Constructing a Langchain Pipeline for Processing Semi-structured Data using RAG
Hey there! Today, we'll walk you through the process of building a Retrieval-Augmented Generation (RAG) pipeline for semi-structured data using the powerful LangChain framework. This tutorial will demystify the challenges of working with unconventional data formats and help you create an efficient system that makes semantic searches a breeze!
Types of Data
Before we dive into the exciting world of RAG and LangChain, let's understand the three major types of data we'll be dealing with:
- Structured Data: Data stored according to a predefined schema with a fixed format like tables, databases, and spreadsheets.
- Unstructured Data: Random data without a structured format, such as images, texts, and PDFs.
- Semi-structured Data: A combination of structured and unstructured data, maintaining a hierarchical order based on markers but not following a rigid schema like XML, CSV, and HTML files with embedded tables.
What is RAG?
RAG stands for Retrieval-Augmented Generation, which is a simple yet effective method of feeding Large Language Models (LLMs) with novel information. In a regular RAG pipeline, we have a set of knowledge sources, embedding models, a vector database, and an LLM. We collect data from various sources, split it, get the embeddings, and store the data in a vector database for efficient content retrieval and answer generation.
What is LangChain?
LangChain is an open-source framework that simplifies the building of AI applications faster. It provides a range of tools, such as vector stores, document loaders, retrievers, embedding models, and text splitters, making it an ideal choice for building advanced RAG pipelines.
Building the RAG Pipeline
Now that you have a basic understanding of the concepts, let's walk through our approach to building the pipeline for handling semi-structured data:
- Data Extraction: Use tools like "unstructured" for extracting valuable information from PDF files, including tables and other relevant data. This open-source tool can handle various unstructured data formats.
- Embedding Generation: UseSentenceTransformers to create embeddings for both the extracted data chunks and user queries.
- Vector Store Creation: Set up a vector store using libraries like Faiss or ChromaDB to store the embeddings of text chunks for efficient content retrieval.
- Retrieval and Generation: Utilize the embeddings of user queries to retrieve relevant data chunks from the vector store, and then pass them along with the query to the LLM for generation of an answer.
- Deployment (Optional): For external access, deploy the pipeline on a cloud service like RunPod using cloud GPU. This involves setting up a persistent pod or API endpoint that processes queries and returns answers.
Key Takeaways
- Conventional RAG often struggles with semi-structured data due to issues like breaking up tables during text splitting and imprecise semantic searches.
- Extracting semi-structured data requires specialized tools like unstructured.
- With LangChain, we can build a multi-vector retriever for storing tables, texts, and summaries in document stores for better semantic search.
That's it! Now you know how to create a RAG pipeline for handling semi-structured data using LangChain. Happy building!
In the realm of data science and data-and-cloud-computing, learning the Retrieval-Augmented Generation (RAG) method with LangChain can revolutionize the way you work with semi-structured data. This approach integrates artificial-intelligence technologies to create an efficient system for learning and dealing with unconventional data formats. To build such a system, you can start by using education-and-self-development resources like videos, tutorials, and forums to understand the underlying concepts and techniques.
