Different language models exist, each with their own unique traits, capabilities, and constraints. These differences make them suitable for specific tasks and underscore the need to carefully select the appropriate model for a given application.
What is LangChain?
LangChain serves as a powerful framework for creating applications driven by language models. These language models are advanced artificial intelligence systems with the capability to comprehend, learn, generate, and manipulate natural language.
Within LangChain, developers have access to modular abstractions that cover essential components for working with language models. The framework also supplies a wide range of implementations for these abstractions, making it convenient to use, whether in conjunction with the rest of the LangChain framework or independently.
A standout feature of LangChain is the availability of ready-to-use chains. These chains are structured combinations of components tailored for specific high-level tasks, such as chatbots, question-answering, data analysis, and more. They serve as a user-friendly entry point for individuals looking to start their projects with a specific use case.
What is LLM?
An LLM, which stands for Large Language Model, is a remarkable type of language model distinguished by its emergent properties resulting from its substantial size. These models are constructed using artificial neural networks and undergo training through self-supervised and semi-supervised learning methods, containing tens of millions to billions of weights. To handle the massive amounts of text data used for training and specialized AI accelerator hardware is employed, primarily gathering information from various sources on the Internet.
The operation of LLMs revolves around input text, where they repeatedly predict the following token or word. Additionally, they have the capacity to generate text from scratch or based on given prompts. LLMs exhibit versatility in performing numerous natural languages processing tasks, including summarization, translation, prediction, and generation.
What is the Difference Between LangChain and LLM?
LangChain serves as a framework created especially for developing software that makes use of language models. On the other hand, LLM is a particular type of language model known for its large size and emergent properties, below are some differences between the two:
| Lang Chain | LLM |
|---|---|
| A system for creating apps that is powered by language models. | A sizeable language model with emergent characteristics. |
| Covers various aspects of working with language models, such as input/output, data connection, chains, agents, memory, and callbacks. | Focuses on the core functionality of understanding, learning, generating, and manipulating natural language. |
| Available in Python (3.8) and JavaScript/TypeScript versions. | Available in Python (3.9) and implemented using artificial neural networks and AI accelerator hardware. |
Conclusion
Due to the diverse capabilities of language models, they find extensive applications in various fields. LLM is a class of language models with vast size and emergent features, while LangChain is a framework for creating applications driven by language models.
