Version: DEV

HTTP API

A complete reference for Swipies AI's RESTful API. Before proceeding, please ensure you [have your Swipies AI API key ready for authentication](https://Swipies AI.io/docs/dev/acquire_Swipies AI_api_key).

ERROR CODES

Code	Message	Description
400	Bad Request	Invalid request parameters
401	Unauthorized	Unauthorized access
403	Forbidden	Access denied
404	Not Found	Resource not found
500	Internal Server Error	Server internal error
1001	Invalid Chunk ID	Invalid Chunk ID
1002	Chunk Update Failed	Chunk update failed

OpenAI-Compatible API

Create chat completion

POST /api/v1/chats_openai/{chat_id}/chat/completions

Creates a model response for a given chat conversation.

This API follows the same request and response format as OpenAI's API. It allows you to interact with the model in a manner similar to how you would with OpenAI's API.

Request

Method: POST
URL: /api/v1/chats_openai/{chat_id}/chat/completions
Headers:
- 'content-Type: application/json'
- 'Authorization: Bearer <YOUR_API_KEY>'
Body:
- "model": string
- "messages": object list
- "stream": boolean

Request example

curl --request POST \
     --url http://{address}/api/v1/chats_openai/{chat_id}/chat/completions \
     --header 'Content-Type: application/json' \
     --header 'Authorization: Bearer <YOUR_API_KEY>' \
     --data '{
        "model": "model",
        "messages": [{"role": "user", "content": "Say this is a test!"}],
        "stream": true
      }'

Request Parameters

model (Body parameter) string, Required
The model used to generate the response. The server will parse this automatically, so you can set it to any value for now.
messages (Body parameter) list[object], Required
A list of historical chat messages used to generate the response. This must contain at least one message with the user role.
stream (Body parameter) boolean
Whether to receive the response as a stream. Set this to false explicitly if you prefer to receive the entire response in one go instead of as a stream.

Response

Stream:

data:{
    "id": "chatcmpl-3b0397f277f511f0b47f729e3aa55728",
    "choices": [
        {
            "delta": {
                "content": "Hello! It seems like you're just greeting me. If you have a specific",
                "role": "assistant",
                "function_call": null,
                "tool_calls": null,
                "reasoning_content": null
            },
            "finish_reason": null,
            "index": 0,
            "logprobs": null
        }
    ],
    "created": 1755084508,
    "model": "model",
    "object": "chat.completion.chunk",
    "system_fingerprint": "",
    "usage": null
}

data:{"id": "chatcmpl-3b0397f277f511f0b47f729e3aa55728", "choices": [{"delta": {"content": " question or need information, feel free to ask, and I'll do my best", "role": "assistant", "function_call": null, "tool_calls": null, "reasoning_content": null}, "finish_reason": null, "index": 0, "logprobs": null}], "created": 1755084508, "model": "model", "object": "chat.completion.chunk", "system_fingerprint": "", "usage": null}

data:{"id": "chatcmpl-3b0397f277f511f0b47f729e3aa55728", "choices": [{"delta": {"content": " to assist you based on the knowledge base provided.", "role": "assistant", "function_call": null, "tool_calls": null, "reasoning_content": null}, "finish_reason": null, "index": 0, "logprobs": null}], "created": 1755084508, "model": "model", "object": "chat.completion.chunk", "system_fingerprint": "", "usage": null}

data:{"id": "chatcmpl-3b0397f277f511f0b47f729e3aa55728", "choices": [{"delta": {"content": null, "role": "assistant", "function_call": null, "tool_calls": null, "reasoning_content": null}, "finish_reason": "stop", "index": 0, "logprobs": null}], "created": 1755084508, "model": "model", "object": "chat.completion.chunk", "system_fingerprint": "", "usage": {"prompt_tokens": 5, "completion_tokens": 188, "total_tokens": 193}}

data:[DONE]

Non-stream:

{
    "choices": [
        {
            "finish_reason": "stop",
            "index": 0,
            "logprobs": null,
            "message": {
                "content": "Hello! I'm your smart assistant. What can I do for you?",
                "role": "assistant"
            }
        }
    ],
    "created": 1755084403,
    "id": "chatcmpl-3b0397f277f511f0b47f729e3aa55728",
    "model": "model",
    "object": "chat.completion",
    "usage": {
        "completion_tokens": 55,
        "completion_tokens_details": {
            "accepted_prediction_tokens": 55,
            "reasoning_tokens": 5,
            "rejected_prediction_tokens": 0
        },
        "prompt_tokens": 5,
        "total_tokens": 60
    }
}

Failure:

{
  "code": 102,
  "message": "The last content of this conversation is not from user."
}

Create agent completion

POST /api/v1/agents_openai/{agent_id}/chat/completions

Creates a model response for a given chat conversation.

This API follows the same request and response format as OpenAI's API. It allows you to interact with the model in a manner similar to how you would with OpenAI's API.

Request

Method: POST
URL: /api/v1/agents_openai/{agent_id}/chat/completions
Headers:
- 'content-Type: application/json'
- 'Authorization: Bearer <YOUR_API_KEY>'
Body:
- "model": string
- "messages": object list
- "stream": boolean

Request example

curl --request POST \
     --url http://{address}/api/v1/agents_openai/{agent_id}/chat/completions \
     --header 'Content-Type: application/json' \
     --header 'Authorization: Bearer <YOUR_API_KEY>' \
     --data '{
        "model": "model",
        "messages": [{"role": "user", "content": "Say this is a test!"}],
        "stream": true
      }'

Request Parameters

model (Body parameter) string, Required
The model used to generate the response. The server will parse this automatically, so you can set it to any value for now.
messages (Body parameter) list[object], Required
A list of historical chat messages used to generate the response. This must contain at least one message with the user role.
stream (Body parameter) boolean
Whether to receive the response as a stream. Set this to false explicitly if you prefer to receive the entire response in one go instead of as a stream.
session_id (Body parameter) string
Agent session id.

Response

Stream:

...

data: {
    "id": "c39f6f9c83d911f0858253708ecb6573",
    "object": "chat.completion.chunk",
    "model": "d1f79142831f11f09cc51795b9eb07c0",
    "choices": [
        {
            "delta": {
                "content": " terminal"
            },
            "finish_reason": null,
            "index": 0
        }
    ]
}

data: {
    "id": "c39f6f9c83d911f0858253708ecb6573",
    "object": "chat.completion.chunk",
    "model": "d1f79142831f11f09cc51795b9eb07c0",
    "choices": [
        {
            "delta": {
                "content": "."
            },
            "finish_reason": null,
            "index": 0
        }
    ]
}

data: {
    "id": "c39f6f9c83d911f0858253708ecb6573",
    "object": "chat.completion.chunk",
    "model": "d1f79142831f11f09cc51795b9eb07c0",
    "choices": [
        {
            "delta": {
                "content": "",
                "reference": {
                    "chunks": {
                        "20": {
                            "id": "4b8935ac0a22deb1",
                            "content": "```cd /usr/ports/editors/neovim/ && make install```## Android[Termux](https://github.com/termux/termux-app) offers a Neovim package.",
                            "document_id": "4bdd2ff65e1511f0907f09f583941b45",
                            "document_name": "INSTALL22.md",
                            "dataset_id": "456ce60c5e1511f0907f09f583941b45",
                            "image_id": "",
                            "positions": [
                                [
                                    12,
                                    11,
                                    11,
                                    11,
                                    11
                                ]
                            ],
                            "url": null,
                            "similarity": 0.5697155305154673,
                            "vector_similarity": 0.7323851005515574,
                            "term_similarity": 0.5000000005,
                            "doc_type": ""
                        }
                    },
                    "doc_aggs": {
                        "INSTALL22.md": {
                            "doc_name": "INSTALL22.md",
                            "doc_id": "4bdd2ff65e1511f0907f09f583941b45",
                            "count": 3
                        },
                        "INSTALL.md": {
                            "doc_name": "INSTALL.md",
                            "doc_id": "4bd7fdd85e1511f0907f09f583941b45",
                            "count": 2
                        },
                        "INSTALL(1).md": {
                            "doc_name": "INSTALL(1).md",
                            "doc_id": "4bdfb42e5e1511f0907f09f583941b45",
                            "count": 2
                        },
                        "INSTALL3.md": {
                            "doc_name": "INSTALL3.md",
                            "doc_id": "4bdab5825e1511f0907f09f583941b45",
                            "count": 1
                        }
                    }
                }
            },
            "finish_reason": null,
            "index": 0
        }
    ]
}

data: [DONE]

Non-stream:

{
    "choices": [
        {
            "finish_reason": "stop",
            "index": 0,
            "logprobs": null,
            "message": {
                "content": "\nTo install Neovim, the process varies depending on your operating system:\n\n### For Windows:\n1. **Download from GitHub**: \n   - Visit the [Neovim releases page](https://github.com/neovim/neovim/releases)\n   - Download the latest Windows installer (nvim-win64.msi)\n   - Run the installer and follow the prompts\n\n2. **Using winget** (Windows Package Manager):\n...",
                "reference": {
                    "chunks": {
                        "20": {
                            "content": "```cd /usr/ports/editors/neovim/ && make install```## Android[Termux](https://github.com/termux/termux-app) offers a Neovim package.",
                            "dataset_id": "456ce60c5e1511f0907f09f583941b45",
                            "doc_type": "",
                            "document_id": "4bdd2ff65e1511f0907f09f583941b45",
                            "document_name": "INSTALL22.md",
                            "id": "4b8935ac0a22deb1",
                            "image_id": "",
                            "positions": [
                                [
                                    12,
                                    11,
                                    11,
                                    11,
                                    11
                                ]
                            ],
                            "similarity": 0.5697155305154673,
                            "term_similarity": 0.5000000005,
                            "url": null,
                            "vector_similarity": 0.7323851005515574
                        }
                    },
                    "doc_aggs": {
                        "INSTALL(1).md": {
                            "count": 2,
                            "doc_id": "4bdfb42e5e1511f0907f09f583941b45",
                            "doc_name": "INSTALL(1).md"
                        },
                        "INSTALL.md": {
                            "count": 2,
                            "doc_id": "4bd7fdd85e1511f0907f09f583941b45",
                            "doc_name": "INSTALL.md"
                        },
                        "INSTALL22.md": {
                            "count": 3,
                            "doc_id": "4bdd2ff65e1511f0907f09f583941b45",
                            "doc_name": "INSTALL22.md"
                        },
                        "INSTALL3.md": {
                            "count": 1,
                            "doc_id": "4bdab5825e1511f0907f09f583941b45",
                            "doc_name": "INSTALL3.md"
                        }
                    }
                },
                "role": "assistant"
            }
        }
    ],
    "created": null,
    "id": "c39f6f9c83d911f0858253708ecb6573",
    "model": "d1f79142831f11f09cc51795b9eb07c0",
    "object": "chat.completion",
    "param": null,
    "usage": {
        "completion_tokens": 415,
        "completion_tokens_details": {
            "accepted_prediction_tokens": 0,
            "reasoning_tokens": 0,
            "rejected_prediction_tokens": 0
        },
        "prompt_tokens": 6,
        "total_tokens": 421
    }
}

Failure:

{
  "code": 102,
  "message": "The last content of this conversation is not from user."
}

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
- "embedding_model": string
- "permission": string
- "chunk_method": string
- "parser_config": object
- "parse_type": int
- "pipeline_id": string

A basic 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"
      }'

A request example specifying ingestion pipeline

WARNING

You must not include "chunk_method" or "parser_config" when specifying an ingestion pipeline.

curl --request POST \
  --url http://{address}/api/v1/datasets \
  --header 'Content-Type: application/json' \
  --header 'Authorization: Bearer <YOUR_API_KEY>' \
  --data '{
   "name": "test-sdk",
   "parse_type": <NUMBER_OF_PARSERS_IN_YOUR_PARSER_COMPONENT>,
   "pipeline_id": "<PIPELINE_ID_32_HEX>"
  }'

Request parameters

"name": (Body parameter), string, Required
The unique name of the dataset to create. It must adhere to the following requirements:
- Basic Multilingual Plane (BMP) only
- Maximum 128 characters
- Case-insensitive
"avatar": (Body parameter), string
Base64 encoding of the avatar.
- Maximum 65535 characters
"description": (Body parameter), string
A brief description of the dataset to create.
- Maximum 65535 characters
"embedding_model": (Body parameter), string
The name of the embedding model to use. For example: "BAAI/bge-large-zh-v1.5@BAAI"
- Maximum 255 characters
- Must follow model_name@model_factory format
"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 default chunk method of the dataset to create. Mutually exclusive with "parse_type" and "pipeline_id". If you set "chunk_method", do not include "parse_type" or "pipeline_id".
Available options:
- "naive": General (default)
- "book": Book
- "email": Email
- "laws": Laws
- "manual": Manual
- "one": One
- "paper": Paper
- "picture": Picture
- "presentation": Presentation
- "qa": Q&A
- "table": Table
- "tag": Tag
"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:
  - "auto_keywords": int
    - Defaults to 0
    - Minimum: 0
    - Maximum: 32
  - "auto_questions": int
    - Defaults to 0
    - Minimum: 0
    - Maximum: 10
  - "chunk_token_num": int
    - Defaults to 512
    - Minimum: 1
    - Maximum: 2048
  - "delimiter": string
    - Defaults to "\n".
  - "html4excel": bool
    - Whether to convert Excel documents into HTML format.
    - Defaults to false
  - "layout_recognize": string
    - Defaults to DeepDOC
  - "tag_kb_ids": array<string>
    - IDs of datasets to be parsed using the Tag chunk method.
    - Before setting this, ensure a tag set is created and properly configured. For details, see [Use tag set](https://Swipies AI.io/docs/dev/use_tag_sets).
  - "task_page_size": int
    - For PDFs only.
    - Defaults to 12
    - Minimum: 1
  - "raptor": object RAPTOR-specific settings.
    - Defaults to: {"use_raptor": false}
  - "graphrag": object GRAPHRAG-specific settings.
    - Defaults to: {"use_graphrag": false}
- If "chunk_method" is "qa", "manuel", "paper", "book", "laws", or "presentation", the "parser_config" object contains the following attribute:
  - "raptor": object RAPTOR-specific settings.
    - Defaults to: {"use_raptor": false}.
- If "chunk_method" is "table", "picture", "one", or "email", "parser_config" is an empty JSON object.
"parse_type": (Body parameter), int
The ingestion pipeline parse type identifier, i.e., the number of parsers in your Parser component.
- Required (along with "pipeline_id") if specifying an ingestion pipeline.
- Must not be included when "chunk_method" is specified.
"pipeline_id": (Body parameter), string
The ingestion pipeline ID. Can be found in the corresponding URL in the Swipies AI UI.
- Required (along with "parse_type") if specifying an ingestion pipeline.
- Must be a 32-character lowercase hexadecimal string, e.g., "d0bebe30ae2211f0970942010a8e0005".
- Must not be included when "chunk_method" is specified.

WARNING

You can choose either of the following ingestion options when creating a dataset, but not both:

Use a built-in chunk method -- specify "chunk_method" (optionally with "parser_config").
Use an ingestion pipeline -- specify both "parse_type" and "pipeline_id".

If none of "chunk_method", "parse_type", or "pipeline_id" are provided, the system defaults to chunk_method = "naive".

Response

Success:

{
    "code": 0,
    "data": {
        "avatar": null,
        "chunk_count": 0,
        "chunk_method": "naive",
        "create_date": "Mon, 28 Apr 2025 18:40:41 GMT",
        "create_time": 1745836841611,
        "created_by": "3af81804241d11f0a6a79f24fc270c7f",
        "description": null,
        "document_count": 0,
        "embedding_model": "BAAI/bge-large-zh-v1.5@BAAI",
        "id": "3b4de7d4241d11f0a6a79f24fc270c7f",
        "language": "English",
        "name": "Swipies AI example",
        "pagerank": 0,
        "parser_config": {
            "chunk_token_num": 128, 
            "delimiter": "\\n!?;。；！？", 
            "html4excel": false, 
            "layout_recognize": "DeepDOC", 
            "raptor": {
                "use_raptor": false
                }
            },
        "permission": "me",
        "similarity_threshold": 0.2,
        "status": "1",
        "tenant_id": "3af81804241d11f0a6a79f24fc270c7f",
        "token_num": 0,
        "update_date": "Mon, 28 Apr 2025 18:40:41 GMT",
        "update_time": 1745836841611,
        "vector_similarity_weight": 0.3,
    },
}

Failure:

{
    "code": 101,
    "message": "Dataset name 'Swipies AI example' already exists"
}

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] or null

Request example

curl --request DELETE \
     --url http://{address}/api/v1/datasets \
     --header 'Content-Type: application/json' \
     --header 'Authorization: Bearer <YOUR_API_KEY>' \
     --data '{
     "ids": ["d94a8dc02c9711f0930f7fbc369eab6d", "e94a8dc02c9711f0930f7fbc369eab6e"]
     }'

Request parameters

"ids": (Body parameter), list[string] or null, Required
Specifies the datasets to delete:
- If null, all datasets will be deleted.
- If an array of IDs, only the specified datasets will be deleted.
- If an empty array, no 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
- "avatar": string
- "description": string
- "embedding_model": string
- "permission": string
- "chunk_method": string
- "pagerank": int
- "parser_config": object

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.
- Basic Multilingual Plane (BMP) only
- Maximum 128 characters
- Case-insensitive
"avatar": (Body parameter), string
The updated base64 encoding of the avatar.
- Maximum 65535 characters
"embedding_model": (Body parameter), string
The updated embedding model name.
- Ensure that "chunk_count" is 0 before updating "embedding_model".
- Maximum 255 characters
- Must follow model_name@model_factory format
"permission": (Body parameter), string
The updated dataset permission. Available options:
- "me": (Default) Only you can manage the dataset.
- "team": All team members can manage the dataset.
"pagerank": (Body parameter), int
refer to [Set page rank](https://Swipies AI.io/docs/dev/set_page_rank)
- Default: 0
- Minimum: 0
- Maximum: 100
"chunk_method": (Body parameter), enum<string>
The chunking method for the dataset. Available options:
- "naive": General (default)
- "book": Book
- "email": Email
- "laws": Laws
- "manual": Manual
- "one": One
- "paper": Paper
- "picture": Picture
- "presentation": Presentation
- "qa": Q&A
- "table": Table
- "tag": Tag
"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:
  - "auto_keywords": int
    - Defaults to 0
    - Minimum: 0
    - Maximum: 32
  - "auto_questions": int
    - Defaults to 0
    - Minimum: 0
    - Maximum: 10
  - "chunk_token_num": int
    - Defaults to 512
    - Minimum: 1
    - Maximum: 2048
  - "delimiter": string
    - Defaults to "\n".
  - "html4excel": bool Indicates whether to convert Excel documents into HTML format.
    - Defaults to false
  - "layout_recognize": string
    - Defaults to DeepDOC
  - "tag_kb_ids": array<string> refer to [Use tag set](https://Swipies AI.io/docs/dev/use_tag_sets)
    - Must include a list of dataset IDs, where each dataset is parsed using the Tag Chunking Method
  - "task_page_size": int For PDF only.
    - Defaults to 12
    - Minimum: 1
  - "raptor": object RAPTOR-specific settings.
    - Defaults to: {"use_raptor": false}
  - "graphrag": object GRAPHRAG-specific settings.
    - Defaults to: {"use_graphrag": false}
- If "chunk_method" is "qa", "manuel", "paper", "book", "laws", or "presentation", the "parser_config" object contains the following attribute:
  - "raptor": object RAPTOR-specific settings.
    - Defaults to: {"use_raptor": false}.
- If "chunk_method" is "table", "picture", "one", or "email", "parser_config" is an empty JSON object.

Response

Success:

{
    "code": 0 
}

Failure:

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

List datasets

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

Lists datasets.

Request

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

Request example

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

Request parameters

page: (Filter parameter)
Specifies the page on which the datasets will be displayed. Defaults to 1.
page_size: (Filter parameter)
The number of datasets on each page. Defaults to 30.
orderby: (Filter parameter)
The field by which datasets should be sorted. Available options:
- create_time (default)
- update_time
desc: (Filter parameter)
Indicates whether the retrieved datasets should be sorted in descending order. Defaults to true.
name: (Filter parameter)
The name of the dataset to retrieve.
id: (Filter parameter)
The ID of the dataset to retrieve.

Response

Success:

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

Failure:

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

Get knowledge graph

GET /api/v1/datasets/{dataset_id}/knowledge_graph

Retrieves the knowledge graph of a specified dataset.

Request

Method: GET
URL: /api/v1/datasets/{dataset_id}/knowledge_graph
Headers:
- 'Authorization: Bearer <YOUR_API_KEY>'

Request example

curl --request GET \
     --url http://{address}/api/v1/datasets/{dataset_id}/knowledge_graph \
     --header 'Authorization: Bearer <YOUR_API_KEY>'

Request parameters

dataset_id: (Path parameter)
The ID of the target dataset.

Response

Success:

{
    "code": 0,
    "data": {
        "graph": {
            "directed": false,
            "edges": [
                {
                    "description": "The notice is a document issued to convey risk warnings and operational alerts.<SEP>The notice is a specific instance of a notification document issued under the risk warning framework.",
                    "keywords": ["9", "8"],
                    "source": "notice",
                    "source_id": ["8a46cdfe4b5c11f0a5281a58e595aa1c"],
                    "src_id": "xxx",
                    "target": "xxx",
                    "tgt_id": "xxx",
                    "weight": 17.0
                }
            ],
            "graph": {
                "source_id": ["8a46cdfe4b5c11f0a5281a58e595aa1c", "8a7eb6424b5c11f0a5281a58e595aa1c"]
            },
            "multigraph": false,
            "nodes": [
                {
                    "description": "xxx",
                    "entity_name": "xxx",
                    "entity_type": "ORGANIZATION",
                    "id": "xxx",
                    "pagerank": 0.10804906590624092,
                    "rank": 3,
                    "source_id": ["8a7eb6424b5c11f0a5281a58e595aa1c"]
                }
            ]
        },
        "mind_map": {}
    }
}

Failure:

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

Delete knowledge graph

DELETE /api/v1/datasets/{dataset_id}/knowledge_graph

Removes the knowledge graph of a specified dataset.

Request

Method: DELETE
URL: /api/v1/datasets/{dataset_id}/knowledge_graph
Headers:
- 'Authorization: Bearer <YOUR_API_KEY>'

Request example

curl --request DELETE \
     --url http://{address}/api/v1/datasets/{dataset_id}/knowledge_graph \
     --header 'Authorization: Bearer <YOUR_API_KEY>'

Request parameters

dataset_id: (Path parameter)
The ID of the target dataset.

Response

Success:

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

Failure:

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

Construct knowledge graph

POST /api/v1/datasets/{dataset_id}/run_graphrag

Constructs a knowledge graph from a specified dataset.

Request

Method: POST
URL: /api/v1/datasets/{dataset_id}/run_graphrag
Headers:
- 'Authorization: Bearer <YOUR_API_KEY>'

Request example

curl --request POST \
     --url http://{address}/api/v1/datasets/{dataset_id}/run_graphrag \
     --header 'Authorization: Bearer <YOUR_API_KEY>'

Request parameters

dataset_id: (Path parameter)
The ID of the target dataset.

Response

Success:

{
    "code":0,
    "data":{
      "graphrag_task_id":"e498de54bfbb11f0ba028f704583b57b"
    }
}

Failure:

{
    "code": 102,
    "message": "Invalid Dataset ID"
}

Get knowledge graph construction status

GET /api/v1/datasets/{dataset_id}/trace_graphrag

Retrieves the knowledge graph construction status for a specified dataset.

Request

Method: GET
URL: /api/v1/datasets/{dataset_id}/trace_graphrag
Headers:
- 'Authorization: Bearer <YOUR_API_KEY>'

Request example

curl --request GET \
     --url http://{address}/api/v1/datasets/{dataset_id}/trace_graphrag \
     --header 'Authorization: Bearer <YOUR_API_KEY>'

Request parameters

dataset_id: (Path parameter)
The ID of the target dataset.

Response

Success:

{
    "code":0,
    "data":{
        "begin_at":"Wed, 12 Nov 2025 19:36:56 GMT",
        "chunk_ids":"",
        "create_date":"Wed, 12 Nov 2025 19:36:56 GMT",
        "create_time":1762947416350,
        "digest":"39e43572e3dcd84f",
        "doc_id":"44661c10bde211f0bc93c164a47ffc40",
        "from_page":100000000,
        "id":"e498de54bfbb11f0ba028f704583b57b",
        "priority":0,
        "process_duration":2.45419,
        "progress":1.0,
        "progress_msg":"19:36:56 created task graphrag\n19:36:57 Task has been received.\n19:36:58 [GraphRAG] doc:083661febe2411f0bc79456921e5745f has no available chunks, skip generation.\n19:36:58 [GraphRAG] build_subgraph doc:44661c10bde211f0bc93c164a47ffc40 start (chunks=1, timeout=10000000000s)\n19:36:58 Graph already contains 44661c10bde211f0bc93c164a47ffc40\n19:36:58 [GraphRAG] build_subgraph doc:44661c10bde211f0bc93c164a47ffc40 empty\n19:36:58 [GraphRAG] kb:33137ed0bde211f0bc93c164a47ffc40 no subgraphs generated successfully, end.\n19:36:58 Knowledge Graph done (0.72s)","retry_count":1,
        "task_type":"graphrag",
        "to_page":100000000,
        "update_date":"Wed, 12 Nov 2025 19:36:58 GMT",
        "update_time":1762947418454
    }
}

Failure:

{
    "code": 102,
    "message": "Invalid Dataset ID"
}

Construct RAPTOR

POST /api/v1/datasets/{dataset_id}/run_raptor

Construct a RAPTOR from a specified dataset.

Request

Method: POST
URL: /api/v1/datasets/{dataset_id}/run_raptor
Headers:
- 'Authorization: Bearer <YOUR_API_KEY>'

Request example

curl --request POST \
     --url http://{address}/api/v1/datasets/{dataset_id}/run_raptor \
     --header 'Authorization: Bearer <YOUR_API_KEY>'

Request parameters

dataset_id: (Path parameter)
The ID of the target dataset.

Response

Success:

{
    "code":0,
    "data":{
        "raptor_task_id":"50d3c31cbfbd11f0ba028f704583b57b"
    }
}

Failure:

{
    "code": 102,
    "message": "Invalid Dataset ID"
}

Get RAPTOR construction status

GET /api/v1/datasets/{dataset_id}/trace_raptor

Retrieves the RAPTOR construction status for a specified dataset.

Request

Method: GET
URL: /api/v1/datasets/{dataset_id}/trace_raptor
Headers:
- 'Authorization: Bearer <YOUR_API_KEY>'

Request example

curl --request GET \
     --url http://{address}/api/v1/datasets/{dataset_id}/trace_raptor \
     --header 'Authorization: Bearer <YOUR_API_KEY>'

Request parameters

dataset_id: (Path parameter)
The ID of the target dataset.

Response

Success:

{
    "code":0,
    "data":{
        "begin_at":"Wed, 12 Nov 2025 19:47:07 GMT",
        "chunk_ids":"",
        "create_date":"Wed, 12 Nov 2025 19:47:07 GMT",
        "create_time":1762948027427,
        "digest":"8b279a6248cb8fc6",
        "doc_id":"44661c10bde211f0bc93c164a47ffc40",
        "from_page":100000000,
        "id":"50d3c31cbfbd11f0ba028f704583b57b",
        "priority":0,
        "process_duration":0.948244,
        "progress":1.0,
        "progress_msg":"19:47:07 created task raptor\n19:47:07 Task has been received.\n19:47:07 Processing...\n19:47:07 Processing...\n19:47:07 Indexing done (0.01s).\n19:47:07 Task done (0.29s)",
        "retry_count":1,
        "task_type":"raptor",
        "to_page":100000000,
        "update_date":"Wed, 12 Nov 2025 19:47:07 GMT",
        "update_time":1762948027948
    }
}

Failure:

{
    "code": 102,
    "message": "Invalid Dataset ID"
}

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": {
                    "use_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
- "meta_fields":object
- "chunk_method":string
- "parser_config":object

Request example

curl --request PUT \
     --url http://{address}/api/v1/datasets/{dataset_id}/documents/{document_id} \
     --header 'Authorization: Bearer <YOUR_API_KEY>' \
     --header 'Content-Type: application/json' \
     --data '
     {
          "name": "manual.txt", 
          "chunk_method": "manual", 
          "parser_config": {"chunk_token_num": 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
"meta_fields": (Body parameter), dict[str, Any] The meta fields of the document.
"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
- "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_num": Defaults to 256.
  - "layout_recognize": Defaults to true.
  - "html4excel": Indicates whether to convert Excel documents into HTML format. Defaults to false.
  - "delimiter": Defaults to "\n".
  - "task_page_size": Defaults to 12. For PDF only.
  - "raptor": RAPTOR-specific settings. Defaults to: {"use_raptor": false}.
- If "chunk_method" is "qa", "manuel", "paper", "book", "laws", or "presentation", the "parser_config" object contains the following attribute:
  - "raptor": RAPTOR-specific settings. Defaults to: {"use_raptor": false}.
- If "chunk_method" is "table", "picture", "one", or "email", "parser_config" is an empty JSON object.
"enabled": (Body parameter), integer
Whether the document should be available in the knowledge base.
- 1 → （available）
- 0 → （unavailable）

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 ./Swipies AI.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}&create_time_from={timestamp}&create_time_to={timestamp}&suffix={file_suffix}&run={run_status}

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}&create_time_from={timestamp}&create_time_to={timestamp}&suffix={file_suffix}&run={run_status}
Headers:
- 'content-Type: application/json'
- 'Authorization: Bearer <YOUR_API_KEY>'

Request examples

A basic request with pagination:

curl --request GET \
     --url http://{address}/api/v1/datasets/{dataset_id}/documents?page=1&page_size=10 \
     --header 'Authorization: Bearer <YOUR_API_KEY>'

Request parameters

dataset_id: (Path parameter)
The associated dataset ID.
keywords: (Filter parameter), string
The keywords used to match document titles.
page: (Filter parameter), integer Specifies the page on which the documents will be displayed. Defaults to 1.
page_size: (Filter parameter), integer
The maximum number of documents on each page. Defaults to 30.
orderby: (Filter parameter), string
The field by which documents should be sorted. Available options:
- create_time (default)
- update_time
desc: (Filter parameter), boolean
Indicates whether the retrieved documents should be sorted in descending order. Defaults to true.
id: (Filter parameter), string
The ID of the document to retrieve.
create_time_from: (Filter parameter), integer
Unix timestamp for filtering documents created after this time. 0 means no filter. Defaults to 0.
create_time_to: (Filter parameter), integer
Unix timestamp for filtering documents created before this time. 0 means no filter. Defaults to 0.
suffix: (Filter parameter), array[string]
Filter by file suffix. Supports multiple values, e.g., pdf, txt, and docx. Defaults to all suffixes.
run: (Filter parameter), array[string]
Filter by document processing status. Supports numeric, text, and mixed formats:
- Numeric format: ["0", "1", "2", "3", "4"]
- Text format: [UNSTART, RUNNING, CANCEL, DONE, FAIL]
- Mixed format: [UNSTART, 1, DONE] (mixing numeric and text formats)
- Status mapping:
  - 0 / UNSTART: Document not yet processed
  - 1 / RUNNING: Document is currently being processed
  - 2 / CANCEL: Document processing was cancelled
  - 3 / DONE: Document processing completed successfully
  - 4 / FAIL: Document processing failed
    Defaults to all statuses.

Usage examples

A request with multiple filtering parameters

curl --request GET \
     --url 'http://{address}/api/v1/datasets/{dataset_id}/documents?suffix=pdf&run=DONE&page=1&page_size=10' \
     --header 'Authorization: Bearer <YOUR_API_KEY>'

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_duration": 0.0,
                "progress": 0.0,
                "progress_msg": "",
                "run": "UNSTART",
                "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_datasets": 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"
}

CHUNK MANAGEMENT WITHIN DATASET

Add chunk

POST /api/v1/datasets/{dataset_id}/documents/{document_id}/chunks

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

Request

Method: POST
URL: /api/v1/datasets/{dataset_id}/documents/{document_id}/chunks
Headers:
- 'content-Type: application/json'
- 'Authorization: Bearer <YOUR_API_KEY>'
Body:
- "content": string
- "important_keywords": list[string]

Request example

curl --request POST \
     --url http://{address}/api/v1/datasets/{dataset_id}/documents/{document_id}/chunks \
     --header 'Content-Type: application/json' \
     --header 'Authorization: Bearer <YOUR_API_KEY>' \
     --data '
     {
          "content": "<CHUNK_CONTENT_HERE>"
     }'

Request parameters

dataset_id: (Path parameter)
The associated dataset ID.
document_ids: (Path parameter)
The associated document ID.
"content": (Body parameter), string, Required
The text content of the chunk.
"important_keywords(Body parameter), list[string]
The key terms or phrases to tag with the chunk.
"questions"(Body parameter), list[string] If there is a given question, the embedded chunks will be based on them

Response

Success:

{
    "code": 0,
    "data": {
        "chunk": {
            "content": "who are you",
            "create_time": "2024-12-30 16:59:55",
            "create_timestamp": 1735549195.969164,
            "dataset_id": "72f36e1ebdf411efb7250242ac120006",
            "document_id": "61d68474be0111ef98dd0242ac120006",
            "id": "12ccdc56e59837e5",
            "important_keywords": [],
            "questions": []
        }
    }
}

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_id: (Path parameter)
The associated document ID.
keywords(Filter parameter), string
The keywords used to match chunk content.
page(Filter parameter), integer
Specifies the page on which the chunks will be displayed. Defaults to 1.
page_size(Filter parameter), integer
The maximum number of chunks on each page. Defaults to 1024.
id(Filter parameter), string
The ID of the chunk to retrieve.

Response

Success:

{
    "code": 0,
    "data": {
        "chunks": [
            {
                "available": true,
                "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": {
                    "use_raptor": false
                }
            },
            "process_begin_at": "Thu, 24 Oct 2024 09:56:44 GMT",
            "process_duration": 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": "Swipies AI123",  
          "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
- "cross_languages": list[string]
- "metadata_condition": object
- "use_kg": boolean
- "toc_enhance": 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 Swipies AI?",
          "dataset_ids": ["b2a62730759d11ef987d0242ac120004"],
          "document_ids": ["77df9ef4759a11ef8bdd0242ac120004"],
          "metadata_condition": {
            "logic": "and",
            "conditions": [
              {
                "name": "author",
                "comparison_operator": "=",
                "value": "Toby"
              },
              {
                "name": "url",
                "comparison_operator": "not contains",
                "value": "amd"
              }
            ]
          }
     }'

Request parameter

"question": (Body parameter), string, Required
The user query or query keywords.
"dataset_ids": (Body parameter) list[string]
The IDs of the datasets to search. If you do not set this argument, ensure that you set "document_ids".
"document_ids": (Body parameter), list[string]
The IDs of the documents to search. Ensure that all selected documents use the same embedding model. Otherwise, an error will occur. If you do not set this argument, ensure that you set "dataset_ids".
"page": (Body parameter), integer
Specifies the page on which the chunks will be displayed. Defaults to 1.
"page_size": (Body parameter)
The maximum number of chunks on each page. Defaults to 30.
"similarity_threshold": (Body parameter)
The minimum similarity score. Defaults to 0.2.
"vector_similarity_weight": (Body parameter), float
The weight of vector cosine similarity. Defaults to 0.3. If x represents the weight of vector cosine similarity, then (1 - x) is the term similarity weight.
"top_k": (Body parameter), integer
The number of chunks engaged in vector cosine computation. Defaults to 1024.
"use_kg": (Body parameter), boolean
Whether to search chunks related to the generated knowledge graph for multi-hop queries. Defaults to False. Before enabling this, ensure you have successfully constructed a knowledge graph for the specified datasets. See [here](https://Swipies AI.io/docs/dev/construct_knowledge_graph) for details.
"toc_enhance": (Body parameter), boolean
Whether to search chunks with extracted table of content. Defaults to False. Before enabling this, ensure you have enabled TOC_Enhance and successfully extracted table of contents for the specified datasets. See [here](https://Swipies AI.io/docs/dev/enable_table_of_contents) for details.
"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).
"cross_languages": (Body parameter) list[string]
The languages that should be translated into, in order to achieve keywords retrievals in different languages.
"metadata_condition": (Body parameter), object
The metadata condition used for filtering chunks:
- "logic": (Body parameter), string
  - "and": Return only results that satisfy every condition (default).
  - "or": Return results that satisfy any condition.
- "conditions": (Body parameter), array
  A list of metadata filter conditions.
  - "name": string - The metadata field name to filter by, e.g., "author", "company", "url". Ensure this parameter before use. See Set metadata for details.
  - comparison_operator: string - The comparison operator. Can be one of:
    - "contains"
    - "not contains"
    - "start with"
    - "empty"
    - "not empty"
    - "="
    - "≠"
    - ">"
    - "<"
    - "≥"
    - "≤"
  - "value": string - The value to compare.

Response

Success:

{
    "code": 0,
    "data": {
        "chunks": [
            {
                "content": "Swipies AI content",
                "content_ltks": "Swipies AI content",
                "document_id": "5c5999ec7be811ef9cab0242ac120005",
                "document_keyword": "1.txt",
                "highlight": "<em>Swipies AI</em> content",
                "id": "d78435d142bd5cf6704da62c778795c5",
                "image_id": "",
                "important_keywords": [
                    ""
                ],
                "kb_id": "c7ee74067a2c11efb21c0242ac120006",
                "positions": [
                    ""
                ],
                "similarity": 0.9669436601210759,
                "term_similarity": 1.0,
                "vector_similarity": 0.8898122004035864
            }
        ],
        "doc_aggs": [
            {
                "count": 1,
                "doc_id": "5c5999ec7be811ef9cab0242ac120005",
                "doc_name": "1.txt"
            }
        ],
        "total": 1
    }
}

Failure:

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

CHAT ASSISTANT MANAGEMENT

Create chat assistant

POST /api/v1/chats

Creates a chat assistant.

Request

Method: POST
URL: /api/v1/chats
Headers:
- 'content-Type: application/json'
- 'Authorization: Bearer <YOUR_API_KEY>'
Body:
- "name": string
- "avatar": string
- "dataset_ids": list[string]
- "llm": object
- "prompt": object

Request example

curl --request POST \
     --url http://{address}/api/v1/chats \
     --header 'Content-Type: application/json' \
     --header 'Authorization: Bearer <YOUR_API_KEY>' \
     --data '{
    "dataset_ids": ["0b2cbc8c877f11ef89070242ac120005"],
    "name":"new_chat_1"
}'

Request parameters

"name": (Body parameter), string, Required
The name of the chat assistant.
"avatar": (Body parameter), string
Base64 encoding of the avatar.
"dataset_ids": (Body parameter), list[string]
The IDs of the associated datasets.
"llm": (Body parameter), object
The LLM settings for the chat assistant to create. If it is not explicitly set, a JSON object with the following values will be generated as the default. An llm JSON object contains the following attributes:
- "model_name", string
  The chat model name. If not set, the user's default chat model will be used.
- "temperature": float
  Controls the randomness of the model's predictions. A lower temperature results in more conservative responses, while a higher temperature yields more creative and diverse responses. Defaults to 0.1.
- "top_p": float
  Also known as “nucleus sampling”, this parameter sets a threshold to select a smaller set of words to sample from. It focuses on the most likely words, cutting off the less probable ones. Defaults to 0.3
- "presence_penalty": float
  This discourages the model from repeating the same information by penalizing words that have already appeared in the conversation. Defaults to 0.4.
- "frequency penalty": float
  Similar to the presence penalty, this reduces the model’s tendency to repeat the same words frequently. Defaults to 0.7.
"prompt": (Body parameter), object
Instructions for the LLM to follow. If it is not explicitly set, a JSON object with the following values will be generated as the default. A prompt JSON object contains the following attributes:
- "similarity_threshold": float Swipies AI employs either a combination of weighted keyword similarity and weighted vector cosine similarity, or a combination of weighted keyword similarity and weighted reranking score during retrieval. This argument sets the threshold for similarities between the user query and chunks. If a similarity score falls below this threshold, the corresponding chunk will be excluded from the results. The default value is 0.2.
- "keywords_similarity_weight": float This argument sets the weight of keyword similarity in the hybrid similarity score with vector cosine similarity or reranking model similarity. By adjusting this weight, you can control the influence of keyword similarity in relation to other similarity measures. The default value is 0.7.
- "top_n": int This argument specifies the number of top chunks with similarity scores above the similarity_threshold that are fed to the LLM. The LLM will only access these 'top N' chunks. The default value is 6.
- "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.
- top_k: int Refers to the process of reordering or selecting the top-k items from a list or set based on a specific ranking criterion. Default to 1024.
- "empty_response": string If nothing is retrieved in the dataset for the user's question, this will be used as the response. To allow the LLM to improvise when nothing is found, leave this blank.
- "opener": string The opening greeting for the user. Defaults to "Hi! I am your assistant, can I help you?".
- "show_quote: boolean Indicates whether the source of text should be displayed. Defaults to true.
- "prompt": string The prompt content.

Response

Success:

{
    "code": 0,
    "data": {
        "avatar": "",
        "create_date": "Thu, 24 Oct 2024 11:18:29 GMT",
        "create_time": 1729768709023,
        "dataset_ids": [
            "527fa74891e811ef9c650242ac120006"
        ],
        "description": "A helpful Assistant",
        "do_refer": "1",
        "id": "b1f2f15691f911ef81180242ac120003",
        "language": "English",
        "llm": {
            "frequency_penalty": 0.7,
            "model_name": "qwen-plus@Tongyi-Qianwen",
            "presence_penalty": 0.4,
            "temperature": 0.1,
            "top_p": 0.3
        },
        "name": "12234",
        "prompt": {
            "empty_response": "Sorry! No relevant content was found in the knowledge base!",
            "keywords_similarity_weight": 0.3,
            "opener": "Hi! I'm your assistant. What can I do for you?",
            "prompt": "You are an intelligent assistant. Please summarize the content of the knowledge base to answer the question. Please list the data in the knowledge base and answer in detail. When all knowledge base content is irrelevant to the question, your answer must include the sentence \"The answer you are looking for is not found in the knowledge base!\" Answers need to consider chat history.\n ",
            "rerank_model": "",
            "similarity_threshold": 0.2,
            "top_n": 6,
            "variables": [
                {
                    "key": "knowledge",
                    "optional": false
                }
            ]
        },
        "prompt_type": "simple",
        "status": "1",
        "tenant_id": "69736c5e723611efb51b0242ac120007",
        "top_k": 1024,
        "update_date": "Thu, 24 Oct 2024 11:18:29 GMT",
        "update_time": 1729768709023
    }
}

Failure:

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

Update chat assistant

PUT /api/v1/chats/{chat_id}

Updates configurations for a specified chat assistant.

Request

Method: PUT
URL: /api/v1/chats/{chat_id}
Headers:
- 'content-Type: application/json'
- 'Authorization: Bearer <YOUR_API_KEY>'
Body:
- "name": string
- "avatar": string
- "dataset_ids": list[string]
- "llm": object
- "prompt": object

Request example

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

Parameters

chat_id: (Path parameter)
The ID of the chat assistant to update.
"name": (Body parameter), string, Required
The revised name of the chat assistant.
"avatar": (Body parameter), string
Base64 encoding of the avatar.
"dataset_ids": (Body parameter), list[string]
The IDs of the associated datasets.
"llm": (Body parameter), object
The LLM settings for the chat assistant to create. If it is not explicitly set, a dictionary with the following values will be generated as the default. An llm object contains the following attributes:
- "model_name", string
  The chat model name. If not set, the user's default chat model will be used.
- "temperature": float
  Controls the randomness of the model's predictions. A lower temperature results in more conservative responses, while a higher temperature yields more creative and diverse responses. Defaults to 0.1.
- "top_p": float
  Also known as “nucleus sampling”, this parameter sets a threshold to select a smaller set of words to sample from. It focuses on the most likely words, cutting off the less probable ones. Defaults to 0.3
- "presence_penalty": float
  This discourages the model from repeating the same information by penalizing words that have already appeared in the conversation. Defaults to 0.2.
- "frequency penalty": float
  Similar to the presence penalty, this reduces the model’s tendency to repeat the same words frequently. Defaults to 0.7.
"prompt": (Body parameter), object
Instructions for the LLM to follow. A prompt object contains the following attributes:
- "similarity_threshold": float Swipies AI employs either a combination of weighted keyword similarity and weighted vector cosine similarity, or a combination of weighted keyword similarity and weighted rerank score during retrieval. This argument sets the threshold for similarities between the user query and chunks. If a similarity score falls below this threshold, the corresponding chunk will be excluded from the results. The default value is 0.2.
- "keywords_similarity_weight": float This argument sets the weight of keyword similarity in the hybrid similarity score with vector cosine similarity or reranking model similarity. By adjusting this weight, you can control the influence of keyword similarity in relation to other similarity measures. The default value is 0.7.
- "top_n": int This argument specifies the number of top chunks with similarity scores above the similarity_threshold that are fed to the LLM. The LLM will only access these 'top N' chunks. The default value is 8.
- "variables": object[] This argument lists the variables to use in the 'System' field of Chat Configurations. Note that:
  - "knowledge" is a reserved variable, which represents the retrieved chunks.
  - All the variables in 'System' should be curly bracketed.
  - The default value is [{"key": "knowledge", "optional": true}]
- "rerank_model": string If it is not specified, vector cosine similarity will be used; otherwise, reranking score will be used.
- "empty_response": string If nothing is retrieved in the dataset for the user's question, this will be used as the response. To allow the LLM to improvise when nothing is found, leave this blank.
- "opener": string The opening greeting for the user. Defaults to "Hi! I am your assistant, can I help you?".
- "show_quote: boolean Indicates whether the source of text should be displayed. Defaults to true.
- "prompt": string The prompt content.

Response

Success:

{
    "code": 0
}

Failure:

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

Delete chat assistants

DELETE /api/v1/chats

Deletes chat assistants by ID.

Request

Method: DELETE
URL: /api/v1/chats
Headers:
- 'content-Type: application/json'
- 'Authorization: Bearer <YOUR_API_KEY>'
Body:
- "ids": list[string]

Request example

curl --request DELETE \
     --url http://{address}/api/v1/chats \
     --header 'Content-Type: application/json' \
     --header 'Authorization: Bearer <YOUR_API_KEY>' \
     --data '
     {
          "ids": ["test_1", "test_2"]
     }'

Request parameters

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

Response

Success:

{
    "code": 0
}

Failure:

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

List chat assistants

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

Lists chat assistants.

Request

Method: GET
URL: /api/v1/chats?page={page}&page_size={page_size}&orderby={orderby}&desc={desc}&name={chat_name}&id={chat_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={chat_name}&id={chat_id} \
     --header 'Authorization: Bearer <YOUR_API_KEY>'

Request parameters

page: (Filter parameter), integer
Specifies the page on which the chat assistants will be displayed. Defaults to 1.
page_size: (Filter parameter), integer
The number of chat assistants on each page. Defaults to 30.
orderby: (Filter parameter), string
The attribute by which the results are sorted. Available options:
- create_time (default)
- update_time
desc: (Filter parameter), boolean
Indicates whether the retrieved chat assistants should be sorted in descending order. Defaults to true.
id: (Filter parameter), string
The ID of the chat assistant to retrieve.
name: (Filter parameter), string
The name of the chat assistant to retrieve.

Response

Success:

{
    "code": 0,
    "data": [
        {
            "avatar": "",
            "create_date": "Fri, 18 Oct 2024 06:20:06 GMT",
            "create_time": 1729232406637,
            "description": "A helpful Assistant",
            "do_refer": "1",
            "id": "04d0d8e28d1911efa3630242ac120006",
            "dataset_ids": ["527fa74891e811ef9c650242ac120006"],
            "language": "English",
            "llm": {
                "frequency_penalty": 0.7,
                "model_name": "qwen-plus@Tongyi-Qianwen",
                "presence_penalty": 0.4,
                "temperature": 0.1,
                "top_p": 0.3
            },
            "name": "13243",
            "prompt": {
                "empty_response": "Sorry! No relevant content was found in the knowledge base!",
                "keywords_similarity_weight": 0.3,
                "opener": "Hi! I'm your assistant. What can I do for you?",
                "prompt": "You are an intelligent assistant. Please summarize the content of the knowledge base to answer the question. Please list the data in the knowledge base and answer in detail. When all knowledge base content is irrelevant to the question, your answer must include the sentence \"The answer you are looking for is not found in the knowledge base!\" Answers need to consider chat history.\n",
                "rerank_model": "",
                "similarity_threshold": 0.2,
                "top_n": 6,
                "variables": [
                    {
                        "key": "knowledge",
                        "optional": false
                    }
                ]
            },
            "prompt_type": "simple",
            "status": "1",
            "tenant_id": "69736c5e723611efb51b0242ac120007",
            "top_k": 1024,
            "update_date": "Fri, 18 Oct 2024 06:20:06 GMT",
            "update_time": 1729232406638
        }
    ]
}

Failure:

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

SESSION MANAGEMENT

Create session with chat assistant

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

Creates a session with a chat assistant.

Request

Method: POST
URL: /api/v1/chats/{chat_id}/sessions
Headers:
- 'content-Type: application/json'
- 'Authorization: Bearer <YOUR_API_KEY>'
Body:
- "name": string
- "user_id": string (optional)

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.
"user_id": (Body parameter), string
Optional user-defined ID.

Response

Success:

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

Failure:

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

Update chat assistant's session

PUT /api/v1/chats/{chat_id}/sessions/{session_id}

Updates a session of a specified chat assistant.

Request

Method: PUT
URL: /api/v1/chats/{chat_id}/sessions/{session_id}
Headers:
- 'content-Type: application/json'
- 'Authorization: Bearer <YOUR_API_KEY>'
Body:
- "name: string
- "user_id: string (optional)

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.
"user_id": (Body parameter), string
Optional user-defined ID.

Response

Success:

{
    "code": 0
}

Failure:

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

List chat assistant's sessions

GET /api/v1/chats/{chat_id}/sessions?page={page}&page_size={page_size}&orderby={orderby}&desc={desc}&name={session_name}&id={session_id}

Lists sessions associated with a specified chat assistant.

Request

Method: GET
URL: /api/v1/chats/{chat_id}/sessions?page={page}&page_size={page_size}&orderby={orderby}&desc={desc}&name={session_name}&id={session_id}&user_id={user_id}
Headers:
- 'Authorization: Bearer <YOUR_API_KEY>'

Request example

curl --request GET \
     --url http://{address}/api/v1/chats/{chat_id}/sessions?page={page}&page_size={page_size}&orderby={orderby}&desc={desc}&name={session_name}&id={session_id} \
     --header 'Authorization: Bearer <YOUR_API_KEY>'

Request Parameters

chat_id: (Path parameter)
The ID of the associated chat assistant.
page: (Filter parameter), integer
Specifies the page on which the sessions will be displayed. Defaults to 1.
page_size: (Filter parameter), integer
The number of sessions on each page. Defaults to 30.
orderby: (Filter parameter), string
The field by which sessions should be sorted. Available options:
- create_time (default)
- update_time
desc: (Filter parameter), boolean
Indicates whether the retrieved sessions should be sorted in descending order. Defaults to true.
name: (Filter parameter) string
The name of the chat session to retrieve.
id: (Filter parameter), string
The ID of the chat session to retrieve.
user_id: (Filter parameter), string
The optional user-defined ID passed in when creating session.

Response

Success:

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

Failure:

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

Delete chat assistant's sessions

DELETE /api/v1/chats/{chat_id}/sessions

Deletes sessions of a chat assistant by ID.

Request

Method: DELETE
URL: /api/v1/chats/{chat_id}/sessions
Headers:
- 'content-Type: application/json'
- 'Authorization: Bearer <YOUR_API_KEY>'
Body:
- "ids": list[string]

Request example

curl --request DELETE \
     --url http://{address}/api/v1/chats/{chat_id}/sessions \
     --header 'Content-Type: application/json' \
     --header 'Authorization: Bearer <YOUR_API_KEY>' \
     --data '
     {
          "ids": ["test_1", "test_2"]
     }'

Request Parameters

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

Response

Success:

{
    "code": 0
}

Failure:

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

Converse with chat assistant

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

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

NOTE

In streaming mode, not all responses include a reference, as this depends on the system's judgement.
In streaming mode, the last message is an empty message:
```
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 (optional)
- "user_id: string (optional)

Request example

curl --request POST \
     --url http://{address}/api/v1/chats/{chat_id}/completions \
     --header 'Content-Type: application/json' \
     --header 'Authorization: Bearer <YOUR_API_KEY>' \
     --data-binary '
     {
     }'

curl --request POST \
     --url http://{address}/api/v1/chats/{chat_id}/completions \
     --header 'Content-Type: application/json' \
     --header 'Authorization: Bearer <YOUR_API_KEY>' \
     --data-binary '
     {
          "question": "Who are you",
          "stream": true,
          "session_id":"9fa7691cb85c11ef9c5f0242ac120005"
     }'

Request Parameters

chat_id: (Path parameter)
The ID of the associated chat assistant.
"question": (Body Parameter), string, Required
The question to start an AI-powered conversation.
"stream": (Body Parameter), boolean
Indicates whether to output responses in a streaming way:
- true: Enable streaming (default).
- false: Disable streaming.
"session_id": (Body Parameter)
The ID of session. If it is not provided, a new session will be generated.
"user_id": (Body parameter), string
The optional user-defined ID. Valid only when no session_id is provided.

Response

Success without session_id:

data:{
    "code": 0,
    "message": "",
    "data": {
        "answer": "Hi! I'm your assistant. What can I do for you?",
        "reference": {},
        "audio_binary": null,
        "id": null,
        "session_id": "b01eed84b85611efa0e90242ac120005"
    }
}
data:{
    "code": 0,
    "message": "",
    "data": true
}

Success with session_id:

data:{
    "code": 0,
    "data": {
        "answer": "I am an intelligent assistant designed to help answer questions by summarizing content from a",
        "reference": {},
        "audio_binary": null,
        "id": "a84c5dd4-97b4-4624-8c3b-974012c8000d",
        "session_id": "82b0ab2a9c1911ef9d870242ac120006"
    }
}
data:{
    "code": 0,
    "data": {
        "answer": "I am an intelligent assistant designed to help answer questions by summarizing content from a knowledge base. My responses are based on the information available in the knowledge base and",
        "reference": {},
        "audio_binary": null,
        "id": "a84c5dd4-97b4-4624-8c3b-974012c8000d",
        "session_id": "82b0ab2a9c1911ef9d870242ac120006"
    }
}
data:{
    "code": 0,
    "data": {
        "answer": "I am an intelligent assistant designed to help answer questions by summarizing content from a knowledge base. My responses are based on the information available in the knowledge base and any relevant chat history.",
        "reference": {},
        "audio_binary": null,
        "id": "a84c5dd4-97b4-4624-8c3b-974012c8000d",
        "session_id": "82b0ab2a9c1911ef9d870242ac120006"
    }
}
data:{
    "code": 0,
    "data": {
        "answer": "I am an intelligent assistant designed to help answer questions by summarizing content from a knowledge base ##0$$. My responses are based on the information available in the knowledge base and any relevant chat history.",
        "reference": {
            "total": 1,
            "chunks": [
                {
                    "id": "faf26c791128f2d5e821f822671063bd",
                    "content": "xxxxxxxx",
                    "document_id": "dd58f58e888511ef89c90242ac120006",
                    "document_name": "1.txt",
                    "dataset_id": "8e83e57a884611ef9d760242ac120006",
                    "image_id": "",
                    "url": null,
                    "similarity": 0.7,
                    "vector_similarity": 0.0,
                    "term_similarity": 1.0,
                    "doc_type": [],
                    "positions": [
                        ""
                    ]
                }
            ],
            "doc_aggs": [
                {
                    "doc_name": "1.txt",
                    "doc_id": "dd58f58e888511ef89c90242ac120006",
                    "count": 1
                }
            ]
        },
        "prompt": "xxxxxxxxxxx",
        "created_at": 1755055623.6401553,
        "id": "a84c5dd4-97b4-4624-8c3b-974012c8000d",
        "session_id": "82b0ab2a9c1911ef9d870242ac120006"
    }
}
data:{
    "code": 0,
    "data": true
}

Failure:

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

Create session with agent

DEPRECATED

This method is deprecated and not recommended. You can still call it but be mindful that calling Converse with agent will automatically generate a session ID for the associated agent.

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

Creates a session with an agent.

Request

Method: POST
URL: /api/v1/agents/{agent_id}/sessions?user_id={user_id}
Headers:
- `'content-Type: application/json'
- 'Authorization: Bearer <YOUR_API_KEY>'
Body:
- the required parameters:str
- other parameters: The variables specified in the Begin component.

Request example

If the Begin component in your agent does not take required parameters:

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.
user_id: (Filter parameter)
The optional user-defined ID for parsing docs (especially images) when creating a session while uploading files.

Response

Success:

{
    "code": 0,
    "data": {
        "agent_id": "dbb4ed366e8611f09690a55a6daec4ef",
        "dsl": {
            "components": {
                "Message:EightyJobsAsk": {
                    "downstream": [],
                    "obj": {
                        "component_name": "Message",
                        "params": {
                            "content": [
                                "{begin@var1}{begin@var2}"
                            ],
                            "debug_inputs": {},
                            "delay_after_error": 2.0,
                            "description": "",
                            "exception_default_value": null,
                            "exception_goto": null,
                            "exception_method": null,
                            "inputs": {},
                            "max_retries": 0,
                            "message_history_window_size": 22,
                            "outputs": {
                                "content": {
                                    "type": "str",
                                    "value": null
                                }
                            },
                            "stream": true
                        }
                    },
                    "upstream": [
                        "begin"
                    ]
                },
                "begin": {
                    "downstream": [
                        "Message:EightyJobsAsk"
                    ],
                    "obj": {
                        "component_name": "Begin",
                        "params": {
                            "debug_inputs": {},
                            "delay_after_error": 2.0,
                            "description": "",
                            "enablePrologue": true,
                            "enable_tips": true,
                            "exception_default_value": null,
                            "exception_goto": null,
                            "exception_method": null,
                            "inputs": {
                                "var1": {
                                    "name": "var1",
                                    "optional": false,
                                    "options": [],
                                    "type": "line",
                                    "value": null
                                },
                                "var2": {
                                    "name": "var2",
                                    "optional": false,
                                    "options": [],
                                    "type": "line",
                                    "value": null
                                }
                            },
                            "max_retries": 0,
                            "message_history_window_size": 22,
                            "mode": "conversational",
                            "outputs": {},
                            "prologue": "Hi! I'm your assistant. What can I do for you?",
                            "tips": "Please fill in the form"
                        }
                    },
                    "upstream": []
                }
            },
            "globals": {
                "sys.conversation_turns": 0,
                "sys.files": [],
                "sys.query": "",
                "sys.user_id": ""
            },
            "graph": {
                "edges": [
                    {
                        "data": {
                            "isHovered": false
                        },
                        "id": "xy-edge__beginstart-Message:EightyJobsAskend",
                        "markerEnd": "logo",
                        "source": "begin",
                        "sourceHandle": "start",
                        "style": {
                            "stroke": "rgba(151, 154, 171, 1)",
                            "strokeWidth": 1
                        },
                        "target": "Message:EightyJobsAsk",
                        "targetHandle": "end",
                        "type": "buttonEdge",
                        "zIndex": 1001
                    }
                ],
                "nodes": [
                    {
                        "data": {
                            "form": {
                                "enablePrologue": true,
                                "inputs": {
                                    "var1": {
                                        "name": "var1",
                                        "optional": false,
                                        "options": [],
                                        "type": "line"
                                    },
                                    "var2": {
                                        "name": "var2",
                                        "optional": false,
                                        "options": [],
                                        "type": "line"
                                    }
                                },
                                "mode": "conversational",
                                "prologue": "Hi! I'm your assistant. What can I do for you?"
                            },
                            "label": "Begin",
                            "name": "begin"
                        },
                        "dragging": false,
                        "id": "begin",
                        "measured": {
                            "height": 112,
                            "width": 200
                        },
                        "position": {
                            "x": 270.64098070942583,
                            "y": -56.320928437811176
                        },
                        "selected": false,
                        "sourcePosition": "left",
                        "targetPosition": "right",
                        "type": "beginNode"
                    },
                    {
                        "data": {
                            "form": {
                                "content": [
                                    "{begin@var1}{begin@var2}"
                                ]
                            },
                            "label": "Message",
                            "name": "Message_0"
                        },
                        "dragging": false,
                        "id": "Message:EightyJobsAsk",
                        "measured": {
                            "height": 57,
                            "width": 200
                        },
                        "position": {
                            "x": 279.5,
                            "y": 190
                        },
                        "selected": true,
                        "sourcePosition": "right",
                        "targetPosition": "left",
                        "type": "messageNode"
                    }
                ]
            },
            "history": [],
            "memory": [],
            "messages": [],
            "path": [],
            "retrieval": [],
            "task_id": "dbb4ed366e8611f09690a55a6daec4ef"
        },
        "id": "0b02fe80780e11f084adcfdc3ed1d902",
        "message": [
            {
                "content": "Hi! I'm your assistant. What can I do for you?",
                "role": "assistant"
            }
        ],
        "source": "agent",
        "user_id": "c3fb861af27a11efa69751e139332ced"
    }
}

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.

NOTE

In streaming mode, not all responses include a reference, as this depends on the system's judgement.
In streaming mode, the last message is an empty message:
```
[DONE]
```

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 (optional)
- "inputs": object (optional)
- "user_id": string (optional)

IMPORTANT

You can include custom parameters in the request body, but first ensure they are defined in the Begin component.

Request example

If the Begin component does not take parameters:

curl --request POST \
     --url http://{address}/api/v1/agents/{agent_id}/completions \
     --header 'Content-Type: application/json' \
     --header 'Authorization: Bearer <YOUR_API_KEY>' \
     --data-binary '
     {
        "question": "Hello",
        "stream": false,
     }'

If the Begin component takes parameters, include their values in the body of "inputs" as follows:

curl --request POST \
     --url http://{address}/api/v1/agents/{agent_id}/completions \
     --header 'Content-Type: application/json' \
     --header 'Authorization: Bearer <YOUR_API_KEY>' \
     --data-binary '
    {
        "question": "Hello",
        "stream": false,
        "inputs": {
            "line_var": {
                "type": "line",
                "value": "I am line_var"
            },
            "int_var": {
                "type": "integer",
                "value": 1
            },
            "paragraph_var": {
                "type": "paragraph",
                "value": "a\nb\nc"
            },
            "option_var": {
                "type": "options",
                "value": "option 2"
            },
            "boolean_var": {
                "type": "boolean",
                "value": true
            }
        }
    }'

The following code will execute the completion process

curl --request POST \
     --url http://{address}/api/v1/agents/{agent_id}/completions \
     --header 'Content-Type: application/json' \
     --header 'Authorization: Bearer <YOUR_API_KEY>' \
     --data-binary '
     {
          "question": "Hello",
          "stream": true,
          "session_id": "cb2f385cb86211efa36e0242ac120005"
     }'

Request Parameters

agent_id: (Path parameter), string
The ID of the associated agent.
"question": (Body Parameter), string, Required
The question to start an AI-powered conversation.
"stream": (Body Parameter), boolean
Indicates whether to output responses in a streaming way:
- true: Enable streaming (default).
- false: Disable streaming.
"session_id": (Body Parameter)
The ID of the session. If it is not provided, a new session will be generated.
"inputs": (Body Parameter)
Variables specified in the Begin component.
"user_id": (Body parameter), string
The optional user-defined ID. Valid only when no session_id is provided.

NOTE

For now, this method does not support a file type input/variable. As a workaround, use the following to upload a file to an agent:
http://{address}/v1/canvas/upload/{agent_id}
You will get a corresponding file ID from its response body.

Response

success without session_id provided and with no variables specified in the Begin component:

Stream:

...

data: {
    "event": "message",
    "message_id": "cecdcb0e83dc11f0858253708ecb6573",
    "created_at": 1756364483,
    "task_id": "d1f79142831f11f09cc51795b9eb07c0",
    "data": {
        "content": " themes"
    },
    "session_id": "cd097ca083dc11f0858253708ecb6573"
}

data: {
    "event": "message",
    "message_id": "cecdcb0e83dc11f0858253708ecb6573",
    "created_at": 1756364483,
    "task_id": "d1f79142831f11f09cc51795b9eb07c0",
    "data": {
        "content": "."
    },
    "session_id": "cd097ca083dc11f0858253708ecb6573"
}

data: {
    "event": "message_end",
    "message_id": "cecdcb0e83dc11f0858253708ecb6573",
    "created_at": 1756364483,
    "task_id": "d1f79142831f11f09cc51795b9eb07c0",
    "data": {
        "reference": {
            "chunks": {
                "20": {
                    "id": "4b8935ac0a22deb1",
                    "content": "```cd /usr/ports/editors/neovim/ && make install```## Android[Termux](https://github.com/termux/termux-app) offers a Neovim package.",
                    "document_id": "4bdd2ff65e1511f0907f09f583941b45",
                    "document_name": "INSTALL22.md",
                    "dataset_id": "456ce60c5e1511f0907f09f583941b45",
                    "image_id": "",
                    "positions": [
                        [
                            12,
                            11,
                            11,
                            11,
                            11
                        ]
                    ],
                    "url": null,
                    "similarity": 0.5705525104787287,
                    "vector_similarity": 0.7351750337624289,
                    "term_similarity": 0.5000000005,
                    "doc_type": ""
                }
            },
            "doc_aggs": {
                "INSTALL22.md": {
                    "doc_name": "INSTALL22.md",
                    "doc_id": "4bdd2ff65e1511f0907f09f583941b45",
                    "count": 3
                },
                "INSTALL.md": {
                    "doc_name": "INSTALL.md",
                    "doc_id": "4bd7fdd85e1511f0907f09f583941b45",
                    "count": 2
                },
                "INSTALL(1).md": {
                    "doc_name": "INSTALL(1).md",
                    "doc_id": "4bdfb42e5e1511f0907f09f583941b45",
                    "count": 2
                },
                "INSTALL3.md": {
                    "doc_name": "INSTALL3.md",
                    "doc_id": "4bdab5825e1511f0907f09f583941b45",
                    "count": 1
                }
            }
        }
    },
    "session_id": "cd097ca083dc11f0858253708ecb6573"
}

data:[DONE]

Non-stream:

{
    "code": 0,
    "data": {
        "created_at": 1756363177,
        "data": {
            "content": "\nTo install Neovim, the process varies depending on your operating system:\n\n### For macOS:\nUsing Homebrew:\n```bash\nbrew install neovim\n```\n\n### For Linux (Debian/Ubuntu):\n```bash\nsudo apt update\nsudo apt install neovim\n```\n\nFor other Linux distributions, you can use their respective package managers or build from source.\n\n### For Windows:\n1. Download the latest Windows installer from the official Neovim GitHub releases page\n2. Run the installer and follow the prompts\n3. Add Neovim to your PATH if not done automatically\n\n### From source (Unix-like systems):\n```bash\ngit clone https://github.com/neovim/neovim.git\ncd neovim\nmake CMAKE_BUILD_TYPE=Release\nsudo make install\n```\n\nAfter installation, you can verify it by running `nvim --version` in your terminal.",
            "created_at": 18129.044975627,
            "elapsed_time": 10.0157331670016,
            "inputs": {
                "var1": {
                    "value": "I am var1"
                },
                "var2": {
                    "value": "I am var2"
                }
            },
            "outputs": {
                "_created_time": 18129.502422278,
                "_elapsed_time": 0.00013378599760471843,
                "content": "\nTo install Neovim, the process varies depending on your operating system:\n\n### For macOS:\nUsing Homebrew:\n```bash\nbrew install neovim\n```\n\n### For Linux (Debian/Ubuntu):\n```bash\nsudo apt update\nsudo apt install neovim\n```\n\nFor other Linux distributions, you can use their respective package managers or build from source.\n\n### For Windows:\n1. Download the latest Windows installer from the official Neovim GitHub releases page\n2. Run the installer and follow the prompts\n3. Add Neovim to your PATH if not done automatically\n\n### From source (Unix-like systems):\n```bash\ngit clone https://github.com/neovim/neovim.git\ncd neovim\nmake CMAKE_BUILD_TYPE=Release\nsudo make install\n```\n\nAfter installation, you can verify it by running `nvim --version` in your terminal."
            },
            "reference": {
                "chunks": {
                    "20": {
                        "content": "```cd /usr/ports/editors/neovim/ && make install```## Android[Termux](https://github.com/termux/termux-app) offers a Neovim package.",
                        "dataset_id": "456ce60c5e1511f0907f09f583941b45",
                        "doc_type": "",
                        "document_id": "4bdd2ff65e1511f0907f09f583941b45",
                        "document_name": "INSTALL22.md",
                        "id": "4b8935ac0a22deb1",
                        "image_id": "",
                        "positions": [
                            [
                                12,
                                11,
                                11,
                                11,
                                11
                            ]
                        ],
                        "similarity": 0.5705525104787287,
                        "term_similarity": 0.5000000005,
                        "url": null,
                        "vector_similarity": 0.7351750337624289
                    }
                },
                "doc_aggs": {
                    "INSTALL(1).md": {
                        "count": 2,
                        "doc_id": "4bdfb42e5e1511f0907f09f583941b45",
                        "doc_name": "INSTALL(1).md"
                    },
                    "INSTALL.md": {
                        "count": 2,
                        "doc_id": "4bd7fdd85e1511f0907f09f583941b45",
                        "doc_name": "INSTALL.md"
                    },
                    "INSTALL22.md": {
                        "count": 3,
                        "doc_id": "4bdd2ff65e1511f0907f09f583941b45",
                        "doc_name": "INSTALL22.md"
                    },
                    "INSTALL3.md": {
                        "count": 1,
                        "doc_id": "4bdab5825e1511f0907f09f583941b45",
                        "doc_name": "INSTALL3.md"
                    }
                }
            }
        },
        "event": "workflow_finished",
        "message_id": "c4692a2683d911f0858253708ecb6573",
        "session_id": "c39f6f9c83d911f0858253708ecb6573",
        "task_id": "d1f79142831f11f09cc51795b9eb07c0"
    }
}

Success without session_id provided and with variables specified in the Begin component:

Stream:

data:{
    "event": "message",
    "message_id": "0e273472783711f0806e1a6272e682d8",
    "created_at": 1755083830,
    "task_id": "99ee29d6783511f09c921a6272e682d8",
    "data": {
        "content": "Hello"
    },
    "session_id": "0e0d1542783711f0806e1a6272e682d8"
}

data:{
    "event": "message",
    "message_id": "0e273472783711f0806e1a6272e682d8",
    "created_at": 1755083830,
    "task_id": "99ee29d6783511f09c921a6272e682d8",
    "data": {
        "content": "!"
    },
    "session_id": "0e0d1542783711f0806e1a6272e682d8"
}

data:{
    "event": "message",
    "message_id": "0e273472783711f0806e1a6272e682d8",
    "created_at": 1755083830,
    "task_id": "99ee29d6783511f09c921a6272e682d8",
    "data": {
        "content": " How"
    },
    "session_id": "0e0d1542783711f0806e1a6272e682d8"
}

...

data:[DONE]

Non-stream:

{
    "code": 0,
    "data": {
        "created_at": 1755083779,
        "data": {
            "created_at": 547400.868004651,
            "elapsed_time": 3.5037803899031132,
            "inputs": {
                "boolean_var": {
                    "type": "boolean",
                    "value": true
                },
                "int_var": {
                    "type": "integer",
                    "value": 1
                },
                "line_var": {
                    "type": "line",
                    "value": "I am line_var"
                },
                "option_var": {
                    "type": "options",
                    "value": "option 2"
                },
                "paragraph_var": {
                    "type": "paragraph",
                    "value": "a\nb\nc"
                }
            },
            "outputs": {
                "_created_time": 547400.869271305,
                "_elapsed_time": 0.0001251999055966735,
                "content": "Hello there! How can I assist you today?"
            }
        },
        "event": "workflow_finished",
        "message_id": "effdad8c783611f089261a6272e682d8",
        "session_id": "efe523b6783611f089261a6272e682d8",
        "task_id": "99ee29d6783511f09c921a6272e682d8"
    }
}

Success with variables specified in the Begin component:

Stream:

data:{
    "event": "message",
    "message_id": "5b62e790783711f0bc531a6272e682d8",
    "created_at": 1755083960,
    "task_id": "99ee29d6783511f09c921a6272e682d8",
    "data": {
        "content": "Hello"
    },
    "session_id": "979e450c781d11f095cb729e3aa55728"
}

data:{
    "event": "message",
    "message_id": "5b62e790783711f0bc531a6272e682d8",
    "created_at": 1755083960,
    "task_id": "99ee29d6783511f09c921a6272e682d8",
    "data": {
        "content": "!"
    },
    "session_id": "979e450c781d11f095cb729e3aa55728"
}

data:{
    "event": "message",
    "message_id": "5b62e790783711f0bc531a6272e682d8",
    "created_at": 1755083960,
    "task_id": "99ee29d6783511f09c921a6272e682d8",
    "data": {
        "content": " You"
    },
    "session_id": "979e450c781d11f095cb729e3aa55728"
}

...

data:[DONE]

Non-stream:

{
    "code": 0,
    "data": {
        "created_at": 1755084029,
        "data": {
            "created_at": 547650.750818867,
            "elapsed_time": 1.6227330720284954,
            "inputs": {},
            "outputs": {
                "_created_time": 547650.752800839,
                "_elapsed_time": 9.628792759031057e-05,
                "content": "Hello! It appears you've sent another \"Hello\" without additional context. I'm here and ready to respond to any requests or questions you may have. Is there something specific you'd like to discuss or learn about?"
            }
        },
        "event": "workflow_finished",
        "message_id": "84eec534783711f08db41a6272e682d8",
        "session_id": "979e450c781d11f095cb729e3aa55728",
        "task_id": "99ee29d6783511f09c921a6272e682d8"
    }
}

Failure:

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

List agent sessions

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

Lists sessions associated with a specified agent.

Request

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

Request example

curl --request GET \
     --url http://{address}/api/v1/agents/{agent_id}/sessions?page={page}&page_size={page_size}&orderby={orderby}&desc={desc}&id={session_id}&user_id={user_id} \
     --header 'Authorization: Bearer <YOUR_API_KEY>'

Request Parameters

agent_id: (Path parameter)
The ID of the associated agent.
page: (Filter parameter), integer
Specifies the page on which the sessions will be displayed. Defaults to 1.
page_size: (Filter parameter), integer
The number of sessions on each page. Defaults to 30.
orderby: (Filter parameter), string
The field by which sessions should be sorted. Available options:
- create_time (default)
- update_time
desc: (Filter parameter), boolean
Indicates whether the retrieved sessions should be sorted in descending order. Defaults to true.
id: (Filter parameter), string
The ID of the agent session to retrieve.
user_id: (Filter parameter), string
The optional user-defined ID passed in when creating session.
dsl: (Filter parameter), boolean
Indicates whether to include the dsl field of the sessions in the response. Defaults to true.

Response

Success:

{
    "code": 0,
    "data": [{
        "agent_id": "e9e2b9c2b2f911ef801d0242ac120006",
        "dsl": {
            "answer": [],
            "components": {
                "Answer:OrangeTermsBurn": {
                    "downstream": [],
                    "obj": {
                        "component_name": "Answer",
                        "params": {}
                    },
                    "upstream": []
                },
                "Generate:SocialYearsRemain": {
                    "downstream": [],
                    "obj": {
                        "component_name": "Generate",
                        "params": {
                            "cite": true,
                            "frequency_penalty": 0.7,
                            "llm_id": "gpt-4o___OpenAI-API@OpenAI-API-Compatible",
                            "message_history_window_size": 12,
                            "parameters": [],
                            "presence_penalty": 0.4,
                            "prompt": "Please summarize the following paragraph. Pay attention to the numbers and do not make things up. The paragraph is as follows:\n{input}\nThis is what you need to summarize.",
                            "temperature": 0.1,
                            "top_p": 0.3
                        }
                    },
                    "upstream": []
                },
                "begin": {
                    "downstream": [],
                    "obj": {
                        "component_name": "Begin",
                        "params": {}
                    },
                    "upstream": []
                }
            },
            "graph": {
                "edges": [],
                "nodes": [
                    {
                        "data": {
                            "label": "Begin",
                            "name": "begin"
                        },
                        "height": 44,
                        "id": "begin",
                        "position": {
                            "x": 50,
                            "y": 200
                        },
                        "sourcePosition": "left",
                        "targetPosition": "right",
                        "type": "beginNode",
                        "width": 200
                    },
                    {
                        "data": {
                            "form": {
                                "cite": true,
                                "frequencyPenaltyEnabled": true,
                                "frequency_penalty": 0.7,
                                "llm_id": "gpt-4o___OpenAI-API@OpenAI-API-Compatible",
                                "maxTokensEnabled": true,
                                "message_history_window_size": 12,
                                "parameters": [],
                                "presencePenaltyEnabled": true,
                                "presence_penalty": 0.4,
                                "prompt": "Please summarize the following paragraph. Pay attention to the numbers and do not make things up. The paragraph is as follows:\n{input}\nThis is what you need to summarize.",
                                "temperature": 0.1,
                                "temperatureEnabled": true,
                                "topPEnabled": true,
                                "top_p": 0.3
                            },
                            "label": "Generate",
                            "name": "Generate Answer_0"
                        },
                        "dragging": false,
                        "height": 105,
                        "id": "Generate:SocialYearsRemain",
                        "position": {
                            "x": 561.3457829707513,
                            "y": 178.7211182312641
                        },
                        "positionAbsolute": {
                            "x": 561.3457829707513,
                            "y": 178.7211182312641
                        },
                        "selected": true,
                        "sourcePosition": "right",
                        "targetPosition": "left",
                        "type": "generateNode",
                        "width": 200
                    },
                    {
                        "data": {
                            "form": {},
                            "label": "Answer",
                            "name": "Dialogue_0"
                        },
                        "height": 44,
                        "id": "Answer:OrangeTermsBurn",
                        "position": {
                            "x": 317.2368194777658,
                            "y": 218.30635555445093
                        },
                        "sourcePosition": "right",
                        "targetPosition": "left",
                        "type": "logicNode",
                        "width": 200
                    }
                ]
            },
            "history": [],
            "messages": [],
            "path": [],
            "reference": []
        },
        "id": "792dde22b2fa11ef97550242ac120006",
        "message": [
            {
                "content": "Hi! I'm your smart assistant. What can I do for you?",
                "role": "assistant"
            }
        ],
        "source": "agent",
        "user_id": ""
    }]
}

Failure:

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

Delete agent's sessions

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

Deletes sessions of an agent by ID.

Request

Method: DELETE
URL: /api/v1/agents/{agent_id}/sessions
Headers:
- 'content-Type: application/json'
- 'Authorization: Bearer <YOUR_API_KEY>'
Body:
- "ids": list[string]

Request example

curl --request DELETE \
     --url http://{address}/api/v1/agents/{agent_id}/sessions \
     --header 'Content-Type: application/json' \
     --header 'Authorization: Bearer <YOUR_API_KEY>' \
     --data '
     {
          "ids": ["test_1", "test_2"]
     }'

Request Parameters

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

Response

Success:

{
    "code": 0
}

Failure:

{
    "code": 102,
    "message": "The agent doesn't own the session cbd31e52f73911ef93b232903b842af6"
}

POST /api/v1/sessions/related_questions

Generates five to ten alternative question strings from the user's original query to retrieve more relevant search results.

This operation requires a Bearer Login Token, which typically expires with in 24 hours. You can find it in the Request Headers in your browser easily as shown below:

![Image](https://raw.githubusercontent.com/infiniflow/Swipies AI-docs/main/images/login_token.jpg)

NOTE

The chat model autonomously determines the number of questions to generate based on the instruction, typically between five and ten.

Request

Method: POST
URL: /api/v1/sessions/related_questions
Headers:
- 'content-Type: application/json'
- 'Authorization: Bearer <YOUR_LOGIN_TOKEN>'
Body:
- "question": string
- "industry": string

Request example

curl --request POST \
     --url http://{address}/api/v1/sessions/related_questions \
     --header 'Content-Type: application/json' \
     --header 'Authorization: Bearer <YOUR_LOGIN_TOKEN>' \
     --data '
     {
          "question": "What are the key advantages of Neovim over Vim?",
          "industry": "software_development"
     }'

Request Parameters

"question": (Body Parameter), string The original user question.
"industry": (Body Parameter), string Industry of the question.

Response

Success:

{
    "code": 0,
    "data": [
        "What makes Neovim superior to Vim in terms of features?",
        "How do the benefits of Neovim compare to those of Vim?",
        "What advantages does Neovim offer that are not present in Vim?",
        "In what ways does Neovim outperform Vim in functionality?",
        "What are the most significant improvements in Neovim compared to Vim?",
        "What unique advantages does Neovim bring to the table over Vim?",
        "How does the user experience in Neovim differ from Vim in terms of benefits?",
        "What are the top reasons to switch from Vim to Neovim?",
        "What features of Neovim are considered more advanced than those in Vim?"
    ],
    "message": "success"
}

Failure:

{
    "code": 401,
    "data": null,
    "message": "<Unauthorized '401: Unauthorized'>"
}

AGENT MANAGEMENT

List agents

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

Lists agents.

Request

Method: GET
URL: /api/v1/agents?page={page}&page_size={page_size}&orderby={orderby}&desc={desc}&title={agent_name}&id={agent_id}
Headers:
- 'Authorization: Bearer <YOUR_API_KEY>'

Request example

curl --request GET \
     --url http://{address}/api/v1/agents?page={page}&page_size={page_size}&orderby={orderby}&desc={desc}&title={agent_name}&id={agent_id} \
     --header 'Authorization: Bearer <YOUR_API_KEY>'

Request parameters

page: (Filter parameter), integer
Specifies the page on which the agents will be displayed. Defaults to 1.
page_size: (Filter parameter), integer
The number of agents on each page. Defaults to 30.
orderby: (Filter parameter), string
The attribute by which the results are sorted. Available options:
- create_time (default)
- update_time
desc: (Filter parameter), boolean
Indicates whether the retrieved agents should be sorted in descending order. Defaults to true.
id: (Filter parameter), string
The ID of the agent to retrieve.
title: (Filter parameter), string
The name of the agent to retrieve.

Response

Success:

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

Failure:

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

Create agent

POST /api/v1/agents

Create an agent.

Request

Method: POST
URL: /api/v1/agents
Headers:
- 'Content-Type: application/json
- 'Authorization: Bearer <YOUR_API_KEY>'
Body:
- "title": string
- "description": string
- "dsl": object

Request example

curl --request POST \
     --url http://{address}/api/v1/agents \
     --header 'Content-Type: application/json' \
     --header 'Authorization: Bearer <YOUR_API_KEY>' \
     --data '{
         "title": "Test Agent",
         "description": "A test agent",
         "dsl": {
           // ... Canvas DSL here ...
         }
     }'

Request parameters

title: (Body parameter), string, Required
The title of the agent.
description: (Body parameter), string
The description of the agent. Defaults to None.
dsl: (Body parameter), object, Required
The canvas DSL object of the agent.

Response

Success:

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

Failure:

{
    "code": 102,
    "message": "Agent with title test already exists."
}

Update agent

PUT /api/v1/agents/{agent_id}

Update an agent by id.

Request

Method: PUT
URL: /api/v1/agents/{agent_id}
Headers:
- 'Content-Type: application/json
- 'Authorization: Bearer <YOUR_API_KEY>'
Body:
- "title": string
- "description": string
- "dsl": object

Request example

curl --request PUT \
     --url http://{address}/api/v1/agents/58af890a2a8911f0a71a11b922ed82d6 \
     --header 'Content-Type: application/json' \
     --header 'Authorization: Bearer <YOUR_API_KEY>' \
     --data '{
         "title": "Test Agent",
         "description": "A test agent",
         "dsl": {
           // ... Canvas DSL here ...
         }
     }'

Request parameters

agent_id: (Path parameter), string
The id of the agent to be updated.
title: (Body parameter), string
The title of the agent.
description: (Body parameter), string
The description of the agent.
dsl: (Body parameter), object
The canvas DSL object of the agent.

Only specify the parameter you want to change in the request body. If a parameter does not exist or is None, it won't be updated.

Response

Success:

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

Failure:

{
    "code": 103,
    "message": "Only owner of canvas authorized for this operation."
}

Delete agent

DELETE /api/v1/agents/{agent_id}

Delete an agent by id.

Request

Method: DELETE
URL: /api/v1/agents/{agent_id}
Headers:
- 'Content-Type: application/json
- 'Authorization: Bearer <YOUR_API_KEY>'

Request example

curl --request DELETE \
     --url http://{address}/api/v1/agents/58af890a2a8911f0a71a11b922ed82d6 \
     --header 'Content-Type: application/json' \
     --header 'Authorization: Bearer <YOUR_API_KEY>' \
     --data '{}'

Request parameters

agent_id: (Path parameter), string
The id of the agent to be deleted.

Response

Success:

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

Failure:

{
    "code": 103,
    "message": "Only owner of canvas authorized for this operation."
}

System

Check system health

GET /v1/system/healthz

Check the health status of Swipies AI’s dependencies (database, Redis, document engine, object storage).

Request

Method: GET
URL: /v1/system/healthz
Headers:
- 'Content-Type: application/json' (no Authorization required)

Request example

curl --request GET
     --url http://{address}/v1/system/healthz
     --header 'Content-Type: application/json'

Request parameters

address: (Path parameter), string
The host and port of the backend service (e.g., localhost:7897).

Responses

200 OK – All services healthy

HTTP/1.1 200 OK
Content-Type: application/json

{
  "db": "ok",
  "redis": "ok",
  "doc_engine": "ok",
  "storage": "ok",
  "status": "ok"
}

500 Internal Server Error – At least one service unhealthy

HTTP/1.1 500 INTERNAL SERVER ERROR
Content-Type: application/json

{
  "db": "ok",
  "redis": "nok",
  "doc_engine": "ok",
  "storage": "ok",
  "status": "nok",
  "_meta": {
    "redis": {
      "elapsed": "5.2",
      "error": "Lost connection!"
    }
  }
}

Explanation:

Each service is reported as "ok" or "nok".
The top-level status reflects overall health.
If any service is "nok", detailed error info appears in _meta.

HTTP API

ERROR CODES​

OpenAI-Compatible API​

Create chat completion​

Request​

Request example​

Request Parameters​

Response​

Create agent completion​

Request​

Request example​

Request Parameters​

Response​

DATASET MANAGEMENT​

Create dataset​

Request​

A basic request example​

A request example specifying ingestion pipeline​

Request parameters​

Response​

Delete datasets​

Request​

Request example​

Request parameters​

Response​

Update dataset​

Request​

Request example​

Request parameters​

Response​

List datasets​

Request​

Request example​

Request parameters​

Response​

Get knowledge graph​

Request​

Request example​

Request parameters​

Response​

Delete knowledge graph​

Request​

Request example​

Request parameters​

Response​

Construct knowledge graph​

Request​

Request example​

Request parameters​

Response​

Get knowledge graph construction status​

Request​

Request example​

Request parameters​

Response​

Construct RAPTOR​

Request​

Request example​

Request parameters​

Response​

Get RAPTOR construction status​

Request​

Request example​

Request parameters​

Response​

FILE MANAGEMENT WITHIN DATASET​

Upload documents​

Request​

Request example​

Request parameters​

Response​

Update document​

Request​

Request example​

Request parameters​

Response​

Download document​

Request​

Request example​

Request parameters​

ERROR CODES

OpenAI-Compatible API

Create chat completion

Request

Request example

Request Parameters

Response

Create agent completion

Request

Request example

Request Parameters

Response

DATASET MANAGEMENT

Create dataset

Request

A basic request example

A request example specifying ingestion pipeline

Request parameters

Response

Delete datasets

Request

Request example

Request parameters

Response

Update dataset

Request

Request example

Request parameters

Response

List datasets

Request

Request example

Request parameters

Response

Get knowledge graph

Request

Request example

Request parameters

Response

Delete knowledge graph

Request

Request example

Request parameters

Response

Construct knowledge graph

Request

Request example

Request parameters

Response

Get knowledge graph construction status

Request

Request example

Request parameters

Response

Construct RAPTOR

Request

Request example

Request parameters

Response

Get RAPTOR construction status

Request

Request example

Request parameters

Response

FILE MANAGEMENT WITHIN DATASET

Upload documents

Request

Request example

Request parameters

Response

Update document

Request

Request example

Request parameters

Response

Download document

Request

Request example

Request parameters

Response