LangChain is an open-source framework that helps developers create applications using large language models (LLMs). LLMs are deep-learning models that are trained on large amounts of data and can generate responses to user queries. LangChain provides tools and abstractions to improve the accuracy, relevancy, and customization of the information generated by LLMs.
LangChain's features include:
- Centralized development environment: A centralized development environment for building applications?
- Module-based approach: A module-based approach for building applications?
- Model interaction: The ability to interact with any language model?
- Data connection and retrieval: The ability to transform data, store it in databases, and retrieve it from those databases?
- Chains: The ability to link multiple LLMs with other components or LLMs?
LangChain can be used to build applications such as chatbots, virtual agents, intelligent search, question-answering, and summarization services.?
LangChain is a framework that simplifies the process of creating generative AI application interfaces. Developers working on these types of interfaces use various tools to create advanced NLP apps; LangChain streamlines this process. For example, LLMs have to access large volumes of big data, so LangChain organizes these large quantities of data so that they can be accessed with ease.
LangChain is made up of the following modules that ensure the multiple components needed to make an effective NLP app can run smoothly:
- Model interaction. Also called model I/O, this module lets LangChain interact with any language model and perform tasks such as managing inputs to the model and extracting information from its outputs.
- Data connection and retrieval. Data that LLMs access can be transformed, stored in databases and retrieved from those databases through queries with this module.
- Chains. When using LangChain to build more complex apps, other components or even more than one LLM might be required. This module links multiple LLMs with other components or LLMs. This is referred to as an LLM chain.
- Agents. The agent module lets LLMs decide the best steps or actions to take to solve problems. It does so by orchestrating a series of complex commands to LLMs and other tools to get them to respond to specific requests.
- Memory. The memory module helps an LLM remember the context of its interactions with users. Both short-term memory and long-term memory can be added to a model, depending on the specific use.
