Welcome back! In the previous chapters, we built the foundation for our Docs-Reader:

We learned how a vector database stores document information for searching by meaning.

We saw how text is turned into numerical vectors that capture meaning.

• We understood why we break large documents into smaller pieces (chunks).
• We put it all together, showing how raw documents are processed and stored in the vector database.

Now that our document information is loaded and ready in Chroma DB, the exciting part begins! How do we actually use this data to answer a user's question?

What Problem Does Query Processing Solve?

The main goal of Docs-Reader is to answer questions about your documents. The vector database is ready, full of document chunks and their embeddings. You, the user, have a question. The problem is bridging the gap between your question and the specific, relevant answer found within the stored document chunks.

Your question is a piece of text. The vector database contains numerical embeddings. Answering requires not just finding relevant information, but also synthesizing that information into a coherent, human-readable answer.

This is where Query Processing comes in.

Enter Query Processing

Query Processing is the central mechanism in our project for taking a user's question and using the loaded document data to generate an answer. It's the "brain" that connects your question to the knowledge stored in the vector database and formulates a response.

Think of it like this:

Imagine the vector database is a massive, perfectly organized library where all the books (document chunks) are filed based on their meaning (their embeddings). When you ask a question, Query Processing acts as a highly efficient librarian and a skilled researcher:

1. The Librarian (Retrieval): Takes your question, understands its core meaning (by converting it to an embedding), and quickly goes to the library shelves (the vector database) to find the books (chunks) that are most relevant to your question's meaning. This step is called Retrieval.
2. The Researcher (Generation): Takes the few relevant books (retrieved chunks) found by the librarian, reads only those specific sections, and uses the information from those sections to write a clear, concise answer to your original question. This step involves using an AI language model and is called Generation.

Query Processing combines these two phases: Retrieval-Augmented Generation (RAG). We retrieve relevant information from our database, and then use it to augment or improve the information used by a language model for generation.

Key Steps in Query Processing

When you type a question into Docs-Reader, here's the sequence of events orchestrated by the Query Processing mechanism:

1. Receive the User Query: The system gets your question as text (e.g., "What is text splitting?").