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Simplest RAG Explanation with Working Code! Beginner Friendly Step-by-Step Example
Keerti Purswani
Overview
This video explains the concept and implementation of Retrieval Augmented Generation (RAG) as a method to enable Large Language Models (LLMs) to answer questions based on specific, private data. It contrasts RAG with fine-tuning, highlighting the cost and complexity issues of the latter. The video then walks through a practical example using LangChain and ChromaDB to build a RAG pipeline that scrapes a website, splits the content into chunks, embeds these chunks into a vector database, and uses them to augment LLM responses. The process involves setting up an API key, loading web content, splitting text, embedding, storing in a vector store, retrieving relevant information, and constructing a prompt for the LLM.
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Chapters
- LLMs are trained on vast datasets but lack knowledge of specific, private company data.
- Fine-tuning LLMs for private data is expensive, computationally intensive, time-consuming, and difficult to update.
- Fine-tuning also makes it hard to remove specific pieces of information.
- RAG offers a more practical solution for integrating private data with LLMs.
Understanding the limitations of standard LLMs and the drawbacks of fine-tuning is crucial for appreciating why RAG is a valuable and efficient alternative for custom data integration.
An e-commerce platform wanting an LLM to answer questions about its specific products, or a course provider wanting an LLM to answer questions about its unique courses.
- RAG involves retrieving relevant information from a database and providing it as context to the LLM along with the user's query.
- The LLM then uses this provided context to generate an answer.
- RAG stands for Retrieval (fetching data), Augmented (enhancing the query with data), and Generation (LLM creating the answer).
- RAG is suitable for building applications that leverage LLMs with custom data, unlike building LLMs from scratch.
This chapter demystifies RAG by breaking down its core components and explaining its purpose, making the subsequent technical implementation easier to grasp.
When a user asks a question, RAG first searches a database for relevant information, then sends that information and the question to the LLM, instructing it to use the provided information to answer.
- The first step is to get your private data into a format accessible by the RAG system, typically a vector database.
- This involves scraping websites (using tools like LangChain's WebBaseLoader) to gather content.
- The scraped content is then split into smaller, manageable chunks (using Text Splitters like RecursiveCharacterTextSplitter) to avoid overwhelming the LLM.
- Chunk overlap is important to maintain context between adjacent text chunks.
Proper data ingestion and preparation, including splitting and managing overlaps, ensures that the information fed into the RAG system is relevant, contextual, and efficiently processed.
Scraping the educates.com website for course information and then splitting the content into chunks of 1000 characters with an overlap of 200 characters.
- Textual data needs to be converted into numerical representations called vectors (embeddings) for storage and retrieval in a vector database.
- This process captures the semantic meaning of the text.
- ChromaDB is used as the vector database in this example.
- OpenAI embeddings are used to convert text chunks into vectors.
Understanding embeddings and vector databases is key to grasping how RAG systems efficiently search and retrieve semantically similar information from a large corpus of text.
Converting text chunks about a course into numerical vectors and storing them in ChromaDB, where each vector represents the meaning of its corresponding text chunk.
- The RAG pipeline is constructed using LangChain, which orchestrates the flow of data and operations.
- A retriever is created from the vector store to fetch relevant document chunks based on a query.
- A pre-defined RAG prompt template is used to structure the input for the LLM, including placeholders for context and the user's question.
- The prompt is augmented with retrieved context, and then sent to an LLM (like OpenAI's GPT) for generation.
LangChain provides the framework to connect different components (retriever, prompt, LLM) into a cohesive RAG application, simplifying the development process.
Using LangChain's `RunnablePassthrough` to pass the user's question directly and a custom function `format_documents` to join retrieved text chunks into a single context string for the prompt.
- The constructed RAG chain is invoked with a user query.
- The chain retrieves relevant information, formats it, constructs a prompt, and sends it to the LLM.
- The LLM generates an answer based on the provided context.
- The output can be optionally inspected to see the exact prompt sent to the LLM, aiding in debugging and understanding.
- The RAG system successfully answers questions about course details, testimonials, and covered projects.
Testing the RAG pipeline with real queries validates its effectiveness and demonstrates its ability to provide accurate, context-aware answers based on the ingested data.
Asking the RAG system 'Are the recordings of the course available for how long?' and receiving the answer 'The recordings of the course are available for lifetime access.'
Key takeaways
- Fine-tuning LLMs for specific data is often impractical due to cost, complexity, and update challenges.
- RAG enhances LLMs by providing relevant external data as context, enabling them to answer questions about private or dynamic information.
- The RAG process involves retrieving relevant data, augmenting the LLM's input with this data, and then generating an answer.
- Text splitting and chunk overlap are critical for managing large documents and preserving context within a RAG system.
- Vector databases and embeddings are essential for efficiently storing and searching through large amounts of text data based on semantic meaning.
- LangChain simplifies the creation of RAG pipelines by providing tools to chain together data loading, splitting, embedding, retrieval, prompting, and LLM interaction.
- A well-implemented RAG system can act as a powerful chatbot or Q&A tool for specific datasets, such as company websites or product catalogs.
Key terms
Retrieval Augmented Generation (RAG)Large Language Model (LLM)Fine-tuningVector DatabaseEmbeddingsLangChainChromaDBText SplitterChunk SizeChunk OverlapRetrieverPrompt Template
Test your understanding
- What are the primary drawbacks of fine-tuning an LLM for company-specific data compared to using RAG?
- How does the 'Retrieval' step in RAG work to prepare information for the LLM?
- Why is it important to split large documents into smaller chunks and use chunk overlap in a RAG pipeline?
- Explain the role of vector databases and embeddings in enabling efficient information retrieval for RAG.
- How does LangChain facilitate the creation of a RAG application by connecting different components?