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AI Agents with LangChain & Autonomous Workflows · Lezione

Integrare gli LLM con LangChain

Impari a collegare diversi provider di LLM, come OpenAI e Hugging Face, ai propri agenti LangChain.

Integrare gli LLM con LangChain è una lezione AI Agents with LangChain & Autonomous Workflows gratuita su CoddyKit. Questa è la lezione 2 di 4. Puoi leggere la lezione completa qui gratuitamente — poi esercitati direttamente nel browser con un editor di codice integrato e un tutor IA disponibile 24/7. Fa parte del percorso di apprendimento AI Agents with LangChain & Autonomous Workflows, e i tuoi progressi si sincronizzano tra il web e l'app CoddyKit. Il corso AI Agents with LangChain & Autonomous Workflows include 4 lezioni in totale.

Parti di questa lezione non sono ancora state tradotte e vengono mostrate in inglese.

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-openai

OpenAI 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-huggingface

Hugging 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., ChatAnthropic for 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!

Domande Frequenti

La lezione «Integrare gli LLM con LangChain» è gratuita?

Sì — il testo completo di «Integrare gli LLM con LangChain» è gratuito qui sul web. Per esercitarvi in modo interattivo (un editor di codice integrato e un tutor IA 24/7) e sbloccare il resto del corso AI Agents with LangChain & Autonomous Workflows, passa a CoddyKit PRO. Il corso AI Agents with LangChain & Autonomous Workflows include 4 lezioni in totale.

Cosa imparerò in «Integrare gli LLM con LangChain»?

Impari a collegare diversi provider di LLM, come OpenAI e Hugging Face, ai propri agenti LangChain. Eserciti AI Agents with LangChain & Autonomous Workflows con codice pratico che esegui direttamente nel browser, e un tutor IA 24/7 risponde alle tue domande mentre lavori sulla lezione.

Ho bisogno di esperienza per iniziare AI Agents with LangChain & Autonomous Workflows?

Non è richiesta alcuna esperienza precedente. AI Agents with LangChain & Autonomous Workflows su CoddyKit è strutturato per principianti e studenti avanzati, quindi puoi iniziare da qui o dall'inizio e procedere al tuo ritmo. Questa è la lezione 2 di 4.

Quanto tempo richiede la lezione «Integrare gli LLM con LangChain»?

La maggior parte delle lezioni CoddyKit richiede circa 5–10 minuti. Ogni lezione è breve e interattiva, quindi fai progressi costanti e riprendi esattamente da dove hai lasciato su web e app.

Posso scrivere ed eseguire codice in questa lezione AI Agents with LangChain & Autonomous Workflows?

Sì. Ogni lezione AI Agents with LangChain & Autonomous Workflows include un editor di codice integrato, quindi scrivi ed esegui codice reale direttamente nel tuo browser e ricevi feedback istantaneo dall'IA — nessuna configurazione locale necessaria.

Tutte le lezioni di questo corso

  1. Tecniche efficaci di progettazione dei prompt
  2. Integrare gli LLM con LangChain
  3. Gestire i parametri e i costi dei modelli
  4. Parsing e validazione di output strutturati
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