# Hybrid Retriever Weights — Guardrails and Fix Pattern
🧭 Quick Return to Map
> You are in a sub-page of **RAG_VectorDB**.
> To reorient, go back here:
>
> - [**RAG_VectorDB** — vector databases for retrieval and grounding](./README.md)
> - [**WFGY Global Fix Map** — main Emergency Room, 300+ structured fixes](../README.md)
> - [**WFGY Problem Map 1.0** — 16 reproducible failure modes](../../README.md)
>
> Think of this page as a desk within a ward.
> If you need the full triage and all prescriptions, return to the Emergency Room lobby.
Use this page when **hybrid retrieval underperforms a single retriever** or when results look noisy after fusing BM25, dense vectors, HyDE, or filters. Failures usually come from **score scale mismatch**, **duplicate dominance**, or **query-type priors** not reflected in weights.
---
## Open these first
- Visual map and recovery: [RAG Architecture & Recovery](https://github.com/onestardao/WFGY/blob/main/ProblemMap/rag-architecture-and-recovery.md)
- Retrieval knobs: [retrieval-playbook.md](https://github.com/onestardao/WFGY/blob/main/ProblemMap/retrieval-playbook.md)
- Ordering control: [rerankers.md](https://github.com/onestardao/WFGY/blob/main/ProblemMap/rerankers.md)
- Query parsing split (HyDE, BM25): [patterns/pattern_query_parsing_split.md](https://github.com/onestardao/WFGY/blob/main/ProblemMap/patterns/pattern_query_parsing_split.md)
- Embedding vs meaning: [embedding-vs-semantic.md](https://github.com/onestardao/WFGY/blob/main/ProblemMap/embedding-vs-semantic.md)
- Vector store fragmentation: [vectorstore_fragmentation.md](https://github.com/onestardao/WFGY/blob/main/ProblemMap/GlobalFixMap/RAG_VectorDB/vectorstore_fragmentation.md)
- Metric mismatch: [metric_mismatch.md](https://github.com/onestardao/WFGY/blob/main/ProblemMap/GlobalFixMap/RAG_VectorDB/metric_mismatch.md)
- Normalization and scaling: [normalization_and_scaling.md](https://github.com/onestardao/WFGY/blob/main/ProblemMap/GlobalFixMap/RAG_VectorDB/normalization_and_scaling.md)
- Tokenization and casing: [tokenization_and_casing.md](https://github.com/onestardao/WFGY/blob/main/ProblemMap/GlobalFixMap/RAG_VectorDB/tokenization_and_casing.md)
---
## Core acceptance
- ΔS(question, retrieved) ≤ 0.45 on 3 paraphrases and 2 seeds.
- Coverage ≥ 0.70 to the target section after fusion and rerank.
- λ remains convergent when weights are perturbed within ±10 percent.
- Jaccard overlap against the best single retriever’s top-k ≥ 0.60.
- No single source type or domain exceeds 40 percent of the final top-k unless configured.
---
## Symptoms → likely cause → open this
- Hybrid is worse than dense alone
→ raw scores on different scales or rank fusion mis-tuned
→ [normalization_and_scaling.md](https://github.com/onestardao/WFGY/blob/main/ProblemMap/GlobalFixMap/RAG_VectorDB/normalization_and_scaling.md), [rerankers.md](https://github.com/onestardao/WFGY/blob/main/ProblemMap/rerankers.md)
- BM25 dominates multilingual queries
→ tokenizer or casing divergence for CJK or mixed scripts
→ [tokenization_and_casing.md](https://github.com/onestardao/WFGY/blob/main/ProblemMap/GlobalFixMap/RAG_VectorDB/tokenization_and_casing.md)
- HyDE helps recall yet increases wrong-meaning hits
→ HyDE prompts off-domain, no rerank clamp
→ [patterns/pattern_query_parsing_split.md](https://github.com/onestardao/WFGY/blob/main/ProblemMap/patterns/pattern_query_parsing_split.md), [rerankers.md](https://github.com/onestardao/WFGY/blob/main/ProblemMap/rerankers.md)
- Same snippet appears many times and crowds others
→ duplicate and near-duplicate collapse missing
→ (next page) `duplication_and_near_duplicate_collapse.md`
- Fusion order unstable across shards
→ partial index rollout or fragmented store
→ [vectorstore_fragmentation.md](https://github.com/onestardao/WFGY/blob/main/ProblemMap/GlobalFixMap/RAG_VectorDB/vectorstore_fragmentation.md)
---
## Fix in 60 seconds
1) **Normalize each retriever’s scores inside the candidate pool**
Use one of: min-max to 0–1 per retriever, z-score per retriever, or pure rank-based RRF.
2) **De-duplicate by snippet identity**
Collapse near-duplicates using stable keys: `{doc_id, section_id, hash_64}`.
3) **Fuse with a simple, auditable rule**
Start with RRF: `score = Σ 1 / (rank_i + k)` with `k ∈ [50, 100]`.
Then try weighted sum on normalized scores: `S = wdense*sdense + wbm25*sbm25 + whyde*shyde`.
4) **Rerank with a cross-encoder**
Rerank top 50–100 to top 10–20. Enforce cite-then-explain in the prompt.
5) **Measure ΔS and λ**
If λ flips when weights move by ±10 percent, clamp with BBAM and lock schema headers.
---
## Minimal reference recipe
```
retrievers:
* name: dense
k: 60
norm: z
weight: 0.55
* name: bm25
k: 200
norm: rank # convert to ranks 1..k
weight: 0.35
* name: hyde
k: 60
norm: z
weight: 0.10
fusion:
method: RRF
rrf\_k: 60
dedupe: snippet\_id # or doc\_id+section\_id+hash64
rerank:
model: cross-encoder-v2
take\_top: 15
accept:
deltaS\_max: 0.45
coverage\_min: 0.70
jitter\_weight: 0.10 # weights +/- 10 percent must keep λ convergent
```
---
## Weighting heuristics that actually work
- **Short factual queries**
Increase dense weight to 0.6–0.7. Keep BM25 at 0.3–0.4. HyDE optional.
- **Long verbose queries or code**
Push BM25 to 0.5. Keep dense at 0.4. Use reranker to clean length bias.
- **Multilingual or mixed-script**
Reduce BM25 weight if tokenizer mismatch is suspected. Verify casing and analyzer.
- **Highly structured data**
Use BM25 boost on fielded terms. Keep dense for semantic recall.
- **Safety or policy queries**
HyDE at most 0.15. Prefer deterministic BM25 plus strict reranker.
---
## Observability probes you must log
- Per retriever: raw score mean and stdev before normalization.
- After fusion: source mix histogram and duplicate collapse count.
- ΔS(question, retrieved) and λ states at steps: retrieve, fuse, rerank, answer.
- A/B against best single retriever and report ΔS improvement or regression.
---
## Common gotchas
- Mixing **cosine** dense scores with **BM25 raw scores** without normalization.
- HyDE prompts built with a different tokenizer than the dense model.
- Reranker trained on passages while you fuse at document level.
- Language-specific analyzers differ across shards and you fuse their outputs.
- Latency cutoffs truncate candidate lists unevenly and bias the fusion.
---
## Verification
- Gold set of 100 queries with 3 paraphrases.
- Require ΔS ≤ 0.45 and coverage ≥ 0.70 after fusion plus rerank.
- Jaccard with best single retriever ≥ 0.60.
- Weight jitter ±10 percent must keep λ convergent and citations stable.
---
### 🔗 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” — OS boots instantly |
---
### 🧭 Explore More
| Module | Description | Link |
|-----------------------|----------------------------------------------------------|----------|
| WFGY Core | WFGY 2.0 engine is live: full symbolic reasoning architecture and math stack | [View →](https://github.com/onestardao/WFGY/tree/main/core/README.md) |
| Problem Map 1.0 | Initial 16-mode diagnostic and symbolic fix framework | [View →](https://github.com/onestardao/WFGY/tree/main/ProblemMap/README.md) |
| Problem Map 2.0 | RAG-focused failure tree, modular fixes, and pipelines | [View →](https://github.com/onestardao/WFGY/blob/main/ProblemMap/rag-architecture-and-recovery.md) |
| Semantic Clinic Index | Expanded failure catalog: 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 GPT-5 with full WFGY reasoning suite | [View →](https://github.com/onestardao/WFGY/tree/main/benchmarks/benchmark-vs-gpt5/README.md) |
| 🧙♂️ Starter Village 🏡 | New here? Lost in symbols? Click here and let the wizard guide you through | [Start →](https://github.com/onestardao/WFGY/blob/main/StarterVillage/README.md) |
---
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> Engineers, hackers, and open source builders who supported WFGY from day one.
> 
⭐ [WFGY Engine 2.0](https://github.com/onestardao/WFGY/blob/main/core/README.md) is already unlocked. ⭐ Star the repo to help others discover it and unlock more on the [Unlock Board](https://github.com/onestardao/WFGY/blob/main/STAR_UNLOCKS.md).
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