Welcome back! In our previous lesson, we explored reranking, a technique that refines search results by ordering them based on relevance. Today, we will focus on multi-field search, an approach that enhances semantic search capabilities by allowing queries across multiple document fields, such as title and content. This lesson will build on your existing knowledge of vector search and introduce you to the practical implementation of multi-field search using ChromaDB.

Multi-field search is crucial in scenarios where documents contain rich information spread across different fields. By searching across these fields, we can provide more comprehensive and relevant results to users. Let's dive into how this works and how you can implement it in your projects.

Let's walk through the process of setting up ChromaDB for multi-field search. We'll use the provided code example to guide us through each step.

First, we load our documents and initialize a ChromaDB client and create a collection with an embedding function. This collection will store our documents, each containing multiple fields like title, content, category, tags, and date. Here's how you can do it:

Next, we add documents to the collection using a batch add function. Each document includes fields such as id, content, and metadata (which can include a title, category, tags, and etc.). This structure allows us to perform multi-field searches effectively.

Now, let's perform a query that searches across the documents' content. We use the where_document and where clauses to specify our search conditions. In this example, we search for documents containing the string "AI" and belonging to the categories "History" or "Technology".

In this query, we utilize several operators to refine our search:

• $contains: Used within the where_document clause, this operator searches for documents that contain the specified string within their content. In our example, it looks for documents that include the term "AI".

• $in: Used within the where clause, this operator checks if a field's value is within a specified list. Here, it filters documents whose category is either "History" or "Technology".

The where_document clause is specifically designed to search within the document's content, while the where clause allows for more complex filtering based on metadata fields. Additionally, ChromaDB supports other logical operators such as:

• $and: Combines multiple conditions that must all be true for a document to be included in the results.
• $or: Combines multiple conditions where at least one must be true for a document to be included.

These operators, along with others not discussed here, provide flexibility in crafting complex queries, enabling you to tailor search results to specific needs and retrieve the most relevant information.

In addition to basic multi-field search, ChromaDB offers advanced query techniques that allow you to refine search results further. For instance, you can filter results by metadata or combine multiple conditions using logical operators like $or and $and.

Consider a scenario where you want to search for documents with a specific keyword in the title or content and filter them by a particular author. You can achieve this by combining conditions in the where clause.

These advanced techniques provide flexibility in crafting complex queries, enabling you to tailor search results to specific needs.

In this lesson, we explored the concept of multi-field search and its role in enhancing semantic search capabilities. We implemented a multi-field search using ChromaDB, allowing queries across documents' content. This approach provides more comprehensive and relevant search results, improving the user experience.

As you move on to the practice exercises, focus on applying what you've learned about multi-field search. Experiment with different queries and document structures to see how they affect the search results. Mastering multi-field search will provide a strong foundation for more advanced search techniques in future lessons. Keep up the great work!