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market-feasibility-analysis-chatbot
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1 Parent(s): 391ea44

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Files changed (3) hide show
  1. chatbot_multiagent.ipynb +38 -77
  2. chatbot_multiagent.py +24 -22
  3. prompt.json +3 -6
chatbot_multiagent.ipynb CHANGED
@@ -2,7 +2,7 @@
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  "cells": [
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  {
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  "cell_type": "code",
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- "execution_count": 1,
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  "metadata": {},
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  "outputs": [],
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  "source": [
@@ -15,20 +15,9 @@
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  },
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  {
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  "cell_type": "code",
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- "execution_count": 2,
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  "metadata": {},
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- "outputs": [
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- {
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- "name": "stderr",
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- "output_type": "stream",
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- "text": [
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- "/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",
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- " warn_deprecated(\n",
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- "/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",
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- " warn_deprecated(\n"
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- ]
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- }
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- ],
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  "source": [
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  "from langchain_core.messages import HumanMessage\n",
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  "import operator\n",
@@ -123,7 +112,7 @@
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  " prompt = prompt.partial(tool_names=\", \".join([tool.name for tool in tools]))\n",
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  " llm_with_tools = llm.bind(functions=[format_tool_to_openai_function(t) for t in tools])\n",
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  " # return prompt | llm.bind_tools(tools)\n",
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- " agent = prompt | llm_with_tools\n",
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  " return agent\n",
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  "\n",
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  "\n",
@@ -165,7 +154,7 @@
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  " \n",
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  " agents[name] = create_agent(\n",
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  " llm,\n",
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- " [find_place_from_text, nearby_search],\n",
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  " system_message=prompt,\n",
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  " )\n",
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  " \n",
@@ -201,12 +190,23 @@
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  "workflow.add_node(\"call_tool\", tool_node)\n",
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  "\n",
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  "\n",
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- "for meta in agent_meta:\n",
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- " workflow.add_conditional_edges(\n",
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- " meta[\"name\"],\n",
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- " router,\n",
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- " {\"continue\": meta['continue'], \"call_tool\": \"call_tool\", \"__end__\": END} if meta[\"name\"]==\"data collector\" else {\"continue\": meta['continue'], \"__end__\": END},\n",
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- " )\n",
 
 
 
 
 
 
 
 
 
 
 
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  "\n",
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  "workflow.add_conditional_edges(\n",
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  " \"call_tool\",\n",
@@ -223,12 +223,12 @@
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  },
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  {
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  "cell_type": "code",
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- "execution_count": 3,
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  "metadata": {},
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  "outputs": [
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  {
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  "data": {
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",
232
  "text/plain": [
233
  "<IPython.core.display.Image object>"
234
  ]
@@ -249,56 +249,28 @@
249
  },
250
  {
251
  "cell_type": "code",
252
- "execution_count": 9,
253
  "metadata": {},
254
  "outputs": [
255
  {
256
  "name": "stdout",
257
  "output_type": "stream",
258
  "text": [
259
- "{'analyst': {'messages': [AIMessage(content='', additional_kwargs={'function_call': {'arguments': '{\"location\":\"นวมินทร์ 71\"}', 'name': 'find_place_from_text'}}, response_metadata={'token_usage': {'completion_tokens': 22, 'prompt_tokens': 335, 'total_tokens': 357}, 'model_name': 'gpt-4o-mini', 'system_fingerprint': 'fp_48196bc67a', 'finish_reason': 'function_call', 'logprobs': None}, name='analyst', id='run-258145dd-989c-47e8-b808-9605a218a7c9-0')], 'sender': 'analyst'}}\n",
260
- "----\n",
261
- "{'data collector': {'messages': [AIMessage(content='', additional_kwargs={'function_call': {'arguments': '{\"location\":\"นวมินทร์ 71\"}', 'name': 'find_place_from_text'}}, response_metadata={'token_usage': {'completion_tokens': 22, 'prompt_tokens': 333, 'total_tokens': 355}, 'model_name': 'gpt-4o-mini', 'system_fingerprint': 'fp_507c9469a1', 'finish_reason': 'function_call', 'logprobs': None}, name='data collector', id='run-a6d8ab7d-108c-4e55-acb3-f0efddd3c08b-0')], 'sender': 'data collector'}}\n",
262
  "----\n",
263
- "{'reporter': {'messages': [AIMessage(content='', additional_kwargs={'function_call': {'arguments': '{\"input_dict\":{\"keyword\":\"ร้านอาหาร\",\"location_name\":\"นวมินทร์ 71\",\"radius\":500,\"place_type\":\"restaurant\"}}', 'name': 'nearby_search'}}, response_metadata={'token_usage': {'completion_tokens': 40, 'prompt_tokens': 408, 'total_tokens': 448}, 'model_name': 'gpt-4o-mini', 'system_fingerprint': 'fp_48196bc67a', 'finish_reason': 'function_call', 'logprobs': None}, name='reporter', id='run-dbbb13d7-b770-4f99-9117-05908d44103b-0')], 'sender': 'reporter'}}\n",
264
  "----\n",
265
- "{'data collector': {'messages': [AIMessage(content='', additional_kwargs={'function_call': {'arguments': '{\"input_dict\":{\"keyword\":\"restaurant\",\"location_name\":\"นวมินทร์ 71\",\"radius\":500,\"place_type\":\"restaurant\"}}', 'name': 'nearby_search'}}, response_metadata={'token_usage': {'completion_tokens': 39, 'prompt_tokens': 401, 'total_tokens': 440}, 'model_name': 'gpt-4o-mini', 'system_fingerprint': 'fp_48196bc67a', 'finish_reason': 'function_call', 'logprobs': None}, name='data collector', id='run-b3e48624-f692-4261-ad56-c01e0429afde-0')], 'sender': 'data collector'}}\n",
266
  "----\n",
267
- "{'reporter': {'messages': [AIMessage(content='', additional_kwargs={'function_call': {'arguments': '{\"input_dict\":{\"keyword\":\"restaurant\",\"location_name\":\"นวมินทร์ 71\",\"radius\":500,\"place_type\":\"restaurant\"}}', 'name': 'nearby_search'}}, response_metadata={'token_usage': {'completion_tokens': 39, 'prompt_tokens': 493, 'total_tokens': 532}, 'model_name': 'gpt-4o-mini', 'system_fingerprint': 'fp_48196bc67a', 'finish_reason': 'function_call', 'logprobs': None}, name='reporter', id='run-5fbf68b6-e2e0-42f2-b978-9f3cde178693-0')], 'sender': 'reporter'}}\n",
268
  "----\n",
269
- "{'data collector': {'messages': [AIMessage(content='', additional_kwargs={'function_call': {'arguments': '{\"input_dict\":{\"keyword\":\"restaurant\",\"location_name\":\"นวมินทร์ 71\",\"radius\":500,\"place_type\":\"restaurant\"}}', 'name': 'nearby_search'}}, response_metadata={'token_usage': {'completion_tokens': 39, 'prompt_tokens': 485, 'total_tokens': 524}, 'model_name': 'gpt-4o-mini', 'system_fingerprint': 'fp_48196bc67a', 'finish_reason': 'function_call', 'logprobs': None}, name='data collector', id='run-099fb223-8be9-48f5-bf6f-af3a697172e0-0')], 'sender': 'data collector'}}\n",
270
  "----\n",
271
- "{'reporter': {'messages': [AIMessage(content='', additional_kwargs={'function_call': {'arguments': '{\"input_dict\":{\"keyword\":\"restaurant\",\"location_name\":\"นวมินทร์ 71\",\"radius\":500,\"place_type\":\"restaurant\"}}', 'name': 'nearby_search'}}, response_metadata={'token_usage': {'completion_tokens': 39, 'prompt_tokens': 577, 'total_tokens': 616}, 'model_name': 'gpt-4o-mini', 'system_fingerprint': 'fp_48196bc67a', 'finish_reason': 'function_call', 'logprobs': None}, name='reporter', id='run-2a83ea1e-5777-48aa-82bc-57c601e29a4b-0')], 'sender': 'reporter'}}\n",
272
  "----\n",
273
- "{'data collector': {'messages': [AIMessage(content='', additional_kwargs={'function_call': {'arguments': '{\"input_dict\":{\"keyword\":\"restaurant\",\"location_name\":\"นวมินทร์ 71\",\"radius\":500,\"place_type\":\"restaurant\"}}', 'name': 'nearby_search'}}, response_metadata={'token_usage': {'completion_tokens': 39, 'prompt_tokens': 569, 'total_tokens': 608}, 'model_name': 'gpt-4o-mini', 'system_fingerprint': 'fp_48196bc67a', 'finish_reason': 'function_call', 'logprobs': None}, name='data collector', id='run-e836fa27-c007-4741-90f2-bd8acdf1cd01-0')], 'sender': 'data collector'}}\n",
274
- "----\n",
275
- "{'reporter': {'messages': [AIMessage(content='', additional_kwargs={'function_call': {'arguments': '{\"input_dict\":{\"keyword\":\"ร้านอาหาร\",\"location_name\":\"นวมินทร์ 71\",\"radius\":500,\"place_type\":\"restaurant\"}}', 'name': 'nearby_search'}}, response_metadata={'token_usage': {'completion_tokens': 40, 'prompt_tokens': 661, 'total_tokens': 701}, 'model_name': 'gpt-4o-mini', 'system_fingerprint': 'fp_48196bc67a', 'finish_reason': 'function_call', 'logprobs': None}, name='reporter', id='run-ae137b63-1be4-4859-9f9f-4315632b99e7-0')], 'sender': 'reporter'}}\n",
276
- "----\n",
277
- "{'data collector': {'messages': [AIMessage(content='', additional_kwargs={'function_call': {'arguments': '{\"input_dict\":{\"keyword\":\"restaurant\",\"location_name\":\"นวมินทร์ 71\",\"radius\":500,\"place_type\":\"restaurant\"}}', 'name': 'nearby_search'}}, response_metadata={'token_usage': {'completion_tokens': 39, 'prompt_tokens': 654, 'total_tokens': 693}, 'model_name': 'gpt-4o-mini', 'system_fingerprint': 'fp_48196bc67a', 'finish_reason': 'function_call', 'logprobs': None}, name='data collector', id='run-cc93ad4f-888e-466f-a29e-03653a7709a7-0')], 'sender': 'data collector'}}\n",
278
- "----\n",
279
- "{'reporter': {'messages': [AIMessage(content='', additional_kwargs={'function_call': {'arguments': '{\"input_dict\":{\"keyword\":\"ร้านอาหาร\",\"location_name\":\"นวมินทร์ 71\",\"radius\":1000,\"place_type\":\"restaurant\"}}', 'name': 'nearby_search'}}, response_metadata={'token_usage': {'completion_tokens': 41, 'prompt_tokens': 746, 'total_tokens': 787}, 'model_name': 'gpt-4o-mini', 'system_fingerprint': 'fp_48196bc67a', 'finish_reason': 'function_call', 'logprobs': None}, name='reporter', id='run-2be60a63-ce04-4f94-8ba9-29969b6f6b4f-0')], 'sender': 'reporter'}}\n",
280
- "----\n",
281
- "{'data collector': {'messages': [AIMessage(content='', additional_kwargs={'function_call': {'arguments': '{\"input_dict\":{\"keyword\":\"restaurant\",\"location_name\":\"นวมินทร์ 71\",\"radius\":1000,\"place_type\":\"restaurant\"}}', 'name': 'nearby_search'}}, response_metadata={'token_usage': {'completion_tokens': 40, 'prompt_tokens': 740, 'total_tokens': 780}, 'model_name': 'gpt-4o-mini', 'system_fingerprint': 'fp_48196bc67a', 'finish_reason': 'function_call', 'logprobs': None}, name='data collector', id='run-f0783436-107b-4513-bebc-f59ff1028878-0')], 'sender': 'data collector'}}\n",
282
- "----\n",
283
- "{'reporter': {'messages': [AIMessage(content='', additional_kwargs={'function_call': {'arguments': '{\"input_dict\":{\"keyword\":\"ร้านอาหาร\",\"location_name\":\"นวมินทร์ 71\",\"radius\":1000,\"place_type\":\"restaurant\"}}', 'name': 'nearby_search'}}, response_metadata={'token_usage': {'completion_tokens': 41, 'prompt_tokens': 833, 'total_tokens': 874}, 'model_name': 'gpt-4o-mini', 'system_fingerprint': 'fp_48196bc67a', 'finish_reason': 'function_call', 'logprobs': None}, name='reporter', id='run-bc595713-4ab3-4b26-a9fa-ab2a73f66f86-0')], 'sender': 'reporter'}}\n",
284
- "----\n",
285
- "{'data collector': {'messages': [AIMessage(content='', additional_kwargs={'function_call': {'arguments': '{\"input_dict\":{\"keyword\":\"restaurant\",\"location_name\":\"นวมินทร์ 71\",\"radius\":1000,\"place_type\":\"restaurant\"}}', 'name': 'nearby_search'}}, response_metadata={'token_usage': {'completion_tokens': 40, 'prompt_tokens': 827, 'total_tokens': 867}, 'model_name': 'gpt-4o-mini', 'system_fingerprint': 'fp_48196bc67a', 'finish_reason': 'function_call', 'logprobs': None}, name='data collector', id='run-5ae4ab19-ae02-4d06-ab74-20f06c34f6fb-0')], 'sender': 'data collector'}}\n",
286
- "----\n",
287
- "{'reporter': {'messages': [AIMessage(content='', additional_kwargs={'function_call': {'arguments': '{\"input_dict\":{\"keyword\":\"ร้านอาหาร\",\"location_name\":\"นวมินทร์ 71\",\"radius\":1000,\"place_type\":\"restaurant\"}}', 'name': 'nearby_search'}}, response_metadata={'token_usage': {'completion_tokens': 41, 'prompt_tokens': 920, 'total_tokens': 961}, 'model_name': 'gpt-4o-mini', 'system_fingerprint': 'fp_507c9469a1', 'finish_reason': 'function_call', 'logprobs': None}, name='reporter', id='run-8287ca4f-d479-4ec8-b4ef-57188ebb2d3a-0')], 'sender': 'reporter'}}\n",
288
  "----\n"
289
  ]
290
- },
291
- {
292
- "ename": "GraphRecursionError",
293
- "evalue": "Recursion limit of 15 reached without hitting a stop condition. You can increase the limit by setting the `recursion_limit` config key.",
294
- "output_type": "error",
295
- "traceback": [
296
- "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
297
- "\u001b[0;31mGraphRecursionError\u001b[0m Traceback (most recent call last)",
298
- "Cell \u001b[0;32mIn[9], line 14\u001b[0m\n\u001b[1;32m 1\u001b[0m graph \u001b[38;5;241m=\u001b[39m workflow\u001b[38;5;241m.\u001b[39mcompile()\n\u001b[1;32m 3\u001b[0m events \u001b[38;5;241m=\u001b[39m graph\u001b[38;5;241m.\u001b[39mstream(\n\u001b[1;32m 4\u001b[0m {\n\u001b[1;32m 5\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 12\u001b[0m {\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mrecursion_limit\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;241m15\u001b[39m},\n\u001b[1;32m 13\u001b[0m )\n\u001b[0;32m---> 14\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 15\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 16\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43mprint\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43m----\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n",
299
- "File \u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/site-packages/langgraph/pregel/__init__.py:1038\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 1036\u001b[0m \u001b[38;5;66;03m# handle exit\u001b[39;00m\n\u001b[1;32m 1037\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m loop\u001b[38;5;241m.\u001b[39mstatus \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mout_of_steps\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n\u001b[0;32m-> 1038\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m GraphRecursionError(\n\u001b[1;32m 1039\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mRecursion limit of \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mconfig[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mrecursion_limit\u001b[39m\u001b[38;5;124m'\u001b[39m]\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m reached \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 1040\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mwithout hitting a stop condition. You can increase the \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 1041\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mlimit by setting the `recursion_limit` config key.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 1042\u001b[0m )\n\u001b[1;32m 1043\u001b[0m \u001b[38;5;66;03m# set final channel values as run output\u001b[39;00m\n\u001b[1;32m 1044\u001b[0m run_manager\u001b[38;5;241m.\u001b[39mon_chain_end(read_channels(loop\u001b[38;5;241m.\u001b[39mchannels, output_keys))\n",
300
- "\u001b[0;31mGraphRecursionError\u001b[0m: Recursion limit of 15 reached without hitting a stop condition. You can increase the limit by setting the `recursion_limit` config key."
301
- ]
302
  }
303
  ],
304
  "source": [
@@ -308,12 +280,12 @@
308
  " {\n",
309
  " \"messages\": [\n",
310
  " HumanMessage(\n",
311
- " content=\"วิเคราะห์ความเป็นไปได้ในการเปิดร้านอาหารแถวนวมินทร์ 71\"\n",
312
  " )\n",
313
  " ],\n",
314
  " },\n",
315
  " # Maximum number of steps to take in the graph\n",
316
- " {\"recursion_limit\": 15},\n",
317
  ")\n",
318
  "for s in events:\n",
319
  " print(s)\n",
@@ -322,20 +294,9 @@
322
  },
323
  {
324
  "cell_type": "code",
325
- "execution_count": 6,
326
  "metadata": {},
327
- "outputs": [
328
- {
329
- "data": {
330
- "text/plain": [
331
- "'รายชื่อร้านกาแฟใกล้มาบุญครอง ได้แก่:\\n\\n1. **ร้านกาแฟ A**\\n - ที่อยู่: ถนนพระราม 1\\n - ระยะทาง: 500 เมตรจากมาบุญครอง\\n - ความคิดเห็น: บริการดี บรรยากาศดี\\n\\n2. **ร้านกาแฟ B**\\n - ที่อยู่: สยามสแควร์\\n - ระยะทาง: 800 เมตรจากมาบุญครอง\\n - ความคิดเห็น: กาแฟอร่อย แนะนำเมนูพิเศษ\\n\\n3. **ร้านกาแฟ C**\\n - ที่อยู่: สวนลุมพินี\\n - ระยะทาง: 1.5 กม.จากมาบุญครอง\\n - ความคิดเห็น: มีมุมสงบ เหมาะสำหรับอ่านหนังสือ\\n\\n4. **ร้านกาแฟ D**\\n - ที่อยู่: ถนนสีลม\\n - ระยะทาง: 2 กม.จากมาบุญครอง\\n - ความคิดเห็น: คาเฟ่สไตล์โมเดิร์น มี Wi-Fi ฟรี\\n\\nหากต้องการข้อมูลเพิ่มเติมหรือคำแนะนำเพิ่มเติม สามารถสอบถามได้ค่ะ!'"
332
- ]
333
- },
334
- "execution_count": 6,
335
- "metadata": {},
336
- "output_type": "execute_result"
337
- }
338
- ],
339
  "source": [
340
  "def submitUserMessage(user_input: str) -> str:\n",
341
  " graph = workflow.compile()\n",
@@ -354,11 +315,11 @@
354
  " \n",
355
  " events = [e for e in events]\n",
356
  " \n",
357
- " response = events[-1]['data collector']['messages'][0].content.replace(\"FINAL ANSWER: \", \"\")\n",
358
  " \n",
359
  " return response\n",
360
  "\n",
361
- "submitUserMessage(\"ค้นหาร้านกาแฟใกล้มาบุญครอง\")"
362
  ]
363
  }
364
  ],
 
2
  "cells": [
3
  {
4
  "cell_type": "code",
5
+ "execution_count": 39,
6
  "metadata": {},
7
  "outputs": [],
8
  "source": [
 
15
  },
16
  {
17
  "cell_type": "code",
18
+ "execution_count": 40,
19
  "metadata": {},
20
+ "outputs": [],
 
 
 
 
 
 
 
 
 
 
 
21
  "source": [
22
  "from langchain_core.messages import HumanMessage\n",
23
  "import operator\n",
 
112
  " prompt = prompt.partial(tool_names=\", \".join([tool.name for tool in tools]))\n",
113
  " llm_with_tools = llm.bind(functions=[format_tool_to_openai_function(t) for t in tools])\n",
114
  " # return prompt | llm.bind_tools(tools)\n",
115
+ " agent = prompt | llm\n",
116
  " return agent\n",
117
  "\n",
118
  "\n",
 
154
  " \n",
155
  " agents[name] = create_agent(\n",
156
  " llm,\n",
157
+ " tools,\n",
158
  " system_message=prompt,\n",
159
  " )\n",
160
  " \n",
 
190
  "workflow.add_node(\"call_tool\", tool_node)\n",
191
  "\n",
192
  "\n",
193
+ "workflow.add_conditional_edges(\n",
194
+ " \"analyst\",\n",
195
+ " router,\n",
196
+ " {\"continue\": \"data collector\", \"call_tool\": \"call_tool\"}\n",
197
+ ")\n",
198
+ "\n",
199
+ "workflow.add_conditional_edges(\n",
200
+ " \"data collector\",\n",
201
+ " router,\n",
202
+ " {\"continue\": \"reporter\", \"call_tool\": \"call_tool\"}\n",
203
+ ")\n",
204
+ "\n",
205
+ "workflow.add_conditional_edges(\n",
206
+ " \"reporter\",\n",
207
+ " router,\n",
208
+ " {\"continue\": \"data collector\", \"call_tool\": \"call_tool\", \"__end__\": END}\n",
209
+ ")\n",
210
  "\n",
211
  "workflow.add_conditional_edges(\n",
212
  " \"call_tool\",\n",
 
223
  },
224
  {
225
  "cell_type": "code",
226
+ "execution_count": 41,
227
  "metadata": {},
228
  "outputs": [
229
  {
230
  "data": {
231
+ "image/jpeg": 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",
232
  "text/plain": [
233
  "<IPython.core.display.Image object>"
234
  ]
 
249
  },
250
  {
251
  "cell_type": "code",
252
+ "execution_count": 42,
253
  "metadata": {},
254
  "outputs": [
255
  {
256
  "name": "stdout",
257
  "output_type": "stream",
258
  "text": [
259
+ "{'analyst': {'messages': [AIMessage(content='เพื่อให้ข้อมูลเกี่ยวกับร้านกาแฟใกล้มาบุญครองและจำนวนประชากรในพื้นที่นั้น ฉันจะเริ่มต้นด้วยการค้นหาร้านกาแฟที่อยู่ใกล้เคียง ก่อนที่จะนำข้อมูลเกี่ยวกับจำนวนประชากรมาวิเคราะห์ต่อไป\\n\\nให้ฉันค้นหาร้านกาแฟที่ใกล้มาบุญครองก่อนนะ', response_metadata={'token_usage': {'completion_tokens': 78, 'prompt_tokens': 234, 'total_tokens': 312}, 'model_name': 'gpt-4o-mini', 'system_fingerprint': 'fp_507c9469a1', 'finish_reason': 'stop', 'logprobs': None}, name='analyst', id='run-00fe7cee-c314-4e71-9f34-0c50b2899153-0')], 'sender': 'analyst'}}\n",
 
 
260
  "----\n",
261
+ "{'data collector': {'messages': [AIMessage(content='กำลังค้นหาร้านกาแฟใกล้มาบุญครอง...', response_metadata={'token_usage': {'completion_tokens': 16, 'prompt_tokens': 289, 'total_tokens': 305}, 'model_name': 'gpt-4o-mini', 'system_fingerprint': 'fp_48196bc67a', 'finish_reason': 'stop', 'logprobs': None}, name='data collector', id='run-1746f90f-ed2a-482b-8678-28a50f14d772-0')], 'sender': 'data collector'}}\n",
262
  "----\n",
263
+ "{'reporter': {'messages': [AIMessage(content='ฉันได้ค้นหาร้านกาแฟที่อยู่ใกล้มาบุญครองแล้ว ต่อไปฉันจะรวบรวมข้อมูลเกี่ยวกับจำนวนประชากรในพื้นที่เพื่อทำการวิเคราะห์ต่อไป\\n\\nให้ฉันค้นหาข้อมูลประชากรในพื้นที่นี้ก่อนนะ', response_metadata={'token_usage': {'completion_tokens': 59, 'prompt_tokens': 359, 'total_tokens': 418}, 'model_name': 'gpt-4o-mini', 'system_fingerprint': 'fp_507c9469a1', 'finish_reason': 'stop', 'logprobs': None}, name='reporter', id='run-5782b0cb-19cf-4eb9-9685-0f427e16e3ef-0')], 'sender': 'reporter'}}\n",
264
  "----\n",
265
+ "{'data collector': {'messages': [AIMessage(content='กำลังค้นหาข้อมูลประชากรในพื้นที่มาบุญครอง...', response_metadata={'token_usage': {'completion_tokens': 18, 'prompt_tokens': 372, 'total_tokens': 390}, 'model_name': 'gpt-4o-mini', 'system_fingerprint': 'fp_48196bc67a', 'finish_reason': 'stop', 'logprobs': None}, name='data collector', id='run-844f383f-6994-4403-b506-8cba7dbea3a9-0')], 'sender': 'data collector'}}\n",
266
  "----\n",
267
+ "{'reporter': {'messages': [AIMessage(content='ข้อมูลประชากรในเขตมาบุญครองยังไม่สามารถค้นหาได้ในขณะนี้ แต่ฉันควรจะนำเสนอข้อมูลเกี่ยวกับร้านกาแฟที่พบได้ในพื้นที่นั้นก่อน\\n\\nให้ฉันแสดงรายชื่อร้านกาแฟที่ใกล้มาบุญครองในตอนนี้:', response_metadata={'token_usage': {'completion_tokens': 68, 'prompt_tokens': 444, 'total_tokens': 512}, 'model_name': 'gpt-4o-mini', 'system_fingerprint': 'fp_48196bc67a', 'finish_reason': 'stop', 'logprobs': None}, name='reporter', id='run-4172aaf2-2838-4244-8f14-585008f3b526-0')], 'sender': 'reporter'}}\n",
268
  "----\n",
269
+ "{'data collector': {'messages': [AIMessage(content=\"ฉันได้ค้นหาร้านกาแฟใกล้มาบุญครอง ซึ่งรวมถึง:\\n\\n1. **ร้านกาแฟ Starbucks** - สาขามาบุญครอง\\n2. **ร้านกาแฟ Cafe Amazon** - ใกล้มาบุญครอง\\n3. **ร้านกาแฟ Dunkin' Donuts** - สาขาใกล้มาบุญครอง\\n4. **ร้านกาแฟ After You** - ใกล้มาบุญครอง\\n5. **ร้านกาแฟ Black Canyon** - สาขาใกล้มาบุญครอง\\n\\nข้อมูลเกี่ยวกับจำนวนประชากรในพื้นที่มาบุญครองยังไม่สามารถรวบรวมได้ในขณะนี้ แต่ถ้าต้องการข้อมูลเพิ่มเติมเกี่ยวกับประชากรในกรุงเทพฯ หรือเขตใกล้เคียงอื่น ๆ ฉันสามารถช่วยค้นหาได้\\n\\nหากต้องการข้อมูลเพิ่มเติมเกี่ยวกับร้านกาแฟหรือรายละเอียดอื่น ๆ โปรดแจ้งให้ฉันทราบ!\", response_metadata={'token_usage': {'completion_tokens': 210, 'prompt_tokens': 466, 'total_tokens': 676}, 'model_name': 'gpt-4o-mini', 'system_fingerprint': 'fp_507c9469a1', 'finish_reason': 'stop', 'logprobs': None}, name='data collector', id='run-e36e9cca-902a-4386-b5ae-7ae517715bce-0')], 'sender': 'data collector'}}\n",
270
  "----\n",
271
+ "{'reporter': {'messages': [AIMessage(content=\"FINAL ANSWER\\n\\nรายชื่อร้านกาแฟที่ใกล้มาบุญครอง ได้แก่:\\n1. Starbucks - สาขามาบุญครอง\\n2. Cafe Amazon - ใกล้มาบุญครอง\\n3. Dunkin' Donuts - สาขาใกล้มาบุญครอง\\n4. After You - ใกล้มาบุญครอง\\n5. Black Canyon - สาขาใกล้มาบุญครอง\\n\\nข้อมูลเกี่ยวกับจำนวนประชากรในพื้นที่นั้นยังไม่สามารถรวบรวมได้ในขณะนี้ หากต้องการข้อมูลเพิ่มเติมเกี่ยวกับประชากรในกรุงเทพฯ หรือเขตใกล้เคียงอื่น ๆ โปรดแจ้งให้ฉันทราบ!\", response_metadata={'token_usage': {'completion_tokens': 154, 'prompt_tokens': 730, 'total_tokens': 884}, 'model_name': 'gpt-4o-mini', 'system_fingerprint': 'fp_507c9469a1', 'finish_reason': 'stop', 'logprobs': None}, name='reporter', id='run-50ede9e8-1d05-41c2-a57f-5bf775c854f1-0')], 'sender': 'reporter'}}\n",
 
 
 
 
 
 
 
 
 
 
 
 
 
 
272
  "----\n"
273
  ]
 
 
 
 
 
 
 
 
 
 
 
 
274
  }
275
  ],
276
  "source": [
 
280
  " {\n",
281
  " \"messages\": [\n",
282
  " HumanMessage(\n",
283
+ " content=\"ค้นหาร้านกาแฟใกล้มาบุญครอง พร้อมวิเคราะห์จำนวนประชากร\"\n",
284
  " )\n",
285
  " ],\n",
286
  " },\n",
287
  " # Maximum number of steps to take in the graph\n",
288
+ " {\"recursion_limit\": 10},\n",
289
  ")\n",
290
  "for s in events:\n",
291
  " print(s)\n",
 
294
  },
295
  {
296
  "cell_type": "code",
297
+ "execution_count": 43,
298
  "metadata": {},
299
+ "outputs": [],
 
 
 
 
 
 
 
 
 
 
 
300
  "source": [
301
  "def submitUserMessage(user_input: str) -> str:\n",
302
  " graph = workflow.compile()\n",
 
315
  " \n",
316
  " events = [e for e in events]\n",
317
  " \n",
318
+ " response = events[-1]['reporter']['messages'][0].content.replace(\"FINAL ANSWER: \", \"\")\n",
319
  " \n",
320
  " return response\n",
321
  "\n",
322
+ "# submitUserMessage(\"ค้นหาร้านกาแฟใกล้มาบุญครอง\")"
323
  ]
324
  }
325
  ],
chatbot_multiagent.py CHANGED
@@ -1,38 +1,29 @@
1
- # %%
2
  import os
3
  import utils
4
 
5
  utils.load_env()
6
  os.environ['LANGCHAIN_TRACING_V2'] = "false"
7
 
8
- # %%
9
- from langchain_core.messages import HumanMessage
10
  import operator
11
  import functools
12
 
13
  # for llm model
 
14
  from langchain_openai import ChatOpenAI
15
- from langchain.agents.format_scratchpad import format_to_openai_function_messages
16
  from tools import find_place_from_text, nearby_search
17
- from typing import Dict, List, Tuple, Annotated, Sequence, TypedDict
18
- from langchain.agents import (
19
- AgentExecutor,
20
- )
21
- from langchain.agents.output_parsers import OpenAIFunctionsAgentOutputParser
22
  from langchain_community.chat_models import ChatOpenAI
23
  from langchain_community.tools.convert_to_openai import format_tool_to_openai_function
24
  from langchain_core.messages import (
25
  AIMessage,
26
- HumanMessage,
27
  BaseMessage,
28
  ToolMessage
29
  )
30
- from langchain_core.pydantic_v1 import BaseModel, Field
31
  from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
32
  from langgraph.graph import END, StateGraph, START
33
 
34
  ## Document vector store for context
35
- from langchain_core.runnables import RunnablePassthrough
36
  from langchain_chroma import Chroma
37
  from langchain_text_splitters import RecursiveCharacterTextSplitter
38
  from langchain_community.document_loaders import CSVLoader
@@ -99,7 +90,7 @@ def create_agent(llm, tools, system_message: str):
99
  prompt = prompt.partial(tool_names=", ".join([tool.name for tool in tools]))
100
  llm_with_tools = llm.bind(functions=[format_tool_to_openai_function(t) for t in tools])
101
  # return prompt | llm.bind_tools(tools)
102
- agent = prompt | llm_with_tools
103
  return agent
104
 
105
 
@@ -141,7 +132,7 @@ for meta in agent_meta:
141
 
142
  agents[name] = create_agent(
143
  llm,
144
- [find_place_from_text, nearby_search],
145
  system_message=prompt,
146
  )
147
 
@@ -177,12 +168,23 @@ for name, node in agent_nodes.items():
177
  workflow.add_node("call_tool", tool_node)
178
 
179
 
180
- for meta in agent_meta:
181
- workflow.add_conditional_edges(
182
- meta["name"],
183
- router,
184
- {"continue": meta['continue'], "call_tool": "call_tool", "__end__": END} if meta["name"]=="data collector" else {"continue": meta['continue'], "__end__": END},
185
- )
 
 
 
 
 
 
 
 
 
 
 
186
 
187
  workflow.add_conditional_edges(
188
  "call_tool",
@@ -196,7 +198,6 @@ workflow.add_conditional_edges(
196
  workflow.add_edge(START, "analyst")
197
  graph = workflow.compile()
198
 
199
- # %%
200
  def submitUserMessage(user_input: str) -> str:
201
  graph = workflow.compile()
202
 
@@ -214,7 +215,8 @@ def submitUserMessage(user_input: str) -> str:
214
 
215
  events = [e for e in events]
216
 
217
- response = events[-1]['data collector']['messages'][0].content.replace("FINAL ANSWER: ", "")
218
 
219
  return response
220
 
 
 
 
1
  import os
2
  import utils
3
 
4
  utils.load_env()
5
  os.environ['LANGCHAIN_TRACING_V2'] = "false"
6
 
7
+
 
8
  import operator
9
  import functools
10
 
11
  # for llm model
12
+ from langchain_core.messages import HumanMessage
13
  from langchain_openai import ChatOpenAI
 
14
  from tools import find_place_from_text, nearby_search
15
+ from typing import Annotated, Sequence, TypedDict
 
 
 
 
16
  from langchain_community.chat_models import ChatOpenAI
17
  from langchain_community.tools.convert_to_openai import format_tool_to_openai_function
18
  from langchain_core.messages import (
19
  AIMessage,
 
20
  BaseMessage,
21
  ToolMessage
22
  )
 
23
  from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
24
  from langgraph.graph import END, StateGraph, START
25
 
26
  ## Document vector store for context
 
27
  from langchain_chroma import Chroma
28
  from langchain_text_splitters import RecursiveCharacterTextSplitter
29
  from langchain_community.document_loaders import CSVLoader
 
90
  prompt = prompt.partial(tool_names=", ".join([tool.name for tool in tools]))
91
  llm_with_tools = llm.bind(functions=[format_tool_to_openai_function(t) for t in tools])
92
  # return prompt | llm.bind_tools(tools)
93
+ agent = prompt | llm
94
  return agent
95
 
96
 
 
132
 
133
  agents[name] = create_agent(
134
  llm,
135
+ tools,
136
  system_message=prompt,
137
  )
138
 
 
168
  workflow.add_node("call_tool", tool_node)
169
 
170
 
171
+ workflow.add_conditional_edges(
172
+ "analyst",
173
+ router,
174
+ {"continue": "data collector", "call_tool": "call_tool"}
175
+ )
176
+
177
+ workflow.add_conditional_edges(
178
+ "data collector",
179
+ router,
180
+ {"continue": "reporter", "call_tool": "call_tool"}
181
+ )
182
+
183
+ workflow.add_conditional_edges(
184
+ "reporter",
185
+ router,
186
+ {"continue": "data collector", "call_tool": "call_tool", "__end__": END}
187
+ )
188
 
189
  workflow.add_conditional_edges(
190
  "call_tool",
 
198
  workflow.add_edge(START, "analyst")
199
  graph = workflow.compile()
200
 
 
201
  def submitUserMessage(user_input: str) -> str:
202
  graph = workflow.compile()
203
 
 
215
 
216
  events = [e for e in events]
217
 
218
+ response = events[-1]['reporter']['messages'][0].content.replace("FINAL ANSWER: ", "")
219
 
220
  return response
221
 
222
+ # submitUserMessage("ค้นหาร้านกาแฟใกล้มาบุญครอง")
prompt.json CHANGED
@@ -1,17 +1,14 @@
1
  [
2
  {
3
  "name": "analyst",
4
- "prompt": "You are the Analyst responsible for understanding the user's needs and guiding the data collection process. When the user asks about opening a shop or business at a specific location, you will: Comprehend the user's request and determine what analytical insights they need about competitors and market opportunities. Identify the necessary data required for analysis, including information about the place nearby, district or location of the specified place, type of community, household expenditures, and population in the district. Clearly communicate these data requirements to the Data Collector.",
5
- "continue": "data collector"
6
  },
7
  {
8
  "name": "data collector",
9
- "prompt": "You are the Data Collector responsible for gathering data based on the Analyst's instructions. When you receive a request from the Analyst, you will: Use the necessary tools to gather data related to the specified location, including information about nearby places, districts, community types, household expenditures, and population demographics. Ensure the data is accurate and comprehensive before sending it to the Reporter.",
10
- "continue": "reporter"
11
  },
12
  {
13
  "name": "reporter",
14
- "prompt": "You are the Reporter responsible for compiling the data into a clear and informative report for the user. When you receive the data from the Data Collector, you will: Organize and analyze the data to generate insights about the competitive landscape and market opportunities at the specified location. 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 the same language as the user's original request. If you want to respond to user, you should to respond to the same language as the user's original request. Except the FINAL ANSWER keep it the same.",
15
- "continue": "data collector"
16
  }
17
  ]
 
1
  [
2
  {
3
  "name": "analyst",
4
+ "prompt": "You are the Analyst responsible for understanding the user's needs and guiding the data collection process. When the user asks about opening a shop or business at a specific location, you will: Comprehend the user's request and determine what analytical insights they need about competitors and market opportunities. Identify the necessary data required for analysis, including information about the place nearby, district or location of the specified place, type of community, household expenditures, and population in the district. Clearly communicate these data requirements to the Data Collector."
 
5
  },
6
  {
7
  "name": "data collector",
8
+ "prompt": "You are the Data Collector responsible for gathering data based on the Analyst's instructions. When you receive a request from the Analyst, you will: Use the necessary tools to gather data related to the specified location, including information about nearby places, districts, community types, household expenditures, and population demographics. Ensure the data is accurate and comprehensive before sending it to the Reporter."
 
9
  },
10
  {
11
  "name": "reporter",
12
+ "prompt": "You are the Reporter responsible for compiling the data into a clear and informative report for the user. When you receive the data from the Data Collector, you will: Organize and analyze the data to generate insights about the competitive landscape and market opportunities at the specified location. 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 the same language as the user's original request. If you want to respond to user, you should to respond to the same language as the user's original request. Except the FINAL ANSWER keep it the same."
 
13
  }
14
  ]