App

Get started with the App API

Chat App

POST /api/v2/chat/completions

Examples

import Tabs from '@theme/Tabs'; import TabItem from '@theme/TabItem';

Stream Chat App

<Tabs defaultValue="python" groupId="chat" values={[ {label: 'Curl', value: 'curl'}, {label: 'Python', value: 'python'}, ] }>

 DBGPT_API_KEY=dbgpt
 APP_ID={YOUR_APP_ID}

 curl -X POST "http://localhost:5670/api/v2/chat/completions" \
    -H "Authorization: Bearer $DBGPT_API_KEY" \
    -H "accept: application/json" \
    -H "Content-Type: application/json" \
    -d "{\"messages\":\"Hello\",\"model\":\"gpt-4o\", \"chat_mode\": \"chat_app\", \"chat_param\": \"$APP_ID\"}"

from dbgpt_client import Client

DBGPT_API_KEY = "dbgpt"
APP_ID="{YOUR_APP_ID}"

client = Client(api_key=DBGPT_API_KEY)

async for data in client.chat_stream(
    messages="Introduce AWEL", 
    model="gpt-4o", 
    chat_mode="chat_app", 
    chat_param=APP_ID
):
    print(data)

Chat Completion Stream Response

data: {"id": "109bfc28-fe87-452c-8e1f-d4fe43283b7d", "created": 1710919480, "model": "gpt-4o", "choices": [{"index": 0, "delta": {"role": "assistant", "content": "```agent-plans\n[{\"name\": \"Introduce Awel\", \"num\": 2, \"status\": \"complete\", \"agent\": \"Human\", \"markdown\": \"```agent-messages\\n[{\\\"sender\\\": \\\"Summarizer\\\", \\\"receiver\\\": \\\"Human\\\", \\\"model\\\": \\\"gpt-4o\\\", \\\"markdown\\\": \\\"Agentic Workflow Expression Language (AWEL) is a specialized language designed for developing large model applications with intelligent agent workflows. It offers flexibility and functionality, allowing developers to focus on business logic for LLMs applications without getting bogged down in model and environment details. AWEL uses a layered API design architecture, making it easier to work with. You can find examples and source code to get started with AWEL, and it supports various operators and environments. AWEL is a powerful tool for building native data applications through workflows and agents.\"}]\n```"}}]}

data: [DONE]

Get App

GET /api/v2/serve/apps/{app_id}

<Tabs defaultValue="curl_get_app" groupId="chat1" values={[ {label: 'Curl', value: 'curl_get_app'}, {label: 'Python', value: 'python_get_app'}, ] }>

DBGPT_API_KEY=dbgpt
APP_ID={YOUR_APP_ID}
curl -X GET "http://localhost:5670/api/v2/serve/apps/$APP_ID" -H "Authorization: Bearer $DBGPT_API_KEY"

from dbgpt_client import Client
from dbgpt_client.app import get_app

DBGPT_API_KEY = "dbgpt"
app_id = "{your_app_id}"

client = Client(api_key=DBGPT_API_KEY)
res = await get_app(client=client, app_id=app_id)

Query Parameters

app_id string Required

app id

Response body

Return App Object

List App

GET /api/v2/serve/apps

<Tabs defaultValue="curl_list_app" groupId="chat1" values={[ {label: 'Curl', value: 'curl_list_app'}, {label: 'Python', value: 'python_list_app'}, ] }>

DBGPT_API_KEY=dbgpt

curl -X GET 'http://localhost:5670/api/v2/serve/apps' -H "Authorization: Bearer $DBGPT_API_KEY"

from dbgpt_client import Client
from dbgpt_client.app import list_app

DBGPT_API_KEY = "dbgpt"
app_id = "{your_app_id}"

client = Client(api_key=DBGPT_API_KEY)
res = await list_app(client=client)

Response body

Return App Object List

The App Model

app_code string

unique app id

app_name string

app name

app_describe string

app describe

team_mode string

team mode

language string

language

team_context string

team context

user_code string

user code

sys_code string

sys code

is_collected string

is collected

icon string

icon

created_at string

created at

updated_at string

updated at

details string

app details List[AppDetailModel]

The App Detail Model

app_code string

app code

app_name string

app name

agent_name string

agent name

node_id string

node id

resources string

resources

prompt_template string

prompt template

llm_strategy string

llm strategy

llm_strategy_value string

llm strategy value

created_at string

created at

updated_at string

updated at

Source: docs/i18n/zh-CN/docusaurus-plugin-content-docs/current/api/app.md