add chat

app.py

@@ -14,9 +14,98 @@ from collections import defaultdict
 import os
 
 
+from langchain.agents import AgentExecutor, AgentType, initialize_agent
+from langchain.agents.structured_chat.prompt import SUFFIX
+from langchain.chat_models import ChatOpenAI
+from langchain.memory import ConversationBufferMemory
+
+from chainlit.action import Action
+from chainlit.input_widget import Select, Switch, Slider
+
+from langchain.tools import StructuredTool, Tool
+
+
+# this is our tool - which is what allows our agent to generate images in the first place!
+# the `description` field is of utmost imporance as it is what the LLM "brain" uses to determine
+# which tool to use for a given input.
+
+
+
 got_csv = False
 
 
+@cl.on_chat_start
+async def start():
+    """
+    This is called when the Chainlit chat is started!
+    We can add some settings to our application to allow users to select the appropriate model, and more!
+    """
+    settings = await cl.ChatSettings(
+        [
+            Select(
+                id="Model",
+                label="OpenAI - Model",
+                values=["gpt-3.5-turbo", "gpt-4-1106-preview"],
+                initial_index=1,
+            ),
+            Switch(id="Streaming", label="OpenAI - Stream Tokens", initial=True),
+            Slider(
+                id="Temperature",
+                label="OpenAI - Temperature",
+                initial=0,
+                min=0,
+                max=2,
+                step=0.1,
+            ),
+        ]
+    ).send()
+    await setup_agent(settings)
+
+
+@cl.on_settings_update
+async def setup_agent(settings):
+    print("Setup agent with following settings: ", settings)
+
+    # We set up our agent with the user selected (or default) settings here.
+    llm = ChatOpenAI(
+        temperature=settings["Temperature"],
+        streaming=settings["Streaming"],
+        model=settings["Model"],
+    )
+
+    # We get our memory here, which is used to track the conversation history.
+    memory = get_memory()
+
+    # This suffix is used to provide the chat history to the prompt.
+    _SUFFIX = "Chat history:\n{chat_history}\n\n" + SUFFIX
+
+    # We initialize our agent here, which is simply being used to decide between responding with text
+    # or an image
+    agent = initialize_agent(
+        llm=llm,  # our LLM (default is GPT-4 Turbo)
+        tools = [
+            generate_most_valuable_feature
+        ],
+        agent=AgentType.STRUCTURED_CHAT_ZERO_SHOT_REACT_DESCRIPTION,  # the agent type we're using today
+        memory=memory,  # our memory!
+        agent_kwargs={
+            "suffix": _SUFFIX,  # adding our chat history suffix
+            "input_variables": ["input", "agent_scratchpad", "chat_history"],
+        },
+    )
+    cl.user_session.set("agent", agent)  # storing our agent in the user session
+
+
+
+@cl.cache
+def get_memory():
+    """
+    This is used to track the conversation history and allow our agent to
+    remember what was said before.
+    """
+    return ConversationBufferMemory(memory_key="chat_history")
+
+
 def find_most_valuable_feature(csv_file):
     print("find_most_valuable_feature")
     print(csv_file)
@@ -75,6 +164,12 @@ def find_most_valuable_feature(csv_file):
     return image_buffer
 
 
+generate_most_valuable_feature = Tool.from_function(
+    func=find_most_valuable_feature,
+    name="Find most valuable feature",
+    description=f"Useful for finding the most valuable feature from a CSV file",
+    return_direct=True,
+)
 
 
 
@@ -109,7 +204,7 @@ def process_and_analyze_data(csv_file):
 
 @cl.on_message
 async def handle_message(message: cl.Message):
-    global got_csv
+    global got_csv, agent
     # Retrieve the CSV file from the message
     csv_file = next(
         (
@@ -139,6 +234,12 @@ async def handle_message(message: cl.Message):
 
             generated_image = cl.user_session.get(name)
 
+            agent = cl.user_session.get("agent")
+
+            res = await cl.make_async(agent.run)(
+                input=message.content, callbacks=[cl.LangchainCallbackHandler()]
+            )
+
             elements = []
             actions = []
 
@@ -159,6 +260,11 @@ async def handle_message(message: cl.Message):
     else:
         if not got_csv:
             await cl.Message(content="Please upload a CSV file.").send()
+        else:
+            res = await cl.make_async(agent.run)(
+                input=message.content, callbacks=[cl.LangchainCallbackHandler()]
+            )
+            await cl.Message(content=res).send()
 
 
 # Run the ChainLit app