AI Workflow Automation with LangChain and LLMs: RAG, Prompt Chains, and Multi-Agents
aha30005480
Early Bird
50%
$2.20Basic / Python, R, multi-agent, vector-database, AI, LangChain, RAG
Part I LangChain Practice in code how to load a PDF as page-by-page Documents, chunk it, convert the chunks into vectors using embeddings, and index them in a vector store Convert questions into vectors and perform similarity searches (including scores, vectors, and MMR), then wrap the search in a Retriever (Runnable) and connect it to RAG Review practical criteria for assessing search quality, security and governance (including prompt injection), and common mistakes Part II Implement in R the Random Forest algorithm, which overcomes the limitations of decision trees, and accurately interpret model results A complete workflow ready for practical use, including hyperparameter tuning, cross-validation, and ROC curve analysis Part III Building an Automated Classification and Response System for Complaints and Inquiries - Practical Prompt Chains and Multi-Agent Systems
Basic
Python, R, multi-agent
