Integrando LLMs ao LangChain
Aprenda a conectar diferentes provedores de LLMs, como OpenAI e Hugging Face, aos seus agentes do LangChain.
Integrando LLMs ao LangChain é uma aula grátis de AI Agents with LangChain & Autonomous Workflows no CoddyKit. Esta é a aula 2 de 4. Você pode ler a aula completa abaixo gratuitamente — depois pratica ao vivo no navegador com um editor de código integrado e um tutor de IA 24/7. Faz parte do caminho de aprendizado de AI Agents with LangChain & Autonomous Workflows, e seu progresso é sincronizado entre a web e o app CoddyKit. O curso de AI Agents with LangChain & Autonomous Workflows inclui 4 aulas no total.
Partes desta aula ainda não foram traduzidas e aparecem em inglês.
Connect Your AI Brains
Imagine your AI agent as a chef. Sometimes they need a specific ingredient (an LLM) for a dish. LangChain lets your agent switch between different LLMs, like having a pantry full of options!
Why is this useful?
- Flexibility: Use the best model for each task.
- Cost: Optimize spending by using cheaper models for simple tasks.
- Performance: Access cutting-edge models as they emerge.
LangChain's Unified Interface
LangChain acts as a universal adapter for Large Language Models (LLMs). Instead of learning a new way to interact with each LLM provider, you learn one LangChain way.
It provides a consistent interface, whether you're talking to OpenAI's GPT models or a model from Hugging Face. This makes your code cleaner and easier to manage.
Integrating OpenAI Models
OpenAI offers powerful LLMs like GPT-3.5 and GPT-4. To use them with LangChain, you'll need an OpenAI API key.
Remember to keep your API key secret! You'll typically set it as an environment variable to avoid hardcoding it in your code.
Before we start, install the necessary library:
pip install langchain-openaiOpenAI Chat Model in Action
Here's how to connect to an OpenAI chat model and get a response. We'll use the ChatOpenAI class, which is optimized for conversational interactions.
Try running this example (ensure OPENAI_API_KEY is set in your environment):
import os
from langchain_openai import ChatOpenAI
from langchain_core.messages import HumanMessage
# Set your OpenAI API key as an environment variable
# os.environ["OPENAI_API_KEY"] = "YOUR_API_KEY_HERE"
# Initialize the chat model
# model_name can be "gpt-3.5-turbo", "gpt-4", etc.
chat = ChatOpenAI(model_name="gpt-3.5-turbo")
# Invoke the model with a simple message
response = chat.invoke([
HumanMessage(content="What is the capital of France?")
])
# Print the model's response
print(response.content)Connecting Hugging Face Models
Hugging Face is a hub for open-source AI models. LangChain allows you to easily integrate models hosted on the Hugging Face Hub or even run local models.
You'll need a Hugging Face API token for models hosted on their Inference API. Get one from your Hugging Face profile settings.
Install the required library:
pip install langchain-huggingfaceHugging Face Hub in Action
Let's use a text generation model from the Hugging Face Hub. We'll use the HuggingFaceHub class, specifying a model repository ID.
Try running this example (ensure HUGGINGFACEHUB_API_TOKEN is set):
import os
from langchain_huggingface import HuggingFaceHub
# Set your Hugging Face API token as an environment variable
# os.environ["HUGGINGFACEHUB_API_TOKEN"] = "YOUR_HF_TOKEN_HERE"
# Initialize the Hugging Face Hub model
# repo_id refers to a model on the Hugging Face Hub (e.g., "google/flan-t5-large")
llm = HuggingFaceHub(
repo_id="google/flan-t5-large",
model_kwargs={"temperature": 0.5, "max_length": 64}
)
# Invoke the model with a simple prompt
response = llm.invoke("What is the capital of Germany?")
# Print the model's response
print(response)Running Local HF Models
For advanced use cases, you might want to run Hugging Face models locally on your machine, especially if you have powerful hardware. LangChain supports this via the HuggingFacePipeline.
This requires installing the transformers library and often torch or tensorflow, and then loading a model directly. It gives you full control and privacy.
Beyond OpenAI & HF
LangChain's modular design means you're not limited to just OpenAI and Hugging Face!
You can integrate with many other LLM providers, including:
- Google: (e.g.,
GooglePalm,VertexAI) - Anthropic: (e.g.,
ChatAnthropicfor Claude models) - Cohere: (e.g.,
Cohere)
The pattern is very similar: install the relevant library, get an API key, and initialize the corresponding LangChain LLM class.
Selecting Your Perfect LLM
With so many options, how do you pick the right LLM?
- Task Complexity: Simple tasks might use smaller, cheaper models.
- Cost: API calls can add up. Compare pricing across providers.
- Performance: Some models are better at specific tasks (e.g., code generation vs. creative writing).
- Privacy: Local models offer maximum data privacy.
- Latency: Response time can vary.
Experimentation is key to finding the best fit!
Integrate & Conquer!
You've learned how LangChain helps you connect to various LLM providers. Let's check your understanding.
Lesson Summary: LLM Integration
Great job! In this lesson, you learned how to connect different Large Language Models to your LangChain agents.
- We explored how LangChain provides a unified interface for various LLMs.
- You saw practical examples of integrating OpenAI and Hugging Face Hub models.
- We touched upon other providers and discussed factors for choosing the right LLM for your needs.
Now your agents have access to a world of AI brains!
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- Cursos
- 12
- Aulas
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Perguntas Frequentes
A aula “Integrando LLMs ao LangChain” é grátis?
Sim — o texto completo de “Integrando LLMs ao LangChain” é grátis para ler aqui na web. Para praticá-la interativamente (um editor de código integrado e um tutor de IA 24/7) e desbloquear o restante do curso de AI Agents with LangChain & Autonomous Workflows, atualize para CoddyKit PRO. O curso de AI Agents with LangChain & Autonomous Workflows inclui 4 aulas no total.
O que vou aprender em “Integrando LLMs ao LangChain”?
Aprenda a conectar diferentes provedores de LLMs, como OpenAI e Hugging Face, aos seus agentes do LangChain. Você pratica AI Agents with LangChain & Autonomous Workflows com código prático que executa diretamente no navegador, e um tutor de IA 24/7 responde suas dúvidas enquanto trabalha na aula.
Preciso ter experiência prévia para começar AI Agents with LangChain & Autonomous Workflows?
Nenhuma experiência prévia é necessária. AI Agents with LangChain & Autonomous Workflows no CoddyKit é estruturado para alunos iniciantes até avançados, então você pode começar aqui ou desde o início e aprender no seu ritmo. Esta é a aula 2 de 4.
Quanto tempo leva a aula “Integrando LLMs ao LangChain”?
A maioria das aulas CoddyKit leva cerca de 5–10 minutos. Cada uma é compacta e interativa, então você faz progresso constante e retoma exatamente de onde parou entre web e app.
Posso escrever e executar código nesta aula de AI Agents with LangChain & Autonomous Workflows?
Sim. Cada aula de AI Agents with LangChain & Autonomous Workflows inclui um editor de código integrado, então você escreve e executa código real direto no navegador e recebe feedback de IA instantaneamente — nenhuma configuração local necessária.
Todas as aulas deste curso
- Técnicas eficazes de design de prompts
- Integrando LLMs ao LangChain
- Gerenciando parâmetros e custos dos modelos
- Análise e validação de saída estruturada