mirror of
https://github.com/furyhawk/agent_alpha.git
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furyhawk
1c203b6bdb
- Introduced `auth_token_repo` for handling auth token operations in Redis. - Created `chat_repo` for managing chat sessions and messages in Redis. - Implemented `ChatService` and `AuthService` to encapsulate business logic for chat and authentication. - Updated routes to utilize new service and repository layers, removing direct database calls. - Enhanced user management with `UserService` for CRUD operations and user authentication. - Revised architecture documentation to reflect the new service-repository pattern.
104 lines
3.9 KiB
Python
104 lines
3.9 KiB
Python
"""Application settings loaded from .env via pydantic-settings."""
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from __future__ import annotations
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from pathlib import Path
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from pydantic import Field
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from pydantic_settings import BaseSettings, SettingsConfigDict
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class Settings(BaseSettings):
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"""Application settings loaded from .env / environment.
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Usage::
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settings = Settings() # load from .env / env vars
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settings = Settings(_env_file=".env.test") # alternate file
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"""
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model_config = SettingsConfigDict(
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env_file=".env",
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env_file_encoding="utf-8",
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extra="ignore",
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)
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debug: bool = False
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llm_base_url: str = "http://localhost:11434/v1"
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llm_model: str = "llama"
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llm_api_key: str = ""
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logfire_token: str = ""
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database_url: str = "postgresql+asyncpg://agent_alpha:agent_alpha@localhost:5432/agent_alpha"
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valkey_url: str = "redis://localhost:6379/0"
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# ── Milvus (Vector Database) ───────────────────────────────────────────
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milvus_uri: str = "http://localhost:19530"
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milvus_token: str = ""
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# ── Media & File Storage ───────────────────────────────────────────────
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media_dir: str = "media"
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max_upload_size_mb: int = 50
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# ── RAG Parsing ────────────────────────────────────────────────────────
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pdf_parser: str = "pymupdf"
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# ── Cross-Encoder Reranker ─────────────────────────────────────────────
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cross_encoder_model: str = "cross-encoder/ms-marco-MiniLM-L6-v2"
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hf_token: str = ""
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models_cache_dir: Path = Path.home() / ".cache" / "agent-alpha" / "models"
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# ── Embeddings ─────────────────────────────────────────────────────────
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embedding_base_url: str = ""
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"""Base URL for the embedding API. If empty, uses the LLM base URL."""
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embedding_api_key: str = ""
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"""API key for the embedding API. If empty, uses the LLM API key."""
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# ── AI Configuration ───────────────────────────────────────────────────
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ai_model: str = "gpt-4o"
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rag_image_description_model: str = ""
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# ── RAG Settings (derived) ────────────────────────────────────────────
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@property
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def rag(self) -> object:
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"""Return a ``RAGSettings`` instance configured from application settings.
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Lazy import to avoid circular dependencies at module level.
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"""
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from backend.services.rag.config import RAGSettings
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return RAGSettings(
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collection_name="documents",
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chunk_size=512,
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chunk_overlap=50,
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chunking_strategy="recursive",
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enable_hybrid_search=False,
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enable_ocr=False,
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enable_image_description=True,
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image_description_model=self.rag_image_description_model,
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)
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# ── Factories ──────────────────────────────────────────────────────────
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@classmethod
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def create(cls, **overrides: str) -> Settings:
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"""Return a Settings instance with selective overrides (useful in tests).
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Example::
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settings = Settings.create(llm_model="test-model")
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"""
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return cls(**overrides)
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settings = Settings()
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