{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"pypi_pypi-groq","slug":"pypi-groq","name":"groq","type":"api","url":"https://pypi.org/project/groq/","page_url":"https://unfragile.ai/pypi-groq","categories":["llm-apis"],"tags":[],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"pypi_pypi-groq__cap_0","uri":"capability://text.generation.language.synchronous.and.asynchronous.chat.completion.streaming.with.unified.interface","name":"synchronous and asynchronous chat completion streaming with unified interface","description":"Provides dual-mode (Groq sync, AsyncGroq async) client classes that expose identical interfaces for chat completions with native streaming support via httpx. Both clients handle authentication, retries, timeouts, and error handling uniformly, with optional aiohttp backend for improved async concurrency. Streaming responses are consumed as iterators, enabling real-time token-by-token processing without buffering entire responses.","intents":["I need to call Groq's chat API from Python with automatic retry and timeout handling","I want to stream chat completions token-by-token for real-time UI updates","I need both sync and async clients with identical method signatures for code reuse","I want to switch between httpx and aiohttp backends without changing application code"],"best_for":["Python developers building LLM applications requiring low-latency inference","Teams building async-first services with high concurrency requirements","Developers migrating from other LLM SDKs (OpenAI, Anthropic) seeking API parity"],"limitations":["Streaming responses consume memory proportional to token generation rate; no built-in backpressure handling for slow consumers","Synchronous client blocks event loop in async contexts; requires explicit thread pool for concurrent sync calls","aiohttp backend requires separate installation and configuration; httpx is default but may have different timeout semantics"],"requires":["Python 3.8+","GROQ_API_KEY environment variable or explicit API key parameter","httpx library (included as core dependency)","aiohttp library (optional, for improved async performance)"],"input_types":["message list (role/content pairs)","model identifier string","optional parameters: temperature, max_tokens, top_p, stop sequences"],"output_types":["ChatCompletion object (sync) or async iterator of ChatCompletionChunk objects (streaming)","Pydantic model with to_json() and to_dict() serialization methods"],"categories":["text-generation-language","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-groq__cap_1","uri":"capability://data.processing.analysis.type.safe.request.response.validation.with.pydantic.models.and.typeddict.parameters","name":"type-safe request/response validation with pydantic models and typeddict parameters","description":"All request parameters are defined as TypedDict structures and response objects as Pydantic models, providing compile-time type hints and runtime validation. Request payloads are validated before transmission, and responses are automatically deserialized and validated against schemas, catching malformed API responses early. Helper methods like to_json() and to_dict() enable flexible serialization for downstream processing.","intents":["I want IDE autocomplete for all Groq API parameters without consulting documentation","I need runtime validation to catch invalid parameters before sending requests","I want to serialize API responses to JSON/dict for logging, caching, or database storage","I need type hints to work with mypy/pyright for static analysis in CI/CD pipelines"],"best_for":["Teams using strict type checking (mypy, pyright) in production codebases","Developers building frameworks or libraries that wrap Groq SDK","Projects requiring audit trails of API requests/responses with guaranteed schema compliance"],"limitations":["Pydantic validation adds ~5-10ms overhead per request for complex nested parameters","TypedDict is Python 3.8+ only; no support for Python 3.7 or earlier","Custom validation rules beyond Pydantic's built-in validators require subclassing models"],"requires":["Python 3.8+","pydantic library (included as core dependency)","typing_extensions for TypedDict support on Python 3.8"],"input_types":["Python dicts matching TypedDict structure","Keyword arguments to client methods"],"output_types":["Pydantic BaseModel instances","JSON strings (via to_json())","Python dicts (via to_dict())"],"categories":["data-processing-analysis","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-groq__cap_10","uri":"capability://text.generation.language.streaming.response.consumption.with.iterator.pattern","name":"streaming response consumption with iterator pattern","description":"Streaming responses (chat completions, audio) are returned as Python iterators that yield chunks as they arrive from the server. Enables real-time processing without buffering entire responses. Iterators support context managers for automatic cleanup. Chunks are Pydantic models with delta fields for incremental updates.","intents":["I want to display chat responses token-by-token as they arrive","I need to process audio chunks in real-time without waiting for completion","I want to implement cancellation or early stopping during streaming","I need to handle streaming errors gracefully without losing partial results"],"best_for":["Real-time chat interfaces (web, mobile, CLI)","Streaming audio processing pipelines","Applications with strict latency requirements"],"limitations":["Iterators are consumed sequentially; no random access to chunks","Early termination (break from loop) may leave connection open; context managers recommended","Chunk structure varies by endpoint; no unified chunk format across resources","Error handling during streaming requires try/except around iteration"],"requires":["Python 3.8+","Streaming parameter enabled in request (e.g., stream=True)","GROQ_API_KEY environment variable or explicit API key"],"input_types":["stream=True parameter in API call"],"output_types":["Iterator yielding chunk objects (Pydantic models)","Chunk fields vary: delta (for chat), audio (for speech synthesis)"],"categories":["text-generation-language","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-groq__cap_11","uri":"capability://safety.moderation.environment.based.api.key.configuration.with.fallback.to.explicit.parameters","name":"environment-based api key configuration with fallback to explicit parameters","description":"SDK automatically reads GROQ_API_KEY from environment variables during client initialization. Supports .env file loading via python-dotenv (optional). Explicit API key parameter overrides environment variable. Enables secure credential management without hardcoding secrets in source code.","intents":["I want to configure API keys via environment variables for production deployments","I need to use .env files for local development without committing secrets","I want to override API keys programmatically for multi-tenant applications","I need to support different API keys for different environments (dev, staging, prod)"],"best_for":["Production applications requiring secure credential management","Teams using environment-based configuration (Docker, Kubernetes, 12-factor apps)","Multi-tenant applications with per-user API keys"],"limitations":["Environment variable lookup is one-time during client initialization; changes require client recreation","No built-in key rotation; clients must be recreated to use new keys",".env file loading requires optional python-dotenv dependency","No key validation; invalid keys are only detected on first API call"],"requires":["Python 3.8+","GROQ_API_KEY environment variable OR explicit api_key parameter","python-dotenv library (optional, for .env file support)"],"input_types":["environment variable GROQ_API_KEY","api_key parameter to Groq() or AsyncGroq() constructor",".env file with GROQ_API_KEY=... (optional)"],"output_types":["Authenticated client instance ready for API calls"],"categories":["safety-moderation","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-groq__cap_12","uri":"capability://safety.moderation.error.handling.with.typed.exception.hierarchy.and.api.error.details","name":"error handling with typed exception hierarchy and api error details","description":"SDK defines a typed exception hierarchy (APIError, APIConnectionError, APITimeoutError, RateLimitError, etc.) that maps to specific failure modes. Exceptions include response status, error message, and request details for debugging. Enables granular error handling based on failure type (e.g., retry on RateLimitError, fail fast on validation errors).","intents":["I want to distinguish between rate limit errors and other API failures","I need to extract error details for logging and monitoring","I want to implement different retry strategies based on error type","I need to provide meaningful error messages to end users"],"best_for":["Production applications requiring robust error handling","Monitoring and observability systems tracking API failures","Applications with custom retry logic based on error types"],"limitations":["Exception hierarchy is fixed; no custom exception types for application-specific errors","Error details depend on API response; some errors may have minimal information","No built-in error recovery strategies; clients must implement custom logic"],"requires":["Python 3.8+","Try/except blocks to catch exceptions"],"input_types":["API response with error status or connection failure"],"output_types":["Typed exception object with status_code, message, headers, body fields"],"categories":["safety-moderation","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-groq__cap_2","uri":"capability://automation.workflow.automatic.retry.and.timeout.management.with.exponential.backoff","name":"automatic retry and timeout management with exponential backoff","description":"Both Groq and AsyncGroq clients implement built-in retry logic with exponential backoff for transient failures (5xx errors, connection timeouts). Timeout values are configurable per-request and globally, with sensible defaults. Retries respect HTTP 429 (rate limit) headers and implement jitter to prevent thundering herd problems in distributed systems.","intents":["I want API calls to automatically retry on transient failures without manual try/except blocks","I need to configure different timeout values for different endpoints (e.g., longer for batch operations)","I want to respect rate limit headers from Groq's API to avoid cascading failures","I need predictable retry behavior with jitter for distributed systems"],"best_for":["Production services requiring high availability and resilience","Applications with variable network conditions (mobile, edge computing)","Teams building multi-tenant platforms where cascading failures are costly"],"limitations":["Retry logic only applies to idempotent operations; non-idempotent requests (e.g., file uploads) may fail if retried","Exponential backoff can add 30+ seconds of latency for max retries; configurable but not eliminable","Rate limit handling is reactive (respects 429 headers) not proactive; no token bucket implementation"],"requires":["Python 3.8+","httpx with timeout support (included)","No external retry libraries needed (built-in implementation)"],"input_types":["timeout parameter (float, seconds)","max_retries parameter (int, default 2)","HTTP response with 5xx status or timeout exception"],"output_types":["Successful response after retry","APIError exception if all retries exhausted"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-groq__cap_3","uri":"capability://data.processing.analysis.audio.transcription.with.file.upload.and.format.support","name":"audio transcription with file upload and format support","description":"The audio.transcriptions resource accepts audio files (WAV, MP3, FLAC, OGG) via multipart form upload and returns transcribed text with optional timestamps. Files are streamed to Groq's API without loading entirely into memory, supporting files larger than available RAM. Language detection is automatic or can be specified explicitly.","intents":["I want to transcribe audio files from disk or memory buffers to text","I need to transcribe large audio files without loading them entirely into RAM","I want to specify the language for transcription to improve accuracy","I need to extract timestamps for each transcribed segment"],"best_for":["Voice-to-text applications (note-taking, meeting transcription)","Accessibility features requiring audio-to-text conversion","Developers building voice-enabled chatbots or voice assistants"],"limitations":["File size limits enforced by Groq API (typically 25MB); larger files must be split client-side","Streaming upload means no progress callback during transmission; entire file must be sent before response","Language detection is automatic but may fail for code-mixed or low-resource languages"],"requires":["Python 3.8+","Audio file in WAV, MP3, FLAC, or OGG format","GROQ_API_KEY environment variable or explicit API key","File handle or bytes buffer with audio data"],"input_types":["file path (string)","file-like object (BinaryIO)","bytes buffer","optional language code (ISO 639-1)"],"output_types":["Transcription object with text field","Optional: timestamp segments with start/end times"],"categories":["data-processing-analysis","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-groq__cap_4","uri":"capability://data.processing.analysis.audio.translation.with.cross.language.support","name":"audio translation with cross-language support","description":"The audio.translations resource accepts audio files in any supported language and translates the transcribed content to English (or specified target language). Uses the same multipart upload mechanism as transcription but adds language pair routing. Translation happens server-side after transcription, so latency includes both speech-to-text and translation steps.","intents":["I want to transcribe and translate audio from non-English languages to English","I need to process multilingual audio files in a single API call","I want to support international users without requiring language-specific models"],"best_for":["Global applications serving multilingual user bases","Accessibility features for non-English speakers","Content localization pipelines requiring audio translation"],"limitations":["Translation target language is fixed (typically English); no support for arbitrary language pairs","Latency is higher than transcription-only because translation happens server-side","Translation quality depends on source language; low-resource languages may have poor translations"],"requires":["Python 3.8+","Audio file in supported format (WAV, MP3, FLAC, OGG)","GROQ_API_KEY environment variable or explicit API key"],"input_types":["file path (string)","file-like object (BinaryIO)","bytes buffer","optional source language code"],"output_types":["Translation object with text field (translated to English)","Optional: original language detection"],"categories":["data-processing-analysis","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-groq__cap_5","uri":"capability://text.generation.language.text.to.speech.synthesis.with.audio.format.selection","name":"text-to-speech synthesis with audio format selection","description":"The audio.speech resource converts text input to audio using Groq's speech synthesis models. Returns binary audio data in specified format (MP3, WAV, OGG, FLAC). Voice selection and speaking rate are configurable. Response is a binary stream that can be written directly to file or piped to audio playback systems.","intents":["I want to generate audio from text for voice-enabled applications","I need to select different voices or speaking rates for different use cases","I want to save synthesized audio to disk or stream it to a user","I need to generate audio in specific formats compatible with my playback system"],"best_for":["Voice assistant applications and chatbots","Accessibility features for text-to-speech","Content creators needing audio narration generation"],"limitations":["Voice selection is limited to Groq's available voices; no custom voice training","Speaking rate adjustments are limited to predefined ranges; fine-grained control not available","Audio quality depends on model; may not match professional voice actors"],"requires":["Python 3.8+","Text input (string, typically 1-4096 characters)","GROQ_API_KEY environment variable or explicit API key"],"input_types":["text string","voice identifier (string)","speaking rate (float, e.g., 1.0 for normal speed)","output format (mp3, wav, ogg, flac)"],"output_types":["Binary audio data (bytes)","Can be written to file or streamed to audio player"],"categories":["text-generation-language","image-visual"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-groq__cap_6","uri":"capability://automation.workflow.batch.operation.submission.retrieval.and.cancellation","name":"batch operation submission, retrieval, and cancellation","description":"The batches resource enables asynchronous processing of multiple requests in a single batch job. Supports create() to submit batch files (JSONL format), retrieve() to poll job status, and cancel() to stop in-progress jobs. Batch results are stored server-side and retrieved via file IDs. Useful for non-time-critical bulk processing (e.g., embedding large datasets, batch inference).","intents":["I want to process thousands of API requests asynchronously without rate limiting","I need to embed a large dataset of documents without hitting rate limits","I want to run batch inference jobs that complete overnight and retrieve results later","I need to cancel long-running batch jobs if requirements change"],"best_for":["Data processing pipelines requiring bulk API calls","ML teams embedding large document collections","Applications with non-time-critical batch workloads"],"limitations":["Batch processing introduces latency (minutes to hours); not suitable for real-time applications","JSONL format requires client-side serialization; no automatic format conversion","Batch results are stored server-side for limited time; clients must retrieve before expiration","No progress callbacks during batch execution; polling required to check status"],"requires":["Python 3.8+","JSONL file with batch requests (one JSON object per line)","GROQ_API_KEY environment variable or explicit API key"],"input_types":["JSONL file (text or bytes)","Batch ID (string, for retrieve/cancel operations)","Request objects serialized to JSON"],"output_types":["Batch object with status (queued, processing, completed, failed)","File ID for retrieving results","Error details if batch fails"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-groq__cap_7","uri":"capability://automation.workflow.file.upload.and.management.with.lifecycle.operations","name":"file upload and management with lifecycle operations","description":"The files resource provides create() to upload files, list() to enumerate uploaded files, retrieve() to fetch file metadata, and delete() to remove files. Files are stored server-side and referenced by ID in batch operations and other API calls. Supports binary file uploads via multipart form encoding.","intents":["I want to upload files to Groq for use in batch operations","I need to list all uploaded files and their metadata","I want to delete files after processing to manage storage","I need to retrieve file metadata without re-uploading"],"best_for":["Batch processing pipelines requiring file uploads","Applications managing multiple files for different users or projects","Data processing workflows with file-based inputs"],"limitations":["File storage is temporary; files expire after retention period (typically 30 days)","No built-in file versioning; overwriting requires delete + re-upload","File size limits enforced by API (typically 100MB); no chunked upload for larger files"],"requires":["Python 3.8+","File path or file-like object (BinaryIO)","GROQ_API_KEY environment variable or explicit API key"],"input_types":["file path (string)","file-like object (BinaryIO)","bytes buffer"],"output_types":["File object with ID, name, size, created_at","List of file objects (from list())","Boolean success (from delete())"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-groq__cap_8","uri":"capability://memory.knowledge.model.listing.and.metadata.retrieval","name":"model listing and metadata retrieval","description":"The models resource provides list() to enumerate available models and retrieve() to fetch metadata for a specific model. Returns model identifiers, context windows, pricing, and availability status. Useful for dynamic model selection based on capabilities or cost constraints.","intents":["I want to list all available Groq models at runtime","I need to check model capabilities (context window, pricing) before making API calls","I want to select models dynamically based on cost or latency requirements","I need to verify that a specific model is available before using it"],"best_for":["Applications with dynamic model selection logic","Cost-optimization systems choosing models based on pricing","Frameworks abstracting multiple LLM providers"],"limitations":["Model list is static per API version; changes require SDK update or API polling","Metadata is limited to public information; no real-time availability or quota status","No filtering or search capabilities; clients must iterate through full list"],"requires":["Python 3.8+","GROQ_API_KEY environment variable or explicit API key"],"input_types":["model ID (string, for retrieve())"],"output_types":["List of Model objects (from list())","Single Model object with metadata (from retrieve())","Model fields: id, name, context_window, pricing, availability"],"categories":["memory-knowledge","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-groq__cap_9","uri":"capability://data.processing.analysis.text.embedding.generation.with.vector.output","name":"text embedding generation with vector output","description":"The embeddings resource converts text input to dense vector embeddings using Groq's embedding models. Accepts single strings or lists of strings and returns vectors of fixed dimensionality. Useful for semantic search, clustering, and similarity comparisons. Vectors are returned as lists of floats.","intents":["I want to embed text for semantic search or similarity comparisons","I need to generate embeddings for a large corpus of documents","I want to cluster documents based on semantic similarity","I need to find similar items in a vector database"],"best_for":["Semantic search applications","RAG (Retrieval-Augmented Generation) systems","Document clustering and similarity analysis","Recommendation systems based on content similarity"],"limitations":["Embedding dimension is fixed by model; no dimensionality reduction built-in","Batch embedding has rate limits; large corpora require pagination","Embeddings are not normalized; cosine similarity requires manual normalization","No built-in vector storage; clients must manage vector databases separately"],"requires":["Python 3.8+","Text input (string or list of strings)","GROQ_API_KEY environment variable or explicit API key"],"input_types":["single text string","list of text strings (batch embedding)","embedding model identifier"],"output_types":["Embedding object with vector field (list of floats)","List of Embedding objects (for batch input)","Metadata: model used, token count"],"categories":["data-processing-analysis","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":27,"verified":false,"data_access_risk":"high","permissions":["Python 3.8+","GROQ_API_KEY environment variable or explicit API key parameter","httpx library (included as core dependency)","aiohttp library (optional, for improved async performance)","pydantic library (included as core dependency)","typing_extensions for TypedDict support on Python 3.8","Streaming parameter enabled in request (e.g., stream=True)","GROQ_API_KEY environment variable or explicit API key","GROQ_API_KEY environment variable OR explicit api_key parameter","python-dotenv library (optional, for .env file support)"],"failure_modes":["Streaming responses consume memory proportional to token generation rate; no built-in backpressure handling for slow consumers","Synchronous client blocks event loop in async contexts; requires explicit thread pool for concurrent sync calls","aiohttp backend requires separate installation and configuration; httpx is default but may have different timeout semantics","Pydantic validation adds ~5-10ms overhead per request for complex nested parameters","TypedDict is Python 3.8+ only; no support for Python 3.7 or earlier","Custom validation rules beyond Pydantic's built-in validators require subclassing models","Iterators are consumed sequentially; no random access to chunks","Early termination (break from loop) may leave connection open; context managers recommended","Chunk structure varies by endpoint; no unified chunk format across resources","Error handling during streaming requires try/except around iteration","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-groq","compare_url":"https://unfragile.ai/compare?artifact=pypi-groq"}},"signature":"sWVFxifOLVsuAy4D+qA2HQRVbvjmyKGLHTqtWZm7fK2vHH8L5BSA0t2G9pzG+LYOPAxEBnSQfMOwS/Y75BliBQ==","signedAt":"2026-06-21T05:51:19.097Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/pypi-groq","artifact":"https://unfragile.ai/pypi-groq","verify":"https://unfragile.ai/api/v1/verify?slug=pypi-groq","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"}}