ChatGroq 集成

export GROQ_API_KEY="your-api-key"

# export LANGSMITH_TRACING="true"
# export LANGSMITH_API_KEY="your-api-key"

import { ChatGroq } from "@langchain/groq"

const llm = new ChatGroq({
    model: "llama-3.3-70b-versatile",
    temperature: 0,
    maxTokens: undefined,
    maxRetries: 2,
    // 其他参数...
})

const aiMsg = await llm.invoke([
    {
      role: "system",
      content: "You are a helpful assistant that translates English to French. Translate the user sentence.",
    },
    { role: "user", content: "I love programming." },
])
aiMsg

AIMessage {
  "content": "I enjoy programming. (The French translation is: \"J'aime programmer.\")\n\nNote: I chose to translate \"I love programming\" as \"J'aime programmer\" instead of \"Je suis amoureux de programmer\" because the latter has a romantic connotation that is not present in the original English sentence.",
  "additional_kwargs": {},
  "response_metadata": {
    "tokenUsage": {
      "completionTokens": 73,
      "promptTokens": 31,
      "totalTokens": 104
    },
    "finish_reason": "stop"
  },
  "tool_calls": [],
  "invalid_tool_calls": []
}

console.log(aiMsg.content)

I enjoy programming. (The French translation is: "J'aime programmer.")

Note: I chose to translate "I love programming" as "J'aime programmer" instead of "Je suis amoureux de programmer" because the latter has a romantic connotation that is not present in the original English sentence.

const messages = [
  {
    role: "system",
    content: "You are a math tutor that handles math exercises and makes output in json in format { result: number }.",
  },
  { role: "user",  content: "2 + 2 * 2" },
];

const aiInvokeMsg = await llm.invoke(messages, { response_format: { type: "json_object" } });

// 如果您不想在每次调用时都传递 response_format，可以将其绑定到实例
const llmWithResponseFormat = llm.bind({ response_format: { type: "json_object" } });
const aiBindMsg = await llmWithResponseFormat.invoke(messages);

// 它们是相同的
console.log({ aiInvokeMsgContent: aiInvokeMsg.content, aiBindMsg: aiBindMsg.content });

{
  aiInvokeMsgContent: '{\n"result": 6\n}',
  aiBindMsg: '{\n"result": 6\n}'
}