From 9e82da243bfb50ca4675bfc971388e5679962771 Mon Sep 17 00:00:00 2001 From: Xu Yang Date: Thu, 13 Jun 2024 16:20:54 +0800 Subject: [PATCH] use token limit to shrink report content (#24) Co-authored-by: xuyang1 --- rdagent/core/conf.py | 4 ++++ rdagent/document_process/document_analysis.py | 17 +++++++++++------ rdagent/document_process/prompts.yaml | 16 +++++++++++++++- .../factor_implementation_config.py | 4 ---- 4 files changed, 30 insertions(+), 11 deletions(-) diff --git a/rdagent/core/conf.py b/rdagent/core/conf.py index be5822be..f6a728b3 100644 --- a/rdagent/core/conf.py +++ b/rdagent/core/conf.py @@ -42,6 +42,10 @@ class FincoSettings(BaseSettings): chat_frequency_penalty: float = 0.0 chat_presence_penalty: float = 0.0 + chat_token_limit: int = ( + 100000 # 100000 is the maximum limit of gpt4, which might increase in the future version of gpt + ) + default_system_prompt: str = "You are an AI assistant who helps to answer user's questions about finance." # Embedding configs diff --git a/rdagent/document_process/document_analysis.py b/rdagent/document_process/document_analysis.py index 984cfb2e..16a5da58 100644 --- a/rdagent/document_process/document_analysis.py +++ b/rdagent/document_process/document_analysis.py @@ -69,14 +69,17 @@ def classify_report_from_dict( res_dict[file_name] = {"class": 0} continue - if not any(substring in content for substring in substrings): + if not any(substring in content for substring in substrings) and False: res_dict[file_name] = {"class": 0} else: - gpt_4_max_token = 128000 - if input_max_token < gpt_4_max_token: - content = enc.encode(content) - max_token_1 = max(0, min(len(content), input_max_token) - 1) - content = enc.decode(content[:max_token_1]) + while ( + APIBackend().build_messages_and_calculate_token( + user_prompt=content, + system_prompt=classify_prompt, + ) + > Config().chat_token_limit + ): + content = content[: -(Config().chat_token_limit // 100)] vote_list = [] for _ in range(vote_time): @@ -450,6 +453,8 @@ def __kmeans_embeddings(embeddings: np.ndarray, k: int = 20) -> list[list[str]]: def __deduplicate_factor_dict(factor_dict: dict[str, dict[str, str]]) -> list[list[str]]: + if len(factor_dict) == 0: + return [] factor_df = pd.DataFrame(factor_dict).T factor_df.index.names = ["factor_name"] diff --git a/rdagent/document_process/prompts.yaml b/rdagent/document_process/prompts.yaml index f08f0048..72ee3721 100644 --- a/rdagent/document_process/prompts.yaml +++ b/rdagent/document_process/prompts.yaml @@ -75,7 +75,7 @@ extract_factor_formulation_user: |- ===========================Factor list in dataframe============================= {{ factor_dict }} -classify_system: |- +classify_system_chinese: |- 你是一个研报分类助手。用户会输入一篇金融研报。请按照要求回答： 因子指能够解释资产收益率或价格等的变量；而模型则指机器学习或深度学习模型，利用因子等变量来预测价格或收益率变化。 @@ -86,6 +86,20 @@ classify_system: |- 请使用json进行回答。json key为：class +classify_system: |- + Your job is classify whether the user input document is a quantitative investment research report. The user will input a document and you should classify it based on the following conditions: + 1. The document is about finance other than other fields like biology, physics, chemistry, etc. + 2. The document is a research report on stock selection (which needs to be strictly separated from time selection and base selection) in the field of metalworking quantification. + 3. The document involves the composition of factors or models, or tests their performance. + + If the document meets all the above conditions, please return 1; otherwise, please return 0. + + Please respond with your decision in JSON format. Just respond the output json string without any interaction and explanation. + The JSON schema should include: + { + "class": 1 + } + factor_viability_system: |- User has designed several factors in quant investment. Please help the user to check the viability of these factors. These factors are used to build a daily frequency strategy in China A-share market. diff --git a/rdagent/factor_implementation/share_modules/factor_implementation_config.py b/rdagent/factor_implementation/share_modules/factor_implementation_config.py index 45d13294..7b43aee7 100644 --- a/rdagent/factor_implementation/share_modules/factor_implementation_config.py +++ b/rdagent/factor_implementation/share_modules/factor_implementation_config.py @@ -29,10 +29,6 @@ class FactorImplementSettings(FincoSettings): v2_error_summary: bool = False v2_knowledge_sampler: float = 1.0 - chat_token_limit: int = ( - 100000 # 100000 is the maximum limit of gpt4, which might increase in the future version of gpt - ) - implementation_factors_per_round: int = 100 # how many factors to choose for each round of evolving evo_multi_proc_n: int = 16 # how many processes to use for evolving (including eval & generation)