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020fc973 · Hyder · 8bce82eb · 020fc973 · 020fc973 · 020fc973
Commit 020fc973 authored Oct 12, 2023 by Hyder
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1_Basic_Chatbot.py 1_Basic_Chatbot.py +44 -0

2_Chatbot_Agent.py 2_Chatbot_Agent.py +63 -0

3_Chat_with_your_Data.py 3_Chat_with_your_Data.py +100 -0

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--- a/1_Basic_Chatbot.py
+++ b/1_Basic_Chatbot.py
+import utils
+import streamlit as st
+from streaming import StreamHandler
+
+from langchain.llms import OpenAI
+from langchain.chains import ConversationChain
+
+st.set_page_config(page_title="Chatbot", page_icon="💬")
+st.header('Basic Chatbot')
+st.write('Allows users to interact with the OpenAI LLMs')
+
+
+class Basic:
+
+    def __init__(self):
+        utils.configure_openai_api_key()
+        self.openai_model = "gpt-3.5-turbo"
+
+    def setup_chain(self):
+        llm = OpenAI(model_name=self.openai_model,
+                     temperature=0, streaming=True)
+        chain = ConversationChain(llm=llm, verbose=True)
+        return chain
+
+    @utils.enable_chat_history
+    def main(self):
+        chain = self.setup_chain()
+        user_query = st.chat_input(placeholder="Ask me anything!")
+        if user_query:
+            utils.display_msg(user_query, 'user')
+            with st.chat_message("assistant"):
+                try:
+                    st_cb = StreamHandler(st.empty())
+                    response = chain.run(user_query, callbacks=[st_cb])
+                    st.session_state.messages.append(
+                        {"role": "assistant", "content": response})
+                except Exception as e:
+                    print(e)
+
+
+
+if __name__ == "__main__":
+    obj = Basic()
+    obj.main()
--- a/2_Chatbot_Agent.py
+++ b/2_Chatbot_Agent.py
+import utils
+import streamlit as st
+
+from langchain.agents import AgentType
+from langchain.chat_models import ChatOpenAI
+from langchain.tools import DuckDuckGoSearchRun
+from langchain.agents import initialize_agent, Tool
+from langchain.callbacks import StreamlitCallbackHandler
+
+st.set_page_config(page_title="ChatWeb", page_icon="🌐")
+st.header('Chatbot with Web Browser Access')
+st.write('Equipped with internet agent, enables users to ask questions about recent events')
+
+
+class ChatbotTools:
+
+    def __init__(self):
+        utils.configure_openai_api_key()
+        self.openai_model = "gpt-3.5-turbo"
+
+    def setup_agent(self):
+        # Define tool
+        ddg_search = DuckDuckGoSearchRun()
+        tools = [
+            Tool(
+                name="DuckDuckGoSearch",
+                func=ddg_search.run,
+                description="Useful for when you need to answer questions about current events. You should ask targeted questions",
+            )
+        ]
+
+        # Setup LLM and Agent
+        llm = ChatOpenAI(model_name=self.openai_model, streaming=True)
+        agent = initialize_agent(
+            tools=tools,
+            llm=llm,
+            agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION,
+            handle_parsing_errors=True,
+            verbose=True
+        )
+        return agent
+
+    @utils.enable_chat_history
+    def main(self):
+        agent = self.setup_agent()
+        user_query = st.chat_input(placeholder="Ask me anything!")
+        if user_query:
+            utils.display_msg(user_query, 'user')
+            with st.chat_message("assistant"):
+                try:
+                    st_cb = StreamlitCallbackHandler(st.container())
+                    response = agent.run(user_query, callbacks=[st_cb])
+                    st.session_state.messages.append(
+                        {"role": "assistant", "content": response})
+                    st.write(response)
+                except Exception as e:
+                    print(e)
+
+
+
+if __name__ == "__main__":
+    obj = ChatbotTools()
+    obj.main()
--- a/3_Chat_with_your_Data.py
+++ b/3_Chat_with_your_Data.py
+import os
+import utils
+import streamlit as st
+from streaming import StreamHandler
+
+from langchain.chat_models import ChatOpenAI
+from langchain.document_loaders import PyPDFLoader
+from langchain.memory import ConversationBufferMemory
+from langchain.chains import ConversationalRetrievalChain
+from langchain.embeddings import OpenAIEmbeddings
+from langchain.vectorstores import FAISS
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+
+st.set_page_config(page_title="ChatPDF", page_icon="📄")
+st.header('Chat with your Documents')
+st.write('Has access to custom documents and can respond to user queries by referring to the content within those documents')
+
+
+class CustomDataChatbot:
+
+    def __init__(self):
+        utils.configure_openai_api_key()
+        self.openai_model = "gpt-3.5-turbo"
+
+    def save_file(self, file):
+        folder = 'tmp'
+        if not os.path.exists(folder):
+            os.makedirs(folder)
+
+        file_path = f'./{folder}/{file.name}'
+        with open(file_path, 'wb') as f:
+            f.write(file.getvalue())
+        return file_path
+
+    @st.spinner('Analyzing documents..')
+    def setup_qa_chain(self, uploaded_files):
+        # Load documents
+        docs = []
+
+        for file in uploaded_files:
+            file_path = self.save_file(file)
+            loader = PyPDFLoader(file_path)
+            docs.extend(loader.load())
+
+        # Split documents
+        text_splitter = RecursiveCharacterTextSplitter(
+            chunk_size=1500,
+            chunk_overlap=200
+        )
+        splits = text_splitter.split_documents(docs)
+
+        # Create embeddings and store in vectordb
+        embeddings = OpenAIEmbeddings()
+        vectordb = FAISS.from_documents(splits, embeddings)
+
+        # Define retriever
+        retriever = vectordb.as_retriever()
+
+        # Setup memory for contextual conversation
+        memory = ConversationBufferMemory(
+            memory_key='chat_history',
+            return_messages=True
+        )
+
+        # Setup LLM and QA chain
+        llm = ChatOpenAI(model_name=self.openai_model,
+                         temperature=0, streaming=True)
+        qa_chain = ConversationalRetrievalChain.from_llm(
+            llm, retriever=retriever, memory=memory, verbose=True)
+        return qa_chain
+
+    @utils.enable_chat_history
+    def main(self):
+        # User Inputs
+        uploaded_files = st.sidebar.file_uploader(label='Upload PDF files', type=[
+                                                  'pdf'], accept_multiple_files=True)
+        if not uploaded_files:
+            st.error("Please upload PDF documents to continue!")
+            st.stop()
+
+        user_query = st.chat_input(placeholder="Ask me anything!")
+
+        if uploaded_files and user_query:
+            qa_chain = self.setup_qa_chain(uploaded_files)
+
+            utils.display_msg(user_query, 'user')
+        
+            with st.chat_message("assistant"):
+                try:
+                    st_cb = StreamHandler(st.empty())
+                    response = qa_chain.run(user_query, callbacks=[st_cb])
+                    st.session_state.messages.append(
+                        {"role": "assistant", "content": response})
+                except Exception as e:
+                     print(e)
+
+
+if __name__ == "__main__":
+    obj = CustomDataChatbot()
+    obj.main()