{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"npm-genkitx-azure-openai","slug":"genkitx-azure-openai","name":"genkitx-azure-openai","type":"framework","url":"https://github.com/genkit-ai/azure-foundry-js-plugin#readme","page_url":"https://unfragile.ai/genkitx-azure-openai","categories":["app-builders"],"tags":["genkit","genkit-plugin","genkit-embedder","genkit-model","openai","gpt-4","gpt-4.1","o3","gpt","gpt-3.5","ai","genai","generative-ai"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"npm-genkitx-azure-openai__cap_0","uri":"capability://tool.use.integration.azure.openai.model.integration.with.genkit.abstraction.layer","name":"azure openai model integration with genkit abstraction layer","description":"Provides a standardized Genkit plugin interface that wraps Azure OpenAI's REST APIs (GPT-4, GPT-4 Turbo, o3, GPT-3.5-Turbo) into Genkit's model registry system. The plugin handles Azure-specific authentication (API keys, managed identity), endpoint configuration, and request/response translation between Genkit's unified model schema and Azure OpenAI's proprietary API contracts, enabling seamless model swapping across cloud providers without application code changes.","intents":["I want to use Azure OpenAI models in my Genkit application without writing Azure-specific API code","I need to switch between Azure OpenAI and other providers (OpenAI, Anthropic, Ollama) without refactoring my application","I want to configure multiple Azure OpenAI deployments and route requests based on model capabilities or cost"],"best_for":["Teams building on Azure infrastructure who want cloud-agnostic LLM application code","Enterprises standardizing on Genkit for multi-provider LLM orchestration","Developers migrating from direct Azure OpenAI SDK usage to Genkit's abstraction layer"],"limitations":["Abstracts away Azure-specific features (e.g., content filtering policies, deployment-level rate limiting) that may require direct API calls for fine-grained control","Requires Azure OpenAI resource provisioning and deployment configuration outside the plugin — no infrastructure-as-code generation","No built-in retry logic or circuit breaker for Azure quota exhaustion scenarios — relies on Genkit's base error handling","Limited to models available in Azure OpenAI's regional deployments; newer models may lag behind OpenAI's public API availability"],"requires":["Node.js 18+","Genkit framework 0.3.0 or later","Azure OpenAI resource with at least one model deployment (e.g., gpt-4, gpt-4-turbo, o3)","Azure API key or managed identity credentials with permissions to call Azure OpenAI endpoints","npm or yarn package manager"],"input_types":["text prompts","structured conversation histories (messages array with role/content)","system prompts","JSON schema for structured output (if using response_format parameter)"],"output_types":["text completions","structured JSON (when response_format is set to json_schema)","token usage metadata (prompt_tokens, completion_tokens, total_tokens)","finish_reason indicators (stop, length, content_filter, tool_calls)"],"categories":["tool-use-integration","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-genkitx-azure-openai__cap_1","uri":"capability://tool.use.integration.multi.model.deployment.routing.with.azure.openai","name":"multi-model deployment routing with azure openai","description":"Allows registration of multiple Azure OpenAI model deployments (e.g., gpt-4 in East US, gpt-4-turbo in West Europe) within a single Genkit application, with automatic routing based on model name or explicit deployment selection. The plugin maintains a registry of deployment-to-endpoint mappings and resolves model requests to the appropriate Azure region/deployment at runtime, enabling cost optimization, latency reduction, and failover patterns.","intents":["I want to use different Azure OpenAI deployments in different regions to minimize latency for global users","I need to route expensive gpt-4 requests to a limited-quota deployment and fallback to gpt-3.5-turbo on quota exhaustion","I want to A/B test different model versions (gpt-4 vs gpt-4-turbo) by routing to separate Azure deployments"],"best_for":["Global applications requiring low-latency LLM inference across multiple Azure regions","Cost-conscious teams with heterogeneous Azure OpenAI quota allocations per region","Organizations implementing canary deployments or gradual model version rollouts"],"limitations":["No built-in load balancing or health checking across deployments — routing is static per model name","Requires manual configuration of all deployment endpoints and API keys; no auto-discovery of Azure OpenAI resources","No cross-deployment fallback logic — if a deployment is unavailable, requests fail rather than automatically retrying another region","Latency overhead from endpoint resolution is negligible (~1-2ms) but adds complexity to error handling if deployments diverge in capability"],"requires":["Multiple Azure OpenAI resources provisioned across regions with model deployments","Separate API keys or managed identity with permissions to all target deployments","Genkit configuration file or code-based plugin initialization with deployment mappings","Network connectivity to all target Azure regions"],"input_types":["model name string (e.g., 'gpt-4', 'gpt-4-turbo')","optional deployment hint or region preference in request context"],"output_types":["resolved Azure OpenAI endpoint URL","model completion with region/deployment metadata in response"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-genkitx-azure-openai__cap_10","uri":"capability://automation.workflow.error.handling.and.retry.logic.for.azure.openai.api.failures","name":"error handling and retry logic for azure openai api failures","description":"Provides automatic retry logic with exponential backoff for transient Azure OpenAI API failures (rate limiting, temporary outages, quota exhaustion), configurable retry budgets, and detailed error classification to distinguish between retryable errors (429, 503) and permanent failures (401, 404). The plugin integrates with Genkit's error handling framework to propagate errors to application code while managing retry state transparently.","intents":["I want to automatically retry requests that fail due to rate limiting or temporary Azure outages without application code changes","I need to implement exponential backoff to avoid overwhelming Azure OpenAI during quota exhaustion","I want to distinguish between transient errors (retry) and permanent errors (fail fast) to optimize error handling"],"best_for":["High-volume applications prone to rate limiting or quota exhaustion","Batch processing pipelines requiring resilience to transient failures","Teams building production LLM applications with strict reliability requirements"],"limitations":["Retry logic increases latency for failed requests (exponential backoff can add 10-60 seconds for multiple retries)","No built-in circuit breaker — if Azure OpenAI is down, all requests will retry until timeout, potentially wasting quota","Retry budgets are per-request; no global rate limiting across concurrent requests to prevent thundering herd during recovery","Streaming requests cannot be retried mid-stream — only full request retries are supported"],"requires":["Genkit 0.3.0+ with error handling support","Azure OpenAI deployment","Optional: custom retry configuration (max retries, backoff multiplier, timeout)"],"input_types":["API request to Azure OpenAI","retry configuration (max retries, backoff strategy, timeout)"],"output_types":["successful response (after retries if applicable)","error with retry metadata (attempts, total latency)","permanent failure if retries exhausted"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-genkitx-azure-openai__cap_2","uri":"capability://data.processing.analysis.structured.output.generation.with.azure.openai.json.schema.mode","name":"structured output generation with azure openai json schema mode","description":"Exposes Azure OpenAI's response_format parameter with json_schema support through Genkit's model interface, enabling deterministic JSON output generation with schema validation. The plugin translates Genkit's structured output requests into Azure OpenAI's JSON schema format, validates responses against the schema, and returns parsed JSON objects with type safety guarantees, eliminating regex-based JSON extraction and hallucination-prone prompt engineering.","intents":["I want to extract structured data (entities, relationships, classifications) from unstructured text with guaranteed JSON output","I need to generate API responses or database records with a specific schema without manual JSON parsing","I want to reduce token usage by avoiding verbose prompt instructions for JSON formatting"],"best_for":["Data extraction pipelines requiring reliable structured output from LLMs","API backends using LLMs to generate typed responses (e.g., classification, entity extraction)","Teams building LLM-powered ETL workflows with strict schema requirements"],"limitations":["Schema complexity is limited by Azure OpenAI's JSON schema validator — deeply nested or recursive schemas may be rejected","Response_format=json_schema is only available on gpt-4-turbo and later models; gpt-3.5-turbo falls back to text generation without schema enforcement","No schema inference from TypeScript types — schemas must be manually defined as JSON Schema objects","Validation errors (schema mismatch) result in model retries, increasing latency and token consumption; no built-in retry budget management"],"requires":["Azure OpenAI deployment with gpt-4-turbo or gpt-4 model","JSON Schema definition conforming to Azure OpenAI's subset of JSON Schema Draft 2020-12","Genkit version with structured output support (0.3.0+)"],"input_types":["text prompt","JSON Schema object defining output structure","system prompt (optional)"],"output_types":["parsed JSON object matching the provided schema","validation error details if response violates schema","token usage including schema overhead"],"categories":["data-processing-analysis","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-genkitx-azure-openai__cap_3","uri":"capability://data.processing.analysis.token.counting.and.cost.estimation.for.azure.openai.models","name":"token counting and cost estimation for azure openai models","description":"Provides token counting utilities that estimate prompt and completion token usage for Azure OpenAI models before or after API calls, enabling cost forecasting and budget management. The plugin uses Azure OpenAI's tokenizer (cl100k_base for GPT-4/3.5) to count tokens in prompts and cached responses, and maps token counts to Azure's per-model pricing to calculate estimated costs, supporting both real-time estimation and batch cost analysis.","intents":["I want to estimate the cost of an LLM request before sending it to avoid budget overruns","I need to analyze token usage across my application to identify expensive operations and optimize prompts","I want to implement token-aware request batching or filtering to stay within Azure OpenAI quota limits"],"best_for":["Cost-conscious teams running high-volume LLM applications on Azure","Developers building LLM-powered SaaS products with per-user cost tracking","Organizations implementing token budgets or rate limiting policies"],"limitations":["Token counting is approximate for structured outputs (JSON schema mode) — actual token usage may vary by 5-10% due to schema encoding overhead","Pricing data must be manually maintained or fetched from Azure's pricing API; plugin does not auto-update when Azure changes model pricing","No support for counting tokens in vision/image inputs — only text tokens are counted","Cost estimation excludes Azure-specific charges (e.g., provisioned throughput, commitment discounts) — only reflects per-token pricing"],"requires":["Azure OpenAI model deployment","Genkit 0.3.0 or later","Optional: Azure pricing data (can be hardcoded or fetched externally)"],"input_types":["text prompt string","conversation history (messages array)","model name and deployment info"],"output_types":["token count (prompt_tokens, completion_tokens)","estimated cost in USD","cost breakdown by token type"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-genkitx-azure-openai__cap_4","uri":"capability://tool.use.integration.function.calling.and.tool.use.with.azure.openai","name":"function calling and tool use with azure openai","description":"Exposes Azure OpenAI's function calling API through Genkit's tool-use abstraction, allowing models to request execution of predefined functions (tools) by returning structured function calls in responses. The plugin translates Genkit's tool definitions into Azure OpenAI's function schema format, parses function call responses, and manages the request-response loop for multi-turn tool interactions, enabling agentic workflows where models decide which tools to invoke based on user requests.","intents":["I want to build an agent that can call APIs or execute code based on user requests without hardcoding decision logic","I need to enable a model to search databases, fetch real-time data, or perform calculations and incorporate results into responses","I want to implement multi-step workflows where a model decides which tools to call in sequence to accomplish a task"],"best_for":["Teams building LLM agents that interact with external APIs or databases","Developers implementing retrieval-augmented generation (RAG) with dynamic tool selection","Organizations automating multi-step workflows (e.g., booking, data processing) with LLM decision-making"],"limitations":["Tool definitions must be manually specified as JSON schemas; no automatic schema generation from TypeScript function signatures","No built-in error handling or validation for tool execution — if a tool fails, the model may hallucinate results or retry indefinitely","Parallel tool calling is supported by Azure OpenAI but requires explicit handling in application code to manage concurrent execution","Tool context window is limited by the model's max tokens; complex tool definitions or many tools may exceed context, forcing tool pruning"],"requires":["Azure OpenAI deployment with gpt-4-turbo or gpt-4 model (gpt-3.5-turbo has limited tool calling support)","Genkit 0.3.0+ with tool-use support","Tool definitions as JSON Schema objects","Application code to implement tool execution handlers"],"input_types":["user prompt or conversation history","tool definitions (JSON Schema format)","tool execution context (e.g., database connection, API credentials)"],"output_types":["function call requests (tool_calls array with function name and arguments)","model response after tool execution","tool execution results (success/error)"],"categories":["tool-use-integration","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-genkitx-azure-openai__cap_5","uri":"capability://data.processing.analysis.embedding.generation.with.azure.openai.text.embedding.models","name":"embedding generation with azure openai text-embedding models","description":"Provides a Genkit embedder plugin that wraps Azure OpenAI's text-embedding-3-small and text-embedding-3-large models, converting text inputs into high-dimensional vector embeddings suitable for semantic search, similarity matching, and RAG applications. The plugin handles batch embedding requests, manages embedding dimensions (1536 for large, 512 for small), and integrates with Genkit's vector storage abstraction for seamless RAG pipeline construction.","intents":["I want to generate embeddings for documents and queries to enable semantic search in my application","I need to build a RAG system that retrieves relevant documents based on semantic similarity to user queries","I want to cluster or classify text based on semantic similarity without fine-tuning a custom model"],"best_for":["Teams building semantic search or RAG systems on Azure infrastructure","Developers implementing document similarity or clustering features in LLM applications","Organizations migrating from OpenAI embeddings to Azure-hosted alternatives for compliance or cost reasons"],"limitations":["Batch embedding requests are limited to 2048 inputs per API call; larger batches require manual chunking and multiple requests","Embedding dimensions are fixed per model (1536 for large, 512 for small); no dimension reduction or custom projection","No built-in caching of embeddings — repeated embedding of the same text incurs redundant API calls and costs","Embedding models are updated periodically by Azure; older embeddings may become incompatible with newer model versions, requiring re-embedding of entire corpora"],"requires":["Azure OpenAI deployment with text-embedding-3-small or text-embedding-3-large model","Genkit 0.3.0+ with embedder support","Text input (string or array of strings)","Optional: vector storage backend (e.g., Pinecone, Weaviate, Chroma) for storing and querying embeddings"],"input_types":["text string","array of text strings (batch embedding)","optional: encoding format (base64, float)"],"output_types":["embedding vector (array of floats, 512 or 1536 dimensions)","batch embeddings (array of vectors)","token usage metadata"],"categories":["data-processing-analysis","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-genkitx-azure-openai__cap_6","uri":"capability://text.generation.language.streaming.response.generation.with.azure.openai","name":"streaming response generation with azure openai","description":"Enables streaming of model responses from Azure OpenAI using Server-Sent Events (SSE), allowing real-time token-by-token delivery to clients instead of waiting for full completion. The plugin implements Genkit's streaming abstraction, handling Azure OpenAI's stream format (delta objects with token increments), managing stream lifecycle (start, chunk, end), and providing error handling for interrupted streams, enabling responsive chat interfaces and real-time content generation.","intents":["I want to build a chat interface that displays model responses in real-time as tokens arrive","I need to reduce perceived latency by streaming tokens to the client while the model is still generating","I want to implement cancellation of long-running requests by closing the stream early"],"best_for":["Web applications and chat interfaces requiring real-time user feedback","Streaming content generation pipelines (e.g., live transcription, real-time summarization)","Teams building interactive LLM applications where latency perception is critical"],"limitations":["Streaming adds complexity to error handling — errors mid-stream may result in partial responses sent to clients","Token counting is approximate during streaming; final token counts are only available after stream completion","Structured output (JSON schema mode) is not supported with streaming — responses must be buffered and validated post-completion","Stream interruption (client disconnect) may leave Azure OpenAI processing requests that are no longer needed, wasting quota"],"requires":["Azure OpenAI deployment with streaming-capable model (gpt-4, gpt-4-turbo, gpt-3.5-turbo)","Genkit 0.3.0+ with streaming support","HTTP client supporting Server-Sent Events (SSE) or WebSocket for client-side streaming","Application code to handle stream chunks and errors"],"input_types":["text prompt","conversation history","system prompt","streaming configuration (chunk size, timeout)"],"output_types":["stream of text chunks (delta tokens)","finish_reason indicator at stream end","token usage metadata (available after stream completion)"],"categories":["text-generation-language","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-genkitx-azure-openai__cap_7","uri":"capability://automation.workflow.caching.and.prompt.optimization.with.azure.openai","name":"caching and prompt optimization with azure openai","description":"Integrates with Azure OpenAI's prompt caching feature (available on gpt-4-turbo and later) to cache frequently-used system prompts, context, or document prefixes, reducing token consumption and latency for repeated requests with similar context. The plugin automatically identifies cacheable content, manages cache keys, and tracks cache hit rates, enabling cost reduction for RAG systems, multi-turn conversations, and batch processing workflows where context is reused across requests.","intents":["I want to reduce token costs for RAG systems by caching large document contexts across multiple queries","I need to optimize multi-turn conversations by caching system prompts and conversation history","I want to batch-process similar requests (e.g., analyzing multiple documents with the same analysis prompt) while reusing cached context"],"best_for":["High-volume RAG applications with repeated document context","Multi-turn conversational systems with stable system prompts","Batch processing pipelines where the same context is applied to multiple inputs"],"limitations":["Prompt caching requires minimum cache size (1024 tokens); small prompts may not benefit from caching overhead","Cache invalidation is manual — if cached content changes, applications must explicitly clear cache or use new cache keys","Cache hit rates depend on request patterns; sparse or highly variable requests may not achieve meaningful cost savings","Cached tokens are charged at 90% of input token rate; savings are modest (~10%) compared to full re-processing, making caching only cost-effective for large, frequently-reused contexts"],"requires":["Azure OpenAI deployment with gpt-4-turbo or gpt-4 model","Genkit 0.3.0+ with caching support","Cacheable content (system prompts, document contexts, conversation history)","Application code to manage cache keys and invalidation"],"input_types":["system prompt or context to cache","cache key (string identifier)","cache TTL (time-to-live, optional)"],"output_types":["cache hit/miss indicator","token usage breakdown (cached vs new tokens)","cost savings estimate"],"categories":["automation-workflow","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-genkitx-azure-openai__cap_8","uri":"capability://image.visual.vision.image.understanding.with.azure.openai.gpt.4v","name":"vision/image understanding with azure openai gpt-4v","description":"Enables image analysis and visual understanding through Azure OpenAI's GPT-4V model, allowing models to process images (JPEG, PNG, GIF, WebP) and answer questions about visual content, extract text (OCR), identify objects, or describe scenes. The plugin handles image encoding (base64 or URL), manages image size constraints (max 20MB), and integrates image inputs with text prompts in a unified message format, enabling multimodal applications like document analysis, visual search, and accessibility features.","intents":["I want to extract text from images (OCR) or analyze document structure without a separate OCR service","I need to build a visual search or image understanding feature that answers questions about image content","I want to implement accessibility features that describe images or extract information from screenshots"],"best_for":["Document processing pipelines requiring OCR and semantic understanding","Visual search or image analysis applications","Accessibility tools that need to describe or extract information from images"],"limitations":["Image processing adds significant latency (2-5 seconds per image) compared to text-only requests","Image inputs are not supported with structured output (JSON schema mode) — responses are text-only","Image size is limited to 20MB; large images or batches of images require preprocessing and resizing","Vision capabilities are limited to GPT-4V; other Azure OpenAI models (gpt-3.5-turbo, gpt-4-turbo) do not support image inputs","OCR accuracy depends on image quality; low-resolution or rotated images may produce poor results"],"requires":["Azure OpenAI deployment with gpt-4-vision model","Genkit 0.3.0+ with multimodal support","Image input (base64-encoded or URL)","Optional: image preprocessing (resizing, format conversion) for large or non-standard images"],"input_types":["image (JPEG, PNG, GIF, WebP, base64 or URL)","text prompt or question about the image","optional: detail level (low, high) for vision processing"],"output_types":["text description or analysis of image content","extracted text (OCR results)","structured data (if combined with function calling)","token usage including image processing overhead"],"categories":["image-visual","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-genkitx-azure-openai__cap_9","uri":"capability://tool.use.integration.authentication.and.credential.management.for.azure.openai","name":"authentication and credential management for azure openai","description":"Provides flexible authentication mechanisms for Azure OpenAI, supporting API key-based authentication, Azure Managed Identity (for Azure-hosted applications), and Azure CLI credential chains. The plugin abstracts credential resolution, allowing applications to use environment variables, configuration files, or runtime credential providers without hardcoding secrets, and integrates with Azure's credential caching to minimize authentication overhead.","intents":["I want to authenticate to Azure OpenAI without hardcoding API keys in my application code","I need to use Azure Managed Identity to authenticate from Azure VMs or App Service without managing credentials","I want to support multiple authentication methods (API key, managed identity, CLI) with automatic fallback"],"best_for":["Azure-hosted applications (VMs, App Service, Functions) using Managed Identity","Development teams using Azure CLI for local authentication","Organizations with strict credential management policies requiring secret rotation"],"limitations":["Managed Identity authentication requires the application to run on Azure infrastructure; local development requires API keys or Azure CLI","Credential caching is transparent but may cause stale credentials if tokens expire during long-running processes","No built-in credential rotation or refresh logic — applications must rely on Azure's token refresh mechanisms","API key authentication requires secure storage (e.g., Azure Key Vault); the plugin does not enforce secret management best practices"],"requires":["Azure OpenAI API key OR Azure Managed Identity OR Azure CLI credentials","Genkit 0.3.0+","Environment variable or configuration file with credentials","Optional: Azure Key Vault for secure credential storage"],"input_types":["API key (string)","Azure Managed Identity (implicit, no input required)","Azure CLI credentials (implicit, requires Azure CLI installation)"],"output_types":["authenticated HTTP client for Azure OpenAI API","credential metadata (expiration, scope)"],"categories":["tool-use-integration","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":36,"verified":false,"data_access_risk":"high","permissions":["Node.js 18+","Genkit framework 0.3.0 or later","Azure OpenAI resource with at least one model deployment (e.g., gpt-4, gpt-4-turbo, o3)","Azure API key or managed identity credentials with permissions to call Azure OpenAI endpoints","npm or yarn package manager","Multiple Azure OpenAI resources provisioned across regions with model deployments","Separate API keys or managed identity with permissions to all target deployments","Genkit configuration file or code-based plugin initialization with deployment mappings","Network connectivity to all target Azure regions","Genkit 0.3.0+ with error handling support"],"failure_modes":["Abstracts away Azure-specific features (e.g., content filtering policies, deployment-level rate limiting) that may require direct API calls for fine-grained control","Requires Azure OpenAI resource provisioning and deployment configuration outside the plugin — no infrastructure-as-code generation","No built-in retry logic or circuit breaker for Azure quota exhaustion scenarios — relies on Genkit's base error handling","Limited to models available in Azure OpenAI's regional deployments; newer models may lag behind OpenAI's public API availability","No built-in load balancing or health checking across deployments — routing is static per model name","Requires manual configuration of all deployment endpoints and API keys; no auto-discovery of Azure OpenAI resources","No cross-deployment fallback logic — if a deployment is unavailable, requests fail rather than automatically retrying another region","Latency overhead from endpoint resolution is negligible (~1-2ms) but adds complexity to error handling if deployments diverge in capability","Retry logic increases latency for failed requests (exponential backoff can add 10-60 seconds for multiple retries)","No built-in circuit breaker — if Azure OpenAI is down, all requests will retry until timeout, potentially wasting quota","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.2549861188471943,"quality":0.32,"ecosystem":0.6000000000000001,"match_graph":0.25,"freshness":0.6,"weights":{"adoption":0.3,"quality":0.2,"ecosystem":0.15,"match_graph":0.23,"freshness":0.12}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:23.902Z","last_scraped_at":"2026-05-03T14:04:47.472Z","last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":17735,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=genkitx-azure-openai","compare_url":"https://unfragile.ai/compare?artifact=genkitx-azure-openai"}},"signature":"PxnDmfQHNZ1Md3PzbVAKlYJlCZ4yOp/idb1O/wvW5gIS+t4HC8qTsy4z86891FM5cWsvum3DB1Rf34ITMJK4Bg==","signedAt":"2026-06-21T14:28:03.954Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/genkitx-azure-openai","artifact":"https://unfragile.ai/genkitx-azure-openai","verify":"https://unfragile.ai/api/v1/verify?slug=genkitx-azure-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"}}