How does LlamaIndex handle vector-based searches?

LlamaIndex handles vector-based searches by converting text data into numerical representations (vectors) and using similarity metrics to find relevant information. When you query a dataset, LlamaIndex transforms both your query and the stored data into vectors using an embedding model. These embeddings capture semantic meaning, allowing the system to compare how closely the query aligns with the indexed content. For example, if you search for “machine learning applications,” LlamaIndex retrieves text chunks whose vectors are closest to the query’s vector based on metrics like cosine similarity or Euclidean distance. This approach works well for semantic searches where exact keyword matches aren’t necessary.

The process involves three key steps: indexing, querying, and post-processing. During indexing, LlamaIndex splits your documents into smaller chunks (e.g., sentences or paragraphs) and generates vectors for each using pre-trained models like OpenAI’s text-embedding-ada-002 or open-source alternatives. These vectors are stored in a vector database such as FAISS, Pinecone, or Chroma. When a query is made, the same embedding model converts the query text into a vector, and the database retrieves the nearest matches. For instance, a query about “neural network architectures” might return results mentioning “CNNs” or “transformers,” even if those exact terms aren’t in the query. Post-processing steps like reranking or filtering can further refine results based on metadata or custom rules.

Developers can customize multiple aspects of this workflow. For example, you might choose a domain-specific embedding model (like BioBERT for medical texts) to improve relevance in specialized contexts. LlamaIndex also supports hybrid approaches, combining keyword-based filtering with vector search for precision. Performance optimizations include adjusting chunk sizes to balance context retention and search speed or using approximate nearest neighbor (ANN) algorithms in databases like FAISS for faster searches over large datasets. These options make LlamaIndex adaptable to use cases ranging from chatbots needing quick answers to research tools requiring deep contextual matches.