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