Claude
a429aa40f6
Co-Authored-By: Claude Sonnet 5.5 <[email protected]> Claude-Session: https://claude.ai/code/session_01Cfo2K4u4rF18QsPXPFnytf
44 lines
2.7 KiB
Markdown
44 lines
2.7 KiB
Markdown
# rerank
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Companion code for [Reranking RAG Results in Java: Cross-Encoder Reranker with ONNX Runtime Before the LLM](https://ankurm.com/reranking-rag-results-java-cross-encoder-onnx-runtime-spring-ai/).
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A bi-encoder (all-MiniLM-L6-v2) retrieves candidates, a cross-encoder (ms-marco-MiniLM-L-6-v2) rescores them, and a Spring AI `DocumentPostProcessor` keeps the top 5 for `RetrievalAugmentationAdvisor`. Both models run in-process through ONNX Runtime; the tokenizers are DJL's Hugging Face tokenizers. No database, no API key, no GPU.
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| Spring Boot | 4.1.2 |
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| Spring AI | 2.0.2 |
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| ONNX Runtime | 1.31.0 (the `spring-ai-transformers` starter would bring 1.21.1) |
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| DJL tokenizers | 0.39.0 (the starter would bring 0.36.0) |
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| JDK | 25 |
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## Layout
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| File | What it is |
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| `BiEncoder` | ONNX Runtime + tokenizer + mean pooling: text to a 384-dim vector |
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| `BiEncoderRetriever` | in-memory cosine search, a Spring AI `DocumentRetriever` |
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| `CrossEncoder` | `[CLS] query [SEP] passage [SEP]` through the model, one logit out |
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| `RerankingPostProcessor` | the `DocumentPostProcessor`: score, sort, keep top N, record both ranks in metadata |
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| `Models` | where the model files come from (Hugging Face, cached in `~/.cache/rerank`) |
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| `KnowledgeBase` | the same 94 passages and 60 labelled questions as the `hybrid-search` module |
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## Run
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`scripts/run-all.sh`. The first run downloads about 250 MB of model files. Every file in `output/` is written by a test that asserts the same facts. Files 06 and 07 contain wall-clock latency and depend on your machine; the others were identical across two consecutive runs.
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| Output | Shows |
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| `01-pair-encoding.txt` | the tokens, `token_type_ids` and attention mask of a (query, passage) pair |
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| `02-one-question-by-hand.txt` | one question's top 8 with cosine and cross-encoder logit side by side |
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| `03-quality-by-pool-size.txt` | hit@1/3/5 and MRR for pools of 5, 10, 20, 50 and 94, and first-stage recall |
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| `04-quality-by-question-kind.txt` | the same, split by exact code, SKU, paraphrase and mixed questions |
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| `05-helped-and-hurt.txt` | every question whose gold-passage rank changed |
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| `06-latency-by-candidates-and-batch.txt` | rerank time by candidate count, batch size and thread count |
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| `07-latency-against-quality.txt` | fp32 and int8, quality and latency in one table |
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| `08-post-processor-in-the-advisor.txt` | retrieve 50, keep 5, and the prompt the model receives |
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| `09-post-processor-edge-cases.txt` | empty input, fewer documents than N |
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| `10`-`14` `-trap-*.txt` | missing input, swapped pair order, score scale, truncation, pool too small |
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The corpus is synthetic and small. It shows how the methods behave, not how they scale.
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