changes

app.py

@@ -5,11 +5,12 @@ import os
 # Constants
 ROLE_USER = "user"
 ROLE_ASSISTANT = "assistant"
+MAX_MESSAGES = 4
 
 st.set_page_config(page_title="Chat with Git", page_icon="🦜")
 st.title("Chat with Git 🤖📚")
 st.markdown("by [Rohan Kataria](https://www.linkedin.com/in/imrohan/) view more at [VEW.AI](https://vew.ai/)")
-st.markdown("This app allows you to chat with Git code files. You can paste link to the Git repository and ask questions about it. In the backround uses the Git Loader and ConversationalRetrival chain from langchain, Streamlit for UI.")
+st.markdown("This app allows you to chat with Git code files. You can paste link to the Git repository and ask questions about it. In the background uses the Git Loader and ConversationalRetrieval chain from langchain, Streamlit for UI.")
 
 @st.cache_resource(ttl="1h")
 def load_agent(url, branch, file_filter):
@@ -19,12 +20,6 @@ def load_agent(url, branch, file_filter):
     return agent
 
 def main():
-    api_key = st.sidebar.text_input("Enter your OpenAI API Key", type="password")
-    if api_key:
-        os.environ["OPENAI_API_KEY"] = api_key
-    else:
-        st.sidebar.error("Please enter your OpenAI API Key.")
-        return
 
     git_link = st.sidebar.text_input("Enter your Git Link")
     branch = st.sidebar.text_input("Enter your Git Branch")
@@ -32,12 +27,14 @@ def main():
 
     if "agent" not in st.session_state:
         st.session_state["agent"] = None
+        st.session_state["user_message_count"] = 0
 
     if st.sidebar.button("Load Agent"):
         if git_link and branch and file_filter:
             try:
                 st.session_state["agent"] = load_agent(git_link, branch, file_filter)
                 st.session_state["messages"] = [{"role": ROLE_ASSISTANT, "content": "How can I help you?"}]
+                st.session_state["user_message_count"] = 0
             except Exception as e:
                 st.sidebar.error(f"Error loading Git repository: {str(e)}")
                 return
@@ -46,21 +43,25 @@ def main():
         for msg in st.session_state.messages:
             st.chat_message(msg["role"]).write(msg["content"])
 
-        user_query = st.chat_input(placeholder="Ask me anything!")
+        if st.session_state["user_message_count"] < MAX_MESSAGES:
+            user_query = st.chat_input(placeholder="Ask me anything!")
 
-        if user_query:
-            st.session_state.messages.append({"role": ROLE_USER, "content": user_query})
-            st.chat_message(ROLE_USER).write(user_query)
+            if user_query:
+                st.session_state.messages.append({"role": ROLE_USER, "content": user_query})
+                st.chat_message(ROLE_USER).write(user_query)
+                st.session_state["user_message_count"] += 1
 
-            # Generate the response
-            with st.spinner("Generating response"):
-                response = st.session_state["agent"](user_query)
+                # Generate the response
+                with st.spinner("Generating response"):
+                    response = st.session_state["agent"](user_query)
 
-            # Display the response immediately
-            st.chat_message(ROLE_ASSISTANT).write(response)
+                # Display the response immediately
+                st.chat_message(ROLE_ASSISTANT).write(response)
 
-            # Add the response to the message history
-            st.session_state.messages.append({"role": ROLE_ASSISTANT, "content": response})
+                # Add the response to the message history
+                st.session_state.messages.append({"role": ROLE_ASSISTANT, "content": response})
+        else:
+            st.warning("Your message limit is over. Contact [Rohan Kataria](https://www.linkedin.com/in/imrohan/) to increase the limit.")
 
 if __name__ == "__main__":
-    main()
+    main()

src/main.py

@@ -16,10 +16,19 @@ from langchain.llms import OpenAI
 from langchain.memory import ConversationBufferMemory
 from langchain.vectorstores import Chroma
 from langchain.embeddings.openai import OpenAIEmbeddings
-from langchain.prompts import PromptTemplate
+from langchain.prompts import PromptTemplate, SystemMessagePromptTemplate, HumanMessagePromptTemplate, AIMessagePromptTemplate, ChatPromptTemplate
 import datetime
 import shutil
 
+
+# Setting up environment variables
+os.environ['LANGCHAIN_TRACING_V2'] = "True"
+os.environ['LANGCHAIN_ENDPOINT']
+os.environ['LANGCHAIN_API_KEY']
+os.environ['LANGCHAIN_PROJECT']
+os.environ["OPENAI_API_KEY"]
+
+
 # Function to load the data from github using langchain with string type url, string type branch, string type file_filter
 def loader(url: str, branch: str, file_filter: str):
     repo_path = "./github_repo"
@@ -60,25 +69,39 @@ def ingest_chunks(chunks):
     return vector_store
 
 #Retreival function to get the data from the database and reply to the user
-def retreival(vector_store):
+def retreival(vector_store, k):
     # Selecting the right model
-    llm_name = "gpt-3.5-turbo"
+    current_date = datetime.datetime.now().date()
+    if current_date < datetime.date(2023, 9, 2):
+        llm_name = "gpt-3.5-turbo-0301"
+    else:
+        llm_name = "gpt-3.5-turbo"
 
     #Creating LLM
-    llm = ChatOpenAI(model=llm_name, temperature=0.5)
+    llm = ChatOpenAI(model=llm_name, temperature=0)
+
+    # Define the system message template
+    system_template = """You're a code summarisation assistant. Given the following extracted parts of a long document as "CONTEXT" create a final answer.
+    If you don't know the answer, just say that you don't know. Don't try to make up an answer.
+    Only If asked to create a "DIAGRAM" for code use "MERMAID SYNTAX LANGUAGE" in your answer from "CONTEXT" and "CHAT HISTORY".
+
+    CONTEXT: {context}
+    =======
+    FINAL ANSWER:"""
+
+    human_template = """{question}"""
+
+    # ai_template = """
+    # FINAL ANSWER:"""
 
-    # Creating Prompt template
-    template = """
-        You're a Git Code summarisation assistant who searches through "SOURCE DOCUMENTS" and provides helpful sumamries with "CODE SNIPPETS". Given the following extracted parts of a long document and a question, create a final answer with "CODE SNIPPETS" from "SOURCE DOCUMENTS".
-        If you don't know the answer, just say that you don't know. Don't try to make up an answer.
-        =========
-        QUESTION: {question}
-        =========
-        CONTEXT: {context}
-        =========
-        FINAL ANSWER:"""
+    # Create the chat prompt templates
+    messages = [
+    SystemMessagePromptTemplate.from_template(system_template),
+    HumanMessagePromptTemplate.from_template(human_template),
+    # AIMessagePromptTemplate.from_template(ai_template)
+    ]
 
-    PROMPT = PromptTemplate(input_variables=["context", "question"], template=template,)
+    PROMPT = ChatPromptTemplate.from_messages(messages)
 
     #Creating memory
     memory = ConversationBufferMemory(
@@ -88,7 +111,7 @@ def retreival(vector_store):
             return_messages=True)
 
     #Creating the retriever, this can also be a contextual compressed retriever
-    retriever = vector_store.as_retriever(search_type="similarity", search_kwargs={"k": 5}) #search_type can be "similarity" or "mmr"
+    retriever = vector_store.as_retriever(search_type="similarity", search_kwargs={"k": k}) #search_type can be "similarity" or "mmr"
 
     chain = ConversationalRetrievalChain.from_llm(
         llm=llm, 
@@ -111,8 +134,8 @@ class ConversationalResponse:
         self.chunks = split_data(self.data)
         self.vector_store = ingest_chunks(self.chunks)
         self.chain_type = "stuff"
-        self.k = 5
-        self.chain = retreival(self.vector_store)
+        self.k = 15
+        self.chain = retreival(self.vector_store, self.k)
 
     def __call__(self, question):
         agent = self.chain(question)