Implementación de chatbot Laura

backend/chatbot_laura/chatbot_logic.py

@@ -1,88 +1,96 @@
 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
+import pandas as pd
+import json
+import faiss
+import numpy as np
+import os
+import pickle
+from django.conf import settings
+from dotenv import load_dotenv
 
-   client = OpenAI(api_key=settings.OPENAI_API_KEY)
+load_dotenv()
 
-   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")
+# Leer la API key de OpenAI desde las variables de entorno
+OPENAI_API_KEY = os.getenv("OPENAI_API_KEY")
+client = OpenAI(api_key=OPENAI_API_KEY)
 
-   def get_embedding(text, model="text-embedding-ada-002"):
-       response = client.embeddings.create(input=text, model=model)
-       return response.data[0].embedding
+# Actualizar las rutas de los archivos
+data_dir = os.path.join(settings.BASE_DIR, "data")
+embedding_file = os.path.join(data_dir, "embeddings.pkl")
+index_file = os.path.join(data_dir, "faiss_index.index")
+json_file = os.path.join(data_dir, "preguntas_respuestas_procesadasV1.json")
 
-   def save_embeddings(embeddings, file_name):
-       with open(file_name, 'wb') as f:
-           pickle.dump(embeddings, f)
+def get_embedding(text, model="text-embedding-ada-002"):
+    response = client.embeddings.create(input=text, model=model)
+    return response.data[0].embedding
 
-   def load_embeddings(file_name):
-       with open(file_name, 'rb') as f:
-           return pickle.load(f)
+def save_embeddings(embeddings, file_name):
+    with open(file_name, 'wb') as f:
+        pickle.dump(embeddings, f)
 
-   def process_data(json_file):
-       with open(json_file, "r") as file:
-           data = json.load(file)
+def load_embeddings(file_name):
+    with open(file_name, 'rb') as f:
+        return pickle.load(f)
 
-       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
+def process_data(json_file):
+    with open(json_file, "r") as file:
+        data = json.load(file)
 
-           processed_data.append({
-               'text_for_embedding': text_for_embedding,
-               'full_content': item['content'],
-               'type': item['type'],
-               'url': item.get('url', ''),
-               'metadata': item.get('metadata', {})
-           })
+    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
 
-       return pd.DataFrame(processed_data)
+        processed_data.append({
+            'text_for_embedding': text_for_embedding,
+            'full_content': item['content'],
+            'type': item['type'],
+            'url': item.get('url', ''),
+            'metadata': item.get('metadata', {})
+        })
 
-   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 pd.DataFrame(processed_data)
 
-       return index, embeddings
+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()
 
-   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
+    return index, embeddings
 
-   # 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)
+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(json_file)
+index, embeddings = initialize_or_load_index(df)

backend/chatbot_laura/views.py

@@ -1,26 +1,26 @@
 from django.http import JsonResponse
-   from channels.generic.websocket import AsyncWebsocketConsumer
-   import json
-   from .chatbot_logic import search, df, index
+from channels.generic.websocket import AsyncWebsocketConsumer
+import json
+from .chatbot_logic import search, df, index
 
-   class ChatConsumer(AsyncWebsocketConsumer):
-       async def connect(self):
-           await self.accept()
+class ChatConsumer(AsyncWebsocketConsumer):
+    async def connect(self):
+        await self.accept()
 
-       async def disconnect(self, close_code):
-           pass
+    async def disconnect(self, close_code):
+        pass
 
-       async def receive(self, text_data):
-           text_data_json = json.loads(text_data)
-           query = text_data_json['message']
+    async def receive(self, text_data):
+        text_data_json = json.loads(text_data)
+        query = text_data_json['message']
 
-           results = search(query, df, index)
+        results = search(query, df, index)
 
-           await self.send(text_data=json.dumps({
-               'message': results
-           }))
+        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})
+def search_view(request):
+    query = request.GET.get('query', '')
+    results = search(query, df, index)
+    return JsonResponse({'results': results})