co:here

Generates coherent, contextually-relevant text across multiple languages using instruction-tuned large language models that follow user directives with high fidelity. The models are trained on diverse instruction datasets and support both zero-shot and few-shot prompting patterns, enabling developers to control output style, length, and format through natural language instructions without requiring fine-tuning.

Converts text inputs into high-dimensional dense vector representations (embeddings) that capture semantic meaning, enabling similarity search, clustering, and retrieval-augmented generation workflows. Cohere's embedding models use transformer-based architectures trained on large text corpora to produce vectors where semantically similar texts have high cosine similarity, supporting both small and large batch processing.

Reranks a list of candidate documents or passages by computing relevance scores using cross-encoder neural networks, which evaluate query-document pairs jointly rather than independently. This two-stage retrieval pattern (dense retrieval followed by reranking) dramatically improves precision by filtering low-relevance results that dense embeddings may have ranked highly, using Cohere's fine-tuned reranker models that understand semantic relevance at scale.

Enables LLMs to invoke external tools and APIs by generating structured function calls based on a schema-defined tool registry. Cohere's implementation parses natural language requests into function names and parameters, supporting multi-turn tool use where the model can chain multiple function calls and reason about results. The system uses JSON schema definitions to constrain outputs and ensure type safety.

Classifies text inputs into predefined categories or intents using fine-tuned or few-shot classification models. Cohere's classify endpoint accepts a list of examples and candidate labels, then predicts the most likely label for new inputs with confidence scores. The system supports both zero-shot (label-only) and few-shot (examples + labels) modes, enabling rapid iteration without retraining.

Processes large volumes of text through generation, embedding, or classification endpoints asynchronously, accepting batches of requests and returning results via webhook callbacks or polling. The batch API decouples request submission from result retrieval, enabling efficient processing of thousands of documents without blocking, and typically offers cost savings compared to real-time API calls.

Manages conversation history and context across multiple turns, enabling stateful dialogue where the model can reference previous messages and maintain coherent conversation flow. Developers pass conversation history as an array of messages (user/assistant pairs), and Cohere's API handles context windowing and token management automatically, truncating or summarizing older messages when context limits are approached.

Analyzes prompts and automatically selects or generates effective few-shot examples to improve model performance on specific tasks. This capability uses meta-learning techniques to identify which examples are most informative for a given task, reducing the number of examples needed and improving accuracy compared to random example selection.

Generates coherent text in multiple languages using language-specific or multilingual models optimized for non-English languages. Cohere's multilingual models are trained on diverse language corpora and support instruction-following in languages beyond English, enabling developers to build truly multilingual applications without language-specific model switching.

Provides rate limiting, quota management, and detailed usage tracking across API calls, enabling developers to monitor consumption, set spending limits, and optimize API usage. The system tracks requests by operation type (generation, embeddings, classification) and provides real-time dashboards and usage reports for cost optimization.