mirror of
https://github.com/hwchase17/langchain
synced 2024-11-06 03:20:49 +00:00
Eric Zhu
GitHub
parent
50626a10ee
commit
b3be83c750
344
docs/modules/agents/examples/human_tools.ipynb
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344
docs/modules/agents/examples/human_tools.ipynb
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{
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"cells": [
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{
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"attachments": {},
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Human as a tool\n",
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"\n",
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"Human are AGI so they can certainly be used as a tool to help out AI agent \n",
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"when it is confused."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"import sys\n",
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"from langchain.chat_models import ChatOpenAI\n",
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"from langchain.llms import OpenAI\n",
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"from langchain.agents import load_tools, initialize_agent\n",
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"\n",
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"llm = ChatOpenAI(temperature=0.0)\n",
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"math_llm = OpenAI(temperature=0.0)\n",
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"tools = load_tools(\n",
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" [\"human\", \"llm-math\"], \n",
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" llm=math_llm, \n",
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" prompt_func=lambda s: sys.stdout.write(s + \"\\n\"), \n",
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" input_func=input,\n",
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")\n",
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"\n",
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"agent_chain = initialize_agent(\n",
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" tools,\n",
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" llm,\n",
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" agent=\"zero-shot-react-description\",\n",
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" verbose=True,\n",
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")"
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]
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"In the above code you can see the tool takes input directly from command line.\n",
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"You can customize `prompt_func` and `input_func` according to your need."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 13,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"\n",
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"\n",
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"\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
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"\n",
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"\n",
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"\u001b[1m> Entering new LLMChain chain...\u001b[0m\n",
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"Prompt after formatting:\n",
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"\u001b[32;1m\u001b[1;3mAnswer the following questions as best you can. You have access to the following tools:\n",
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"\n",
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"Human: A human. Useful for clarification, confirmation, or anything other tools cannot help.Input should be a question you need help with.\n",
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"Calculator: Useful for when you need to answer questions about math.\n",
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"\n",
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"Use the following format:\n",
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"\n",
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"Question: the input question you must answer\n",
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"Thought: you should always think about what to do\n",
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"Action: the action to take, should be one of [Human, Calculator]\n",
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"Action Input: the input to the action\n",
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"Observation: the result of the action\n",
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"... (this Thought/Action/Action Input/Observation can repeat N times)\n",
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"Thought: I now know the final answer\n",
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"Final Answer: the final answer to the original input question\n",
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"\n",
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"Begin!\n",
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"\n",
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"Question: What is Eric Zhu's birthday?\n",
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"Thought:\u001b[0m\n",
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"\n",
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"\u001b[1m> Finished chain.\u001b[0m\n",
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"\u001b[32;1m\u001b[1;3mI don't have any information on Eric Zhu's birthday, so I'll need to ask a human.\n",
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"Action: Human\n",
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"Action Input: \"Do you know Eric Zhu's birthday?\"\u001b[0mDo you know Eric Zhu's birthday?\n",
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"\n",
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"Observation: \u001b[36;1m\u001b[1;3mYes\u001b[0m\n",
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"Thought:\n",
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"\n",
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"\u001b[1m> Entering new LLMChain chain...\u001b[0m\n",
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"Prompt after formatting:\n",
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"\u001b[32;1m\u001b[1;3mAnswer the following questions as best you can. You have access to the following tools:\n",
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"\n",
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"Human: A human. Useful for clarification, confirmation, or anything other tools cannot help.Input should be a question you need help with.\n",
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"Calculator: Useful for when you need to answer questions about math.\n",
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"\n",
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"Use the following format:\n",
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"\n",
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"Question: the input question you must answer\n",
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"Thought: you should always think about what to do\n",
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"Action: the action to take, should be one of [Human, Calculator]\n",
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"Action Input: the input to the action\n",
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"Observation: the result of the action\n",
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"... (this Thought/Action/Action Input/Observation can repeat N times)\n",
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"Thought: I now know the final answer\n",
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"Final Answer: the final answer to the original input question\n",
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"\n",
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"Begin!\n",
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"\n",
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"Question: What is Eric Zhu's birthday?\n",
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"Thought:I don't have any information on Eric Zhu's birthday, so I'll need to ask a human.\n",
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"Action: Human\n",
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"Action Input: \"Do you know Eric Zhu's birthday?\"\n",
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"Observation: Yes\n",
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"Thought:\u001b[0m\n",
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"\n",
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"\u001b[1m> Finished chain.\u001b[0m\n",
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"\u001b[32;1m\u001b[1;3mGreat, now I just need to wait for the human to provide the answer.\n",
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"Action: Human\n",
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"Action Input: \"What is Eric Zhu's birthday?\"\u001b[0mWhat is Eric Zhu's birthday?\n",
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"\n",
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"Observation: \u001b[36;1m\u001b[1;3mIt's today\u001b[0m\n",
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"Thought:\n",
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"\n",
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"\u001b[1m> Entering new LLMChain chain...\u001b[0m\n",
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"Prompt after formatting:\n",
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"\u001b[32;1m\u001b[1;3mAnswer the following questions as best you can. You have access to the following tools:\n",
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"\n",
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"Human: A human. Useful for clarification, confirmation, or anything other tools cannot help.Input should be a question you need help with.\n",
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"Calculator: Useful for when you need to answer questions about math.\n",
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"\n",
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"Use the following format:\n",
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"\n",
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"Question: the input question you must answer\n",
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"Thought: you should always think about what to do\n",
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"Action: the action to take, should be one of [Human, Calculator]\n",
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"Action Input: the input to the action\n",
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"Observation: the result of the action\n",
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"... (this Thought/Action/Action Input/Observation can repeat N times)\n",
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"Thought: I now know the final answer\n",
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"Final Answer: the final answer to the original input question\n",
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"\n",
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"Begin!\n",
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"\n",
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"Question: What is Eric Zhu's birthday?\n",
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"Thought:I don't have any information on Eric Zhu's birthday, so I'll need to ask a human.\n",
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"Action: Human\n",
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"Action Input: \"Do you know Eric Zhu's birthday?\"\n",
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"Observation: Yes\n",
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"Thought:Great, now I just need to wait for the human to provide the answer.\n",
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"Action: Human\n",
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"Action Input: \"What is Eric Zhu's birthday?\"\n",
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"Observation: It's today\n",
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"Thought:\u001b[0m\n",
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"\n",
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"\u001b[1m> Finished chain.\u001b[0m\n",
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"\u001b[32;1m\u001b[1;3mI need to confirm if the human is sure about the answer.\n",
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"Action: Human\n",
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"Action Input: \"Are you sure it's today?\"\u001b[0mAre you sure it's today?\n",
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"\n",
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"Observation: \u001b[36;1m\u001b[1;3m\u001b[0m\n",
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"Thought:\n",
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"\n",
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"\u001b[1m> Entering new LLMChain chain...\u001b[0m\n",
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"Prompt after formatting:\n",
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"\u001b[32;1m\u001b[1;3mAnswer the following questions as best you can. You have access to the following tools:\n",
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"\n",
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"Human: A human. Useful for clarification, confirmation, or anything other tools cannot help.Input should be a question you need help with.\n",
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"Calculator: Useful for when you need to answer questions about math.\n",
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"\n",
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"Use the following format:\n",
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"\n",
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"Question: the input question you must answer\n",
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"Thought: you should always think about what to do\n",
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"Action: the action to take, should be one of [Human, Calculator]\n",
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"Action Input: the input to the action\n",
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"Observation: the result of the action\n",
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"... (this Thought/Action/Action Input/Observation can repeat N times)\n",
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"Thought: I now know the final answer\n",
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"Final Answer: the final answer to the original input question\n",
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"\n",
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"Begin!\n",
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"\n",
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"Question: What is Eric Zhu's birthday?\n",
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"Thought:I don't have any information on Eric Zhu's birthday, so I'll need to ask a human.\n",
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"Action: Human\n",
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"Action Input: \"Do you know Eric Zhu's birthday?\"\n",
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"Observation: Yes\n",
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"Thought:Great, now I just need to wait for the human to provide the answer.\n",
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"Action: Human\n",
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"Action Input: \"What is Eric Zhu's birthday?\"\n",
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"Observation: It's today\n",
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"Thought:I need to confirm if the human is sure about the answer.\n",
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"Action: Human\n",
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"Action Input: \"Are you sure it's today?\"\n",
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"Observation: \n",
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"Thought:\u001b[0m\n",
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"\n",
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"\u001b[1m> Finished chain.\u001b[0m\n",
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"\u001b[32;1m\u001b[1;3mThe human confirmed that it's today, but I still need to verify it with a calculator.\n",
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"Action: Calculator\n",
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"Action Input: Today's date\u001b[0m\n",
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"\n",
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"\u001b[1m> Entering new LLMMathChain chain...\u001b[0m\n",
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"Today's date\n",
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"\n",
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"\u001b[1m> Entering new LLMChain chain...\u001b[0m\n",
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"Prompt after formatting:\n",
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"\u001b[32;1m\u001b[1;3mTranslate a math problem into Python code that can be executed in Python 3 REPL. Use the output of running this code to answer the question.\n",
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"\n",
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"Question: ${Question with math problem.}\n",
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"```python\n",
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"${Code that solves the problem and prints the solution}\n",
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"```\n",
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"```output\n",
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"${Output of running the code}\n",
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"```\n",
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"Answer: ${Answer}\n",
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"\n",
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"Begin.\n",
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"\n",
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"Question: What is 37593 * 67?\n",
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"\n",
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"```python\n",
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"print(37593 * 67)\n",
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"```\n",
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"```output\n",
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"2518731\n",
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"```\n",
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"Answer: 2518731\n",
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"\n",
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"Question: Today's date\n",
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"\u001b[0m\n",
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"\n",
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"\u001b[1m> Finished chain.\u001b[0m\n",
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"\u001b[32;1m\u001b[1;3m\n",
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"```python\n",
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"import datetime\n",
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"\n",
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"print(datetime.date.today())\n",
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"```\n",
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"\u001b[0m\n",
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"Answer: \u001b[33;1m\u001b[1;3m2023-03-22\n",
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"\u001b[0m\n",
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"\u001b[1m> Finished chain.\u001b[0m\n",
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"\n",
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"Observation: \u001b[33;1m\u001b[1;3mAnswer: 2023-03-22\n",
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"\u001b[0m\n",
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"Thought:\n",
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"\n",
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"\u001b[1m> Entering new LLMChain chain...\u001b[0m\n",
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"Prompt after formatting:\n",
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"\u001b[32;1m\u001b[1;3mAnswer the following questions as best you can. You have access to the following tools:\n",
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"\n",
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"Human: A human. Useful for clarification, confirmation, or anything other tools cannot help.Input should be a question you need help with.\n",
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"Calculator: Useful for when you need to answer questions about math.\n",
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"\n",
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"Use the following format:\n",
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"\n",
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"Question: the input question you must answer\n",
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"Thought: you should always think about what to do\n",
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"Action: the action to take, should be one of [Human, Calculator]\n",
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"Action Input: the input to the action\n",
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"Observation: the result of the action\n",
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"... (this Thought/Action/Action Input/Observation can repeat N times)\n",
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"Thought: I now know the final answer\n",
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"Final Answer: the final answer to the original input question\n",
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"\n",
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"Begin!\n",
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"\n",
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"Question: What is Eric Zhu's birthday?\n",
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"Thought:I don't have any information on Eric Zhu's birthday, so I'll need to ask a human.\n",
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"Action: Human\n",
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"Action Input: \"Do you know Eric Zhu's birthday?\"\n",
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"Observation: Yes\n",
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"Thought:Great, now I just need to wait for the human to provide the answer.\n",
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"Action: Human\n",
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"Action Input: \"What is Eric Zhu's birthday?\"\n",
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"Observation: It's today\n",
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"Thought:I need to confirm if the human is sure about the answer.\n",
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"Action: Human\n",
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"Action Input: \"Are you sure it's today?\"\n",
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"Observation: \n",
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"Thought:The human confirmed that it's today, but I still need to verify it with a calculator.\n",
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"Action: Calculator\n",
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"Action Input: Today's date\n",
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"Observation: Answer: 2023-03-22\n",
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"\n",
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"Thought:\u001b[0m\n",
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"\n",
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"\u001b[1m> Finished chain.\u001b[0m\n",
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"\u001b[32;1m\u001b[1;3mI now know the final answer\n",
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"Final Answer: Eric Zhu's birthday is March 22, 2023.\u001b[0m\n",
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"\n",
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"\u001b[1m> Finished chain.\u001b[0m\n"
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]
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},
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{
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"data": {
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"text/plain": [
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"\"Eric Zhu's birthday is March 22, 2023.\""
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]
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},
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"execution_count": 13,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"\n",
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"agent_chain.run(\"What is Eric Zhu's birthday?\")\n",
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"# Answer with \"it is today.\""
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": ".venv",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.11.2"
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},
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"orig_nbformat": 4
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}
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@ -13,6 +13,7 @@ from langchain.requests import RequestsWrapper
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from langchain.tools.base import BaseTool
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from langchain.tools.bing_search.tool import BingSearchRun
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from langchain.tools.google_search.tool import GoogleSearchResults, GoogleSearchRun
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from langchain.tools.human.tool import HumanInputRun
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from langchain.tools.python.tool import PythonREPLTool
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from langchain.tools.requests.tool import RequestsGetTool
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from langchain.tools.wikipedia.tool import WikipediaQueryRun
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@ -177,6 +178,10 @@ def _get_bing_search(**kwargs: Any) -> BaseTool:
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return BingSearchRun(api_wrapper=BingSearchAPIWrapper(**kwargs))
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def _get_human_tool(**kwargs: Any) -> BaseTool:
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return HumanInputRun(**kwargs)
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_EXTRA_LLM_TOOLS = {
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"news-api": (_get_news_api, ["news_api_key"]),
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"tmdb-api": (_get_tmdb_api, ["tmdb_bearer_token"]),
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@ -195,6 +200,7 @@ _EXTRA_OPTIONAL_TOOLS = {
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"serpapi": (_get_serpapi, ["serpapi_api_key", "aiosession"]),
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"searx-search": (_get_searx_search, ["searx_host"]),
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"wikipedia": (_get_wikipedia, ["top_k_results"]),
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"human": (_get_human_tool, ["prompt_func", "input_func"]),
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}
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1
langchain/tools/human/__init__.py
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1
langchain/tools/human/__init__.py
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"""Tool for asking for human input."""
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27
langchain/tools/human/tool.py
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27
langchain/tools/human/tool.py
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"""Tool for asking human input."""
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from typing import Callable
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from langchain.tools.base import BaseTool
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class HumanInputRun(BaseTool):
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"""Tool that adds the capability to ask user for input."""
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name = "Human"
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description = (
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"A human. "
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"Useful for clarification, confirmation, or anything other tools cannot help. "
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"Input should be a question you need help with."
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)
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prompt_func: Callable[[str], None]
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input_func: Callable
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def _run(self, query: str) -> str:
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"""Use the Human input tool."""
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self.prompt_func(query)
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return self.input_func()
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async def _arun(self, query: str) -> str:
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"""Use the Human tool asynchronously."""
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raise NotImplementedError("Human tool does not support async")
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Block a user