LangChain Has a Free Framework for Building LLM-Powered Applications

LangChain connects LLMs to your data, tools, and APIs. RAG, agents, chains, and memory — the building blocks for AI applications beyond simple chat. Beyond ChatGPT Wrappers Calling an LLM API is easy. Building a production AI app is hard: How do you search YOUR documents? (RAG) How do you give the LLM access to tools? (Agents) How do you maintain conversation history? (Memory) How do you chain multiple LLM calls? (Chains) LangChain provides abstractions for all of this. What You Get for Free RAG (Retrieval-Augmented Generation): from langchain_community.document_loaders import PDFLoader from langchain_openai import OpenAIEmbeddings , ChatOpenAI from langchain_community.vectorstores import Chroma from langchain.chains import RetrievalQA # Load your documents loader = PDFLoader ( ' company_docs.pdf ' ) docs = loader . load () # Create vector store vectorstore = Chroma . from_documents ( docs , OpenAIEmbeddings ()) # Ask questions about your data qa = RetrievalQA . from_chain_type ( llm =