In today’s data-driven world, efficiently navigating through vast amounts of textual information is paramount. But how do we make sense of it all? Enter RAG — a powerful combination of retriever and generator models, revolutionizing text search and answering like never before.
Ever struggled with wrangling PDFs or HTMLs? With RAG, it’s a breeze.
file_id and content? Problem: How do we handle large documents efficiently?
Solution: Enter chunking — the process of breaking down texts into smaller, digestible sections.
?Let’s try out baseline chunking strategy to seamlessly parse PDFs and text files. But what happens when a paragraph exceeds the defined chunk size? If a paragraph is still too lengthy, we seamlessly divide it until it fits within the specified fixed chunk size.
?? Implementation: We typically store chunked datasets in Spark tables. However, for local usage, an alternative option is to save them in CSV files. These files include essential information such as file_id, chunk_id and chunk_content
To explore chunking and various strategies with code examples. Check out my blog for more details!
? Curious about what and why embeddings? Imagine turning words and phrases into numerical vectors that capture their meaning and context. But why do we need them?
? But how do embeddings benefit our project?
??? Solution: By utilizing BAAI/bge-large-en or any other embeddings model transform chunks of text into rich, semantic representations. In the realm of RAG, embeddings are pivotal. They empower us to search for top similar chunks from our vast dataset, thereby enhancing the accuracy of our responses to user questions. Don’t worry if this seems complex for now; as we progress through the next steps, it will become clearer.
?? Implementation: Once we’ve generated embeddings for our individual chunks, we store them alongside other information in a CSV file. This includes essential details such as file_id, chunk_id, chunk_content, and their corresponding embeddings.
VectorDB integration takes RAG to the next level. In VectorDB, we store chunks and their corresponding embeddings using semantic indexing. With semantic indexing, search becomes lightning-fast, providing instant access to the most relevant chunks based on your query. We utilize vectorDBs like Faiss, pgvector , Pinecode, or Chromedb for efficient storage and retrieval of embeddings in VectorDB.
Pass your query through VectorDB to fetch the top 5 most relevant embeddings and associated chunks. In this step, we leverage cosine similarity to retrieve the top K similar results from VectorDB. Say goodbye to endless scrolling — the information you need is right at your fingertips.
Introducing GPT3.5 — your ultimate text generation companion. With LLM, generating answers to your queries is as straightforward as taking API details and defining your agent. Just sit back and let the magic happen.
Combine your question, top 5 chunks, and prompt to create the perfect input for the LLM model to deliver insightful answers.
Watch as RAG effortlessly generates accurate and insightful answers to your queries. From complex research projects to everyday inquiries, RAG has you covered.
Tags: llm, GenAI, NLP, RAG, Chunking
