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LangChain Mind Map

Introduction

What is LangChain?

• Open-source framework for AI development
• Combines large language models (LLMs) with external computation & data
• Available in Python and TypeScript (JavaScript)

Why Use LangChain?

• Popularity surged after GPT-4 (March 2023)
• Enables referencing personal data sources (e.g., PDFs, databases)
• Facilitates automated actions (e.g., sending emails)

Core Concepts

1. LLM Wrappers

• Connects to models like GPT-4, Hugging Face models

2. Prompt Templates

• Avoid hardcoding text
• Allows dynamic text input

3. Indexes

• Extracts relevant information for LLMs
• Stores data in vector format

4. Chains

• Combines multiple components to build LLM applications
• Enables sequential processing (e.g., multi-step reasoning)

5. Agents

• Interacts with external APIs
• Can execute Python code

LangChain Pipeline

Step 1: User Query

• Input question provided by the user

Step 2: Vector Representation

• Question converted into vector format
• Similarity search performed in vector database

Step 3: Data Retrieval

• Relevant document chunks fetched

Step 4: Response Generation

• LLM generates response based on query & retrieved data

Practical Applications

Data-Aware Applications

• References personal/company data
• Uses vector databases (e.g., Pinecone)

Action-Oriented Applications

• Automates tasks (e.g., booking flights, transferring money, paying taxes)

Education & Learning

• LLMs assist in studying by referencing syllabi

Coding & Data Science

• Enhances analytics by connecting to company data

Setting Up LangChain

Install Dependencies

• pip install python-dotenv langchain pinecone-client

Environment Variables

• OpenAI API key
• Pinecone API key & environment

Using LLM Wrappers

Text-based Model

• Uses OpenAI’s text-davinci-003

Chat-based Model

• Uses OpenAI’s GPT-3.5-turbo or GPT-4
• Implements message schemas (AI, human, system messages)

Prompt Templates

• Enables dynamic input formatting
• Example: Inject user query into a predefined text structure

Chains

Simple Chain

• Combines LLM with a prompt template

Sequential Chain

• Output of one chain feeds into another
• Example:
• Chain 1: Explain a concept
• Chain 2: Explain it for a 5-year-old

Embeddings & Vector Stores

Step 1: Splitting Text into Chunks

• Uses RecursiveCharacterTextSplitter

Step 2: Generating Embeddings

• Uses OpenAI’s Ada model

Step 3: Storing in Pinecone

• Uploads vectors for similarity search

Step 4: Retrieving Information

• Queries stored vectors to fetch relevant information

Agents & Python Execution

Using Agents for Code Execution

• Implements OpenAI model with Python execution capabilities
• Example: Finding quadratic equation roots with NumPy

Conclusion

• LangChain simplifies LLM integration with external data & APIs
• Expanding capabilities enable powerful applications
• Ongoing advancements enhance AI automation and accessibility