SEC 文件
SEC 文件 是提交给美国证券交易委员会 (SEC) 的财务报表或其他正式文件。上市公司、某些内部人士和经纪交易商需要定期进行
SEC 文件的提交。投资者和金融专业人士依赖这些文件获取他们评估的公司的信息，以便进行投资决策。
SEC 文件数据由 Kay.ai 和 Cybersyn 提供，来源于 Snowflake Marketplace。
设置
首先，您需要安装 kay 包。您还需要一个 API 密钥：您可以在 https://kay.ai 免费获 取一个。获得 API 密钥后，您必须将其设置为环境变量 KAY_API_KEY。
在这个示例中，我们将使用 KayAiRetriever。请查看 kay notebook 以获取有关其接受的参数的更详细信息。
# Setup API keys for Kay and OpenAI
from getpass import getpass
KAY_API_KEY = getpass()
OPENAI_API_KEY = getpass()
········
········
import os
os.environ["KAY_API_KEY"] = KAY_API_KEY
os.environ["OPENAI_API_KEY"] = OPENAI_API_KEY
示例
<!--IMPORTS:[{"imported": "ConversationalRetrievalChain", "source": "langchain.chains", "docs": "https://python.langchain.com/api_reference/langchain/chains/langchain.chains.conversational_retrieval.base.ConversationalRetrievalChain.html", "title": "SEC filing"}, {"imported": "KayAiRetriever", "source": "langchain_community.retrievers", "docs": "https://python.langchain.com/api_reference/community/retrievers/langchain_community.retrievers.kay.KayAiRetriever.html", "title": "SEC filing"}, {"imported": "ChatOpenAI", "source": "langchain_openai", "docs": "https://python.langchain.com/api_reference/openai/chat_models/langchain_openai.chat_models.base.ChatOpenAI.html", "title": "SEC filing"}]-->
from langchain.chains import ConversationalRetrievalChain
from langchain_community.retrievers import KayAiRetriever
from langchain_openai import ChatOpenAI
model = ChatOpenAI(model="gpt-3.5-turbo")
retriever = KayAiRetriever.create(
dataset_id="company", data_types=["10-K", "10-Q"], num_contexts=6
)
qa = ConversationalRetrievalChain.from_llm(model, retriever=retriever)
questions = [
"What are patterns in Nvidia's spend over the past three quarters?",
# "What are some recent challenges faced by the renewable energy sector?",
]
chat_history = []
for question in questions:
result = qa({"question": question, "chat_history": chat_history})
chat_history.append((question, result["answer"]))
print(f"-> **Question**: {question} \n")
print(f"**Answer**: {result['answer']} \n")
-> **Question**: What are patterns in Nvidia's spend over the past three quarters?
**Answer**: Based on the provided information, here are the patterns in NVIDIA's spend over the past three quarters:
1. Research and Development Expenses:
- Q3 2022: Increased by 34% compared to Q3 2021.
- Q1 2023: Increased by 40% compared to Q1 2022.
- Q2 2022: Increased by 25% compared to Q2 2021.
Overall, research and development expenses have been consistently increasing over the past three quarters.
2. Sales, General and Administrative Expenses:
- Q3 2022: Increased by 8% compared to Q3 2021.
- Q1 2023: Increased by 14% compared to Q1 2022.
- Q2 2022: Decreased by 16% compared to Q2 2021.
The pattern for sales, general and administrative expenses is not as consistent, with some quarters showing an increase and others showing a decrease.
3. Total Operating Expenses:
- Q3 2022: Increased by 25% compared to Q3 2021.
- Q1 2023: Increased by 113% compared to Q1 2022.
- Q2 2022: Increased by 9% compared to Q2 2021.
Total operating expenses have generally been increasing over the past three quarters, with a significant increase in Q1 2023.
Overall, the pattern indicates a consistent increase in research and development expenses and total operating expenses, while sales, general and administrative expenses show some fluctuations.
