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import os
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import requests
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from langchain.tools import tool
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from langchain.text_splitter import CharacterTextSplitter
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from langchain.embeddings import OpenAIEmbeddings
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from langchain_community.vectorstores import FAISS
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from sec_api import QueryApi
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from unstructured.partition.html import partition_html
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class SECTools():
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@tool("Search 10-Q form")
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def search_10q(data):
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"""
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Useful to search information from the latest 10-Q form for a
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given stock.
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The input to this tool should be a pipe (|) separated text of
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length two, representing the stock ticker you are interested and what
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question you have from it.
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For example, `AAPL|what was last quarter's revenue`.
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"""
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stock, ask = data.split("|")
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queryApi = QueryApi(api_key=os.environ['SEC_API_API_KEY'])
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query = {
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"query": {
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"query_string": {
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"query": f"ticker:{stock} AND formType:\"10-Q\""
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}
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},
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"from": "0",
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"size": "1",
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"sort": [{ "filedAt": { "order": "desc" }}]
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}
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fillings = queryApi.get_filings(query)['filings']
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if len(fillings) == 0:
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return "Sorry, I couldn't find any filling for this stock, check if the ticker is correct."
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link = fillings[0]['linkToFilingDetails']
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answer = SECTools.__embedding_search(link, ask)
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return answer
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@tool("Search 10-K form")
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def search_10k(data):
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"""
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Useful to search information from the latest 10-K form for a
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given stock.
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The input to this tool should be a pipe (|) separated text of
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length two, representing the stock ticker you are interested, what
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question you have from it.
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For example, `AAPL|what was last year's revenue`.
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"""
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stock, ask = data.split("|")
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queryApi = QueryApi(api_key=os.environ['SEC_API_API_KEY'])
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query = {
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"query": {
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"query_string": {
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"query": f"ticker:{stock} AND formType:\"10-K\""
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}
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},
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"from": "0",
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"size": "1",
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"sort": [{ "filedAt": { "order": "desc" }}]
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}
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fillings = queryApi.get_filings(query)['filings']
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if len(fillings) == 0:
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return "Sorry, I couldn't find any filling for this stock, check if the ticker is correct."
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link = fillings[0]['linkToFilingDetails']
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answer = SECTools.__embedding_search(link, ask)
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return answer
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def __embedding_search(url, ask):
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text = SECTools.__download_form_html(url)
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elements = partition_html(text=text)
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content = "\n".join([str(el) for el in elements])
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text_splitter = CharacterTextSplitter(
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separator = "\n",
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chunk_size = 1000,
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chunk_overlap = 150,
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length_function = len,
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is_separator_regex = False,
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)
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docs = text_splitter.create_documents([content])
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retriever = FAISS.from_documents(
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docs, OpenAIEmbeddings()
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).as_retriever()
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answers = retriever.get_relevant_documents(ask, top_k=4)
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answers = "\n\n".join([a.page_content for a in answers])
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return answers
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def __download_form_html(url):
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headers = {
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'Accept': 'text/html,application/xhtml+xml,application/xml;q=0.9,image/avif,image/webp,image/apng,*/*;q=0.8,application/signed-exchange;v=b3;q=0.7',
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'Accept-Encoding': 'gzip, deflate, br',
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'Accept-Language': 'en-US,en;q=0.9,pt-BR;q=0.8,pt;q=0.7',
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'Cache-Control': 'max-age=0',
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'Dnt': '1',
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'Sec-Ch-Ua': '"Not_A Brand";v="8", "Chromium";v="120"',
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'Sec-Ch-Ua-Mobile': '?0',
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'Sec-Ch-Ua-Platform': '"macOS"',
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'Sec-Fetch-Dest': 'document',
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'Sec-Fetch-Mode': 'navigate',
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'Sec-Fetch-Site': 'none',
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'Sec-Fetch-User': '?1',
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'Upgrade-Insecure-Requests': '1',
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'User-Agent': 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/120.0.0.0 Safari/537.36'
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}
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response = requests.get(url, headers=headers)
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return response.text
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