Upload 6 files

deploying on cloud

.gitignore

@@ -0,0 +1 @@
+.env

Pipfile

@@ -0,0 +1,11 @@
+[[source]]
+url = "https://pypi.org/simple"
+verify_ssl = true
+name = "pypi"
+
+[packages]
+
+[dev-packages]
+
+[requires]
+python_version = "3.11"

app.py

@@ -0,0 +1,141 @@
+from dotenv import load_dotenv
+from langchain_core.messages import AIMessage, HumanMessage
+from langchain_core.prompts import ChatPromptTemplate
+from langchain_core.runnables import RunnablePassthrough
+from langchain_community.utilities import SQLDatabase
+from langchain_core.output_parsers import StrOutputParser
+from langchain_openai import ChatOpenAI
+import streamlit as st
+
+load_dotenv()
+
+st.set_page_config(page_title="Chat with SQL", page_icon=":speech_ballon")
+st.title("Chat with my MySQL")
+
+# session state variable
+if "chat_history" not in st.session_state:
+    st.session_state.chat_history = [AIMessage(content="Hello! I'm a SQL assistant. ASk me anything about your database."),]
+
+
+def init_database(user: str, password: str, host: str, port: str, database: str) -> SQLDatabase:
+    # connceting to mysql db using mysql-connector-python  driver
+    db_uri = f"mysql+mysqlconnector://{user}:{password}@{host}:{port}/{database}"
+    return SQLDatabase.from_uri(db_uri)
+
+
+def get_sql_chain(db):
+    template = """
+            You are a data analyst at a company. You are interacting with a user who is asking you questions about the company's database.
+            Based on the table schema below, write a SQL query that would answer the user's question. Take the conversation history into account.
+
+            <SCHEMA>{schema}</SCHEMA>
+
+            Conversation History: {chat_history}
+
+            Write only the SQL query and nothing else. Do not wrap the SQL query in any other text, not even backticks.
+
+            For example(few shot learning):
+            Question: which 3 artists have the most tracks?
+            SQL Query: SELECT ArtistId, COUNT(*) as track_count FROM Track GROUP BY ArtistId ORDER BY track_count DESC LIMIT 3;
+            Question: Name 10 artists
+            SQL Query: SELECT Name FROM Artist LIMIT 10;
+
+            Your turn:
+
+            Question: {question}
+            SQL Query:
+        """
+    
+    prompt = ChatPromptTemplate.from_template(template)
+    llm = ChatOpenAI(model="gpt-4")
+
+    def get_schema(_):
+        return db.get_table_info()
+    
+    sql_chain = RunnablePassthrough.assign(schema=get_schema) | prompt | llm | StrOutputParser()
+
+    return sql_chain
+
+
+def get_response(user_query: str, db: SQLDatabase, chat_history: list):
+    sql_chain = get_sql_chain(db)
+    template = """
+        You are a data analyst at a company. You are interacting with a user who is asking you questions about the company's database.
+        Based on the table schema below, question, sql query, and sql response, write a natural language response.
+        <SCHEMA>{schema}</SCHEMA>
+
+        Conversation History: {chat_history}
+        SQL Query: <SQL>{query}</SQL>
+        User Question: {question}
+        SQL Response: {response}
+        """
+    prompt = ChatPromptTemplate.from_template(template)
+    llm = ChatOpenAI()
+
+    response_chain =  (
+        RunnablePassthrough.assign(query=sql_chain)
+                            .assign(schema=lambda _: db.get_table_info(),
+                            # response=lambda vars: print("variables: ", vars),
+                            response=lambda vars: db.run(vars["query"]),
+                            )
+                            | prompt
+                            | llm
+                            | StrOutputParser()
+    )
+
+    return response_chain.invoke({"question": user_query, "chat_history": chat_history})
+    
+
+
+with st.sidebar:
+    st.subheader("Settings")
+    st.write("This is a simple chat application using LLM and MySQL")
+    st.write("Connect to the databse and satrt chatting.")
+
+    st.text_input("Host", value="localhost", key="Host")
+    st.text_input("Port", value="3306", key="Port")
+    st.text_input("User", value="root", key="User")
+    st.text_input("Password", type="password", value="admin", key="Password")
+    st.text_input("Database", value="Chinook", key="Database")
+
+    if st.button("Connect"):
+        with st.spinner("Connecting to database..."):
+            db = init_database(
+                st.session_state["User"],
+                st.session_state["Password"],
+                st.session_state["Host"],
+                st.session_state["Port"],
+                st.session_state["Database"],
+            )
+            st.session_state.db = db
+            st.success("Connected to database!")
+
+# printing out messages/ chat
+for message in st.session_state.chat_history:
+    if isinstance(message, AIMessage):
+        with st.chat_message("AI"):
+            st.markdown(message.content)
+    elif isinstance(message, HumanMessage):
+        with st.chat_message("Human"):
+            st.markdown(message.content)
+
+
+user_query = st.chat_input("Type a message...")
+if user_query is not None and user_query.strip() != "":
+    # adding to chat history
+    st.session_state.chat_history.append(HumanMessage(content=user_query))
+
+    # displaying user query// with manages the lifecycle of an object
+    with st.chat_message("Human"):
+        st.markdown(user_query)
+
+    with st.chat_message("AI"):
+        response = get_response(user_query, st.session_state.db, st.session_state.chat_history)
+        # sql_chain = get_sql_chain(st.session_state.db)
+        # response = sql_chain.invoke({
+        #     "chat_history": st.session_state.chat_history,  # scheam has already been populated in func getsqlchain
+        #     "question" : user_query
+        # })
+        st.markdown(response)
+
+    st.session_state.chat_history.append(AIMessage(content=response))

requirements.txt

@@ -0,0 +1,8 @@
+streamlit
+langchain
+langchain-openai
+langchain_community 
+langchain-groq
+mysql-connector-python
+mysql
+python-dotenv