“openai”的搜索结果

Manage the credentials Arize uses to call LLM providers such as OpenAI and Anthropic. Create, inspect, update, or delete connections, configure allowed models and authentication methods, and retrieve connection IDs for use in evaluations.

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• Connect an LLM provider to Arize
• Review existing connections and allowed models
• Rotate stored provider API keys

Turn text into audio files for narration, product demos, phone prompts, or accessibility reads using the OpenAI Audio API. Choose built-in voices and delivery directions, then generate a single clip or a batch of files.

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• Create narration from an explainer script
• Generate a voiceover for a product demo
• Produce a batch of phone menu prompts

Deploy language model APIs and run batch generation with vLLM. Configure GPU memory use, quantization—lower precision model storage—and distribution across GPUs, then test response times and monitor how many requests the server handles.

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• Deploying an OpenAI-compatible language model API
• Generating responses for a large set of prompts
• Fitting a large model into limited GPU memory

Azure OpenAI SDK for .NET. Client library for Azure OpenAI and OpenAI services. Use for chat completions, embeddings, image generation, audio transcription, and assistants. Triggers: "Azure OpenAI", "AzureOpenAIClient", "ChatClient", "chat completions .NET", "GPT-4", "embeddings", "DALL-E", "Whisper", "OpenAI .NET".

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OpenAI API (developers.openai.com) の運用・管理リファレンス。 Administration API, admin API keys, RBAC, roles, groups, invites, projects, spend limits, audit logs, usage / costs API, Terraform provider (openai/openai), workload identity federation (WIF, OIDC / SPIFFE / X.509 / Idira), moderation API, misalignment monitoring, safety alerts / cases, safety best practices, under-18 guidance, ZDR Private Safety Processing (PSP), production best practices, deployment checklist, cost / latency optimization, fast / ultrafast mode, Realtime API costs。 rate limits・error handling・prompt caching・webhooks は openai-api-core、Codex WIF・管理は openai-codex。

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依据OpenAI的前端制作指引，以精简的页面布局创建视觉协调的落地页、网站和应用界面，用于规划界面外观与元素排列。

• 制作布局简洁的落地页
• 创建视觉协调的网站
• 制作应用界面

Ask AI questions, generate code, and summarize text through 9Router using OpenAI or Anthropic request formats. Receive responses incrementally with streaming, and use provider combinations that automatically try another provider when needed.

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• Ask an AI model questions through 9Router
• Generate code from written instructions
• Summarize text with a selected model

Add Codex as the agent that plans tasks and coordinates tools in NanoClaw. Choose it for individual groups alongside Claude, with conversation history and session resume using a ChatGPT subscription or OpenAI API key.

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• Adding Codex as a NanoClaw agent provider
• Choosing Claude or Codex for each group
• Setting up ChatGPT or API key authentication

Turn speech from audio or video recordings into text using OpenAI. Save transcripts as text or JSON, with optional speaker labels and reference recordings to help identify known speakers in meetings or interviews.

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• Transcribe recorded meetings
• Label speakers in interviews
• Extract spoken text from audio or video

Build and troubleshoot ChatGPT apps with an MCP server that exposes tools and an optional interactive widget. Use current official documentation and examples to create scaffolds, configure security settings, and guide local validation and deployment when requested.

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• Create a ChatGPT app scaffold from an official example
• Design tools and connect an MCP server to a widget
• Refactor or troubleshoot an existing Apps SDK app

Improve research through up to four rounds of external language model review, fixes, and reassessment. Use an OpenAI-compatible API to identify weaknesses, prioritize changes, and keep a cumulative record of reviewer responses, actions, and experiment results.

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• Identify weaknesses before submitting research
• Revise research using an external model's feedback
• Track review scores, changes, and experiment results

Find and run local AI models with llama.cpp. Choose GGUF model files and quantization levels that fit available memory, launch a command line session or local server, and test an OpenAI-compatible API on your computer.

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• Find models to run on a local computer
• Choose quantization for available memory
• Launch and test a local OpenAI-compatible API

Build, refactor, and troubleshoot ChatGPT apps with an MCP server, which exposes tools to the assistant, and an interactive widget. Use official documentation and examples to plan tools, scaffold code, configure security settings, and validate local operation.

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• Scaffolding a ChatGPT app with a server and widget
• Planning app tools and their inputs
• Refactoring an existing Apps SDK demo

Azure OpenAI is Microsoft's hosted service for OpenAI models (GPT, gpt-image, Whisper, embeddings) on Azure infrastructure. Use when calling OpenAI models through Azure, authenticating with Managed Identity or Entra ID, setting up content filters, or deploying with enterprise compliance, regional data residency or private networking.

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Azure OpenAI SDK for .NET. Client library for Azure OpenAI and OpenAI services. Use for chat completions, embeddings, image generation, audio transcription, and assistants. Triggers: "Azure OpenAI", "AzureOpenAIClient", "ChatClient", "chat completions .NET", "GPT-4", "embeddings", "DALL-E", "Whis...

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OpenAI Apps SDK / ChatGPT plugins (developers.openai.com/plugins) のリファレンス。 ChatGPT 内アプリを MCP サーバーとして公開する側の API。 window.openai, useOpenAiGlobal, registerAppTool, ui://widget, _meta, openai/outputTemplate, OAuth, plugin.json / mcp.json (Agent Plugins), MCP Events, Plugin Extensions, Checkout API, 接続テスト・審査・申請。

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为council整理的重要决策草案追加一次Codex外部批评，检查结论的薄弱之处、遗漏的失败情形，以及是否充分处理最有力的反对意见。

• 审视难以判断的重要决策草案
• 检查草案遗漏的失败可能性
• 确认是否忽略有力的反对意见

Connect AI agents to OmniRoute for text and image generation, speech synthesis, transcription, and web search. Use OpenAI-compatible APIs and create embeddings, numerical representations of text, through documented requests.

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• Add text generation to an AI agent
• Generate or edit images from instructions
• Turn text into speech or transcribe audio

Helps design focused AI products built on APIs such as OpenAI and Anthropic. Covers product strategy, prompts, model selection, user experience, usage tracking, and cost management for tools aimed at solving specific customer problems.

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• Planning an AI SaaS product for a specific problem
• Designing prompts for product features
• Balancing model choices, costs, and user experience

Build voice applications and phone agents with guidance on OpenAI Realtime API, Vapi, Deepgram, ElevenLabs, and LiveKit. Choose services for speech recognition, voice generation, and live communication, while addressing response delays and interruptions.

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• Build a voice assistant that responds in real time
• Create a phone support agent with Vapi
• Connect speech recognition and voice generation

Set up Langfuse to record LLM application activity, costs, and response times. Manage prompt versions, organize evaluation datasets, and compare results to investigate failures and guide improvements using recorded evidence.

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• Investigating failures in LLM applications
• Comparing prompt versions and evaluation scores
• Monitoring LLM costs and response times

Add OpenCode as an agent runtime in NanoClaw to use models from OpenAI, Google, OpenRouter, DeepSeek, and other supported services. The skill covers installation, authentication, group selection, and default model changes.

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• Adding OpenCode to an existing NanoClaw installation
• Switching an agent group to OpenCode
• Changing the default model without signing in again

Call Gemini and OpenMaaS models such as Llama, DeepSeek, and Qwen through Google Cloud Agent Platform. Prepare authentication and SDK code, run prompts, configure connection endpoints, and troubleshoot common request errors.

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• Send a test prompt to Gemini
• Call Llama or DeepSeek with the OpenAI SDK
• Write code to call an existing custom endpoint

Guides you through running large language models on CPUs, Apple Silicon, and AMD or Intel GPUs. Covers choosing GGUF model files, reducing memory through quantization, and starting an OpenAI-compatible chat server.

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• Running a local LLM on an Apple Silicon Mac
• Trying LLM inference on a CPU-only machine
• Choosing a model format that fits available memory