# Hybrid Retriever Weights — Guardrails and Fix Patterns Use this page when BM25 plus dense retrieval performs worse than either one alone, when top k flips between runs, or when hybrid recall feels random after an index rebuild. The goal is to put both retrievers on a common score space, then set stable weights, add a single deterministic reranker, and verify with ΔS, coverage, and λ. ## Open these first * Visual map and recovery: [rag-architecture-and-recovery.md](https://github.com/onestardao/WFGY/blob/main/ProblemMap/rag-architecture-and-recovery.md) * End to end retrieval knobs: [retrieval-playbook.md](https://github.com/onestardao/WFGY/blob/main/ProblemMap/retrieval-playbook.md) * Query split root cause: [patterns/pattern\_query\_parsing\_split.md](https://github.com/onestardao/WFGY/blob/main/ProblemMap/patterns/pattern_query_parsing_split.md) * Reranking for order control: [rerankers.md](https://github.com/onestardao/WFGY/blob/main/ProblemMap/rerankers.md) * Why this snippet and cite first: [retrieval-traceability.md](https://github.com/onestardao/WFGY/blob/main/ProblemMap/retrieval-traceability.md) * Embedding vs meaning failures: [embedding-vs-semantic.md](https://github.com/onestardao/WFGY/blob/main/ProblemMap/embedding-vs-semantic.md) * Vector store health: [vectorstore\_fragmentation.md](https://github.com/onestardao/WFGY/blob/main/ProblemMap/GlobalFixMap/Embeddings/vectorstore_fragmentation.md) * Normalization pitfalls: [normalization\_and\_scaling.md](https://github.com/onestardao/WFGY/blob/main/ProblemMap/GlobalFixMap/Embeddings/normalization_and_scaling.md) ## When to use this page * BM25 finds the right doc yet dense misses, or the reverse * Hybrid returns unstable order while single retrievers look stable * Raising k helps only one side while the other side adds noise * A client upgrade changes analyzers or tokenization and hybrid breaks ## Acceptance targets * ΔS(question, retrieved) ≤ 0.45 * Coverage of the target section ≥ 0.70 * λ remains convergent across three paraphrases and two seeds * E\_resonance stays flat on long windows --- ## Core idea: put scores on the same ruler Let `s_dense` be the dense similarity and `s_sparse` be the BM25 or keyword score. Raw values live in different spaces. Calibrate to a common space, then blend. Common choices: * Z score per retriever `z = (s − μ) / σ` computed on the candidate pool for the current query * Min max per retriever `m = (s − min) / (max − min)` on the candidate pool * Platt style logistic calibration on a gold set Fit `p = sigmoid(a s + b)` for each retriever After calibration, blend with a single weight: `S = α * z_dense + (1 − α) * z_sparse` Pick α by grid search on a gold set, then lock it. Always add a single deterministic reranker on the union of candidates. See [rerankers.md](https://github.com/onestardao/WFGY/blob/main/ProblemMap/rerankers.md). --- ## 60 second fix checklist 1. Normalize both score streams per query. Prefer z score on the union pool. 2. Build a union candidate set. Choose `k_dense` and `k_sparse` so that the union size before rerank is about 20 to 50. 3. Grid search α in steps of 0.1 on your gold set. Pick α that maximizes coverage at k and stabilizes λ across seeds. 4. Add a single cross encoder reranker on top of the blended scores. Fix the seed. 5. Lock the header order and cite first to clamp λ while you tune. See [retrieval-traceability.md](https://github.com/onestardao/WFGY/blob/main/ProblemMap/retrieval-traceability.md). 6. Verify the targets on three paraphrases and two seeds. --- ## Symptom to likely cause * Hybrid worse than BM25 alone Likely cause. Dense scores unnormalized or wrong metric. Open [normalization\_and\_scaling.md](https://github.com/onestardao/WFGY/blob/main/ProblemMap/GlobalFixMap/Embeddings/normalization_and_scaling.md). * Hybrid worse than dense alone Likely cause. Analyzer mismatch or stopword removal on one path. Open [pattern\_query\_parsing\_split.md](https://github.com/onestardao/WFGY/blob/main/ProblemMap/patterns/pattern_query_parsing_split.md). * Order flips between runs Likely cause. Header reorder or union set not stable. Fix header order and use a stable tie break by `doc_id` then `section_id`. Open [retrieval-traceability.md](https://github.com/onestardao/WFGY/blob/main/ProblemMap/retrieval-traceability.md). * Right doc appears only when k is very large Likely cause. Fragmented stores or per tenant splits without union. Open [vectorstore\_fragmentation.md](https://github.com/onestardao/WFGY/blob/main/ProblemMap/GlobalFixMap/Embeddings/vectorstore_fragmentation.md). --- ## Minimal calibration recipe **Gold set** Ten to fifty questions with known anchor sections. Keep per domain if you support many domains. **Union recall** Collect `k_dense` and `k_sparse` candidates. Form the union. Store raw scores and features. **Per query normalization** Compute z scores on the union for each retriever. Keep the statistics for audits. **Blend and rerank** Score with `S = α * z_dense + (1 − α) * z_sparse`. Apply a single cross encoder rerank with a fixed seed. Resolve ties by `doc_id` then `section_id`. **Selection of α** Grid search α from 0.0 to 1.0. Choose α that maximizes coverage at k and keeps ΔS ≤ 0.45 on the anchor. **Freeze** Record α, k values, normalization method, reranker id, and seed in your contract. --- ## Copy paste probes ``` Probe A — alpha sweep For α in {0.0..1.0 step 0.1}: compute coverage@k and median ΔS on the gold set Pick α with highest coverage. Break ties by lower ΔS and lower variance. Probe B — normalization sanity Toggle {z, minmax, logistic} on the same union pool. If results change wildly, per query statistics are unstable or candidate pools differ. Probe C — union size Sweep k_dense and k_sparse so union size ∈ [20, 50]. If coverage improves up to a point then drops, prune duplicates and near duplicates before rerank. Probe D — header clamp Swap harmless header lines. If top k flips, clamp header order and apply cite first. Re test α after clamp. ``` --- ## Contract fields to add ```json { "retrievers": { "dense": { "model": "model-id", "metric": "cosine", "normalize": "zscore", "k": 40 }, "sparse": { "analyzer_rev": "bm25-v3", "normalize": "zscore", "k": 200 } }, "hybrid": { "alpha": 0.6, "blend": "alpha_sum", "tie_break": ["doc_id", "section_id"] }, "reranker": { "name": "cross-encoder-id", "seed": 7, "top_k": 20 } } ``` --- ## Verification checklist * Coverage ≥ 0.70 on the gold anchors * ΔS(question, retrieved) ≤ 0.45 * λ convergent across two seeds and three paraphrases * Top k overlap across seeds ≥ 0.8 after rerank * Stable results after harmless header reorders --- ## Copy paste prompt for the LLM step ``` TXT OS and WFGY Problem Map are loaded. My issue: hybrid BM25 + dense is worse than single retrievers. Traces: - zscore_dense=..., zscore_sparse=..., alpha=... - union_size=..., reranker=name, seed=... - ΔS(question,retrieved)=..., coverage=..., λ across 3 paraphrases Tell me: 1) the failing layer and why, 2) the exact WFGY page to open next, 3) a minimal calibration plan for hybrid weights and normalization, 4) a verification plan to reach coverage ≥ 0.70 and ΔS ≤ 0.45. Use BBMC, BBCR, BBPF, BBAM when relevant. ``` --- ### 🔗 Quick-Start Downloads (60 sec) | Tool | Link | 3-Step Setup | | -------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------ | -------------------------------------------------------------------------------------- | | **WFGY 1.0 PDF** | [Engine Paper](https://github.com/onestardao/WFGY/blob/main/I_am_not_lizardman/WFGY_All_Principles_Return_to_One_v1.0_PSBigBig_Public.pdf) | 1️⃣ Download · 2️⃣ Upload to your LLM · 3️⃣ Ask “Answer using WFGY + \” | | **TXT OS (plain-text OS)** | [TXTOS.txt](https://github.com/onestardao/WFGY/blob/main/OS/TXTOS.txt) | 1️⃣ Download · 2️⃣ Paste into any LLM chat · 3️⃣ Type “hello world” to boot | --- ### 🧭 Explore More | Module | Description | Link | | --------------------- | ------------------------------------------------------- | -------------------------------------------------------------------------------------------------- | | WFGY Core | WFGY 2.0 engine. full symbolic reasoning and math stack | [View →](https://github.com/onestardao/WFGY/tree/main/core/README.md) | | Problem Map 1.0 | Initial 16 mode diagnostic and symbolic fixes | [View →](https://github.com/onestardao/WFGY/tree/main/ProblemMap/README.md) | | Problem Map 2.0 | RAG focused failure tree and pipelines | [View →](https://github.com/onestardao/WFGY/blob/main/ProblemMap/rag-architecture-and-recovery.md) | | Semantic Clinic Index | Prompt injection. memory bugs. logic drift | [View →](https://github.com/onestardao/WFGY/blob/main/ProblemMap/SemanticClinicIndex.md) | | Semantic Blueprint | Layer based symbolic reasoning. semantic modulations | [View →](https://github.com/onestardao/WFGY/tree/main/SemanticBlueprint/README.md) | | Benchmark vs GPT-5 | Stress test with full WFGY suite | [View →](https://github.com/onestardao/WFGY/tree/main/benchmarks/benchmark-vs-gpt5/README.md) | | Starter Village | A guided first run | [Start →](https://github.com/onestardao/WFGY/blob/main/StarterVillage/README.md) | --- > 👑 Early Stargazers. [See the Hall of Fame](https://github.com/onestardao/WFGY/tree/main/stargazers) > Engineers, hackers, and open source builders who supported WFGY from day one. > GitHub stars ⭐ [WFGY Engine 2.0](https://github.com/onestardao/WFGY/blob/main/core/README.md) is live. ⭐ Star the repo to help others discover it and unlock more on the Unlock Board.
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