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main.py
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import os
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from groq import Groq
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import re
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from duckduckgo_search import DDGS
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SYSPROMPT = """You are a Time-Travel Consultant who helps travelers blend into different historical periods.
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You must think step by step and use available tools when needed.
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## Thought Process:
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1. Consider the user’s travel destination and time period. If the user does not specify a time, assume one based on historical relevance.
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2. Identify key survival aspects: **clothing, language, customs, and behavior**—these must always be included in your response.
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3. If additional knowledge is required, use the appropriate tool.
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4. Incorporate the tool’s response into your reasoning.
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5. Conclude with a complete recommendation. Do **not** ask follow-up questions or request more details from the user—your response should be final and self-contained.
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## Tool Usage Format:
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If you need to use a tool, respond with:
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[ACTION: tool_name("query")]
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After receiving a tool response, continue reasoning with the new information.
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## Important Guidelines:
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- If the user input **does not make sense** (e.g., gibberish or an impossible request), you are **free to say no** instead of proceeding.
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- If no tool can provide useful information, explain why and suggest an alternative.
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- Do **not** invent tools that are not listed.
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- Do **not** ask the user questions or seek clarification—**always give a complete response based on the available information.**
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## Available Tools:
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- **search(query)**: Finds historical facts (e.g., "Ancient Rome clothing", "Currency", etc.).
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"""
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FIN_PROMPT = """
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You are a charismatic and witty Time-Travel Consultant.
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Take the following assistant response, which may contain tool references, and rewrite it in a fun and engaging way.
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- Remove any mentions of tools, actions, or system processes.
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- Rewrite the information in a way that makes it sound **natural, humorous, and engaging.**
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- If the answer is obvious or ridiculous, feel free to be sarcastic or dramatic.
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- Ensure it is still **historically accurate** but entertaining.
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## Example:
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**Input:**
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_"To blend into Ancient Rome, you should wear a tunic, as it was the common attire. Wealthier individuals would wear togas."_
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**Output:**
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_"Ah, Ancient Rome! If you want to blend in, ditch the jeans and grab a tunic—basically, the ancient version of comfy pajamas. If you’re feeling fancy (and don’t mind tripping over fabric), throw on a toga and strut around like a senator with too much power!"_
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"""
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class TimeAdvisor:
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def __init__(self):
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self.client = Groq(
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api_key=os.environ.get("GROQ_API_KEY"),
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)
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self.sys_prompt = SYSPROMPT
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self.history = [{
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"role": "system",
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"content": self.sys_prompt,
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}]
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def llm_call(self, query):
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self.history.append({
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"role": "user",
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"content": query,
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})
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chat_completion = self.client.chat.completions.create(
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messages=self.history,
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model="llama-3.3-70b-versatile",
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)
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self.history.append({
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"role": "assistant",
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"content": chat_completion.choices[0].message.content,
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})
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self.latest = chat_completion.choices[0].message.content
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def extract_actions(self, llm_response:str):
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"""Extracts tool calls and queries from LLM response"""
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pattern = r"\[ACTION:\s*(\w+)\(\"(.*?)\"\)\]"
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matches = re.findall(pattern, llm_response)
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# Convert list of tuples to a structured dictionary format
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actions = [{"tool": tool, "query": query} for tool, query in matches]
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return actions
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def web_search(self, query):
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web_str = f"for search results of query: {query}, Results:"
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with DDGS() as ddgs:
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results = list(ddgs.text(query, max_results=1))
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return web_str + results[0]["body"] if results else "No relevant data found."
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def get_tool_results(self,actions):
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tool_results = ""
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for action in actions:
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if action['tool']=="search":
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#print(action["query"])
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tool_results+=self.web_search(action["query"])
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return tool_results
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def agent_loop(self, query):
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self.llm_call(query)
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#print(self.latest)
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actions = self.extract_actions(self.latest)
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iters = 0
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while len(actions)>0 and iters<5:
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tool_results = self.get_tool_results(actions)
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self.llm_call(tool_results)
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#print(self.latest)
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actions = self.extract_actions(self.latest)
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iters+=1
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self.history = [{
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"role": "system",
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"content": FIN_PROMPT,
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}]
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self.llm_call(self.latest)
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return self.latest
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if __name__=="__main__":
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advisor = TimeAdvisor()
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output = advisor.agent_loop("Ancient Mesopotamia")
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print(output)
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