Building a Retrieval Augmented Generation App with Langchain and OpenAI

Introduction

• Purpose: Create an app to interact with documents or data sources using AI for Q&A or chatbot purposes.
• Libraries/Tools: Langchain and OpenAI in Python.
• Technique: Retrieval Augmented Generation (RAG).

Overview of RAG

• Example: Asking the agent questions about AWS documentation for Lambda.
• Benefits: Ensures responses are based on provided data sources, avoiding AI hallucinations.

Project Steps

1. Data Preparation: How to prepare and load textual data.
2. Creating a Vector Database: Turn the loaded data into a searchable vector database.
3. Querying the Database: Retrieve relevant data chunks to answer questions.
4. Generating Responses: Use retrieved data to generate informed AI responses.

Step-by-Step Guide

1. Data Preparation

• Data Source: PDF, collection of text, or markdown files (e.g., documentation files, customer support handbook, podcast transcripts).
• Loading Data: Use Langchain's directory loader module to load markdown files. Group documents into appropriate folders.
• Splitting Data: Chunk long documents for more focused and relevant search results. Use recursive character text splitter.
  • Example:
    • Chunk size: 1000 characters
    • Overlap: 500 characters
  • Outcome: Large documents split into smaller, manageable chunks.

2. Creating a Vector Database

• Tool: ChromaDB for vector embeddings.
• **Process: **
  1. Generate vector embeddings using OpenAI.
  2. Create and persist ChromaDB with the embeddings.

3. Understanding Vector Embeddings

• Vector embeddings capture the meaning of text; they are lists of numbers representing text in multi-dimensional space.
• Distance Measurement:
  • Cosine similarity or Euclidean distance can be used to measure the closeness of embeddings.
• **Practical Example: **
  • Using OpenAI embeddings for words like 'apple,' 'orange,' 'beach,' and 'iPhone'.
  • Evaluator function in Langchain to compare distances.

4. Querying the Database

• Objective: Find database chunks most relevant to a query.
• **Process: **
  1. Use the same embedding function for the query.
  2. Retrieve top chunks based on embedding distance.
  3. Check relevancy before processing results.

5. Generating AI Response

• Prompt Template: Use placeholders for context (chunks) and the query.
  • Example: Provide context and query in a structured prompt to OpenAI.
• Code Implementation: Use models like chatOpenAI to generate responses.
• Referencing Sources: Extract source metadata to ensure response traceability.
• Final Script: Main function with all steps integrated for querying and AI-generated responses. Example outputs for queries like “How does Alice meet the Mad Hatter?” and AWS Lambda documentation queries.

Conclusion

• Applications: Retrieval augmented generation with varied datasets (e.g., books, documentation, customer support data).
• Next Steps: Link to GitHub code, suggestions for further tutorials, and closing remarks.