global fix map for llm + rag — the problem map upgrade (mit)
tl;dr
the original WFGY Problem Map 1.0 grew into a cross-stack Global Fix Map. it routes real failures to measured fixes for providers, agents, vector DBs, RAG, OCR, local deploy, safety, eval, and governance. everything stays store-agnostic and model-agnostic. acceptance targets are explicit:
- ΔS(question, retrieved) ≤ 0.45
- coverage of the correct span ≥ 0.70
- λ remains convergent across 3 paraphrases
single index + quick start + all pages:
WFGY Global Fix Map → https://github.com/onestardao/WFGY/tree/main/ProblemMap/README.md
why this matters to hf users
you ship spaces, demos, notebooks that mix retrieval, parsing, agents, and local inference. most failures repeat across stacks. instead of patching after the model speaks, we block unstable states before generation. when gates pass, the bug class is sealed. if drift shows up later, it is a new class to map once.
MIT license. no sdk. no plugin. keep your infra.
what “global fix map” actually is
a repair manual with a single layout per page:
- symptoms that match logs or screenshots
- minimal fix you can do today
- hard fix for production
- verify section with the same acceptance targets
- adapters when a vendor is special
the map is organized for long tail search so you can land on the exact page:
providers and agents
openai, anthropic, gemini, mistral, llama, bedrock, together, openrouter. frameworks like langchain, langgraph, autogen, assistants v2. topics include role order, tool loops, cold boot, schema drift.
data and retrieval
faiss, qdrant, weaviate, milvus, pgvector, redis, elastic, pinecone, vespa. issues like metric mismatch, normalization and scaling, tokenization and casing, hybrid retriever weights, vectorstore fragmentation, duplication and near duplicates, poisoning and contamination.
input and parsing
document ai and OCR for scanned pdfs, tables and columns, headers and footers, images and figures. unicode normalization, fullwidth vs halfwidth, CJK segmentation, RTL bidi control, diacritics and folding.
reasoning and memory
logic collapse and recovery, entropy overload, symbolic collapse, proof dead ends, context stitching and window joins, chain-of-thought variance clamp, session memory coherence.
automation and ops
idempotency, warmups, backpressure, rate limits, cache invalidation, staged rollout, vector index build and swap, version pinning, rollback and fast recovery.
safety and prompt integrity
prompt injection, jailbreaks, role confusion, memory fences, json mode and tool calls, citation-first answers, tool selection and timeouts, system vs user role order.
eval and governance
sdk-free evals you can run inside a notebook, coverage tracking, ΔS thresholds, drift alarms, audit and lineage, sign-off gates, policy baselines.
all written to be store-agnostic and model-agnostic so you can keep FAISS or Qdrant or Milvus or pgvector, and still apply the same gates.
how to use it in 60 seconds
- open the link below, grab the quick start.
- attach the tiny engine pdf to a fresh chat with your model.
- run the one-liner demo: answer normally, then “use WFGY” and compare depth, accuracy, understanding.
you will see a bridge or recovery step when the chain stalls. that is the firewall acting before output.
global index and quick start
https://github.com/onestardao/WFGY/tree/main/ProblemMap/README.md
example routes for common hf pain points
-
rag looks plausible but citations drift
go to Retrieval → Playbook, then RAG_VectorDB → metric mismatch, tokenization and casing. watch ΔS and coverage move. -
ocr tables parse, retrieval still misses the right cell
Input & Parsing → tables and columns, pdf layouts and ocr. then LanguageLocale → unicode normalization, width differences. -
quantized local model behaves unlike fp16
LocalDeploy_Inference → vllm or llama.cpp specifics. then Embeddings → dimension mismatch and projection. -
agents produce different stories depending on run order
Agents & Orchestration. then Safety_PromptIntegrity → system user role order, tool timeouts.
each page ends with “verify” so you can know it is actually fixed and not just pretty.
what changed vs problem map 1.0
- one index for the whole stack, not only 16 classes
- unified acceptance targets, same numbers everywhere
- vendor adapters so you do not rebuild your pipeline
- minimal sandboxes and recipes to prove a fix before refactors
who should read this
- practitioners maintaining RAG in production
- people publishing Spaces or notebooks that combine OCR, retrieval, and tool use
- teams doing multi-agent orchestration on top of vector stores
- anyone tired of patching the same failure again
call for counterexamples
if a page does not help your case, open the index link and share a short trace: inputs, calls, wrong output, which page you tried. we will map it to a number and route you to the right fix. that is how the map gets better.
single entry point, MIT, reproducible:
WFGY Global Fix Map → https://github.com/onestardao/WFGY/tree/main/ProblemMap/README.md
