Upload 11 files

chatbot.ipynb

@@ -35,6 +35,7 @@
     "\n",
     "\n",
     "tool_node = tools.tool_node\n",
+    "tools.tools\n",
     "\n",
     "# model = ChatAnthropic(model=\"claude-3-5-sonnet-20240620\", temperature=0).bind_tools(tools)\n",
     "model = ChatOpenAI(model=\"gpt-4o-mini\")\n",

chatbot.py

@@ -9,13 +9,17 @@ from langchain_core.messages import HumanMessage
 from langchain_openai import ChatOpenAI
 from langgraph.checkpoint.memory import MemorySaver
 from langgraph.graph import END, StateGraph, MessagesState
+from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
+from langchain.agents import AgentExecutor, create_openai_tools_agent
+import functools
 import tools
 
 # define tools node.
 tool_node = tools.tool_node
 
+
 # load models.
-model = ChatOpenAI(model="gpt-4o-mini")
+llm = ChatOpenAI(model="gpt-4o-mini")
 
 # Define the function that determines whether to continue or not
 def should_continue(state: MessagesState) -> Literal["tools", END]:
@@ -28,11 +32,21 @@ def should_continue(state: MessagesState) -> Literal["tools", END]:
     return END
 
 
+# Define the function that calls the model
 # Define the function that calls the model
 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
+    
+    # Check if this is the first message in the conversation
+    if len(messages) == 1:
+        # Add the initial prompt before the user's first message
+        initial_prompt = HumanMessage(content="You are a market analyst specializing in feasibility studies. Your task is to analyze the potential of a new business location based on specific criteria. You will gather data from Google Maps about the presence and distribution of relevant points of interest, such as coffee shops, restaurants, or other businesses, near a specified location.")
+        messages.insert(0, initial_prompt)
+    
+    # Call the model with the updated messages
+    response = llm.invoke(messages)
+    
+    # Return the updated messages state
     return {"messages": [response]}
 
 
@@ -46,6 +60,7 @@ workflow.add_node("tools", tool_node)
 # Set the entrypoint as `agent`
 workflow.set_entry_point("agent")
 
+
 # We now add a conditional edge
 workflow.add_conditional_edges(
     # First, we define the start node. We use `agent`.