LLM OpenAI
LLM OpenAI 组件提供与 OpenAI 及 OpenAI 兼容 API(Azure OpenAI、Ollama 等)的集成。
{ "component-llm-openai": { "api_key": "sk-xxx...", "api_base": "https://api.openai.com/v1", "default_model": "gpt-4" }}用于 Azure OpenAI
Section titled “用于 Azure OpenAI”{ "component-llm-openai": { "api_key": "your-azure-key", "api_base": "https://your-resource.openai.azure.com", "api_type": "azure", "api_version": "2024-02-15-preview", "deployment_name": "gpt-4" }}在 Flows 中使用
Section titled “在 Flows 中使用”基础 Completion
Section titled “基础 Completion”- id: ask_llm type: llm config: model: "gpt-4" prompt: "What is the capital of France?" next: use_response配合 System Prompt
Section titled “配合 System Prompt”- id: classify type: llm config: model: "gpt-4" system_prompt: | You are a customer service intent classifier. Classify the user's message into one of: greeting, question, complaint, other. Respond with JSON: {"intent": "...", "confidence": 0.0-1.0} prompt: "{{message}}" output_format: json next: route_intentChat Completion
Section titled “Chat Completion”- id: chat type: llm config: model: "gpt-4" messages: - role: system content: "You are a helpful assistant." - role: user content: "{{user_message}}" temperature: 0.7 max_tokens: 500配合 Function Calling
Section titled “配合 Function Calling”- id: extract_entities type: llm config: model: "gpt-4" prompt: "Extract order information from: {{message}}" functions: - name: extract_order description: "Extract order details" parameters: type: object properties: order_id: type: string description: "The order ID" product: type: string description: "Product name" required: ["order_id"] function_call: "auto"| 参数 | 类型 | 默认值 | 说明 |
|---|---|---|---|
model | string | ”gpt-4” | 模型名称 |
prompt | string | - | 用户提示词 |
system_prompt | string | - | 系统消息 |
messages | array | - | 完整消息数组 |
temperature | float | 1.0 | 创造性(0-2) |
max_tokens | int | - | 最大响应 token 数 |
output_format | string | ”text” | "text" 或 "json" |
functions | array | - | 函数定义 |
function_call | string | ”auto” | 函数调用模式 |
- id: ask_llm type: llm config: model: "gpt-4" prompt: "{{question}}" output: llm_response next: use_response
- id: use_response type: reply config: message: "{{llm_response}}"- id: safe_llm_call type: llm config: model: "gpt-4" prompt: "{{message}}" timeout: 30000 retry_count: 3 on_error: handle_error next: success
- id: handle_error type: reply config: message: "I'm having trouble processing that. Please try again."