Implementación de chatbot Laura

backend/chatbot_laura/chatbot_logic.py

@@ -0,0 +1,88 @@
+from openai import OpenAI
+   import pandas as pd
+   import json
+   import faiss
+   import numpy as np
+   import os
+   import pickle
+   from django.conf import settings
+
+   client = OpenAI(api_key=settings.OPENAI_API_KEY)
+
+   embedding_file = os.path.join(settings.BASE_DIR, "chatbot_laura", "embeddings.pkl")
+   index_file = os.path.join(settings.BASE_DIR, "chatbot_laura", "faiss_index.index")
+
+   def get_embedding(text, model="text-embedding-ada-002"):
+       response = client.embeddings.create(input=text, model=model)
+       return response.data[0].embedding
+
+   def save_embeddings(embeddings, file_name):
+       with open(file_name, 'wb') as f:
+           pickle.dump(embeddings, f)
+
+   def load_embeddings(file_name):
+       with open(file_name, 'rb') as f:
+           return pickle.load(f)
+
+   def process_data(json_file):
+       with open(json_file, "r") as file:
+           data = json.load(file)
+
+       processed_data = []
+       for item in data:
+           if item['type'] == 'qa':
+               text_for_embedding = f"{item['content']['pregunta']} {item['content']['respuesta']}"
+           elif item['type'] == 'info':
+               text_for_embedding = f"{item['content']['titulo']} {item['content'].get('descripcion', '')}"
+           else:
+               continue
+
+           processed_data.append({
+               'text_for_embedding': text_for_embedding,
+               'full_content': item['content'],
+               'type': item['type'],
+               'url': item.get('url', ''),
+               'metadata': item.get('metadata', {})
+           })
+
+       return pd.DataFrame(processed_data)
+
+   def initialize_or_load_index(df):
+       if os.path.exists(embedding_file) and os.path.exists(index_file):
+           embeddings = load_embeddings(embedding_file)
+           index = faiss.read_index(index_file)
+       else:
+           df['embedding'] = df['text_for_embedding'].apply(lambda x: get_embedding(x))
+           embedding_matrix = np.array(df['embedding'].tolist()).astype('float32')
+           embedding_matrix /= np.linalg.norm(embedding_matrix, axis=1)[:, None]
+
+           index = faiss.IndexFlatIP(embedding_matrix.shape[1])
+           index.add(embedding_matrix)
+
+           save_embeddings(df['embedding'].tolist(), embedding_file)
+           faiss.write_index(index, index_file)
+           embeddings = df['embedding'].tolist()
+
+       return index, embeddings
+
+   def search(query, df, index, k=3):
+       query_embedding = np.array(get_embedding(query)).astype('float32')
+       query_embedding /= np.linalg.norm(query_embedding)
+       D, I = index.search(np.array([query_embedding]), k)
+
+       results = []
+       for i in range(k):
+           result = df.iloc[I[0][i]]
+           results.append({
+               'content': result['full_content'],
+               'url': result['url'],
+               'type': result['type'],
+               'metadata': result['metadata'],
+               'similarity_score': float(D[0][i])
+           })
+
+       return results
+
+   # Initialize data and index
+   df = process_data(os.path.join(settings.BASE_DIR, "chatbot_laura", "preguntas_respuestas_procesadasV1.json"))
+   index, embeddings = initialize_or_load_index(df)

backend/chatbot_laura/models.py

@@ -0,0 +1,11 @@
+from django.db import models
+
+   class SearchResult(models.Model):
+       content = models.JSONField()
+       url = models.URLField(blank=True)
+       type = models.CharField(max_length=50)
+       metadata = models.JSONField(default=dict)
+       similarity_score = models.FloatField()
+
+       def __str__(self):
+           return f"{self.type} - {self.similarity_score}"

backend/chatbot_laura/routing.py

@@ -1,7 +1,9 @@
-# chatbot_laura/routing.py
-from django.urls import path
-from .consumers import LauraChatConsumer
+from channels.routing import ProtocolTypeRouter, URLRouter
+   from django.urls import path
+   from chatbot_laura.views import ChatConsumer
 
-websocket_urlpatterns = [
-    path('ws/laura-chat/', LauraChatConsumer.as_asgi()),
-]
+   application = ProtocolTypeRouter({
+       "websocket": URLRouter([
+           path("ws/chat/", ChatConsumer.as_asgi()),
+       ]),
+   })

backend/chatbot_laura/urls.py

@@ -1,6 +1,6 @@
 from django.urls import path
-from .views import chatbot_laura_view
+from . import views
 
 urlpatterns = [
-    path('chatbot-laura/', chatbot_laura_view, name='chatbot_laura'),
-]
+    path('search/', views.search_view, name='search'),
+]

backend/chatbot_laura/views.py

@@ -1,26 +1,26 @@
 from django.http import JsonResponse
-from rest_framework.decorators import api_view
-from .chatbot_laura_logic import ChatbotLauraLogic
+   from channels.generic.websocket import AsyncWebsocketConsumer
+   import json
+   from .chatbot_logic import search, df, index
 
-# Inicializar el chatbot globalmente para que se cargue solo una vez
-chatbot_logic = ChatbotLauraLogic()
+   class ChatConsumer(AsyncWebsocketConsumer):
+       async def connect(self):
+           await self.accept()
 
-@api_view(['POST'])
-def chatbot_laura_view(request):
-    """
-    Vista para procesar las solicitudes del chatbot Laura.
-    """
-    try:
-        # Obtener el mensaje enviado por el usuario desde el cuerpo del POST
-        mensaje = request.data.get('mensaje', '')
-        if not mensaje:
-            return JsonResponse({'error': 'No se proporcionó el mensaje'}, status=400)
-
-        # Realizar la búsqueda utilizando la lógica del chatbot
-        resultados = chatbot_logic.search(mensaje)
+       async def disconnect(self, close_code):
+           pass
 
-        # Devolver los resultados como una respuesta JSON
-        return JsonResponse({'resultados': resultados}, status=200)
-
-    except Exception as e:
-        return JsonResponse({'error': str(e)}, status=500)
+       async def receive(self, text_data):
+           text_data_json = json.loads(text_data)
+           query = text_data_json['message']
+
+           results = search(query, df, index)
+
+           await self.send(text_data=json.dumps({
+               'message': results
+           }))
+
+   def search_view(request):
+       query = request.GET.get('query', '')
+       results = search(query, df, index)
+       return JsonResponse({'results': results})

backend/mi_portafolio/urls.py

@@ -5,5 +5,5 @@
     path('admin/', admin.site.urls),
     path('api/', include('conversaciones.urls')),
     path('api/', include('proyectos.urls')),
-    path('api/', include('chatbot_laura.urls')),
+    path('chatbot_laura/', include('chatbot_laura.urls')),
 ]