How does LlamaIndex handle document ranking?

LlamaIndex handles document ranking primarily through semantic similarity and vector-based retrieval, augmented by customizable filtering and reranking techniques. When you index documents using LlamaIndex, it typically converts text into vector embeddings using models like OpenAI’s text-embedding-ada-002 or other open-source alternatives. These embeddings capture the semantic meaning of the text. During a query, the user’s input is also converted into an embedding, and LlamaIndex compares this query embedding against stored document embeddings using metrics like cosine similarity. Documents with higher similarity scores are ranked higher. For example, if a user searches for “climate change effects,” documents containing phrases like “global warming impacts” or “CO2 emission consequences” would rank highly due to semantic alignment, even if keyword overlap is minimal.

Beyond semantic search, LlamaIndex supports metadata-based filtering to refine rankings. Developers can attach metadata (e.g., publication dates, categories) to documents during indexing. When querying, they can apply filters to prioritize documents that meet specific criteria. For instance, a medical app might rank documents tagged as “peer-reviewed studies” higher than general articles. Hybrid approaches, such as combining keyword matching (BM25) with vector search, are also possible. For example, a query for “Python async frameworks” might first retrieve documents containing “Python” and “async” via keyword matching, then reorder results using vector similarity to emphasize frameworks like FastAPI or Tornado. This flexibility allows developers to balance precision and recall based on their use case.

Finally, LlamaIndex enables post-processing steps to improve ranking quality. After an initial retrieval, developers can use “node postprocessors” to rerank results. One common technique is employing cross-encoder models (e.g., from Hugging Face’s sentence-transformers) that compare the query with each document more thoroughly than simple vector similarity. While slower, cross-encoders provide finer-grained rankings by evaluating pairwise relevance. For example, after a vector search returns 100 documents about “machine learning,” a cross-encoder could identify the top 10 most relevant to “unsupervised learning techniques.” Developers can also implement custom logic, such as boosting documents from trusted sources or penalizing outdated content. These layers ensure that rankings align closely with domain-specific needs.