Create knowledge/mindseye_knowledge.py

engine_03/knowledge/mindseye_knowledge.py

@@ -0,0 +1,172 @@
+from typing import List, Dict
+import random
+
+FACTS: List[Dict] = [
+    {
+        "id": "mindseye_overview",
+        "title": "What is MindsEye?",
+        "keywords": [
+            "what is mindseye",
+            "what is minds eye",
+            "mindseye os",
+            "minds eye os",
+            "mindseye overview",
+            "os overview",
+            "system overview",
+        ],
+        "answer": (
+            "MindsEye is a multi-surface AI operating system being built under "
+            "SAGEWORKS AI. It treats data as time-labeled binary flows across "
+            "devices, browsers, and cloud services.\n\n"
+            "Instead of random scripts everywhere, you get:\n"
+            "- A shared sense of **time** for all events\n"
+            "- Ledgers that remember what agents did and why\n"
+            "- Layers like LAW-N / LAW-T / Workspace / SQL / Cloud Fabric that "
+            "cooperate instead of fighting each other."
+        ),
+    },
+    {
+        "id": "workspace_layer",
+        "title": "Workspace Automation Layer",
+        "keywords": [
+            "workspace",
+            "gmail",
+            "docs",
+            "sheets",
+            "drive",
+            "workspace automation",
+            "google workspace",
+            "apps script",
+        ],
+        "answer": (
+            "The Workspace layer is the front-of-house for MindsEye.\n\n"
+            "Gmail, Docs, Sheets, and Drive events land here first. They get:\n"
+            "- Labeled with time and context\n"
+            "- Routed into flows\n"
+            "- Optionally logged into ledgers\n\n"
+            "It’s the part that makes MindsEye feel like an assistant living "
+            "inside your existing tools, not just another random bot."
+        ),
+    },
+    {
+        "id": "ledger_layer",
+        "title": "MindsEye Ledger / Time-Labeled Truth",
+        "keywords": [
+            "ledger",
+            "google ledger",
+            "time labeled",
+            "time-labeled",
+            "tlb",
+            "binary ledger",
+        ],
+        "answer": (
+            "The ledger layer is where MindsEye becomes ruthless about time.\n\n"
+            "Every event, prompt, and response becomes a **time-labeled row**. "
+            "Agents can look back and see:\n"
+            "- What happened\n"
+            "- When it happened\n"
+            "- Which node or agent acted\n\n"
+            "That lets MindsEye reason over history instead of treating each prompt "
+            "like amnesia mode."
+        ),
+    },
+    {
+        "id": "law_n_layer",
+        "title": "LAW-N — Network-Native Layer",
+        "keywords": [
+            "law-n",
+            "law n",
+            "lawn",
+            "network layer",
+            "network-native",
+            "api graph",
+            "service graph",
+        ],
+        "answer": (
+            "LAW-N is the network-native layer of MindsEye.\n\n"
+            "Instead of hard-coding API calls everywhere, LAW-N treats services, "
+            "agents, and tools as a **graph of time-aware nodes**. Each node knows:\n"
+            "- What data it consumes\n"
+            "- What it outputs\n"
+            "- How it fits into the wider flow."
+        ),
+    },
+    {
+        "id": "roadmap",
+        "title": "MindsEye Roadmap",
+        "keywords": [
+            "roadmap",
+            "what next",
+            "next steps",
+            "plan",
+            "how to scale",
+        ],
+        "answer": (
+            "High-level roadmap:\n\n"
+            "1. Sandbox — this Space: local chatbot + flow studio.\n"
+            "2. Glue — bridge sandbox into ledgers + Workspace flows.\n"
+            "3. Network — orchestrate services via LAW-N.\n"
+            "4. Full Stack — OS experience across web, mobile, and cloud."
+        ),
+    },
+]
+
+GENERIC_FALLBACKS = [
+    "I don’t have that topic wired into the sandbox brain yet. Try asking about MindsEye, the ledger, LAW-N, the roadmap, or SAGEWORKS.",
+    "This version of the bot is intentionally small. If this topic matters, it probably deserves its own node or ledger later.",
+]
+
+GREETING_TRIGGERS = [
+    "hi", "hello", "hey", "yo", "morning", "afternoon", "evening", "sup", "what's up", "whats up"
+]
+
+
+def normalize(text: str) -> str:
+    return text.strip().lower()
+
+
+def is_greeting(message: str) -> bool:
+    msg = normalize(message)
+    return any(trigger in msg for trigger in GREETING_TRIGGERS)
+
+
+def greeting_reply() -> str:
+    options = [
+        "Yo 👋 This is the MindsEye sandbox brain. No APIs, just local logic.",
+        "Hey 👋 You’re talking to the MindsEye sandbox bot — the tiny version before the 21-repo beast shows up.",
+        "Hi 👋 This is the clean-slate MindsEye chatbot. Ask about MindsEye, the ledger, LAW-N, or the roadmap.",
+    ]
+    return random.choice(options)
+
+
+def find_best_fact(user_message: str) -> str:
+    msg = normalize(user_message)
+
+    if is_greeting(msg):
+        return greeting_reply()
+
+    # Exact keyword match
+    for fact in FACTS:
+        for kw in fact["keywords"]:
+            if kw in msg:
+                return fact["answer"]
+
+    # Soft scoring
+    best_fact = None
+    best_score = 0
+    tokens = set(msg.split())
+
+    for fact in FACTS:
+        score = 0
+        for kw in fact["keywords"]:
+            for token in kw.split():
+                if token in tokens:
+                    score += 1
+        if score > best_score:
+            best_score = score
+            best_fact = fact
+
+    if best_fact and best_score > 0:
+        return best_fact["answer"]
+
+    return random.choice(GENERIC_FALLBACKS)