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chore: finalize repo audit hygiene (#257)

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This commit is contained in:
Elliot Chen
2026-06-06 13:59:12 +08:00
committed by GitHub
parent ab23e40b28
commit 00f1dfaec5
27 changed files with 459 additions and 199 deletions
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212
use-cases/openher/demo/evermemos_demo.py
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@ -1,18 +1,18 @@
#!/usr/bin/env python3
"""
OpenHer × EverCore Integration Demo
OpenHer × EverOS Integration Demo
Demonstrates how EverCore provides long-term memory to the
Demonstrates how EverOS provides long-term memory to the
AI Being persona engine. Shows session context loading, memory
storage, search, and relationship vector evolution.
Usage:
# With EverCore Cloud
# With EverMind Cloud
export EVERMEMOS_BASE_URL=https://api.evermind.ai/v1
export EVERMEMOS_API_KEY=your_key
python demo/evermemos_demo.py
# With self-hosted EverCore
# With self-hosted EverOS
export EVERMEMOS_BASE_URL=http://localhost:1995/api/v1
python demo/evermemos_demo.py
"""
@ -20,12 +20,10 @@ Usage:
import asyncio
import os
import sys
import json
from datetime import datetime
from typing import Optional
# ──────────────────────────────────────────────
# EverCore Client (minimal standalone version)
# EverOS Client (minimal standalone version)
# ──────────────────────────────────────────────
try:
@ -35,8 +33,8 @@ except ImportError:
sys.exit(1)
class EverCoreClient:
"""Minimal EverCore client for demo purposes."""
class EverOSClient:
"""Minimal EverOS client for demo purposes."""
def __init__(self, base_url: str, api_key: str = ""):
self.base_url = base_url.rstrip("/")
@ -51,7 +49,7 @@ class EverCoreClient:
return h
async def health_check(self) -> bool:
"""Check if EverCore is reachable."""
"""Check if EverOS is reachable."""
try:
# Try the health endpoint (remove /api/v1 suffix)
health_url = self.base_url.replace("/api/v1", "") + "/health"
@ -129,12 +127,13 @@ class EverCoreClient:
# ──────────────────────────────────────────────
# Relationship Vector (from EverCore session)
# Relationship Vector (from EverOS session)
# ──────────────────────────────────────────────
def compute_relationship_vector(profile_data: dict) -> dict:
"""
Extract 4D relationship vector from EverCore profile data.
Extract 4D relationship vector from EverOS profile data.
These 4 dimensions expand the persona engine's neural network
from 8D to 12D input, allowing it to differentiate behavior
@ -152,12 +151,12 @@ def apply_relationship_ema(
prior: dict,
delta: dict,
conversation_depth: float,
prev_ema: Optional[dict] = None,
prev_ema: dict | None = None,
) -> dict:
"""
Semi-emergent relationship update (Step 2.5 of ChatAgent lifecycle).
Blends EverCore prior with LLM-judged delta through EMA:
Blends EverOS prior with LLM-judged delta through EMA:
- alpha modulated by conversation depth (deeper = trust LLM more)
- Clips to valid ranges
- Preserves momentum through prev_ema
@ -181,25 +180,26 @@ def apply_relationship_ema(
# Demo
# ──────────────────────────────────────────────
async def main():
base_url = os.getenv("EVERMEMOS_BASE_URL", "")
api_key = os.getenv("EVERMEMOS_API_KEY", "")
if not base_url:
print("=" * 60)
print("OpenHer × EverCore Integration Demo")
print("OpenHer × EverOS Integration Demo")
print("=" * 60)
print()
print("⚠️ EVERMEMOS_BASE_URL not set.")
print()
print("To run this demo, set up EverCore:")
print("To run this demo, set up EverOS:")
print()
print(" Option A — Cloud:")
print(" Option A — EverMind Cloud:")
print(" export EVERMEMOS_BASE_URL=https://api.evermind.ai/v1")
print(" export EVERMEMOS_API_KEY=your_key")
print()
print(" Option B — Self-hosted:")
print(" cd vendor/EverCore && docker compose up -d")
print(" cd vendor/EverOS && docker compose up -d")
print(" uv run python src/run.py")
print(" export EVERMEMOS_BASE_URL=http://localhost:1995/api/v1")
print()
@ -209,20 +209,20 @@ async def main():
await demo_simulation()
return
client = EverCoreClient(base_url, api_key)
client = EverOSClient(base_url, api_key)
print("=" * 60)
print("OpenHer × EverCore Integration Demo")
print("OpenHer × EverOS Integration Demo")
print("=" * 60)
print(f"\n📡 EverCore: {base_url}")
print(f"\n📡 EverOS: {base_url}")
# Health check
healthy = await client.health_check()
if not healthy:
print("❌ EverCore is not reachable. Check your URL and try again.")
print("❌ EverOS is not reachable. Check your URL and try again.")
await client.close()
return
print("✅ EverCore is healthy\n")
print("✅ EverOS is healthy\n")
# ── Demo conversation ──
user_id = "demo_user"
@ -232,8 +232,15 @@ async def main():
group_id = f"{persona_id}__{user_id}"
conversations = [
("My name is Alex, I'm a software engineer", "Nice to meet you Alex! What kind of software do you work on?"),
("I love hiking in the mountains on weekends", "That sounds wonderful! There's something about being up high that makes everything else feel small."),
(
"My name is Alex, I'm a software engineer",
"Nice to meet you Alex! What kind of software do you work on?",
),
(
"I love hiking in the mountains on weekends",
"That sounds wonderful! There's something about being up high "
"that makes everything else feel small.",
),
("I drink my coffee black, no sugar", "Noted! A purist. I respect that."),
]
@ -249,7 +256,7 @@ async def main():
agent_reply=agent_reply,
)
status = "✅" if "error" not in result else "❌"
print(f" {status} User: \"{user_msg[:50]}...\"")
print(f' {status} User: "{user_msg[:50]}..."')
# Wait for indexing
print("\n⏳ Waiting for memory indexing (3s)...")
@ -270,7 +277,7 @@ async def main():
group_id=group_id,
)
memories = result.get("result", {}).get("memories", [])
print(f" Q: \"{query}\"")
print(f' Q: "{query}"')
if memories:
for mem in memories[:2]:
content = str(mem)[:100]
@ -281,7 +288,12 @@ async def main():
# Relationship vector
print("📊 Relationship Vector Evolution:\n")
prior = {"relationship_depth": 0.0, "emotional_valence": 0.0, "trust_level": 0.0, "pending_foresight": 0.0}
prior = {
"relationship_depth": 0.0,
"emotional_valence": 0.0,
"trust_level": 0.0,
"pending_foresight": 0.0,
}
deltas = [
{"relationship_depth": 0.1, "emotional_valence": 0.2, "trust_level": 0.05},
{"relationship_depth": 0.05, "emotional_valence": 0.1, "trust_level": 0.1},
@ -290,61 +302,147 @@ async def main():
ema = None
for i, delta in enumerate(deltas):
ema = apply_relationship_ema(prior, delta, conversation_depth=0.2 * (i + 1), prev_ema=ema)
print(f" Turn {i+1}: depth={ema['relationship_depth']:.3f} "
f"valence={ema['emotional_valence']:.3f} "
f"trust={ema['trust_level']:.3f}")
ema = apply_relationship_ema(
prior, delta, conversation_depth=0.2 * (i + 1), prev_ema=ema
)
print(
f" Turn {i + 1}: depth={ema['relationship_depth']:.3f} "
f"valence={ema['emotional_valence']:.3f} "
f"trust={ema['trust_level']:.3f}"
)
prior = ema
print(f"\n → After 3 turns: no longer a stranger (depth={ema['relationship_depth']:.3f})")
print(f" → Neural network now produces warmer, more familiar behavioral signals\n")
print(
"\n → After 3 turns: no longer a stranger "
f"(depth={ema['relationship_depth']:.3f})"
)
print(" → Neural network now produces warmer, more familiar behavioral signals\n")
await client.close()
print("✅ Demo complete!")
async def demo_simulation():
"""Run demo in simulation mode (no EverCore connection)."""
"""Run demo in simulation mode (no EverOS connection)."""
print("📊 Simulating Relationship Vector Evolution:\n")
print(" This shows how the 4D EverCore relationship vector")
print(" This shows how the 4D EverOS relationship vector")
print(" deepens over multiple conversation turns.\n")
prior = {"relationship_depth": 0.0, "emotional_valence": 0.0, "trust_level": 0.0, "pending_foresight": 0.0}
prior = {
"relationship_depth": 0.0,
"emotional_valence": 0.0,
"trust_level": 0.0,
"pending_foresight": 0.0,
}
# Simulate 10 turns of conversation
simulated_deltas = [
(0.3, {"relationship_depth": 0.10, "emotional_valence": 0.15, "trust_level": 0.05}),
(0.4, {"relationship_depth": 0.08, "emotional_valence": 0.10, "trust_level": 0.08}),
(0.5, {"relationship_depth": 0.05, "emotional_valence": 0.20, "trust_level": 0.12}),
(0.6, {"relationship_depth": 0.06, "emotional_valence": -0.10, "trust_level": 0.03}),
(0.7, {"relationship_depth": 0.04, "emotional_valence": 0.08, "trust_level": 0.10}),
(0.7, {"relationship_depth": 0.03, "emotional_valence": 0.12, "trust_level": 0.08}),
(0.8, {"relationship_depth": 0.02, "emotional_valence": 0.05, "trust_level": 0.06}),
(0.8, {"relationship_depth": 0.03, "emotional_valence": 0.10, "trust_level": 0.05}),
(0.9, {"relationship_depth": 0.01, "emotional_valence": 0.08, "trust_level": 0.04}),
(0.9, {"relationship_depth": 0.02, "emotional_valence": 0.06, "trust_level": 0.03}),
(
0.3,
{
"relationship_depth": 0.10,
"emotional_valence": 0.15,
"trust_level": 0.05,
},
),
(
0.4,
{
"relationship_depth": 0.08,
"emotional_valence": 0.10,
"trust_level": 0.08,
},
),
(
0.5,
{
"relationship_depth": 0.05,
"emotional_valence": 0.20,
"trust_level": 0.12,
},
),
(
0.6,
{
"relationship_depth": 0.06,
"emotional_valence": -0.10,
"trust_level": 0.03,
},
),
(
0.7,
{
"relationship_depth": 0.04,
"emotional_valence": 0.08,
"trust_level": 0.10,
},
),
(
0.7,
{
"relationship_depth": 0.03,
"emotional_valence": 0.12,
"trust_level": 0.08,
},
),
(
0.8,
{
"relationship_depth": 0.02,
"emotional_valence": 0.05,
"trust_level": 0.06,
},
),
(
0.8,
{
"relationship_depth": 0.03,
"emotional_valence": 0.10,
"trust_level": 0.05,
},
),
(
0.9,
{
"relationship_depth": 0.01,
"emotional_valence": 0.08,
"trust_level": 0.04,
},
),
(
0.9,
{
"relationship_depth": 0.02,
"emotional_valence": 0.06,
"trust_level": 0.03,
},
),
]
ema = None
for i, (depth, delta) in enumerate(simulated_deltas, 1):
alpha = max(0.15, min(0.65, 0.15 + 0.5 * depth))
ema = apply_relationship_ema(prior, delta, conversation_depth=depth, prev_ema=ema)
ema = apply_relationship_ema(
prior, delta, conversation_depth=depth, prev_ema=ema
)
bar_d = "█" * int(ema["relationship_depth"] * 20)
bar_v = "█" * int(max(0, ema["emotional_valence"]) * 20)
bar_t = "█" * int(ema["trust_level"] * 20)
print(f" Turn {i:2d} (α={alpha:.2f}): "
f"depth={ema['relationship_depth']:.3f} {bar_d}")
print(f" "
f"valence={ema['emotional_valence']:+.3f} {bar_v}")
print(f" "
f"trust={ema['trust_level']:.3f} {bar_t}")
print(
f" Turn {i:2d} (α={alpha:.2f}): "
f"depth={ema['relationship_depth']:.3f} {bar_d}"
)
print(f" valence={ema['emotional_valence']:+.3f} {bar_v}")
print(f" trust={ema['trust_level']:.3f} {bar_t}")
print()
prior = ema
print(" ──────────────────────────────────")
print(f" Final state: depth={ema['relationship_depth']:.3f}, "
f"valence={ema['emotional_valence']:+.3f}, "
f"trust={ema['trust_level']:.3f}")
print(
f" Final state: depth={ema['relationship_depth']:.3f}, "
f"valence={ema['emotional_valence']:+.3f}, "
f"trust={ema['trust_level']:.3f}"
)
print()
print(" Turn 4 shows a negative emotional event (valence delta = -0.10),")
print(" but the EMA smoothing prevents overreaction — the relationship")