cr

examples/chatbots/prompt-generator.ipynb

@@ -1,15 +1,51 @@
 {
  "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "d72fae4e-f7de-42b7-91ee-bdd0a57ae46c",
+   "metadata": {},
+   "source": [
+    "# Prompt Generator\n",
+    "\n",
+    "In this example we will create a chat bot that helps a user generate a prompt.\n",
+    "It will first collect requirements from the user, and then will generate the prompt (and refine it based on user input).\n",
+    "These are split into two separate states, and the LLM decides when to transition between them.\n",
+    "\n",
+    "A graphical representation of the system can be found below.\n",
+    "\n",
+    "![](imgs/prompt-generator.png)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6d78b593-ba26-4c90-b2e2-83119e47679f",
+   "metadata": {},
+   "source": [
+    "## Gather information\n",
+    "\n",
+    "First, let's define the part of the graph that will gather user requirements. This will be an LLM call with a specific system message. It will have access to a tool that it can call when it is ready to generate the prompt."
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": 1,
-   "id": "c0aa20b8-fe59-4c91-a06f-32002f142a41",
+   "execution_count": 6,
+   "id": "53216ab5-2cd3-48a4-8778-41ba10f72519",
    "metadata": {},
    "outputs": [],
    "source": [
-    "from langgraph.graph import MessageGraph\n",
     "from langchain_core.messages import SystemMessage\n",
-    "\n",
+    "from langchain_openai import ChatOpenAI\n",
+    "from langchain_core.pydantic_v1 import BaseModel, Field\n",
+    "from typing import List"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "5f795b78-004d-40ca-95d6-069f67e4f9c9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
     "template = \"\"\"Your job is to get information from a user about what type of prompt template they want to create.\n",
     "\n",
     "You should get the following information from them:\n",
@@ -21,35 +57,87 @@
     "\n",
     "If you are not able to discerne this info, ask them to clarify! Do not attempt to wildly guess.\n",
     "\n",
-    "After you are able to discerne all the information, call the relevant tool\"\"\"\n",
+    "After you are able to discerne all the information, call the relevant tool\"\"\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "153a7c89-ec75-430e-96ac-6294d4e6ea2b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "llm = ChatOpenAI(temperature=0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "8325ed0e-177a-4129-b4a4-cd54d527c634",
+   "metadata": {},
+   "outputs": [],
+   "source": [
     "def get_messages_info(messages):\n",
     "    return [SystemMessage(content=template)] + messages\n",
     "\n",
-    "from langchain_openai import ChatOpenAI\n",
-    "\n",
-    "llm = ChatOpenAI(temperature=0)\n",
-    "\n",
-    "from langchain_core.pydantic_v1 import BaseModel, Field\n",
-    "from typing import List\n",
-    "\n",
     "\n",
     "class PromptInstructions(BaseModel):\n",
     "    \"\"\"Instructions on how to prompt the LLM.\"\"\"\n",
     "    objective: str\n",
     "    variables: List[str]\n",
     "    constraints: List[str]\n",
-    "    requirements: List[str]\n",
-    "\n",
+    "    requirements: List[str]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "95bb557a-47f2-46fd-adf7-9cf0e8ffe51c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
     "llm_with_tool = llm.bind_tools([PromptInstructions])\n",
     "\n",
-    "chain = get_messages_info | llm_with_tool\n",
+    "chain = get_messages_info | llm_with_tool"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bb40630f-83c7-4283-a6dd-04231805a7ed",
+   "metadata": {},
+   "source": [
+    "## Generate Prompt\n",
     "\n",
+    "We now set up the state that will generate the prompt.\n",
+    "This will require a separate system message, as well as a function to filter out all message PRIOR to the tool invocation (as that is when the previous state decided it was time to generate the prompt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "5b6736fb-21d7-44d3-a3fe-460f15945654",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Helper function for determining if tool was called\n",
     "def _is_tool_call(msg):\n",
-    "    return hasattr(msg, \"additional_kwargs\") and 'tool_calls' in msg.additional_kwargs\n",
-    "\n",
+    "    return hasattr(msg, \"additional_kwargs\") and 'tool_calls' in msg.additional_kwargs"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "ca9a0234-bbeb-4bff-8276-8dde499c3390",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# New system prompt\n",
     "prompt_system = \"\"\"Based on the following requirements, write a good prompt template:\n",
     "\n",
     "{reqs}\"\"\"\n",
+    "\n",
+    "# Function to get the messages for the prompt\n",
+    "# Will only get messages AFTER the tool call\n",
     "def get_prompt_messages(messages):\n",
     "    tool_call = None\n",
     "    other_msgs = []\n",
@@ -59,10 +147,40 @@
     "        elif tool_call is not None:\n",
     "            other_msgs.append(m)\n",
     "    return [SystemMessage(content=prompt_system.format(reqs=tool_call))] + other_msgs\n",
-    "    \n",
-    "\n",
-    "prompt_gen_chain = get_prompt_messages | llm\n",
+    "    "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3293d2d3-e060-4c98-8065-3696b049b5fc",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "prompt_gen_chain = get_prompt_messages | llm"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8dbabda8-34f0-4eef-bce2-ad3ff505366b",
+   "metadata": {},
+   "source": [
+    "## Define the state logic\n",
     "\n",
+    "This is the logic for what state the chatbot is in.\n",
+    "If the last message is a tool call, then we are in the state where the \"prompt creator\" (`prompt`) should respond.\n",
+    "Otherwise, if the last message is not a HumanMessage, then we know the human should respond next and so we are in the `END` state.\n",
+    "If the last message is a HumanMessage, then if there was a tool call previously we are in the `prompt` state.\n",
+    "Otherwise, we are in the \"info gathering\" (`info`) state."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "74f29e15-20e2-420c-a450-84e929f16e4e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
     "def get_state(messages):\n",
     "    if _is_tool_call(messages[-1]):\n",
     "        return \"prompt\"\n",
@@ -74,6 +192,17 @@
     "    return \"info\""
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "b76bea78-07a5-418f-9b7c-71c376d4b6f7",
+   "metadata": {},
+   "source": [
+    "## Create the graph\n",
+    "\n",
+    "We can now the create the graph.\n",
+    "We will use a SqliteSaver to persist conversation history."
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 2,
@@ -96,9 +225,19 @@
     "graph = workflow.compile(checkpointer=memory)"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "afcf523c-265d-45cf-a981-fc50c50c1738",
+   "metadata": {},
+   "source": [
+    "## Use the graph\n",
+    "\n",
+    "We can now use the created chatbot."
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 5,
    "id": "25793988-45a2-4e65-b33c-64e72aadb10e",
    "metadata": {},
    "outputs": [
@@ -240,6 +379,20 @@
       "---\n",
       "\n"
      ]
+    },
+    {
+     "name": "stdin",
+     "output_type": "stream",
+     "text": [
+      "User (q/Q to quit):  q\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "AI: Byebye\n"
+     ]
     }
    ],
    "source": [