mirror of
https://github.com/onestardao/WFGY.git
synced 2026-04-30 12:39:55 +00:00
237 lines
12 KiB
Markdown
237 lines
12 KiB
Markdown
# 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 + \<your question>” |
|
||
| **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.
|
||
|
||
> <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.
|
||
|
||
<div align="center">
|
||
|
||
[](https://github.com/onestardao/WFGY)
|
||
|
||
[](https://github.com/onestardao/WFGY/tree/main/OS)
|
||
|
||
[](https://github.com/onestardao/WFGY/tree/main/OS/BlahBlahBlah)
|
||
|
||
[](https://github.com/onestardao/WFGY/tree/main/OS/BlotBlotBlot)
|
||
|
||
[](https://github.com/onestardao/WFGY/tree/main/OS/BlocBlocBloc)
|
||
|
||
[](https://github.com/onestardao/WFGY/tree/main/OS/BlurBlurBlur)
|
||
|
||
[](https://github.com/onestardao/WFGY/tree/main/OS/BlowBlowBlow)
|
||
|
||
|
||
</div>
|