mirror of
https://github.com/Monadical-SAS/reflector.git
synced 2025-12-20 20:29:06 +00:00
9fe261406c
* initial * add LLM features * update LLM logic * update llm functions: change control flow * add generation config * update return types * update processors and tests * update rtc_offer * revert new title processor change * fix unit tests * add comments and fix HTTP 500 * adjust prompt * test with reflector app * revert new event for final title * update * move onus onto processors * move onus onto processors * stash * add provision for gen config * dynamically pack the LLM input using context length * tune final summary params * update consolidated class structures * update consolidated class structures * update precommit * add broadcast processors * working baseline * Organize LLMParams * minor fixes * minor fixes * minor fixes * fix unit tests * fix unit tests * fix unit tests * update tests * update tests * edit pipeline response events * update summary return types * configure tests * alembic db migration * change LLM response flow * edit main llm functions * edit main llm functions * change llm name and gen cf * Update transcript_topic_detector.py * PR review comments * checkpoint before db event migration * update DB migration of past events * update DB migration of past events * edit LLM classes * Delete unwanted file * remove List typing * remove List typing * update oobabooga API call * topic enhancements * update UI event handling * move ensure_casing to llm base * update tests * update tests
102 lines
3.0 KiB
Python
102 lines
3.0 KiB
Python
from pydantic_settings import BaseSettings, SettingsConfigDict
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class Settings(BaseSettings):
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model_config = SettingsConfigDict(env_file=".env", env_file_encoding="utf-8")
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OPENMP_KMP_DUPLICATE_LIB_OK: bool = False
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# Database
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DATABASE_URL: str = "sqlite:///./reflector.sqlite3"
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# local data directory (audio for no)
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DATA_DIR: str = "./data"
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# Whisper
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WHISPER_MODEL_SIZE: str = "tiny"
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WHISPER_REAL_TIME_MODEL_SIZE: str = "tiny"
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# Summarizer
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SUMMARIZER_MODEL: str = "facebook/bart-large-cnn"
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SUMMARIZER_INPUT_ENCODING_MAX_LENGTH: int = 1024
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SUMMARIZER_MAX_LENGTH: int = 2048
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SUMMARIZER_BEAM_SIZE: int = 6
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SUMMARIZER_MAX_CHUNK_LENGTH: int = 1024
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SUMMARIZER_USING_CHUNKS: bool = True
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# Audio
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AUDIO_BLACKHOLE_INPUT_AGGREGATOR_DEVICE_NAME: str = "aggregator"
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AUDIO_AV_FOUNDATION_DEVICE_ID: int = 1
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AUDIO_CHANNELS: int = 2
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AUDIO_SAMPLING_RATE: int = 48000
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AUDIO_SAMPLING_WIDTH: int = 2
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AUDIO_BUFFER_SIZE: int = 256 * 960
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# Audio Transcription
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# backends: whisper, banana, modal
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TRANSCRIPT_BACKEND: str = "whisper"
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TRANSCRIPT_URL: str | None = None
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TRANSCRIPT_TIMEOUT: int = 90
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# Audio transcription banana.dev configuration
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TRANSCRIPT_BANANA_API_KEY: str | None = None
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TRANSCRIPT_BANANA_MODEL_KEY: str | None = None
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# Audio transcription modal.com configuration
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TRANSCRIPT_MODAL_API_KEY: str | None = None
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# Audio transcription storage
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TRANSCRIPT_STORAGE_BACKEND: str = "aws"
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# Storage configuration for AWS
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TRANSCRIPT_STORAGE_AWS_BUCKET_NAME: str = "reflector-bucket/chunks"
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TRANSCRIPT_STORAGE_AWS_REGION: str = "us-east-1"
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TRANSCRIPT_STORAGE_AWS_ACCESS_KEY_ID: str | None = None
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TRANSCRIPT_STORAGE_AWS_SECRET_ACCESS_KEY: str | None = None
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# LLM
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# available backend: openai, banana, modal, oobabooga
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LLM_BACKEND: str = "oobabooga"
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# LLM common configuration
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LLM_URL: str | None = None
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LLM_HOST: str = "localhost"
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LLM_PORT: int = 7860
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LLM_OPENAI_KEY: str | None = None
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LLM_OPENAI_MODEL: str = "gpt-3.5-turbo"
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LLM_OPENAI_TEMPERATURE: float = 0.7
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LLM_TIMEOUT: int = 60 * 5 # take cold start into account
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LLM_MAX_TOKENS: int = 1024
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LLM_TEMPERATURE: float = 0.7
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# LLM Banana configuration
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LLM_BANANA_API_KEY: str | None = None
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LLM_BANANA_MODEL_KEY: str | None = None
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# LLM Modal configuration
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LLM_MODAL_API_KEY: str | None = None
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# Sentry
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SENTRY_DSN: str | None = None
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# User authentication (none, fief)
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AUTH_BACKEND: str = "none"
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# User authentication using fief
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AUTH_FIEF_URL: str | None = None
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AUTH_FIEF_CLIENT_ID: str | None = None
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AUTH_FIEF_CLIENT_SECRET: str | None = None
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# API public mode
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# if set, all anonymous record will be public
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PUBLIC_MODE: bool = False
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# Default LLM model name
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DEFAULT_LLM: str = "lmsys/vicuna-13b-v1.5"
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# Cache directory for all model storage
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CACHE_DIR: str = "data"
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settings = Settings()
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