added langgraph sample

Author: yorek

chainlit/app-chainlit-tools.py

@@ -0,0 +1,73 @@
+import os
+from dotenv import load_dotenv
+
+from typing import Annotated, Literal, TypedDict
+
+from langchain_openai import AzureChatOpenAI
+
+from langchain.schema.runnable.config import RunnableConfig
+from langchain_core.messages import SystemMessage, HumanMessage
+
+from langchain_core.messages import HumanMessage
+from langchain_core.tools import tool
+from langgraph.checkpoint.memory import MemorySaver
+from langgraph.graph import END, START, StateGraph, MessagesState
+from langgraph.prebuilt import ToolNode
+
+from utilities import get_similar_sessions
+
+import chainlit as cl
+
+load_dotenv()
+
+tools = [get_similar_sessions]
+model = AzureChatOpenAI(openai_api_version=os.environ["AZURE_OPENAI_API_VERSION"], azure_deployment=os.environ["AZURE_OPENAI_CHAT_DEPLOYMENT_NAME"])
+model = model.bind_tools(tools)
+
+def should_continue(state: MessagesState) -> Literal["tools", END]:
+    messages = state["messages"]
+    last_message = messages[-1]
+    # If the LLM makes a tool call, then we route to the "tools" node
+    if last_message.tool_calls:
+        return "tools"
+    # Otherwise, we stop (reply to the user)
+    return END
+
+def call_model(state: MessagesState):
+    messages = state["messages"]
+    response = model.invoke(messages)
+    # We return a list, because this will get added to the existing list
+    return {"messages": [response]}
+
+tool_node = ToolNode(tools=tools)
+
+workflow = StateGraph(MessagesState)
+
+workflow.add_node("agent", call_model)
+workflow.add_node("tools", tool_node)
+
+workflow.add_edge(START, "agent")
+
+workflow.add_conditional_edges("agent", should_continue)
+
+workflow.add_edge("tools", "agent")
+
+checkpointer = MemorySaver()
+
+graph = workflow.compile(checkpointer=checkpointer)
+
+@cl.on_message
+async def on_message(msg: cl.Message):
+    config = {"configurable": {"thread_id": cl.context.session.id}}
+    final_answer = cl.Message(content="")
+    
+    for msg, metadata in await cl.make_async(graph.stream)(
+        {"messages": [HumanMessage(content=msg.content)]}, 
+        stream_mode="messages", 
+        config=RunnableConfig(callbacks=[cl.LangchainCallbackHandler()], **config)
+        ):        
+            if metadata["langgraph_node"] == "agent":
+                await final_answer.stream_token(msg.content)
+
+    await final_answer.send()
+

chainlit/app-langgraph.py

@@ -56,12 +56,12 @@ async def on_chat_start():
 async def on_message(message: cl.Message):
     runnable = cl.user_session.get("runnable")  # type: Runnable
 
-    msg = cl.Message(content="")
+    response_message = cl.Message(content="")
 
     for chunk in await cl.make_async(runnable.stream)(
         input=message.content,
         config=RunnableConfig(callbacks=[cl.LangchainCallbackHandler()]),
     ):
-        await msg.stream_token(chunk)
+        await response_message.stream_token(chunk)
 
-    await msg.send()
+    await response_message.send()

chainlit/app.py

@@ -1,6 +1,14 @@
-chainlit
-langchain
-langchain_openai
 python-dotenv
+
 pyodbc
-azure-identity
+azure-identity
+
+langchain 
+langchain-openai
+langchain-community 
+langchain-sqlserver
+langgraph
+
+chainlit
+
+

chainlit/requirements.txt

@@ -0,0 +1 @@
+@chainlit run app-langgraph.py -w

chainlit/run-langgraph.bat

@@ -25,26 +25,33 @@ def get_mssql_connection(source_variable_name: str) -> pyodbc.Connection:
 
     return conn
 
-def get_similar_sessions(search_text:str) -> str:
+def get_similar_sessions(topic:str) -> str:
+    """
+    Use this function to get a list of sessions that are potentially relevant for the specified topic.
+    The sessions are provided in the format of `id|title|abstract|speakers|start-time|end-time`. 
+    
+    """
     conn = get_mssql_connection("AZURE_SQL_CONNECTION_STRING")
     logging.info("Querying MSSQL...")
-    logging.info(f"Message content: '{search_text}'")
+    logging.info(f"Topic: '{topic}'")
     try:        
         cursor = conn.cursor()            
-        results = cursor.execute("SET NOCOUNT ON; EXEC web.find_sessions @text=?", (search_text)).fetchall()
+        results = cursor.execute("SET NOCOUNT ON; EXEC web.find_sessions @text=?", (topic)).fetchall()
 
         logging.info(f"Found {len(results)} similar sessions.")
 
         payload = ""
         for row in results:
             description = str(row[2]).replace("\n", " ")
             speakers = ", ".join(json.loads(row[7]))    
-            payload += f'{row[1]}|{description}|{speakers}|{row[4]}|{row[5]}' 
+            payload += f'{row[0]}|{row[1]}|{description}|{speakers}|{row[4]}|{row[5]}' 
             payload += "\n"
 
         return payload    
     finally:
         cursor.close()    
 
 if __name__ == "__main__":
+    from dotenv import load_dotenv
+    load_dotenv()
     print(get_similar_sessions("SQL and AI"))