deepseek

DeepSeek Model

A DeepSeek model implementation for Eino that implements the ToolCallingChatModel interface. This enables seamless integration with Eino's LLM capabilities for enhanced natural language processing and generation.

Features

• Implements github.com/cloudwego/eino/components/model.Model
• Easy integration with Eino's model system
• Configurable model parameters
• Support for chat completion
• Support for streaming responses
• Custom response parsing support
• Flexible model configuration

Installation

go get github.com/cloudwego/eino-ext/components/model/deepseek@latest

Quick Start

Here's a quick example of how to use the DeepSeek model:

package main

import (
	"context"
	"fmt"
	"log"
	"os"
	"time"

	"github.com/cloudwego/eino-ext/components/model/deepseek"
	"github.com/cloudwego/eino/schema"
)

func main() {
	ctx := context.Background()
	apiKey := os.Getenv("DEEPSEEK_API_KEY")
	if apiKey == "" {
		log.Fatal("DEEPSEEK_API_KEY environment variable is not set")
	}
	cm, err := deepseek.NewChatModel(ctx, &deepseek.ChatModelConfig{
		APIKey:  apiKey,                          
		Model:    os.Getenv("MODEL_NAME"),           
		BaseURL: "https://api.deepseek.com/beta", 
	

	})
	if err != nil {
		log.Fatal(err)
	}

	messages := []*schema.Message{
		{
			Role:    schema.System,
			Content: "You are a helpful AI assistant. Be concise in your responses.",
		},
		{
			Role:    schema.User,
			Content: "What is the capital of France?",
		},
	}

	resp, err := cm.Generate(ctx, messages)
	if err != nil {
		log.Printf("Generate error: %v", err)
		return
	}

	reasoning, ok := deepseek.GetReasoningContent(resp)
	if !ok {
		fmt.Printf("Unexpected: non-reasoning")
	} else {
		fmt.Printf("Reasoning Content: %s\n", reasoning)
	}
	fmt.Printf("Assistant: %s\n", resp.Content)
	if resp.ResponseMeta != nil && resp.ResponseMeta.Usage != nil {
		fmt.Printf("Tokens used: %d (prompt) + %d (completion) = %d (total)\n",
			resp.ResponseMeta.Usage.PromptTokens,
			resp.ResponseMeta.Usage.CompletionTokens,
			resp.ResponseMeta.Usage.TotalTokens)
	}

}

Configuration

The model can be configured using the deepseek.ChatModelConfig struct:

type ChatModelConfig struct {
	// APIKey is your authentication key
	// Required
	APIKey string `json:"api_key"`

	// Timeout specifies the maximum duration to wait for API responses
	// Optional. Default: 5 minutes
	Timeout time.Duration `json:"timeout"`

	// HTTPClient specifies the client to send HTTP requests.
	// Optional. Default http.DefaultClient
	HTTPClient *http.Client `json:"http_client"`

	// BaseURL is your custom deepseek endpoint url
	// Optional. Default: https://api.deepseek.com/
	BaseURL string `json:"base_url"`

	// Path sets the path for the API request. Defaults to "chat/completions", if not set.
	// Example usages would be "/c/chat/" or any http after the baseURL extension
	// Path 用于设置 API 请求的路径。如果未设置，则默认为 "chat/completions"。
	// 用法示例可以是 "/c/chat/" 或 baseURL 之后的任何 http 路径。
	Path string `json:"path"`

	// The following fields correspond to DeepSeek's chat API parameters
	// Ref: https://api-docs.deepseek.com/api/create-chat-completion

	// Model specifies the ID of the model to use
	// Required
	Model string `json:"model"`

	// MaxTokens limits the maximum number of tokens that can be generated in the chat completion
	// Range: [1, 8192].
	// Optional. Default: 4096
	MaxTokens int `json:"max_tokens,omitempty"`

	// Temperature specifies what sampling temperature to use
	// Generally recommend altering this or TopP but not both.
	// Range: [0.0, 2.0]. Higher values make output more random
	// Optional. Default: 1.0
	Temperature float32 `json:"temperature,omitempty"`

	// TopP controls diversity via nucleus sampling
	// Generally recommend altering this or Temperature but not both.
	// Range: [0.0, 1.0]. Lower values make output more focused
	// Optional. Default: 1.0
	TopP float32 `json:"top_p,omitempty"`

	// Stop sequences where the API will stop generating further tokens
	// Optional. Example: []string{"\n", "User:"}
	Stop []string `json:"stop,omitempty"`

	// PresencePenalty prevents repetition by penalizing tokens based on presence
	// Range: [-2.0, 2.0]. Positive values increase likelihood of new topics
	// Optional. Default: 0
	PresencePenalty float32 `json:"presence_penalty,omitempty"`

	// ResponseFormat specifies the format of the model's response
	// Optional. Use for structured outputs
	ResponseFormatType ResponseFormatType `json:"response_format_type,omitempty"`

	// FrequencyPenalty prevents repetition by penalizing tokens based on frequency
	// Range: [-2.0, 2.0]. Positive values decrease likelihood of repetition
	// Optional. Default: 0
	FrequencyPenalty float32 `json:"frequency_penalty,omitempty"`

	// LogProbs specifies whether to return log probabilities of the output tokens.
	LogProbs bool `json:"log_probs"`

	// TopLogProbs specifies the number of most likely tokens to return at each token position, each with an associated log probability.
	TopLogProbs int `json:"top_log_probs"`
}

Examples

See the following examples for more usage:

• Basic Generation
• Prefix Generation
• Intent & Tool Calling
• Streaming Response
• Tool Call Reasoning

For More Details

• Eino Documentation
• DeepSeek Documentation