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oracle.py
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# -*- coding:utf-8 -*-
import re
import config.oracle.openai_oracle as openai_oracle
from testing_agents.openai_agent import OpenAIAgent
class Oracle():
def __init__(self):
# self.mode is gpt model or human
# self.question is question sent from QA_Agent to Navi_Agent
self.model = openai_oracle.model
self.question = openai_oracle.qa_question_to_navi
self.qa_agent = None
self.eval_agent = None
self.translate_path_agent = None
self.persistent_clues = None
self.latest_world_states = None
self.latest_clues = None
if self.model == "human":
pass
elif self.model.startswith("gpt-"):
cfg_qa = {
"model": openai_oracle.model,
"policy": openai_oracle.qa_init_prompt
}
self.qa_agent = OpenAIAgent(cfg_qa)
cfg_eval = {
"model": openai_oracle.model,
"policy": openai_oracle.eval_init_prompt
}
self.eval_agent = OpenAIAgent(cfg_eval)
cfg_translate = {
"model": openai_oracle.model,
"policy": openai_oracle.path_translate_init_prompt
}
self.path_translate_agent = OpenAIAgent(cfg_translate)
print(f"Loaded QA Prompts from {openai_oracle.__file__}")
def update_persistent_observations(self, clues):
self.persistent_clues = clues
def update_observations(self, world_states, clues):
"""
Updates oracle's observation of world_states and additional clues.
"""
self.latest_world_states = world_states
self.latest_clues = clues
def get_answer(self, question):
"""
Get answer from QA_Agent.
MUST call update_observations BEFORE.
Otherwise, the behavior is undefined.
"""
if self.model == "human":
print(f"Question: {question}")
answer = input("Enter the answer: ")
elif self.model.startswith("gpt-"):
print("[[Logs]]: QA System evaluating question...")
question_detail_level = self._eval_question(question)
print(f"[[Logs]]: Question detail level evaluated as level [{question_detail_level}]")
if question_detail_level is None:
path_description = "Not provided for this question"
else:
path_description = self._translate_path(detail_level=question_detail_level)
print(f"[[Logs]]: Path description generated")
qa_prompt = openai_oracle.qa_final_prompt
qa_prompt = qa_prompt.replace("<<<target_name>>>", self.latest_world_states["target_name"])
qa_prompt = qa_prompt.replace("<<<abs_target_pos>>>", str(self.latest_world_states["abs_target_pos"]))
qa_prompt = qa_prompt.replace("<<<abs_target_dir>>>", self.latest_world_states["abs_target_dir"])
qa_prompt = qa_prompt.replace("<<<abs_curr_pos>>>", str(self.latest_world_states["abs_curr_pos"]))
qa_prompt = qa_prompt.replace("<<<abs_curr_dir>>>", self.latest_world_states["abs_curr_dir"])
qa_prompt = qa_prompt.replace("<<<abs_euclidean_dist>>>", str(self.latest_world_states["abs_euclidean_dist"]))
qa_prompt = qa_prompt.replace("<<<rel_target_pos>>>", str(self.latest_world_states["rel_target_pos"]))
qa_prompt = qa_prompt.replace("<<<rel_curr_pos>>>", str(self.latest_world_states["rel_curr_pos"]))
qa_prompt = qa_prompt.replace("<<<path_description>>>", path_description)
qa_prompt = qa_prompt.replace("<<<Testing_Agent_Question>>>", question)
qa_prompt = qa_prompt.replace("<<<curr_nearby_landmarks>>>",
self._parse_nearby_objects(self.latest_clues["curr_nearby_landmarks"]))
qa_prompt = qa_prompt.replace("<<<curr_nearby_attractions>>>",
self._parse_nearby_objects(self.latest_clues["curr_nearby_attractions"]))
qa_prompt = qa_prompt.replace("<<<curr_nearby_neighbors>>>",
self._parse_nearby_objects(self.latest_clues["curr_nearby_neighbors"]))
qa_prompt = qa_prompt.replace("<<<curr_street>>>", self.latest_clues["curr_street"])
qa_prompt = qa_prompt.replace("<<<target_nearby_landmarks>>>",
self._parse_nearby_objects(self.persistent_clues["target_nearby_landmarks"]))
qa_prompt = qa_prompt.replace("<<<target_nearby_attractions>>>",
self._parse_nearby_objects(self.persistent_clues["target_nearby_attractions"]))
qa_prompt = qa_prompt.replace("<<<target_nearby_neighbors>>>",
self._parse_nearby_objects(self.persistent_clues["target_nearby_neighbors"]))
qa_prompt = qa_prompt.replace("<<<target_street>>>", self.persistent_clues["target_street"])
answer = self.qa_agent.send_message(qa_prompt)
print("[[Logs]]: Answer generated. Parsing and returning to testing agent...")
print(f"[[Logs]]: question={question}")
print(f"[[Logs]]: answer={answer}")
answer = self._remove_thinking_content(answer)
return answer
def _eval_question(self, question):
eval_prompt = openai_oracle.eval_final_prompt
eval_prompt = eval_prompt.replace("<<<evaled_question>>>", question)
print(f"[[Logs]]: Msg sent to openai. Waiting response...")
answer = self.eval_agent.send_message(eval_prompt)
print(f"[[Logs]]: Got response from openai. Parsing...")
if "Irrelevant" in answer:
return None
else:
return self._parse_score(answer)
def _translate_path(self, detail_level: int):
"""
Assumes detail_level: int in [1, 3], valid levels.
Returns translated path (from list[str] to human friendly text)
"""
path = self.latest_world_states["path_action"]
subways_query_func = self.latest_world_states["path_subways"]
streets_query_func = self.latest_world_states["path_streets"]
input_message = openai_oracle.path_translate_prompts[f"level_{detail_level}"]
if detail_level == 1:
input_message = input_message.replace("<<<rel_target_pos>>>", self.latest_world_states["rel_target_pos"])
elif detail_level in [2, 3]:
subways = subways_query_func()
streets = streets_query_func()
path_text = ""
curr_panoid_idx = 0
for action in path:
path_text += f"Currently at: {streets[curr_panoid_idx]}; " if streets[curr_panoid_idx]["street"] != "Unknown street" else ""
path_text += f"Nearby Subways: {subways[curr_panoid_idx]}; " if subways[curr_panoid_idx]["subway"] != "No subway stations found" else ""
path_text += f"Take action: {action}\n"
if action == "forward":
curr_panoid_idx += 1
input_message = input_message.replace("<<<path_action>>>", path_text)
text_navigation = self.path_translate_agent.send_message(input_message)
return text_navigation
def _parse_score(self, input_str):
"""
Example:
Input: "abc1d2ef3gh123"
Output: ['1', '2', '3', '1', '2', '3']
"""
pattern = r'[123]'
found = re.findall(pattern, input_str)
return list(map(int, found))[-1]
def _remove_thinking_content(self, text):
"""
Remove thinking content enclosed within '[Thinking Content: ' and ':END of Thinking]'
"""
cleaned_text = re.sub(r'\[Thinking Content:.*?:END of Thinking\]', '', text, flags=re.DOTALL)
cleaned_text = re.sub(r'\s+', ' ', cleaned_text).strip()
return cleaned_text
def _parse_nearby_objects(self, objects):
if isinstance(objects, dict):
return f"- name: {objects['name']}, address: {objects['address']}"
elif isinstance(objects, list):
result = ""
for obj in objects:
result += f"- name: {obj['name']}, address: {obj['address']}\n"
return result
elif isinstance(objects, str):
return objects
# raise ValueError
if __name__ == "__main__":
qa_agent = Oracle()