LangChain

The Large Language Model (LLM) is becoming a revolutionary technology that enables developers to build applications that were previously impossible to build. However, using these LLMs alone is often not enough to create a truly powerful application – when you can combine them with other computing or knowledge sources, you may realize their true capabilities.
LangChain is a framework for developing applications driven by language models, allowing developers to connect language models to other data sources and interact with their environment. LangChain aims to assist developers in the following six main areas, in order of increasing complexity:
📃 LLMs and Prompts: This includes prompt management, prompt optimization, a universal interface for all LLMs, and a universal utility for handling LLMs.
🔗 Chains: Chains are not just individual LLM calls, but call sequences (whether for LLM or different utilities). LangChain provides standard interfaces for chains, extensive integration with other tools, and end-to-end chains for common applications.
📚 Data Augmented Generation involves specific types of chains that first interact with external data sources to obtain data for use in the generation step. Examples in this regard include summarizing long texts and answering questions about specific data sources.
🤖 Agents: Agents involve LLM making decisions about what actions to take, taking those actions, seeing an observation, and repeating until completed. LangChain provides a standard interface for agents to choose from, as well as examples of end-to-end agents.
🧠 Memory: Memory is the concept of persistent state between chain/proxy calls. LangChain provides a standard interface for memory, a collection of memory implementations, and examples of using memory chains/proxies.
🧐 Evaluation: [BETA] It is well known that generative models are difficult to evaluate using traditional metrics. A new method of evaluating them is to use the language model itself for evaluation, and LangChain provides some hints/chains to assist in this.