mirror of
https://github.com/NicolasBohn/NexQuant.git
synced 2026-08-03 10:27:42 +00:00
Xu Yang
1e77557293
* run the code * update code * remove some redundant code --------- Co-authored-by: xuyang1 <xuyang1@microsoft.com>
94 lines
2.9 KiB
Python
94 lines
2.9 KiB
Python
# TODO: use pydantic for other modules in Qlib
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# from pydantic_settings import BaseSettings
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import os
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from typing import Union
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from dotenv import load_dotenv
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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 FincoSettings(BaseSettings):
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use_azure: bool = True
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use_azure_token_provider: bool = False
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max_retry: int = 10
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retry_wait_seconds: int = 1
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continuous_mode: bool = False
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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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workspace: str = "./finco_workspace"
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prompt_cache_path: str = os.getcwd() + "/prompt_cache.db"
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session_cache_folder_location: str = os.getcwd() + "/session_cache_folder/"
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max_past_message_include: int = 10
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use_vector_only: bool = False
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log_llm_chat_content: bool = True
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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: Union[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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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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# 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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# finco v2 configs
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azure_document_intelligence_key: str = ""
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azure_document_intelligence_endpoint: str = ""
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# fincov2 llama2 endpoint
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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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