Skip to main content

Nage KLM — Knowledge Lifecycle Model API client

Project description

nage

Python SDK for Nage KLM — Knowledge Lifecycle Model API.

pip install nage

Quick Start

import nage

client = nage.Client("nk_live_...")

# Temel kullanım
response = client.think("Python'da async/await nedir?")
print(response.response)
print(response.stemma)
# STEMMA(MING/coding: 0.62, FEHM/turkish-context: 0.38)

# STEMMA attribution
for varve, weight in response.stemma.top(3):
    print(f"  {varve}: {weight:.2%}")

# Platform seç
response = client.think(
    "Merge sort nasıl çalışır?",
    platform="nm/ming",   # MING ağırlıklı
    max_tokens=1024,
)

# Streaming
for chunk in client.think_stream("Merhaba!"):
    print(chunk, end="", flush=True)

# VARVE listesi
knowledge = client.knowledge
print(f"Platform: {knowledge.platform}")
print(f"Toplam VARVE: {knowledge.total_varves}")
for layer, varves in knowledge.layers.items():
    print(f"  {layer}: {[v.varve_id for v in varves]}")

# 4 katman tanımları
layers = client.layers()
print(layers["CORTEX"]["question"])  # "Nasıl düşünülür ve sentezlenir?"

# Sağlık kontrolü
health = client.health()
print(health["status"])  # "ok"

Async

import asyncio
import nage

async def main():
    async with nage.AsyncClient("nk_live_...") as client:
        response = await client.think("Merhaba!")
        print(response.stemma)

        async for chunk in client.think_stream("Streaming test"):
            print(chunk, end="", flush=True)

asyncio.run(main())

STEMMA

Her yanıt STEMMA (kaynak atıf vektörü) içerir:

response.stemma.weights        # {"MING/coding": 0.62, "FEHM/turkish-context": 0.38}
response.stemma.dominant_layer # "MING"
response.stemma.dominant_varve # "MING/coding"
response.stemma.entropy        # 0.67
response.stemma.top(2)         # [("MING/coding", 0.62), ("FEHM/turkish-context", 0.38)]

Platform Aliases

Alias Model Ağırlık
nage-8b Genel Dengeli
nage-14b Derin düşünme CORTEX
nm/fehm Türkçe odaklı FEHM
nm/ming Kod odaklı MING

Katmanlar

  • FEHM — İletişim, kültür, diyalog
  • MING — Teknik, kod, sistemler
  • CHI — Alan bilgisi, olgusal
  • CORTEX — Akıl yürütme, derin sentez

License

MIT

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

nage_ai-0.1.0.tar.gz (5.4 kB view details)

Uploaded Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

nage_ai-0.1.0-py3-none-any.whl (5.7 kB view details)

Uploaded Python 3

File details

Details for the file nage_ai-0.1.0.tar.gz.

File metadata

  • Download URL: nage_ai-0.1.0.tar.gz
  • Upload date:
  • Size: 5.4 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.2.0 CPython/3.14.3

File hashes

Hashes for nage_ai-0.1.0.tar.gz
Algorithm Hash digest
SHA256 c1ad73d8ea68f7a67b3b506577bddf7882444740f49415f9f567af4ca532f6ab
MD5 e514e9b3620cbc941494b862dc85a07a
BLAKE2b-256 f477d0f97954b2948ec70d1bade8275ca135da9da6a932c41065f6dfa39d52cd

See more details on using hashes here.

File details

Details for the file nage_ai-0.1.0-py3-none-any.whl.

File metadata

  • Download URL: nage_ai-0.1.0-py3-none-any.whl
  • Upload date:
  • Size: 5.7 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.2.0 CPython/3.14.3

File hashes

Hashes for nage_ai-0.1.0-py3-none-any.whl
Algorithm Hash digest
SHA256 ba9a05bd096f1e9f019509cb7bc2c0ef6db3f83a4b0fd306504410e181555970
MD5 b010c5bf3dbc8a66ff477e635169302d
BLAKE2b-256 ccafc87e6cc2520724a5972170e3c865879aa0b8132d8560e754c078cdd9669a

See more details on using hashes here.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page