api-docs

"use strict";
Object.defineProperty(exports, "__esModule", { value: true });
exports.cohereDocs = void 0;
exports.cohereDocs = [
    {
        id: "cohere-chat",
        title: "Cohere Chat API",
        category: "chat",
        provider: "cohere",
        keywords: ["cohere", "chat", "chatbot", "conversation"],
        content: `# Cohere Chat API

## Endpoint
POST https://api.cohere.ai/v2/chat

## Headers
- Authorization: Bearer YOUR_API_KEY
- Content-Type: application/json

## Request Body
\`\`\`json
{
  "model": "command-r-plus" | "command-r" | "command" | "command-light" | "command-r7b-2025",
  "messages": [
    {
      "role": "system" | "user" | "assistant" | "tool",
      "content": "string" | [
        {"type": "text", "text": "string"},
        {"type": "image", "image": {"url": "string"}}
      ]
    }
  ],
  "temperature": number (0-5, default 0.3),
  "p": number (0-1, top_p),
  "k": number (0-500, top_k),
  "max_tokens": number,
  "stop_sequences": string[],
  "stream": boolean,
  "tools": [
    {
      "name": "string",
      "description": "string",
      "parameter_definitions": {
        "param_name": {
          "description": "string",
          "type": "string" | "number" | "boolean",
          "required": boolean,
          "value": "default value"
        }
      }
    }
  ],
  "tool_results": [
    {
      "call": {
        "name": "string",
        "parameters": {"param": "value"}
      },
      "outputs": [{"text": "result"}]
    }
  ],
  "documents": [
    {
      "id": "string",
      "data": {"text": "content"},
      "title": "string",
      "url": "string"
    }
  ],
  "search_queries_only": boolean,
  "prompt_truncation": "AUTO" | "OFF"
}
\`\`\`

## Response
\`\`\`json
{
  "id": "xxx",
  "message": {
    "role": "assistant",
    "content": [
      {"type": "text", "text": "Hello!"},
      {
        "type": "tool_call",
        "tool_call": {
          "name": "get_weather",
          "parameters": {"location": "London"}
        }
      }
    ],
    "tool_calls": [...]
  },
  "finish_reason": "COMPLETE" | "MAX_TOKENS" | "ERROR",
  "usage": {
    "billed_tokens": 0,
    "input_tokens": 0,
    "output_tokens": 0
  },
  "citations": [...],
  "search_results": [...]
}
\`\`\`

## Available Models
- command-r-plus: Most capable, 128K context
- command-r: Balanced, 128K context
- command: Standard, 4K context
- command-light: Fast, 4K context
- command-r7b-2025: New 7B model

## Python SDK Example
\`\`\`python
import cohere

co = cohere.Client("YOUR_API_KEY")

response = co.chat(
    model="command-r-plus",
    message="Hello!",
    temperature=0.3,
    max_tokens=1024
)

print(response.message.content[0].text)
\`\`\`

## Node.js Example
\`\`\`javascript
import { CohereClient } from "cohere-ai";

const cohere = new CohereClient({ token: "YOUR_API_KEY" });

const response = await cohere.chat({
  model: "command-r-plus",
  message: "Hello!",
  maxTokens: 1024
});

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

## cURL Example
\`\`\`bash
curl https://api.cohere.ai/v2/chat \\
  -H "Authorization: Bearer $COHERE_API_KEY" \\
  -H "Content-Type: application/json" \\
  -d '{
    "model": "command-r-plus",
    "messages": [{"role": "user", "content": "Hello!"}],
    "max_tokens": 1024
  }'
\`\`\``
    },
    {
        id: "cohere-embed",
        title: "Cohere Embed API",
        category: "embeddings",
        provider: "cohere",
        keywords: ["cohere", "embed", "embeddings", "vector"],
        content: `# Cohere Embed API

## Endpoint
POST https://api.cohere.ai/v1/embed

## Request
\`\`\`json
{
  "model": "embed-english-v3.0" | "embed-multilingual-v3.0" | "embed-english-light-v3.0" | "embed-multilingual-light-v3.0",
  "texts": ["text1", "text2"],
  "input_type": "search_document" | "search_query" | "classification" | "clustering",
  "truncate": "NONE" | "LEFT" | "RIGHT",
  "embedding_types": ["float"] | ["int8"] | ["uint8"] | ["binary"] | ["ubinary"]
}
\`\`\`

## Response
\`\`\`json
{
  "id": "xxx",
  "texts": ["text1", "text2"],
  "embeddings": [[0.1, 0.2, ...], [0.3, 0.4, ...]],
  "response_type": "embeddings_by_type",
  "meta": {
    "billed_units": {"input_tokens": 10}
  }
}
\`\`\`

## Model Details
- embed-english-v3.0: 1024 dimensions, best quality
- embed-multilingual-v3.0: 1024 dimensions, 100+ languages
- embed-english-light-v3.0: 384 dimensions, faster
- embed-multilingual-light-v3.0: 384 dimensions, multilingual fast

## Input Types
- search_document: For embedding documents to store
- search_query: For embedding search queries
- classification: For classification tasks
- clustering: For clustering tasks

## Python Example
\`\`\`python
import cohere

co = cohere.Client("YOUR_API_KEY")

response = co.embed(
    model="embed-english-v3.0",
    texts=["Hello world"],
    input_type="search_document"
)

embeddings = response.embeddings
\`\`\``
    },
    {
        id: "cohere-rerank",
        title: "Cohere Rerank API",
        category: "rerank",
        provider: "cohere",
        keywords: ["cohere", "rerank", "ranking", "search", "relevance"],
        content: `# Cohere Rerank API

## Endpoint
POST https://api.cohere.ai/v1/rerank

## Request
\`\`\`json
{
  "model": "rerank-v3.5" | "rerank-english-v3.0" | "rerank-multilingual-v3.0",
  "query": "What is Python?",
  "documents": [
    "Python is a programming language",
    "Python is a snake",
    "Python was created by Guido van Rossum"
  ],
  "top_n": 3,
  "return_documents": true,
  "max_chunks_per_doc": 10
}
\`\`\`

## Response
\`\`\`json
{
  "id": "xxx",
  "results": [
    {
      "index": 2,
      "relevance_score": 0.95,
      "document": {"text": "Python was created by Guido van Rossum"}
    },
    {
      "index": 0,
      "relevance_score": 0.85,
      "document": {"text": "Python is a programming language"}
    }
  ],
  "meta": {"billed_units": {"search_units": 1}}
}
\`\`\`

## Model Details
- rerank-v3.5: Latest, best quality
- rerank-english-v3.0: English optimized
- rerank-multilingual-v3.0: Multilingual

## Python Example
\`\`\`python
import cohere

co = cohere.Client("YOUR_API_KEY")

response = co.rerank(
    model="rerank-v3.5",
    query="What is Python?",
    documents=[
        "Python is a programming language",
        "Python is a snake",
        "Python was created by Guido van Rossum"
    ],
    top_n=2
)

for result in response.results:
    print(f"Index {result.index}: {result.relevance_score}")
\`\`\`

## Use in RAG Pipeline
1. Retrieve documents with embeddings (fast, approximate)
2. Rerank with Cohere Rerank (precise ordering)
3. Pass top N to LLM for generation`
    },
    {
        id: "cohere-generate",
        title: "Cohere Generate (Text Completion)",
        category: "completion",
        provider: "cohere",
        keywords: ["cohere", "generate", "completion", "text"],
        content: `# Cohere Generate API

## Endpoint
POST https://api.cohere.ai/v1/generate

## Request
\`\`\`json
{
  "model": "command" | "command-light",
  "prompt": "Once upon a time",
  "max_tokens": 1024,
  "temperature": 0.7,
  "p": 0.75,
  "k": 0,
  "stop_sequences": ["\\n\\n"],
  "num_generations": 1,
  "return_likelihoods": "NONE" | "GENERATION" | "ALL"
}
\`\`\`

## Response
\`\`\`json
{
  "id": "xxx",
  "generations": [
    {
      "id": "xxx",
      "text": "there was a...",
      "finish_reason": "COMPLETE",
      "likelihood": -12.34
    }
  ],
  "prompt": "Once upon a time",
  "meta": {"billed_units": {"input_tokens": 4, "output_tokens": 50}}
}
\`\`\`

## Note
The Generate API is legacy. Use Chat API (v2) for new applications.`
    },
    {
        id: "cohere-rate-limits",
        title: "Cohere Rate Limits & Pricing",
        category: "limits",
        provider: "cohere",
        keywords: ["cohere", "rate limit", "pricing", "cost"],
        content: `# Cohere Rate Limits & Pricing

## Rate Limits
- Free tier: 5-10 RPM (varies by endpoint)
- Paid tier: Higher limits based on plan

## Pricing (per 1M tokens)
### Chat
- command-r-plus: Input $2.50, Output $10.00
- command-r: Input $0.50, Output $1.50
- command: Input $1.00, Output $2.00
- command-light: Input $0.30, Output $0.60

### Embed
- embed-english-v3.0: $0.10
- embed-multilingual-v3.0: $0.10
- embed-english-light-v3.0: $0.10

### Rerank
- rerank-v3.5: $2.00 per 1K documents
- rerank-english-v3.0: $2.00 per 1K documents

## Context Windows
- command-r-plus: 128K tokens
- command-r: 128K tokens
- command: 4K tokens
- command-light: 4K tokens

## Error Codes
- 400: Bad Request
- 401: Unauthorized
- 403: Forbidden
- 429: Rate limit exceeded
- 500: Internal error`
    }
];
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