Practical Applications of Vector Search
In this quest, you’ll explore the practical applications of vector search through text-to-text matching. Instead of working with images, this quest focuses on how we can apply vector search techniques to textual data. Using pre-trained models like SentenceTransformers, you’ll generate vector embeddings for a collection of text entries, store them in ChromaDB, and perform searches based on vector similarity.
Text embeddings capture the semantic meaning of sentences, allowing us to perform intelligent searches based on context, not just exact keyword matches.
For technical help on the StackUp platform & quest-related questions, join our Discord, head to the quest-helpdesk channel and look for the correct thread to ask your question.
Learning Outcomes
By the end of this quest, you will be able to:
- Understand the key concepts of vector search and how it applies to text-to-text matching.
- Implement a basic text-to-text search system using pre-trained models to generate vector embeddings.
- Use ChromaDB to store and query embeddings efficiently, facilitating fast and accurate similarity searches.
- Evaluate the accuracy and relevance of search results based on vector similarity.
Tutorial Steps
Total steps: 6
-
Step 1: Environment Setup
-
Step 2: Setting Up ChromaDB
-
Step 3: Loading the Pre-Trained Embedding Model and Ingesting Text Data
-
Step 4: Making Queries and Displaying the Results
-
Step 5: Analysis of the Vector Search Results
-
Step 6: Conclusion
Need help?
Find articles to support you through your journey or chat with our support team.
Help Center
