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Official Cohere TypeScript SDK patterns -- CohereClientV2, chat, embeddings, rerank, RAG with citations, tool use, streaming, and model selection

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Overview

Official Cohere TypeScript SDK patterns -- CohereClientV2, chat, embeddings, rerank, RAG with citations, tool use, streaming, and model selection

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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: 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 environment variables)

Auto-detection: Cohere, cohere-ai, CohereClientV2, CohereClient, command-a, command-r, command-r-plus, embed-v4, rerank-v4, chatStream, content-delta, inputType, search_document, search_query, embeddingTypes, topN, CO_API_KEY, COHERE_API_KEY

When to use:

Building applications with Cohere Command models (chat, generation, summarization)
Creating semantic search pipelines with Cohere embeddings
Adding relevance scoring to search results with Cohere Rerank
Implementing RAG with inline document grounding and automatic citations
Building agentic workflows with Cohere tool use / function calling
Streaming chat responses for real-time user interfaces

Key patterns covered:

Client setup with CohereClientV2 (token, timeout, platform configs)
Chat and streaming (chat, chatStream, event types)
Embeddings with inputType for search/classification/clustering
Rerank for relevance scoring and search result ordering
RAG with documents and automatic citation handling
Tool use / function calling with multi-step loops
Model selection (Command-A, Command-R, Embed v4, Rerank v4)

When NOT to use:

Multi-provider applications needing OpenAI/Anthropic/Google switching -- use a unified provider SDK
React-specific chat UI hooks -- use a framework-integrated AI SDK
Simple text completion without Cohere-specific features (rerank, citations)

Examples Index

Core: Setup, Chat & Error Handling -- CohereClientV2 init, basic chat, streaming, error handling
Embeddings & Rerank -- Semantic search, input types, rerank scoring, RAG pipeline
Tool Use & RAG -- Function calling, document grounding, citation handling
Quick API Reference -- Model IDs, method signatures, event types, error classes

Philosophy

The Cohere TypeScript SDK (cohere-ai) provides direct access to Cohere's API surface -- chat, embeddings, rerank, and RAG with citations. The SDK is auto-generated from Cohere's API spec using Fern.

Core principles:

V2 API is current -- CohereClientV2 provides the modern API. model is required on every call. V1 methods on CohereClient are legacy.
Embeddings are typed -- The inputType parameter (search_document, search_query, classification, clustering) is mandatory for v3+ models. Mismatching input types between indexing and querying silently degrades results.
RAG is first-class -- Pass documents directly to chat() and the model returns grounded answers with inline citations. No external retrieval framework required for the grounding step.
Rerank is a standalone primitive -- Score and reorder search results without building a full RAG pipeline. Feed any list of documents and a query, get relevance scores back.
Citations are automatic -- When documents are provided (via RAG or tool results), the model generates fine-grained citations with start/end positions and source references.

When to use the Cohere SDK directly:

You want Cohere-specific features: rerank, citation grounding, multilingual embeddings
You need semantic search with embed + rerank pipeline
You want RAG with automatic inline citations
You are building on Cohere's platform (or Bedrock/Azure/OCI with Cohere models)

When NOT to use:

You need to switch between multiple LLM providers -- use a unified provider SDK
You want React-specific chat UI hooks -- use a framework-integrated AI SDK
You only need basic chat completion without Cohere differentiators

Core Patterns

Pattern 1: Client Setup

Initialize CohereClientV2. The token parameter is required (pass from environment).

// lib/cohere.ts -- basic setup
import { CohereClientV2 } from "cohere-ai";

const client = new CohereClientV2({
  token: process.env.CO_API_KEY,
});

export { client };

// lib/cohere.ts -- production configuration
const TIMEOUT_MS = 30_000;

const client = new CohereClientV2({
  token: process.env.CO_API_KEY,
  timeout: TIMEOUT_MS,
});

Why good: Explicit token from env var, named timeout constant, named export

// BAD: Hardcoded key, default CohereClient (V1)
import { CohereClient } from "cohere-ai";
const client = new CohereClient({ token: "sk-abc123" });

Why bad: Hardcoded API key is a security breach risk, CohereClient is the legacy V1 client

See: examples/core.md for error handling, platform configs (Bedrock, Azure)

Pattern 2: Chat Completion

V2 chat uses messages array with system, user, assistant, and tool roles.

const response = await client.chat({
  model: "command-a-03-2025",
  messages: [
    { role: "system", content: "You are a helpful coding assistant." },
    { role: "user", content: "Explain TypeScript generics." },
  ],
});

console.log(response.message.content[0].text);

Why good: System message for instruction, model explicitly specified, correct V2 content access path

// BAD: Missing model (required in V2), wrong response access
const response = await client.chat({
  messages: [{ role: "user", content: "Hello" }],
});
console.log(response.text); // WRONG: V2 uses response.message.content[0].text

Why bad: V2 requires model, response shape is response.message.content[0].text not response.text

See: examples/core.md for multi-turn, token tracking, temperature control

Pattern 3: Streaming

Use chatStream with for await and check event type for content-delta.

const stream = await client.chatStream({
  model: "command-a-03-2025",
  messages: [{ role: "user", content: "Explain async/await." }],
});

for await (const event of stream) {
  if (event.type === "content-delta") {
    process.stdout.write(event.delta?.message?.content?.text ?? "");
  }
}

Why good: Checks event type before accessing delta, handles nullable content safely

// BAD: Not checking event type
for await (const event of stream) {
  console.log(event.delta?.message); // Many events don't have message delta
}

Why bad: Only content-delta events have text content -- other events (message-start, citation-start, tool-plan-delta) have different shapes

See: examples/core.md for full streaming with all event types

Pattern 4: Embeddings

inputType is required for v3+ models. Mismatching types between indexing and querying silently degrades results.

const EMBEDDING_MODEL = "embed-v4.0";

// Index documents with search_document
const docEmbeddings = await client.embed({
  model: EMBEDDING_MODEL,
  inputType: "search_document",
  texts: ["TypeScript is a typed superset of JavaScript."],
  embeddingTypes: ["float"],
});

// Query with search_query
const queryEmbedding = await client.embed({
  model: EMBEDDING_MODEL,
  inputType: "search_query",
  texts: ["What is TypeScript?"],
  embeddingTypes: ["float"],
});

Why good: Correct inputType pairing, embeddingTypes explicitly specified, named model constant

// BAD: Same inputType for both indexing and querying
const docs = await client.embed({
  model: "embed-v4.0",
  inputType: "search_query", // WRONG for documents
  texts: documents,
  embeddingTypes: ["float"],
});

Why bad: Using search_query for document indexing silently produces worse similarity scores -- documents must use search_document

See: examples/embeddings-rerank.md for cosine similarity, dimension control, batch embedding

Pattern 5: Rerank

Score documents by relevance to a query. Returns ordered results with relevance scores.

const RERANK_MODEL = "rerank-v4.0-pro";
const TOP_N = 3;

const result = await client.rerank({
  model: RERANK_MODEL,
  query: "What is TypeScript?",
  documents: [
    "TypeScript is a typed superset of JavaScript.",
    "Python is a general-purpose language.",
    "TypeScript compiles to JavaScript.",
  ],
  topN: TOP_N,
});

for (const item of result.results) {
  console.log(`Doc ${item.index}: score ${item.relevanceScore}`);
}

Why good: Named constants, topN limits results, accesses index and relevanceScore

See: examples/embeddings-rerank.md for embed + rerank pipeline, rank fields

Pattern 6: RAG with Documents

Pass documents to chat() and the model returns grounded answers with inline citations.

const response = await client.chat({
  model: "command-a-03-2025",
  messages: [{ role: "user", content: "What is TypeScript?" }],
  documents: [
    {
      data: {
        text: "TypeScript is a typed superset of JavaScript.",
        title: "TS Docs",
      },
    },
    {
      data: {
        text: "TypeScript was developed by Microsoft.",
        title: "History",
      },
    },
  ],
});

console.log(response.message.content[0].text);

// Citations reference which documents support each claim
if (response.message.citations) {
  for (const citation of response.message.citations) {
    console.log(`"${citation.text}" from doc ${citation.sources}`);
  }
}

Why good: Documents passed inline with metadata, citations accessed from response, no external retrieval framework needed

See: examples/tools-rag.md for full RAG pipeline with embed + rerank + chat

Pattern 7: Tool Use / Function Calling

4-step loop: user message -> model returns tool_calls -> execute tools -> return results with tool_call_id.

const tools = [
  {
    type: "function" as const,
    function: {
      name: "get_weather",
      description: "Get weather for a city",
      parameters: {
        type: "object",
        properties: {
          location: { type: "string", description: "City name" },
        },
        required: ["location"],
      },
    },
  },
];

const response = await client.chat({
  model: "command-a-03-2025",
  messages: [{ role: "user", content: "Weather in Paris?" }],
  tools,
});

// Ch

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File metadata

name: ai-provider-cohere-sdk
description: Official Cohere TypeScript SDK patterns -- CohereClientV2, chat, embeddings, rerank, RAG with citations, tool use, streaming, and model selection

View original text

---
name: ai-provider-cohere-sdk
description: Official Cohere TypeScript SDK patterns -- CohereClientV2, chat, embeddings, rerank, RAG with citations, tool use, streaming, and model selection
---

# 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 environment variables)**

</critical_requirements>

---

**Auto-detection:** Cohere, cohere-ai, CohereClientV2, CohereClient, command-a, command-r, command-r-plus, embed-v4, rerank-v4, chatStream, content-delta, inputType, search_document, search_query, embeddingTypes, topN, CO_API_KEY, COHERE_API_KEY

**When to use:**

- Building applications with Cohere Command models (chat, generation, summarization)
- Creating semantic search pipelines with Cohere embeddings
- Adding relevance scoring to search results with Cohere Rerank
- Implementing RAG with inline document grounding and automatic citations
- Building agentic workflows with Cohere tool use / function calling
- Streaming chat responses for real-time user interfaces

**Key patterns covered:**

- Client setup with `CohereClientV2` (token, timeout, platform configs)
- Chat and streaming (`chat`, `chatStream`, event types)
- Embeddings with `inputType` for search/classification/clustering
- Rerank for relevance scoring and search result ordering
- RAG with documents and automatic citation handling
- Tool use / function calling with multi-step loops
- Model selection (Command-A, Command-R, Embed v4, Rerank v4)

**When NOT to use:**

- Multi-provider applications needing OpenAI/Anthropic/Google switching -- use a unified provider SDK
- React-specific chat UI hooks -- use a framework-integrated AI SDK
- Simple text completion without Cohere-specific features (rerank, citations)

---

## Examples Index

- [Core: Setup, Chat & Error Handling](examples/core.md) -- CohereClientV2 init, basic chat, streaming, error handling
- [Embeddings & Rerank](examples/embeddings-rerank.md) -- Semantic search, input types, rerank scoring, RAG pipeline
- [Tool Use & RAG](examples/tools-rag.md) -- Function calling, document grounding, citation handling
- [Quick API Reference](reference.md) -- Model IDs, method signatures, event types, error classes

---

<philosophy>

## Philosophy

The Cohere TypeScript SDK (`cohere-ai`) provides **direct access to Cohere's API surface** -- chat, embeddings, rerank, and RAG with citations. The SDK is auto-generated from Cohere's API spec using Fern.

**Core principles:**

1. **V2 API is current** -- `CohereClientV2` provides the modern API. `model` is required on every call. V1 methods on `CohereClient` are legacy.
2. **Embeddings are typed** -- The `inputType` parameter (`search_document`, `search_query`, `classification`, `clustering`) is mandatory for v3+ models. Mismatching input types between indexing and querying silently degrades results.
3. **RAG is first-class** -- Pass `documents` directly to `chat()` and the model returns grounded answers with inline citations. No external retrieval framework required for the grounding step.
4. **Rerank is a standalone primitive** -- Score and reorder search results without building a full RAG pipeline. Feed any list of documents and a query, get relevance scores back.
5. **Citations are automatic** -- When documents are provided (via RAG or tool results), the model generates fine-grained citations with start/end positions and source references.

**When to use the Cohere SDK directly:**

- You want Cohere-specific features: rerank, citation grounding, multilingual embeddings
- You need semantic search with embed + rerank pipeline
- You want RAG with automatic inline citations
- You are building on Cohere's platform (or Bedrock/Azure/OCI with Cohere models)

**When NOT to use:**

- You need to switch between multiple LLM providers -- use a unified provider SDK
- You want React-specific chat UI hooks -- use a framework-integrated AI SDK
- You only need basic chat completion without Cohere differentiators

</philosophy>

---

<patterns>

## Core Patterns

### Pattern 1: Client Setup

Initialize `CohereClientV2`. The `token` parameter is required (pass from environment).

```typescript
// lib/cohere.ts -- basic setup
import { CohereClientV2 } from "cohere-ai";

const client = new CohereClientV2({
  token: process.env.CO_API_KEY,
});

export { client };
```

```typescript
// lib/cohere.ts -- production configuration
const TIMEOUT_MS = 30_000;

const client = new CohereClientV2({
  token: process.env.CO_API_KEY,
  timeout: TIMEOUT_MS,
});
```

**Why good:** Explicit token from env var, named timeout constant, named export

```typescript
// BAD: Hardcoded key, default CohereClient (V1)
import { CohereClient } from "cohere-ai";
const client = new CohereClient({ token: "sk-abc123" });
```

**Why bad:** Hardcoded API key is a security breach risk, `CohereClient` is the legacy V1 client

**See:** [examples/core.md](examples/core.md) for error handling, platform configs (Bedrock, Azure)

---

### Pattern 2: Chat Completion

V2 chat uses `messages` array with `system`, `user`, `assistant`, and `tool` roles.

```typescript
const response = await client.chat({
  model: "command-a-03-2025",
  messages: [
    { role: "system", content: "You are a helpful coding assistant." },
    { role: "user", content: "Explain TypeScript generics." },
  ],
});

console.log(response.message.content[0].text);
```

**Why good:** System message for instruction, `model` explicitly specified, correct V2 content access path

```typescript
// BAD: Missing model (required in V2), wrong response access
const response = await client.chat({
  messages: [{ role: "user", content: "Hello" }],
});
console.log(response.text); // WRONG: V2 uses response.message.content[0].text
```

**Why bad:** V2 requires `model`, response shape is `response.message.content[0].text` not `response.text`

**See:** [examples/core.md](examples/core.md) for multi-turn, token tracking, temperature control

---

### Pattern 3: Streaming

Use `chatStream` with `for await` and check event `type` for `content-delta`.

```typescript
const stream = await client.chatStream({
  model: "command-a-03-2025",
  messages: [{ role: "user", content: "Explain async/await." }],
});

for await (const event of stream) {
  if (event.type === "content-delta") {
    process.stdout.write(event.delta?.message?.content?.text ?? "");
  }
}
```

**Why good:** Checks event type before accessing delta, handles nullable content safely

```typescript
// BAD: Not checking event type
for await (const event of stream) {
  console.log(event.delta?.message); // Many events don't have message delta
}
```

**Why bad:** Only `content-delta` events have text content -- other events (`message-start`, `citation-start`, `tool-plan-delta`) have different shapes

**See:** [examples/core.md](examples/core.md) for full streaming with all event types

---

### Pattern 4: Embeddings

`inputType` is required for v3+ models. Mismatching types between indexing and querying silently degrades results.

```typescript
const EMBEDDING_MODEL = "embed-v4.0";

// Index documents with search_document
const docEmbeddings = await client.embed({
  model: EMBEDDING_MODEL,
  inputType: "search_document",
  texts: ["TypeScript is a typed superset of JavaScript."],
  embeddingTypes: ["float"],
});

// Query with search_query
const queryEmbedding = await client.embed({
  model: EMBEDDING_MODEL,
  inputType: "search_query",
  texts: ["What is TypeScript?"],
  embeddingTypes: ["float"],
});
```

**Why good:** Correct `inputType` pairing, `embeddingTypes` explicitly specified, named model constant

```typescript
// BAD: Same inputType for both indexing and querying
const docs = await client.embed({
  model: "embed-v4.0",
  inputType: "search_query", // WRONG for documents
  texts: documents,
  embeddingTypes: ["float"],
});
```

**Why bad:** Using `search_query` for document indexing silently produces worse similarity scores -- documents must use `search_document`

**See:** [examples/embeddings-rerank.md](examples/embeddings-rerank.md) for cosine similarity, dimension control, batch embedding

---

### Pattern 5: Rerank

Score documents by relevance to a query. Returns ordered results with relevance scores.

```typescript
const RERANK_MODEL = "rerank-v4.0-pro";
const TOP_N = 3;

const result = await client.rerank({
  model: RERANK_MODEL,
  query: "What is TypeScript?",
  documents: [
    "TypeScript is a typed superset of JavaScript.",
    "Python is a general-purpose language.",
    "TypeScript compiles to JavaScript.",
  ],
  topN: TOP_N,
});

for (const item of result.results) {
  console.log(`Doc ${item.index}: score ${item.relevanceScore}`);
}
```

**Why good:** Named constants, `topN` limits results, accesses `index` and `relevanceScore`

**See:** [examples/embeddings-rerank.md](examples/embeddings-rerank.md) for embed + rerank pipeline, rank fields

---

### Pattern 6: RAG with Documents

Pass `documents` to `chat()` and the model returns grounded answers with inline citations.

```typescript
const response = await client.chat({
  model: "command-a-03-2025",
  messages: [{ role: "user", content: "What is TypeScript?" }],
  documents: [
    {
      data: {
        text: "TypeScript is a typed superset of JavaScript.",
        title: "TS Docs",
      },
    },
    {
      data: {
        text: "TypeScript was developed by Microsoft.",
        title: "History",
      },
    },
  ],
});

console.log(response.message.content[0].text);

// Citations reference which documents support each claim
if (response.message.citations) {
  for (const citation of response.message.citations) {
    console.log(`"${citation.text}" from doc ${citation.sources}`);
  }
}
```

**Why good:** Documents passed inline with metadata, citations accessed from response, no external retrieval framework needed

**See:** [examples/tools-rag.md](examples/tools-rag.md) for full RAG pipeline with embed + rerank + chat

---

### Pattern 7: Tool Use / Function Calling

4-step loop: user message -> model returns `tool_calls` -> execute tools -> return results with `tool_call_id`.

```typescript
const tools = [
  {
    type: "function" as const,
    function: {
      name: "get_weather",
      description: "Get weather for a city",
      parameters: {
        type: "object",
        properties: {
          location: { type: "string", description: "City name" },
        },
        required: ["location"],
      },
    },
  },
];

const response = await client.chat({
  model: "command-a-03-2025",
  messages: [{ role: "user", content: "Weather in Paris?" }],
  tools,
});

// Ch

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Source repository: agents-inc/skills
License: MIT
Version: Unknown
Last GitHub push: Sep 7, 2026
Registry updated: Sep 13, 2026
Instruction path: dist/plugins/ai-provider-cohere-sdk/skills/ai-provider-cohere-sdk/SKILL.md @ 3a51ef571e99

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Permission surface: secrets or environment access, shell or command execution
Review status: AI review approval is missing

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ai-provider-cohere-sdk

Official Cohere TypeScript SDK patterns -- CohereClientV2, chat, embeddings, rerank, RAG with citations, tool use, streaming, and model selection

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Price unconfirmed★ 24 GitHub starsRegistry updated · Sep 13, 2026agent-skill

Overview

Official Cohere TypeScript SDK patterns -- CohereClientV2, chat, embeddings, rerank, RAG with citations, tool use, streaming, and model selection

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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: 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 environment variables)

When to use:

Building applications with Cohere Command models (chat, generation, summarization)
Creating semantic search pipelines with Cohere embeddings
Adding relevance scoring to search results with Cohere Rerank
Implementing RAG with inline document grounding and automatic citations
Building agentic workflows with Cohere tool use / function calling
Streaming chat responses for real-time user interfaces

Key patterns covered:

Client setup with CohereClientV2 (token, timeout, platform configs)
Chat and streaming (chat, chatStream, event types)
Embeddings with inputType for search/classification/clustering
Rerank for relevance scoring and search result ordering
RAG with documents and automatic citation handling
Tool use / function calling with multi-step loops
Model selection (Command-A, Command-R, Embed v4, Rerank v4)

When NOT to use:

Multi-provider applications needing OpenAI/Anthropic/Google switching -- use a unified provider SDK
React-specific chat UI hooks -- use a framework-integrated AI SDK
Simple text completion without Cohere-specific features (rerank, citations)

Examples Index

Core: Setup, Chat & Error Handling -- CohereClientV2 init, basic chat, streaming, error handling
Embeddings & Rerank -- Semantic search, input types, rerank scoring, RAG pipeline
Tool Use & RAG -- Function calling, document grounding, citation handling
Quick API Reference -- Model IDs, method signatures, event types, error classes

Philosophy

Core principles:

V2 API is current -- CohereClientV2 provides the modern API. model is required on every call. V1 methods on CohereClient are legacy.
Embeddings are typed -- The inputType parameter (search_document, search_query, classification, clustering) is mandatory for v3+ models. Mismatching input types between indexing and querying silently degrades results.
RAG is first-class -- Pass documents directly to chat() and the model returns grounded answers with inline citations. No external retrieval framework required for the grounding step.
Rerank is a standalone primitive -- Score and reorder search results without building a full RAG pipeline. Feed any list of documents and a query, get relevance scores back.
Citations are automatic -- When documents are provided (via RAG or tool results), the model generates fine-grained citations with start/end positions and source references.

When to use the Cohere SDK directly:

You want Cohere-specific features: rerank, citation grounding, multilingual embeddings
You need semantic search with embed + rerank pipeline
You want RAG with automatic inline citations
You are building on Cohere's platform (or Bedrock/Azure/OCI with Cohere models)

When NOT to use:

You need to switch between multiple LLM providers -- use a unified provider SDK
You want React-specific chat UI hooks -- use a framework-integrated AI SDK
You only need basic chat completion without Cohere differentiators

Core Patterns

Pattern 1: Client Setup

Initialize CohereClientV2. The token parameter is required (pass from environment).

// lib/cohere.ts -- basic setup
import { CohereClientV2 } from "cohere-ai";

const client = new CohereClientV2({
  token: process.env.CO_API_KEY,
});

export { client };

// lib/cohere.ts -- production configuration
const TIMEOUT_MS = 30_000;

const client = new CohereClientV2({
  token: process.env.CO_API_KEY,
  timeout: TIMEOUT_MS,
});

Why good: Explicit token from env var, named timeout constant, named export

// BAD: Hardcoded key, default CohereClient (V1)
import { CohereClient } from "cohere-ai";
const client = new CohereClient({ token: "sk-abc123" });

Why bad: Hardcoded API key is a security breach risk, CohereClient is the legacy V1 client

See: examples/core.md for error handling, platform configs (Bedrock, Azure)

Pattern 2: Chat Completion

V2 chat uses messages array with system, user, assistant, and tool roles.

const response = await client.chat({
  model: "command-a-03-2025",
  messages: [
    { role: "system", content: "You are a helpful coding assistant." },
    { role: "user", content: "Explain TypeScript generics." },
  ],
});

console.log(response.message.content[0].text);

Why good: System message for instruction, model explicitly specified, correct V2 content access path

// BAD: Missing model (required in V2), wrong response access
const response = await client.chat({
  messages: [{ role: "user", content: "Hello" }],
});
console.log(response.text); // WRONG: V2 uses response.message.content[0].text

Why bad: V2 requires model, response shape is response.message.content[0].text not response.text

See: examples/core.md for multi-turn, token tracking, temperature control

Pattern 3: Streaming

Use chatStream with for await and check event type for content-delta.

const stream = await client.chatStream({
  model: "command-a-03-2025",
  messages: [{ role: "user", content: "Explain async/await." }],
});

for await (const event of stream) {
  if (event.type === "content-delta") {
    process.stdout.write(event.delta?.message?.content?.text ?? "");
  }
}

Why good: Checks event type before accessing delta, handles nullable content safely

// BAD: Not checking event type
for await (const event of stream) {
  console.log(event.delta?.message); // Many events don't have message delta
}

Why bad: Only content-delta events have text content -- other events (message-start, citation-start, tool-plan-delta) have different shapes

See: examples/core.md for full streaming with all event types

Pattern 4: Embeddings

inputType is required for v3+ models. Mismatching types between indexing and querying silently degrades results.

const EMBEDDING_MODEL = "embed-v4.0";

// Index documents with search_document
const docEmbeddings = await client.embed({
  model: EMBEDDING_MODEL,
  inputType: "search_document",
  texts: ["TypeScript is a typed superset of JavaScript."],
  embeddingTypes: ["float"],
});

// Query with search_query
const queryEmbedding = await client.embed({
  model: EMBEDDING_MODEL,
  inputType: "search_query",
  texts: ["What is TypeScript?"],
  embeddingTypes: ["float"],
});

Why good: Correct inputType pairing, embeddingTypes explicitly specified, named model constant

// BAD: Same inputType for both indexing and querying
const docs = await client.embed({
  model: "embed-v4.0",
  inputType: "search_query", // WRONG for documents
  texts: documents,
  embeddingTypes: ["float"],
});

Why bad: Using search_query for document indexing silently produces worse similarity scores -- documents must use search_document

See: examples/embeddings-rerank.md for cosine similarity, dimension control, batch embedding

Pattern 5: Rerank

Score documents by relevance to a query. Returns ordered results with relevance scores.

const RERANK_MODEL = "rerank-v4.0-pro";
const TOP_N = 3;

const result = await client.rerank({
  model: RERANK_MODEL,
  query: "What is TypeScript?",
  documents: [
    "TypeScript is a typed superset of JavaScript.",
    "Python is a general-purpose language.",
    "TypeScript compiles to JavaScript.",
  ],
  topN: TOP_N,
});

for (const item of result.results) {
  console.log(`Doc ${item.index}: score ${item.relevanceScore}`);
}

Why good: Named constants, topN limits results, accesses index and relevanceScore

See: examples/embeddings-rerank.md for embed + rerank pipeline, rank fields

Pattern 6: RAG with Documents

Pass documents to chat() and the model returns grounded answers with inline citations.

const response = await client.chat({
  model: "command-a-03-2025",
  messages: [{ role: "user", content: "What is TypeScript?" }],
  documents: [
    {
      data: {
        text: "TypeScript is a typed superset of JavaScript.",
        title: "TS Docs",
      },
    },
    {
      data: {
        text: "TypeScript was developed by Microsoft.",
        title: "History",
      },
    },
  ],
});

console.log(response.message.content[0].text);

// Citations reference which documents support each claim
if (response.message.citations) {
  for (const citation of response.message.citations) {
    console.log(`"${citation.text}" from doc ${citation.sources}`);
  }
}

Why good: Documents passed inline with metadata, citations accessed from response, no external retrieval framework needed

See: examples/tools-rag.md for full RAG pipeline with embed + rerank + chat

Pattern 7: Tool Use / Function Calling

4-step loop: user message -> model returns tool_calls -> execute tools -> return results with tool_call_id.

const tools = [
  {
    type: "function" as const,
    function: {
      name: "get_weather",
      description: "Get weather for a city",
      parameters: {
        type: "object",
        properties: {
          location: { type: "string", description: "City name" },
        },
        required: ["location"],
      },
    },
  },
];

const response = await client.chat({
  model: "command-a-03-2025",
  messages: [{ role: "user", content: "Weather in Paris?" }],
  tools,
});

// Ch

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File metadata

name: ai-provider-cohere-sdk
description: Official Cohere TypeScript SDK patterns -- CohereClientV2, chat, embeddings, rerank, RAG with citations, tool use, streaming, and model selection

View original text

---
name: ai-provider-cohere-sdk
description: Official Cohere TypeScript SDK patterns -- CohereClientV2, chat, embeddings, rerank, RAG with citations, tool use, streaming, and model selection
---

# 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 environment variables)**

</critical_requirements>

---

**Auto-detection:** Cohere, cohere-ai, CohereClientV2, CohereClient, command-a, command-r, command-r-plus, embed-v4, rerank-v4, chatStream, content-delta, inputType, search_document, search_query, embeddingTypes, topN, CO_API_KEY, COHERE_API_KEY

**When to use:**

- Building applications with Cohere Command models (chat, generation, summarization)
- Creating semantic search pipelines with Cohere embeddings
- Adding relevance scoring to search results with Cohere Rerank
- Implementing RAG with inline document grounding and automatic citations
- Building agentic workflows with Cohere tool use / function calling
- Streaming chat responses for real-time user interfaces

**Key patterns covered:**

- Client setup with `CohereClientV2` (token, timeout, platform configs)
- Chat and streaming (`chat`, `chatStream`, event types)
- Embeddings with `inputType` for search/classification/clustering
- Rerank for relevance scoring and search result ordering
- RAG with documents and automatic citation handling
- Tool use / function calling with multi-step loops
- Model selection (Command-A, Command-R, Embed v4, Rerank v4)

**When NOT to use:**

- Multi-provider applications needing OpenAI/Anthropic/Google switching -- use a unified provider SDK
- React-specific chat UI hooks -- use a framework-integrated AI SDK
- Simple text completion without Cohere-specific features (rerank, citations)

---

## Examples Index

- [Core: Setup, Chat & Error Handling](examples/core.md) -- CohereClientV2 init, basic chat, streaming, error handling
- [Embeddings & Rerank](examples/embeddings-rerank.md) -- Semantic search, input types, rerank scoring, RAG pipeline
- [Tool Use & RAG](examples/tools-rag.md) -- Function calling, document grounding, citation handling
- [Quick API Reference](reference.md) -- Model IDs, method signatures, event types, error classes

---

<philosophy>

## Philosophy

The Cohere TypeScript SDK (`cohere-ai`) provides **direct access to Cohere's API surface** -- chat, embeddings, rerank, and RAG with citations. The SDK is auto-generated from Cohere's API spec using Fern.

**Core principles:**

1. **V2 API is current** -- `CohereClientV2` provides the modern API. `model` is required on every call. V1 methods on `CohereClient` are legacy.
2. **Embeddings are typed** -- The `inputType` parameter (`search_document`, `search_query`, `classification`, `clustering`) is mandatory for v3+ models. Mismatching input types between indexing and querying silently degrades results.
3. **RAG is first-class** -- Pass `documents` directly to `chat()` and the model returns grounded answers with inline citations. No external retrieval framework required for the grounding step.
4. **Rerank is a standalone primitive** -- Score and reorder search results without building a full RAG pipeline. Feed any list of documents and a query, get relevance scores back.
5. **Citations are automatic** -- When documents are provided (via RAG or tool results), the model generates fine-grained citations with start/end positions and source references.

**When to use the Cohere SDK directly:**

- You want Cohere-specific features: rerank, citation grounding, multilingual embeddings
- You need semantic search with embed + rerank pipeline
- You want RAG with automatic inline citations
- You are building on Cohere's platform (or Bedrock/Azure/OCI with Cohere models)

**When NOT to use:**

- You need to switch between multiple LLM providers -- use a unified provider SDK
- You want React-specific chat UI hooks -- use a framework-integrated AI SDK
- You only need basic chat completion without Cohere differentiators

</philosophy>

---

<patterns>

## Core Patterns

### Pattern 1: Client Setup

Initialize `CohereClientV2`. The `token` parameter is required (pass from environment).

```typescript
// lib/cohere.ts -- basic setup
import { CohereClientV2 } from "cohere-ai";

const client = new CohereClientV2({
  token: process.env.CO_API_KEY,
});

export { client };
```

```typescript
// lib/cohere.ts -- production configuration
const TIMEOUT_MS = 30_000;

const client = new CohereClientV2({
  token: process.env.CO_API_KEY,
  timeout: TIMEOUT_MS,
});
```

**Why good:** Explicit token from env var, named timeout constant, named export

```typescript
// BAD: Hardcoded key, default CohereClient (V1)
import { CohereClient } from "cohere-ai";
const client = new CohereClient({ token: "sk-abc123" });
```

**Why bad:** Hardcoded API key is a security breach risk, `CohereClient` is the legacy V1 client

**See:** [examples/core.md](examples/core.md) for error handling, platform configs (Bedrock, Azure)

---

### Pattern 2: Chat Completion

V2 chat uses `messages` array with `system`, `user`, `assistant`, and `tool` roles.

```typescript
const response = await client.chat({
  model: "command-a-03-2025",
  messages: [
    { role: "system", content: "You are a helpful coding assistant." },
    { role: "user", content: "Explain TypeScript generics." },
  ],
});

console.log(response.message.content[0].text);
```

**Why good:** System message for instruction, `model` explicitly specified, correct V2 content access path

```typescript
// BAD: Missing model (required in V2), wrong response access
const response = await client.chat({
  messages: [{ role: "user", content: "Hello" }],
});
console.log(response.text); // WRONG: V2 uses response.message.content[0].text
```

**Why bad:** V2 requires `model`, response shape is `response.message.content[0].text` not `response.text`

**See:** [examples/core.md](examples/core.md) for multi-turn, token tracking, temperature control

---

### Pattern 3: Streaming

Use `chatStream` with `for await` and check event `type` for `content-delta`.

```typescript
const stream = await client.chatStream({
  model: "command-a-03-2025",
  messages: [{ role: "user", content: "Explain async/await." }],
});

for await (const event of stream) {
  if (event.type === "content-delta") {
    process.stdout.write(event.delta?.message?.content?.text ?? "");
  }
}
```

**Why good:** Checks event type before accessing delta, handles nullable content safely

```typescript
// BAD: Not checking event type
for await (const event of stream) {
  console.log(event.delta?.message); // Many events don't have message delta
}
```

**Why bad:** Only `content-delta` events have text content -- other events (`message-start`, `citation-start`, `tool-plan-delta`) have different shapes

**See:** [examples/core.md](examples/core.md) for full streaming with all event types

---

### Pattern 4: Embeddings

`inputType` is required for v3+ models. Mismatching types between indexing and querying silently degrades results.

```typescript
const EMBEDDING_MODEL = "embed-v4.0";

// Index documents with search_document
const docEmbeddings = await client.embed({
  model: EMBEDDING_MODEL,
  inputType: "search_document",
  texts: ["TypeScript is a typed superset of JavaScript."],
  embeddingTypes: ["float"],
});

// Query with search_query
const queryEmbedding = await client.embed({
  model: EMBEDDING_MODEL,
  inputType: "search_query",
  texts: ["What is TypeScript?"],
  embeddingTypes: ["float"],
});
```

**Why good:** Correct `inputType` pairing, `embeddingTypes` explicitly specified, named model constant

```typescript
// BAD: Same inputType for both indexing and querying
const docs = await client.embed({
  model: "embed-v4.0",
  inputType: "search_query", // WRONG for documents
  texts: documents,
  embeddingTypes: ["float"],
});
```

**Why bad:** Using `search_query` for document indexing silently produces worse similarity scores -- documents must use `search_document`

**See:** [examples/embeddings-rerank.md](examples/embeddings-rerank.md) for cosine similarity, dimension control, batch embedding

---

### Pattern 5: Rerank

Score documents by relevance to a query. Returns ordered results with relevance scores.

```typescript
const RERANK_MODEL = "rerank-v4.0-pro";
const TOP_N = 3;

const result = await client.rerank({
  model: RERANK_MODEL,
  query: "What is TypeScript?",
  documents: [
    "TypeScript is a typed superset of JavaScript.",
    "Python is a general-purpose language.",
    "TypeScript compiles to JavaScript.",
  ],
  topN: TOP_N,
});

for (const item of result.results) {
  console.log(`Doc ${item.index}: score ${item.relevanceScore}`);
}
```

**Why good:** Named constants, `topN` limits results, accesses `index` and `relevanceScore`

**See:** [examples/embeddings-rerank.md](examples/embeddings-rerank.md) for embed + rerank pipeline, rank fields

---

### Pattern 6: RAG with Documents

Pass `documents` to `chat()` and the model returns grounded answers with inline citations.

```typescript
const response = await client.chat({
  model: "command-a-03-2025",
  messages: [{ role: "user", content: "What is TypeScript?" }],
  documents: [
    {
      data: {
        text: "TypeScript is a typed superset of JavaScript.",
        title: "TS Docs",
      },
    },
    {
      data: {
        text: "TypeScript was developed by Microsoft.",
        title: "History",
      },
    },
  ],
});

console.log(response.message.content[0].text);

// Citations reference which documents support each claim
if (response.message.citations) {
  for (const citation of response.message.citations) {
    console.log(`"${citation.text}" from doc ${citation.sources}`);
  }
}
```

**Why good:** Documents passed inline with metadata, citations accessed from response, no external retrieval framework needed

**See:** [examples/tools-rag.md](examples/tools-rag.md) for full RAG pipeline with embed + rerank + chat

---

### Pattern 7: Tool Use / Function Calling

4-step loop: user message -> model returns `tool_calls` -> execute tools -> return results with `tool_call_id`.

```typescript
const tools = [
  {
    type: "function" as const,
    function: {
      name: "get_weather",
      description: "Get weather for a city",
      parameters: {
        type: "object",
        properties: {
          location: { type: "string", description: "City name" },
        },
        required: ["location"],
      },
    },
  },
];

const response = await client.chat({
  model: "command-a-03-2025",
  messages: [{ role: "user", content: "Weather in Paris?" }],
  tools,
});

// Ch

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License: MIT

Permission surface may require sandboxing
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GitHub adoption: 24 GitHub stars
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Source & usage notes

IndexedStatic Checked

Repository metadata and review signals are advisory. Popularity, source discovery and successful execution are different facts.

Source repository: agents-inc/skills
License: MIT
Version: Unknown
Last GitHub push: Sep 7, 2026
Registry updated: Sep 13, 2026
Instruction path: dist/plugins/ai-provider-cohere-sdk/skills/ai-provider-cohere-sdk/SKILL.md @ 3a51ef571e99

Version reported in registry metadata; check source releases before relying on it.

Quality

55/100

Promising

Trust

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Sandbox only

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Low GitHub adoption signal
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GitHub adoption: 24 GitHub stars
Stars/forks activity: 24 stars, 8 forks; issue activity unavailable in current metadata
Permission surface: secrets or environment access, shell or command execution
Review status: AI review approval is missing

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More details

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    "minimum_review_before_use": [
      "Trust: 68/100 Manual review",
      "Audit: 72/100 Needs review",
      "Safety: 28/100 Avoid automatic install",
      "Review repository, license, install command, and permission surface before production use."
    ],
    "expected_agent_output": {
      "selected_skill": "agents-inc-ai-provider-cohere-sdk (ai-provider-cohere-sdk)",
      "install_command": "npx skills add agents-inc/skills --skill ai-provider-cohere-sdk",
      "risk_summary": "Needs review; Blocked for auto-install; Review before production",
      "verification_result": "Report the smallest successful task, files touched, warnings, and any missing setup."
    }
  },
  "outcome_feedback": {
    "endpoint": "https://www.openagentskill.com/api/agent/outcome",
    "method": "POST",
    "requires_resolve_event_id": true,
    "event_id_source": "Use install_receipt.outcome_feedback.event_id or feedback.event_id returned by /api/agent/resolve for the current task.",
    "expected_outcomes": [
      "success",
      "failed",
      "not_relevant",
      "blocked_by_risk",
      "setup_required"
    ],
    "payload_template": {
      "event_id": "<install_receipt.outcome_feedback.event_id or feedback.event_id from /api/agent/resolve>",
      "skill_slug": "agents-inc-ai-provider-cohere-sdk",
      "task": "Use ai-provider-cohere-sdk in an agent workflow",
      "agent": "codex",
      "outcome": "success",
      "install_used": true,
      "risk_blocked": false,
      "setup_required": false,
      "task_success": true,
      "output_quality": 4,
      "error_type": null,
      "human_review_required": false,
      "workspace": "sandbox",
      "time_to_useful_ms": 120000,
      "notes": "Report the smallest successful task, setup friction, files touched, and risk notes."
    }
  },
  "endpoints": {
    "web": "https://www.openagentskill.com/skills/agents-inc-ai-provider-cohere-sdk",
    "api": "https://www.openagentskill.com/api/agent/skills/agents-inc-ai-provider-cohere-sdk",
    "audit": "https://www.openagentskill.com/skills/agents-inc-ai-provider-cohere-sdk/audit",
    "eval": "https://www.openagentskill.com/api/agent/evals?slug=agents-inc-ai-provider-cohere-sdk&task=Use%20ai-provider-cohere-sdk%20in%20an%20agent%20workflow&max_risk=medium",
    "resolve": "https://www.openagentskill.com/api/agent/resolve?task=Use%20ai-provider-cohere-sdk%20in%20an%20agent%20workflow&agent=codex&max_risk=medium",
    "receipt": "https://www.openagentskill.com/api/agent/receipt?task=Use%20ai-provider-cohere-sdk%20in%20an%20agent%20workflow&agent=codex&max_risk=medium&format=text",
    "install": "https://www.openagentskill.com/api/skills/agents-inc-ai-provider-cohere-sdk/install",
    "manifest": "https://www.openagentskill.com/api/registry/manifest/agents-inc-ai-provider-cohere-sdk"
  }
}

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Creator: agents-inc
Source: agents-inc/skills
Indexed by: OpenAgentSkill community index

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Overview

Cohere SDK Patterns

CRITICAL: Before Using This Skill

Examples Index

Philosophy

Core Patterns

Pattern 1: Client Setup

Pattern 2: Chat Completion

Pattern 3: Streaming

Pattern 4: Embeddings

Pattern 5: Rerank

Pattern 6: RAG with Documents

Pattern 7: Tool Use / Function Calling

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Overview

Cohere SDK Patterns

CRITICAL: Before Using This Skill

Examples Index

Philosophy

Core Patterns

Pattern 1: Client Setup

Pattern 2: Chat Completion

Pattern 3: Streaming

Pattern 4: Embeddings

Pattern 5: Rerank

Pattern 6: RAG with Documents

Pattern 7: Tool Use / Function Calling

Review the source

Price & running costs

Start with one small task

Source & usage notes

Agent access

Related skills

For the creator

Registry indexed

Claim this skill listing

Add the evidence badges to your README

Community signal