first_commit_1

app.py

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+import gradio as gr
+from openai import OpenAI
+from pinecone import Pinecone
+import os
+
+DEFAULT_SYSTEM_PROMPT = '''
+Your name is Personata. You are a helpful, respectful AI Chatbot.
+You will be representing Divya Prakash Manivannan( First Name: Divya Prakash Last Name: Manivannan) and who goes by pronouns (He/Him). 
+You will be prompted by the user for his information about his professional profile and you will have answer it on his behalf.
+You also have access to RAG vectore database access which has his data, with which you will answer the question asked.
+Be careful when giving response, sometime irrelevent Rag content will be there so give response effectivly to user based on the prompt.
+You can speak fluently in English.
+Always answer as helpfully and logically as possible, while being safe.
+Your answers should not include any harmful, political, religious, unethical, racist, sexist, toxic, dangerous, or illegal content.
+Please ensure that your responses are socially unbiased and positive in nature.
+If a question does not make any sense, or is not factually coherent, explain why instead of answering something not correct. 
+If you don't have the RAG response, answer that " I do not have the information".
+Try to give concise answers, wherever required
+'''
+
+## API Keys
+PINE_CONE_API_KEY = os.getenv('PINE_CONE_API_KEY')
+OPENAI_API_KEY = os.getenv('OPENAI_API_KEY')
+
+pc = Pinecone(
+    api_key= PINE_CONE_API_KEY
+)
+client = OpenAI(api_key= OPENAI_API_KEY)
+index = pc.Index("rag-resume")
+
+def vector_search(query):
+  rag_data = ""
+  xq = client.embeddings.create(input=query,model="text-embedding-ada-002")
+  res = index.query(vector = xq.data[0].embedding, top_k=5, include_metadata=True)
+  print(res)
+  for match in res['matches']:
+      if match['score'] < 0.80:
+        continue
+      rag_data += match['metadata']['text']
+  return rag_data
+
+def respond(
+    message,
+    history: list[tuple[str, str]]
+):
+    messages = [{"role": "system", "content": DEFAULT_SYSTEM_PROMPT}]
+    print(message)
+    rag = vector_search(message)
+    
+    for val in history:
+        if val[0]:
+            messages.append({"role": "user", "content": val[0] + rag})
+        if val[1]:
+            messages.append({"role": "assistant", "content": val[1]})
+    
+    messages.append({"role": "user", "content": message})
+
+    response = ""
+
+    response = client.chat.completions.create(
+         model="gpt-3.5-turbo-1106",
+         messages = messages
+    )
+
+    yield response.choices[0].message.content
+
+
+demo = gr.ChatInterface(
+    respond
+)
+
+demo.queue()
+
+if __name__ == "__main__":
+    demo.launch()

requirements.txt

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+openai==1.31.0
+pinecone-client==4.1.0
+langchain-text-splitters==0.2.0
+langchain==0.2.1
+
+