Indexing Essentials: How RAG Organizes Data?

In RAG systems, pinpointing exact answers to our questions involves a process akin to finding the most relevant book within a huge library. This library isn't just large; it can hypothetically be infinite, containing every conceivable text, document, and article. To navigate this immense data trove efficiently, we rely on a concept called Indexing.

How does indexing enhance data retrieval?

Vectorization involves converting data into a suitable numeric format known as a vector. This is a crucial step that prepares data for the next stage—indexing. Indexing is the process of organizing this vectorized data into structures that support efficient querying and retrieval.

It is the backbone of any RAG system and fundamentally transforms large volumes of text into a structured, searchable format that computers can quickly understand and process. This transformation is essential for the efficient retrieval of information in response to user queries.

Without indexing, searching through vast datasets would be like flipping through every page of every book in an extensive library to find a single piece of information—a highly time-consuming and inefficient task. By organizing data in a structured way, indexing allows the system to quickly locate relevant information by referring to the index rather than scanning every document.

What does indexing do in RAG systems?

Now, let’s dive into the mechanics of how indexing is actually carried out, from document collection to vectorization.

1. Data collection: The initial step involves ingesting data from diverse sources that might include internal databases, documents, web pages, and other data. This data forms the basis of the knowledge base on which the RAG system will draw to answer queries.

2. Split and parse the data: Once the data is ingested, it needs to be broken down into manageable chunks. This is necessary because the LLMs used in RAG systems typically have a maximum context window that they can process in one go. During this step, the data is not only split The action of dividing the data into smaller, more manageable parts or chunks. This division is necessary because the LLMs used in RAG systems typically have a maximum context window that limits the amount of data they can process at once. but also parsed to extract useful metadata. Metadata might include information like document titles, authors, publication dates, and any other relevant data that could aid in retrieval and contextual understanding.

3. Vector embeddings: With the data chunked and metadata extracted, the next step is to convert these chunks into vector embeddingsA learned numerical representation of a piece of data. . This process involves using an embedding model (such as BERT, GPT, or other neural network models) that transforms text into a high-dimensional space where semantic relationships and textual similarities are numerically represented.

4. Vector database: The final step in the indexing pipeline is to store the generated embeddings along with their metadata in a vector database (such as ChromaDB, Pinecone, and Milvus) Such databases are optimized for handling large volumes of high-dimensional data and allow for efficient querying.