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Building LLM Apps with Lang Chain

Aug 1, 2024

Langston Crash Course: Building LLM Applications with Lang Chain

Overview

  • Langston: Framework for building applications on top of Large Language Models (LLMs).
  • Example Application: Restaurant Name Generator using Streamlit.
  • Key Concepts: Lang Chain, OpenAI API, limitations of LLM-based applications.

What is Lang Chain?

  • Lang Chain: A framework to build applications using LLMs like GPT-3.5 or GPT-4.
  • Problem Addressed: Simplifies the process of building LLM applications by providing a standardized architecture.
  • Example: Restaurant Idea Generator using OpenAI API.

Limitations of Direct API Calls

  • Cost: OpenAI API charges per token, which can be expensive for startups.
  • Data Limitations: Knowledge limited to September 2021; cannot access latest information or internal data.
  • Industry Demand: Businesses want custom LLM solutions to access internal data and reduce costs.
  • OpenAI’s Stance: Focus on providing foundational APIs rather than complete solutions.

Framework Features: Lang Chain

  • Integration: Supports LLMs like GPT-3, GPT-4, Hugging Face models, etc.
  • Extensible: Plug-and-play support for different models without changing code structure.
  • Additional Integrations: Google Search, Wikipedia, organizational databases.

Initial Setup

  1. OpenAI Account: Create and acquire API key.
  2. Environment Setup: Store API keys securely, import necessary libraries.
  3. Install Modules: pip install langchain openai

Building the Application

Basic Example

  • OpenAI LLM Initialization: Setting up the temperature parameter for creativity.
  • Example Prompt: Generate a restaurant name based on cuisine.

Using Prompt Templates

  • Prompt Template: Define templates for various inputs like cuisine.
  • Example: {cuisine} Palace for generating a restaurant name.
  • LLM Chain: Combines LLM and prompt template for dynamic input processing.

Sequential Chains

  • Concept: Chain multiple LLM calls where the output of one is the input for another.
  • Example: Generate restaurant name and then generate menu items.
  • Implementation: Use SequentialChain to link multiple prompt templates.

Streamlit Integration

  • Streamlit: Easily build POC applications with minimal code.
  • UI Elements: Create dropdowns, headers, and other UI elements for user interaction.
  • Application Structure: Modularize code, create helper functions, and integrate Lang Chain logic.

Advanced Features: Agents

  • Agents: Use LLMs with external tools for complex tasks (e.g., Google Search, Wikipedia).
  • Examples: Fetching real-time data using plugins like SERP API, Expedia.
  • Implementation: Setting up agents to use multiple tools and LLM reasoning.

Memory in LLM Chains

  • Default Behavior: Stateless, does not remember past interactions.
  • Conversational Buffer Memory: Attaches memory to remember past exchanges.
  • Window Memory: Limits the number of past exchanges to optimize API cost.
  • Applications: Useful in chatbot applications for context-aware conversations.

Future Topics

  • Upcoming Features: Retrieval QA chain, Vector stores, advanced LLM applications.
  • Community Engagement: Encourages subscribing, liking, and sharing for continued learning and development.

Note: The transcript provides detailed step-by-step instructions and code snippets for building an LLM-based application using Lang Chain and Streamlit. Check video description for code links and further resources.

Full transcript