Yes, you can use LlamaIndex to perform semantic search. LlamaIndex is designed to help developers build and manage data structures that support semantic search functionality, making it easier to retrieve information based on meaning rather than just matching keywords. With semantic search, the goal is to understand the context and intent behind a user’s query to provide more relevant results.
To implement semantic search with LlamaIndex, you typically start by indexing your documents. This involves transforming the content of your documents into vector representations, which capture the meaning of the text. LlamaIndex supports various embedding models, allowing you to convert your documents into vectors that reflect their semantic content. Once your documents are indexed, you can run queries against this structured data. The search process will compare the vector of a user’s query with the stored vectors to find matches based on similarity, rather than exact word matches.
For example, if you have a collection of technical documents related to machine learning and a user searches for "neural networks for classification," LlamaIndex can retrieve documents that discuss similar concepts, even if the exact terms “neural networks” or “classification” aren’t present. By utilizing semantic search, you can provide users with more relevant results, enhancing their experience and making it easier to find the information they need. Overall, LlamaIndex provides a robust framework for building semantic search applications efficiently.