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Visão Geral

O LangChain é um framework popular para a construção de aplicações de LLM. O LemonData funciona perfeitamente com a integração OpenAI do LangChain.

Instalação

pip install langchain langchain-openai

Configuração Básica

from langchain_openai import ChatOpenAI

llm = ChatOpenAI(
    model="gpt-4o",
    api_key="sk-your-lemondata-key",
    base_url="https://api.lemondata.cc/v1"
)

response = llm.invoke("Hello, how are you?")
print(response.content)

Usando Diferentes Modelos

Acesse qualquer modelo do LemonData:
# OpenAI GPT-4o
gpt4 = ChatOpenAI(
    model="gpt-4o",
    api_key="sk-your-key",
    base_url="https://api.lemondata.cc/v1"
)

# Anthropic Claude
claude = ChatOpenAI(
    model="claude-sonnet-4-5",
    api_key="sk-your-key",
    base_url="https://api.lemondata.cc/v1"
)

# Google Gemini
gemini = ChatOpenAI(
    model="gemini-2.5-flash",
    api_key="sk-your-key",
    base_url="https://api.lemondata.cc/v1"
)

# DeepSeek
deepseek = ChatOpenAI(
    model="deepseek-r1",
    api_key="sk-your-key",
    base_url="https://api.lemondata.cc/v1"
)

Chat com Histórico de Mensagens

from langchain_core.messages import HumanMessage, SystemMessage

messages = [
    SystemMessage(content="You are a helpful assistant."),
    HumanMessage(content="What is the capital of France?")
]

response = llm.invoke(messages)
print(response.content)

Streaming

for chunk in llm.stream("Write a poem about coding"):
    print(chunk.content, end="", flush=True)

Uso Assíncrono

import asyncio

async def main():
    response = await llm.ainvoke("Hello!")
    print(response.content)

asyncio.run(main())

Chains

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

prompt = ChatPromptTemplate.from_messages([
    ("system", "You are a helpful assistant that translates {input_language} to {output_language}."),
    ("