8.9 KiB
Pipedream — Guardrails and Fix Patterns
🧭 Quick Return to Map
You are in a sub-page of Automation Platforms.
To reorient, go back here:
- Automation Platforms — stabilize no-code workflows and integrations
- WFGY Global Fix Map — main Emergency Room, 300+ structured fixes
- WFGY Problem Map 1.0 — 16 reproducible failure modes
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 when your integration is built on Pipedream (HTTP triggers, Node/Python steps, marketplace components) and answers look plausible but wrong, citations don’t line up, or flows pass step-by-step while users still see inconsistencies.
Acceptance targets
- ΔS(question, retrieved) ≤ 0.45
- Coverage ≥ 0.70 to the intended section/record
- λ stays convergent across 3 paraphrases
Typical breakpoints → exact fixes
-
Output sounds right but cites the wrong snippet or section
Fix No.1: Hallucination & Chunk Drift →
Hallucination ·
Retrieval Playbook -
High vector similarity, wrong meaning in answers
Fix No.5: Embedding ≠ Semantic →
Embedding ≠ Semantic -
Indexed facts exist (S3/GSheet/Notion/DB) but never appear in top-k
Pattern: Vectorstore Fragmentation →
Vectorstore Fragmentation -
Can’t show “why this snippet?” from within step logs
Fix No.8: Retrieval Traceability + snippet/citation schema →
Retrieval Traceability ·
Data Contracts -
Long multi-step flows drift in tone or logic (especially with retries)
Fix No.3/No.9: Context Drift and Entropy Collapse →
Context Drift ·
Entropy Collapse -
Works in test events, fails in scheduled/production runs (secrets/env mismatch)
Infra: Pre-Deploy / Bootstrap / Deadlock →
Pre-Deploy Collapse ·
Bootstrap Ordering ·
Deployment Deadlock -
Model answers confidently with wrong claims
Fix No.4: Bluffing / Overconfidence →
Bluffing
Minimal Pipedream pattern with WFGY checks
A compact flow outline that enforces cite-first schema, observable retrieval, and ΔS/λ validation.
Trigger: HTTP / Webhook (POST)
Step 1 — Parse input
- Extract "question" and optional "k" (default 10)
Step 2 — Retrieve context (custom component or HTTP)
- POST to your retriever: { question, k }
- Return: snippets[], each with { snippet_id, text, source, section_id }
Step 3 — Assemble prompt (Node step)
SYSTEM:
Cite lines before any explanation. Keep per-source fences.
TASK:
Answer only from the provided context. Return citations as [snippet_id].
CONTEXT:
<joined snippets with snippet_id + source + text>
QUESTION:
<user question>
Step 4 — Call LLM (component or HTTP)
- Input: prompt from Step 3
- Output: answer + raw citations if available
Step 5 — WFGY post-check (HTTP to your wfgyCheck function)
- Body: { question, context, answer }
- Return: { deltaS, lambda, coverage, notes }
Step 6 — Gate
IF deltaS ≥ 0.60 OR lambda != "→"
→ Fail fast with 422 and include trace table (snippet_id↔citation)
ELSE
→ 200 OK with { answer, deltaS, lambda, coverage, citations[] }
Reference specs: RAG Architecture & Recovery · Retrieval Playbook · Retrieval Traceability · Data Contracts
Pipedream-specific gotchas
-
Event truncation: large contexts exceed step memory or event size. Use external store for snippets, inject only ids + short preview into the prompt, and re-fetch on demand. See Data Contracts
-
Package/runtime drift: Node/Python versions or package pins differ between components. Pin versions and rebuild embeddings/index with the same runtime. See Embedding ≠ Semantic
-
Concurrent runs reorder records and break implicit ranking. Add a rerank step after per-source ΔS ≤ 0.50. See Rerankers
-
Secret/connection mismatch across sources: different tokens for ingestion vs query cause empty/partial retrieval. Verify in a boot check before first LLM call. See Pre-Deploy Collapse
-
Marketplace components hide prompts: wrap LLM calls in your own component so the cite-first schema and fences are explicit in code. See Retrieval Traceability
When to escalate
-
ΔS stays ≥ 0.60 after chunking/retrieval fixes → rebuild index with explicit metric flags and unit normalization. Retrieval Playbook
-
Answers flip between preview and deployed sources → verify version skew, secret scope, and environment variables. Bootstrap Ordering · Deployment Deadlock
🔗 Quick-Start Downloads (60 sec)
| Tool | Link | 3-Step Setup |
|---|---|---|
| WFGY 1.0 PDF | Engine Paper | 1️⃣ Download · 2️⃣ Upload to your LLM · 3️⃣ Ask “Answer using WFGY + <your question>” |
| TXT OS (plain-text 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 | External citations, integrations, and ecosystem proof |
| Engine | WFGY 1.0 | Original PDF based tension engine |
| Engine | WFGY 2.0 | Production tension kernel and math engine for RAG and agents |
| Engine | WFGY 3.0 | TXT based Singularity tension engine, 131 S class set |
| Map | Problem Map 1.0 | Flagship 16 problem RAG failure checklist and fix map |
| Map | Problem Map 2.0 | RAG focused recovery pipeline |
| Map | Problem Map 3.0 | Global Debug Card, image as a debug protocol layer |
| Map | Semantic Clinic | Symptom to family to exact fix |
| Map | Grandma’s Clinic | Plain language stories mapped to Problem Map 1.0 |
| Onboarding | Starter Village | Guided tour for newcomers |
| App | TXT OS | TXT semantic OS, fast boot |
| App | Blah Blah Blah | Abstract and paradox Q and A built on TXT OS |
| App | Blur Blur Blur | Text to image with semantic control |
| App | Blow Blow Blow | Reasoning game engine and memory demo |
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