Upload 4 files

browser_tools.py

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+import requests
+from bs4 import BeautifulSoup
+from crewai import Agent, Task
+from langchain.tools import tool
+import os
+from langchain_google_genai import ChatGoogleGenerativeAI
+
+class BrowserTools:
+    @tool("Scrape website content")
+    def scrape_and_summarize_website(website):
+        """Useful to scrape and summarize a website content"""
+        # Fetch the webpage content
+        headers = {
+            'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/91.0.4472.124 Safari/537.36'
+        }
+        print("=====================used===================")
+        response = requests.get(website, headers=headers)
+
+        # Parse the HTML content
+        soup = BeautifulSoup(response.content, 'html.parser')
+
+        # Extract text content
+        for script in soup(["script", "style"]):
+            script.decompose()
+        content = soup.get_text(separator="\n")
+
+        # Clean up the text
+        lines = (line.strip() for line in content.splitlines())
+        chunks = (phrase.strip() for line in lines for phrase in line.split("  "))
+        content = '\n'.join(chunk for chunk in chunks if chunk)
+        print(content)
+        # Split content into chunks
+        content_chunks = [content[i:i + 8000] for i in range(0, len(content), 8000)]
+
+        summaries = []
+        for chunk in content_chunks:
+            agent = Agent(
+                role='Principal Researcher',
+                goal='Do amazing research and summaries based on the content you are working with',
+                backstory="You're a Principal Researcher at a big company and you need to do research about a given topic.",
+                llm = ChatGoogleGenerativeAI(model="gemini-1.5-flash",google_api_key = os.getenv("GOOGLE_API_KEY")),
+                allow_delegation=False
+            )
+            task = Task(
+                agent=agent,
+                description=f'Analyze and summarize the content below, make sure to include the most relevant information in the summary, return only the summary nothing else.\n\nCONTENT\n----------\n{chunk}',
+
+
+            )
+            summary = task.execute()
+            summaries.append(summary)
+
+        return "\n\n".join(summaries)

calculator_tools.py

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+from langchain.tools import tool
+
+
+class CalculatorTools:
+
+    @tool("Make a calculation")
+    def calculate(operation):
+        """Useful to perform any mathematical calculations,
+        like sum, minus, multiplication, division, etc.
+        The input to this tool should be a mathematical
+        expression, a couple examples are `200*7` or `5000/2*10`
+        """
+        try:
+            return eval(operation)
+        except SyntaxError:
+            return "Error: Invalid syntax in mathematical expression"
+
+from pydantic import BaseModel, Field
+# from langchain.tools import tool
+
+# # Define a Pydantic model for the tool's input parameters
+# class CalculationInput(BaseModel):
+#     operation: str = Field(..., description="The mathematical operation to perform")
+#     factor: float = Field(..., description="A factor by which to multiply the result of the operation")
+
+# # Use the tool decorator with the args_schema parameter pointing to the Pydantic model
+# @tool("perform_calculation", args_schema=CalculationInput, return_direct=True)
+# def perform_calculation(operation: str, factor: float) -> str:
+#     """
+#     Performs a specified mathematical operation and multiplies the result by a given factor.
+
+#     Parameters:
+#     - operation (str): A string representing a mathematical operation (e.g., "10 + 5").
+#     - factor (float): A factor by which to multiply the result of the operation.
+
+#     Returns:
+#     - A string representation of the calculation result.
+#     """
+#     # Perform the calculation
+#     result = eval(operation) * factor
+
+#     # Return the result as a string
+#     return f"The result of '{operation}' multiplied by {factor} is {result}."

search_tools.py

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+import json
+import os
+import requests
+from langchain.tools import tool
+
+
+class SearchTools():
+
+    @tool("Search the internet")
+    def search_internet(query):
+        """Useful to search the internet
+        about a a given topic and return relevant results"""
+        top_result_to_return = 4
+        url = "https://google.serper.dev/search"
+        payload = json.dumps({"q": query})
+        headers = {
+            'X-API-KEY': os.environ['SERPER_API_KEY'],
+            'content-type': 'application/json'
+        }
+        response = requests.request("POST", url, headers=headers, data=payload)
+        # check if there is an organic key
+        if 'organic' not in response.json():
+            return "Sorry, I couldn't find anything about that, there could be an error with you serper api key."
+        else:
+            results = response.json()['organic']
+            string = []
+            for result in results[:top_result_to_return]:
+                try:
+                    string.append('\n'.join([
+                        f"Title: {result['title']}", f"Link: {result['link']}",
+                        f"Snippet: {result['snippet']}", "\n-----------------"
+                    ]))
+                except KeyError:
+                    next
+
+            return '\n'.join(string)

sec_tools.py

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+import os
+
+import requests
+
+from langchain.tools import tool
+from langchain.text_splitter import CharacterTextSplitter
+from langchain_google_genai import GoogleGenerativeAIEmbeddings
+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, 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']
+    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']
+    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, GoogleGenerativeAIEmbeddings(model="models/embedding-001", api_key = os.getenv("GOOGLE_API_KEY"))
+    ).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