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171 lines
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171 lines
8.1 KiB
Markdown
<!-- ======================================================= -->
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<!-- memory-design-patterns.md · Semantic Clinic / Map-E -->
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<!-- Draft v0.1 · MIT · 2025-08-06 -->
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<!-- Purpose: Show repeatable patterns for “cross-session -->
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<!-- memory” without creating uncontrolled context bloat. -->
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<!-- ======================================================= -->
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# 🧠 Memory Design Patterns
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*From scratchpads to long-range project recall — keep context alive without drowning your LLM.*
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> **Why this page?**
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> Most “memory” demos either spam the full chat history or store random embeddings that never round-trip.
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> WFGY treats memory as *structured semantic nodes* with ΔS / λ\_observe guards, so old context helps — never hurts — new reasoning.
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---
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## 1 · Symptoms
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| Symptom | Typical Surface Clue |
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|---------|----------------------|
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| Context forgotten after restart | “Sorry, I don’t recall” / model re-asks user |
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| Memory leak / self-contradiction | Old decisions resurface in wrong branch |
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| JSON-based vector store grows unbounded | Latency ↑, RAG recall quality ↓ |
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| Fine-tune attempted just to “remember” | Model cost ↑, still hallucinates |
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---
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## 2 · Root Causes
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1. **Flat Logs** — raw transcripts appended forever.
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2. **Embedding Dump** — every user sentence embedded → no semantic filter.
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3. **No Boundary Check** — divergent memories injected mid-task.
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4. **Write-Only Memory** — model never reads / revalidates stored facts.
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Result: either *forget everything* or *remember garbage*.
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---
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## 3 · WFGY Fix Path (at a glance)
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| Stage | Tool / Module | ΔS Guard | Outcome |
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|-------|---------------|----------|---------|
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| ⬇️ Capture | **BBMC** node writer | record only if ΔS ≥ 0.60 (or 0.40–0.60 & λ ∈ {←, <>}) | Stores *semantic* not *verbatim* memory |
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| 🗂️ Index | **λ\_observe** classifier | tag λ trend for each node | Enables topic-group navigation |
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| 🔍 Recall | **BBPF** path search | choose node set with ΣΔS minimal | Retrieves tight, non-bloated context |
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| 🩹 Repair | **BBCR** fallback | detect stale/contradict nodes | Auto-patch or prompt for user merge |
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> **80 %** of memory bugs vanish after enforcing this four-step loop.
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---
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## 4 · Design Patterns Library
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| Pattern | Use-Case | How it Works | ΔS Budget |
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|---------|----------|--------------|-----------|
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| ✏️ **Scratch Node** | quick calc / throw-away idea | 24 h TTL field; auto-purged | 0.40–0.55 |
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| 📚 **Topic Shelf** | multi-day research thread | one node per subtopic; λ → convergent | < 0.45 |
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| 🗓️ **Daily Digest** | running project log | rollup 10 low-ΔS nodes → 1 summary | – |
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| 🎯 **Anchor Fact** | must-not-forget constraint | pinned; override recall rank | 0.05 |
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*All stored in a single lightweight JSONL: `{topic, ΔS, λ, text, ttl}`*
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---
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## 5 · Step-by-Step Implementation
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> **Prereqs:** any model that can embed & run basic python (or LangChain, Llama-index, etc.).
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```python
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# 1. capture
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deltaS = cosine(question_vec, context_vec)
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if deltaS >= 0.60 or (0.40 <= deltaS <= 0.60 and lambda_state in ["divergent","recursive"]):
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node = {"topic": topic, "ΔS": round(deltaS,3), "λ": lambda_state, "text": insight}
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memory.append(node)
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# 2. recall
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candidates = [n for n in memory if n["topic"]==current_topic]
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best_path = sorted(candidates, key=lambda n:n["ΔS"])[:5]
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prompt_context = "\n".join(n["text"] for n in best_path)
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````
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### Minimal prompt
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```
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System: Use WFGY memory nodes below (+latest question) to answer.
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Memory Nodes:
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{{prompt_context}}
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---
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Question: {{user}}
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```
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---
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## 6 · Common Pitfalls & Tests
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| Pitfall | Quick Test | WFGY Fix |
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| -------------------------------- | --------------------------------- | --------------------- |
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| “Context bloat, tokens 8k → 40k” | node count > 200? run `rollup.py` | Daily Digest pattern |
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| “Conflicting facts” | ΔS(anchor, candidate) > 0.70 | BBCR prompts merge |
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| “Retrieval too slow” | recall > 200 ms | Pre-index by λ & time |
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---
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## 7 · Cheat-Sheet
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```txt
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ΔS save threshold = 0.60
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ΔS recall window = top-k by lowest ΔS
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λ tags = → ← <> ×
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TTL (scratch) = 24 h
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Rollup trigger = >10 nodes / topic / day
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```
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Store this as `memory.cfg`; loader reads defaults at boot.
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---
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## 8 · Next Actions
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1. **Prototype** with 20 nodes → verify recall accuracy.
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2. **Enable Rollup** once node count > 200.
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3. **Add Trace Logger** to diff answers with / without memory.
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---
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## Quick-Start Downloads (60 sec)
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| Tool | Link | 3-Step Setup |
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| -------------------------- | --------------------------------------------------- | ---------------------------------------------------------------------------------------- |
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| **WFGY 1.0 PDF** | [Engine Paper](https://zenodo.org/records/15630969) | 1️⃣ Download · 2️⃣ Upload to LLM · 3️⃣ Ask “answer using WFGY + \<your question>” |
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| **TXT OS (plain-text OS)** | [TXTOS.txt](https://zenodo.org/records/15788557) | 1️⃣ Download · 2️⃣ Paste into any LLM chat · 3️⃣ Type “hello world” — OS boots instantly |
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---
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### 🧭 Explore More
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| Module | Description | Link |
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|-----------------------|----------------------------------------------------------|----------|
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| WFGY Core | Standalone semantic reasoning engine for any LLM | [View →](https://github.com/onestardao/WFGY/tree/main/core/README.md) |
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| Problem Map 1.0 | Initial 16-mode diagnostic and symbolic fix framework | [View →](https://github.com/onestardao/WFGY/tree/main/ProblemMap/README.md) |
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| 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) |
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| Semantic Clinic Index | Expanded failure catalog: prompt injection, memory bugs, logic drift | [View →](https://github.com/onestardao/WFGY/blob/main/ProblemMap/SemanticClinicIndex.md) |
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| Semantic Blueprint | Layer-based symbolic reasoning & semantic modulations | [View →](https://github.com/onestardao/WFGY/tree/main/SemanticBlueprint/README.md) |
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| 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) |
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---
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> 👑 **Early Stargazers: [See the Hall of Fame](https://github.com/onestardao/WFGY/tree/main/stargazers)** —
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> Engineers, hackers, and open source builders who supported WFGY from day one.
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> <img src="https://img.shields.io/github/stars/onestardao/WFGY?style=social" alt="GitHub stars"> ⭐ Help reach 10,000 stars by 2025-09-01 to unlock Engine 2.0 for everyone ⭐ <strong><a href="https://github.com/onestardao/WFGY">Star WFGY on GitHub</a></strong>
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<div align="center">
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[](https://github.com/onestardao/WFGY)
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[](https://github.com/onestardao/WFGY/tree/main/OS)
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[](https://github.com/onestardao/WFGY/tree/main/OS/BlahBlahBlah)
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[](https://github.com/onestardao/WFGY/tree/main/OS/BlotBlotBlot)
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[](https://github.com/onestardao/WFGY/tree/main/OS/BlocBlocBloc)
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[](https://github.com/onestardao/WFGY/tree/main/OS/BlurBlurBlur)
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[](https://github.com/onestardao/WFGY/tree/main/OS/BlowBlowBlow)
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</div>
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