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import re |
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from typing import Any, Literal |
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from langchain_community.tools import DuckDuckGoSearchResults |
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from langchain_core.messages import SystemMessage, AnyMessage |
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from langchain_core.runnables import Runnable |
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from langchain_core.tools import BaseTool |
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from langchain_ollama import ChatOllama |
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from langgraph.constants import START, END |
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from langgraph.graph import MessagesState, StateGraph |
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from langgraph.graph.graph import CompiledGraph |
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from langchain_huggingface import ChatHuggingFace, HuggingFaceEndpoint |
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from langgraph.prebuilt import ToolNode |
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from pydantic import BaseModel |
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from tools import ( |
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get_excel_table_content, |
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get_youtube_video_transcript, |
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reverse_string, |
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transcribe_audio_file, |
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web_page_info_retriever, |
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youtube_video_to_frame_captions, sum_list, execute_python_script, |
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) |
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class AgentFactory: |
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""" |
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A factory for the agent. It is assumed that an Ollama server is running |
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on the machine where the factory is used. |
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""" |
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__system_prompt: str = ( |
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"You have to answer to some test questions.\n" |
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"Sometimes auxiliary files may be attached to the question.\n" |
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"Each question is a JSON string with the following fields:\n" |
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"1. task_id: unique hash identifier of the question.\n" |
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"2. question: the text of the question.\n" |
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"3. Level: ignore this field.\n" |
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"4. file_name: the name of the file needed to answer the question. " |
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"This is empty if the question does not refer to any file. " |
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"IMPORTANT: The text of the question may mention a file name that is " |
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"different from what is reported into the \"file_name\" JSON field. " |
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"YOU HAVE TO IGNORE THE FILE NAME MENTIONED INTO \"question\" AND " |
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"YOU MUST USE THE FILE NAME PROVIDED INTO THE \"file_name\" FIELD.\n" |
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"\n" |
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"Achieve the solution by dividing your reasoning in steps, and\n" |
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"provide an explanation for each step.\n" |
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"\n" |
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"The format of your final answer must be\n" |
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"\n" |
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"<ANSWER>your_final_answer</Answer>, where your_final_answer is a\n" |
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"number OR as few words as possible OR a comma separated list of\n" |
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"numbers and/or strings. If you are asked for\n" |
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"a number, don't use comma to write your number neither use units\n" |
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"such as $ or percent sign unless specified otherwise. If you are\n" |
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"asked for a string, don't use articles, neither abbreviations (e.g.\n" |
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"for cities), and write the digits in plain text unless specified\n" |
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"otherwise. If you are asked for a comma separated list, apply the\n" |
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"above rules depending of whether the element to be put in the list\n" |
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"is a number or a string.\n" |
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"ALWAYS PRESENT THE FINAL ANSWER BETWEEN THE <ANSWER> AND </ANSWER>\n" |
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"TAGS.\n" |
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"\n" |
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"When, for achieving the solution, you have to perform a sum, DON'T\n" |
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"try to do that yourself. Exploit the tool that is able to sum a list\n" |
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" of numbers. If you have to sum the results of previous sums, use\n" |
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"again the same tool, by calling it again.\n" |
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"You are advised to cycle between reasoning and tool calling also\n" |
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"multiple times. Provide an answer only when you are sure you don't\n" |
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"have to call any tool again." |
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) |
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__llm: Runnable |
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__tools: list[BaseTool] |
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def __init__( |
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self, |
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model: str = "qwen2.5-coder:32b", |
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temperature: float = 0.0, |
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num_ctx: int = 8192 |
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) -> None: |
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""" |
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Constructor. |
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Args: |
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model: The name of the Ollama model to use. |
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temperature: Temperature parameter. |
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num_ctx: Size of the context window used to generate the |
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next token. |
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""" |
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search_tool = DuckDuckGoSearchResults( |
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description=( |
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"A wrapper around Duck Duck Go Search. Useful for when you " |
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"need to answer questions about information you can find on " |
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"the web. Input should be a search query. It is advisable to " |
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"use this tool to retrieve web page URLs and use another tool " |
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"to analyze the pages. If the web source is suggested by the " |
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"user query, prefer retrieving information from that source. " |
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"For example, the query may suggest to search on Wikipedia or " |
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"Medium. In those cases, prepend the query with " |
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"'site: <name of the source>'. For example: " |
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"'site: wikipedia.org'" |
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), |
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output_format="list" |
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) |
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search_tool.with_retry() |
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self.__tools = [ |
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execute_python_script, |
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get_excel_table_content, |
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get_youtube_video_transcript, |
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reverse_string, |
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search_tool, |
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sum_list, |
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transcribe_audio_file, |
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web_page_info_retriever, |
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youtube_video_to_frame_captions |
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] |
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self.__llm = ChatOllama( |
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model=model, |
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temperature=temperature, |
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num_ctx=num_ctx |
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).bind_tools(tools=self.__tools) |
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def __run_llm(self, state: MessagesState) -> dict[str, Any]: |
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answer = self.__llm.invoke(state["messages"]) |
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pattern = r'\n*<think>.*?</think>\n*' |
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answer.content = re.sub( |
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pattern, "", answer.content, flags=re.DOTALL |
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) |
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return {"messages": [answer]} |
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@staticmethod |
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def __extract_last_message( |
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state: list[AnyMessage] | dict[str, Any] | BaseModel, |
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messages_key: str |
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) -> str: |
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if isinstance(state, list): |
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last_message = state[-1] |
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elif isinstance(state, dict) and (messages := state.get(messages_key, [])): |
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last_message = messages[-1] |
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elif messages := getattr(state, messages_key, []): |
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last_message = messages[-1] |
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else: |
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raise ValueError(f"No messages found in input state to tool_edge: {state}") |
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return last_message |
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def __route_from_llm( |
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self, |
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state: list[AnyMessage] | dict[str, Any] | BaseModel, |
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messages_key: str = "messages", |
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) -> Literal["tools", "extract_final_answer"]: |
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ai_message = self.__extract_last_message(state, messages_key) |
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if hasattr(ai_message, "tool_calls") and len(ai_message.tool_calls) > 0: |
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return "tools" |
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return "extract_final_answer" |
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@staticmethod |
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def __extract_final_answer(state: MessagesState) -> dict[str, Any]: |
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last_message = state["messages"][-1].content |
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pattern = r"<ANSWER>(?P<answer>.*?)</ANSWER>" |
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m = re.search(pattern, last_message, flags=re.DOTALL) |
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answer = m.group("answer").strip() if m else "" |
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return {"messages": [answer]} |
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@property |
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def system_prompt(self) -> SystemMessage: |
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""" |
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Returns: |
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The system prompt to use with the agent. |
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""" |
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return SystemMessage(content=self.__system_prompt) |
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def get(self) -> CompiledGraph: |
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""" |
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Factory method. |
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Returns: |
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The instance of the agent. |
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""" |
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graph_builder = StateGraph(MessagesState) |
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graph_builder.add_node("LLM", self.__run_llm) |
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graph_builder.add_node("tools", ToolNode(tools=self.__tools)) |
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graph_builder.add_node( |
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"extract_final_answer", |
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self.__extract_final_answer |
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) |
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graph_builder.add_edge(start_key=START, end_key="LLM") |
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graph_builder.add_conditional_edges( |
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source="LLM", |
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path=self.__route_from_llm, |
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path_map={ |
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"tools": "tools", |
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"extract_final_answer": "extract_final_answer" |
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} |
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) |
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graph_builder.add_edge(start_key="tools", end_key="LLM") |
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graph_builder.add_edge(start_key="extract_final_answer", end_key=END) |
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return graph_builder.compile() |
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