ai-provider-cohere-sdk

# Cohere SDK Patterns > **Quick Guide:** Use the `cohere-ai` npm package with `CohereClientV2` for all new Cohere integrations. V2 API requires `model` on every call. Use `chatStream` for streaming with `content-delta` events. Embeddings require `inputType` matching your use case (`search_document` for indexing, `search_query` for querying). Rerank scores documents by relevance. RAG works by passing `documents` to `chat()` -- the model returns inline citations automatically. Tool use follows a 4-step loop: user message, model returns `tool_calls`, you execute and return results, model generates cited response. --- <critical_requirements> ## CRITICAL: Before Using This Skill > **All code must follow project conventions in CLAUDE.md** (kebab-case, named exports, import ordering, `import type`, named constants) **(You MUST use `CohereClientV2` (not `CohereClient`) for all new code -- V2 is the current API with required `model` parameter)** **(You MUST specify `inputType` on every embed call -- `search_document` for indexing, `search_query` for querying -- mismatched types produce garbage similarity scores)** **(You MUST handle the tool use loop correctly: append the full assistant message (with `tool_calls`) to messages, then append `tool` role results with matching `tool_call_id`)** **(You MUST check `finish_reason` in responses -- `MAX_TOKENS` means the output was truncated)** **(You MUST never hardcode API keys -- pass via `token` constructor parameter sourced from ...