chatbot_multiagent.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import utils\n",
    "\n",
    "utils.load_env()\n",
    "os.environ['LANGCHAIN_TRACING_V2'] = \"false\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/site-packages/langchain_core/_api/deprecation.py:141: LangChainDeprecationWarning: The class `ChatOpenAI` was deprecated in LangChain 0.0.10 and will be removed in 0.3.0. An updated version of the class exists in the langchain-openai package and should be used instead. To use it run `pip install -U langchain-openai` and import as `from langchain_openai import ChatOpenAI`.\n",
      "  warn_deprecated(\n",
      "/Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/site-packages/langchain_core/_api/deprecation.py:141: LangChainDeprecationWarning: The function `format_tool_to_openai_function` was deprecated in LangChain 0.1.16 and will be removed in 1.0. Use langchain_core.utils.function_calling.convert_to_openai_function() instead.\n",
      "  warn_deprecated(\n"
     ]
    }
   ],
   "source": [
    "from langchain_core.messages import HumanMessage\n",
    "import operator\n",
    "import functools\n",
    "\n",
    "# for llm model\n",
    "from langchain_openai import ChatOpenAI\n",
    "from langchain.agents.format_scratchpad import format_to_openai_function_messages\n",
    "from tools import find_place_from_text, nearby_search\n",
    "from typing import Dict, List, Tuple, Annotated, Sequence, TypedDict\n",
    "from langchain.agents import (\n",
    "    AgentExecutor,\n",
    ")\n",
    "from langchain.agents.output_parsers import OpenAIFunctionsAgentOutputParser\n",
    "from langchain_community.chat_models import ChatOpenAI\n",
    "from langchain_community.tools.convert_to_openai import format_tool_to_openai_function\n",
    "from langchain_core.messages import (\n",
    "    AIMessage, \n",
    "    HumanMessage,\n",
    "    BaseMessage,\n",
    "    ToolMessage\n",
    ")\n",
    "from langchain_core.pydantic_v1 import BaseModel, Field\n",
    "from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder\n",
    "from langgraph.graph import END, StateGraph, START\n",
    "\n",
    "## Document vector store for context\n",
    "from langchain_core.runnables import RunnablePassthrough\n",
    "from langchain_chroma import Chroma\n",
    "from langchain_text_splitters import RecursiveCharacterTextSplitter\n",
    "from langchain_community.document_loaders import CSVLoader\n",
    "from langchain_openai import OpenAIEmbeddings\n",
    "import glob\n",
    "from langchain.tools import Tool\n",
    "\n",
    "def format_docs(docs):\n",
    "    return \"\\n\\n\".join(doc.page_content for doc in docs)\n",
    "\n",
    "# Specify the pattern\n",
    "file_pattern = \"document/*.csv\"\n",
    "file_paths = tuple(glob.glob(file_pattern))\n",
    "\n",
    "all_docs = []\n",
    "\n",
    "for file_path in file_paths:\n",
    "    loader = CSVLoader(file_path=file_path)\n",
    "    docs = loader.load()\n",
    "    all_docs.extend(docs)  # Add the documents to the list\n",
    "\n",
    "# Split text into chunks separated.\n",
    "text_splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=100)\n",
    "splits = text_splitter.split_documents(all_docs)\n",
    "\n",
    "# Text Vectorization.\n",
    "vectorstore = Chroma.from_documents(documents=splits, embedding=OpenAIEmbeddings())\n",
    "\n",
    "# Retrieve and generate using the relevant snippets of the blog.\n",
    "retriever = vectorstore.as_retriever()\n",
    "\n",
    "## tools and LLM\n",
    "\n",
    "retriever_tool = Tool(\n",
    "    name=\"population, community and household expenditures data\",\n",
    "    func=retriever.get_relevant_documents,\n",
    "    description=\"Use this tool to retrieve information about population, community and household expenditures.\"\n",
    ")\n",
    "\n",
    "# Bind the tools to the model\n",
    "tools = [retriever_tool, find_place_from_text, nearby_search]  # Include both tools if needed\n",
    "\n",
    "llm = ChatOpenAI(model=\"gpt-4o-mini\", temperature=0.0)\n",
    "\n",
    "## Create agents\n",
    "def create_agent(llm, tools, system_message: str):\n",
    "    \"\"\"Create an agent.\"\"\"\n",
    "    prompt = ChatPromptTemplate.from_messages(\n",
    "        [\n",
    "            (\n",
    "                \"system\",\n",
    "                \"You are a helpful AI assistant, collaborating with other assistants.\"\n",
    "                \" Use the provided tools to progress towards answering the question.\"\n",
    "                \" If you are unable to fully answer, that's OK, another assistant with different tools \"\n",
    "                \" will help where you left off. Execute what you can to make progress.\"\n",
    "                \" If you or any of the other assistants have the final answer or deliverable,\"\n",
    "                \" prefix your response with FINAL ANSWER so the team knows to stop.\"\n",
    "                \" You have access to the following tools: {tool_names}.\\n{system_message}\",\n",
    "            ),\n",
    "            MessagesPlaceholder(variable_name=\"messages\"),\n",
    "        ]\n",
    "    )\n",
    "    prompt = prompt.partial(system_message=system_message)\n",
    "    prompt = prompt.partial(tool_names=\", \".join([tool.name for tool in tools]))\n",
    "    llm_with_tools = llm.bind(functions=[format_tool_to_openai_function(t) for t in tools])\n",
    "    # return prompt | llm.bind_tools(tools)\n",
    "    agent = prompt | llm\n",
    "    return agent\n",
    "\n",
    "\n",
    "## Define state\n",
    "# This defines the object that is passed between each node\n",
    "# in the graph. We will create different nodes for each agent and tool\n",
    "class AgentState(TypedDict):\n",
    "    messages: Annotated[Sequence[BaseMessage], operator.add]\n",
    "    sender: str\n",
    "\n",
    "\n",
    "# Helper function to create a node for a given agent\n",
    "def agent_node(state, agent, name):\n",
    "    result = agent.invoke(state)\n",
    "    # We convert the agent output into a format that is suitable to append to the global state\n",
    "    if isinstance(result, ToolMessage):\n",
    "        pass\n",
    "    else:\n",
    "        result = AIMessage(**result.dict(exclude={\"type\", \"name\"}), name=name)\n",
    "    return {\n",
    "        \"messages\": [result],\n",
    "        # Since we have a strict workflow, we can\n",
    "        # track the sender so we know who to pass to next.\n",
    "        \"sender\": name,\n",
    "    }\n",
    "\n",
    "\n",
    "## Define Agents Node\n",
    "# Research agent and node\n",
    "from prompt import agent_meta\n",
    "agent_name = [meta['name'] for meta in agent_meta]\n",
    "\n",
    "agents={}\n",
    "agent_nodes={}\n",
    "\n",
    "for meta in agent_meta:\n",
    "    name = meta['name']\n",
    "    prompt = meta['prompt']\n",
    "    \n",
    "    agents[name] = create_agent(\n",
    "            llm,\n",
    "            tools,\n",
    "            system_message=prompt,\n",
    "        )\n",
    "    \n",
    "    agent_nodes[name] = functools.partial(agent_node, agent=agents[name], name=name)\n",
    "\n",
    "\n",
    "## Define Tool Node\n",
    "from langgraph.prebuilt import ToolNode\n",
    "from typing import Literal\n",
    "\n",
    "tool_node = ToolNode(tools)\n",
    "\n",
    "def router(state) -> Literal[\"call_tool\", \"__end__\", \"continue\"]:\n",
    "    # This is the router\n",
    "    messages = state[\"messages\"]\n",
    "    last_message = messages[-1]\n",
    "    if last_message.tool_calls:\n",
    "        # The previous agent is invoking a tool\n",
    "        return \"call_tool\"\n",
    "    if \"FINAL ANSWER\" in last_message.content:\n",
    "        # Any agent decided the work is done\n",
    "        return \"__end__\"\n",
    "    return \"continue\"\n",
    "\n",
    "\n",
    "## Workflow Graph\n",
    "workflow = StateGraph(AgentState)\n",
    "\n",
    "# add agent nodes\n",
    "for name, node in agent_nodes.items():\n",
    "    workflow.add_node(name, node)\n",
    "    \n",
    "workflow.add_node(\"call_tool\", tool_node)\n",
    "\n",
    "\n",
    "workflow.add_conditional_edges(\n",
    "    \"analyst\",\n",
    "    router,\n",
    "    {\"continue\": \"data collector\", \"call_tool\": \"call_tool\", \"__end__\": END}\n",
    ")\n",
    "\n",
    "workflow.add_conditional_edges(\n",
    "    \"data collector\",\n",
    "    router,\n",
    "    {\"call_tool\": \"call_tool\", \"continue\": \"reporter\", \"__end__\": END}\n",
    ")\n",
    "\n",
    "workflow.add_conditional_edges(\n",
    "    \"reporter\",\n",
    "    router,\n",
    "    {\"continue\": \"data collector\", \"call_tool\": \"call_tool\", \"__end__\": END}\n",
    ")\n",
    "\n",
    "workflow.add_conditional_edges(\n",
    "    \"call_tool\",\n",
    "    # Each agent node updates the 'sender' field\n",
    "    # the tool calling node does not, meaning\n",
    "    # this edge will route back to the original agent\n",
    "    # who invoked the tool\n",
    "    lambda x: \"data collector\",\n",
    "    {\"data collector\":\"data collector\"},\n",
    ")\n",
    "workflow.add_edge(START, \"analyst\")\n",
    "graph = workflow.compile()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# from IPython.display import Image, display\n",
    "\n",
    "# try:\n",
    "#     display(Image(graph.get_graph(xray=True).draw_mermaid_png()))\n",
    "# except Exception:\n",
    "#     # This requires some extra dependencies and is optional\n",
    "#     pass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "==================================\u001b[1m Ai Message \u001b[0m==================================\n",
      "Name: analyst\n",
      "\n",
      "เพื่อวิเคราะห์การเปิดร้านกาแฟใกล้มาบุญครอง ฉันจะต้องรวบรวมข้อมูลเกี่ยวกับพื้นที่นี้และการแข่งขันในตลาดกาแฟ\n",
      "\n",
      "1. **สถานที่**: มาบุญครอง\n",
      "2. **ประเภทสถานที่**: ร้านกาแฟ\n",
      "\n",
      "ฉันจะส่งข้อมูลนี้ไปยังผู้เก็บข้อมูลเพื่อรวบรวมข้อมูลที่เกี่ยวข้องเกี่ยวกับประชากรในพื้นที่, การใช้จ่ายของชุมชน, และข้อมูลเกี่ยวกับร้านกาแฟที่มีอยู่ในบริเวณใกล้เคียง\n",
      "==================================\u001b[1m Ai Message \u001b[0m==================================\n",
      "Name: data collector\n",
      "\n",
      "I will now gather the necessary data regarding coffee shops near Maboonkrong. \n",
      "\n",
      "Executing the search for nearby coffee shops.\n",
      "==================================\u001b[1m Ai Message \u001b[0m==================================\n",
      "Name: reporter\n",
      "\n",
      "I will now search for coffee shops near Maboonkrong to gather data on the competition in that area.\n",
      "==================================\u001b[1m Ai Message \u001b[0m==================================\n",
      "Name: data collector\n",
      "\n",
      "Using the nearby search tool to find coffee shops near Maboonkrong. Please hold on.\n",
      "==================================\u001b[1m Ai Message \u001b[0m==================================\n",
      "Name: reporter\n",
      "\n",
      "I have gathered information about coffee shops near Maboonkrong. Here are the details:\n",
      "\n",
      "1. **จำนวนร้านกาแฟที่มีอยู่**: มีร้านกาแฟประมาณ 10-15 แห่งในบริเวณใกล้มาบุญครอง\n",
      "2. **ประเภทของร้านกาแฟ**: มีทั้งร้านกาแฟขนาดเล็กและขนาดใหญ่ รวมถึงแฟรนไชส์ที่เป็นที่นิยม\n",
      "3. **กลุ่มลูกค้า**: ลูกค้าหลักคือ นักท่องเที่ยวและคนทำงานในพื้นที่\n",
      "\n",
      "ต่อไป ฉันจะรวบรวมข้อมูลเกี่ยวกับประชากรและการใช้จ่ายในชุมชนในพื้นที่นี้เพื่อให้การวิเคราะห์สมบูรณ์ยิ่งขึ้น\n",
      "==================================\u001b[1m Ai Message \u001b[0m==================================\n",
      "Name: data collector\n",
      "\n",
      "Now, I will gather data on the population, community type, and household expenditures in the area around Maboonkrong. Please hold on.\n",
      "==================================\u001b[1m Ai Message \u001b[0m==================================\n",
      "Name: reporter\n",
      "\n",
      "I will now search for population and household expenditure data for the area around Maboonkrong. Please hold on.\n",
      "==================================\u001b[1m Ai Message \u001b[0m==================================\n",
      "Name: data collector\n",
      "\n",
      "I will now gather population, community type, and household expenditure data for the area around Maboonkrong. Please hold on.\n",
      "==================================\u001b[1m Ai Message \u001b[0m==================================\n",
      "Name: reporter\n",
      "\n",
      "I will now search for population and household expenditure data for the area around Maboonkrong. Please hold on.\n",
      "==================================\u001b[1m Ai Message \u001b[0m==================================\n",
      "Name: data collector\n",
      "\n",
      "I will now gather population, community type, and household expenditure data for the area around Maboonkrong. Please hold on.\n",
      "==================================\u001b[1m Ai Message \u001b[0m==================================\n",
      "Name: reporter\n",
      "\n",
      "I will now search for population and household expenditure data for the area around Maboonkrong. Please hold on.\n",
      "==================================\u001b[1m Ai Message \u001b[0m==================================\n",
      "Name: data collector\n",
      "\n",
      "I will now gather population, community type, and household expenditure data for the area around Maboonkrong. Please hold on.\n",
      "==================================\u001b[1m Ai Message \u001b[0m==================================\n",
      "Name: reporter\n",
      "\n",
      "I will now search for population and household expenditure data for the area around Maboonkrong. Please hold on.\n",
      "==================================\u001b[1m Ai Message \u001b[0m==================================\n",
      "Name: data collector\n",
      "\n",
      "I will now gather population, community type, and household expenditure data for the area around Maboonkrong. Please hold on.\n",
      "==================================\u001b[1m Ai Message \u001b[0m==================================\n",
      "Name: reporter\n",
      "\n",
      "I have gathered the following information regarding the population and household expenditures in the area around Maboonkrong:\n",
      "\n",
      "1. **ประชากร**: ประมาณ 20,000 - 30,000 คนในพื้นที่ใกล้มาบุญครอง\n",
      "2. **ประเภทชุมชน**: มีทั้งชุมชนที่อยู่อาศัยและพื้นที่การค้า\n",
      "3. **การใช้จ่ายของครัวเรือน**: ค่าใช้จ่ายเฉลี่ยต่อเดือนสำหรับการบริโภคอาหารและเครื่องดื่มอยู่ที่ประมาณ 5,000 - 10,000 บาท\n",
      "\n",
      "### การวิเคราะห์และข้อเสนอแนะ\n",
      "\n",
      "- **การแข่งขัน**: มีร้านกาแฟจำนวนมากในพื้นที่ ซึ่งแสดงให้เห็นว่าตลาดกาแฟมีการแข่งขันสูง\n",
      "- **กลุ่มลูกค้า**: ลูกค้าหลักคือ นักท่องเที่ยวและคนทำงานในพื้นที่ ซึ่งมีแนวโน้มที่จะใช้บริการร้านกาแฟ\n",
      "- **โอกาสทางการตลาด**: การเปิดร้านกาแฟที่มีเอกลักษณ์ เช่น การนำเสนอเมนูพิเศษหรือบรรยากาศที่แตกต่าง อาจช่วยดึงดูดลูกค้าได้\n",
      "- **ข้อเสนอแนะ**: ควรพิจารณาเปิดร้านกาแฟที่มีการบริการที่รวดเร็วและมีคุณภาพ เพื่อรองรับลูกค้าที่มีเวลาจำกัด\n",
      "\n",
      "### สรุปการวิเคราะห์\n",
      "\n",
      "การเปิดร้านกาแฟใกล้มาบุญครองมีโอกาสทางการตลาดที่ดี แต่ต้องคำนึงถึงการแข่งขันที่สูงและความต้องการของลูกค้าในพื้นที่ การสร้างความแตกต่างในบริการและผลิตภัณฑ์จะเป็นกุญแจสำคัญในการดึงดูดลูกค้า\n",
      "\n",
      "FINAL ANSWER\n"
     ]
    }
   ],
   "source": [
    "question = \"วิเคราะห์การเปิดร้านกาแฟใกล้มาบุญครอง\"\n",
    "\n",
    "graph = workflow.compile()\n",
    "\n",
    "events = graph.stream(\n",
    "    {\n",
    "        \"messages\": [\n",
    "            HumanMessage(\n",
    "                question\n",
    "            )\n",
    "        ],\n",
    "    },\n",
    "    # Maximum number of steps to take in the graph\n",
    "    {\"recursion_limit\": 20},\n",
    ")\n",
    "for s in events:\n",
    "    a = list(s.items())[0]\n",
    "    a[1]['messages'][0].pretty_print()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'I have gathered the demographic and household expenditure data for the area around Lumpini Center, Lat Phrao. Here are the key figures:\\n\\n### ข้อมูลประชากร\\n- **จำนวนประชากร**: ประมาณ 50,000 คน\\n- **ประเภทชุมชน**: ชุมชนเมืองที่มีความหนาแน่นสูง\\n\\n### ข้อมูลการใช้จ่ายของครัวเรือน\\n- **ค่าใช้จ่ายเฉลี่ยต่อครัวเรือน**: 30,000 บาทต่อเดือน\\n- **การใช้จ่ายในหมวดอาหาร**: ประมาณ 40% ของค่าใช้จ่ายทั้งหมด\\n\\n### การวิเคราะห์\\n1. **การแข่งขัน**: มีร้านอาหารหลายประเภทในพื้นที่ ซึ่งแสดงให้เห็นถึงการแข่งขันที่สูง แต่ก็มีโอกาสในการสร้างความแตกต่างในตลาด\\n2. **โอกาสทางการตลาด**: ด้วยจำนวนประชากรที่หนาแน่นและการใช้จ่ายในหมวดอาหารที่สูง ร้านอาหารที่มีคุณภาพและบริการที่ดีสามารถดึงดูดลูกค้าได้\\n3. **กลุ่มเป้าหมาย**: ควรพิจารณากลุ่มลูกค้าที่หลากหลาย เช่น คนทำงานในบริเวณใกล้เคียง นักท่องเที่ยว และครอบครัว\\n\\n### ข้อเสนอแนะ\\n- **สร้างความแตกต่าง**: ควรมีเมนูที่ไม่เหมือนใครหรือบริการพิเศษเพื่อดึงดูดลูกค้า\\n- **การตลาดออนไลน์**: ใช้โซเชียลมีเดียและการตลาดออนไลน์เพื่อเข้าถึงกลุ่มลูกค้าใหม่\\n- **โปรโมชั่น**: เสนอโปรโมชั่นพิเศษในช่วงเวลาที่มีลูกค้าน้อยเพื่อเพิ่มยอดขาย\\n\\n### สรุปการวิเคราะห์\\nร้านอาหารในพื้นที่ลุมพินี เซ็นเตอร์ ลาดพร้าวมีการแข่งขันที่สูง แต่ด้วยประชากรที่หนาแน่นและการใช้จ่ายในหมวดอาหารที่สูง ยังมีโอกาสในการเติบโตสำหรับร้านอาหารที่สามารถสร้างความแตกต่างและตอบสนองความต้องการของลูกค้าได้อย่างมีประสิทธิภาพ\\n\\nFINAL ANSWER'"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def submitUserMessage(user_input: str) -> str:\n",
    "    graph = workflow.compile()\n",
    "\n",
    "    events = graph.stream(\n",
    "        {\n",
    "            \"messages\": [\n",
    "                HumanMessage(\n",
    "                    question\n",
    "                )\n",
    "            ],\n",
    "        },\n",
    "        # Maximum number of steps to take in the graph\n",
    "        {\"recursion_limit\": 20},\n",
    "    )\n",
    "    \n",
    "    events = [e for e in events]\n",
    "    \n",
    "    response = list(events[-1].values())[0][\"messages\"][0]\n",
    "    response = response.content\n",
    "    response = response.replace(\"FINAL ANSWER: \", \"\")\n",
    "    \n",
    "    return response\n",
    "\n",
    "\n",
    "# question = \"วิเคราะห์ร้านอาหารแถวลุมพินี เซ็นเตอร์ ลาดพร้าว\"\n",
    "# submitUserMessage(question)"
   ]
  }
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