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The library formats prompt strings into Azure's completion API format, handles response parsing, and returns completion choices with finish reasons, enabling use cases that don't fit the chat paradigm (code generation from raw prompts, text continuation, few-shot learning).","intents":["Generate code or text from a raw prompt without chat message structure","Use few-shot learning by embedding examples directly in the prompt","Work with fine-tuned completion models deployed on Azure"],"best_for":["Legacy applications using GPT-3 completion models","Few-shot learning scenarios with structured prompt engineering","Code generation tasks that don't require multi-turn conversation"],"limitations":["Completion API is deprecated in favor of chat completions — Azure may sunset this endpoint","No streaming support documented for completion requests","Prompt engineering is entirely manual — no built-in prompt templates or optimization","No support for logit_bias or other advanced sampling parameters"],"requires":["Node.js 12.0+","Authenticated Azure OpenAI client","Azure OpenAI deployment with completion model (GPT-3 or fine-tuned variant)"],"input_types":["prompt string","optional parameters (temperature, max_tokens, top_p, frequency_penalty, presence_penalty)"],"output_types":["completion object (choices array with text, finish_reason, logprobs)"],"categories":["text-generation-language","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-azure-openai__cap_3","uri":"capability://data.processing.analysis.embedding.generation.for.semantic.search.and.similarity","name":"embedding generation for semantic search and similarity","description":"Generates vector embeddings for text inputs using Azure OpenAI's embedding models (text-embedding-ada-002 or similar). 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The library routes requests to the correct Azure endpoint based on deployment configuration, enabling multi-model setups (e.g., different deployments for chat vs embeddings) and A/B testing across model versions without code changes.","intents":["Route requests to different Azure OpenAI deployments (e.g., gpt-4 vs gpt-3.5-turbo)","Switch model versions in production by changing deployment configuration","Run A/B tests comparing different model versions or deployments"],"best_for":["Teams managing multiple Azure OpenAI deployments across environments","Applications requiring model version flexibility without code redeploy","Experimentation workflows comparing model performance"],"limitations":["No built-in load balancing across deployments — caller must implement round-robin or weighted routing","Deployment names are opaque strings — no validation that deployment exists before API call","No fallback mechanism if primary deployment is unavailable","Configuration is static per client instance — dynamic deployment switching requires client recreation"],"requires":["Node.js 12.0+","Authenticated Azure OpenAI client","Pre-created Azure OpenAI deployments with known names"],"input_types":["deployment name string (e.g., 'gpt-4-deployment')","optional model version parameter"],"output_types":["request routed to correct Azure endpoint"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-azure-openai__cap_5","uri":"capability://safety.moderation.error.handling.and.azure.specific.exception.mapping","name":"error handling and azure-specific exception mapping","description":"Catches Azure OpenAI API errors (rate limits, authentication failures, model unavailability) and maps them to meaningful exception types or error objects, preserving Azure error codes and messages. The library distinguishes between transient errors (429, 500) and permanent failures (401, 404), enabling developers to implement appropriate retry logic or user-facing error messages without parsing raw HTTP status codes.","intents":["Distinguish between rate limiting (retry) and authentication errors (fail fast)","Extract Azure error details (error code, message) for logging and debugging","Implement conditional retry logic based on error type"],"best_for":["Production applications requiring robust error handling","Teams building resilience layers (retry policies, circuit breakers) on top of Azure OpenAI","Debugging and monitoring systems that need structured error information"],"limitations":["No built-in retry logic — developers must implement exponential backoff manually","Error classification is basic (transient vs permanent) — no granular error categories","Azure error messages are opaque strings — no structured error codes for programmatic handling","No timeout handling — long-running requests may hang without explicit timeout configuration"],"requires":["Node.js 12.0+","Authenticated Azure OpenAI client"],"input_types":["HTTP response from Azure OpenAI API"],"output_types":["exception object with error code, message, and retry-ability flag"],"categories":["safety-moderation","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-azure-openai__cap_6","uri":"capability://safety.moderation.request.parameter.validation.and.sanitization","name":"request parameter validation and sanitization","description":"Validates input parameters (temperature, max_tokens, top_p, etc.) against Azure OpenAI API constraints before sending requests, rejecting invalid values early with descriptive error messages. 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