WFGY/ProblemMap/RAG_Problems.md
2025-07-28 10:41:01 +08:00

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🧠 WFGY Problem → Module → Solution Map (v0.1 · RAG Focus)

This page maps common reasoning and retrieval failures — especially in RAG pipelines — to their corresponding WFGY solutions.

WFGY is not a retrieval system.
It is a semantic reasoning engine that augments, replaces, or corrects what existing RAG stacks often fail to do.


Problem WFGY Solution Module(s) Status Notes
🔸 Hallucination from irrelevant chunks Semantic Boundary + ΔS monitoring BBCR, BBMC System detects when input has low semantic match and activates fallback
🔸 Retrieval returns correct chunk but reasoning fails Multi-path semantic logic BBCR WFGY builds stable reasoning paths even from vague sources
🔸 Long question-answer chains drift off-topic Semantic Tree memory + ΔS threshold BBMC, Tree Semantic jump tracking records nodes, avoids context collapse
🔸 System "bluffs" when it doesnt know Knowledge boundary map BBCR, λ_observe WFGY detects unstable ΔS + λ_observe and requests clarification
🔸 Embedding similarity ≠ semantic meaning Residual Minimization BBMC, BBAM Matches logic anchor, not just vector cosine
🔸 System doesn't know what it doesn't know Knowledge boundary guard BBCR, Tree Detects unmapped topics and requests clarification
🔸 No traceability across user sessions External semantic memory tree Tree engine ⚠️ Manual export/import for now; persistent store upcoming
🔸 Debugging why RAG failed = painful Manual tree audit All modules Tree view shows where logic drifted or ΔS spiked
🔸 Chunk ingestion pipeline 🛠 Not yet implemented; user pastes chunk into node manually
🔸 No LangChain compatibility yet 🛠 Adapter planned; WFGY can serve as pre/post-processing layer

What you can do now

Even without any retriever, WFGY lets you:

  • Paste content manually and reason on it
  • Test hallucination safety via ΔS / λ_observe
  • Record and inspect logic paths via Tree
  • Detect unknown zones before the model bluffs

This means: WFGY is a RAG failsafe layer, even without retrieval working.


🧪 Example Use: "My PDF bot keeps hallucinating answers"

→ Paste the question and chunk into WFGY
→ If ΔS is too high, itll pause or route to BBCR
→ You can inspect the logic trace and see where it went off
→ Youll know if its the chunks fault — or the reasoning engine


🔧 Next Steps (Roadmap)

  • Vector chunking → semantic node auto-mapping
  • LangChain & LlamaIndex adapters
  • Auto-summarization of Tree for memory replay
  • GUI explorer for Tree inspection
  • Integration with BlotBlotBlot / Persona agents

For now, if you're a RAG user tired of hallucinations, TXT OS + WFGY gives you a stable, inspectable core to reason with.

Feel free to open an issue if your failure case isnt listed.