Lang Chain Master Class for Beginners

Overview

• Purpose: Teach everything needed to start AI development with Lang Chain.
• Outcome: Ability to create chat bots, agents, and tools using Lang Chain.
• Structure: Begin with basics, gradually introduce advanced features.
• Resources: 20 examples and source code available for free download.
• Community Support: Free AI developer community with 1500+ members, free coaching calls.

Environment Setup

Steps

1. Install Python
2. Install Poetry (dependency management tool)
3. Run poetry install to install dependencies
4. Confirm Poetry works with poetry shell
5. Update environment variables (e.g., OpenAI key)

Working with Lang Chain Core Components

Chat Models

Overview

• Purpose: Interface with large language models (LLMs) like ChatGPT, Claude, Gemini.
• Features: Tool calling outputs, multimodal support, JSON content output.
• Setup: Create chat models, define prompt templates, automate tasks using chains.

Basic Example

1. Create Chat Model.
2. Define and format prompt templates.
3. Invoke function to get results.
4. Handle metadata and token usage.
5. Use result content for further automation.

Conversation Example

1. Initialize Chat History and System Message.
2. Alternate between Human and AI messages.
3. Use message history for context-aware conversations.

Advanced Chat Models

1. Use different LLMs (e.g., Anthropic's Claude, Google's Gemini).
2. Compare and contrast models based on task performance, cost, and speed.

Agent and Tool Integration

1. Chat models can make decisions and act autonomously using tools.
2. Examples: Writers generating summaries, conversing with documents using Rag, accessing external APIs.
3. Combine chat models, prompts, tools, and chains to automate workflows.

Detailed Modules

Prompt Templates

Usage

1. Create structured prompts that include variables and specific instructions.
2. Use templates to dynamically generate prompts for different tasks (e.g., jokes, emails).
3. Test automation with different levels of complexity.

Chains

Overview

1. Combine tasks into a sequence of operations.
2. Use LLM Expression Language to chain tasks using the pipe operator.
3. Execute and manage complex workflows by chaining together prompts and models.

Practical Examples

1. Basic sequential task chaining (prompt output -> model input).
2. Advanced chaining with branching and parallel execution.

Rag (Retrieval Augmented Generation)

Concept

1. Augment LLMs knowledge by retrieving external documents (PDFs, websites, etc.) using vector stores and embeddings.
2. Enhance AI responses by including specific document context in the prompts.

Implementation

1. Set up vector stores and create embeddings for text chunks.
2. Retrieve and integrate relevant document chunks to answer questions.
3. Use metadata to improve document retrieval and enhance response accuracy.

Agents and Tools

Concept

1. Agents are LLMs with specialized prompts (state machines) guiding behavior through specific steps.
2. Tools are scripts or functions that agents can use to perform actions (e.g., web searches, executing code).
3. Interaction: Agents use tools to fetch data or execute routines, improving task automation.

Setup and Examples

1. Create simple tools (e.g., current time, Wikipedia search) and package them with descriptive prompts for agents.
2. Build agents using structured prompts and configure them to interact with various tools and data sources.

Final Notes

• The course includes comprehensive source code and practical examples for various AI automation tasks.
• Community involvement is encouraged for continuous learning and support.
• Additional resources and tutorials on related AI topics are available on the instructor’s YouTube channel.