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api-and-clients

api-and-clients

Use DeepAnalyze from Python, OpenAI-style clients, and the API server.

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Documentation de la Skill

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name: "api-and-clients" description: "Use DeepAnalyze from Python, OpenAI-style clients, and the API server." metadata: disco-role: operating disable-model-invocation: true license: MIT

api-and-clients

Use this sub-skill when the task is about:

  • DeepAnalyzeVLLM(model_name, api_url='http://localhost:8000/v1/chat/completions', max_rounds=30) and generate(prompt, workspace, temperature=0.5, max_tokens=32768, top_p=None, top_k=None)
  • execute_code(code_str) and the <Analyze>, <Understand>, <Code>, <Execute>, <File>, and <Answer> runtime contract
  • POST /v1/files, GET /v1/files/{file_id}, GET /v1/files/{file_id}/content, DELETE /v1/files/{file_id}, POST /v1/chat/completions, /v1/models, /health, and /v1/admin/*
  • requests and OpenAI client flows that place file_ids on the latest user message and thread_id on the latest user message when continuing a conversation
  • streaming chunks, generated file reporting, and persistent thread workspaces

Start with these local references:

  • references/api-reference.md
  • references/programmatic-usage.md
  • references/case-study-patterns.md
  • references/troubleshooting.md

Use these bundled scripts:

  • scripts/deepanalyze_vllm_smoke.py
  • scripts/api_client_smoke.py
  • scripts/openai_client_smoke.py
  • scripts/mock_vllm_server.py

Route away when the task is mainly:

  • browser UI, Jupyter, or CLI navigation -> interactive-frontends
  • vLLM launch, model download, memory, or quantization -> model-serving
  • SFT, RL, benchmarks, or other training/evaluation -> training-and-evaluation
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