers:
'content-Type: application/json'
'Authorization: Bearer <YOUR_API_KEY>'
- Body:
"name"
:string
"avatar"
:string
"description"
:string
"language"
:string
"embedding_model"
:string
"permission"
:string
"chunk_method"
:string
"parser_config"
:object
Request example
curl --request POST \
--url http://{address}/api/v1/datasets \
--header 'Content-Type: application/json' \
--header 'Authorization: Bearer <YOUR_API_KEY>' \
--data '{
"name": "test_1"
}'
Request parameters
-
"name"
: (Body parameter),string
, Required
The unique name of the dataset to create. It must adhere to the following requirements:- Permitted characters include:
- English letters (a-z, A-Z)
- Digits (0-9)
- "_" (underscore)
- Must begin with an English letter or underscore.
- Maximum 65,535 characters.
- Case-insensitive.
- Permitted characters include:
-
"avatar"
: (Body parameter),string
Base64 encoding of the avatar. -
"description"
: (Body parameter),string
A brief description of the dataset to create. -
"language"
: (Body parameter),string
The language setting of the dataset to create. Available options:"English"
(default)"Chinese"
-
"embedding_model"
: (Body parameter),string
The name of the embedding model to use. For example:"BAAI/bge-zh-v1.5"
-
"permission"
: (Body parameter),string
Specifies who can access the dataset to create. Available options:"me"
: (Default) Only you can manage the dataset."team"
: All team members can manage the dataset.
-
"chunk_method"
: (Body parameter),enum<string>
The chunking method of the dataset to create. Available options:"naive"
: General (default)"manual"
: Manual"qa"
: Q&A"table"
: Table"paper"
: Paper"book"
: Book"laws"
: Laws"presentation"
: Presentation"picture"
: Picture"one"
: One"knowledge_graph"
: Knowledge Graph
Ensure your LLM is properly configured on the Settings page before selecting this. Please also note that Knowledge Graph consumes a large number of Tokens!"email"
: Email
-
"parser_config"
: (Body parameter),object
The configuration settings for the dataset parser. The attributes in this JSON object vary with the selected"chunk_method"
:- If
"chunk_method"
is"naive"
, the"parser_config"
object contains the following attributes:"chunk_token_count"
: Defaults to128
."layout_recognize"
: Defaults totrue
."html4excel"
: Indicates whether to convert Excel documents into HTML format. Defaults tofalse
."delimiter"
: Defaults to"\n!?。;!?"
."task_page_size"
: Defaults to12
. For PDF only."raptor"
: Raptor-specific settings. Defaults to:{"use_raptor": false}
.
- If
"chunk_method"
is"qa"
,"manuel"
,"paper"
,"book"
,"laws"
, or"presentation"
, the"parser_config"
object contains the following attribute:"raptor"
: Raptor-specific settings. Defaults to:{"use_raptor": false}
.
- If
"chunk_method"
is"table"
,"picture"
,"one"
, or"email"
,"parser_config"
is an empty JSON object. - If
"chunk_method"
is"knowledge_graph"
, the"parser_config"
object contains the following attributes:"chunk_token_count"
: Defaults to128
."delimiter"
: Defaults to"\n!?。;!?"
."entity_types"
: Defaults to["organization","person","location","event","time"]
- If
Response
Success:
{
"code": 0,
"data": {
"avatar": null,
"chunk_count": 0,
"chunk_method": "naive",
"create_date": "Thu, 24 Oct 2024 09:14:07 GMT",
"create_time": 1729761247434,
"created_by": "69736c5e723611efb51b0242ac120007",
"description": null,
"document_count": 0,
"embedding_model": "BAAI/bge-large-zh-v1.5",
"id": "527fa74891e811ef9c650242ac120006",
"language": "English",
"name": "test_1",
"parser_config": {
"chunk_token_num": 128,
"delimiter": "\\n!?;。;!?",
"html4excel": false,
"layout_recognize": true,
"raptor": {
"user_raptor": false
}
},
"permission": "me",
"similarity_threshold": 0.2,
"status": "1",
"tenant_id": "69736c5e723611efb51b0242ac120007",
"token_num": 0,
"update_date": "Thu, 24 Oct 2024 09:14:07 GMT",
"update_time": 1729761247434,
"vector_similarity_weight": 0.3
}
}
Failure:
{
"code": 102,
"message": "Duplicated knowledgebase name in creating dataset."
}
Delete datasets
DELETE /api/v1/datasets
Deletes datasets by ID.
Request
- Method: DELETE
- URL:
/api/v1/datasets
- Headers:
'content-Type: application/json'
'Authorization: Bearer <YOUR_API_KEY>'
- Body:
"ids"
:list[string]
Request example
curl --request DELETE \
--url http://{address}/api/v1/datasets \
--header 'Content-Type: application/json' \
--header 'Authorization: Bearer <YOUR_API_KEY>' \
--data '{
"ids": ["test_1", "test_2"]
}'
Request parameters
"ids"
: (Body parameter),list[string]
The IDs of the datasets to delete. If it is not specified, all datasets will be deleted.
Response
Success:
{
"code": 0
}
Failure:
{
"code": 102,
"message": "You don't own the dataset."
}
Update dataset
PUT /api/v1/datasets/{dataset_id}
Updates configurations for a specified dataset.
Request
- Method: PUT
- URL:
/api/v1/datasets/{dataset_id}
- Headers:
'content-Type: application/json'
'Authorization: Bearer <YOUR_API_KEY>'
- Body:
"name"
:string
"embedding_model"
:string
"chunk_method"
:enum<string>
Request example
curl --request PUT \
--url http://{address}/api/v1/datasets/{dataset_id} \
--header 'Content-Type: application/json' \
--header 'Authorization: Bearer <YOUR_API_KEY>' \
--data '
{
"name": "updated_dataset"
}'
Request parameters
dataset_id
: (Path parameter)
The ID of the dataset to update."name"
: (Body parameter),string
The revised name of the dataset."embedding_model"
: (Body parameter),string
The updated embedding model name.- Ensure that
"chunk_count"
is0
before updating"embedding_model"
.
- Ensure that
"chunk_method"
: (Body parameter),enum<string>
The chunking method for the dataset. Available options:"naive"
: General"manual
: Manual"qa"
: Q&A"table"
: Table"paper"
: Paper"book"
: Book"laws"
: Laws"presentation"
: Presentation"picture"
: Picture"one"
:One"email"
: Email"knowledge_graph"
: Knowledge Graph
Ensure your LLM is properly configured on the Settings page before selecting this. Please also note that Knowledge Graph consumes a large number of Tokens!
Response
Success:
{
"code": 0
}
Failure:
{
"code": 102,
"message": "Can't change tenant_id."
}
List datasets
GET /api/v1/datasets?page={page}&page_size={page_size}&orderby={orderby}&desc={desc}&name={dataset_name}&id={dataset_id}
Lists datasets.
Request
- Method: GET
- URL:
/api/v1/datasets?page={page}&page_size={page_size}&orderby={orderby}&desc={desc}&name={dataset_name}&id={dataset_id}
- Headers:
'Authorization: Bearer <YOUR_API_KEY>'
Request example
curl --request GET \
--url http://{address}/api/v1/datasets?page={page}&page_size={page_size}&orderby={orderby}&desc={desc}&name={dataset_name}&id={dataset_id} \
--header 'Authorization: Bearer <YOUR_API_KEY>'
Request parameters
page
: (Filter parameter)
Specifies the page on which the datasets will be displayed. Defaults to1
.page_size
: (Filter parameter)
The number of datasets on each page. Defaults to30
.orderby
: (Filter parameter)
The field by which datasets should be sorted. Available options:create_time
(default)update_time
desc
: (Filter parameter)
Indicates whether the retrieved datasets should be sorted in descending order. Defaults totrue
.name
: (Filter parameter)
The name of the dataset to retrieve.id
: (Filter parameter)
The ID of the dataset to retrieve.
Response
Success:
{
"code": 0,
"data": [
{
"avatar": "",
"chunk_count": 59,
"create_date": "Sat, 14 Sep 2024 01:12:37 GMT",
"create_time": 1726276357324,
"created_by": "69736c5e723611efb51b0242ac120007",
"description": null,
"document_count": 1,
"embedding_model": "BAAI/bge-large-zh-v1.5",
"id": "6e211ee0723611efa10a0242ac120007",
"language": "English",
"name": "mysql",
"chunk_method": "knowledge_graph",
"parser_config": {
"chunk_token_num": 8192,
"delimiter": "\\n!?;。;!?",
"entity_types": [
"organization",
"person",
"location",
"event",
"time"
]
},
"permission": "me",
"similarity_threshold": 0.2,
"status": "1",
"tenant_id": "69736c5e723611efb51b0242ac120007",
"token_num": 12744,
"update_date": "Thu, 10 Oct 2024 04:07:23 GMT",
"update_time": 1728533243536,
"vector_similarity_weight": 0.3
}
]
}
Failure:
{
"code": 102,
"message": "The dataset doesn't exist"
}
File Management within Dataset
Upload documents
POST /api/v1/datasets/{dataset_id}/documents
Uploads documents to a specified dataset.
Request
- Method: POST
- URL:
/api/v1/datasets/{dataset_id}/documents
- Headers:
'Content-Type: multipart/form-data'
'Authorization: Bearer <YOUR_API_KEY>'
- Form:
'file=@{FILE_PATH}'
Request example
curl --request POST \
--url http://{address}/api/v1/datasets/{dataset_id}/documents \
--header 'Content-Type: multipart/form-data' \
--header 'Authorization: Bearer <YOUR_API_KEY>' \
--form 'file=@./test1.txt' \
--form 'file=@./test2.pdf'
Request parameters
dataset_id
: (Path parameter)
The ID of the dataset to which the documents will be uploaded.'file'
: (Body parameter)
A document to upload.
Response
Success:
{
"code": 0,
"data": [
{
"chunk_method": "naive",
"created_by": "69736c5e723611efb51b0242ac120007",
"dataset_id": "527fa74891e811ef9c650242ac120006",
"id": "b330ec2e91ec11efbc510242ac120004",
"location": "1.txt",
"name": "1.txt",
"parser_config": {
"chunk_token_num": 128,
"delimiter": "\\n!?;。;!?",
"html4excel": false,
"layout_recognize": true,
"raptor": {
"user_raptor": false
}
},
"run": "UNSTART",
"size": 17966,
"thumbnail": "",
"type": "doc"
}
]
}
Failure:
{
"code": 101,
"message": "No file part!"
}
Update document
PUT /api/v1/datasets/{dataset_id}/documents/{document_id}
Updates configurations for a specified document.
Request
- Method: PUT
- URL:
/api/v1/datasets/{dataset_id}/documents/{document_id}
- Headers:
'content-Type: application/json'
'Authorization: Bearer <YOUR_API_KEY>'
- Body:
"name"
:string
"chunk_method"
:string
"parser_config"
:object
Request example
curl --request PUT \
--url http://{address}/api/v1/datasets/{dataset_id}/info/{document_id} \
--header 'Authorization: Bearer <YOUR_API_KEY>' \
--header 'Content-Type: application/json' \
--data '
{
"name": "manual.txt",
"chunk_method": "manual",
"parser_config": {"chunk_token_count": 128}
}'
Request parameters
dataset_id
: (Path parameter)
The ID of the associated dataset.document_id
: (Path parameter)
The ID of the document to update."name"
: (Body parameter),string
"chunk_method"
: (Body parameter),string
The parsing method to apply to the document:"naive"
: General"manual
: Manual"qa"
: Q&A"table"
: Table"paper"
: Paper"book"
: Book"laws"
: Laws"presentation"
: Presentation"picture"
: Picture"one"
: One"knowledge_graph"
: Knowledge Graph
Ensure your LLM is properly configured on the Settings page before selecting this. Please also note that Knowledge Graph consumes a large number of Tokens!"email"
: Email
"parser_config"
: (Body parameter),object
The configuration settings for the dataset parser. The attributes in this JSON object vary with the selected"chunk_method"
:- If
"chunk_method"
is"naive"
, the"parser_config"
object contains the following attributes:"chunk_token_count"
: Defaults to128
."layout_recognize"
: Defaults totrue
."html4excel"
: Indicates whether to convert Excel documents into HTML format. Defaults tofalse
."delimiter"
: Defaults to"\n!?。;!?"
."task_page_size"
: Defaults to12
. For PDF only."raptor"
: Raptor-specific settings. Defaults to:{"use_raptor": false}
.
- If
"chunk_method"
is"qa"
,"manuel"
,"paper"
,"book"
,"laws"
, or"presentation"
, the"parser_config"
object contains the following attribute:"raptor"
: Raptor-specific settings. Defaults to:{"use_raptor": false}
.
- If
"chunk_method"
is"table"
,"picture"
,"one"
, or"email"
,"parser_config"
is an empty JSON object. - If
"chunk_method"
is"knowledge_graph"
, the"parser_config"
object contains the following attributes:"chunk_token_count"
: Defaults to128
."delimiter"
: Defaults to"\n!?。;!?"
."entity_types"
: Defaults to["organization","person","location","event","time"]
- If
Response
Success:
{
"code": 0
}
Failure:
{
"code": 102,
"message": "The dataset does not have the document."
}
Download document
GET /api/v1/datasets/{dataset_id}/documents/{document_id}
Downloads a document from a specified dataset.
Request
- Method: GET
- URL:
/api/v1/datasets/{dataset_id}/documents/{document_id}
- Headers:
'Authorization: Bearer <YOUR_API_KEY>'
- Output:
'{PATH_TO_THE_FILE}'
Request example
curl --request GET \
--url http://{address}/api/v1/datasets/{dataset_id}/documents/{document_id} \
--header 'Authorization: Bearer <YOUR_API_KEY>' \
--output ./ragflow.txt
Request parameters
dataset_id
: (Path parameter)
The associated dataset ID.documents_id
: (Path parameter)
The ID of the document to download.
Response
Success:
This is a test to verify the file download feature.
Failure:
{
"code": 102,
"message": "You do not own the dataset 7898da028a0511efbf750242ac1220005."
}
List documents
GET /api/v1/datasets/{dataset_id}/documents?page={page}&page_size={page_size}&orderby={orderby}&desc={desc}&keywords={keywords}&id={document_id}&name={document_name}
Lists documents in a specified dataset.
Request
- Method: GET
- URL:
/api/v1/datasets/{dataset_id}/documents?page={page}&page_size={page_size}&orderby={orderby}&desc={desc}&keywords={keywords}&id={document_id}&name={document_name}
- Headers:
'content-Type: application/json'
'Authorization: Bearer <YOUR_API_KEY>'
Request example
curl --request GET \
--url http://{address}/api/v1/datasets/{dataset_id}/documents?page={page}&page_size={page_size}&orderby={orderby}&desc={desc}&keywords={keywords}&id={document_id}&name={document_name} \
--header 'Authorization: Bearer <YOUR_API_KEY>'
Request parameters
dataset_id
: (Path parameter)
The associated dataset ID.keywords
: (Filter parameter),string
The keywords used to match document titles.page
: (Filter parameter),integer
Specifies the page on which the documents will be displayed. Defaults to1
.page_size
: (Filter parameter),integer
The maximum number of documents on each page. Defaults to30
.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 totrue
.id
: (Filter parameter),string
The ID of the document to retrieve.
Response
Success:
{
"code": 0,
"data": {
"docs": [
{
"chunk_count": 0,
"create_date": "Mon, 14 Oct 2024 09:11:01 GMT",
"create_time": 1728897061948,
"created_by": "69736c5e723611efb51b0242ac120007",
"id": "3bcfbf8a8a0c11ef8aba0242ac120006",
"knowledgebase_id": "7898da028a0511efbf750242ac120005",
"location": "Test_2.txt",
"name": "Test_2.txt",
"parser_config": {
"chunk_token_count": 128,
"delimiter": "\n!?。;!?",
"layout_recognize": true,
"task_page_size": 12
},
"chunk_method": "naive",
"process_begin_at": null,
"process_duation": 0.0,
"progress": 0.0,
"progress_msg": "",
"run": "0",
"size": 7,
"source_type": "local",
"status": "1",
"thumbnail": null,
"token_count": 0,
"type": "doc",
"update_date": "Mon, 14 Oct 2024 09:11:01 GMT",
"update_time": 1728897061948
}
],
"total": 1
}
}
Failure:
{
"code": 102,
"message": "You don't own the dataset 7898da028a0511efbf750242ac1220005. "
}
Delete documents
DELETE /api/v1/datasets/{dataset_id}/documents
Deletes documents by ID.
Request
- Method: DELETE
- URL:
/api/v1/datasets/{dataset_id}/documents
- Headers:
'Content-Type: application/json'
'Authorization: Bearer <YOUR_API_KEY>'
- Body:
"ids"
:list[string]
Request example
curl --request DELETE \
--url http://{address}/api/v1/datasets/{dataset_id}/documents \
--header 'Content-Type: application/json' \
--header 'Authorization: Bearer <YOUR_API_KEY>' \
--data '
{
"ids": ["id_1","id_2"]
}'
Request parameters
dataset_id
: (Path parameter)
The associated dataset ID."ids"
: (Body parameter),list[string]
The IDs of the documents to delete. If it is not specified, all documents in the specified dataset will be deleted.
Response
Success:
{
"code": 0
}.
Failure:
{
"code": 102,
"message": "You do not own the dataset 7898da028a0511efbf750242ac1220005."
}
Parse documents
POST /api/v1/datasets/{dataset_id}/chunks
Parses documents in a specified dataset.
Request
- Method: POST
- URL:
/api/v1/datasets/{dataset_id}/chunks
- Headers:
'content-Type: application/json'
'Authorization: Bearer <YOUR_API_KEY>'
- Body:
"document_ids"
:list[string]
Request example
curl --request POST \
--url http://{address}/api/v1/datasets/{dataset_id}/chunks \
--header 'Content-Type: application/json' \
--header 'Authorization: Bearer <YOUR_API_KEY>' \
--data '
{
"document_ids": ["97a5f1c2759811efaa500242ac120004","97ad64b6759811ef9fc30242ac120004"]
}'
Request parameters
dataset_id
: (Path parameter)
The dataset ID."document_ids"
: (Body parameter),list[string]
, Required
The IDs of the documents to parse.
Response
Success:
{
"code": 0
}
Failure:
{
"code": 102,
"message": "`document_ids` is required"
}
Stop parsing documents
DELETE /api/v1/datasets/{dataset_id}/chunks
Stops parsing specified documents.
Request
- Method: DELETE
- URL:
/api/v1/datasets/{dataset_id}/chunks
- Headers:
'content-Type: application/json'
'Authorization: Bearer <YOUR_API_KEY>'
- Body:
"document_ids"
:list[string]
Request example
curl --request DELETE \
--url http://{address}/api/v1/datasets/{dataset_id}/chunks \
--header 'Content-Type: application/json' \
--header 'Authorization: Bearer <YOUR_API_KEY>' \
--data '
{
"document_ids": ["97a5f1c2759811efaa500242ac120004","97ad64b6759811ef9fc30242ac120004"]
}'
Request parameters
dataset_id
: (Path parameter)
The associated dataset ID."document_ids"
: (Body parameter),list[string]
, Required
The IDs of the documents for which the parsing should be stopped.
Response
Success:
{
"code": 0
}
Failure:
{
"code": 102,
"message": "`document_ids` is required"
}
Add chunks
POST /api/v1/datasets/{dataset_id}/documents/{document_id}/chunks
Adds a chunk to a specified document in a specified dataset.
Request
- Method: POST
- URL:
/api/v1/datasets/{dataset_id}/documents/{document_id}/chunks
- Headers:
'content-Type: application/json'
'Authorization: Bearer <YOUR_API_KEY>'
- Body:
"content"
:string
"important_keywords"
:list[string]
Request example
curl --request POST \
--url http://{address}/api/v1/datasets/{dataset_id}/documents/{document_id}/chunks \
--header 'Content-Type: application/json' \
--header 'Authorization: Bearer <YOUR_API_KEY>' \
--data '
{
"content": "<SOME_CHUNK_CONTENT_HERE>"
}'
Request parameters
dataset_id
: (Path parameter)
The associated dataset ID.document_ids
: (Path parameter)
The associated document ID."content"
: (Body parameter),string
, Required
The text content of the chunk."important_keywords
(Body parameter),list[string]
The key terms or phrases to tag with the chunk.
Response
Success:
{
"code": 0,
"data": {
"chunk": {
"content": "ragflow content",
"create_time": "2024-10-16 08:05:04",
"create_timestamp": 1729065904.581025,
"dataset_id": [
"c7ee74067a2c11efb21c0242ac120006"
],
"document_id": "5c5999ec7be811ef9cab0242ac120005",
"id": "d78435d142bd5cf6704da62c778795c5",
"important_keywords": []
}
}
}
Failure:
{
"code": 102,
"message": "`content` is required"
}
List chunks
GET /api/v1/datasets/{dataset_id}/documents/{document_id}/chunks?keywords={keywords}&page={page}&page_size={page_size}&id={id}
Lists chunks in a specified document.
Request
- Method: GET
- URL:
/api/v1/datasets/{dataset_id}/documents/{document_id}/chunks?keywords={keywords}&page={page}&page_size={page_size}&id={chunk_id}
- Headers:
'Authorization: Bearer <YOUR_API_KEY>'
Request example
curl --request GET \
--url http://{address}/api/v1/datasets/{dataset_id}/documents/{document_id}/chunks?keywords={keywords}&page={page}&page_size={page_size}&id={chunk_id} \
--header 'Authorization: Bearer <YOUR_API_KEY>'
Request parameters
dataset_id
: (Path parameter)
The associated dataset ID.document_ids
: (Path parameter)
The associated document ID.keywords
(Filter parameter),string
The keywords used to match chunk content.page
(Filter parameter),integer
Specifies the page on which the chunks will be displayed. Defaults to1
.page_size
(Filter parameter),integer
The maximum number of chunks on each page. Defaults to1024
.id
(Filter parameter),string
The ID of the chunk to retrieve.
Response
Success:
{
"code": 0,
"data": {
"chunks": [
{
"available_int": 1,
"content": "This is a test content.",
"docnm_kwd": "1.txt",
"document_id": "b330ec2e91ec11efbc510242ac120004",
"id": "b48c170e90f70af998485c1065490726",
"image_id": "",
"important_keywords": "",
"positions": [
""
]
}
],
"doc": {
"chunk_count": 1,
"chunk_method": "naive",
"create_date": "Thu, 24 Oct 2024 09:45:27 GMT",
"create_time": 1729763127646,
"created_by": "69736c5e723611efb51b0242ac120007",
"dataset_id": "527fa74891e811ef9c650242ac120006",
"id": "b330ec2e91ec11efbc510242ac120004",
"location": "1.txt",
"name": "1.txt",
"parser_config": {
"chunk_token_num": 128,
"delimiter": "\\n!?;。;!?",
"html4excel": false,
"layout_recognize": true,
"raptor": {
"user_raptor": false
}
},
"process_begin_at": "Thu, 24 Oct 2024 09:56:44 GMT",
"process_duation": 0.54213,
"progress": 0.0,
"progress_msg": "Task dispatched...",
"run": "2",
"size": 17966,
"source_type": "local",
"status": "1",
"thumbnail": "",
"token_count": 8,
"type": "doc",
"update_date": "Thu, 24 Oct 2024 11:03:15 GMT",
"update_time": 1729767795721
},
"total": 1
}
}
Failure:
{
"code": 102,
"message": "You don't own the document 5c5999ec7be811ef9cab0242ac12000e5."
}
Delete chunks
DELETE /api/v1/datasets/{dataset_id}/documents/{document_id}/chunks
Deletes chunks by ID.
Request
- Method: DELETE
- URL:
/api/v1/datasets/{dataset_id}/documents/{document_id}/chunks
- Headers:
'content-Type: application/json'
'Authorization: Bearer <YOUR_API_KEY>'
- Body:
"chunk_ids"
:list[string]
Request example
curl --request DELETE \
--url http://{address}/api/v1/datasets/{dataset_id}/documents/{document_id}/chunks \
--header 'Content-Type: application/json' \
--header 'Authorization: Bearer <YOUR_API_KEY>' \
--data '
{
"chunk_ids": ["test_1", "test_2"]
}'
Request parameters
dataset_id
: (Path parameter)
The associated dataset ID.document_ids
: (Path parameter)
The associated document ID."chunk_ids"
: (Body parameter),list[string]
The IDs of the chunks to delete. If it is not specified, all chunks of the specified document will be deleted.
Response
Success:
{
"code": 0
}
Failure:
{
"code": 102,
"message": "`chunk_ids` is required"
}
Update chunk
PUT /api/v1/datasets/{dataset_id}/documents/{document_id}/chunks/{chunk_id}
Updates content or configurations for a specified chunk.
Request
- Method: PUT
- URL:
/api/v1/datasets/{dataset_id}/documents/{document_id}/chunks/{chunk_id}
- Headers:
'content-Type: application/json'
'Authorization: Bearer <YOUR_API_KEY>'
- Body:
"content"
:string
"important_keywords"
:list[string]
"available"
:boolean
Request example
curl --request PUT \
--url http://{address}/api/v1/datasets/{dataset_id}/documents/{document_id}/chunks/{chunk_id} \
--header 'Content-Type: application/json' \
--header 'Authorization: Bearer <YOUR_API_KEY>' \
--data '
{
"content": "ragflow123",
"important_keywords": []
}'
Request parameters
dataset_id
: (Path parameter)
The associated dataset ID.document_ids
: (Path parameter)
The associated document ID.chunk_id
: (Path parameter)
The ID of the chunk to update."content"
: (Body parameter),string
The text content of the chunk."important_keywords"
: (Body parameter),list[string]
A list of key terms or phrases to tag with the chunk."available"
: (Body parameter)boolean
The chunk's availability status in the dataset. Value options:true
: Available (default)false
: Unavailable
Response
Success:
{
"code": 0
}
Failure:
{
"code": 102,
"message": "Can't find this chunk 29a2d9987e16ba331fb4d7d30d99b71d2"
}
Retrieve chunks
POST /api/v1/retrieval
Retrieves chunks from specified datasets.
Request
- Method: POST
- URL:
/api/v1/retrieval
- Headers:
'content-Type: application/json'
'Authorization: Bearer <YOUR_API_KEY>'
- Body:
"question"
:string
"dataset_ids"
:list[string]
"document_ids"
:list[string]
"page"
:integer
"page_size"
:integer
"similarity_threshold"
:float
"vector_similarity_weight"
:float
"top_k"
:integer
"rerank_id"
:string
"keyword"
:boolean
"highlight"
:boolean
Request example
curl --request POST \
--url http://{address}/api/v1/retrieval \
--header 'Content-Type: application/json' \
--header 'Authorization: Bearer <YOUR_API_KEY>' \
--data '
{
"question": "What is advantage of ragflow?",
"dataset_ids": ["b2a62730759d11ef987d0242ac120004"],
"document_ids": ["77df9ef4759a11ef8bdd0242ac120004"]
}'
Request parameter
"question"
: (Body parameter),string
, Required
The user query or query keywords."dataset_ids"
: (Body parameter)list[string]
The IDs of the datasets to search. If you do not set this argument, ensure that you set"document_ids"
."document_ids"
: (Body parameter),list[string]
The IDs of the documents to search. Ensure that all selected documents use the same embedding model. Otherwise, an error will occur. If you do not set this argument, ensure that you set"dataset_ids"
."page"
: (Body parameter),integer
Specifies the page on which the chunks will be displayed. Defaults to1
."page_size"
: (Body parameter)
The maximum number of chunks on each page. Defaults to30
."similarity_threshold"
: (Body parameter)
The minimum similarity score. Defaults to0.2
."vector_similarity_weight"
: (Body parameter),float
The weight of vector cosine similarity. Defaults to0.3
. If x represents the weight of vector cosine similarity, then (1 - x) is the term similarity weight."top_k"
: (Body parameter),integer
The number of chunks engaged in vector cosine computaton. Defaults to1024
."rerank_id"
: (Body parameter),integer
The ID of the rerank model."keyword"
: (Body parameter),boolean
Indicates whether to enable keyword-based matching:true
: Enable keyword-based matching.false
: Disable keyword-based matching (default).
"highlight"
: (Body parameter),boolean
Specifies whether to enable highlighting of matched terms in the results:true
: Enable highlighting of matched terms.false
: Disable highlighting of matched terms (default).
Response
Success:
{
"code": 0,
"data": {
"chunks": [
{
"content": "ragflow content",
"content_ltks": "ragflow content",
"document_id": "5c5999ec7be811ef9cab0242ac120005",
"document_keyword": "1.txt",
"highlight": "<em>ragflow</em> content",
"id": "d78435d142bd5cf6704da62c778795c5",
"image_id": "",
"important_keywords": [
""
],
"kb_id": "c7ee74067a2c11efb21c0242ac120006",
"positions": [
""
],
"similarity": 0.9669436601210759,
"term_similarity": 1.0,
"vector_similarity": 0.8898122004035864
}
],
"doc_aggs": [
{
"count": 1,
"doc_id": "5c5999ec7be811ef9cab0242ac120005",
"doc_name": "1.txt"
}
],
"total": 1
}
}
Failure:
{
"code": 102,
"message": "`datasets` is required."
}
Chat Assistant Management
Create chat assistant
POST /api/v1/chats
Creates a chat assistant.
Request
- Method: POST
- URL:
/api/v1/chats
- Headers:
'content-Type: application/json'
'Authorization: Bearer <YOUR_API_KEY>'
- Body:
"name"
:string
"avatar"
:string
"dataset_ids"
:list[string]
"llm"
:object
"prompt"
:object
Request example
curl --request POST \
--url http://{address}/api/v1/chats \
--header 'Content-Type: application/json' \
--header 'Authorization: Bearer <YOUR_API_KEY>'
--data '{
"dataset_ids": ["0b2cbc8c877f11ef89070242ac120005"],
"name":"new_chat_1"
}'
Request parameters
"name"
: (Body parameter),string
, Required
The name of the chat assistant."avatar"
: (Body parameter),string
Base64 encoding of the avatar."dataset_ids"
: (Body parameter),list[string]
The IDs of the associated datasets."llm"
: (Body parameter),object
The LLM settings for the chat assistant to create. If it is not explicitly set, a JSON object with the following values will be generated as the default. Anllm
JSON object contains the following attributes:"model_name"
,string
The chat model name. If not set, the user's default chat model will be used."temperature"
:float
Controls the randomness of the model's predictions. A lower temperature increases the model's confidence in its responses; a higher temperature increases creativity and diversity. Defaults to0.1
."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 to0.3
"presence_penalty"
:float
This discourages the model from repeating the same information by penalizing words that have already appeared in the conversation. Defaults to0.2
."frequency penalty"
:float
Similar to the presence penalty, this reduces the model’s tendency to repeat the same words frequently. Defaults to0.7
."max_token"
:integer
The maximum length of the model’s output, measured in the number of tokens (words or pieces of words). Defaults to512
.
"prompt"
: (Body parameter),object
Instructions for the LLM to follow. If it is not explicitly set, a JSON object with the following values will be generated as the default. Aprompt
JSON object contains the following attributes:"similarity_threshold"
:float
RAGFlow employs either a combination of weighted keyword similarity and weighted vector cosine similarity, or a combination of weighted keyword similarity and weighted reranking score during retrieval. This argument sets the threshold for similarities between the user query and chunks. If a similarity score falls below this threshold, the corresponding chunk will be excluded from the results. The default value is0.2
."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 is0.7
."top_n"
:int
This argument specifies the number of top chunks with similarity scores above thesimilarity_threshold
that are fed to the LLM. The LLM will only access these 'top N' chunks. The default value is8
."variables"
:object[]
This argument lists the variables to use in the 'System' field of Chat Configurations. Note that:"knowledge"
is a reserved variable, which represents the retrieved chunks.- All the variables in 'System' should be curly bracketed.
- The default value is
[{"key": "knowledge", "optional": true}]
.
"rerank_model"
:string
If it is not specified, vector cosine similarity will be used; otherwise, reranking score will be used."empty_response"
:string
If nothing is retrieved in the dataset for the user's question, this will be used as the response. To allow the LLM to improvise when nothing is found, leave this blank."opener"
:string
The opening greeting for the user. Defaults to"Hi! I am your assistant, can I help you?"
."show_quote
:boolean
Indicates whether the source of text should be displayed. Defaults totrue
."prompt"
:string
The prompt content.
Response
Success:
{
"code": 0,
"data": {
"avatar": "",
"create_date": "Thu, 24 Oct 2024 11:18:29 GMT",
"create_time": 1729768709023,
"dataset_ids": [
"527fa74891e811ef9c650242ac120006"
],
"description": "A helpful Assistant",
"do_refer": "1",
"id": "b1f2f15691f911ef81180242ac120003",
"language": "English",
"llm": {
"frequency_penalty": 0.7,
"max_tokens": 512,
"model_name": "qwen-plus@Tongyi-Qianwen",
"presence_penalty": 0.4,
"temperature": 0.1,
"top_p": 0.3
},
"name": "12234",
"prompt": {
"empty_response": "Sorry! No relevant content was found in the knowledge base!",
"keywords_similarity_weight": 0.3,
"opener": "Hi! I'm your assistant, what can I do for you?",
"prompt": "You are an intelligent assistant. Please summarize the content of the knowledge base to answer the question. Please list the data in the knowledge base and answer in detail. When all knowledge base content is irrelevant to the question, your answer must include the sentence \"The answer you are looking for is not found in the knowledge base!\" Answers need to consider chat history.\n ",
"rerank_model": "",
"similarity_threshold": 0.2,
"top_n": 6,
"variables": [
{
"key": "knowledge",
"optional": false
}
]
},
"prompt_type": "simple",
"status": "1",
"tenant_id": "69736c5e723611efb51b0242ac120007",
"top_k": 1024,
"update_date": "Thu, 24 Oct 2024 11:18:29 GMT",
"update_time": 1729768709023
}
}
Failure:
{
"code": 102,
"message": "Duplicated chat name in creating dataset."
}
Update chat assistant
PUT /api/v1/chats/{chat_id}
Updates configurations for a specified chat assistant.
Request
- Method: PUT
- URL:
/api/v1/chats/{chat_id}
- Headers:
'content-Type: application/json'
'Authorization: Bearer <YOUR_API_KEY>'
- Body:
"name"
:string
"avatar"
:string
"dataset_ids"
:list[string]
"llm"
:object
"prompt"
:object
Request example
curl --request PUT \
--url http://{address}/api/v1/chats/{chat_id} \
--header 'Content-Type: application/json' \
--header 'Authorization: Bearer <YOUR_API_KEY>' \
--data '
{
"name":"Test"
}'
Parameters
chat_id
: (Path parameter)
The ID of the chat assistant to update."name"
: (Body parameter),string
, Required
The revised name of the chat assistant."avatar"
: (Body parameter),string
Base64 encoding of the avatar."dataset_ids"
: (Body parameter),list[string]
The IDs of the associated datasets."llm"
: (Body parameter),object
The LLM settings for the chat assistant to create. If it is not explicitly set, a dictionary with the following values will be generated as the default. Anllm
object contains the following attributes:"model_name"
,string
The chat model name. If not set, the user's default chat model will be used."temperature"
:float
Controls the randomness of the model's predictions. A lower temperature increases the model's confidence in its responses; a higher temperature increases creativity and diversity. Defaults to0.1
."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 to0.3
"presence_penalty"
:float
This discourages the model from repeating the same information by penalizing words that have already appeared in the conversation. Defaults to0.2
."frequency penalty"
:float
Similar to the presence penalty, this reduces the model’s tendency to repeat the same words frequently. Defaults to0.7
."max_token"
:integer
The maximum length of the model’s output, measured in the number of tokens (words or pieces of words). Defaults to512
.
"prompt"
: (Body parameter),object
Instructions for the LLM to follow. Aprompt
object contains the following attributes:"similarity_threshold"
:float
RAGFlow employs either a combination of weighted keyword similarity and weighted vector cosine similarity, or a combination of weighted keyword similarity and weighted rerank score during retrieval. This argument sets the threshold for similarities between the user query and chunks. If a similarity score falls below this threshold, the corresponding chunk will be excluded from the results. The default value is0.2
."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 is0.7
."top_n"
:int
This argument specifies the number of top chunks with similarity scores above thesimilarity_threshold
that are fed to the LLM. The LLM will only access these 'top N' chunks. The default value is8
."variables"
:object[]
This argument lists the variables to use in the 'System' field of Chat Configurations. Note that:"knowledge"
is a reserved variable, which represents the retrieved chunks.- All the variables in 'System' should be curly bracketed.
- The default value is
[{"key": "knowledge", "optional": true}]
"rerank_model"
:string
If it is not specified, vector cosine similarity will be used; otherwise, reranking score will be used."empty_response"
:string
If nothing is retrieved in the dataset for the user's question, this will be used as the response. To allow the LLM to improvise when nothing is found, leave this blank."opener"
:string
The opening greeting for the user. Defaults to"Hi! I am your assistant, can I help you?"
."show_quote
:boolean
Indicates whether the source of text should be displayed. Defaults totrue
."prompt"
:string
The prompt content.
Response
Success:
{
"code": 0
}
Failure:
{
"code": 102,
"message": "Duplicated chat name in updating dataset."
}
Delete chat assistants
DELETE /api/v1/chats
Deletes chat assistants by ID.
Request
- Method: DELETE
- URL:
/api/v1/chats
- Headers:
'content-Type: application/json'
'Authorization: Bearer <YOUR_API_KEY>'
- Body:
"ids"
:list[string]
Request example
curl --request DELETE \
--url http://{address}/api/v1/chats \
--header 'Content-Type: application/json' \
--header 'Authorization: Bearer <YOUR_API_KEY>' \
--data '
{
"ids": ["test_1", "test_2"]
}'
Request parameters
"ids"
: (Body parameter),list[string]
The IDs of the chat assistants to delete. If it is not specified, all chat assistants in the system will be deleted.
Response
Success:
{
"code": 0
}
Failure:
{
"code": 102,
"message": "ids are required"
}
List chat assistants
GET /api/v1/chats?page={page}&page_size={page_size}&orderby={orderby}&desc={desc}&name={chat_name}&id={chat_id}
Lists chat assistants.
Request
- Method: GET
- URL:
/api/v1/chats?page={page}&page_size={page_size}&orderby={orderby}&desc={desc}&name={dataset_name}&id={dataset_id}
- Headers:
'Authorization: Bearer <YOUR_API_KEY>'
Request example
curl --request GET \
--url http://{address}/api/v1/chats?page={page}&page_size={page_size}&orderby={orderby}&desc={desc}&name={dataset_name}&id={dataset_id} \
--header 'Authorization: Bearer <YOUR_API_KEY>'
Request parameters
page
: (Filter parameter),integer
Specifies the page on which the chat assistants will be displayed. Defaults to1
.page_size
: (Filter parameter),integer
The number of chat assistants on each page. Defaults to30
.orderby
: (Filter parameter),string
The attribute by which the results are sorted. Available options:create_time
(default)update_time
desc
: (Filter parameter),boolean
Indicates whether the retrieved chat assistants should be sorted in descending order. Defaults totrue
.id
: (Filter parameter),string
The ID of the chat assistant to retrieve.name
: (Filter parameter),string
The name of the chat assistant to retrieve.
Response
Success:
{
"code": 0,
"data": [
{
"avatar": "",
"create_date": "Fri, 18 Oct 2024 06:20:06 GMT",
"create_time": 1729232406637,
"description": "A helpful Assistant",
"do_refer": "1",
"id": "04d0d8e28d1911efa3630242ac120006",
"dataset_ids": ["527fa74891e811ef9c650242ac120006"],
"language": "English",
"llm": {
"frequency_penalty": 0.7,
"max_tokens": 512,
"model_name": "qwen-plus@Tongyi-Qianwen",
"presence_penalty": 0.4,
"temperature": 0.1,
"top_p": 0.3
},
"name": "13243",
"prompt": {
"empty_response": "Sorry! No relevant content was found in the knowledge base!",
"keywords_similarity_weight": 0.3,
"opener": "Hi! I'm your assistant, what can I do for you?",
"prompt": "You are an intelligent assistant. Please summarize the content of the knowledge base to answer the question. Please list the data in the knowledge base and answer in detail. When all knowledge base content is irrelevant to the question, your answer must include the sentence \"The answer you are looking for is not found in the knowledge base!\" Answers need to consider chat history.\n",
"rerank_model": "",
"similarity_threshold": 0.2,
"top_n": 6,
"variables": [
{
"key": "knowledge",
"optional": false
}
]
},
"prompt_type": "simple",
"status": "1",
"tenant_id": "69736c5e723611efb51b0242ac120007",
"top_k": 1024,
"update_date": "Fri, 18 Oct 2024 06:20:06 GMT",
"update_time": 1729232406638
}
]
}
Failure:
{
"code": 102,
"message": "The chat doesn't exist"
}
Create session with chat assistant
POST /api/v1/chats/{chat_id}/sessions
Creates a session with a chat assistant.
Request
- Method: POST
- URL:
/api/v1/chats/{chat_id}/sessions
- Headers:
'content-Type: application/json'
'Authorization: Bearer <YOUR_API_KEY>'
- Body:
"name"
:string
Request example
curl --request POST \
--url http://{address}/api/v1/chats/{chat_id}/sessions \
--header 'Content-Type: application/json' \
--header 'Authorization: Bearer <YOUR_API_KEY>' \
--data '
{
"name": "new session"
}'
Request parameters
chat_id
: (Path parameter)
The ID of the associated chat assistant."name"
: (Body parameter),string
The name of the chat session to create.
Response
Success:
{
"code": 0,
"data": {
"chat_id": "2ca4b22e878011ef88fe0242ac120005",
"create_date": "Fri, 11 Oct 2024 08:46:14 GMT",
"create_time": 1728636374571,
"id": "4606b4ec87ad11efbc4f0242ac120006",
"messages": [
{
"content": "Hi! I am your assistant,can I help you?",
"role": "assistant"
}
],
"name": "new session",
"update_date": "Fri, 11 Oct 2024 08:46:14 GMT",
"update_time": 1728636374571
}
}
Failure:
{
"code": 102,
"message": "Name can not be empty."
}
Update session
PUT /api/v1/chats/{chat_id}/sessions/{session_id}
Updates a session of a specified chat assistant.
Request
- Method: PUT
- URL:
/api/v1/chats/{chat_id}/sessions/{session_id}
- Headers:
'content-Type: application/json'
'Authorization: Bearer <YOUR_API_KEY>'
- Body:
"name
:string
Request example
curl --request PUT \
--url http://{address}/api/v1/chats/{chat_id}/sessions/{session_id} \
--header 'Content-Type: application/json' \
--header 'Authorization: Bearer <YOUR_API_KEY>' \
--data '
{
"name": "<REVISED_SESSION_NAME_HERE>"
}'
Request Parameter
chat_id
: (Path parameter)
The ID of the associated chat assistant.session_id
: (Path parameter)
The ID of the session to update."name"
: (*Body Parameter),string
The revised name of the session.
Response
Success:
{
"code": 0
}
Failure:
{
"code": 102,
"message": "Name cannot be empty."
}
List sessions
GET /api/v1/chats/{chat_id}/sessions?page={page}&page_size={page_size}&orderby={orderby}&desc={desc}&name={session_name}&id={session_id}
Lists sessions associated with a specified chat assistant.
Request
- Method: GET
- URL:
/api/v1/chats/{chat_id}/sessions?page={page}&page_size={page_size}&orderby={orderby}&desc={desc}&name={session_name}&id={session_id}
- Headers:
'Authorization: Bearer <YOUR_API_KEY>'
Request example
curl --request GET \
--url http://{address}/api/v1/chats/{chat_id}/sessions?page={page}&page_size={page_size}&orderby={orderby}&desc={desc}&name={session_name}&id={session_id} \
--header 'Authorization: Bearer <YOUR_API_KEY>'
Request Parameters
chat_id
: (Path parameter)
The ID of the associated chat assistant.page
: (Filter parameter),integer
Specifies the page on which the sessions will be displayed. Defaults to1
.page_size
: (Filter parameter),integer
The number of sessions on each page. Defaults to30
.orderby
: (Filter parameter),string
The field by which sessions should be sorted. Available options:create_time
(default)update_time
desc
: (Filter parameter),boolean
Indicates whether the retrieved sessions should be sorted in descending order. Defaults totrue
.name
: (Filter parameter)string
The name of the chat session to retrieve.id
: (Filter parameter),string
The ID of the chat session to retrieve.
Response
Success:
{
"code": 0,
"data": [
{
"chat": "2ca4b22e878011ef88fe0242ac120005",
"create_date": "Fri, 11 Oct 2024 08:46:43 GMT",
"create_time": 1728636403974,
"id": "578d541e87ad11ef96b90242ac120006",
"messages": [
{
"content": "Hi! I am your assistant,can I help you?",
"role": "assistant"
}
],
"name": "new session",
"update_date": "Fri, 11 Oct 2024 08:46:43 GMT",
"update_time": 1728636403974
}
]
}
Failure:
{
"code": 102,
"message": "The session doesn't exist"
}
Delete sessions
DELETE /api/v1/chats/{chat_id}/sessions
Deletes sessions of a chat assistant by ID.
Request
- Method: DELETE
- URL:
/api/v1/chats/{chat_id}/sessions
- Headers:
'content-Type: application/json'
'Authorization: Bearer <YOUR_API_KEY>'
- Body:
"ids"
:list[string]
Request example
# Either id or name must be provided, but not both.
curl --request DELETE \
--url http://{address}/api/v1/chats/{chat_id}/sessions \
--header 'Content-Type: application/json' \
--header 'Authorization: Bearer <YOUR_API_KEY>' \
--data '
{
"ids": ["test_1", "test_2"]
}'
Request Parameters
chat_id
: (Path parameter)
The ID of the associated chat assistant."ids"
: (Body Parameter),list[string]
The IDs of the sessions to delete. If it is not specified, all sessions associated with the specified chat assistant will be deleted.
Response
Success:
{
"code": 0
}
Failure:
{
"code": 102,
"message": "The chat doesn't own the session"
}
Converse with chat assistant
POST /api/v1/chats/{chat_id}/completions
Asks a specified chat assistant a question to start an AI-powered conversation.
-
In streaming mode, not all responses include a reference, as this depends on the system's judgement.
-
In streaming mode, the last message is an empty message:
data:
{
"code": 0,
"data": true
}
Request
- Method: POST
- URL:
/api/v1/chats/{chat_id}/completions
- Headers:
'content-Type: application/json'
'Authorization: Bearer <YOUR_API_KEY>'
- Body:
"question"
:string
"stream"
:boolean
"session_id"
:string
Request example
curl --request POST \
--url http://{address}/api/v1/chats/{chat_id}/completions \
--header 'Content-Type: application/json' \
--header 'Authorization: Bearer <YOUR_API_KEY>' \
--data-binary '
{
"question": "What is RAGFlow?",
"stream": true
}'
Request Parameters
chat_id
: (Path parameter)
The ID of the associated chat assistant."question"
: (Body Parameter),string
, Required
The question to start an AI-powered conversation."stream"
: (Body Parameter),boolean
Indicates whether to output responses in a streaming way:true
: Enable streaming (default).false
: Disable streaming.
"session_id"
: (Body Parameter)
The ID of session. If it is not provided, a new session will be generated.
Response
Success:
data:{
"code": 0,
"data": {
"answer": "I am an intelligent assistant designed to help answer questions by summarizing content from a",
"reference": {},
"audio_binary": null,
"id": "a84c5dd4-97b4-4624-8c3b-974012c8000d",
"session_id": "82b0ab2a9c1911ef9d870242ac120006"
}
}
data:{
"code": 0,
"data": {
"answer": "I am an intelligent assistant designed to help answer questions by summarizing content from a knowledge base. My responses are based on the information available in the knowledge base and",
"reference": {},
"audio_binary": null,
"id": "a84c5dd4-97b4-4624-8c3b-974012c8000d",
"session_id": "82b0ab2a9c1911ef9d870242ac120006"
}
}
data:{
"code": 0,
"data": {
"answer": "I am an intelligent assistant designed to help answer questions by summarizing content from a knowledge base. My responses are based on the information available in the knowledge base and any relevant chat history.",
"reference": {},
"audio_binary": null,
"id": "a84c5dd4-97b4-4624-8c3b-974012c8000d",
"session_id": "82b0ab2a9c1911ef9d870242ac120006"
}
}
data:{
"code": 0,
"data": {
"answer": "I am an intelligent assistant designed to help answer questions by summarizing content from a knowledge base ##0$$. My responses are based on the information available in the knowledge base and any relevant chat history.",
"reference": {
"total": 1,
"chunks": [
{
"id": "faf26c791128f2d5e821f822671063bd",
"content": "xxxxxxxx",
"document_id": "dd58f58e888511ef89c90242ac120006",
"document_name": "1.txt",
"dataset_id": "8e83e57a884611ef9d760242ac120006",
"image_id": "",
"similarity": 0.7,
"vector_similarity": 0.0,
"term_similarity": 1.0,
"positions": [
""
]
}
],
"doc_aggs": [
{
"doc_name": "1.txt",
"doc_id": "dd58f58e888511ef89c90242ac120006",
"count": 1
}
]
},
"prompt": "xxxxxxxxxxx",
"id": "a84c5dd4-97b4-4624-8c3b-974012c8000d",
"session_id": "82b0ab2a9c1911ef9d870242ac120006"
}
}
data:{
"code": 0,
"data": true
}
Failure:
{
"code": 102,
"message": "Please input your question."
}
Create session with an agent
POST /api/v1/agents/{agent_id}/sessions
Creates a session with an agent.
Request
- Method: POST
- URL:
/api/v1/agents/{agent_id}/sessions
- Headers:
'content-Type: application/json'
'Authorization: Bearer <YOUR_API_KEY>'
- Body:
Request example
curl --request POST \
--url http://{address}/api/v1/agents/{agent_id}/sessions \
--header 'Content-Type: application/json' \
--header 'Authorization: Bearer <YOUR_API_KEY>' \
--data '{
}'
Request parameters
agent_id
: (Path parameter)
The ID of the associated agent assistant.
Response
Success:
{
"code": 0,
"data": {
"agent_id": "2e45b5209c1011efa3e90242ac120006",
"id": "7869e9e49c1711ef92840242ac120006",
"message": [
{
"content": "Hello! I am a recruiter at InfiniFlow. I learned that you are an expert in the field, and took the liberty of reaching out to you. There is an opportunity I would like to share with you. RAGFlow is currently looking for a senior engineer for your position. I was wondering if you might be interested?",
"role": "assistant"
}
],
"source": "agent",
"user_id": ""
}
}
Failure:
{
"code": 102,
"message": "Agent not found."
}
Converse with agent
POST /api/v1/agents/{agent_id}/completions
Asks a specified agent a question to start an AI-powered conversation.
-
In streaming mode, not all responses include a reference, as this depends on the system's judgement.
-
In streaming mode, the last message is an empty message:
data:
{
"code": 0,
"data": true
}
Request
- Method: POST
- URL:
/api/v1/agents/{agent_id}/completions
- Headers:
'content-Type: application/json'
'Authorization: Bearer <YOUR_API_KEY>'
- Body:
"question"
:string
"stream"
:boolean
"session_id"
:string
Request example
curl --request POST \
--url http://{address}/api/v1/agents/{agent_id}/completions \
--header 'Content-Type: application/json' \
--header 'Authorization: Bearer <YOUR_API_KEY>' \
--data-binary '
{
"question": "What is RAGFlow?",
"stream": true
}'
Request Parameters
agent_id
: (Path parameter),string
The ID of the associated agent assistant."question"
: (Body Parameter),string
, Required
The question to start an AI-powered conversation."stream"
: (Body Parameter),boolean
Indicates whether to output responses in a streaming way:true
: Enable streaming (default).false
: Disable streaming.
"session_id"
: (Body Parameter)
The ID of the session. If it is not provided, a new session will be generated.
Response
Success:
data:{
"code": 0,
"message": "",
"data": {
"answer": "",
"reference": [],
"id": "7ed5c2e4-aa28-4397-bbed-59664a332aa0",
"session_id": "ce1b4fa89c1811ef85720242ac120006"
}
}
data:{
"code": 0,
"data": {
"answer": "Hello",
"reference": [],
"id": "7ed5c2e4-aa28-4397-bbed-59664a332aa0",
"session_id": "ce1b4fa89c1811ef85720242ac120006"
}
}
data:{
"code": 0,
"data": {
"answer": "Hello!",
"reference": [],
"id": "7ed5c2e4-aa28-4397-bbed-59664a332aa0",
"session_id": "ce1b4fa89c1811ef85720242ac120006"
}
}
data:{
"code": 0,
"data": {
"answer": "Hello! How",
"reference": [],
"id": "7ed5c2e4-aa28-4397-bbed-59664a332aa0",
"session_id": "ce1b4fa89c1811ef85720242ac120006"
}
}
data:{
"code": 0,
"data": {
"answer": "Hello! How can",
"reference": [],
"id": "7ed5c2e4-aa28-4397-bbed-59664a332aa0",
"session_id": "ce1b4fa89c1811ef85720242ac120006"
}
}
data:{
"code": 0,
"data": {
"answer": "Hello! How can I",
"reference": [],
"id": "7ed5c2e4-aa28-4397-bbed-59664a332aa0",
"session_id": "ce1b4fa89c1811ef85720242ac120006"
}
}
data:{
"code": 0,
"data": {
"answer": "Hello! How can I assist",
"reference": [],
"id": "7ed5c2e4-aa28-4397-bbed-59664a332aa0",
"session_id": "ce1b4fa89c1811ef85720242ac120006"
}
}
data:{
"code": 0,
"data": {
"answer": "Hello! How can I assist you",
"reference": [],
"id": "7ed5c2e4-aa28-4397-bbed-59664a332aa0",
"session_id": "ce1b4fa89c1811ef85720242ac120006"
}
}
data:{
"code": 0,
"data": {
"answer": "Hello! How can I assist you today",
"reference": [],
"id": "7ed5c2e4-aa28-4397-bbed-59664a332aa0",
"session_id": "ce1b4fa89c1811ef85720242ac120006"
}
}
data:{
"code": 0,
"data": {
"answer": "Hello! How can I assist you today?",
"reference": [],
"id": "7ed5c2e4-aa28-4397-bbed-59664a332aa0",
"session_id": "ce1b4fa89c1811ef85720242ac120006"
}
}
data:{
"code": 0,
"data": {
"answer": "Hello! How can I assist you today?",
"reference": [],
"id": "7ed5c2e4-aa28-4397-bbed-59664a332aa0",
"session_id": "ce1b4fa89c1811ef85720242ac120006"
}
}
data:{
"code": 0,
"data": true
}
Failure:
{
"code": 102,
"message": "`question` is required."
}