forked from innovacion/Mayacontigo
55 lines
1.9 KiB
Python
55 lines
1.9 KiB
Python
from hvac import Client
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from pydantic import Field
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from pydantic_settings import BaseSettings
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client = Client(url="https://vault.ia-innovacion.work")
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if not client.is_authenticated():
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raise Exception("Vault authentication failed")
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secret_map = client.secrets.kv.v2.read_secret_version(
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path="banortegpt", mount_point="secret"
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)["data"]["data"]
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class Settings(BaseSettings):
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# Config básico
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model: str = "gpt-4o"
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model_temperature: int = 0
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message_limit: int = 10
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host: str = "0.0.0.0"
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port: int = 8000
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# AGREGAR ESTAS LÍNEAS (igual que OCP):
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embedding_model: str = "text-embedding-3-large"
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storage_bucket: str = "normativa-bucket" # Ajusta el nombre
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vector_index: str = "MayaNormativaLLM"
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search_limit: int = 3
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# API Keys existentes
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azure_endpoint: str = Field(default_factory=lambda: secret_map["azure_endpoint"])
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openai_api_key: str = Field(default_factory=lambda: secret_map["openai_api_key"])
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openai_api_version: str = Field(default_factory=lambda: secret_map["openai_api_version"])
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mongodb_url: str = Field(default_factory=lambda: secret_map["cosmosdb_connection_string"])
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# AGREGAR ESTAS LÍNEAS (igual que OCP):
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azure_blob_connection_string: str = Field(
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default_factory=lambda: secret_map["azure_blob_connection_string"]
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)
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qdrant_url: str = Field(default_factory=lambda: secret_map["qdrant_api_url"])
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qdrant_api_key: str | None = Field(
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default_factory=lambda: secret_map["qdrant_api_key"]
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)
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async def init_mongo_db(self):
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from banortegpt.database.mongo_memory.models import Conversation
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from beanie import init_beanie
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from motor.motor_asyncio import AsyncIOMotorClient
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client = AsyncIOMotorClient(self.mongodb_url)
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await init_beanie(
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database=client.banortegptdos,
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document_models=[Conversation],
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)
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config = Settings() |