Spaces:
Sleeping
Sleeping
File size: 7,394 Bytes
7d6d833 |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 |
from typing import Union
from mistralai import Mistral
import yaml
from pathlib import Path
import re
import json
import time
class AIAgent:
def __init__(
self,
name,
personal_context,
character,
emotions,
attitudes,
goal,
general_context,
client,
arbitrary_agent=None
):
"""
Initialise l'agent IA avec ses attributs de base.
:param name: Nom de l'agent
:param character: dict décrivant la personnalité de l'IA
:param emotions: dict des émotions actuelles de l'IA
:param goal: Objectif principal de l'IA
:param general_context: Contexte général (ex: sujet du débat)
:param arbitrary_agent: Objet gérant la logique de mise à jour des émotions
"""
self.client = client
self.model = "mistral-large-latest"
self.name = name
self.personal_context = personal_context
self.character = character
self.emotions = emotions
self.attitudes = attitudes
self.goal = goal
self.general_context = general_context
self.arbitrary_agent = arbitrary_agent
self.context_memory = ""
@classmethod
def from_yaml(cls, character_yaml: Union[Path, str], general_context_yaml: Union[Path, str], client, arbitrary_agent=None):
"""
Initialize an object using YAML content.
"""
character_data = cls.parse_yaml_to_dict(str(character_yaml))
context_data = cls.parse_yaml_to_dict(str(general_context_yaml))
if character_data:
return cls(
client=client,
name=character_data.get("name"),
personal_context =character_data.get("personal_context"),
character=character_data.get("character"),
emotions=character_data.get("emotions"),
attitudes=character_data.get("attitudes"),
goal=character_data.get("goal"),
general_context = context_data.get("general_context"),
arbitrary_agent = arbitrary_agent
)
else:
raise ValueError("Failed to parse YAML content.")
@staticmethod
def parse_yaml_to_dict(yaml_content):
"""
Parse YAML content into a Python dictionary.
"""
try:
with open(yaml_content, 'r') as file:
return yaml.safe_load(file)
except FileNotFoundError:
print(f"Error: The file '{yaml_content}' was not found.")
except yaml.YAMLError as e:
print(f"Error parsing YAML file: {e}")
except Exception as e:
print(f"Unexpected error: {e}")
return None
def __repr__(self):
return (f"TrumpProfile(\n"
f" general_context={self.general_context},\n"
f" {self.name}_character={self.character},\n"
f" {self.name}_emotions={self.emotions},\n"
f" {self.name}_attitudes={self.attitudes},\n"
f" {self.name}_goal='{self.goal}'\n"
f")")
def respond(self, input_text):
"""
Génère une réponse basée sur le contexte fourni.
:param input_text: Texte reçu
:param opponent_state: État actuel de l'adversaire (dict)
:return: Réponse de l'IA
"""
response = self._generate_response(
instructions=input_text,
environment_description="N/A"
)
return response
def update_emotions(self, input_text):
"""
Met à jour les émotions en fonction d'une analyse via l'arbitrary_agent (LLM).
"""
self.context_memory = self.create_memory_context(input_text)
if self.arbitrary_agent is not None:
self.emotions, self.attitudes = self.arbitrary_agent.update_emotions(character=self)
else:
print("No arbitrary agent provided. Emotions remain unchanged.")
def _generate_response(self, instructions, environment_description, max_tokens=None):
messages = [
{
"role": "system",
"content": (
f"General context: {self.general_context}\n"
#f"Personal context: {self.personal_context}\n"
f"Character: {self.character}\n"
f"Goal: {self.goal}\n"
f"Emotions: {self.emotions}\n"
f"Attitudes: {self.attitudes}\n"
f"Environment: {environment_description}\n"
),
},
{
"role": "user",
"content": (
f"Instructions: {instructions}\n"
f"Conversation history: {self.context_memory}"
),
},
]
# ------------------------------
# Hypothetical call to LLM API
# ------------------------------
time.sleep(1)
chat_response = self.client.chat.complete(
model=self.model,
messages=messages,
max_tokens=max_tokens if max_tokens else None
)
# ------------------------------------------------
return chat_response.choices[0].message.content
def create_memory_context(
self,
current_input,
additional_instructions="Summarize recent key points and emotional undertones."
):
"""
Calls LLM to produce a memory context (summary, emotional tone, etc.) in valid JSON.
"""
system_prompt = (
"You are a memory transformation engine. "
"Based on the agent's current input, current context memory, character personality, and current emotions, "
"produce a concise memory context in valid JSON."
)
user_prompt = f"""
Character: {self.name}
Emotions: {self.emotions}
Current answer he get: {current_input}
Context memory: {self.context_memory}
Task:
- {additional_instructions}
- Return structured memory context in valid JSON. Example:
{{
"summary": "...",
"emotionalTone": "..."
}}
"""
messages = [
{"role": "system", "content": system_prompt},
{"role": "user", "content": user_prompt},
]
time.sleep(1)
response = self.client.chat.complete(
model=self.model,
messages=messages,
response_format={"type": "json_object"},
max_tokens=300
)
raw_text = response.choices[0].message.content.strip()
cleaned_response = re.sub(r"```(json)?", "", raw_text).strip()
try:
memory_context = json.loads(cleaned_response)
except json.JSONDecodeError:
print(f"Error: Could not parse JSON for memory context. Using fallback.\nResponse: {cleaned_response}")
memory_context = {
"summary": "No valid summary",
"emotionalTone": "neutral"
}
return memory_context
|