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