Welcome back! In the previous lesson, you learned how to insert and store embeddings in ChromaDB, setting the stage for more advanced operations. Today, we will focus on querying in ChromaDB, a crucial step in leveraging the power of vector-based search systems. Our goal is to guide you through performing a search query in ChromaDB and interpreting the results. This lesson will build on your existing knowledge and help you understand how to retrieve relevant information from your vector database efficiently.

Vector queries are at the heart of ChromaDB's search capabilities. Unlike traditional keyword searches, vector queries leverage the numerical representations of text, known as embeddings, to find semantically similar documents. In ChromaDB, a query is structured with query_texts, which are the input texts you want to search for, and n_results, which specifies the number of results you wish to retrieve. This approach allows you to perform more nuanced searches, capturing the meaning behind the text rather than just matching keywords.

Before proceeding to search in ChromaDB, let's define the data we'll be working with, as we learned in our previous lesson. Here is a code snippet that was deeply explained earlier:

In this setup, we have a collection of sample documents, each with a unique identifier and content. These documents are inserted into ChromaDB, making them available for querying. This step ensures that our database is populated with data, allowing us to perform meaningful search operations in the subsequent example.

Let's walk through an example to demonstrate how to perform a search query in ChromaDB. Consider the following code snippet:

In this example, we define a query_text with the question "What is ChromaDB?" and use the collection.query() method to perform the search. The query_texts parameter takes a list of input texts, and n_results specifies that we want the top two results. The results are then iterated over, displaying each document along with its distance score. The distance score indicates how closely the document matches the query, with lower scores representing more relevant results. When you run this code, you should see output similar to:

In ChromaDB, similarity metrics play a crucial role in determining which documents are returned in response to a query. When you perform a search, ChromaDB calculates the similarity between the query embeddings and the document embeddings stored in the database. This is typically done using distance measures such as cosine similarity or Euclidean distance.

The similarity metric computes a score for each document, indicating how closely it matches the query. These scores are then used to rank the documents. ChromaDB sorts the documents based on their similarity scores and selects the top n_results to display. This process ensures that the most relevant documents, as determined by their proximity in the vector space, are presented to the user.

Understanding this mechanism allows you to better interpret the search results and refine your queries. By experimenting with different query texts and adjusting the number of results, you can optimize the search outcomes to suit your specific needs.

Interpreting the search results is key to understanding the relevance of the documents returned by ChromaDB. The distance scores provide a measure of similarity between the query and the documents. A lower distance score indicates a closer match, meaning the document is more relevant to the query. To refine your search outcomes, consider adjusting the query_texts or experimenting with different n_results values. This flexibility allows you to tailor your queries to better meet your specific needs.

In this lesson, you learned how to perform a search query in ChromaDB and interpret the results. We explored the structure of vector queries, focusing on query_texts and n_results, and walked through a practical example to demonstrate the process. As you move forward, you'll have the opportunity to practice these concepts through exercises that reinforce what you've learned. Experiment with different queries and explore the capabilities of ChromaDB further. Keep up the great work, and let's continue enhancing your skills with ChromaDB!