changed structure

app.py

@@ -0,0 +1,137 @@
+# Import necessary libraries and modules
+import chat_utils
+import openai
+from dotenv import load_dotenv
+import os
+import streamlit as st
+from streaming import StreamHandler
+
+from langchain.memory import StreamlitChatMessageHistory
+
+from langchain.llms import OpenAI
+from langchain.chat_models import ChatOpenAI
+from langchain.chains import ConversationalRetrievalChain
+from langchain.memory import ConversationBufferMemory
+from langchain.vectorstores import Pinecone
+import pinecone
+from langchain.embeddings.openai import OpenAIEmbeddings  # For generating embeddings with OpenAI's embedding model
+from langchain.prompts.chat import (
+    ChatPromptTemplate,
+    SystemMessagePromptTemplate,
+    HumanMessagePromptTemplate,
+)
+
+# Load environment variables from .env file
+load_dotenv()
+
+# Set the title of the Streamlit app
+st.title("LawyerAI - Chatbot 🤖")
+
+# Check if the chat history is present in session state, if not, initialize it
+if "session_chat_history" not in st.session_state:
+    st.session_state.session_chat_history = []
+
+if "chat_history" not in st.session_state:
+    st.session_state["chat_history"] = []
+
+# Initialize OpenAI embeddings
+embeddings = OpenAIEmbeddings()
+
+if "index" not in st.session_state:
+    try:
+        pinecone.init(api_key=os.environ["PINECONE_API_KEY"],environment=os.environ["PINECONE_ENVIRONMENT"])
+        pinecone_start = Pinecone.from_existing_index(os.environ["PINECONE_INDEX_NAME"], embeddings)
+        st.session_state["index"] = pinecone_start.as_retriever(k=3)
+
+    except Exception as e:
+        st.error(f"FAISS index couldn't be loaded.\n\n{e}")
+
+# Define a class for the custom chatbot
+class CustomDataChatbot:
+
+    def __init__(self):
+        if "chat_messages" not in st.session_state:
+            st.session_state.chat_messages = []
+        openai.api_key = os.getenv("OPENAI_API_KEY")
+
+    def create_qa_chain(self):
+
+        # Define the system message template
+        system_template = """You are a helpful assistant. Always end your sentence asking your users if they need more help. Use the following pieces of context to answer the users question at the end. 
+        If you cannot find the answer from the pieces of context, just say that you don't know, don't try to make up an answer. If the question is a greeting or a goodbye, then be flexible and respond accordingly.
+        ----------------
+        {context}
+        ----------------
+        
+        This is the history of your conversation with the user so far:
+        ----------------
+        {chat_history}
+        ----------------"""
+
+        # Create the chat prompt templates
+        messages = [
+            SystemMessagePromptTemplate.from_template(system_template),
+            HumanMessagePromptTemplate.from_template(
+                "Question:```{question}```")
+        ]
+
+        qa_prompt = ChatPromptTemplate.from_messages(messages)
+
+        # Optionally, specify your own session_state key for storing messages
+        msgs = StreamlitChatMessageHistory(key="special_app_key")
+
+        memory = ConversationBufferMemory(
+            memory_key="chat_history", chat_memory=msgs)
+
+        # Create a faiss vector store using an existing index and OpenAI embeddings
+        vectorstore = st.session_state.index
+
+        # Create a ConversationalRetrievalChain for question answering
+        st.session_state.qa = ConversationalRetrievalChain.from_llm(
+            ChatOpenAI(streaming=True,
+                       temperature=0, model="gpt-3.5-turbo"),  # Chat model configuration
+            # Use the faiss vector store for retrieval
+            vectorstore,
+            # Another OpenAI model for condensing questions
+            condense_question_llm=OpenAI(temperature=0),
+            combine_docs_chain_kwargs={"prompt": qa_prompt},
+            # memory=memory,  # Provide the conversation memory
+            return_source_documents=True,
+        )
+
+    @chat_utils.enable_chat_history
+    def main(self):
+
+        user_query = st.chat_input(placeholder="Ask me anything!")
+
+        if user_query:
+            chat_utils.display_msg(user_query, 'user')
+
+            # Display assistant's response with avatar
+            with st.chat_message("assistant", avatar="https://icon-library.com/images/law-icon-png/law-icon-png-3.jpg"):
+                st_callback = StreamHandler(st.empty())
+                if 'qa' not in st.session_state:
+                    st.session_state.qa = None
+                    self.create_qa_chain()
+                result = st.session_state.qa({"question": user_query, "chat_history": st.session_state.chat_history}, callbacks=[
+                                             st_callback])
+
+
+                # Display the source documents and metadata
+                with st.expander("See sources"):
+                    for doc in result['source_documents']:
+                        st.info(f"\nPage Content: {doc.page_content}")
+                        st.json(doc.metadata, expanded=False)
+
+                st.session_state.messages.append(
+                    {"role": "assistant", "content": result['answer'], "matching_docs": result['source_documents']})
+                st.session_state.session_chat_history.append(
+                    (user_query, result["answer"]))
+                st.session_state.chat_history.append(
+                    (user_query, result["answer"]))
+
+# Entry point for the script
+if __name__ == "__main__":
+    if "index" in st.session_state:
+        obj = CustomDataChatbot()
+        obj.main()

utilities/chat_utilities.py → chat_utils.py

@@ -1,4 +1,7 @@
+import os
+import random
 import streamlit as st
+import uuid 
 
 #decorator
 def enable_chat_history(func):
@@ -19,7 +22,15 @@ def enable_chat_history(func):
         st.session_state["messages"] = [{"role": "assistant", "content": "How can I help you?"}]
     for msg in st.session_state["messages"]:
         if msg["role"] == "assistant":
-            st.chat_message(msg["role"],avatar="https://icon-library.com/images/law-icon-png/law-icon-png-3.jpg").write(msg["content"])
+            if "index" in st.session_state:
+            # print(msg["content"])
+                with st.chat_message(msg["role"],avatar="https://icon-library.com/images/law-icon-png/law-icon-png-3.jpg"):
+                    st.write(msg["content"])
+                    if "matching_docs" in msg:
+                        with st.expander("See sources"):
+                            for doc in msg['matching_docs']:
+                                st.info(f"\nPage Content: {doc.page_content}")
+                                st.json(doc.metadata, expanded= False)
         else:
             st.chat_message(msg["role"]).write(msg["content"])
 
@@ -28,6 +39,7 @@ def execute(*args, **kwargs):
     return execute
 
 def display_msg(msg, author):
+
     """Method to display message on the UI
 
     Args:

requirements.txt

@@ -1,6 +1,6 @@
-langchain==0.0.263
+langchain==0.0.275
+openai==0.27.9
 pinecone_client==2.2.2
 python-dotenv==1.0.0
 streamlit==1.25.0
 tiktoken==0.3.1
-openai

utilities/create_index.py

@@ -18,7 +18,7 @@
 load_dotenv()
 
 # Replace with the name of the directory carrying your data  
-data_directory = "data"
+data_directory = "E:\\DESKTOP\\FreeLanceProjects\\muhammad_thaqib\\LawyerAI\\data"
 
 # Load your documents from different sources
 def get_documents():