{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"pypi_pypi-cohere","slug":"pypi-cohere","name":"cohere","type":"framework","url":"https://pypi.org/project/cohere/","page_url":"https://unfragile.ai/pypi-cohere","categories":["frameworks-sdks"],"tags":[],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"pypi_pypi-cohere__cap_0","uri":"capability://tool.use.integration.multi.platform.llm.client.abstraction.with.unified.api","name":"multi-platform llm client abstraction with unified api","description":"Provides a unified Python client interface (Client, AsyncClient, ClientV2, AsyncClientV2) that abstracts away platform-specific differences across Cohere's hosted API, AWS Bedrock, AWS SageMaker, Azure, GCP, and Oracle Cloud. Uses a layered architecture with BaseClientWrapper handling authentication token management and HTTP headers, while SyncClientWrapper and AsyncClientWrapper extend this for synchronous and asynchronous execution modes respectively. Developers write once and deploy across multiple cloud providers without changing application code.","intents":["I need to switch my LLM backend from Cohere's hosted API to AWS Bedrock without rewriting my application","I want to support multiple cloud providers for redundancy and cost optimization","I need both sync and async execution patterns for different deployment scenarios"],"best_for":["teams building multi-cloud AI applications","enterprises with existing AWS/Azure/GCP infrastructure","developers avoiding vendor lock-in"],"limitations":["API feature parity varies across platforms — some advanced features only available on Cohere hosted API","Platform-specific authentication setup required (AWS credentials, Azure tokens, etc.)","Latency varies significantly by platform and region"],"requires":["Python 3.9+","API key for Cohere or credentials for target cloud platform (AWS, Azure, GCP, Oracle)","Network access to selected platform endpoints"],"input_types":["text prompts","conversation messages","structured parameters"],"output_types":["text responses","streaming response objects","structured metadata"],"categories":["tool-use-integration","multi-cloud-abstraction"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-cohere__cap_1","uri":"capability://text.generation.language.streaming.chat.api.with.token.level.response.streaming","name":"streaming chat api with token-level response streaming","description":"Implements real-time chat response streaming via the chat_stream endpoint, allowing developers to consume LLM responses token-by-token as they're generated rather than waiting for complete responses. Uses HTTP streaming (chunked transfer encoding) to deliver partial responses, enabling low-latency UI updates and progressive text rendering. Supports both synchronous and asynchronous streaming patterns through dedicated stream methods that yield response chunks.","intents":["I want to display LLM responses in real-time as they're generated for better UX","I need to build a chatbot that shows typing-like behavior with token-level granularity","I want to reduce perceived latency by streaming partial results while the model is still computing"],"best_for":["web/mobile applications requiring real-time response rendering","chatbot interfaces with progressive text display","applications with strict latency requirements"],"limitations":["Streaming adds complexity to error handling — errors may occur mid-stream after partial content is consumed","Token-level streaming requires client-side buffering and rendering logic","Some platforms (e.g., SageMaker) may have limited streaming support"],"requires":["Python 3.9+","Cohere API key or platform credentials","Client-side code to handle streaming response iteration"],"input_types":["chat messages with conversation history","system prompts","optional parameters"],"output_types":["streaming response objects yielding text chunks","metadata about stream completion"],"categories":["text-generation-language","streaming"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-cohere__cap_10","uri":"capability://data.processing.analysis.batch.api.request.processing.with.optimized.throughput","name":"batch api request processing with optimized throughput","description":"Supports batch processing of multiple inputs in single API calls for endpoints like embed, classify, and rerank, reducing overhead and improving throughput compared to individual requests. Batch operations accept lists of inputs and return lists of outputs with consistent ordering, enabling efficient processing of large datasets. Batch sizes are limited per endpoint (typically 96 items) to balance throughput and latency, with automatic batching handled by the application.","intents":["I need to embed 10,000 documents efficiently without making 10,000 individual API calls","I want to classify a batch of customer support tickets in a single operation","I need to rerank multiple candidate lists efficiently for a search system"],"best_for":["batch processing pipelines for document indexing","bulk classification and embedding operations","cost-optimized data processing workflows"],"limitations":["Batch size limits apply (typically 96 items per request) — larger batches require manual chunking","Batch processing adds latency compared to streaming — not suitable for real-time applications","No built-in batching orchestration — applications must implement batch chunking logic"],"requires":["Python 3.9+","Cohere API key","Lists of inputs within batch size limits"],"input_types":["lists of texts for embedding/classification","lists of documents for reranking"],"output_types":["lists of embeddings/classifications/reranked results with consistent ordering"],"categories":["data-processing-analysis","batch-processing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-cohere__cap_11","uri":"capability://data.processing.analysis.response.metadata.and.usage.tracking","name":"response metadata and usage tracking","description":"Includes detailed metadata in API responses such as token usage (input/output tokens), model version, generation ID, and finish reason (complete, max_tokens, etc.). This metadata enables cost tracking, quota management, and debugging of model behavior. The SDK automatically includes this information in response objects, allowing applications to monitor API consumption without additional tracking logic.","intents":["I need to track token usage for billing and cost optimization","I want to understand why a generation stopped (max tokens, end-of-sequence, etc.)","I need to monitor API consumption metrics for quota management"],"best_for":["applications with strict cost budgets requiring usage tracking","systems implementing token-based rate limiting","monitoring and observability pipelines"],"limitations":["Token counts are approximate for billing — actual charges may vary slightly","Metadata structure varies between API versions (v1 vs v2)","No aggregated usage reporting — applications must implement their own analytics"],"requires":["Python 3.9+","Cohere API key"],"input_types":["API responses from any endpoint"],"output_types":["structured metadata objects with token counts, model info, finish reasons"],"categories":["data-processing-analysis","monitoring"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-cohere__cap_2","uri":"capability://data.processing.analysis.text.embedding.generation.with.multi.modal.support","name":"text embedding generation with multi-modal support","description":"Generates dense vector embeddings (typically 1024-4096 dimensions) for text and image inputs via the embed endpoint, converting unstructured content into fixed-size numerical representations suitable for semantic search, clustering, and similarity comparisons. Supports batch processing of multiple inputs in a single API call, with configurable embedding dimensions and input types. Returns embedding vectors alongside metadata about token usage and model version.","intents":["I need to convert documents into vectors for semantic search over a knowledge base","I want to find similar texts or images by comparing their embeddings","I need to build a RAG system that requires dense vector representations of documents"],"best_for":["developers building semantic search systems","teams implementing RAG (Retrieval-Augmented Generation) pipelines","applications requiring similarity-based document clustering"],"limitations":["Embedding dimensions are fixed per model — cannot customize output dimensionality","Batch size limits apply — typically 96 texts per request","Embeddings are model-specific — switching models invalidates existing vectors"],"requires":["Python 3.9+","Cohere API key","Text or image inputs (images require base64 encoding)"],"input_types":["text strings","base64-encoded images","lists of mixed text/image inputs"],"output_types":["numpy arrays or lists of float vectors","embedding metadata with token counts"],"categories":["data-processing-analysis","embeddings"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-cohere__cap_3","uri":"capability://search.retrieval.semantic.reranking.with.relevance.scoring","name":"semantic reranking with relevance scoring","description":"Reorders a list of documents or texts based on their relevance to a query using a specialized reranking model, producing relevance scores for each item. Takes a query and a list of candidate texts, then returns the same texts sorted by relevance with associated scores (typically 0-1 range). Useful for post-processing search results or ranking candidates from a larger corpus. Operates via the rerank endpoint with support for batch processing.","intents":["I have search results from BM25 or keyword search and want to rerank them by semantic relevance","I need to filter and sort a large list of candidates by how well they match a query","I want to improve RAG retrieval quality by reranking initial search results before passing to the LLM"],"best_for":["RAG systems improving retrieval quality","search applications combining keyword and semantic ranking","information retrieval pipelines with multi-stage ranking"],"limitations":["Reranking adds latency — typically 100-500ms for 100 documents","Requires pre-filtering to a reasonable candidate set (typically <100 items) for cost efficiency","Scores are relative within a batch — not comparable across separate rerank calls"],"requires":["Python 3.9+","Cohere API key","Query string and list of candidate documents"],"input_types":["query string","list of document/text strings"],"output_types":["reranked list with relevance scores","index mappings to original documents"],"categories":["search-retrieval","ranking"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-cohere__cap_4","uri":"capability://data.processing.analysis.text.classification.into.predefined.categories","name":"text classification into predefined categories","description":"Classifies input text into one or more predefined categories using a fine-tuned classification model via the classify endpoint. Accepts a list of texts and a list of category labels, returning predicted class labels and confidence scores for each input. Supports both single-label and multi-label classification scenarios. Uses the model's semantic understanding to match text to categories without requiring training data.","intents":["I need to automatically categorize customer support tickets into predefined buckets","I want to classify user-generated content by sentiment, topic, or intent","I need to route documents to different processing pipelines based on their category"],"best_for":["content moderation and categorization systems","customer support ticket routing","document classification pipelines"],"limitations":["Classification quality depends on category label clarity — vague labels produce poor results","Limited to predefined categories — cannot discover new categories from data","Batch size limits apply — typically 96 texts per request","No confidence threshold filtering — must implement client-side filtering if needed"],"requires":["Python 3.9+","Cohere API key","List of texts to classify and list of category labels"],"input_types":["text strings","category label strings"],"output_types":["predicted class labels","confidence scores per prediction"],"categories":["data-processing-analysis","classification"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-cohere__cap_5","uri":"capability://data.processing.analysis.token.level.text.processing.with.bidirectional.conversion","name":"token-level text processing with bidirectional conversion","description":"Provides tokenize and detokenize endpoints for converting between text and token representations using Cohere's tokenizer. The tokenize endpoint breaks text into tokens (subword units) and returns token IDs and counts, useful for understanding token consumption and managing context windows. The detokenize endpoint reverses this process, converting token IDs back into readable text. Both operations use the same tokenizer as the LLM models, ensuring consistency.","intents":["I need to count tokens in my prompts to stay within API rate limits and cost budgets","I want to understand how my text will be tokenized before sending it to the LLM","I need to debug token-level issues or reconstruct text from token sequences"],"best_for":["developers managing token budgets and API costs","applications with strict context window constraints","debugging and testing LLM input processing"],"limitations":["Tokenization is model-specific — different models may tokenize identically but future models could differ","Detokenization may not perfectly reconstruct original text due to whitespace normalization","Token counts are approximate for billing purposes — actual charges may vary slightly"],"requires":["Python 3.9+","Cohere API key"],"input_types":["text strings for tokenization","token ID sequences for detokenization"],"output_types":["token ID lists and token counts","reconstructed text strings"],"categories":["data-processing-analysis","text-processing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-cohere__cap_6","uri":"capability://tool.use.integration.synchronous.and.asynchronous.execution.with.dual.client.interfaces","name":"synchronous and asynchronous execution with dual client interfaces","description":"Provides parallel client implementations for both synchronous (Client, ClientV2) and asynchronous (AsyncClient, AsyncClientV2) execution patterns, allowing developers to choose the execution model that fits their application architecture. Synchronous clients use blocking HTTP calls suitable for scripts and simple applications, while asynchronous clients use async/await patterns with non-blocking I/O, enabling high-concurrency scenarios. Both client types share identical API method signatures, allowing easy switching between execution modes.","intents":["I need to make concurrent API calls from a web server without blocking request handlers","I want to use async/await patterns in my FastAPI or asyncio-based application","I need a simple blocking client for scripts or batch processing jobs"],"best_for":["web applications using async frameworks (FastAPI, Starlette, aiohttp)","high-concurrency services requiring non-blocking I/O","batch processing scripts and CLI tools"],"limitations":["Async clients require event loop setup and async context management","Mixing sync and async clients in the same application requires careful thread/event loop isolation","Async overhead adds ~5-10ms per request compared to sync for single-threaded workloads"],"requires":["Python 3.9+","Cohere API key","asyncio event loop for async clients (automatic in async frameworks)"],"input_types":["identical API parameters for both sync and async"],"output_types":["identical response objects for both sync and async"],"categories":["tool-use-integration","async-patterns"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-cohere__cap_7","uri":"capability://tool.use.integration.api.versioning.with.v1.and.v2.client.support","name":"api versioning with v1 and v2 client support","description":"Supports both Cohere API v1 and v2 through separate client implementations (Client/AsyncClient for v1, ClientV2/AsyncClientV2 for v2), allowing developers to use legacy v1 endpoints or adopt v2's enhanced features. Each API version has its own request/response models, endpoint signatures, and capabilities. Developers can instantiate either version based on their requirements, with v2 providing newer models and improved API design while v1 maintains backward compatibility.","intents":["I have existing code using Cohere v1 API and want to maintain compatibility","I want to gradually migrate from v1 to v2 API by updating endpoints incrementally","I need access to v2-only features like improved chat models or new endpoints"],"best_for":["teams maintaining legacy v1 integrations","gradual migration scenarios from v1 to v2","applications requiring v2-specific features"],"limitations":["v1 and v2 clients have different method signatures and response structures — not interchangeable","v1 API is in maintenance mode — new features only added to v2","Maintaining both versions in production adds testing and deployment complexity"],"requires":["Python 3.9+","Cohere API key with access to desired API version"],"input_types":["version-specific request models"],"output_types":["version-specific response models"],"categories":["tool-use-integration","api-versioning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-cohere__cap_8","uri":"capability://tool.use.integration.environment.based.authentication.with.token.management","name":"environment-based authentication with token management","description":"Implements flexible authentication through the BaseClientWrapper that supports both explicit API key passing and environment variable-based configuration (CO_API_KEY). The wrapper handles token lifecycle management, including header construction with Bearer token authentication and automatic token injection into all HTTP requests. Supports multiple authentication methods across different platforms (Cohere API key, AWS credentials, Azure tokens, etc.) with platform-specific credential handling.","intents":["I want to configure API credentials via environment variables for secure deployment","I need to pass API keys programmatically without exposing them in code","I want to support multiple authentication methods for different deployment environments"],"best_for":["production deployments using environment-based secrets management","containerized applications with external credential injection","multi-environment applications (dev/staging/prod with different credentials)"],"limitations":["Environment variable names are fixed (CO_API_KEY) — cannot customize for multiple credentials","No built-in credential rotation — requires manual token refresh or application restart","Platform-specific credentials (AWS, Azure) require separate setup beyond the SDK"],"requires":["Python 3.9+","CO_API_KEY environment variable OR explicit API key parameter","Proper environment variable configuration in deployment environment"],"input_types":["API key string or environment variable reference"],"output_types":["authenticated HTTP headers for all requests"],"categories":["tool-use-integration","authentication"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-cohere__cap_9","uri":"capability://tool.use.integration.structured.error.handling.with.platform.specific.exceptions","name":"structured error handling with platform-specific exceptions","description":"Implements comprehensive error handling that captures HTTP errors, authentication failures, rate limiting, and API-specific errors, returning structured exception objects with error codes, messages, and metadata. The error handling layer in the client wrapper catches HTTP exceptions and transforms them into SDK-specific exceptions, providing context about the failure (e.g., 401 for auth failures, 429 for rate limits, 500 for server errors). Supports graceful degradation and retry logic at the application level.","intents":["I need to distinguish between authentication errors, rate limits, and server errors to implement appropriate retry logic","I want to provide meaningful error messages to users when API calls fail","I need to monitor and log API failures with structured error information"],"best_for":["production applications requiring robust error handling","systems implementing exponential backoff and retry strategies","applications with detailed error logging and monitoring"],"limitations":["No built-in retry logic — applications must implement their own retry strategies","Streaming errors may occur mid-stream after partial content is consumed","Error messages are API-provided — may not be consistent across platforms"],"requires":["Python 3.9+","Exception handling code in application logic"],"input_types":["HTTP error responses from Cohere API"],"output_types":["structured exception objects with error codes and messages"],"categories":["tool-use-integration","error-handling"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":31,"verified":false,"data_access_risk":"high","permissions":["Python 3.9+","API key for Cohere or credentials for target cloud platform (AWS, Azure, GCP, Oracle)","Network access to selected platform endpoints","Cohere API key or platform credentials","Client-side code to handle streaming response iteration","Cohere API key","Lists of inputs within batch size limits","Text or image inputs (images require base64 encoding)","Query string and list of candidate documents","List of texts to classify and list of category labels"],"failure_modes":["API feature parity varies across platforms — some advanced features only available on Cohere hosted API","Platform-specific authentication setup required (AWS credentials, Azure tokens, etc.)","Latency varies significantly by platform and region","Streaming adds complexity to error handling — errors may occur mid-stream after partial content is consumed","Token-level streaming requires client-side buffering and rendering logic","Some platforms (e.g., SageMaker) may have limited streaming support","Batch size limits apply (typically 96 items per request) — larger batches require manual chunking","Batch processing adds latency compared to streaming — not suitable for real-time applications","No built-in batching orchestration — applications must implement batch chunking logic","Token counts are approximate for billing — actual charges may vary slightly","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.59,"ecosystem":0.39999999999999997,"match_graph":0.25,"freshness":0.52,"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":"2026-05-24T12:16:25.060Z","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-cohere","compare_url":"https://unfragile.ai/compare?artifact=pypi-cohere"}},"signature":"DnSEc/JGoQLX1y8VaujAQjEJNth8rdyfD1qOGcB9lpZTeGjnbzVzppq8WWD1vA237N+9PzqtZZXyb1ga4wroCQ==","signedAt":"2026-06-20T22:43:22.169Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/pypi-cohere","artifact":"https://unfragile.ai/pypi-cohere","verify":"https://unfragile.ai/api/v1/verify?slug=pypi-cohere","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"}}