In this tutorial, we guide you through creating a Python-based Retrieval-Augmented Generation (RAG) application. This tool enables natural language queries on PDF documents, providing insightful responses and referencing source material. We’ll focus on creating an efficient, locally-run solution using board game manuals as sample data.
Key Features and Steps
- Document Loading
- Use Langchain’s PDF loader to import your PDF files.
- For flexibility, explore alternative document loaders for formats like CSV, HTML, and Markdown.
- Data Preprocessing
- Break down large documents into manageable chunks with Langchain’s recursive text splitter, improving data organization and response relevance.
- Generating Embeddings
- Choose the appropriate embedding function for your needs. Options include AWS Bedrock and local solutions like Ollama for embeddings.
- Maintain embedding consistency for optimal database querying.
- Vector Database Setup
- Store and update your embeddings in ChromaDB.
- Use unique chunk IDs to allow for database updates without duplication, enabling easy additions and modifications.
- Building the Querying Mechanism
- Create prompts to fetch contextually relevant data chunks from your vector database.
- Use a Local LLM via Ollama to generate natural language responses based on fetched data.
- Quality Assurance with Unit Testing
- Establish a framework for testing response accuracy with sample questions and expected answers.
- Implement positive and negative test cases to validate output reliability, and consider an 80–90% success threshold.
Takeaways
By following this guide, you’ll learn to set up a locally-operable RAG app capable of answering queries based on PDF content. Through embedding generation, vector storage, and effective testing, your app will deliver precise, context-rich responses tailored to your data sources.
