What query types does it detect?

Factual lookups, multi-hop questions that need chaining several facts, comparisons, hypotheticals and counterfactuals, summarization or list requests, and temporal queries that depend on dates. Each maps to a different retrieval challenge and recommended strategy.

Why does query type matter for RAG?

Single-vector similarity search handles simple factual lookups well but fails on multi-hop and comparison queries, which need decomposition or multi-query retrieval. Knowing the type up front lets you route the query to the right strategy instead of getting silent retrieval misses.

How is difficulty estimated?

The tool weighs length, the number of distinct entities, conjunctions that imply multiple sub-questions, vague or open-ended phrasing, and dependence on time. More of these signals means harder retrieval. It is a heuristic guide for prioritizing which queries to test against your index.

Does it connect to my vector store?

No. It analyzes the query text only, entirely in the browser. Use it to triage queries and decide on retrieval strategy before you run them against your real index.

RAG Query Analyzer — Gera Tools

Predict how your RAG pipeline will handle a query

Most RAG failures are not embedding-quality problems — they are query-type mismatches. A plain similarity search nails “What is our refund window?” but quietly fails on “How does our refund policy compare to our competitor’s, and has it changed since last year?” This analyzer classifies a query and predicts its retrieval difficulty, then tells you which strategy will actually work.

How it works

The tool inspects the query for structural signals: length, the number of distinct entities, conjunctions that hint at multiple sub-questions, comparison and hypothetical phrasing, summarization cues, and temporal dependence. From those signals it assigns a query type and an easy / moderate / hard difficulty rating, then maps the type to a concrete tactic — query decomposition for multi-hop, multi-query expansion for broad asks, HyDE for sparse-match factuals, and reranking for comparisons.

Tips for using the analysis

Route, don’t retrieve blindly. If a query is multi-hop, decompose it into sub-questions and retrieve for each before answering.
Comparisons need both sides. Make sure your retriever pulls passages for every entity being compared, not just the first one mentioned.
Temporal queries need metadata filters, not just semantic similarity — filter your index by date before ranking.
Test your hardest-rated queries first; they expose retrieval gaps fastest.