babymore87
MyBoerse.bz Pro Member
Free Download Master Langchain v1 and Ollama - Chatbot, RAG and AI Agents
Last updated 4/2026
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz
Language: English | Size: 12.39 GB | Duration: 19h 19m
Deploy Langchain v1 AI App at AWS, Local LLM Projects, Ollama, DeepSeek, LLAMA, Qwen3, Gemma3, GPT-OSS, Text to MySQL
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.
Requirements
Basic Python programming knowledge
Familiarity with APIs and web requests
Basic understanding of machine learning concepts
Access to a computer with internet for installations and setups
Curiosity to learn LLMs, AI agents, and RAG systems - everything else will be taught step-by-step.
Description
2026 Upgrade: Course completely re-recorded with LangChain v1 and LangGraph v1.All projects, agents, tools, and RAG pipelines rebuilt from scratch.**Perfect for developers, AI engineers, and serious learners who want production-grade GenAI skills.**This course is a comprehensive, practical guide to integrating Langchain v1 (latest release) and Ollama to build, automate, and deploy production-ready AI applications. Updated with the newest technologies and frameworks, you'll learn to set up these cutting-edge tools, create advanced prompt templates, build autonomous AI agents, implement RAG (Retrieval-Augmented Generation) systems, and deploy real-world applications on AWS. Each section is designed to provide you with hands-on skills and real-world experience with the latest AI development practices.What You Will Learn1. Ollama & Langchain SetupComplete installation and configuration of Ollama and LangchainWork with the latest models: GPT-OSS, Gemma3, Qwen3, DeepSeek R1, and LLAMA 3.2Master Ollama commands, custom model creation, and raw API integrationConfigure local LLM environments for optimal performance2. Advanced Prompt EngineeringDesign effective AI, human, and system message promptsUse ChatPromptTemplate and MessagesPlaceholder for dynamic conversationsMaster the invoke method and structured prompt patternsImplement best practices for prompt tuning and optimization3. LCEL Chains for Workflow AutomationBuild Sequential, Parallel, and Router Chains with Langchain Expression Language (LCEL)Create custom chains using RunnableLambda and RunnablePassthroughImplement chain decorators for simplified workflow automationDesign conditional logic and dynamic chain routing for complex applications4. Structured Output ParsingParse LLM outputs using Pydantic, JSON, CSV, and custom parsersUse with_structured_output method for type-safe responsesHandle date-time parsing and structured data extractionFormat data for downstream processing and integration5. Chat Memory and Conversation ManagementImplement chat history with BaseChatMessageHistory and InMemoryChatMessageHistoryUse MessagesPlaceholder for dynamic conversation flowBuild stateful conversational AI applicationsManage long-term chat sessions efficiently6. Build Production-Ready ChatbotsCreate interactive chatbot applications using StreamlitImplement streaming responses like ChatGPTMaintain persistent chat history and session stateDeploy user-friendly chat interfaces with real-time updates7. Document Processing with Multiple LoadersProcess PDFs using PyMuPDF and create QA systemsWork with Microsoft Office files (PPTX, DOCX, Excel)Use Microsoft's MarkItDown for universal document conversionImplement IBM's Docling for advanced OCR and document processingExtract tables, images, and figures from any document type8. Vector Stores and RAG ImplementationBuild Retrieval-Augmented Generation (RAG) systems with FAISS and ChromaCreate and manage vector embeddings using OllamaEmbeddingsImplement document chunking strategies with RecursiveTextSplitterOptimize chunk sizes for better retrieval performanceDesign RAG prompt templates for context-aware responses9. Agentic RAG SystemsBuild autonomous RAG agents that retrieve and reasonCreate custom tool decorators for agent capabilitiesImplement real-time streaming for agent responsesIntegrate vector stores with intelligent agent workflows10. Tool Calling and Function ExecutionSet up built-in tools: Tavily Search, DuckDuckGo, PubMed, WikipediaCreate custom tools and bind them to LLMsImplement tool calling loops for multi-step reasoningPass tool results back to LLMs for informed responses11. AI Agents with LangchainMaster the create_agent API for building intelligent agentsBuild web search agents with DuckDuckGo integrationImplement agent state management and middlewareCreate dynamic model selection for intelligent agent routingStream agent responses in real-time using values, updates, and messages12. Text-to-SQL Agent (MySQL Integration)Build natural language to SQL query systemsCreate schema inspection, query generation, and validation toolsImplement automatic SQL error correction with LLMsExecute complex database queries from natural language13. Real-World AI ProjectsStock Market News Analysis: Scrape web data and generate comprehensive reportsLinkedIn Profile Scraper: Extract and parse profile data with LLMsResume Parser: Build AI-powered CV analysis and JSON extraction systemHealth Supplements QA: Create domain-specific RAG question-answering systems14. Production Deployment on AWSLaunch and configure AWS EC2 instances for LLM applicationsInstall Ollama and Langchain on cloud serversDeploy Streamlit applications in production environmentsConnect VS Code to remote servers for seamless developmentBy the end of this course, you'll have the expertise to build, deploy, and manage production-grade AI-powered applications using Langchain and Ollama. You'll be able to create intelligent chatbots, RAG systems, autonomous agents, and document processors that are ready for real-world deployment.Start building the future of AI applications today.
Developers who want to build AI-powered applications, chatbots, and intelligent automation tools.,Data Scientists & ML Engineers who want hands-on experience with LangChain v1, LangGraph workflows, and real-world RAG systems.,AI enthusiasts and students who want to go beyond theory and build practical GenAI projects using open-source LLMs.,Professionals who want practical experience with tool calling, AI agents, retrieval systems, document processing, and production deployments.,Anyone with basic Python knowledge looking to build end-to-end AI applications that run locally using Ollama.
Homepage
Recommend Download Link Hight Speed | Please Say Thanks Keep Topic Live
No Password - Links are Interchangeable
