Model OpenAI di LangChain!

Model seri GPT dari OpenAI termasuk LLM berkinerja tertinggi saat ini. Model ini tersedia melalui API OpenAI, yang dapat Anda gunakan dengan mudah menggunakan LangChain.

Biasanya, menggunakan model OpenAI memerlukan kunci API pribadi untuk penagihan biaya model. Dalam kursus ini, Anda tidak perlu membuat atau memberikan kunci API OpenAI. Placeholder "<OPENAI_API_TOKEN>" akan mengirim permintaan yang valid ke API. Jika Anda membuat banyak permintaan dalam waktu singkat, Anda mungkin akan mengalami RateLimitError. Jika ini terjadi, harap jeda sejenak lalu coba lagi.

Kelas ChatOpenAI sudah diimpor.

Latihan ini merupakan bagian dari kursus

Mengembangkan Aplikasi LLM dengan LangChain

Instruksi latihan

Definisikan model chat OpenAI menggunakan kelas LangChain; biarkan placeholder kunci API apa adanya.
Panggil LLM yang Anda definisikan untuk merespons prompt yang disediakan (silakan bereksperimen dengan prompt lain di sini).

Latihan interaktif langsung praktik

Cobalah latihan ini dengan melengkapi kode contoh ini.

# Define the LLM
llm = ____(model="gpt-4o-mini", api_key="")

# Predict the words following the text in question
prompt = 'Three reasons for using LangChain for LLM application development.'
response = ____(prompt)

print(response.content)

Edit dan Jalankan Kode

Latihan ini merupakan bagian dari kursus

Mengembangkan Aplikasi LLM dengan LangChain

SkillTag.level.intermediateSkillTag.label

4.8+

Mulai Kursus Gratis

Welcome to the LangChain framework for building applications on LLMs! You'll learn about the main components of LangChain, including models, chains, agents, prompts, and parsers. You'll create chatbots using both open-source models from Hugging Face and proprietary models from OpenAI, create prompt templates, and integrate different chatbot memory strategies to manage context and resources during conversations.

Exercise 1: Ekosistem LangChain Exercise 2: Model OpenAI di LangChain!

Latihan Saat Ini

Exercise 3: Model Hugging Face di LangChain!Exercise 4: Templat prompt Exercise 5: Templat prompt dan perangkaian Exercise 6: Templat prompt chat Exercise 7: Few-shot prompting Exercise 8: Membuat himpunan contoh few-shot Exercise 9: Membangun templat prompt few-shot Exercise 10: Menerapkan few-shot prompting

Time to level up your LangChain chains! You'll learn to use the LangChain Expression Language (LCEL) for defining chains with greater flexibility. You'll create sequential chains, where inputs are passed between components to create more advanced applications. You'll also begin to integrate agents, which use LLMs for decision-making.

Exercise 1: Sequential chains Exercise 2: Building prompts for sequential chains Exercise 3: Sequential chains with LCEL Exercise 4: Introduction to LangChain agents Exercise 5: What's an agent?Exercise 6: ReAct agents Exercise 7: Custom tools for agents Exercise 8: Defining a function for tool use Exercise 9: Creating custom tools Exercise 10: Integrating custom tools with agents

One limitation of LLMs is that they have a knowledge cut-off due to being trained on data up to a certain point. In this chapter, you'll learn to create applications that use Retrieval Augmented Generation (RAG) to integrate external data with LLMs. The RAG workflow contains a few different processes, including splitting data, creating and storing the embeddings using a vector database, and retrieving the most relevant information for use in the application. You'll learn to master the entire workflow!

Exercise 1: Integrating document loaders Exercise 2: PDF document loaders Exercise 3: CSV document loaders Exercise 4: HTML document loaders Exercise 5: Splitting external data for retrieval Exercise 6: Splitting by character Exercise 7: Recursively splitting by character Exercise 8: Splitting HTML Exercise 9: RAG storage and retrieval using vector databases Exercise 10: Preparing the documents and vector database Exercise 11: Building a retrieval prompt template Exercise 12: Creating a RAG chain Exercise 13: Wrap-up!