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Fixed all ruff lint and formatting errors

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Utkarsh-Patel-13 2025-07-24 14:43:48 -07:00
parent 0a03c42cc5
commit d359a59f6d
85 changed files with 5520 additions and 3870 deletions
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53
surfsense_backend/app/config/__init__.py
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@ -1,13 +1,11 @@
import os
from pathlib import Path
import shutil
from pathlib import Path
from chonkie import AutoEmbeddings, CodeChunker, RecursiveChunker
from dotenv import load_dotenv
from rerankers import Reranker
# Get the base directory of the project
BASE_DIR = Path(__file__).resolve().parent.parent.parent
@ -18,37 +16,37 @@ load_dotenv(env_file)
def is_ffmpeg_installed():
"""
Check if ffmpeg is installed on the current system.
Returns:
bool: True if ffmpeg is installed, False otherwise.
"""
return shutil.which("ffmpeg") is not None
class Config:
# Check if ffmpeg is installed
if not is_ffmpeg_installed():
import static_ffmpeg
# ffmpeg installed on first call to add_paths(), threadsafe.
static_ffmpeg.add_paths()
# check if ffmpeg is installed again
if not is_ffmpeg_installed():
raise ValueError("FFmpeg is not installed on the system. Please install it to use the Surfsense Podcaster.")
raise ValueError(
"FFmpeg is not installed on the system. Please install it to use the Surfsense Podcaster."
)
# Database
DATABASE_URL = os.getenv("DATABASE_URL")
NEXT_FRONTEND_URL = os.getenv("NEXT_FRONTEND_URL")
# AUTH: Google OAuth
AUTH_TYPE = os.getenv("AUTH_TYPE")
if AUTH_TYPE == "GOOGLE":
GOOGLE_OAUTH_CLIENT_ID = os.getenv("GOOGLE_OAUTH_CLIENT_ID")
GOOGLE_OAUTH_CLIENT_SECRET = os.getenv("GOOGLE_OAUTH_CLIENT_SECRET")
# LLM instances are now managed per-user through the LLMConfig system
# Legacy environment variables removed in favor of user-specific configurations
@ -56,12 +54,12 @@ class Config:
EMBEDDING_MODEL = os.getenv("EMBEDDING_MODEL")
embedding_model_instance = AutoEmbeddings.get_embeddings(EMBEDDING_MODEL)
chunker_instance = RecursiveChunker(
chunk_size=getattr(embedding_model_instance, 'max_seq_length', 512)
chunk_size=getattr(embedding_model_instance, "max_seq_length", 512)
)
code_chunker_instance = CodeChunker(
chunk_size=getattr(embedding_model_instance, 'max_seq_length', 512)
chunk_size=getattr(embedding_model_instance, "max_seq_length", 512)
)
# Reranker's Configuration | Pinecode, Cohere etc. Read more at https://github.com/AnswerDotAI/rerankers?tab=readme-ov-file#usage
RERANKERS_MODEL_NAME = os.getenv("RERANKERS_MODEL_NAME")
RERANKERS_MODEL_TYPE = os.getenv("RERANKERS_MODEL_TYPE")
@ -69,45 +67,46 @@ class Config:
model_name=RERANKERS_MODEL_NAME,
model_type=RERANKERS_MODEL_TYPE,
)
# OAuth JWT
SECRET_KEY = os.getenv("SECRET_KEY")
# ETL Service
ETL_SERVICE = os.getenv("ETL_SERVICE")
if ETL_SERVICE == "UNSTRUCTURED":
# Unstructured API Key
UNSTRUCTURED_API_KEY = os.getenv("UNSTRUCTURED_API_KEY")
elif ETL_SERVICE == "LLAMACLOUD":
# LlamaCloud API Key
LLAMA_CLOUD_API_KEY = os.getenv("LLAMA_CLOUD_API_KEY")
# Firecrawl API Key
FIRECRAWL_API_KEY = os.getenv("FIRECRAWL_API_KEY", None)
FIRECRAWL_API_KEY = os.getenv("FIRECRAWL_API_KEY", None)
# Litellm TTS Configuration
TTS_SERVICE = os.getenv("TTS_SERVICE")
TTS_SERVICE_API_BASE = os.getenv("TTS_SERVICE_API_BASE")
TTS_SERVICE_API_KEY = os.getenv("TTS_SERVICE_API_KEY")
# Litellm STT Configuration
STT_SERVICE = os.getenv("STT_SERVICE")
STT_SERVICE_API_BASE = os.getenv("STT_SERVICE_API_BASE")
STT_SERVICE_API_KEY = os.getenv("STT_SERVICE_API_KEY")
# Validation Checks
# Check embedding dimension
if hasattr(embedding_model_instance, 'dimension') and embedding_model_instance.dimension > 2000:
if (
hasattr(embedding_model_instance, "dimension")
and embedding_model_instance.dimension > 2000
):
raise ValueError(
f"Embedding dimension for Model: {EMBEDDING_MODEL} "
f"has {embedding_model_instance.dimension} dimensions, which "
f"exceeds the maximum of 2000 allowed by PGVector."
)
@classmethod
def get_settings(cls):
"""Get all settings as a dictionary."""