Upload 17 files

chatbot_multiagent.ipynb

@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -15,20 +15,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 8,
    "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"
-     ]
-    }
-   ],
+   "outputs": [],
+   "source": [
+    "from langchain.globals import set_debug, set_verbose\n",
+    "\n",
+    "set_verbose(True)\n",
+    "set_debug(False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "from langchain_core.messages import HumanMessage\n",
     "import operator\n",
@@ -36,6 +37,7 @@
     "\n",
     "# for llm model\n",
     "from langchain_openai import ChatOpenAI\n",
+    "# from langchain_community.chat_models 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",
@@ -43,7 +45,6 @@
     "    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",
@@ -67,6 +68,8 @@
     "def format_docs(docs):\n",
     "    return \"\\n\\n\".join(doc.page_content for doc in docs)\n",
     "\n",
+    "\n",
+    "## Document csv\n",
     "# Specify the pattern\n",
     "file_pattern = \"document/*.csv\"\n",
     "file_paths = tuple(glob.glob(file_pattern))\n",
@@ -88,8 +91,8 @@
     "# Retrieve and generate using the relevant snippets of the blog.\n",
     "retriever = vectorstore.as_retriever()\n",
     "\n",
-    "## tools and LLM\n",
     "\n",
+    "## tools and LLM\n",
     "retriever_tool = Tool(\n",
     "    name=\"population, community and household expenditures data\",\n",
     "    func=retriever.get_relevant_documents,\n",
@@ -97,7 +100,8 @@
     ")\n",
     "\n",
     "# Bind the tools to the model\n",
-    "tools = [retriever_tool, find_place_from_text, nearby_search]  # Include both tools if needed\n",
+    "# tools = [retriever_tool, find_place_from_text, nearby_search]  # Include both tools if needed\n",
+    "tools = [find_place_from_text, nearby_search]\n",
     "\n",
     "llm = ChatOpenAI(model=\"gpt-4o-mini\", temperature=0.0)\n",
     "\n",
@@ -123,7 +127,7 @@
     "    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",
+    "    agent = prompt | llm_with_tools\n",
     "    return agent\n",
     "\n",
     "\n",
@@ -225,8 +229,12 @@
     "    # 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",
+    "    lambda x: x[\"sender\"],\n",
+    "    {\n",
+    "        \"data collector\":\"data collector\",\n",
+    "        \"analyst\":\"analyst\",\n",
+    "        \"reporter\":\"reporter\",\n",
+    "        },\n",
     ")\n",
     "workflow.add_edge(START, \"analyst\")\n",
     "graph = workflow.compile()"
@@ -234,7 +242,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -249,83 +257,57 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "==================================\u001b[1m Ai Message \u001b[0m==================================\n",
-      "Name: analyst\n",
-      "\n",
-      "เพื่อวิเค��าะห์การเปิดร้านกาแฟใกล้มาบุญครอง ฉันจะต้องรวบรวมข้อมูลเกี่ยวกับพื้นที่นี้ รวมถึงข้อมูลประชากรและการใช้จ่ายของชุมชนในบริเวณนั้น\n",
-      "\n",
-      "ข้อมูลที่ต้องการคือ:\n",
-      "- สถานที่: มาบุญครอง\n",
-      "- ประเภทสถานที่: ร้านกาแฟ\n",
-      "\n",
-      "ฉันจะส่งข้อมูลนี้ไปยังผู้เก็บข้อมูลเพื่อรวบรวมข้อมูลที่เกี่ยวข้องต่อไป\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",
-      "First, I will find the location details for Maboonkrong.\n",
-      "==================================\u001b[1m Ai Message \u001b[0m==================================\n",
-      "Name: reporter\n",
-      "\n",
-      "Finding the location details for Maboonkrong.\n",
-      "==================================\u001b[1m Ai Message \u001b[0m==================================\n",
-      "Name: data collector\n",
-      "\n",
-      "I have found the location details for Maboonkrong. Now, I will proceed to search for nearby coffee shops and gather the required data.\n",
-      "==================================\u001b[1m Ai Message \u001b[0m==================================\n",
-      "Name: reporter\n",
-      "\n",
-      "Searching for nearby coffee shops around Maboonkrong.\n",
-      "==================================\u001b[1m Ai Message \u001b[0m==================================\n",
-      "Name: data collector\n",
-      "\n",
-      "I have gathered the following data regarding coffee shops near Maboonkrong:\n",
-      "\n",
-      "1. **Number of Competitors**: 5\n",
-      "2. **List of Competitors Nearby**:\n",
-      "   - **Coffee Shop A**\n",
-      "     - Address: 123 Maboonkrong Rd, Bangkok\n",
-      "     - Opening Hours: 8 AM - 10 PM\n",
-      "     - Rating: 4.5\n",
-      "   - **Coffee Shop B**\n",
-      "     - Address: 456 Maboonkrong Rd, Bangkok\n",
-      "     - Opening Hours: 7 AM - 9 PM\n",
-      "     - Rating: 4.0\n",
-      "   - **Coffee Shop C**\n",
-      "     - Address: 789 Maboonkrong Rd, Bangkok\n",
-      "     - Opening Hours: 9 AM - 11 PM\n",
-      "     - Rating: 4.2\n",
-      "   - **Coffee Shop D**\n",
-      "     - Address: 321 Maboonkrong Rd, Bangkok\n",
-      "     - Opening Hours: 8 AM - 8 PM\n",
-      "     - Rating: 4.3\n",
-      "   - **Coffee Shop E**\n",
-      "     - Address: 654 Maboonkrong Rd, Bangkok\n",
-      "     - Opening Hours: 10 AM - 10 PM\n",
-      "     - Rating: 4.1\n",
-      "\n",
-      "3. **Products Sold by Competitors**: Coffee, pastries, sandwiches, and snacks.\n",
-      "4. **Number of Population Nearby**: Approximately 50,000\n",
-      "5. **Community Type**: Urban\n",
-      "6. **Household Expenditures**: Average household expenditure on food and beverages is around $300 per month.\n",
-      "7. **Population Data**: The population density in the area is high, with a mix of residents and tourists.\n",
-      "\n",
-      "This data should provide a comprehensive overview for analyzing the potential of opening a coffee shop near Maboonkrong. \n",
-      "\n",
-      "FINAL ANSWER\n"
+      "*********name: analyst\n",
+      "{'analyst': {'messages': [AIMessage(content=\"To assist with the feasibility analysis for a bookstore near New York, I will extract the relevant information:\\n\\n- **Location**: New York\\n- **Keyword**: Bookstore\\n\\nI will now instruct the Data Collector to gather relevant data based on this input. \\n\\nLet's proceed with the data collection.\", additional_kwargs={'function_call': {'arguments': '{\"input_dict\":{\"keyword\":\"bookstore\",\"location_name\":\"New York\",\"radius\":5000,\"place_type\":\"bookstore\"}}', 'name': 'nearby_search'}, 'refusal': None}, response_metadata={'token_usage': {'completion_tokens': 98, 'prompt_tokens': 335, 'total_tokens': 433}, 'model_name': 'gpt-4o-mini-2024-07-18', 'system_fingerprint': 'fp_48196bc67a', 'finish_reason': 'function_call', 'logprobs': None}, name='analyst', id='run-f7896776-cbec-4359-93ca-71d0f7fedeb7-0', usage_metadata={'input_tokens': 335, 'output_tokens': 98, 'total_tokens': 433})], 'sender': 'analyst'}}\n",
+      "*********name: data collector\n",
+      "{'data collector': {'messages': [AIMessage(content='', additional_kwargs={'function_call': {'arguments': '{\"location\":\"New York\"}', 'name': 'find_place_from_text'}, 'refusal': None}, response_metadata={'token_usage': {'completion_tokens': 17, 'prompt_tokens': 592, 'total_tokens': 609}, 'model_name': 'gpt-4o-mini-2024-07-18', 'system_fingerprint': 'fp_48196bc67a', 'finish_reason': 'function_call', 'logprobs': None}, name='data collector', id='run-28ebbebf-faad-44a5-b972-29b6f8bc3455-0', usage_metadata={'input_tokens': 592, 'output_tokens': 17, 'total_tokens': 609})], 'sender': 'data collector'}}\n",
+      "*********name: reporter\n"
+     ]
+    },
+    {
+     "ename": "BadRequestError",
+     "evalue": "Error code: 400 - {'error': {'message': \"Invalid 'messages[3].name': string does not match pattern. Expected a string that matches the pattern '^[a-zA-Z0-9_-]+$'.\", 'type': 'invalid_request_error', 'param': 'messages[3].name', 'code': 'invalid_value'}}",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mBadRequestError\u001b[0m                           Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn[11], line 16\u001b[0m\n\u001b[1;32m      3\u001b[0m graph \u001b[38;5;241m=\u001b[39m workflow\u001b[38;5;241m.\u001b[39mcompile()\n\u001b[1;32m      5\u001b[0m events \u001b[38;5;241m=\u001b[39m graph\u001b[38;5;241m.\u001b[39mstream(\n\u001b[1;32m      6\u001b[0m     {\n\u001b[1;32m      7\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmessages\u001b[39m\u001b[38;5;124m\"\u001b[39m: [\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m     14\u001b[0m     {\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mrecursion_limit\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;241m20\u001b[39m},\n\u001b[1;32m     15\u001b[0m )\n\u001b[0;32m---> 16\u001b[0m \u001b[38;5;28;43;01mfor\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43ms\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01min\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mevents\u001b[49m\u001b[43m:\u001b[49m\n\u001b[1;32m     17\u001b[0m \u001b[43m    \u001b[49m\u001b[38;5;28;43mprint\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43ms\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     18\u001b[0m     \u001b[38;5;66;03m# a = list(s.items())[0]\u001b[39;00m\n\u001b[1;32m     19\u001b[0m     \u001b[38;5;66;03m# a[1]['messages'][0].pretty_print()\u001b[39;00m\n",
+      "File \u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/site-packages/langgraph/pregel/__init__.py:1018\u001b[0m, in \u001b[0;36mPregel.stream\u001b[0;34m(self, input, config, stream_mode, output_keys, interrupt_before, interrupt_after, debug)\u001b[0m\n\u001b[1;32m   1015\u001b[0m         \u001b[38;5;28;01mdel\u001b[39;00m fut, task\n\u001b[1;32m   1017\u001b[0m \u001b[38;5;66;03m# panic on failure or timeout\u001b[39;00m\n\u001b[0;32m-> 1018\u001b[0m \u001b[43m_panic_or_proceed\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdone\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43minflight\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mloop\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mstep\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1019\u001b[0m \u001b[38;5;66;03m# don't keep futures around in memory longer than needed\u001b[39;00m\n\u001b[1;32m   1020\u001b[0m \u001b[38;5;28;01mdel\u001b[39;00m done, inflight, futures\n",
+      "File \u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/site-packages/langgraph/pregel/__init__.py:1419\u001b[0m, in \u001b[0;36m_panic_or_proceed\u001b[0;34m(done, inflight, step, timeout_exc_cls)\u001b[0m\n\u001b[1;32m   1417\u001b[0m             inflight\u001b[38;5;241m.\u001b[39mpop()\u001b[38;5;241m.\u001b[39mcancel()\n\u001b[1;32m   1418\u001b[0m         \u001b[38;5;66;03m# raise the exception\u001b[39;00m\n\u001b[0;32m-> 1419\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m exc\n\u001b[1;32m   1421\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m inflight:\n\u001b[1;32m   1422\u001b[0m     \u001b[38;5;66;03m# if we got here means we timed out\u001b[39;00m\n\u001b[1;32m   1423\u001b[0m     \u001b[38;5;28;01mwhile\u001b[39;00m inflight:\n\u001b[1;32m   1424\u001b[0m         \u001b[38;5;66;03m# cancel all pending tasks\u001b[39;00m\n",
+      "File \u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/site-packages/langgraph/pregel/executor.py:60\u001b[0m, in \u001b[0;36mBackgroundExecutor.done\u001b[0;34m(self, task)\u001b[0m\n\u001b[1;32m     58\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mdone\u001b[39m(\u001b[38;5;28mself\u001b[39m, task: concurrent\u001b[38;5;241m.\u001b[39mfutures\u001b[38;5;241m.\u001b[39mFuture) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m     59\u001b[0m     \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m---> 60\u001b[0m         \u001b[43mtask\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mresult\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     61\u001b[0m     \u001b[38;5;28;01mexcept\u001b[39;00m GraphInterrupt:\n\u001b[1;32m     62\u001b[0m         \u001b[38;5;66;03m# This exception is an interruption signal, not an error\u001b[39;00m\n\u001b[1;32m     63\u001b[0m         \u001b[38;5;66;03m# so we don't want to re-raise it on exit\u001b[39;00m\n\u001b[1;32m     64\u001b[0m         \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtasks\u001b[38;5;241m.\u001b[39mpop(task)\n",
+      "File \u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/concurrent/futures/_base.py:449\u001b[0m, in \u001b[0;36mFuture.result\u001b[0;34m(self, timeout)\u001b[0m\n\u001b[1;32m    447\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m CancelledError()\n\u001b[1;32m    448\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_state \u001b[38;5;241m==\u001b[39m FINISHED:\n\u001b[0;32m--> 449\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m__get_result\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    451\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_condition\u001b[38;5;241m.\u001b[39mwait(timeout)\n\u001b[1;32m    453\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_state \u001b[38;5;129;01min\u001b[39;00m [CANCELLED, CANCELLED_AND_NOTIFIED]:\n",
+      "File \u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/concurrent/futures/_base.py:401\u001b[0m, in \u001b[0;36mFuture.__get_result\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    399\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_exception:\n\u001b[1;32m    400\u001b[0m     \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 401\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_exception\n\u001b[1;32m    402\u001b[0m     \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[1;32m    403\u001b[0m         \u001b[38;5;66;03m# Break a reference cycle with the exception in self._exception\u001b[39;00m\n\u001b[1;32m    404\u001b[0m         \u001b[38;5;28mself\u001b[39m \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n",
+      "File \u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/concurrent/futures/thread.py:58\u001b[0m, in \u001b[0;36m_WorkItem.run\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m     55\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m\n\u001b[1;32m     57\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m---> 58\u001b[0m     result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfn\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     59\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mBaseException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m exc:\n\u001b[1;32m     60\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfuture\u001b[38;5;241m.\u001b[39mset_exception(exc)\n",
+      "File \u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/site-packages/langgraph/pregel/retry.py:25\u001b[0m, in \u001b[0;36mrun_with_retry\u001b[0;34m(task, retry_policy)\u001b[0m\n\u001b[1;32m     23\u001b[0m task\u001b[38;5;241m.\u001b[39mwrites\u001b[38;5;241m.\u001b[39mclear()\n\u001b[1;32m     24\u001b[0m \u001b[38;5;66;03m# run the task\u001b[39;00m\n\u001b[0;32m---> 25\u001b[0m \u001b[43mtask\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mproc\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43minvoke\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtask\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43minput\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtask\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mconfig\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     26\u001b[0m \u001b[38;5;66;03m# if successful, end\u001b[39;00m\n\u001b[1;32m     27\u001b[0m \u001b[38;5;28;01mbreak\u001b[39;00m\n",
+      "File \u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/site-packages/langchain_core/runnables/base.py:2876\u001b[0m, in \u001b[0;36mRunnableSequence.invoke\u001b[0;34m(self, input, config, **kwargs)\u001b[0m\n\u001b[1;32m   2874\u001b[0m context\u001b[38;5;241m.\u001b[39mrun(_set_config_context, config)\n\u001b[1;32m   2875\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m i \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m0\u001b[39m:\n\u001b[0;32m-> 2876\u001b[0m     \u001b[38;5;28minput\u001b[39m \u001b[38;5;241m=\u001b[39m \u001b[43mcontext\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrun\u001b[49m\u001b[43m(\u001b[49m\u001b[43mstep\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43minvoke\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43minput\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mconfig\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   2877\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m   2878\u001b[0m     \u001b[38;5;28minput\u001b[39m \u001b[38;5;241m=\u001b[39m context\u001b[38;5;241m.\u001b[39mrun(step\u001b[38;5;241m.\u001b[39minvoke, \u001b[38;5;28minput\u001b[39m, config)\n",
+      "File \u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/site-packages/langgraph/utils.py:102\u001b[0m, in \u001b[0;36mRunnableCallable.invoke\u001b[0;34m(self, input, config, **kwargs)\u001b[0m\n\u001b[1;32m    100\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m accepts_config(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfunc):\n\u001b[1;32m    101\u001b[0m         kwargs[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mconfig\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m config\n\u001b[0;32m--> 102\u001b[0m     ret \u001b[38;5;241m=\u001b[39m \u001b[43mcontext\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrun\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfunc\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43minput\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    103\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(ret, Runnable) \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mrecurse:\n\u001b[1;32m    104\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m ret\u001b[38;5;241m.\u001b[39minvoke(\u001b[38;5;28minput\u001b[39m, config)\n",
+      "Cell \u001b[0;32mIn[9], line 112\u001b[0m, in \u001b[0;36magent_node\u001b[0;34m(state, agent, name)\u001b[0m\n\u001b[1;32m    110\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21magent_node\u001b[39m(state, agent, name):\n\u001b[1;32m    111\u001b[0m     \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m*********name: \u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;241m+\u001b[39m name)\n\u001b[0;32m--> 112\u001b[0m     result \u001b[38;5;241m=\u001b[39m \u001b[43magent\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43minvoke\u001b[49m\u001b[43m(\u001b[49m\u001b[43mstate\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    113\u001b[0m     \u001b[38;5;66;03m# We convert the agent output into a format that is suitable to append to the global state\u001b[39;00m\n\u001b[1;32m    114\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(result, ToolMessage):\n",
+      "File \u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/site-packages/langchain_core/runnables/base.py:2878\u001b[0m, in \u001b[0;36mRunnableSequence.invoke\u001b[0;34m(self, input, config, **kwargs)\u001b[0m\n\u001b[1;32m   2876\u001b[0m             \u001b[38;5;28minput\u001b[39m \u001b[38;5;241m=\u001b[39m context\u001b[38;5;241m.\u001b[39mrun(step\u001b[38;5;241m.\u001b[39minvoke, \u001b[38;5;28minput\u001b[39m, config, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[1;32m   2877\u001b[0m         \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m-> 2878\u001b[0m             \u001b[38;5;28minput\u001b[39m \u001b[38;5;241m=\u001b[39m context\u001b[38;5;241m.\u001b[39mrun(step\u001b[38;5;241m.\u001b[39minvoke, \u001b[38;5;28minput\u001b[39m, config)\n\u001b[1;32m   2879\u001b[0m \u001b[38;5;66;03m# finish the root run\u001b[39;00m\n\u001b[1;32m   2880\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mBaseException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n",
+      "File \u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/site-packages/langchain_core/runnables/base.py:5094\u001b[0m, in \u001b[0;36mRunnableBindingBase.invoke\u001b[0;34m(self, input, config, **kwargs)\u001b[0m\n\u001b[1;32m   5088\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21minvoke\u001b[39m(\n\u001b[1;32m   5089\u001b[0m     \u001b[38;5;28mself\u001b[39m,\n\u001b[1;32m   5090\u001b[0m     \u001b[38;5;28minput\u001b[39m: Input,\n\u001b[1;32m   5091\u001b[0m     config: Optional[RunnableConfig] \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m,\n\u001b[1;32m   5092\u001b[0m     \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs: Optional[Any],\n\u001b[1;32m   5093\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m Output:\n\u001b[0;32m-> 5094\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mbound\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43minvoke\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m   5095\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;28;43minput\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m   5096\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_merge_configs\u001b[49m\u001b[43m(\u001b[49m\u001b[43mconfig\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   5097\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43m{\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m}\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   5098\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/site-packages/langchain_core/language_models/chat_models.py:276\u001b[0m, in \u001b[0;36mBaseChatModel.invoke\u001b[0;34m(self, input, config, stop, **kwargs)\u001b[0m\n\u001b[1;32m    265\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21minvoke\u001b[39m(\n\u001b[1;32m    266\u001b[0m     \u001b[38;5;28mself\u001b[39m,\n\u001b[1;32m    267\u001b[0m     \u001b[38;5;28minput\u001b[39m: LanguageModelInput,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    271\u001b[0m     \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs: Any,\n\u001b[1;32m    272\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m BaseMessage:\n\u001b[1;32m    273\u001b[0m     config \u001b[38;5;241m=\u001b[39m ensure_config(config)\n\u001b[1;32m    274\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m cast(\n\u001b[1;32m    275\u001b[0m         ChatGeneration,\n\u001b[0;32m--> 276\u001b[0m         \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgenerate_prompt\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    277\u001b[0m \u001b[43m            \u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_convert_input\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43minput\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    278\u001b[0m \u001b[43m            \u001b[49m\u001b[43mstop\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mstop\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    279\u001b[0m \u001b[43m            \u001b[49m\u001b[43mcallbacks\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mconfig\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mcallbacks\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    280\u001b[0m \u001b[43m            \u001b[49m\u001b[43mtags\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mconfig\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mtags\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    281\u001b[0m \u001b[43m            \u001b[49m\u001b[43mmetadata\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mconfig\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mmetadata\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    282\u001b[0m \u001b[43m            \u001b[49m\u001b[43mrun_name\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mconfig\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mrun_name\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    283\u001b[0m \u001b[43m            \u001b[49m\u001b[43mrun_id\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mconfig\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mpop\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mrun_id\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    284\u001b[0m \u001b[43m            \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    285\u001b[0m \u001b[43m        \u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241m.\u001b[39mgenerations[\u001b[38;5;241m0\u001b[39m][\u001b[38;5;241m0\u001b[39m],\n\u001b[1;32m    286\u001b[0m     )\u001b[38;5;241m.\u001b[39mmessage\n",
+      "File \u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/site-packages/langchain_core/language_models/chat_models.py:776\u001b[0m, in \u001b[0;36mBaseChatModel.generate_prompt\u001b[0;34m(self, prompts, stop, callbacks, **kwargs)\u001b[0m\n\u001b[1;32m    768\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mgenerate_prompt\u001b[39m(\n\u001b[1;32m    769\u001b[0m     \u001b[38;5;28mself\u001b[39m,\n\u001b[1;32m    770\u001b[0m     prompts: List[PromptValue],\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    773\u001b[0m     \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs: Any,\n\u001b[1;32m    774\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m LLMResult:\n\u001b[1;32m    775\u001b[0m     prompt_messages \u001b[38;5;241m=\u001b[39m [p\u001b[38;5;241m.\u001b[39mto_messages() \u001b[38;5;28;01mfor\u001b[39;00m p \u001b[38;5;129;01min\u001b[39;00m prompts]\n\u001b[0;32m--> 776\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgenerate\u001b[49m\u001b[43m(\u001b[49m\u001b[43mprompt_messages\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mstop\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mstop\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcallbacks\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcallbacks\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/site-packages/langchain_core/language_models/chat_models.py:633\u001b[0m, in \u001b[0;36mBaseChatModel.generate\u001b[0;34m(self, messages, stop, callbacks, tags, metadata, run_name, run_id, **kwargs)\u001b[0m\n\u001b[1;32m    631\u001b[0m         \u001b[38;5;28;01mif\u001b[39;00m run_managers:\n\u001b[1;32m    632\u001b[0m             run_managers[i]\u001b[38;5;241m.\u001b[39mon_llm_error(e, response\u001b[38;5;241m=\u001b[39mLLMResult(generations\u001b[38;5;241m=\u001b[39m[]))\n\u001b[0;32m--> 633\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m e\n\u001b[1;32m    634\u001b[0m flattened_outputs \u001b[38;5;241m=\u001b[39m [\n\u001b[1;32m    635\u001b[0m     LLMResult(generations\u001b[38;5;241m=\u001b[39m[res\u001b[38;5;241m.\u001b[39mgenerations], llm_output\u001b[38;5;241m=\u001b[39mres\u001b[38;5;241m.\u001b[39mllm_output)  \u001b[38;5;66;03m# type: ignore[list-item]\u001b[39;00m\n\u001b[1;32m    636\u001b[0m     \u001b[38;5;28;01mfor\u001b[39;00m res \u001b[38;5;129;01min\u001b[39;00m results\n\u001b[1;32m    637\u001b[0m ]\n\u001b[1;32m    638\u001b[0m llm_output \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_combine_llm_outputs([res\u001b[38;5;241m.\u001b[39mllm_output \u001b[38;5;28;01mfor\u001b[39;00m res \u001b[38;5;129;01min\u001b[39;00m results])\n",
+      "File \u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/site-packages/langchain_core/language_models/chat_models.py:623\u001b[0m, in \u001b[0;36mBaseChatModel.generate\u001b[0;34m(self, messages, stop, callbacks, tags, metadata, run_name, run_id, **kwargs)\u001b[0m\n\u001b[1;32m    620\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m i, m \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28menumerate\u001b[39m(messages):\n\u001b[1;32m    621\u001b[0m     \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m    622\u001b[0m         results\u001b[38;5;241m.\u001b[39mappend(\n\u001b[0;32m--> 623\u001b[0m             \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_generate_with_cache\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    624\u001b[0m \u001b[43m                \u001b[49m\u001b[43mm\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    625\u001b[0m \u001b[43m                \u001b[49m\u001b[43mstop\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mstop\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    626\u001b[0m \u001b[43m                \u001b[49m\u001b[43mrun_manager\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mrun_managers\u001b[49m\u001b[43m[\u001b[49m\u001b[43mi\u001b[49m\u001b[43m]\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mif\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mrun_managers\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01melse\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[1;32m    627\u001b[0m \u001b[43m                \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    628\u001b[0m \u001b[43m            \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    629\u001b[0m         )\n\u001b[1;32m    630\u001b[0m     \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mBaseException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[1;32m    631\u001b[0m         \u001b[38;5;28;01mif\u001b[39;00m run_managers:\n",
+      "File \u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/site-packages/langchain_core/language_models/chat_models.py:845\u001b[0m, in \u001b[0;36mBaseChatModel._generate_with_cache\u001b[0;34m(self, messages, stop, run_manager, **kwargs)\u001b[0m\n\u001b[1;32m    843\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m    844\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m inspect\u001b[38;5;241m.\u001b[39msignature(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_generate)\u001b[38;5;241m.\u001b[39mparameters\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mrun_manager\u001b[39m\u001b[38;5;124m\"\u001b[39m):\n\u001b[0;32m--> 845\u001b[0m         result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_generate\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    846\u001b[0m \u001b[43m            \u001b[49m\u001b[43mmessages\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mstop\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mstop\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrun_manager\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mrun_manager\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\n\u001b[1;32m    847\u001b[0m \u001b[43m        \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    848\u001b[0m     \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m    849\u001b[0m         result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_generate(messages, stop\u001b[38;5;241m=\u001b[39mstop, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n",
+      "File \u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/site-packages/langchain_openai/chat_models/base.py:635\u001b[0m, in \u001b[0;36mBaseChatOpenAI._generate\u001b[0;34m(self, messages, stop, run_manager, **kwargs)\u001b[0m\n\u001b[1;32m    633\u001b[0m     generation_info \u001b[38;5;241m=\u001b[39m {\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mheaders\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;28mdict\u001b[39m(raw_response\u001b[38;5;241m.\u001b[39mheaders)}\n\u001b[1;32m    634\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m--> 635\u001b[0m     response \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mclient\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcreate\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mpayload\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    636\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_create_chat_result(response, generation_info)\n",
+      "File \u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/site-packages/openai/_utils/_utils.py:274\u001b[0m, in \u001b[0;36mrequired_args.<locals>.inner.<locals>.wrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    272\u001b[0m             msg \u001b[38;5;241m=\u001b[39m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mMissing required argument: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mquote(missing[\u001b[38;5;241m0\u001b[39m])\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    273\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m(msg)\n\u001b[0;32m--> 274\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/site-packages/openai/resources/chat/completions.py:668\u001b[0m, in \u001b[0;36mCompletions.create\u001b[0;34m(self, messages, model, frequency_penalty, function_call, functions, logit_bias, logprobs, max_tokens, n, parallel_tool_calls, presence_penalty, response_format, seed, service_tier, stop, stream, stream_options, temperature, tool_choice, tools, top_logprobs, top_p, user, extra_headers, extra_query, extra_body, timeout)\u001b[0m\n\u001b[1;32m    633\u001b[0m \u001b[38;5;129m@required_args\u001b[39m([\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmessages\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmodel\u001b[39m\u001b[38;5;124m\"\u001b[39m], [\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmessages\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmodel\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mstream\u001b[39m\u001b[38;5;124m\"\u001b[39m])\n\u001b[1;32m    634\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mcreate\u001b[39m(\n\u001b[1;32m    635\u001b[0m     \u001b[38;5;28mself\u001b[39m,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    665\u001b[0m     timeout: \u001b[38;5;28mfloat\u001b[39m \u001b[38;5;241m|\u001b[39m httpx\u001b[38;5;241m.\u001b[39mTimeout \u001b[38;5;241m|\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;241m|\u001b[39m NotGiven \u001b[38;5;241m=\u001b[39m NOT_GIVEN,\n\u001b[1;32m    666\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m ChatCompletion \u001b[38;5;241m|\u001b[39m Stream[ChatCompletionChunk]:\n\u001b[1;32m    667\u001b[0m     validate_response_format(response_format)\n\u001b[0;32m--> 668\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_post\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    669\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43m/chat/completions\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m    670\u001b[0m \u001b[43m        \u001b[49m\u001b[43mbody\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmaybe_transform\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    671\u001b[0m \u001b[43m            \u001b[49m\u001b[43m{\u001b[49m\n\u001b[1;32m    672\u001b[0m \u001b[43m                \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mmessages\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mmessages\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    673\u001b[0m \u001b[43m                \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mmodel\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mmodel\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    674\u001b[0m \u001b[43m                \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mfrequency_penalty\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mfrequency_penalty\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    675\u001b[0m \u001b[43m                \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mfunction_call\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mfunction_call\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    676\u001b[0m \u001b[43m                \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mfunctions\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mfunctions\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    677\u001b[0m \u001b[43m                \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mlogit_bias\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mlogit_bias\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    678\u001b[0m \u001b[43m                \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mlogprobs\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mlogprobs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    679\u001b[0m \u001b[43m                \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mmax_tokens\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mmax_tokens\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    680\u001b[0m \u001b[43m                \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mn\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mn\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    681\u001b[0m \u001b[43m                \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mparallel_tool_calls\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mparallel_tool_calls\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    682\u001b[0m \u001b[43m                \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mpresence_penalty\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mpresence_penalty\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    683\u001b[0m \u001b[43m                \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mresponse_format\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mresponse_format\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    684\u001b[0m \u001b[43m                \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mseed\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mseed\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    685\u001b[0m \u001b[43m                \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mservice_tier\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mservice_tier\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    686\u001b[0m \u001b[43m                \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mstop\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mstop\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    687\u001b[0m \u001b[43m                \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mstream\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mstream\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    688\u001b[0m \u001b[43m                \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mstream_options\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mstream_options\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    689\u001b[0m \u001b[43m                \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mtemperature\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mtemperature\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    690\u001b[0m \u001b[43m                \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mtool_choice\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mtool_choice\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    691\u001b[0m \u001b[43m                \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mtools\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mtools\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    692\u001b[0m \u001b[43m                \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mtop_logprobs\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mtop_logprobs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    693\u001b[0m \u001b[43m                \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mtop_p\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mtop_p\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    694\u001b[0m \u001b[43m                \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43muser\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43muser\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    695\u001b[0m \u001b[43m            \u001b[49m\u001b[43m}\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    696\u001b[0m \u001b[43m            \u001b[49m\u001b[43mcompletion_create_params\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mCompletionCreateParams\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    697\u001b[0m \u001b[43m        \u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    698\u001b[0m \u001b[43m        \u001b[49m\u001b[43moptions\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmake_request_options\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    699\u001b[0m \u001b[43m            \u001b[49m\u001b[43mextra_headers\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mextra_headers\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mextra_query\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mextra_query\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mextra_body\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mextra_body\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtimeout\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtimeout\u001b[49m\n\u001b[1;32m    700\u001b[0m \u001b[43m        \u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    701\u001b[0m \u001b[43m        \u001b[49m\u001b[43mcast_to\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mChatCompletion\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    702\u001b[0m \u001b[43m        \u001b[49m\u001b[43mstream\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mstream\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01mor\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[1;32m    703\u001b[0m \u001b[43m        \u001b[49m\u001b[43mstream_cls\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mStream\u001b[49m\u001b[43m[\u001b[49m\u001b[43mChatCompletionChunk\u001b[49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    704\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/site-packages/openai/_base_client.py:1260\u001b[0m, in \u001b[0;36mSyncAPIClient.post\u001b[0;34m(self, path, cast_to, body, options, files, stream, stream_cls)\u001b[0m\n\u001b[1;32m   1246\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mpost\u001b[39m(\n\u001b[1;32m   1247\u001b[0m     \u001b[38;5;28mself\u001b[39m,\n\u001b[1;32m   1248\u001b[0m     path: \u001b[38;5;28mstr\u001b[39m,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m   1255\u001b[0m     stream_cls: \u001b[38;5;28mtype\u001b[39m[_StreamT] \u001b[38;5;241m|\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m,\n\u001b[1;32m   1256\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m ResponseT \u001b[38;5;241m|\u001b[39m _StreamT:\n\u001b[1;32m   1257\u001b[0m     opts \u001b[38;5;241m=\u001b[39m FinalRequestOptions\u001b[38;5;241m.\u001b[39mconstruct(\n\u001b[1;32m   1258\u001b[0m         method\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mpost\u001b[39m\u001b[38;5;124m\"\u001b[39m, url\u001b[38;5;241m=\u001b[39mpath, json_data\u001b[38;5;241m=\u001b[39mbody, files\u001b[38;5;241m=\u001b[39mto_httpx_files(files), \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39moptions\n\u001b[1;32m   1259\u001b[0m     )\n\u001b[0;32m-> 1260\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m cast(ResponseT, \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrequest\u001b[49m\u001b[43m(\u001b[49m\u001b[43mcast_to\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mopts\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mstream\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mstream\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mstream_cls\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mstream_cls\u001b[49m\u001b[43m)\u001b[49m)\n",
+      "File \u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/site-packages/openai/_base_client.py:937\u001b[0m, in \u001b[0;36mSyncAPIClient.request\u001b[0;34m(self, cast_to, options, remaining_retries, stream, stream_cls)\u001b[0m\n\u001b[1;32m    928\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mrequest\u001b[39m(\n\u001b[1;32m    929\u001b[0m     \u001b[38;5;28mself\u001b[39m,\n\u001b[1;32m    930\u001b[0m     cast_to: Type[ResponseT],\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    935\u001b[0m     stream_cls: \u001b[38;5;28mtype\u001b[39m[_StreamT] \u001b[38;5;241m|\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m,\n\u001b[1;32m    936\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m ResponseT \u001b[38;5;241m|\u001b[39m _StreamT:\n\u001b[0;32m--> 937\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_request\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    938\u001b[0m \u001b[43m        \u001b[49m\u001b[43mcast_to\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcast_to\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    939\u001b[0m \u001b[43m        \u001b[49m\u001b[43moptions\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43moptions\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    940\u001b[0m \u001b[43m        \u001b[49m\u001b[43mstream\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mstream\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    941\u001b[0m \u001b[43m        \u001b[49m\u001b[43mstream_cls\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mstream_cls\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    942\u001b[0m \u001b[43m        \u001b[49m\u001b[43mremaining_retries\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mremaining_retries\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    943\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/site-packages/openai/_base_client.py:1041\u001b[0m, in \u001b[0;36mSyncAPIClient._request\u001b[0;34m(self, cast_to, options, remaining_retries, stream, stream_cls)\u001b[0m\n\u001b[1;32m   1038\u001b[0m         err\u001b[38;5;241m.\u001b[39mresponse\u001b[38;5;241m.\u001b[39mread()\n\u001b[1;32m   1040\u001b[0m     log\u001b[38;5;241m.\u001b[39mdebug(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mRe-raising status error\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m-> 1041\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_make_status_error_from_response(err\u001b[38;5;241m.\u001b[39mresponse) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m   1043\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_process_response(\n\u001b[1;32m   1044\u001b[0m     cast_to\u001b[38;5;241m=\u001b[39mcast_to,\n\u001b[1;32m   1045\u001b[0m     options\u001b[38;5;241m=\u001b[39moptions,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m   1049\u001b[0m     retries_taken\u001b[38;5;241m=\u001b[39moptions\u001b[38;5;241m.\u001b[39mget_max_retries(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mmax_retries) \u001b[38;5;241m-\u001b[39m retries,\n\u001b[1;32m   1050\u001b[0m )\n",
+      "\u001b[0;31mBadRequestError\u001b[0m: Error code: 400 - {'error': {'message': \"Invalid 'messages[3].name': string does not match pattern. Expected a string that matches the pattern '^[a-zA-Z0-9_-]+$'.\", 'type': 'invalid_request_error', 'param': 'messages[3].name', 'code': 'invalid_value'}}"
      ]
     }
    ],
    "source": [
-    "question = \"วิเคราะห์การเปิดร้านกาแฟใกล้มาบุญครอง\"\n",
+    "question = \"Feasibility analysis for a bookstore near New york\"\n",
     "\n",
     "graph = workflow.compile()\n",
     "\n",
@@ -341,13 +323,14 @@
     "    {\"recursion_limit\": 20},\n",
     ")\n",
     "for s in events:\n",
-    "    a = list(s.items())[0]\n",
-    "    a[1]['messages'][0].pretty_print()"
+    "    print(s)\n",
+    "    # a = list(s.items())[0]\n",
+    "    # a[1]['messages'][0].pretty_print()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [

chatbot_multiagent.py

@@ -5,6 +5,12 @@ import utils
 utils.load_env()
 os.environ['LANGCHAIN_TRACING_V2'] = "false"
 
+# %%
+from langchain.globals import set_debug, set_verbose
+
+set_verbose(True)
+set_debug(False)
+
 # %%
 from langchain_core.messages import HumanMessage
 import operator
@@ -12,6 +18,7 @@ import functools
 
 # for llm model
 from langchain_openai import ChatOpenAI
+# from langchain_community.chat_models import ChatOpenAI
 from langchain.agents.format_scratchpad import format_to_openai_function_messages
 from tools import find_place_from_text, nearby_search
 from typing import Dict, List, Tuple, Annotated, Sequence, TypedDict
@@ -19,7 +26,6 @@ from langchain.agents import (
     AgentExecutor,
 )
 from langchain.agents.output_parsers import OpenAIFunctionsAgentOutputParser
-from langchain_community.chat_models import ChatOpenAI
 from langchain_community.tools.convert_to_openai import format_tool_to_openai_function
 from langchain_core.messages import (
     AIMessage, 
@@ -43,6 +49,8 @@ from langchain.tools import Tool
 def format_docs(docs):
     return "\n\n".join(doc.page_content for doc in docs)
 
+
+## Document csv
 # Specify the pattern
 file_pattern = "document/*.csv"
 file_paths = tuple(glob.glob(file_pattern))
@@ -64,8 +72,8 @@ vectorstore = Chroma.from_documents(documents=splits, embedding=OpenAIEmbeddings
 # Retrieve and generate using the relevant snippets of the blog.
 retriever = vectorstore.as_retriever()
 
-## tools and LLM
 
+## tools and LLM
 retriever_tool = Tool(
     name="population, community and household expenditures data",
     func=retriever.get_relevant_documents,
@@ -73,7 +81,8 @@ retriever_tool = Tool(
 )
 
 # Bind the tools to the model
-tools = [retriever_tool, find_place_from_text, nearby_search]  # Include both tools if needed
+# tools = [retriever_tool, find_place_from_text, nearby_search]  # Include both tools if needed
+tools = [find_place_from_text, nearby_search]
 
 llm = ChatOpenAI(model="gpt-4o-mini", temperature=0.0)
 
@@ -99,7 +108,7 @@ def create_agent(llm, tools, system_message: str):
     prompt = prompt.partial(tool_names=", ".join([tool.name for tool in tools]))
     llm_with_tools = llm.bind(functions=[format_tool_to_openai_function(t) for t in tools])
     # return prompt | llm.bind_tools(tools)
-    agent = prompt | llm
+    agent = prompt | llm_with_tools
     return agent
 
 
@@ -201,8 +210,12 @@ workflow.add_conditional_edges(
     # the tool calling node does not, meaning
     # this edge will route back to the original agent
     # who invoked the tool
-    lambda x: "data collector",
-    {"data collector":"data collector"},
+    lambda x: x["sender"],
+    {
+        "data collector":"data collector",
+        "analyst":"analyst",
+        "reporter":"reporter",
+        },
 )
 workflow.add_edge(START, "analyst")
 graph = workflow.compile()
@@ -217,24 +230,7 @@ graph = workflow.compile()
 #     pass
 
 # %%
-# question = "วิเคราะห์ร้านอาหารแถวลุมพินี เซ็นเตอร์ ลาดพร้าว"
 
-# graph = workflow.compile()
-
-# events = graph.stream(
-#     {
-#         "messages": [
-#             HumanMessage(
-#                 question
-#             )
-#         ],
-#     },
-#     # Maximum number of steps to take in the graph
-#     {"recursion_limit": 20},
-# )
-# for s in events:
-#     a = list(s.items())[0]
-#     a[1]['messages'][0].pretty_print()
 
 # %%
 def submitUserMessage(user_input: str) -> str:
@@ -244,17 +240,19 @@ def submitUserMessage(user_input: str) -> str:
         {
             "messages": [
                 HumanMessage(
-                    user_input
+                    question
                 )
             ],
         },
         # Maximum number of steps to take in the graph
-        {"recursion_limit": 40},
+        {"recursion_limit": 20},
     )
-    for s in events:
-        a = list(s.items())[0]
-
-    response = a[1]['messages'][0].content.replace("FINAL ANSWER", "")
+    
+    events = [e for e in events]
+    
+    response = list(events[-1].values())[0]["messages"][0]
+    response = response.content
+    response = response.replace("FINAL ANSWER: ", "")
     
     return response
 

prompt.py

@@ -3,6 +3,7 @@ agent_meta = [
         "name": "analyst",
         "prompt": """
         You are the Analyst responsible for extracting key information from the user and guiding the data collection process. When the user asks about analyzing a location for a business opportunity, you will:
+        Ensure that all communications and data are handled in English
         - Extract the location the user wants to analyze and the keyword representing the type of place (e.g., “shop,” “coffee shop,” which represents the competitor).
         - Communicate this information clearly to the Data Collector, instructing them to gather relevant data based on the user’s input.
         """
@@ -22,7 +23,7 @@ agent_meta = [
 	    -	Population data.
 	    - The tools at your disposal include:
 	    1.	Population, Community, and Household Expenditures Data: Contains community type by district, household expenditures by province, and population data by district.
-	    2.	find_place_from_text: Provides address (district, province), geometric location, and name of the place.
+	    2.	find_place_from_text: Provides address (district, province), and name of the place.
 	    3.	nearby_search: Provides a list of competitors nearby according to the keyword, including address, location, name, opening hours, rating, and plus code.
 	    - After collecting the data, send it to the Reporter. Ensure that all communications and data are handled in English.
         """
@@ -31,12 +32,11 @@ agent_meta = [
         "name": "reporter",
         "prompt": """
         You are the Reporter responsible for feasibility analysis . You role is to compiling the data into a clear and informative report for the user. When you receive the data from the Data Collector, you will:
-        
+        Ensure that all communications and data are handled in English 
 	    -	Organize and analyze the data to generate insights about the competitive landscape and market opportunities at the specified location.
 	    -	Ensure that your report includes both numerical data (such as the number of competitors, population figures, and household expenditures) and analytical insights (such as market opportunities and recommendations).
 	    -	If the Data Collector is unable to find certain data(or not povide data anymore), you will still provide a final answer based on the available information.
 	    -	Create a well-structured report that provides the user with actionable recommendations based on the analysis.
-	    -	Ensure the report is clear, concise, and delivered in Thai language if it is the final answer.
         -   Don't forget to give Descriptive anlytical summary at last.
         """
     }

tools.py

@@ -17,7 +17,7 @@ def find_place_from_text(location:str):
     return f"""
     address: {r['formatted_address']}\n
     location: {r['geometry']['location']}\n
-    name: {r['name']}\n
+    location_name: {r['name']}\n
     """
     
 # def nearby_search(keyword:str, location:str, radius=2000, place_type=None):
@@ -64,7 +64,7 @@ def nearby_search(input_dict: NearbySearchInput):
         strout += f"""
         address: {address}\n
         location: {location_info}\n
-        name: {name}\n
+        lacation_name: {name}\n
         opening hours: {opening_hours}\n
         rating: {rating}\n
         plus code: {plus_code}\n\n