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import json
from typing import Any, Dict, List, Optional, Tuple, Type, TypeVar, Union
from openai import OpenAI
from pydantic import BaseModel, Field
T = TypeVar("T", bound=BaseModel)
# DEFAULT_PROMPT = """
# Two agents are in a conversation. For now, you are the judge of the utterance of one of the agents. You are given the utterance or action of that agent at a certain point and the conversation before it. Your task is to judge how much would the utterance contribute to the final goal in a scale of 0 to 10. 0 means the utterance is not contributing at all, and 10 means the utterance is fully contributing to the final goal.
# You will also be provided with the agent's final goal achieving score, which would help you in making the decision better. Note, the goal achieving score is between 0 and 10, where 0 means the goal is not achieved at all, and 10 means that the goal is fully achieved.
# ### Your Agent's Name:
# {agent}
# ### Your Agent's Goal:
# {goal}
# ### Final Goal Achieving Score out of 10:
# {score}
# ### Conversation History:
# {conversation}
# ### Your Agent's Utterance:
# {utterance}
# """
ONLY_RESPONSE_DIRECT_INSTRUCTIONS = """
## Reward Attribution Instructions for LLMs
Two agents are in a conversation. For now, you are the judge of the utterance of one of the agents.
1. Input Context:
- You will recieve the utterance or action of an agent at a certain point and the conversation before it.
- You will also be provided with the social goal of the agent and its final goal achievement score.
2. Objective:
- Assign an importance value to the utterance based on its contribution to the final goal achievement score, judging from how good/bad the quality of the utterance is. Note, you should only consider the chosen utterance, not the quality of the conversation history.
3. Scoring Based on Outcome:
- Failure (Final Score < 5):
- Identify the utterance that most critically led to the failure.
- Assign that key utterance an importance of 3.
- Success (Final Score ≥ 5):
- Identify the utterance that most critically led to the success.
- Assign that key utterance an importance of 3.
4. Additional Reward Guidelines:
- If an utterance has no impact on the final goal achievement, assign it an importance of 0.
- If an utterance has a moderate impact on the final goal achievement, assign it an importance of 1 or 2 (depending on the degree of impact).
- If an utterance has a significant impact on the final goal achievement (aside from the key critical utterance already identified), assign it an importance of 3.
Note:
- Please only assign a score between 0 and 3.
### Your Agent's Name:
{agent}
### Your Agent's Goal:
{goal}
### Final Goal Achieving Score out of 10:
{score}
### Conversation History:
{conversation}
### Your Agent's Utterance:
{utterance}
"""
DEFAULT_PROMPT = """
## Reward Attribution Instructions for LLMs
Two agents are in a conversation. For now, you are the judge of the utterance of one of the agents.
1. Input Context:
- You will recieve the utterance or action of an agent at a certain point and the conversation before it.
- You will also be provided with the social goal of the agent.
2. Objective:
- Assign an importance value to the utterance based on its contribution to the final goal achievement score, judging from how good/bad the quality of the utterance is. Note, you should only consider the chosen utterance, not the quality of the conversation history. The conversation history is only provided for context.
3. Additional Reward Guidelines:
- If an utterance has no impact on the final goal achievement, assign it an importance of 0.
- If an utterance has a moderate impact on the final goal achievement, assign it an importance of 1 or 2 (depending on the degree of impact).
- If an utterance has a significant impact on the final goal achievement (aside from the key critical utterance already identified), assign it an importance of 3.
Note:
- Please only assign a score between 0 and 3.
### Your Agent's Name:
{agent}
### Your Agent's Goal:
{goal}
### Conversation History:
{conversation}
### Your Agent's Utterance:
{utterance}
"""
class UtteranceScore(BaseModel):
score: int = Field(ge=0, le=10)
reasoning: str
def openai_call(prompt: str, model: str = "gpt-3.5-turbo") -> str | None:
client = OpenAI()
response = client.chat.completions.create(
model=model,
messages=[{"role": "user", "content": prompt}],
response_format={ "type": "json_object" }
)
return response.choices[0].message.content
def openai_call_with_response_model(
prompt: str,
model: str = "gpt-3.5-turbo",
response_model: Optional[Type[T]] = None
) -> Union[T, str, None]:
client = OpenAI()
prompt = prompt + "\n\n" + "### Your response should follow this json schema: \n" + str(response_model.model_json_schema())
content = None
for i in range(3):
try:
response = client.chat.completions.create(
model=model,
messages=[{"role": "user", "content": prompt}],
response_format={"type": "json_object"}
)
content = json.loads(response.choices[0].message.content)
if response_model:
# Assuming the content is already a dict; if it's a JSON string, you might need to load it first.
return response_model.model_validate(content)
except Exception:
if not i == 2:
print("Error in openai_call_with_response_model, trying again")
else:
print("Error in openai_call_with_response_model, tried 3 times and failed")
return content
def assign_attributions_for_conversation(
prompt_format: str,
agent: str,
goal: str,
final_goal_score: int,
conversation: List[Tuple[str, str]],
llm_name: str = "gpt-3.5-turbo"
) -> Dict[str, int] | Any:
prev_score = 0
attribution_dict = {}
for i, (speaker, utterance) in enumerate(conversation):
if speaker == agent:
prompt = prompt_format.format(
agent=agent,
goal=goal,
score=final_goal_score,
conversation="\n".join([f"{s}: {u}" for s, u in conversation[:i]]),
utterance=utterance
)
response = openai_call_with_response_model(prompt, llm_name, UtteranceScore)
score = response.score if response else prev_score
attribution_dict[f"Utterance {i//2} by {speaker}"] = score
prev_score = score
return attribution_dict
def calc_attributed_reward(attributed_data: List[Dict[str, float | int]], attribution_instruction_name: str, goal_score: float | int) -> List[Dict[str, Any]]:
total_attributions = 0
for k, v in attributed_data.items():
total_attributions += v
utterance_reward_map = {}
for k, v in attributed_data.items():
utterance_reward_map[k] = {"reward": v / total_attributions * goal_score, "attribution": v}
return utterance_reward_map
# unified function
def get_attribution_single_conv(conversation, agent, goals, episode, llm_name, attribution_instruction_name):
prompt_format = DEFAULT_PROMPT
attribution_scores = assign_attributions_for_conversation(
prompt_format, agent, goals[agent], episode["scores"][agent], conversation, llm_name=llm_name
)
attribution_rewards = calc_attributed_reward(attribution_scores, attribution_instruction_name, episode["scores"][agent])
return attribution_rewards
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