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{
"cells": [
{
"cell_type": "code",
"execution_count": 39,
"metadata": {},
"outputs": [],
"source": [
"import os\n",
"import utils\n",
"\n",
"utils.load_env()\n",
"os.environ['LANGCHAIN_TRACING_V2'] = \"false\""
]
},
{
"cell_type": "code",
"execution_count": 40,
"metadata": {},
"outputs": [],
"source": [
"from langchain_core.messages import HumanMessage\n",
"import operator\n",
"import functools\n",
"\n",
"# for llm model\n",
"from langchain_openai import ChatOpenAI\n",
"from langchain.agents.format_scratchpad import format_to_openai_function_messages\n",
"from tools import find_place_from_text, nearby_search\n",
"from typing import Dict, List, Tuple, Annotated, Sequence, TypedDict\n",
"from langchain.agents import (\n",
" AgentExecutor,\n",
")\n",
"from langchain.agents.output_parsers import OpenAIFunctionsAgentOutputParser\n",
"from langchain_community.chat_models import ChatOpenAI\n",
"from langchain_community.tools.convert_to_openai import format_tool_to_openai_function\n",
"from langchain_core.messages import (\n",
" AIMessage, \n",
" HumanMessage,\n",
" BaseMessage,\n",
" ToolMessage\n",
")\n",
"from langchain_core.pydantic_v1 import BaseModel, Field\n",
"from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder\n",
"from langgraph.graph import END, StateGraph, START\n",
"\n",
"## Document vector store for context\n",
"from langchain_core.runnables import RunnablePassthrough\n",
"from langchain_chroma import Chroma\n",
"from langchain_text_splitters import RecursiveCharacterTextSplitter\n",
"from langchain_community.document_loaders import CSVLoader\n",
"from langchain_openai import OpenAIEmbeddings\n",
"import glob\n",
"from langchain.tools import Tool\n",
"\n",
"def format_docs(docs):\n",
" return \"\\n\\n\".join(doc.page_content for doc in docs)\n",
"\n",
"# Specify the pattern\n",
"file_pattern = \"document/*.csv\"\n",
"file_paths = tuple(glob.glob(file_pattern))\n",
"\n",
"all_docs = []\n",
"\n",
"for file_path in file_paths:\n",
" loader = CSVLoader(file_path=file_path)\n",
" docs = loader.load()\n",
" all_docs.extend(docs) # Add the documents to the list\n",
"\n",
"# Split text into chunks separated.\n",
"text_splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=100)\n",
"splits = text_splitter.split_documents(all_docs)\n",
"\n",
"# Text Vectorization.\n",
"vectorstore = Chroma.from_documents(documents=splits, embedding=OpenAIEmbeddings())\n",
"\n",
"# Retrieve and generate using the relevant snippets of the blog.\n",
"retriever = vectorstore.as_retriever()\n",
"\n",
"## tools and LLM\n",
"\n",
"retriever_tool = Tool(\n",
" name=\"Retriever\",\n",
" func=retriever.get_relevant_documents,\n",
" description=\"Use this tool to retrieve information about population, community and household expenditures.\"\n",
")\n",
"\n",
"# Bind the tools to the model\n",
"tools = [retriever_tool, find_place_from_text, nearby_search] # Include both tools if needed\n",
"\n",
"llm = ChatOpenAI(model=\"gpt-4o-mini\")\n",
"\n",
"## Create agents\n",
"def create_agent(llm, tools, system_message: str):\n",
" \"\"\"Create an agent.\"\"\"\n",
" prompt = ChatPromptTemplate.from_messages(\n",
" [\n",
" (\n",
" \"system\",\n",
" \"You are a helpful AI assistant, collaborating with other assistants.\"\n",
" \" Use the provided tools to progress towards answering the question.\"\n",
" \" If you are unable to fully answer, that's OK, another assistant with different tools \"\n",
" \" will help where you left off. Execute what you can to make progress.\"\n",
" \" If you or any of the other assistants have the final answer or deliverable,\"\n",
" \" prefix your response with FINAL ANSWER so the team knows to stop.\"\n",
" \" You have access to the following tools: {tool_names}.\\n{system_message}\",\n",
" ),\n",
" MessagesPlaceholder(variable_name=\"messages\"),\n",
" ]\n",
" )\n",
" prompt = prompt.partial(system_message=system_message)\n",
" prompt = prompt.partial(tool_names=\", \".join([tool.name for tool in tools]))\n",
" llm_with_tools = llm.bind(functions=[format_tool_to_openai_function(t) for t in tools])\n",
" # return prompt | llm.bind_tools(tools)\n",
" agent = prompt | llm\n",
" return agent\n",
"\n",
"\n",
"## Define state\n",
"# This defines the object that is passed between each node\n",
"# in the graph. We will create different nodes for each agent and tool\n",
"class AgentState(TypedDict):\n",
" messages: Annotated[Sequence[BaseMessage], operator.add]\n",
" sender: str\n",
"\n",
"\n",
"# Helper function to create a node for a given agent\n",
"def agent_node(state, agent, name):\n",
" result = agent.invoke(state)\n",
" # We convert the agent output into a format that is suitable to append to the global state\n",
" if isinstance(result, ToolMessage):\n",
" pass\n",
" else:\n",
" result = AIMessage(**result.dict(exclude={\"type\", \"name\"}), name=name)\n",
" return {\n",
" \"messages\": [result],\n",
" # Since we have a strict workflow, we can\n",
" # track the sender so we know who to pass to next.\n",
" \"sender\": name,\n",
" }\n",
"\n",
"\n",
"## Define Agents Node\n",
"# Research agent and node\n",
"agent_meta = utils.load_agent_meta()\n",
"agent_name = [meta['name'] for meta in agent_meta]\n",
"\n",
"agents={}\n",
"agent_nodes={}\n",
"\n",
"for meta in agent_meta:\n",
" name = meta['name']\n",
" prompt = meta['prompt']\n",
" \n",
" agents[name] = create_agent(\n",
" llm,\n",
" tools,\n",
" system_message=prompt,\n",
" )\n",
" \n",
" agent_nodes[name] = functools.partial(agent_node, agent=agents[name], name=name)\n",
"\n",
"\n",
"## Define Tool Node\n",
"from langgraph.prebuilt import ToolNode\n",
"from typing import Literal\n",
"\n",
"tool_node = ToolNode(tools)\n",
"\n",
"def router(state) -> Literal[\"call_tool\", \"__end__\", \"continue\"]:\n",
" # This is the router\n",
" messages = state[\"messages\"]\n",
" last_message = messages[-1]\n",
" if last_message.tool_calls:\n",
" # The previous agent is invoking a tool\n",
" return \"call_tool\"\n",
" if \"FINAL ANSWER\" in last_message.content:\n",
" # Any agent decided the work is done\n",
" return \"__end__\"\n",
" return \"continue\"\n",
"\n",
"\n",
"## Workflow Graph\n",
"workflow = StateGraph(AgentState)\n",
"\n",
"# add agent nodes\n",
"for name, node in agent_nodes.items():\n",
" workflow.add_node(name, node)\n",
" \n",
"workflow.add_node(\"call_tool\", tool_node)\n",
"\n",
"\n",
"workflow.add_conditional_edges(\n",
" \"analyst\",\n",
" router,\n",
" {\"continue\": \"data collector\", \"call_tool\": \"call_tool\"}\n",
")\n",
"\n",
"workflow.add_conditional_edges(\n",
" \"data collector\",\n",
" router,\n",
" {\"continue\": \"reporter\", \"call_tool\": \"call_tool\"}\n",
")\n",
"\n",
"workflow.add_conditional_edges(\n",
" \"reporter\",\n",
" router,\n",
" {\"continue\": \"data collector\", \"call_tool\": \"call_tool\", \"__end__\": END}\n",
")\n",
"\n",
"workflow.add_conditional_edges(\n",
" \"call_tool\",\n",
" # Each agent node updates the 'sender' field\n",
" # the tool calling node does not, meaning\n",
" # this edge will route back to the original agent\n",
" # who invoked the tool\n",
" lambda x: x[\"sender\"],\n",
" {name: name for name in agent_name},\n",
")\n",
"workflow.add_edge(START, \"analyst\")\n",
"graph = workflow.compile()"
]
},
{
"cell_type": "code",
"execution_count": 41,
"metadata": {},
"outputs": [
{
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",
"text/plain": [
"<IPython.core.display.Image object>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"from IPython.display import Image, display\n",
"\n",
"try:\n",
" display(Image(graph.get_graph(xray=True).draw_mermaid_png()))\n",
"except Exception:\n",
" # This requires some extra dependencies and is optional\n",
" pass"
]
},
{
"cell_type": "code",
"execution_count": 42,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"{'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",
"----\n",
"{'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",
"----\n",
"{'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",
"----\n",
"{'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",
"----\n",
"{'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",
"----\n",
"{'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",
"----\n",
"{'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",
"----\n"
]
}
],
"source": [
"graph = workflow.compile()\n",
"\n",
"events = graph.stream(\n",
" {\n",
" \"messages\": [\n",
" HumanMessage(\n",
" content=\"ค้นหาร้านกาแฟใกล้มาบุญครอง พร้อมวิเคราะห์จำนวนประชากร\"\n",
" )\n",
" ],\n",
" },\n",
" # Maximum number of steps to take in the graph\n",
" {\"recursion_limit\": 10},\n",
")\n",
"for s in events:\n",
" print(s)\n",
" print(\"----\")"
]
},
{
"cell_type": "code",
"execution_count": 43,
"metadata": {},
"outputs": [],
"source": [
"def submitUserMessage(user_input: str) -> str:\n",
" graph = workflow.compile()\n",
"\n",
" events = graph.stream(\n",
" {\n",
" \"messages\": [\n",
" HumanMessage(\n",
" content=user_input\n",
" )\n",
" ],\n",
" },\n",
" # Maximum number of steps to take in the graph\n",
" {\"recursion_limit\": 15},\n",
" )\n",
" \n",
" events = [e for e in events]\n",
" \n",
" response = events[-1]['reporter']['messages'][0].content.replace(\"FINAL ANSWER: \", \"\")\n",
" \n",
" return response\n",
"\n",
"# submitUserMessage(\"ค้นหาร้านกาแฟใกล้มาบุญครอง\")"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.11.9"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
|