When comparing retrievers or vector search configurations for RAG systems, focus on three core criteria: relevance, ranking quality, and efficiency. These metrics help determine which configuration retrieves the most useful information for downstream tasks while balancing performance and resource constraints. Below, we break down each category with practical examples.
First, relevance measures how well retrieved documents match the query’s intent. Key metrics include precision (the percentage of retrieved documents that are relevant) and recall (the percentage of all relevant documents retrieved). For example, if a query about “Python threading” returns 5 documents, and 3 are about multithreading while 2 are unrelated, precision is 60%. However, recall depends on how many relevant documents exist in the entire dataset. A retriever with higher recall ensures fewer critical documents are missed, which is vital for RAG’s answer quality. You can also evaluate context relevance—whether retrieved text snippets contain specific details needed to answer the query (e.g., code examples for a technical question).
Second, ranking quality assesses whether the most relevant documents appear at the top of results. Metrics like Mean Reciprocal Rank (MRR) or top-k accuracy (e.g., whether the correct document is in the top 3 results) are critical here. For instance, if Retriever A places the correct answer in the first position 80% of the time, while Retriever B does so 60% of the time, A is likely better for RAG’s generator, which often prioritizes top results. Additionally, test how configurations handle ambiguous queries. If a query for “Java” refers to the language but returns coffee-related articles, the ranking logic (or embedding model) may need adjustment.
Finally, efficiency evaluates speed and resource usage. This includes latency (time to return results) and throughput (queries processed per second). For example, a brute-force vector search might have perfect accuracy but be too slow for real-time applications, while an approximate method like HNSW indexing could offer faster results with minimal accuracy loss. Also, consider memory usage—some vector databases require heavy RAM allocation, which may not scale. Balance these factors against your application’s needs: a research tool might prioritize recall, while a customer-facing chatbot needs low latency.
In summary, prioritize relevance to ensure quality inputs for the generator, ranking to surface the best results quickly, and efficiency to meet performance requirements. Test configurations with real-world queries and datasets to identify trade-offs (e.g., a 5% drop in recall for a 2x speed improvement). This structured approach ensures you select the retriever best aligned with your RAG system’s goals.