LangChain has emerged as a robust framework for constructing functions that leverage massive language fashions (LLMs). Whether or not you’re engaged on chatbots 🤖, serps 🔍, or AI-driven assistants 🧠, LangChain offers important instruments to streamline improvement. On this weblog, we are going to discover three core ideas of LangChain: Chains, Brokers, and Retrieval Methods, that are essential for constructing environment friendly AI functions.
Chains are sequences of steps the place the output of 1 element serves because the enter for the subsequent. In LangChain, chains allow builders to construct structured workflows involving LLMs, exterior APIs, and databases.
- đź”— Easy Chains: A primary sequential move the place every step processes the enter and passes it to the subsequent.
- đź”— Sequential Chains: A number of chains linked collectively to create a extra complicated workflow.
- đź”— LlamaIndex Chains: Combine LLMs with structured information sources.
- đź”— Customized Chains: Builders can outline their very own logic for chaining totally different features.
