In the field of large models, there is a popular bird that is favored by more and more developers. This little bird comes from the official logo of LangChain and is one of the animal emojis we often use on social platforms. Some netizens speculated that the reason why the parrot was chosen as the logo of LangChain is because the large model is often jokingly called "random parrot", that is, it only imitates the text output of humans and lacks real comprehension. LangChain, on the other hand, combines parrot (🦜) with chain (🔗) in an ingenious way, implying its role in improving the capabilities of large models.
Open AI's GPT, Meta's LLaMA, Google's Gemini...... Over the past year or so, we've seen countless large models emerge and evolve, and their so-called superpowers for generating content and processing data have gradually changed from unbelievable to uncommon. Although the large model has broken into our lives with lightning speed, and even become a right-hand man in office and study scenes, it still has great potential waiting to be explored. It's not easy to get a large model to communicate with your application fluently, and we need a special tool to help us do that.
Imagine you're planning a trip abroad, and there's an app that helps us take care of everything from making an itinerary to booking flights and hotels, without you having to do it yourself. In order to do the above, it needs to connect the following.
- Large models such as GPT: This app uses large models to analyze your travel preferences and use this data to generate tailored travel recommendations. If necessary, it can also help you communicate with locals without barriers with real-time translation.
- Your data: This data includes things like your travel budget, the places you've traveled, hotels and things you've been involved in, and may be stored in databases or documents.
- Map services: The app also integrates with map services to ensure accurate routes and provide you with navigation.
- Third-party booking platforms: The app connects to multiple booking platforms, making it easy to book flights and hotels.
This all-powerful travel assistant requires the use of large models, user data, and various third-party platforms. As you might have guessed, that's where LangChain comes in.
LangChain acts as a glue that provides a set of tools and features to help developers integrate multiple large models, data sources, and tools into their applications, and simplifies the application development process. For example, with the memory component, it allows the model to gain a "memory" of the context, thus maintaining the coherence of the user's conversation with the model; With the help of the retrieval component, the model can easily retrieve and organize data from different data sources, and respond to the needs of users more quickly. With LLM classes, users can switch between various models, or use multiple models, without being constrained by a particular model...... With LangChain, developers can easily create smarter and more efficient applications.
In summary, LangChain allows developers to interact directly with large models according to their needs, and developing a large model application is no longer out of reach. As an open-source framework, LangChain has become a popular project on GitHub in just a few months since its release, and has garnered 80k+ stars so far.
🌟 Have you heard of LangChain, read this article and want to develop your own large-scale model application?
💡 Never heard of LangChain, but now full of curiosity?
Whether you're an AI enthusiast who wants to feel the mysterious power of large models, or you want to dive deeper into the basics of LangChain, this new book recommended today is your first choice to get started! 👇
"LangChain Actual Combat" by Cui Hao|
Article source: Turing Editorial Department