Yes, you can use LlamaIndex to perform semantic search. LlamaIndex is designed to help developers build applications that connect large language models (LLMs) with external data, and semantic search is a core use case. Instead of relying on keyword matching, semantic search uses the meaning of text to find relevant results. LlamaIndex achieves this by converting your data into numerical representations (embeddings) and storing them in a way that allows efficient similarity-based retrieval. When you query the system, it compares the semantic meaning of your input to stored data to return the most contextually relevant results.
To implement semantic search with LlamaIndex, you typically start by indexing your data. For example, suppose you have a collection of documents or a database. LlamaIndex can parse these into smaller chunks (like paragraphs or sentences), generate embeddings for each chunk using a model like OpenAI’s text-embedding-ada-002, and store these embeddings in a vector database (such as Pinecone, FAISS, or Chroma). When you run a search query, LlamaIndex converts your query into an embedding and retrieves the closest-matching data chunks from the vector store. This approach works well for tasks like finding technical documentation snippets, matching user questions to FAQ answers, or retrieving relevant passages from large text corpora.
Customization is straightforward. You can adjust how data is chunked (e.g., splitting text by sentence vs. fixed token lengths), choose different embedding models, or fine-tune the retrieval process. For instance, if you’re building a support chatbot, you might combine semantic search with keyword filters to narrow results by product or date. LlamaIndex also supports hybrid search, where semantic results are combined with traditional keyword-based rankings for improved accuracy. While setup requires some initial effort—like configuring the vector database and tuning parameters—the library abstracts much of the complexity, letting you focus on integrating search into your application.