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You can subscribe to model assets (optional), training resources, and inference resources.
Using APIs to Call a Third-Party Model After a pre-trained or trained model is deployed, you can use the text dialog API to call the model. The third-party inference service can be invoked using Pangu inference APIs (V1 inference APIs) or OpenAI APIs (V2 inference APIs).
__init__(url="") def process(self, req): rst = {'result': "success", 'suggestion': "pass"} return rst, 200 dependency folder: This folder is mandatory and is used to store dependency packages that are not included in the base image or whose versions are
Enter the load balancer name (in the format of mas-xxx), set the listening port (ranging from 30000 to 40000), and set the protocol type (using HTTP or HTTPS requests for calling the inference model). Click OK.
They include services like Object Storage Service (OBS) and Content Delivery Network (CDN). Click OK. Figure 4 Selecting a scope Click OK.
Constraints N/A Range N/A Default Value N/A Response Parameters Streaming (with stream set to true in the header) Status code: 200 Table 4 Data units output in streaming mode Parameter Type Description data String Definition If stream is set to true, agent execution messages will
/usr/bin/env python # -*- coding: utf-8 -*- class Template: def __init__(self): # todo pass def process(self, params_dict): # Obtain input parameters.
Constraints N/A Range N/A Default Value N/A Response Parameters Non-streaming (with stream set to false in the header) Status code: 200 Table 5 Data units output in non-streaming mode Parameter Type Description outputs Map<String, Object> Definition Final output of the workflow.
Model configuration is supported. Variables in prompts can be defined. Outcome preview is supported. Historical records can be viewed. Prompt comparison Candidate prompts can be compared (prompt difference comparison and outcome comparison).
Flexible MCP service MCP uses the universal standard language to supply tools and data through the MCP server (one-off development and unlimited connections). In this way, AI agents can communicate with thousands of external tools and data more efficiently and conveniently.
