Cohere

Cohere's Chat endpoint runs text generation through the ‘Command’ family of models, supporting both conversational interfaces and broader generative AI applications. Developers can pass user messages, keep conversation state across system, user, and assistant turns, or stream responses as they're generated. This flexibility helps chatbots feel more natural and continuous, rather than responding in disconnected bursts.

Grounding responses in external documents is where Chat's RAG support comes in, cutting down on hallucinations by tying answers back to actual source material. Documents pulled from a vector database or similar source get passed to the model as context. Built-in citation generation then points to the exact text behind each response, so answers can be checked against their source rather than taken at face value.

Building agents that reason through tasks and take action means connecting to outside tools, and that's what tool use in the Chat endpoint supports. Function calling and multi-step reasoning work alongside citation generation, so developers can define a tool's schema, execute the calls the model generates, and feed results back for a final response.

Cohere features ‘Embed’ that powers semantic search by converting multilingual text and images into vector representations. A user's query gets embedded the same way and compared against document embeddings to surface the closest matches. Applications end up retrieving results based on meaning and context, not just exact word matches.

The platform’s ‘Rerank’ takes results already pulled from a keyword or vector search and reorders them by actual relevance to the query. Query and retrieved documents are passed through the Rerank endpoint. Search systems end up surfacing better results without needing to rebuild the retrieval pipeline from scratch.

The ‘Transcribe’ model converts business audio, whether from calls, meetings, or training recordings, into precise text even in noisy settings or with varied speech patterns. It covers 14 languages and supports private deployment with modest GPU requirements, which means sensitive audio can be processed locally or at the edge instead of routing through external servers.