# %% import os 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 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 from langchain.agents import ( AgentExecutor, ) from langchain.agents.output_parsers import OpenAIFunctionsAgentOutputParser from langchain_community.tools.convert_to_openai import format_tool_to_openai_function from langchain_core.messages import ( AIMessage, HumanMessage, BaseMessage, ToolMessage ) from langchain_core.pydantic_v1 import BaseModel, Field from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder from langgraph.graph import END, StateGraph, START ## Document vector store for context from langchain_core.runnables import RunnablePassthrough from langchain_chroma import Chroma from langchain_text_splitters import RecursiveCharacterTextSplitter from langchain_community.document_loaders import CSVLoader from langchain_openai import OpenAIEmbeddings import glob from langchain.tools.retriever import create_retriever_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)) all_docs = [] for file_path in file_paths: loader = CSVLoader(file_path=file_path) docs = loader.load() all_docs.extend(docs) # Add the documents to the list # Split text into chunks separated. text_splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=100) splits = text_splitter.split_documents(all_docs) # Text Vectorization. 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 retriever_tool = create_retriever_tool( retriever, "search_population_community_household_expenditures_data", "Use this tool to retrieve information about population, community and household expenditures. by searching distinct or province" ) # Bind the tools to the model 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) ## Create agents def create_agent(llm, tools, system_message: str): """Create an agent.""" prompt = ChatPromptTemplate.from_messages( [ ( "system", "You are a helpful AI assistant, collaborating with other assistants." " Use the provided tools to progress towards answering the question." " If you are unable to fully answer, that's OK, another assistant with different tools " " will help where you left off. Execute what you can to make progress." " If you or any of the other assistants have the final answer or deliverable," " " " You have access to the following tools: {tool_names}.\n{system_message}", ), MessagesPlaceholder(variable_name="messages"), ] ) prompt = prompt.partial(system_message=system_message) 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_with_tools #return agent ## Define state # This defines the object that is passed between each node # in the graph. We will create different nodes for each agent and tool class AgentState(TypedDict): messages: Annotated[Sequence[BaseMessage], operator.add] sender: str # Helper function to create a node for a given agent def agent_node(state, agent, name): result = agent.invoke(state) # We convert the agent output into a format that is suitable to append to the global state if isinstance(result, ToolMessage): pass else: result = AIMessage(**result.dict(exclude={"type", "name"}), name=name) return { "messages": [result], # Since we have a strict workflow, we can # track the sender so we know who to pass to next. "sender": name, } ## Define Agents Node # Research agent and node from prompt import agent_meta agent_name = [meta['name'] for meta in agent_meta] agents={} agent_nodes={} for meta in agent_meta: name = meta['name'] prompt = meta['prompt'] agents[name] = create_agent( llm, tools, system_message=prompt, ) agent_nodes[name] = functools.partial(agent_node, agent=agents[name], name=name) ## Define Tool Node from langgraph.prebuilt import ToolNode from typing import Literal tool_node = ToolNode(tools) def router(state) -> Literal["call_tool", "__end__", "continue"]: # This is the router messages = state["messages"] last_message = messages[-1] if "continue" in last_message.content: return "continue" if last_message.tool_calls: # The previous agent is invoking a tool return "call_tool" if "FINAL ANSWER" in last_message.content: # Any agent decided the work is done return "__end__" else: return "continue" ## Workflow Graph workflow = StateGraph(AgentState) # add agent nodes for name, node in agent_nodes.items(): workflow.add_node(name, node) workflow.add_node("call_tool", tool_node) workflow.add_conditional_edges( "analyst", router, {"continue": "data_collector", "call_tool": "call_tool", "__end__": END} ) workflow.add_conditional_edges( "data_collector", router, {"call_tool": "call_tool", "continue": "reporter", "__end__": END} ) workflow.add_conditional_edges( "reporter", router, {"continue": "data_collector", "call_tool": "call_tool", "__end__": END} ) workflow.add_conditional_edges( "call_tool", # Each agent node updates the 'sender' field # the tool calling node does not, meaning # this edge will route back to the original agent # who invoked the tool lambda x: x["sender"], {name:name for name in agent_name}, ) workflow.add_edge(START, "analyst") graph = workflow.compile() # %% # from IPython.display import Image, display # try: # display(Image(graph.get_graph(xray=True).draw_mermaid_png())) # except Exception: # # This requires some extra dependencies and is optional # pass # %% # question = "อยากจะเปิดร้านหนังสือแถวๆคู้บอนช่วยวิเคราะห์หน่อย" # 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: # # print(s) # a = list(s.items())[0] # a[1]['messages'][0].pretty_print() # %% def submitUserMessage(user_input: str) -> str: graph = workflow.compile() events = graph.stream( { "messages": [ HumanMessage( user_input ) ], }, # Maximum number of steps to take in the graph {"recursion_limit": 20}, ) events = [e for e in events] response = list(events[-1].values())[0]["messages"][0] response = response.content response = response.replace("FINAL ANSWER", "") return response # question = "วิเคราะห์ร้านอาหารแถวลุมพินี เซ็นเตอร์ ลาดพร้าว" # submitUserMessage(question)