Enhance generative AI systems by integrating internal data with large language models using RAG
Keith Bourne
Part 1 – Introduction to Retrieval-Augmented Generation (RAG)
Chapter 2: Code Lab – An Entire RAG Pipeline
2.5. Indexing
The document outlines the indexing stage of data processing, particularly focusing on web loading, data pre-processing, and vectorization for use in a generative AI application. The steps involved include:
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Web Loading and Crawling: This step involves fetching data from a specified web page using the WebBaseLoader class from the langchain_community document_loaders module, which pulls in necessary content based on specified CSS classes.
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Data Pre-processing and Splitting: After collecting the documents, the content is split into manageable chunks using a text splitter, specifically the SemanticChunker. This chunker preserves the semantic context of the text rather than splitting it arbitrarily, which is important for maintaining coherence in the data.
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Embedding and Indexing: The chunks are then converted into vector embeddings using OpenAIEmbeddings and stored in a Chroma vector database. This process involves creating a vector store from the split documents and generating mathematical representations (embeddings) of the content.
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Retriever Creation: A retriever mechanism is established to facilitate vector similarity searches within the vector database, enabling efficient retrieval of relevant documents based on user queries.
The document encourages experimentation with different web pages, text splitters, and embedding methods to understand their effects on data processing and retrieval outcomes. Overall, it serves as a foundational guide for setting up data indexing in a generative AI context.
2.6. Retrieval and generation
This document explains setting up a Retrieval-Augmented Generation (RAG) process using LangChain components. The RAG process involves combining retrieval and generation stages to answer user queries using a language model (LLM). Key steps include vectorizing the query, retrieving relevant documents, formatting the content, and generating a response using the LLM.
The document describes using a prompt template from LangChain Hub, which helps structure the interaction with the LLM. A specific template for RAG applications is used, requiring context and question inputs to generate an answer.
A function formats retrieved documents into a string suitable for the prompt. The LLM is defined using the ChatOpenAI class with a specific model.
The LangChain Expression Language (LCEL) is used to define a chain that includes retrieval, formatting, and LLM operations. The chain processes the query by retrieving documents, formatting them, feeding the formatted prompt into the LLM, and parsing the response.
Overall, the document outlines setting up a RAG pipeline with LangChain, focusing on the retrieval and formatting of data to optimize interaction with the LLM.