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# modules/chatbot/chat_process.py
import os
import anthropic
import logging
from typing import Dict, Generator
logger = logging.getLogger(__name__)
class ChatProcessor:
def __init__(self):
"""
Inicializa el procesador de chat con la API de Claude
"""
api_key = os.environ.get("ANTHROPIC_API_KEY")
if not api_key:
raise ValueError("No se encontr贸 la clave API de Anthropic. Aseg煤rate de configurarla en las variables de entorno.")
self.client = anthropic.Anthropic(api_key=api_key)
self.conversation_history = []
def process_chat_input(self, message: str, lang_code: str) -> Generator[str, None, None]:
"""
Procesa el mensaje y genera una respuesta
"""
try:
# Agregar mensaje a la historia
self.conversation_history.append(f"Human: {message}")
full_message = "\n".join(self.conversation_history)
# Generar respuesta usando la API de Claude
response = self.client.completions.create(
model="claude-2",
prompt=f"{full_message}\n\nAssistant:",
max_tokens_to_sample=300,
temperature=0.7,
stop_sequences=["Human:"]
)
# Procesar la respuesta
claude_response = response.completion.strip()
self.conversation_history.append(f"Assistant: {claude_response}")
# Mantener un historial limitado
if len(self.conversation_history) > 10:
self.conversation_history = self.conversation_history[-10:]
# Dividir la respuesta en palabras para streaming
words = claude_response.split()
for word in words:
yield word + " "
except Exception as e:
logger.error(f"Error en process_chat_input: {str(e)}")
yield f"Error: {str(e)}"
def get_conversation_history(self) -> list:
"""
Retorna el historial de la conversaci贸n
"""
return self.conversation_history
def clear_history(self):
"""
Limpia el historial de la conversaci贸n
"""
self.conversation_history = [] |