Files
VRCT/src-python/models/translation/translation_ollama.py
misyaguziya 965bee818a LM Studio と Ollama の翻訳クライアントとプロンプトを追加、requirements に langchain-ollama を追記
- src-python/models/translation に LM Studio 用 (translation_lmstudio.py / translation_lmstudio.yml) を追加
- Ollama 用クライアント (translation_ollama.py / translation_ollama.yml) を追加
- 各クライアントでプロンプト YAML から system_prompt / supported_languages を読み込み、認証チェック・モデル一覧取得・モデル設定・クライアント更新・translate 呼び出しを実装
- requirements.txt と requirements_cuda.txt に langchain-ollama==0.3.10 を追記
2025-10-17 15:58:50 +09:00

115 lines
4.0 KiB
Python

import requests
from langchain_ollama import ChatOllama
import yaml
from os import path as os_path
def _authentication_check(base_url: str | None = None) -> bool:
"""Check authentication for Ollama API.
"""
try:
response = requests.get(f"{base_url}/api/ping")
if response.status_code == 200:
return True
else:
return False
except Exception:
return False
def _get_available_text_models(base_url: str | None = None) -> list[str]:
"""Extract available text models from Ollama.
"""
response = requests.get(f"{base_url}/api/tags")
models = response.json()["models"]
allowed_models = []
for model in models:
allowed_models.append(model["name"])
allowed_models.sort()
return allowed_models
def _load_prompt_config(root_path: str = None) -> dict:
prompt_filename = "translation_ollama.yml"
# PyInstaller 展開後
if root_path and os_path.exists(os_path.join(root_path, "_internal", "prompt", prompt_filename)):
prompt_path = os_path.join(root_path, "_internal", "prompt", prompt_filename)
# src-python 直下実行
elif os_path.exists(os_path.join(os_path.dirname(__file__), "models", "translation", "prompt", prompt_filename)):
prompt_path = os_path.join(os_path.dirname(__file__), "models", "translation", "prompt", prompt_filename)
# translation フォルダ直下実行
elif os_path.exists(os_path.join(os_path.dirname(__file__), "prompt", prompt_filename)):
prompt_path = os_path.join(os_path.dirname(__file__), "prompt", prompt_filename)
else:
raise FileNotFoundError(f"Prompt file not found: {prompt_filename}")
with open(prompt_path, "r", encoding="utf-8") as f:
return yaml.safe_load(f)
class OllamaClient:
"""Ollama Translation simple wrapper.
prompt/translation_ollama.yml から system_prompt / supported_languages を読み込む。
"""
def __init__(self, root_path: str = None):
self.model = None
self.base_url = "http://localhost:11434"
prompt_config = _load_prompt_config(root_path)
self.supported_languages = prompt_config["supported_languages"]
self.prompt_template = prompt_config["system_prompt"]
self.openai_llm = None
def getModelList(self) -> list[str]:
if _authentication_check(self.base_url):
return _get_available_text_models(self.base_url)
return []
def getModel(self) -> str:
return self.model
def setModel(self, model: str) -> bool:
if model in self.getModelList():
self.model = model
return True
else:
return False
def updateClient(self) -> None:
self.openai_llm = ChatOllama(
base_url=self.base_url,
model=self.model,
streaming=False,
)
def translate(self, text: str, input_lang: str, output_lang: str) -> str:
system_prompt = self.prompt_template.format(
supported_languages=self.supported_languages,
input_lang=input_lang,
output_lang=output_lang,
)
messages = [
{"role": "system", "content": system_prompt},
{"role": "user", "content": text},
]
resp = self.openai_llm.invoke(messages)
content = ""
if isinstance(resp.content, str):
content = resp.content
elif isinstance(resp.content, list):
for part in resp.content:
if isinstance(part, str):
content += part
elif isinstance(part, dict) and "content" in part and isinstance(part["content"], str):
content += part["content"]
return content.strip()
if __name__ == "__main__":
client = OllamaClient()
models = client.getModelList()
if models:
print("Available models:", models)
model = input("Select a model: ")
client.setModel(model)
client.updateClient()
print(client.translate("こんにちは世界", "Japanese", "English"))