mirror of
https://github.com/NicolasBohn/NexQuant.git
synced 2026-08-04 18:57:44 +00:00
XianBW
eee2b3c56a
* remove ruff comment in log.py * change log framework and fix llm_utils.py's logs * Some thoughts for logging * fix SingletonMeta's definition, maintain an instance dict for each class that inherits it * adjust log codes directory, add some tag for factor implementation logging * Update rdagent/core/conf.py * fix factor task app & log * fix log import * Streamlet framework * fix log tag to path logic * Add todos * Add example in docstring * add log tag for llm_utils.py * Capture lost content --------- Co-authored-by: Young <afe.young@gmail.com> Co-authored-by: you-n-g <you-n-g@users.noreply.github.com>
105 lines
3.3 KiB
Python
105 lines
3.3 KiB
Python
from __future__ import annotations
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from pathlib import Path
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from dotenv import load_dotenv
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from pydantic_settings import BaseSettings
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# TODO: use pydantic for other modules in Qlib
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# from pydantic_settings import BaseSettings
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# make sure that env variable is loaded while calling Config()
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load_dotenv(verbose=True, override=True)
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from pydantic_settings import BaseSettings
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class RDAgentSettings(BaseSettings):
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# TODO: (xiao) I think LLMSetting may be a better name.
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# TODO: (xiao) I think most of the config should be in oai.config
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# Log configs
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# TODO: (xiao) think it can be a seperate config.
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log_trace_path: str | None = None
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log_llm_chat_content: bool = True
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use_azure: bool = True
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use_azure_token_provider: bool = False
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managed_identity_client_id: str | None = None
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max_retry: int = 10
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retry_wait_seconds: int = 1
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dump_chat_cache: bool = False
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use_chat_cache: bool = False
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dump_embedding_cache: bool = False
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use_embedding_cache: bool = False
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prompt_cache_path: str = str(Path.cwd() / "prompt_cache.db")
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session_cache_folder_location: str = str(Path.cwd() / "session_cache_folder/")
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max_past_message_include: int = 10
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# Chat configs
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chat_openai_api_key: str = ""
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chat_azure_api_base: str = ""
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chat_azure_api_version: str = ""
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chat_model: str = ""
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chat_max_tokens: int = 3000
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chat_temperature: float = 0.5
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chat_stream: bool = True
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chat_seed: int | None = None
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chat_frequency_penalty: float = 0.0
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chat_presence_penalty: float = 0.0
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chat_token_limit: int = (
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100000 # 100000 is the maximum limit of gpt4, which might increase in the future version of gpt
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)
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default_system_prompt: str = "You are an AI assistant who helps to answer user's questions about finance."
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# Embedding configs
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embedding_openai_api_key: str = ""
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embedding_azure_api_base: str = ""
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embedding_azure_api_version: str = ""
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embedding_model: str = ""
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# offline llama2 related config
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use_llama2: bool = False
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llama2_ckpt_dir: str = "Llama-2-7b-chat"
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llama2_tokenizer_path: str = "Llama-2-7b-chat/tokenizer.model"
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llams2_max_batch_size: int = 8
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# azure document intelligence configs
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azure_document_intelligence_key: str = ""
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azure_document_intelligence_endpoint: str = ""
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# server served endpoints
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use_gcr_endpoint: bool = False
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gcr_endpoint_type: str = "llama2_70b" # or "llama3_70b", "phi2", "phi3_4k", "phi3_128k"
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llama2_70b_endpoint: str = ""
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llama2_70b_endpoint_key: str = ""
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llama2_70b_endpoint_deployment: str = ""
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llama3_70b_endpoint: str = ""
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llama3_70b_endpoint_key: str = ""
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llama3_70b_endpoint_deployment: str = ""
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phi2_endpoint: str = ""
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phi2_endpoint_key: str = ""
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phi2_endpoint_deployment: str = ""
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phi3_4k_endpoint: str = ""
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phi3_4k_endpoint_key: str = ""
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phi3_4k_endpoint_deployment: str = ""
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phi3_128k_endpoint: str = ""
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phi3_128k_endpoint_key: str = ""
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phi3_128k_endpoint_deployment: str = ""
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gcr_endpoint_temperature: float = 0.7
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gcr_endpoint_top_p: float = 0.9
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gcr_endpoint_do_sample: bool = False
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gcr_endpoint_max_token: int = 100
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# factor extraction conf
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max_input_duplicate_factor_group: int = 600
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max_output_duplicate_factor_group: int = 20
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RD_AGENT_SETTINGS = RDAgentSettings()
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