To evaluate LlamaIndex’s performance, focus on three key aspects: retrieval accuracy, response quality, and system efficiency. LlamaIndex is designed to connect large language models (LLMs) with external data, so its effectiveness hinges on how well it retrieves relevant information and processes it into useful outputs. Start by testing retrieval accuracy using standard metrics like precision (the percentage of retrieved documents that are relevant) and recall (the percentage of relevant documents retrieved). For example, if you query a document store for “climate change impacts,” check if the top results include key studies or articles and exclude unrelated content. Tools like precision@k (accuracy of top k results) and MRR (Mean Reciprocal Rank) can quantify this. Run tests with varied query types—factual, open-ended, or multi-step—to identify weaknesses in indexing or search logic.
Next, assess the quality of responses generated when LlamaIndex feeds retrieved data into an LLM. Use benchmarks like BLEU or ROUGE scores to compare generated text against human-written references, but also include human evaluation for relevance, coherence, and factual correctness. For instance, if LlamaIndex powers a question-answering system, verify if answers directly address the query and avoid hallucinations. Test edge cases, like ambiguous queries or data gaps, to see how the system handles uncertainty. Additionally, measure latency and throughput—how quickly LlamaIndex processes a query and how many requests it can handle per second. If a query takes 5 seconds to return results in a real-time application, that might be unacceptable. Tools like Locust or Apache Benchmark can simulate load and stress-test the system.
Finally, evaluate scalability and resource usage. As your data grows, LlamaIndex should maintain performance without excessive memory or compute costs. Measure indexing time for datasets of increasing size—for example, how long it takes to index 10,000 documents versus 100,000. Monitor RAM/CPU usage during indexing and querying to identify bottlenecks. If indexing 1GB of data requires 16GB of RAM, you might need optimization. Test distributed setups if scaling horizontally, and check if response times remain consistent. Also, validate customization: if you tweak retrieval parameters (like chunk size or embedding models), does performance improve? For example, switching from a generic embedding to a domain-specific one (like BioBERT for medical data) might boost accuracy. Document these metrics to establish baselines and track improvements over time.