Update data_collector.py

data_collector.py

@@ -6,64 +6,64 @@ from sentence_transformers import SentenceTransformer,util
 from transformers import pipeline
 import requests
 
-# def consume_llm_api(prompt):
-#     """
-#     Sends a prompt to the LLM API and processes the streamed response.
-#     """
-#     url = "https://8417-201-238-124-65.ngrok-free.app/api/llm-response"
-#     headers = {"Content-Type": "application/json"}
-#     payload = {"prompt": prompt,"extension":"1"}
+def consume_llm_api(prompt):
+    """
+    Sends a prompt to the LLM API and processes the streamed response.
+    """
+    url = "https://8417-201-238-124-65.ngrok-free.app/api/llm-response"
+    headers = {"Content-Type": "application/json"}
+    payload = {"prompt": prompt,"extension":"1"}
 
     
-#     print("Sending prompt to the LLM API...")
-#     response_ = requests.post(url, json=payload,verify=False)
-#     response_data = response_.json()
-#     return response_data['text']
-def consume_llm_api(prompt):
+    print("Sending prompt to the LLM API...")
+    response_ = requests.post(url, json=payload,verify=False)
+    response_data = response_.json()
+    return response_data['text']
+# def consume_llm_api(prompt):
 
-    import requests
-    from sentence_transformers import SentenceTransformer
-    from pinecone import Pinecone, ServerlessSpec
-    Gen_api = "https://8417-201-238-124-65.ngrok-free.app/api/llm-response"
-    api_key = "pcsk_2EhvKP_GqGkpAjF4p4ziL7PgrgM9xuKcthX9gtqhyLxV3UaMmWTQufW4qKZrjhLrf2d1ma"
-    pc = Pinecone(api_key=api_key)
-    model = SentenceTransformer("all-mpnet-base-v2")
-    try:
-        index_name = "quickstart"
-        pc.create_index(
-            name=index_name,
-            dimension=768, 
-            metric="cosine", 
-            spec=ServerlessSpec(
-                cloud="aws",
-                region="us-east-1"
-            ) 
-        )
-    except:
-        pass
-    index = pc.Index(index_name)
-    index.upsert(
-        vectors=[
-            {
-                "id": "lorum", 
-                "values": [float(i) for i in list(model.encode("lorum"))],  
-                "metadata": {"string":str(prompt)}
+#     import requests
+#     from sentence_transformers import SentenceTransformer
+#     from pinecone import Pinecone, ServerlessSpec
+#     Gen_api = "https://8417-201-238-124-65.ngrok-free.app/api/llm-response"
+#     api_key = "pcsk_2EhvKP_GqGkpAjF4p4ziL7PgrgM9xuKcthX9gtqhyLxV3UaMmWTQufW4qKZrjhLrf2d1ma"
+#     pc = Pinecone(api_key=api_key)
+#     model = SentenceTransformer("all-mpnet-base-v2")
+#     try:
+#         index_name = "quickstart"
+#         pc.create_index(
+#             name=index_name,
+#             dimension=768, 
+#             metric="cosine", 
+#             spec=ServerlessSpec(
+#                 cloud="aws",
+#                 region="us-east-1"
+#             ) 
+#         )
+#     except:
+#         pass
+#     index = pc.Index(index_name)
+#     index.upsert(
+#         vectors=[
+#             {
+#                 "id": "lorum", 
+#                 "values": [float(i) for i in list(model.encode("lorum"))],  
+#                 "metadata": {"string":str(prompt)}
                 
-            }
-        ]
-    )
+#             }
+#         ]
+#     )
 
-    gen_api_response = requests.post(url = Gen_api,json={"api_key": api_key},verify=False)
+#     gen_api_response = requests.post(url = Gen_api,json={"api_key": api_key},verify=False)
 
-    if gen_api_response.json().get("status"):
-        response = index.query(
-        vector=[float(i) for i in model.encode(str(prompt))],
-        top_k=1,
-        include_metadata=True,
-    )
+#     if gen_api_response.json().get("status"):
+#         response = index.query(
+#         vector=[float(i) for i in model.encode(str(prompt))],
+#         top_k=1,
+#         include_metadata=True,
+#     )
 
         
-    return response['matches'][0]['metadata']['string']
+#     return response['matches'][0]['metadata']['string']
 
 
 def relevent_value(long_query,count=3):