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Unclaimed ProfileMastering local AI orchestration is becoming a critical skill for modern developers, and this comprehensive course by Laxmi Kant (KGP Talkie) provides the exact blueprint needed to succeed. Focused on LangChain v1 and Ollama, the training guides you through running powerful open-source LLMs—like DeepSeek R1, LLaMA, and Gemma—entirely on your local machine. Students appreciate the deep dive into LangChain Expression Language (LCEL), which simplifies creating complex runnable pipelines, prompt templates, and structured JSON output parsers. The course shines in its practical, hands-on approach, teaching you how to build highly functional conversational chatbots with persistent memory, optimized Retrieval-Augmented Generation (RAG) pipelines, and autonomous AI agents capable of tool calling. Beyond the basics, you will work on highly relevant real-world projects, including a sophisticated Text-to-SQL agent for MySQL databases, automated LinkedIn scrapers, and resume parsing pipelines. Finally, the curriculum bridges the gap between local development and production by walking through AWS EC2 deployment strategies. Whether you are an aspiring AI engineer or an experienced developer looking to transition to local LLM infrastructure, this course offers the practical skills needed to build secure, cost-effective, and robust AI applications.
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KGP Talkie | Laxmi Kant
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Laxmi Kant, widely recognized online as the creator of KGP Talkie, is an experienced educator and software engineer specializing in artificial intelligence, machine learning, and modern backend integration. With a passion for demystifying cutting-edge technology, Laxmi focuses heavily on practical, hands-on tutorials that bridge the gap between academic theory and deployment-ready software. His teaching methodology revolves around building real-world projects, which is evident in his extensive curriculum covering LangChain, LangGraph, Ollama, FastAPI, and Model Context Protocol. Laxmi believes that the best way to master artificial intelligence is through direct implementation. He guides students through the step-by-step process of designing private…Show more
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Learning format
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What You'll Learn
- Install and integrate LangChain v1 and Ollama to run Qwen3, Gemma3, DeepSeek R1, GPT-OSS, LLAMA, and custom GGUF models locally.
- Build complete chatbots with memory, history, streaming responses, and a Streamlit UI.
- Use prompt templates, LCEL chains, chain routing, parallel chains, custom chains, and runnable pipelines to structure LLM workflows.
- Parse structured output using Pydantic, JSON, CSV parsers, and .with_structured_output() methods.
- Implement advanced retrieval systems including similarity search, MMR search, threshold search, and optimized chunking.
- Use tool calling and function calling with DuckDuckGo, Tavily, Wikipedia, PubMed, and custom tools.
- Build production-ready AI agents using LangChain v1 agent API, dynamic model selection, middleware, state management, and real-time streaming.
- Create Agentic RAG systems including autonomous retrieval, context citation, custom FAISS tools, and streamed agentic responses.
- Build a complete Text-to-SQL Agent for MySQL with schema extraction, SQL generation, validation, execution, and automated error correction.
- Build LinkedIn scraper, resume parser, and data extraction workflows using Selenium, BeautifulSoup, LLM parsing, and Streamlit apps.
- Deploy LangChain v1 + Ollama applications to AWS EC2, configure remote servers, and run production-level AI apps.
Best For
- Developers looking to build and deploy privacy-focused, local AI applications using open-source LLMs.
- Engineers who want to master LangChain Expression Language (LCEL) and advanced AI agent orchestration.
- Professionals aiming to integrate RAG pipelines and autonomous tool-calling capabilities into their projects.
- Data scientists or software architects interested in cost-effective alternatives to proprietary LLM APIs.
Not For
- Absolute beginners with no prior experience in Python programming.
- Individuals seeking a theoretical overview of AI without hands-on coding or implementation.
- Developers exclusively interested in cloud-based API integrations who have no interest in local machine infrastructure.
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