import os from dotenv import load_dotenv from typing import TypedDict, List, Dict, Any, Optional from langgraph.graph import StateGraph, START, END, MessagesState from langchain.agents import create_tool_calling_agent, ConversationalAgent, AgentExecutor, initialize_agent, create_react_agent from langchain_google_genai import ChatGoogleGenerativeAI from langchain_groq import ChatGroq from langchain_core.tools import tool from langchain_core.messages import HumanMessage, SystemMessage from langchain.memory import ConversationBufferMemory from langchain_core.prompts import ChatPromptTemplate, PromptTemplate from langgraph.prebuilt import ToolNode from langgraph.prebuilt import tools_condition # 1. Web Browsing from langchain_community.tools import DuckDuckGoSearchResults from langchain_community.document_loaders import ImageCaptionLoader import requests, time import pandas as pd from pathlib import Path from langchain_community.tools import WikipediaQueryRun from langchain_community.utilities import WikipediaAPIWrapper from langchain_community.document_loaders import YoutubeLoader from langchain_community.document_loaders import UnstructuredExcelLoader from langchain_community.document_loaders import AssemblyAIAudioTranscriptLoader from langchain.text_splitter import CharacterTextSplitter from langchain_community.utilities import GoogleSerperAPIWrapper load_dotenv() DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space" @tool def duckduck_websearch(query: str) -> str: """Allows search through DuckDuckGo. Args: query: what you want to search """ search = DuckDuckGoSearchResults() results = search.invoke(query) return "\n".join(results) @tool def serper_websearch(query: str) -> str: """Allows search through Serper. Args: query: what you want to search """ search = GoogleSerperAPIWrapper(serper_api_key=os.getenv("SERPER_API_KEY")) results = search.run(query) return results @tool def visit_webpage(url: str) -> str: """Fetches raw HTML content of a web page. Args: url: the webpage url """ try: response = requests.get(url, timeout=5) return response.text[:5000] except Exception as e: return f"[ERROR fetching {url}]: {str(e)}" @tool def wiki_search(query: str) -> str: """Wiki search tools. Args: query: what you want to wiki """ api_wrapper = WikipediaAPIWrapper(top_k_results=1, doc_content_chars_max=100) wikipediatool = WikipediaQueryRun(api_wrapper=api_wrapper) return wikipediatool.run({"query": query}) @tool def text_splitter(text: str) -> List[str]: """Splits text into chunks using LangChain's CharacterTextSplitter. Args: text: A string of text to split. """ splitter = CharacterTextSplitter(chunk_size=450, chunk_overlap=10) return splitter.split_text(text) @tool def youtube_transcript(video_url: str) -> str: """Fetched youtube transcript Args: video_url: YouTube video url """ try: loader = YoutubeLoader.from_youtube_url(video_url) # video_id = video_url.split("v=")[-1].split("&")[0] # transcript = YouTubeTranscriptApi.get_transcript(video_id) return loader.load() except Exception as e: return f"Error fetching transcript: {str(e)}" # 4. File Reading @tool def read_file(task_id: str) -> str: """First download the file, then read its content Args: dir: the task_id """ file_url = f'{DEFAULT_API_URL}/files/{task_id}' r = requests.get(file_url, timeout=15, allow_redirects=True) with open('temp', "wb") as fp: fp.write(r.content) with open('temp') as f: return f.read() @tool def excel_read(task_id: str) -> str: """First download the excel file, then read its content Args: dir: the task_id """ try: file_url = f'{DEFAULT_API_URL}/files/{task_id}' r = requests.get(file_url, timeout=15, allow_redirects=True) with open('temp.xlsx', "wb") as fp: fp.write(r.content) # Read the Excel file df = pd.read_excel('temp.xlsx') # Run various analyses based on the query result = ( f"Excel file loaded with {len(df)} rows and {len(df.columns)} columns.\n" ) result += f"Columns: {', '.join(df.columns)}\n\n" # Add summary statistics result += "Summary statistics:\n" result += str(df.describe()) return result except Exception as e: return f"Error analyzing Excel file: {str(e)}" @tool def csv_read(task_id: str) -> str: """First download the csv file, then read its content Args: dir: the task_id """ try: file_url = f'{DEFAULT_API_URL}/files/{task_id}' r = requests.get(file_url, timeout=15, allow_redirects=True) with open('temp.csv', "wb") as fp: fp.write(r.content) # Read the CSV file df = pd.read_csv(temp.csv) # Run various analyses based on the query result = ( f"Excel file loaded with {len(df)} rows and {len(df.columns)} columns.\n" ) result += f"Columns: {', '.join(df.columns)}\n\n" # Add summary statistics result += "Summary statistics:\n" result += str(df.describe()) return result except Exception as e: return f"Error analyzing CSV file: {str(e)}" @tool def mp3_listen(task_id: str) -> str: """First download the mp3 file, then listen to it Args: dir: the task_id """ file_url = f'{DEFAULT_API_URL}/files/{task_id}' r = requests.get(file_url, timeout=15, allow_redirects=True) with open('temp.mp3', "wb") as fp: fp.write(r.content) loader = AssemblyAIAudioTranscriptLoader(file_path="temp.mp3", api_key=os.getenv("AssemblyAI_API_KEY")) docs = loader.load() contents = [doc.page_content for doc in docs] return "\n".join(contents) # 5. Image Open @tool def image_caption(dir: str) -> str: """Understand the content of the provided image Args: dir: the image url link """ loader = ImageCaptionLoader(images=[dir]) metadata = loader.load() return metadata[0].page_content # 2. Coding from langchain_experimental.tools import PythonREPLTool @tool def run_python(code: str): """ Run the given python code Args: code: the python code """ return PythonREPLTool().run(code) @tool def multiply(a: float, b: float) -> float: """Multiply two numbers. Args: a: first float b: second float """ return a * b @tool def add(a: float, b: float) -> float: """Add two numbers. Args: a: first float b: second float """ return a + b @tool def subtract(a: float, b: float) -> float: """Subtract two numbers. Args: a: first float b: second float """ return a - b @tool def divide(a: float, b: float) -> float: """Divide two numbers. Args: a: first float b: second float """ if b == 0: raise ValueError("Cannot divide by zero.") return a / b # 3. Multi-Modality # - multiply: multiply two numbers, A and B # - add: add two numbers, A and B # - subtract: Subtract A by B with passing A as the first argument # - divide: Divide A by B with passing A as the first argument # ("human", f"Question: {question}\nReport to validate: {final_answer}") class BasicAgent: def __init__(self): self.model = ChatGoogleGenerativeAI( model="gemini-2.0-flash-lite", temperature=0, max_tokens=1024, candidate_count=1, google_api_key=os.getenv("GEMINI_API_KEY"), ) # System Prompt for few shot prompting self.sys_prompt = """" You are a general AI assistant. I will ask you a question. Report your thoughts, and finish your answer with the following template: FINAL ANSWER: [YOUR FINAL ANSWER]. YOUR FINAL ANSWER should be a number OR as few words as possible OR a comma separared list of numbers and/or strings. If you are asked for a number, don't use comma to write your number neither use units such as $ or percent sign unless specified otherwise. If you are asked for a string, don't use articles, neither abbreviations (eg. for cities), and write the digits in plain text unless specified otherwise. If you are asked for a comma separated list, apply the above rules depending of whether the element to put in the list is a number or a string. You have access to the following tools: - serper_websearch: web search the content of the query by passing the query as input with Serper Search Engine - duckduck_websearch: web search the content of the query by passing the query as input with DuckDuckGo Search Engine - visit_webpage: visit the given webpage url by passing the url as input - wiki_search: wiki search the content of the query by passing the query as input if the question asks for wiki search it - text_splitter: split text into chunks - youtube_transcript: fetch the transcript of the Youtube video by passing the video url as input if the question asks for watching a Youtube video - read_file: read the content of the attached file by passing the TASK-ID as input - excel_read: read the content of the attached excel file by passing the TASK-ID as input - csv_read: read the content of the attached csv file by passing the TASK-ID as input - mp3_listen: listen to the content of the attached mp3 file by passing the TASK-ID as input - image_caption: understand the visual content of the attached image by passing the TASK-ID as input - run_python: run the python code If Task ID is included in the question, remember to call the relevant read tools [ie. read_file, excel_read, csv_read, mp3_listen, image_caption] Note: python_tool is called when the question mentions the term "Python" or any math calculation. """ self.tools = [duckduck_websearch, serper_websearch, visit_webpage, wiki_search, text_splitter, youtube_transcript, read_file, excel_read, csv_read, mp3_listen, image_caption, run_python] # Setup memory self.memory = ConversationBufferMemory( memory_key="chat_history", return_messages=True ) self.agent = self.__setup__agent__() # self.prompt = ChatPromptTemplate.from_messages([ # ("system", self.sys_prompt), # ("human", "{input}") # ]) # self.agent = initialize_agent( # tools=self.tools, # llm=self.model, # agent="zero-shot-react-description", # ReAct agent type # verbose=True, # system_prompt=self.prompt, # handle_parsing_errors=True, # max_iterations=30 # # "Check your output and make sure it conforms, use the Action/Action Input syntax" # ) print("BasicAgent initialized.") def __call__(self, task: dict) -> str: task_id, question, file_name = task["task_id"], task["question"], task["file_name"] print(f"Agent received question (first 50 chars): {question[:50]}...") if file_name == "" or file_name is None: question = question else: question = f"{question} with TASK-ID: {task_id}" # fixed_answer = self.agent.run(f'{question} with TASK-ID: {task_id}') fixed_answer = "This is a default answer." max_retries = 3 base_sleep = 1 for attempt in range(max_retries): try: fixed_answer = self.agent.run(question) print(f"Agent returning fixed answer: {fixed_answer}") time.sleep(60) return fixed_answer except Exception as e: sleep_time = base_sleep * (attempt + 1) if attempt < max_retries - 1: print(f"Attempt {attempt + 1} failed. Retrying in {sleep_time} seconds...") time.sleep(sleep_time) continue return f"Error processing query after {max_retries} attempts: {str(e)}" return fixed_answer def __setup__agent__(self) -> AgentExecutor: PREFIX = """ You are a general AI assistant that can use various tools to answer question. I will ask you a question. Report your thoughts, and finish your answer with the following template: FINAL ANSWER: [YOUR FINAL ANSWER]. YOUR FINAL ANSWER should be a number OR as few words as possible OR a comma separared list of numbers and/or strings. If you are asked for a number, don't use comma to write your number neither use units such as $ or percent sign unless specified otherwise. If you are asked for a string, don't use articles, neither abbreviations (eg. for cities), and write the digits in plain text unless specified otherwise. If you are asked for a comma separated list, apply the above rules depending of whether the element to put in the list is a number or a string. NOTE: - If Task ID is included in the question, remember to call the relevant read tools [ie. read_file, excel_read, csv_read, mp3_listen, image_caption] - python_tool is called when the question mentions the term "Python" or any math calculation. """ FORMAT_INSTRUCTIONS = """ To use a tool, use the following format: Thought: Do I need to use a tool? Yes Action: the action to take, should be one of [{tool_names}] Action Input: the input to the action Observation: the result of the action When you have a response to say to the Human, or if you do not need to use a tool, you MUST use the format: Thought: Do I need to use a tool? No Final Answer: [your response here] Begin! Remember to ALWAYS include 'Thought:', 'Action:', 'Action Input:', and 'Final Answer:' in your responses. """ SUFFIX = """ Previous conversation history: {chat_history} New question: {input} {agent_scratchpad} """ agent = ConversationalAgent.from_llm_and_tools( llm=self.model, tools=self.tools, prefix=PREFIX, format_instructions=FORMAT_INSTRUCTIONS, suffix=SUFFIX, input_variables=["input", "chat_history", "agent_scratchpad", "tool_names"], handle_parsing_errors=True ) return AgentExecutor.from_agent_and_tools( agent=agent, tools=self.tools, memory=self.memory, max_iterations=5, verbose=True, handle_parsing_errors=True, return_only_outputs=True # This ensures we only get the final output )