Master Langchain Llm Integration: Build Smarter Ai Solutions

Develop Intelligent AI Solutions with LangChain - Chatbots, Custom Workflow, LLMs, and Prompt Optimization Techniques

What you'll learn

Master LangChain architecture and LLM integration, harnessing advanced agents, chains, and document loaders to design intelligent, scalable AI solutions

Design and implement robust end-to-end LangChain workflows, leveraging document splitters, embeddings, and vector stores for dynamic AI retrieval

Integrate and optimize multiple vector stores and retrieval systems, mastering FAISS, ChromaDB, PineCone, and others to elevate AI model performance

Leverage diverse document loaders, text splitters, and embedding techniques to efficiently transform unstructured data for AI processing

Implement interactive LangChain applications with dynamic chain runnables, parallel execution, and robust fallback strategies for resilience

Utilize advanced prompt templates and output parsers, including JSON, YAML, and custom formats to optimize and enhance AI model interactions for accuracy

Apply LangSmith and Phoenix Arize tools for end-to-end tracing and evaluation, ensuring reliable performance of your LangChain QA applications

Build and deploy robust AI solutions by integrating LLMs with LangChain, using agents, retrievers, prompt engineering, and scalable vector systems

Requirements

Python Basics: Familiarity with Python is beneficial; beginners will receive guided tutorials to ramp up quickly using Conda environments

AI/ML Fundamentals: Basic knowledge of AI and machine learning concepts (like LLMs and embeddings) is helpful, though foundational concepts are covered

Command-Line Skills: Some comfort with terminal or command prompt operations is useful for environment setup and running scripts

Data Format Handling: An understanding of formats like CSV, JSON, PDF, and Markdown is advantageous; tutorials will assist you in working with these data types

Access to APIs: While access to OpenAI’s paid API can enhance learning, alternatives like Ollama are provided, ensuring a low entry barrier

Reliable Equipment: A computer with a stable internet connection capable of running Python and necessary packages is required for a smooth learning experience

Description

Master LangChain and build smarter AI solutions with large language model (LLM) integration! This course covers everything you need to know to build robust AI applications using LangChain. We’ll start by introducing you to key concepts like AI, large language models, and retrieval-augmented generation (RAG). From there, you’ll set up your environment and learn how to process data with document loaders and splitters, making sure your AI has the right data to work with.Next, we’ll dive deep into embeddings and vector stores, essential for creating powerful AI search and retrieval systems. You’ll explore different vector store solutions such as FAISS, ChromaDB, and Pinecone, and learn how to select the best one for your needs. Our retriever modules will teach you how to make your AI smarter with multi-query and context-aware retrieval techniques.In the second half of the course, we’ll focus on building AI chat models and composing effective prompts to get the best responses. You’ll also explore advanced workflow integration using the LangChain Component Execution Layer (LCEL), where you’ll learn to create dynamic, modular AI solutions. Finally, we’ll wrap up with essential debugging and tracing techniques to ensure your AI workflows are optimized and running efficiently.What Will You Learn?How to set up LangChain and Ollama for local AI developmentUsing document loaders and splitters to process text, PDFs, JSON, and other formatsCreating embeddings for smarter AI search and retrievalWorking with vector stores like FAISS, ChromaDB, Pinecone, and moreBuilding interactive AI chat models and workflows using LangChainOptimizing and debugging AI workflows with tools like LangSmith and custom retriever tracingCourse HighlightsStep-by-step guidance: Learn everything from setup to building advanced workflowsHands-on projects: Apply what you learn with real-world examples and exercisesReference code: All code is provided in a GitHub repository for easy access and practiceAdvanced techniques: Explore embedding caching, context-aware retrievers, and LangChain Component Execution Layer (LCEL)What Will You Gain?Practical experience with LangChain, Ollama, and AI integrationsA deep understanding of vector stores, embeddings, and document processingThe ability to build scalable, efficient AI workflowsSkills to debug and optimize AI solutions for real-world use casesHow Is This Course Taught?Clear, step-by-step explanationsHands-on demos and practical projectsReference code provided on GitHub for all exercisesReal-world applications to reinforce learningJoin Me on This Exciting Journey!Build smarter AI solutions with LangChain and LLMsStay ahead of the curve with cutting-edge AI integration techniquesGain practical skills that you can apply immediately in your projectsLet’s get started and unlock the full potential of LangChain together!

Overview

Section 1: Introduction

Lecture 1 Introduction

Lecture 2 Git Repository for Demos

Lecture 3 Foundation Lectures

Lecture 4 Getting Started with LangChain: A Framework for Smarter AI Apps

Lecture 5 LangChain Components: Building Blocks of AI-Powered Workflows

Lecture 6 Real-World LangChain Applications: AI in Action

Section 2: Setup

Lecture 7 Setting Up LangChain: Your First Step Towards AI Development

Lecture 8 Conda Setup for LangChain: Managing Environments Easily

Lecture 9 Run Your First LangChain Program & See AI in Action

Lecture 10 Ollama 101: An Intro to Local AI Model Deployment

Lecture 11 Setting Up Ollama: Running AI Models Without the Cloud

Lecture 12 Ollama & LangChain: Seamless LLM Integration for Smarter AI

Lecture 13 Bringing LangChain & Ollama Together: Hands-on Integration Guide

Lecture 14 Exploring the LangChain Ecosystem: Tools, Features & Capabilities

Section 3: Document Loaders

Lecture 15 Intro to Document Loaders: Feeding AI the Right Data

Lecture 16 PDF Loader: Extracting Insights from PDF Files

Lecture 17 CSV & JSON Loaders: Structuring AI-Friendly Data

Lecture 18 Handling Unstructured Documents: Making Sense of Raw Text

Lecture 19 Directory Loader: Managing Multiple Files for AI Processing

Section 4: Document Splitter

Lecture 20 Splitting Documents: Why It’s Crucial for AI Processing

Lecture 21 Character-Based Text Splitters: Breaking Down Large Texts

Lecture 22 Hands-on Demo: Using Character Splitters in LangChain

Lecture 23 Structured Text Splitting: Keeping AI Organized

Lecture 24 Splitting HTML Documents: Extracting AI-Readable Content

Lecture 25 Splitting JSON Files: Making Complex Data AI-Friendly

Lecture 26 Markdown Splitter: Preparing Notes & Code for AI Processing

Lecture 27 Splitting Code & Text: Processing Language & Markdown Efficiently

Section 5: Embeddings

Lecture 28 Intro to Embeddings: Transforming Text into AI-Readable Data

Lecture 29 Embeddings Playground: Experimenting with AI’s Understanding

Lecture 30 Using Ollama for Embeddings: Running Models Locally

Lecture 31 OpenAI Embeddings: Exploring Cloud-Based Vectorization

Lecture 32 Creating Embeddings for Text Files: Structuring Raw Data

Lecture 33 Embedding PDFs: Enhancing AI Search & Retrieval

Lecture 34 HuggingFace Embeddings: Open-Source Models in Action

Lecture 35 Caching Embeddings: Optimizing Speed & Efficiency

Lecture 36 Fake Embeddings: Understanding AI Testing Techniques

Section 6: Vector Store

Lecture 37 Intro to Vector Stores: Storing AI’s Knowledge Smartly

Lecture 38 Vector Store Demo: How AI Remembers & Retrieves Data

Lecture 39 FAISS Vector Store: Optimizing Search for Speed & Accuracy

Lecture 40 FAISS with HuggingFace: Supercharging AI Storage & Retrieval

Lecture 41 ChromaDB & WebStore: Efficient Data Storage for AI Apps

Lecture 42 ChromaDB for PDFs: Storing & Searching AI-Friendly Documents

Lecture 43 Sqlite Vector Store: Lightweight Storage for AI Data

Lecture 44 Weaviate Vector Store: Scalable AI Search & Discovery

Lecture 45 Qdrant Vector Store (InMemory): Fast & Efficient Retrieval

Lecture 46 Qdrant Vector Store (Container): Deploying AI Search at Scale

Lecture 47 PineCone Vector Store: The Powerhouse for AI Indexing

Lecture 48 Vector Stores Recap: Choosing the Right Storage for Your AI

Section 7: Retrievers

Lecture 49 Retrievers 101: How AI Finds the Right Information

Lecture 50 Different Retrieval Methods: Which One Suits Your AI?

Lecture 51 Retrievers with Scoring: Ranking AI Results for Accuracy

Lecture 52 Multi-Query Retrieval: Enhancing AI’s Search Capabilities

Lecture 53 Ensemble Retrieval: Combining BM25 & FAISS for Best Results

Lecture 54 Context Reordering: Making AI Smarter with Better Context

Lecture 55 Parent-Child Document Retrieval: Understanding Relationships

Section 8: Chat Model & Messages

Lecture 56 Intro to Chat Models: How AI Conversations Work

Lecture 57 Understanding Chat Messages: Structuring AI Interactions

Lecture 58 Chat Model Demo: Creating Your First AI Chatbot

Lecture 59 LangChain Chat Model: Connecting AI with Workflow Chains

Lecture 60 Chat Models & Tool Integration: Expanding AI Capabilities

Lecture 61 Binding & Invoking Tools: Making AI More Interactive

Lecture 62 Human-In-The-Loop AI: When to Let Users Control AI

Lecture 63 Managing Model Token Usage: Optimizing AI Costs

Lecture 64 Rate Limiting in AI: Keeping Performance in Check

Lecture 65 Few-Shot Prompting: Teaching AI with Small Examples

Lecture 66 Prompt Templates: Structuring AI Requests for Better Output

Lecture 67 Composing Effective Prompts: Mastering AI Communication

Section 9: Output Parsers

Lecture 68 String Output Parser: Extracting AI Responses as Text

Lecture 69 JSON Output Parser: Formatting AI Outputs for Apps

Lecture 70 YAML Output Parser: Structured AI Outputs Made Simple

Lecture 71 Custom Output Parsing: Tailoring AI’s Responses to Your Needs

Section 10: LCEL

Lecture 72 Runnable Interface: Connecting AI Components Dynamically

Lecture 73 LCEL Demo: Running LangChain Workflows in Action

Lecture 74 Working with Chain Runnables: Streamlining AI Execution

Lecture 75 Runnable PassThrough: Making AI More Modular

Lecture 76 Parallel Execution: Speeding Up AI Tasks Efficiently

Lecture 77 Streaming with Runnables: Handling AI Data in Real-Time

Lecture 78 Default Invocation: Optimizing LangChain Workflow Calls

Lecture 79 Sub-Chain Routing: Directing AI Processes Smartly

Lecture 80 Self-Constructing Chains: AI That Adapts & Evolves

Lecture 81 Inspecting Runnables: Debugging AI Workflows Effectively

Lecture 82 LLM & Chain Fallbacks: Handling AI Failures Gracefully

Section 11: Example Selector

Lecture 83 Example Selection: Optimizing AI Responses with Context

Lecture 84 Selecting by Length: Keeping AI Answers Concise

Lecture 85 Selecting by Similarity: Matching AI Responses to Input

Lecture 86 Selecting by N-Gram Overlap: Enhancing AI Relevance

Lecture 87 MMR-Based Selection: Improving AI’s Answer Diversity

Section 12: Tracing & Evaluation

Lecture 88 LangSmith Introduction: Tracing AI Workflows Effectively

Section 13: Foundation

Lecture 89 AI & ML Basics: Understanding How Machines Learn & Think

Lecture 90 Intro to LLMs: How Large Language Models Transform AI

Lecture 91 Gen AI & RAG: Unlocking Smarter AI with Retrieval-Augmented Generation

Lecture 92 Vectors & Similarity: How AI Finds Meaning in Data

Lecture 93 Embeddings & Vectors: The Foundation of AI Understanding

Lecture 94 Vector Databases: Storing & Retrieving AI Knowledge Efficiently

Lecture 95 Indexes in AI: Optimizing Search & Retrieval for Faster Responses

Lecture 96 AI Agents: How They Think, Act & Automate Tasks

Lecture 97 Chains & Workflows: Connecting AI Components for Smart Execution

Section 14: Conclusion

Lecture 98 Conclusion

Aspiring AI Developers: Ideal for developers with basic Python skills who want to master LangChain and integrate LLMs to build advanced, intelligent applications,Data Scientists: Perfect for data professionals eager to enhance AI pipelines with efficient document loaders, embeddings, and vector databases for smarter data processing,Machine Learning Enthusiasts: Designed for those familiar with AI/ML fundamentals who seek to expand their knowledge into cutting-edge LangChain architectures and workflows,Software Engineers: Suited for engineers aiming to incorporate advanced prompt engineering, chain runnables, and agent integrations into robust AI solutions,Generative AI Beginners: Great for learners new to generative models and LLMs, offering step-by-step guidance and accessible resources to build a strong foundation,Tech Innovators & Integrators: Beneficial for professionals looking to integrate multiple AI tools—like Ollama and OpenAI—into scalable, production-ready systems