在 LangChain 中使用

pip install langchain-openai langchain-anthropic

npm install @langchain/openai @langchain/core

import os
from langchain_openai import ChatOpenAI

llm = ChatOpenAI(
    model="gpt-5.4-mini",
    base_url="https://open.tutujin.com/v1",
    api_key=os.environ["TUTUJIN_API_KEY"],
    temperature=0.3,
    max_retries=3,
    timeout=60,
)

print(llm.invoke("用一句话解释向量数据库。").content)

# 流式
for chunk in llm.stream("写一段 100 字的产品介绍"):
    print(chunk.content, end="", flush=True)

import { ChatOpenAI } from '@langchain/openai'

const llm = new ChatOpenAI({
  model: 'gpt-5.4-mini',
  apiKey: process.env.TUTUJIN_API_KEY,
  configuration: { baseURL: 'https://open.tutujin.com/v1' },
  temperature: 0.3,
})

const res = await llm.invoke('用一句话解释向量数据库。')
console.log(res.content)

// 流式
for await (const chunk of await llm.stream('写一段 100 字的产品介绍')) {
  process.stdout.write(String(chunk.content))
}

from langchain_core.output_parsers import StrOutputParser
from langchain_core.prompts import ChatPromptTemplate

prompt = ChatPromptTemplate.from_messages([
    ("system", "你是电商文案编辑，输出不超过 60 字。"),
    ("user", "为这件商品写一句卖点：{product}"),
])
chain = prompt | llm | StrOutputParser()
print(chain.invoke({"product": "304 不锈钢保温杯，500ml"}))

import os
from langchain_openai import OpenAIEmbeddings

embeddings = OpenAIEmbeddings(
    model="text-embedding-3-small",
    base_url="https://open.tutujin.com/v1",
    api_key=os.environ["TUTUJIN_API_KEY"],
)
vectors = embeddings.embed_documents(["退货政策是什么？", "多久发货？"])
query_vector = embeddings.embed_query("怎么退货")

embeddings = OpenAIEmbeddings(
    model="qwen3-embedding-8b",
    base_url="https://open.tutujin.com/v1",
    api_key=os.environ["TUTUJIN_API_KEY"],
    check_embedding_ctx_length=False,
)

import os
from langchain_anthropic import ChatAnthropic

claude = ChatAnthropic(
    model="claude-sonnet-5-5",
    base_url="https://open.tutujin.com",
    api_key=os.environ["TUTUJIN_API_KEY"],
    max_tokens=1024,
)
print(claude.invoke("用三点总结这份会议纪要的要点：……").content)