Update app.py

app.py

@@ -1,30 +1,84 @@
+# Benim hesap
+
 import gradio as gr
+import pinecone
 import openai
 
+index_name = 'gpt-4-langchain-docs'
+
+# initialize connection to pinecone
+pinecone.init(
+    api_key="08dc4515-1799-4a23-81a0-b9f86975f84f",  # app.pinecone.io (console)
+    environment="us-west4-gcp"  # next to API key in console
+)
+
+# check if index already exists (it shouldn't if this is first time)
+if index_name not in pinecone.list_indexes():
+    # if does not exist, create index
+    pinecone.create_index(
+        index_name,
+        dimension=len(res['data'][0]['embedding']),
+        metric='dotproduct'
+    )
+# connect to index
+index = pinecone.GRPCIndex(index_name)
 
-def Question(OpenAI_Key, Ask_Question):
-  # pass the generated text to audio
-  openai.api_key = OpenAI_Key
-  # Set up the model and prompt
-  model_engine = "text-davinci-003"
-  #prompt = "who is alon musk?"
-  # Generate a response
-  completion = openai.Completion.create(
-  engine=model_engine,
-  prompt=(f"{Ask_Question}"),
-  max_tokens=1024,
-  n=1,
-  stop=None,
-  temperature=0.5,)
-  response = completion.choices[0].text
-  #out_result=resp['message']
-  return  response
+
+def ask(OpenAI_key,query):
+    
+    
+    openai.api_key = OpenAI_key  #platform.openai.com
+
+    embed_model = "text-embedding-ada-002"
+
+    res = openai.Embedding.create(
+    input=[
+        "Sample document text goes here",
+        "there will be several phrases in each batch"
+    ], engine=embed_model
+)
+    
+    index_name = 'gpt-4-langchain-docs'
+
+    
+    
   
     
-demo = gr.Interface(
-    title='OpenAI ChatGPT Application',
-    fn=Question,
-    inputs=["text", "text"],
-    outputs="text",)
+    
+    res = openai.Embedding.create(
+    input=[query],
+    engine=embed_model
+)
+
+# retrieve from Pinecone
+    xq = res['data'][0]['embedding']
+
+# get relevant contexts (including the questions)
+    res = index.query(xq, top_k=5, include_metadata=True)
+
+    contexts = [item['metadata']['text'] for item in res['matches']]
 
-demo.launch() 
+    augmented_query = "\n\n---\n\n".join(contexts)+"\n\n-----\n\n"+query
+
+    primer = f"""You are Q&A bot. A highly intelligent system that answers
+user questions based on the information provided by the user above
+each question. If the information can not be found in the information
+provided by the user you truthfully say "I don't know".
+"""
+
+    res = openai.ChatCompletion.create(
+    model="gpt-3.5-turbo",
+    messages=[
+        {"role": "system", "content": primer},
+        {"role": "user", "content": augmented_query}
+    ]
+)
+    from IPython.display import Markdown
+
+    response = (res['choices'][0]['message']['content'])
+  
+    
+    return response
+     
+demo = gr.Interface(title = 'ShipsGo AI Assistant' , fn=ask, inputs=["text","text"] , outputs="text")
+demo.launch()