search added

app.py

@@ -2,11 +2,15 @@ import os
 
 import openai
 import streamlit as st
+from langchain.agents import AgentType, initialize_agent, load_tools
+from langchain.llms import OpenAI as l_OpenAI
 from transformers import pipeline
 
 from helpers.foundation_models import *
 
-openai_client = openai.OpenAI(api_key=os.environ["OPENAI_API_KEY"])
+OPENAI_API_KEY = os.environ["OPENAI_API_KEY"]
+SERPAPI_API_KEY = os.environ["SERPAPI_API_KEY"]
+openai_client = openai.OpenAI(api_key=OPENAI_API_KEY)
 
 
 # Initialize chat history
@@ -34,7 +38,7 @@ with st.expander("Instructions"):
 
 option = st.sidebar.selectbox(
     "Which task do you want to do?",
-    ("Sentiment Analysis", "Medical Summarization", "ChatGPT"),
+    ("Sentiment Analysis", "Medical Summarization", "ChatGPT", "ChatGPT (with Google)"),
 )
 
 
@@ -74,6 +78,16 @@ if prompt := st.chat_input("What is up?"):
             if prompt:
                 out = call_chatgpt(query=prompt)
                 doc = out
+        elif option == "ChatGPT (with Google)":
+            if prompt:
+                ans_langchain = call_langchain(prompt)
+                prompt = f"""
+                    Based on the internet search results: {ans_langchain};
+
+                    Answer the user question: {prompt}
+                """
+                out = call_chatgpt(query=prompt)
+                doc = out
         else:
             doc = ""
 

helpers/foundation_models.py

@@ -3,8 +3,12 @@ from typing import List, Tuple
 
 import openai
 import streamlit as st
+from langchain.agents import AgentType, initialize_agent, load_tools
+from langchain.llms import OpenAI as l_OpenAI
 
-openai_client = openai.OpenAI(api_key=os.environ["OPENAI_API_KEY"])
+OPENAI_API_KEY = os.environ["OPENAI_API_KEY"]
+SERPAPI_API_KEY = os.environ["SERPAPI_API_KEY"]
+openai_client = openai.OpenAI(api_key=OPENAI_API_KEY)
 
 
 def call_chatgpt(query: str, model: str = "gpt-3.5-turbo") -> str:
@@ -36,3 +40,16 @@ def call_chatgpt(query: str, model: str = "gpt-3.5-turbo") -> str:
 
     # Return the generated content.
     return content
+
+
+def call_langchain(prompt: str) -> str:
+    llm = l_OpenAI(temperature=0, openai_api_key=OPENAI_API_KEY)
+    tools = load_tools(
+        ["serpapi", "llm-math"], llm=llm, serpapi_api_key=SERPAPI_API_KEY
+    )
+    agent = initialize_agent(
+        tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True
+    )
+    output = agent.run(prompt)
+
+    return output

requirements.txt

@@ -1,3 +1,5 @@
 torch
+google-search-results
+langchain
 openai
 transformers