npcpy

from npcpy import get_llm_response
from pydantic import BaseModel
from typing import List

class Planet(BaseModel):
    name: str
    distance_au: float
    num_moons: int

class SolarSystem(BaseModel):
    planets: List[Planet]

response = get_llm_response(
    "List the first 4 planets from the sun.",
    model='qwen3.5:2b', provider='ollama',
    format=SolarSystem
)
for p in response['response']['planets']:
    print(f"{p['name']}: {p['distance_au']} AU, {p['num_moons']} moons")

from npcpy.llm_funcs import gen_image, gen_video
from npcpy.gen.audio_gen import text_to_speech

# Image — OpenAI, Gemini, Ollama, or diffusers
images = gen_image("A sunset over the mountains", model='gpt-image-1', provider='openai')
images[0].save("sunset.png")

# Audio — OpenAI, Gemini, ElevenLabs, Kokoro, gTTS
audio_bytes = text_to_speech("Hello from npcpy!", engine="openai", voice="alloy")
with open("hello.wav", "wb") as f:
    f.write(audio_bytes)

# Video — Gemini Veo
result = gen_video("A cat riding a skateboard", model='veo-3.1-fast-generate-preview', provider='gemini')
print(result['output'])

from mcp.server.fastmcp import FastMCP

mcp = FastMCP("Database Tools")

@mcp.tool()
def query_orders(customer_id: str, limit: int = 10) -> str:
    """Query recent orders for a customer."""
    # Your database logic here
    return f"Found {limit} orders for customer {customer_id}"

@mcp.tool()
def search_products(query: str) -> str:
    """Search the product catalog."""
    return f"Products matching: {query}"

if __name__ == "__main__":
    mcp.run()

from npcpy.memory.knowledge_graph import (
    kg_initial, kg_evolve_incremental, kg_sleep_process,
    kg_dream_process, kg_hybrid_search,
)
from npcpy.data.load_file import load_file_contents

# Seed the KG from a design doc PDF and a migration script
design_doc = load_file_contents("docs/auth_migration_plan.pdf")
migration_sql = load_file_contents("migrations/003_clerk_auth.sql")

kg = kg_initial(
    content=design_doc + "\n\n" + migration_sql,
    model="qwen3:4b", provider="ollama",
)

# Assimilate follow-up commits and PR descriptions
kg, _ = kg_evolve_incremental(
    kg,
    new_content_text=(
        "PR #412: Replaced Stripe customer-session lookup with Clerk JWT verification. "
        "Removed /api/stripe/webhook endpoint. Added ClerkMiddleware to all protected routes. "
        "CSP headers updated to allow clerk.accounts.dev origin."
    ),
    model="qwen3:4b", provider="ollama", get_concepts=True,
)

# Consolidate — merge redundant nodes, strengthen high-frequency edges
kg, sleep_report = kg_sleep_process(kg, model="qwen3:4b", provider="ollama")

# Dream — generate speculative connections between loosely related concepts
kg, dream_report = kg_dream_process(kg, model="qwen3:4b", provider="ollama")

# Search across facts, concepts, and speculative edges
results = kg_hybrid_search(kg, "How does auth propagate through GraphQL resolvers?",
                           model="qwen3:4b", provider="ollama")
for r in results:
    print(r['score'], r['text'])
print(f"{len(kg['facts'])} facts, {len(kg['concepts'])} concepts")

from npcpy.llm_funcs import get_facts

conversation = """
User: We're ripping out Stripe entirely and moving auth to Clerk. The JWT verification
      will happen in ClerkMiddleware instead of the custom verify_stripe_session helper.
Assistant: Got it. I'll update the middleware chain. What about the existing session store?
User: Kill the Redis session cache — Clerk handles session state on their end.
      Also, the CSP headers need clerk.accounts.dev and clerk.enpisi.com added to connect-src.
"""

facts = get_facts(conversation, model="qwen3:4b", provider="ollama")
for f in facts:
    print(f"[{f.get('category', 'general')}] {f['statement']}")
# [architecture] Auth provider migrated from Stripe to Clerk with JWT verification via ClerkMiddleware
# [infrastructure] Redis session cache removed — Clerk manages session state
# [security] CSP connect-src updated to include clerk.accounts.dev and clerk.enpisi.com

from pathlib import Path
from npcpy.memory.kg_population import SememolutionPopulation
from npcpy.data.load_file import load_file_contents

pop = SememolutionPopulation(population_size=100, sample_size=10)
pop.initialize()

# Ingest a heterogeneous corpus — PDFs, DOCX, source code, meeting transcripts
corpus_dirs = [Path("docs/architecture"), Path("docs/meeting_notes"), Path("src/auth")]
for d in corpus_dirs:
    for f in sorted(d.glob("*")):
        if f.suffix in (".pdf", ".docx", ".md", ".py", ".ts", ".txt"):
            text = load_file_contents(str(f))
            pop.assimilate_text(text)

# Sleep/dream cycle — each individual consolidates according to its genome
pop.sleep_cycle()

# Query: sample 10 individuals, generate competing responses, rank them
rankings = pop.query_and_rank("How does the auth middleware chain interact with the GraphQL context?")
for rank, entry in enumerate(rankings[:3], 1):
    print(f"#{rank} (individual {entry['id']}, score {entry['score']:.3f}): {entry['response'][:120]}...")

# Selection + reproduction — top performers breed, bottom are replaced
pop.evolve_generation()

stats = pop.get_stats()
print(f"Generation {stats['generation']} | avg fitness {stats['avg_fitness']:.3f} | "
      f"best fitness {stats['best_fitness']:.3f} | diversity {stats['diversity']:.3f}")

from npcpy.ft.sft import run_sft

# Train a model to extract structured decisions from meeting notes
# LoRA fine-tuning — auto-uses MLX on Apple Silicon
X_train = [
    "Meeting: Auth Migration Sync (2025-01-15)\nAttendees: Sarah, Mike, Priya\n"
    "Discussion: Evaluated Clerk vs Auth0 for replacing Stripe auth. Clerk chosen "
    "for lower latency and native Next.js support. Migration starts sprint 12. "
    "Redis session store will be removed once Clerk JWT verification is stable.",

    "Meeting: API Rate Limiting Review (2025-01-22)\nAttendees: Mike, Jordan\n"
    "Discussion: Current per-session token bucket is incompatible with Clerk's "
    "stateless JWTs. Agreed to switch to per-IP sliding window with 100 req/min "
    "default. Premium tier gets 500 req/min. Jordan to implement by Friday.",

    "Meeting: GraphQL Schema Freeze (2025-02-01)\nAttendees: Sarah, Priya, Jordan\n"
    "Discussion: Schema v2 locked for release. Nested auth context propagation "
    "through dataloaders confirmed working. New 'viewer' pattern adopted for "
    "all authenticated queries. Breaking changes documented in CHANGELOG.",

    "Meeting: Deployment Postmortem (2025-02-10)\nAttendees: full team\n"
    "Discussion: Production outage caused by missing CSP header for clerk.accounts.dev. "
    "Root cause: deploy script didn't pick up new env vars. Fix: added CSP validation "
    "to CI pipeline. New rule: all external origins must be in csp_allowlist.json.",
]
y_train = [
    '{"decisions": [{"what": "Adopt Clerk for auth", "why": "Lower latency, native Next.js support", "owner": "team", "deadline": "sprint 12"}, {"what": "Remove Redis session store", "why": "Clerk handles session state", "owner": "team", "deadline": "after JWT verification stable"}]}',
    '{"decisions": [{"what": "Switch to per-IP sliding window rate limiter", "why": "Token bucket incompatible with stateless JWTs", "owner": "Jordan", "deadline": "Friday"}, {"what": "Set rate limits to 100/min default, 500/min premium", "why": "Tiered access control", "owner": "Jordan", "deadline": "Friday"}]}',
    '{"decisions": [{"what": "Freeze GraphQL schema v2", "why": "Release readiness", "owner": "Sarah", "deadline": "immediate"}, {"what": "Adopt viewer pattern for authenticated queries", "why": "Consistent auth context in nested resolvers", "owner": "Priya", "deadline": "immediate"}]}',
    '{"decisions": [{"what": "Add CSP validation to CI pipeline", "why": "Prevent missing CSP headers in deploys", "owner": "team", "deadline": "immediate"}, {"what": "Require external origins in csp_allowlist.json", "why": "Enforce explicit approval of external domains", "owner": "team", "deadline": "immediate"}]}',
]

model_path = run_sft(X_train=X_train, y_train=y_train)