File size: 4,523 Bytes
6bf64e3 ce8cf32 6bf64e3 22b0142 6bf64e3 a8f6f00 344e113 6bf64e3 a8f6f00 6bf64e3 a8f6f00 6bf64e3 ce8cf32 4e4ac35 6f5a43e 6bf64e3 4d78302 6bf64e3 0dad5de 6bf64e3 d506d13 6524196 d506d13 b02ca9a d506d13 b02ca9a | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 | from typing import Dict, Any, Optional
import os
import time
import json
from openai import OpenAI
class BaseAgent:
"""Base class for all agents with common functionality."""
def __init__(self, model):
"""Initialize the base agent with the specified model."""
print(f"Initializing Base Agent with {model}")
if model is None:
model = os.getenv("DEFAULT_LLM_MODEL")
if model is None:
raise ValueError("No model specified and DEFAULT_LLM_MODEL not set")
openai_key = os.getenv("OPENAI_API_KEY")
if openai_key is None:
raise ValueError("OPENAI_API_KEY environment variable not set")
self.model = model
print("Init Client")
self.client = OpenAI(api_key="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")
print("Client successfully initialized")
def __call__(self, question: str, file_name: str = None, **kwargs) -> str:
"""Process the question and return an answer."""
raise NotImplementedError("Subclasses must implement this method")
def extract_search_query(self, question: str) -> str:
"""
Extract the key search terms from the question.
Args:
question: The original question
Returns:
A reformulated search query
"""
system_prompt = """
You are an expert at extracting search terms from questions.
Given a question, identify the key terms that would be most effective for a web search.
Remove any unnecessary words and focus on specific names, dates, or key concepts.
Return only the search terms, with no additional text.
"""
user_prompt = f"Extract the most effective search terms from this question: {question}"
response = self.llm_call(system_prompt, user_prompt)
return response.strip()
def llm_call(self, system_prompt: str, user_prompt: str,
temperature: float = 0.1, model: Optional[str] = None) -> str:
"""
Make a call to the LLM with the given prompts.
Args:
system_prompt: The system prompt to guide the LLM behavior
user_prompt: The user prompt containing the question or task
temperature: Controls randomness (lower is more deterministic)
model: Optional override for the model to use
Returns:
The LLM response content as a string
"""
if model is None:
model = self.model
try:
response = self.client.chat.completions.create(
model=model,
messages=[
{"role": "system", "content": system_prompt},
{"role": "user", "content": user_prompt}
],
temperature=temperature
)
return response.choices[0].message.content
except Exception as e:
print(f"Error calling LLM: {str(e)}")
return f"Error: {str(e)}"
def ask_james(self, question) -> str:
system_prompt = """
You are a general AI Assistant.
You must provide only the exact answer requested by the user, in the correct type and minimal format, based purely on the provided information.
YOUR FINAL ANSWER should be a number OR as few words as possible OR a comma separated 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 (e.g. 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 be put in the list is a number or a string.
If you cannot find the answer based on the provided information, reply only with "Unknown".
You must not explain your answer, restate the question, or add any commentary.
Output must be a single line, without punctuation or extra whitespace.
"""
final_answer = self.llm_call(system_prompt, question, temperature=0.0)
return final_answer |