# 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 | Layer | Page | What it’s for | | --- | --- | --- | | ⭐ Proof | [WFGY Recognition Map](/recognition/README.md) | External citations, integrations, and ecosystem proof | | ⚙️ Engine | [WFGY 1.0](/legacy/README.md) | Original PDF tension engine and early logic sketch (legacy reference) | | ⚙️ Engine | [WFGY 2.0](/core/README.md) | Production tension kernel for RAG and agent systems | | ⚙️ Engine | [WFGY 3.0](/TensionUniverse/EventHorizon/README.md) | TXT based Singularity tension engine (131 S class set) | | 🗺️ Map | [Problem Map 1.0](/ProblemMap/README.md) | Flagship 16 problem RAG failure taxonomy and fix map | | 🗺️ Map | [Problem Map 2.0](/ProblemMap/wfgy-rag-16-problem-map-global-debug-card.md) | Global Debug Card for RAG and agent pipeline diagnosis | | 🗺️ Map | [Problem Map 3.0](/ProblemMap/wfgy-ai-problem-map-troubleshooting-atlas.md) | Global AI troubleshooting atlas and failure pattern map | | 🧰 App | [TXT OS](/OS/README.md) | .txt semantic OS with fast bootstrap | | 🧰 App | [Blah Blah Blah](/OS/BlahBlahBlah/README.md) | Abstract and paradox Q&A built on TXT OS | | 🧰 App | [Blur Blur Blur](/OS/BlurBlurBlur/README.md) | Text to image generation with semantic control | | 🏡 Onboarding | [Starter Village](/StarterVillage/README.md) | Guided entry point for new users | If this repository helped, starring it improves discovery so more builders can find the docs and tools. 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