{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"pypi_pypi-openai","slug":"pypi-openai","name":"openai","type":"api","url":"https://pypi.org/project/openai/","page_url":"https://unfragile.ai/pypi-openai","categories":["llm-apis"],"tags":[],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"pypi_pypi-openai__cap_0","uri":"capability://text.generation.language.type.safe.synchronous.chat.completions.with.ide.autocomplete","name":"type-safe synchronous chat completions with ide autocomplete","description":"Provides a synchronous OpenAI client class that wraps the Chat Completions API with full Pydantic-based type definitions for all request parameters and response models. The SDK is generated from OpenAI's OpenAPI specification using Stainless, enabling compile-time type checking and IDE autocomplete for all parameters (model, temperature, max_tokens, tools, etc.). Requests are validated against Pydantic schemas before transmission, and responses are automatically deserialized into typed Python objects with nested model support for complex structures like tool calls and function definitions.","intents":["I want to call GPT-4 from Python with full IDE autocomplete for all parameters","I need type safety to catch parameter errors before making API calls","I want response objects that are properly typed so I can access nested fields without casting"],"best_for":["Python developers building production LLM applications","Teams requiring static type checking and IDE support","Developers migrating from untyped REST clients to strongly-typed SDKs"],"limitations":["Synchronous blocking I/O — not suitable for high-concurrency scenarios without thread pools","Type validation adds ~5-10ms overhead per request due to Pydantic schema validation","Python 3.9+ only — no support for older Python versions"],"requires":["Python 3.9+","OpenAI API key (OPENAI_API_KEY environment variable or explicit parameter)","httpx library (included as dependency)"],"input_types":["list of message dicts with role/content","system prompts (string)","tool definitions (structured dicts)","numeric parameters (temperature, max_tokens, top_p)"],"output_types":["ChatCompletion typed object","nested Choice objects with Message containing content and tool_calls","ChoiceLogprobs for token probability data"],"categories":["text-generation-language","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-openai__cap_1","uri":"capability://text.generation.language.asynchronous.streaming.chat.completions.with.event.iteration","name":"asynchronous streaming chat completions with event iteration","description":"Provides AsyncOpenAI client with native async/await support for streaming chat completions, returning an async iterator that yields server-sent events (SSE) as they arrive. The implementation uses httpx's async HTTP client with chunked transfer encoding to stream tokens in real-time without buffering the entire response. Each streamed chunk is parsed into typed ServerSentEvent objects, and the SDK provides convenience methods to extract delta content and tool calls from the stream, enabling token-by-token processing for real-time UI updates or token counting.","intents":["I need to stream tokens from GPT-4 in real-time for a web app or CLI","I want to process each token as it arrives without waiting for the full response","I need to count tokens or implement early stopping based on streamed output"],"best_for":["Web applications with WebSocket or Server-Sent Events backends","CLI tools requiring real-time token display","High-concurrency services handling multiple concurrent streams"],"limitations":["Streaming responses cannot be retried mid-stream — connection loss requires full restart","SSE parsing adds ~2-3ms per chunk due to JSON deserialization","Tool calls in streaming mode arrive as deltas and must be reassembled by the client"],"requires":["Python 3.9+","asyncio event loop","OpenAI API key","httpx with async support (included)"],"input_types":["list of message dicts","stream=True parameter","optional tool definitions"],"output_types":["AsyncIterator[ChatCompletionStreamResponse]","ServerSentEvent objects with delta content","tool_call_delta objects for function calls"],"categories":["text-generation-language","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-openai__cap_10","uri":"capability://automation.workflow.automatic.retry.with.exponential.backoff.and.rate.limit.handling","name":"automatic retry with exponential backoff and rate-limit handling","description":"Implements a sophisticated retry mechanism at the HTTP client level that automatically retries failed requests with exponential backoff, jitter, and rate-limit awareness. The SDK detects rate-limit errors (429 status), timeout errors, and transient failures (5xx), then retries with configurable max attempts and backoff strategy. Respects Retry-After headers from the API and implements jitter to prevent thundering herd problems. The retry logic is transparent to the caller — failed requests are automatically retried without explicit error handling code.","intents":["I want my API calls to automatically retry on rate limits without crashing","I need robust error handling for transient network failures","I want to avoid implementing custom retry logic in my application"],"best_for":["Production applications requiring high reliability","Batch processing jobs that can tolerate delays","Applications with unpredictable load patterns"],"limitations":["Retry logic only applies to idempotent operations — POST requests with side effects may be retried multiple times","Max retry attempts are configurable but default to 2 — very aggressive rate limiting may still fail","Exponential backoff can add significant latency (up to minutes for max retries)","Streaming requests cannot be retried mid-stream — connection loss requires full restart"],"requires":["Python 3.9+","OpenAI API key","Network connectivity (retries assume transient failures)"],"input_types":["any API request (automatic, no explicit input)"],"output_types":["successful response after retry","APIError after max retries exceeded"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-openai__cap_11","uri":"capability://data.processing.analysis.pagination.with.automatic.cursor.management.for.list.endpoints","name":"pagination with automatic cursor management for list endpoints","description":"Provides automatic pagination for list endpoints (e.g., list messages, list files, list fine-tuning jobs) that return large result sets. The SDK abstracts away cursor/offset management and provides a unified iterator interface that automatically fetches the next page when needed. Supports both limit-offset and cursor-based pagination depending on the endpoint, and provides convenience methods to iterate over all results or fetch a specific page. The implementation handles page size configuration and automatically retries failed page fetches.","intents":["I want to iterate over all messages in a thread without manually handling pagination","I need to fetch all fine-tuning jobs but the API returns them in pages","I want to avoid implementing custom pagination logic for list endpoints"],"best_for":["Applications listing large datasets (threads, files, jobs)","Batch processing scripts iterating over API results","Developers avoiding manual pagination implementation"],"limitations":["Pagination is lazy — pages are fetched on-demand, adding latency when iterating","No built-in caching — repeated iteration over the same data makes multiple API calls","Page size is fixed per endpoint — cannot customize batch size for all endpoints","Cursor-based pagination may skip items if data is modified during iteration"],"requires":["Python 3.9+","OpenAI API key","List endpoint that supports pagination"],"input_types":["list endpoint parameters (limit, order, etc.)"],"output_types":["SyncCursorPage or AsyncCursorPage iterator","individual items from the paginated result set"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-openai__cap_12","uri":"capability://safety.moderation.webhook.signature.verification.for.event.authenticity","name":"webhook signature verification for event authenticity","description":"Provides utility functions to verify webhook signatures from OpenAI, ensuring that incoming webhook events are authentic and have not been tampered with. The SDK uses HMAC-SHA256 to verify the signature header against the webhook payload and a secret key, and provides a convenience function that validates the timestamp to prevent replay attacks. Supports both raw webhook verification and integration with web frameworks (Flask, FastAPI, etc.).","intents":["I want to verify that webhook events from OpenAI are authentic","I need to prevent replay attacks on my webhook endpoint","I want a simple function to validate webhook signatures without implementing crypto manually"],"best_for":["Applications receiving webhooks from OpenAI (fine-tuning events, etc.)","Security-conscious teams requiring event authenticity verification","Webhook handlers in production environments"],"limitations":["Webhook signature verification requires the webhook secret key — must be stored securely","Timestamp validation window is fixed (5 minutes default) — may reject legitimate delayed webhooks","No built-in rate limiting — webhook handlers must implement their own DDoS protection"],"requires":["Python 3.9+","Webhook secret key from OpenAI dashboard","Webhook payload and signature header from incoming request"],"input_types":["webhook payload (bytes or string)","signature header (string, format: 't=timestamp,v1=signature')","webhook secret key (string)"],"output_types":["boolean (True if signature is valid)","exception (WebhookSignatureVerificationError if invalid)"],"categories":["safety-moderation","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-openai__cap_13","uri":"capability://tool.use.integration.custom.http.client.and.proxy.configuration.for.network.control","name":"custom http client and proxy configuration for network control","description":"Allows users to provide custom httpx.Client or httpx.AsyncClient instances to the OpenAI client, enabling fine-grained control over HTTP behavior including proxy configuration, custom headers, SSL/TLS settings, and connection pooling. The SDK accepts a custom_client parameter that replaces the default HTTP client, allowing integration with corporate proxies, custom certificate authorities, or specialized network configurations. Supports both synchronous and asynchronous custom clients.","intents":["I need to route API calls through a corporate proxy","I want to use a custom SSL certificate authority for internal security","I need to add custom headers or authentication to all API requests"],"best_for":["Enterprise environments with network restrictions","Teams requiring custom SSL/TLS configuration","Applications needing fine-grained HTTP control"],"limitations":["Custom client must be httpx-compatible — other HTTP libraries not supported","User is responsible for managing client lifecycle (connection pooling, cleanup)","Custom client bypasses some SDK defaults (e.g., retry logic) — must be re-implemented if needed","Debugging custom client issues requires httpx knowledge"],"requires":["Python 3.9+","httpx library","Custom httpx.Client or httpx.AsyncClient instance"],"input_types":["httpx.Client or httpx.AsyncClient instance","proxy URL (string)","custom headers (dict)","SSL certificate path (string)"],"output_types":["OpenAI or AsyncOpenAI client using custom HTTP client"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-openai__cap_14","uri":"capability://tool.use.integration.azure.openai.client.with.managed.identity.and.endpoint.configuration","name":"azure openai client with managed identity and endpoint configuration","description":"Provides a specialized AzureOpenAI client that integrates with Microsoft Azure's OpenAI service, handling Azure-specific authentication (API keys, managed identities, Azure AD tokens) and endpoint configuration. The SDK automatically maps OpenAI model names to Azure deployment names, manages Azure-specific headers and authentication flows, and provides the same API surface as the standard OpenAI client. Supports both key-based and token-based authentication, with automatic token refresh for managed identities.","intents":["I want to use Azure OpenAI instead of the public OpenAI API","I need to authenticate using Azure managed identity instead of API keys","I want to use the same code for both OpenAI and Azure OpenAI with minimal changes"],"best_for":["Organizations using Azure cloud infrastructure","Teams requiring Azure AD integration","Applications needing Azure-specific compliance or data residency"],"limitations":["Model names must be mapped to Azure deployment names — no automatic discovery","Azure API versions may lag behind OpenAI API — some features may not be available","Managed identity authentication requires Azure SDK (azure-identity) — adds dependency","Azure endpoint URLs are region-specific — must be configured correctly"],"requires":["Python 3.9+","Azure OpenAI resource deployed in Azure","Azure API key OR Azure managed identity credentials","azure-identity library (for managed identity authentication)"],"input_types":["azure_endpoint (string, e.g., https://myresource.openai.azure.com/)","api_key (string) OR DefaultAzureCredential","api_version (string, e.g., 2024-02-15-preview)","deployment_id (string, maps to model name)"],"output_types":["AzureOpenAI client with same API as OpenAI client","responses identical to OpenAI API"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-openai__cap_2","uri":"capability://data.processing.analysis.structured.output.parsing.with.json.schema.validation","name":"structured output parsing with json schema validation","description":"Implements parsed responses capability that automatically validates and deserializes chat completion responses against a provided Pydantic model or JSON schema. When response_format={'type': 'json_schema', 'json_schema': {...}} is specified, the SDK enforces that the model returns valid JSON matching the schema, then automatically parses the response into the provided Python type. This enables type-safe extraction of structured data (e.g., extracting entities, classifications, or complex nested objects) with automatic validation and error handling for malformed responses.","intents":["I want to extract structured data (JSON) from LLM responses with guaranteed schema compliance","I need to parse LLM outputs into Python dataclasses or Pydantic models automatically","I want validation to fail fast if the LLM returns invalid JSON or schema-violating data"],"best_for":["Data extraction pipelines requiring strict schema compliance","Applications building LLM-powered APIs that return typed JSON","Teams using LLMs for classification, entity extraction, or structured reasoning"],"limitations":["Requires explicit JSON schema definition — no automatic inference from Python types in all cases","LLM may still violate schema despite json_schema mode; SDK validates but doesn't auto-retry","Schema complexity impacts token usage — overly detailed schemas increase prompt size"],"requires":["Python 3.9+","Pydantic model or JSON schema definition","OpenAI API key","Model supporting structured outputs (gpt-4-turbo, gpt-4o, or later)"],"input_types":["Pydantic BaseModel class","JSON schema dict","response_format parameter with json_schema"],"output_types":["Parsed Python object matching the provided type","Validated JSON dict","ParsedChatCompletion wrapper with .parsed attribute"],"categories":["data-processing-analysis","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-openai__cap_3","uri":"capability://tool.use.integration.tool.calling.with.multi.provider.function.registry","name":"tool calling with multi-provider function registry","description":"Provides a schema-based function calling system that converts Python functions or Pydantic models into OpenAI tool definitions, handles tool call responses from the model, and provides utilities for executing called functions. The SDK automatically generates JSON schemas from function signatures and type hints, manages the tool_calls list in responses, and provides helpers to extract and invoke the called functions. Supports both function definitions (for stateless calls) and tool objects with nested schemas for complex multi-step interactions.","intents":["I want to let GPT-4 call Python functions by automatically converting function signatures to tool schemas","I need to handle tool_calls from the model response and execute the corresponding Python functions","I want to build multi-turn conversations where the model calls tools and I feed results back"],"best_for":["Developers building LLM agents with function calling","Teams implementing ReAct or similar agentic patterns","Applications requiring tool-use loops (model calls tool → execute → feed back result)"],"limitations":["Function signatures must be type-hinted for schema generation — untyped functions require manual schema definition","Tool execution is not automatic — SDK provides helpers but developers must implement the loop","No built-in sandboxing — executing arbitrary tool calls requires careful validation and security controls"],"requires":["Python 3.9+","Type hints on functions (for automatic schema generation)","OpenAI API key","Model supporting tool_use (gpt-4-turbo, gpt-4o, or later)"],"input_types":["Python function with type hints","Pydantic model","Manual tool definition dict"],"output_types":["tool_calls list with ToolCall objects","ToolCall.function.name and ToolCall.function.arguments (JSON string)","tool_results for feeding back into next turn"],"categories":["tool-use-integration","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-openai__cap_4","uri":"capability://planning.reasoning.assistants.api.with.stateful.thread.and.message.management","name":"assistants api with stateful thread and message management","description":"Provides a high-level Assistants API client that manages stateful conversations through Thread objects, automatically handling message history, run execution, and response streaming. The SDK abstracts away the complexity of creating threads, appending messages, polling run status, and retrieving results. Supports streaming assistant events (on_message_created, on_text_delta, on_tool_call_created) for real-time UI updates, and handles file uploads and retrieval for document-based assistants. The implementation uses polling with exponential backoff for run completion and provides convenience methods to extract final messages from completed runs.","intents":["I want to build a stateful chatbot where conversation history is managed server-side","I need to create an assistant with custom instructions and tools that persists across sessions","I want to stream assistant responses in real-time as the model thinks and executes tools"],"best_for":["Chatbot applications requiring persistent conversation state","Document-based Q&A systems using file retrieval","Multi-turn agentic workflows with tool use and state management"],"limitations":["Run polling adds latency — no native WebSocket support for real-time updates","File uploads are limited to 20MB per file and specific formats (PDF, DOCX, etc.)","Thread state is server-side only — no local caching or offline support","Beta API — breaking changes possible in future versions"],"requires":["Python 3.9+","OpenAI API key","Model supporting assistants (gpt-4-turbo, gpt-4o, or later)","Optional: file uploads for document retrieval"],"input_types":["assistant_id (string)","thread_id (string or None to create new)","message content (text or file)","tool definitions"],"output_types":["Thread object with thread_id","Message objects with role/content","Run object with status (queued, in_progress, completed, failed)","streaming events (MessageStreamEvent)"],"categories":["planning-reasoning","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-openai__cap_5","uri":"capability://automation.workflow.fine.tuning.job.submission.and.status.monitoring","name":"fine-tuning job submission and status monitoring","description":"Provides a fine-tuning API client that manages the full lifecycle of fine-tuning jobs: uploading training/validation files, submitting fine-tuning jobs with hyperparameter configuration, polling job status, and retrieving the resulting model ID. The SDK handles file format validation (JSONL), automatic retry on transient failures, and provides convenience methods to list jobs and check completion status. Supports both standard fine-tuning and custom hyperparameter tuning with validation set evaluation.","intents":["I want to fine-tune a base model on my custom dataset and track the job progress","I need to upload JSONL training data and submit a fine-tuning job with specific hyperparameters","I want to retrieve the fine-tuned model ID once training completes"],"best_for":["ML teams customizing models for domain-specific tasks","Applications requiring model personalization at scale","Developers building automated ML pipelines"],"limitations":["Fine-tuning jobs can take hours or days — no real-time progress streaming, only status polling","Training data must be in JSONL format with specific schema — no automatic format conversion","Fine-tuned models are stored in OpenAI's account — no local model export","Cost is per token trained — large datasets can be expensive"],"requires":["Python 3.9+","OpenAI API key with fine-tuning permissions","Training data in JSONL format (one JSON object per line)","Sufficient API account credits"],"input_types":["JSONL file path (training data)","JSONL file path (validation data, optional)","hyperparameters dict (learning_rate, batch_size, epochs)","base model name (gpt-3.5-turbo, gpt-4, etc.)"],"output_types":["FineTuningJob object with job_id and status","fine_tuned_model string (model ID after completion)","training metrics (loss, accuracy)"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-openai__cap_6","uri":"capability://data.processing.analysis.embeddings.generation.with.vector.output.and.batch.processing","name":"embeddings generation with vector output and batch processing","description":"Provides an embeddings API client that converts text or token sequences into dense vector representations using OpenAI's embedding models. The SDK handles batching of inputs (up to 2048 per request), automatic retry on rate limits, and returns Embedding objects with vector data and usage statistics. Supports both single and batch embedding generation with configurable models (text-embedding-3-small, text-embedding-3-large) and encoding formats (float, base64).","intents":["I want to convert text into embeddings for semantic search or similarity comparison","I need to embed a large corpus of documents efficiently using batch processing","I want to store embeddings in a vector database for RAG applications"],"best_for":["Developers building semantic search or RAG systems","Teams implementing vector database pipelines","Applications requiring similarity-based retrieval"],"limitations":["Batch size limited to 2048 inputs per request — very large corpora require multiple API calls","Embeddings are model-specific — changing models requires re-embedding entire corpus","No local embedding generation — all computation happens on OpenAI servers","Embedding dimensions vary by model (1536 for text-embedding-3-small, 3072 for large)"],"requires":["Python 3.9+","OpenAI API key","Text input (string or list of strings)","Model name (text-embedding-3-small or text-embedding-3-large)"],"input_types":["string (single text)","list of strings (batch)","list of token sequences (for token-based embeddings)"],"output_types":["Embedding object with vector (list of floats)","CreateEmbeddingResponse with list of embeddings and usage stats","base64-encoded vectors (optional)"],"categories":["data-processing-analysis","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-openai__cap_7","uri":"capability://data.processing.analysis.audio.transcription.and.translation.with.multiple.formats","name":"audio transcription and translation with multiple formats","description":"Provides audio API clients for transcribing and translating audio files using Whisper models. The SDK handles file upload, format detection (MP3, WAV, M4A, FLAC, etc.), and returns transcription/translation results with optional timestamp granularity (segment or word-level). Supports both synchronous and asynchronous operations, with streaming transcription for real-time speech-to-text applications. The implementation uses multipart form-data for file uploads and provides convenience methods to extract text or structured results with timing information.","intents":["I want to transcribe audio files to text using Whisper","I need to translate audio from other languages to English","I want real-time transcription with word-level timestamps for video captioning"],"best_for":["Speech-to-text applications and voice assistants","Video captioning and subtitle generation","Multilingual transcription pipelines"],"limitations":["File size limited to 25MB — larger files must be split before upload","Supported formats limited to MP3, WAV, M4A, FLAC, OGG — no raw PCM or other formats","Timestamp granularity (word-level) adds latency and token usage","Translation only supports output to English"],"requires":["Python 3.9+","OpenAI API key","Audio file in supported format (MP3, WAV, M4A, FLAC, OGG)","File size ≤ 25MB"],"input_types":["file path (string)","file object (binary)","audio bytes"],"output_types":["Transcript object with text","TranscriptionVerbose with segments and word-level timestamps","Translation object with translated text"],"categories":["data-processing-analysis","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-openai__cap_8","uri":"capability://image.visual.image.generation.with.dall.e.models.and.size.quality.control","name":"image generation with dall-e models and size/quality control","description":"Provides image generation API client that creates images from text prompts using DALL-E 3 or DALL-E 2 models. The SDK handles prompt submission, configurable image sizes (256x256, 512x512, 1024x1024, 1024x1792, 1792x1024), quality settings (standard, hd), and style options (natural, vivid). Returns Image objects with URLs or base64-encoded image data, and supports batch generation of multiple variations. The implementation manages API rate limits and provides convenience methods to download or save generated images.","intents":["I want to generate images from text descriptions using DALL-E","I need to create multiple variations of an image with different prompts","I want to control image quality, size, and style for specific use cases"],"best_for":["Content creation and design automation tools","Applications generating product images or mockups","Creative tools requiring on-demand image generation"],"limitations":["Generated images are owned by OpenAI unless explicitly purchased — usage rights vary by plan","Image generation is slow (10-60 seconds per image) — not suitable for real-time applications","DALL-E 3 limited to 1 image per request — DALL-E 2 supports up to 10","Prompt moderation may reject requests with certain content"],"requires":["Python 3.9+","OpenAI API key with image generation credits","Text prompt (string)","Model name (dall-e-3 or dall-e-2)"],"input_types":["text prompt (string)","size parameter (256x256, 512x512, 1024x1024, etc.)","quality parameter (standard, hd)","style parameter (natural, vivid)"],"output_types":["Image object with url or b64_json","ImagesResponse with list of Image objects","base64-encoded image data (optional)"],"categories":["image-visual","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-openai__cap_9","uri":"capability://image.visual.image.analysis.and.vision.understanding.with.multi.modal.inputs","name":"image analysis and vision understanding with multi-modal inputs","description":"Provides vision capability within chat completions that allows models to analyze images and answer questions about them. The SDK accepts images as URLs or base64-encoded data within message content, automatically formats them for the API, and returns text responses analyzing the image. Supports multiple image formats (JPEG, PNG, GIF, WebP) and image detail levels (low, high, auto) for controlling token usage. Works seamlessly with chat completions — images are just another content type in the messages list.","intents":["I want to ask GPT-4V questions about images (OCR, object detection, scene understanding)","I need to analyze multiple images in a single conversation","I want to control token usage by adjusting image detail level"],"best_for":["Document analysis and OCR applications","Visual Q&A systems","Accessibility tools (image description generation)"],"limitations":["Image detail level (high) significantly increases token usage — low detail is ~85 tokens, high is ~170-2000 tokens","GIF images are treated as static (first frame only) — no video/animation support","Image size must be ≤ 20MB — very large images must be resized","Vision capability only available in gpt-4-vision, gpt-4o, and later models"],"requires":["Python 3.9+","OpenAI API key","Model supporting vision (gpt-4-vision, gpt-4o, or later)","Image URL or base64-encoded image data"],"input_types":["image URL (string)","base64-encoded image (string)","image file path (converted to base64)"],"output_types":["ChatCompletion with text response analyzing the image","nested content objects with image_url or base64 data"],"categories":["image-visual","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":27,"verified":false,"data_access_risk":"high","permissions":["Python 3.9+","OpenAI API key (OPENAI_API_KEY environment variable or explicit parameter)","httpx library (included as dependency)","asyncio event loop","OpenAI API key","httpx with async support (included)","Network connectivity (retries assume transient failures)","List endpoint that supports pagination","Webhook secret key from OpenAI dashboard","Webhook payload and signature header from incoming request"],"failure_modes":["Synchronous blocking I/O — not suitable for high-concurrency scenarios without thread pools","Type validation adds ~5-10ms overhead per request due to Pydantic schema validation","Python 3.9+ only — no support for older Python versions","Streaming responses cannot be retried mid-stream — connection loss requires full restart","SSE parsing adds ~2-3ms per chunk due to JSON deserialization","Tool calls in streaming mode arrive as deltas and must be reassembled by the client","Retry logic only applies to idempotent operations — POST requests with side effects may be retried multiple times","Max retry attempts are configurable but default to 2 — very aggressive rate limiting may still fail","Exponential backoff can add significant latency (up to minutes for max retries)","Streaming requests cannot be retried mid-stream — connection loss requires full restart","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.35,"ecosystem":0.39999999999999997,"match_graph":0.25,"freshness":0.52,"weights":{"adoption":0.25,"quality":0.25,"ecosystem":0.1,"match_graph":0.28,"freshness":0.12}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:25.060Z","last_scraped_at":"2026-05-03T15:20:10.823Z","last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=pypi-openai","compare_url":"https://unfragile.ai/compare?artifact=pypi-openai"}},"signature":"cLwxfEuX9ZPc5/bF+J4tYNbcIgLVjalK+EaZrVpsADABVbHCruS8jcIxdCoMaOETJZY1q4RZHZvQox2OcjGZAg==","signedAt":"2026-06-20T22:44:49.652Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/pypi-openai","artifact":"https://unfragile.ai/pypi-openai","verify":"https://unfragile.ai/api/v1/verify?slug=pypi-openai","publicKey":"https://unfragile.ai/api/v1/trust-passport-public-key","spec":"https://unfragile.ai/trust","schema":"https://unfragile.ai/schema.json","docs":"https://unfragile.ai/docs"}}