09_rag/02_langchain_llama2_chatbot.ipynb

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   "metadata": {
    "id": "JPdQvYmlWmNc"
   },
   "source": [
    "# Llama2 Chatbot in Hugging Face and LangChain\n",
    "\n",
    "In this notebook we'll explore how we can use the open source **Llama-70b-chat** model in both Hugging Face transformers and LangChain.\n",
    "At the time of writing, you must first request access to Llama 2 models via [this form]() (access is typically granted within a few hours).\n",
    "\n",
    "We start by doing a `pip install` of all required libraries."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "K_fRq0BSGMBk",
    "outputId": "94aaa24d-53c2-4fc0-f89f-4902a65a8123",
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n",
      "\u001b[0m\n",
      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.2.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m23.3\u001b[0m\n",
      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\n"
     ]
    }
   ],
   "source": [
    "!pip install -qU \\\n",
    "    transformers==4.31.0 \\\n",
    "    accelerate==0.21.0 \\\n",
    "    einops==0.6.1 \\\n",
    "    langchain==0.0.309 \\\n",
    "    xformers==0.0.20 \\\n",
    "    bitsandbytes==0.41.0 \\\n",
    "    torch==2.0.1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "VHQwEeW9Zps2"
   },
   "source": [
    "## Initializing the Hugging Face Pipeline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "mElf068NXout"
   },
   "source": [
    "The first thing we need to do is initialize a `text-generation` pipeline with Hugging Face transformers. The Pipeline requires three things that we must initialize first, those are:\n",
    "\n",
    "* A LLM, in this case it will be a variant of `meta-llama/Llama-2-70b-chat-hf`.\n",
    "\n",
    "* The respective tokenizer for the model.\n",
    "\n",
    "We'll explain these as we get to them, let's begin with our model.\n",
    "\n",
    "We initialize the model and move it to our CUDA-enabled GPU. Using Colab this can take 5-10 minutes to download and initialize the model."
   ]
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    },
    "id": "ikzdi_uMI7B-",
    "outputId": "d4ded4bf-5cab-4e25-d231-d981a90dcece",
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "4704ba9f21fc49cdbe17c629310c5ebb",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Loading checkpoint shards:   0%|          | 0/2 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model loaded on cuda:0\n"
     ]
    }
   ],
   "source": [
    "from torch import cuda, bfloat16\n",
    "import transformers\n",
    "\n",
    "model_id = \"NousResearch/Llama-2-7b-chat-hf\" # not gated\n",
    "\n",
    "device = f'cuda:{cuda.current_device()}' if cuda.is_available() else 'cpu'\n",
    "\n",
    "# set quantization configuration to load large model with less GPU memory\n",
    "# this requires the `bitsandbytes` library\n",
    "bnb_config = transformers.BitsAndBytesConfig(\n",
    "    load_in_4bit=True,\n",
    "    bnb_4bit_quant_type='nf4',\n",
    "    bnb_4bit_use_double_quant=True,\n",
    "    bnb_4bit_compute_dtype=bfloat16\n",
    ")\n",
    "\n",
    "# begin initializing HF items, need auth token for these\n",
    "#hf_auth = '<YOUR_API_KEY>'\n",
    "model_config = transformers.AutoConfig.from_pretrained(\n",
    "    model_id,\n",
    "#    use_auth_token=hf_auth\n",
    ")\n",
    "\n",
    "model = transformers.AutoModelForCausalLM.from_pretrained(\n",
    "    model_id,\n",
    "    trust_remote_code=True,\n",
    "    config=model_config,\n",
    "    quantization_config=bnb_config,\n",
    "    device_map='auto',\n",
    "#    use_auth_token=hf_auth\n",
    ")\n",
    "model.eval()\n",
    "print(f\"Model loaded on {device}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "JzX9LqWSX9ot"
   },
   "source": [
    "The pipeline requires a tokenizer which handles the translation of human readable plaintext to LLM readable token IDs. The Llama 2 70B models were trained using the Llama 2 70B tokenizer, which we initialize like so:"
   ]
  },
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      "e45e5ebbb96c4d6b95ce8da95f2f8ee3",
      "1f75a225da9e4c61b90f499d3dcccf44",
      "b3057dcd2bad4bdca91bb0462c7f022e",
      "19a416b2255a459eb5e38ae16659573e",
      "05816b481eae4bf182fdbb7afb4c46e0",
      "2661f1212bf24795a3dd6aef1f0ef480",
      "24eb4d140be8444599a9a5deceb2c795",
      "57965686944149148385c5a4d7b25f7b",
      "ac9ddc3b629c4cfd9361c837522459a0",
      "3b6dc7f60d164613b10c0cfd5f392b8e",
      "f327c96becb04d379c081cc93702154a",
      "44cb035df14f4244a09615f4df4a1ba7",
      "eece86f33c224f09854751079ee04bcd",
      "1783c6a2114f4fe1b18f217e86b99244",
      "3d5ce661c27648f2897ad6834f7d3b66",
      "0c267c7eb24f4c3da05c540507bd4e3f",
      "23c934ab5d704d279d76d56e831cde02",
      "489398d37a734f01b95d0d709984f9ca",
      "f50066ed61e54105b59ca0b321134b63"
     ]
    },
    "id": "v0iPv1GDGxgT",
    "outputId": "eb0f2f34-7daf-48a3-ce7d-8bb4fdfd189f",
    "tags": []
   },
   "outputs": [],
   "source": [
    "tokenizer = transformers.AutoTokenizer.from_pretrained(\n",
    "    model_id,\n",
    "#    use_auth_token=hf_auth\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "bNysQFtPoaj7"
   },
   "source": [
    "Now we're ready to initialize the HF pipeline. There are a few additional parameters that we must define here. Comments explaining these have been included in the code."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "id": "qAYXi8ayKusU",
    "tags": []
   },
   "outputs": [],
   "source": [
    "generate_text = transformers.pipeline(\n",
    "    model=model, \n",
    "    tokenizer=tokenizer,\n",
    "    return_full_text=True,  # langchain expects the full text\n",
    "    task='text-generation',\n",
    "    # we pass model parameters here too\n",
    "    temperature=0.0,  # 'randomness' of outputs, 0.0 is the min and 1.0 the max\n",
    "    max_new_tokens=512,  # mex number of tokens to generate in the output\n",
    "    repetition_penalty=1.1  # without this output begins repeating\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "8DG1WNTnJF1o"
   },
   "source": [
    "Confirm this is working:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "lhFgmMr0JHUF",
    "outputId": "6b2c7bb1-db8c-4e0f-a6ab-e0f224a79bf1",
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.10/site-packages/transformers/generation/utils.py:1270: UserWarning: You have modified the pretrained model configuration to control generation. This is a deprecated strategy to control generation and will be removed soon, in a future version. Please use a generation configuration file (see https://huggingface.co/docs/transformers/main_classes/text_generation )\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Explain to me the difference between nuclear fission and fusion. Unterscheidung zwischen Nuklearfusion und -fission.\n",
      "Nuclear fission is a process in which an atomic nucleus splits into two or more smaller nuclei, releasing energy in the process. This is typically accomplished through the use of neutron bombardment, where a neutron is absorbed by the nucleus, causing it to split. Fission reactions are typically used in nuclear reactors to generate electricity.\n",
      "Nuclear fusion, on the other hand, is the process by which two or more atomic nuclei combine to form a single, heavier nucleus. This process also releases energy, but it is not as commonly used for generating electricity as fission. Instead, fusion reactions are often studied for their potential to provide a nearly limitless source of clean energy.\n",
      "The main difference between nuclear fission and fusion is the direction of the energy release. In fission, the energy is released in the form of kinetic energy of the fragments, while in fusion, the energy is released in the form of electromagnetic radiation (such as light and heat). Additionally, fission reactions typically involve the splitting of heavy elements into lighter ones, while fusion reactions involve the merging of light elements into heavier ones.\n",
      "In summary, nuclear fission is the process of splitting an atom into two or more smaller atoms, releasing energy in the process, while nuclear fusion is the process of combining two or more atomic nuclei into a single, heavier nucleus, also releasing energy.\n"
     ]
    }
   ],
   "source": [
    "res = generate_text(\"Explain to me the difference between nuclear fission and fusion.\")\n",
    "print(res[0][\"generated_text\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "0N3W3cj3Re1K"
   },
   "source": [
    "Now to implement this in LangChain"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "id": "-8RxQYwHRg0N",
    "tags": []
   },
   "outputs": [],
   "source": [
    "from langchain.llms import HuggingFacePipeline\n",
    "\n",
    "llm = HuggingFacePipeline(pipeline=generate_text)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 178
    },
    "id": "aiW0_FoQWG6J",
    "outputId": "05b4b748-dea8-464b-ee3a-e2aea84a4ae4",
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "' Unterscheidung zwischen Nuklearfusion und -fission.\\nNuclear fission is a process in which an atomic nucleus splits into two or more smaller nuclei, releasing energy in the process. This is typically accomplished through the use of neutron bombardment, where a neutron is absorbed by the nucleus, causing it to split. Fission reactions are typically used in nuclear reactors to generate electricity.\\nNuclear fusion, on the other hand, is the process by which two or more atomic nuclei combine to form a single, heavier nucleus. This process also releases energy, but it is not as commonly used for generating electricity as fission. Instead, fusion reactions are often studied for their potential to provide a nearly limitless source of clean energy.\\nThe main difference between nuclear fission and fusion is the direction of the energy release. In fission, the energy is released in the form of kinetic energy of the fragments, while in fusion, the energy is released in the form of electromagnetic radiation (such as light and heat). Additionally, fission reactions typically involve the splitting of heavy elements into lighter ones, while fusion reactions involve the merging of light elements into heavier ones.\\nIn summary, nuclear fission is the process of splitting an atom into two or more smaller atoms, releasing energy in the process, while nuclear fusion is the process of combining two or more atomic nuclei into a single, heavier nucleus, also releasing energy.'"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "llm(prompt=\"Explain to me the difference between nuclear fission and fusion.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "5tv0KxJLvsIa"
   },
   "source": [
    "We still get the same output as we're not really doing anything differently here, but we have now added **Llama 2 70B Chat** to the LangChain library. Using this we can now begin using LangChain's advanced agent tooling, chains, etc, with **Llama 2**."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "UjTR6XL54fc8"
   },
   "source": [
    "## Initializing an Conversational Agent\n",
    "\n",
    "Getting a conversational agent to work with open source models is incredibly hard. However, with Llama 2 70B it is now possible. Let's see how we can get it running!\n",
    "\n",
    "We first need to initialize the agent. Conversational agents require several things such as conversational `memory`, access to `tools`, and an `llm` (which we have already initialized)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "id": "hmfX0_usXTx3",
    "tags": []
   },
   "outputs": [],
   "source": [
    "from langchain.memory import ConversationBufferWindowMemory\n",
    "from langchain.agents import load_tools\n",
    "\n",
    "memory = ConversationBufferWindowMemory(\n",
    "    memory_key=\"chat_history\", k=5, return_messages=True, output_key=\"output\"\n",
    ")\n",
    "tools = load_tools([\"llm-math\"], llm=llm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "id": "h0jhbRMw48ep",
    "tags": []
   },
   "outputs": [],
   "source": [
    "from langchain.agents import AgentOutputParser\n",
    "from langchain.agents.conversational_chat.prompt import FORMAT_INSTRUCTIONS\n",
    "from langchain.output_parsers.json import parse_json_markdown\n",
    "from langchain.schema import AgentAction, AgentFinish\n",
    "\n",
    "class OutputParser(AgentOutputParser):\n",
    "    def get_format_instructions(self) -> str:\n",
    "        return FORMAT_INSTRUCTIONS\n",
    "\n",
    "    def parse(self, text: str) -> AgentAction | AgentFinish:\n",
    "        try:\n",
    "            # this will work IF the text is a valid JSON with action and action_input\n",
    "            response = parse_json_markdown(text)\n",
    "            action, action_input = response[\"action\"], response[\"action_input\"]\n",
    "            if action == \"Final Answer\":\n",
    "                # this means the agent is finished so we call AgentFinish\n",
    "                return AgentFinish({\"output\": action_input}, text)\n",
    "            else:\n",
    "                # otherwise the agent wants to use an action, so we call AgentAction\n",
    "                return AgentAction(action, action_input, text)\n",
    "        except Exception:\n",
    "            # sometimes the agent will return a string that is not a valid JSON\n",
    "            # often this happens when the agent is finished\n",
    "            # so we just return the text as the output\n",
    "            return AgentFinish({\"output\": text}, text)\n",
    "\n",
    "    @property\n",
    "    def _type(self) -> str:\n",
    "        return \"conversational_chat\"\n",
    "\n",
    "# initialize output parser for agent\n",
    "parser = OutputParser()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "id": "CPwa9SWP4-dR",
    "tags": []
   },
   "outputs": [],
   "source": [
    "from langchain.agents import initialize_agent\n",
    "\n",
    "# initialize agent\n",
    "agent = initialize_agent(\n",
    "    agent=\"chat-conversational-react-description\",\n",
    "    tools=tools,\n",
    "    llm=llm,\n",
    "    verbose=True,\n",
    "    early_stopping_method=\"generate\",\n",
    "    memory=memory,\n",
    "    #agent_kwargs={\"output_parser\": parser}\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "Re7e6Ir361xV",
    "outputId": "a0acc2e3-4fec-49c7-fd7b-c962a2ff5e46",
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ChatPromptTemplate(input_variables=['input', 'chat_history', 'agent_scratchpad'], input_types={'chat_history': typing.List[typing.Union[langchain.schema.messages.AIMessage, langchain.schema.messages.HumanMessage, langchain.schema.messages.ChatMessage, langchain.schema.messages.SystemMessage, langchain.schema.messages.FunctionMessage]], 'agent_scratchpad': typing.List[typing.Union[langchain.schema.messages.AIMessage, langchain.schema.messages.HumanMessage, langchain.schema.messages.ChatMessage, langchain.schema.messages.SystemMessage, langchain.schema.messages.FunctionMessage]]}, messages=[SystemMessagePromptTemplate(prompt=PromptTemplate(input_variables=[], template='Assistant is a large language model trained by OpenAI.\\n\\nAssistant is designed to be able to assist with a wide range of tasks, from answering simple questions to providing in-depth explanations and discussions on a wide range of topics. As a language model, Assistant is able to generate human-like text based on the input it receives, allowing it to engage in natural-sounding conversations and provide responses that are coherent and relevant to the topic at hand.\\n\\nAssistant is constantly learning and improving, and its capabilities are constantly evolving. It is able to process and understand large amounts of text, and can use this knowledge to provide accurate and informative responses to a wide range of questions. Additionally, Assistant is able to generate its own text based on the input it receives, allowing it to engage in discussions and provide explanations and descriptions on a wide range of topics.\\n\\nOverall, Assistant is a powerful system that can help with a wide range of tasks and provide valuable insights and information on a wide range of topics. Whether you need help with a specific question or just want to have a conversation about a particular topic, Assistant is here to assist.')), MessagesPlaceholder(variable_name='chat_history'), HumanMessagePromptTemplate(prompt=PromptTemplate(input_variables=['input'], template='TOOLS\\n------\\nAssistant can ask the user to use tools to look up information that may be helpful in answering the users original question. The tools the human can use are:\\n\\n> Calculator: Useful for when you need to answer questions about math.\\n\\nRESPONSE FORMAT INSTRUCTIONS\\n----------------------------\\n\\nWhen responding to me, please output a response in one of two formats:\\n\\n**Option 1:**\\nUse this if you want the human to use a tool.\\nMarkdown code snippet formatted in the following schema:\\n\\n```json\\n{{\\n    \"action\": string, \\\\ The action to take. Must be one of Calculator\\n    \"action_input\": string \\\\ The input to the action\\n}}\\n```\\n\\n**Option #2:**\\nUse this if you want to respond directly to the human. Markdown code snippet formatted in the following schema:\\n\\n```json\\n{{\\n    \"action\": \"Final Answer\",\\n    \"action_input\": string \\\\ You should put what you want to return to use here\\n}}\\n```\\n\\nUSER\\'S INPUT\\n--------------------\\nHere is the user\\'s input (remember to respond with a markdown code snippet of a json blob with a single action, and NOTHING else):\\n\\n{input}')), MessagesPlaceholder(variable_name='agent_scratchpad')])"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "agent.agent.llm_chain.prompt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "CjFtnFTcOH9_"
   },
   "source": [
    "We need to add special tokens used to signify the beginning and end of instructions, and the beginning and end of system messages. These are described in the Llama-2 model cards on Hugging Face."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "id": "Mt8Ah5cRMBqN",
    "tags": []
   },
   "outputs": [],
   "source": [
    "B_INST, E_INST = \"[INST]\", \"[/INST]\"\n",
    "B_SYS, E_SYS = \"<<SYS>>\\n\", \"\\n<</SYS>>\\n\\n\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "id": "VzrpXdVA8K9r",
    "tags": []
   },
   "outputs": [],
   "source": [
    "sys_msg = B_SYS + \"\"\"Assistant is a expert JSON builder designed to assist with a wide range of tasks.\n",
    "\n",
    "Assistant is able to respond to the User and use tools using JSON strings that contain \"action\" and \"action_input\" parameters.\n",
    "\n",
    "All of Assistant's communication is performed using this JSON format.\n",
    "\n",
    "Assistant can also use tools by responding to the user with tool use instructions in the same \"action\" and \"action_input\" JSON format. Tools available to Assistant are:\n",
    "\n",
    "- \"Calculator\": Useful for when you need to answer questions about math.\n",
    "  - To use the calculator tool, Assistant should write like so:\n",
    "    ```json\n",
    "    {{\"action\": \"Calculator\",\n",
    "      \"action_input\": \"sqrt(4)\"}}\n",
    "    ```\n",
    "\n",
    "Here are some previous conversations between the Assistant and User:\n",
    "\n",
    "User: Hey how are you today?\n",
    "Assistant: ```json\n",
    "{{\"action\": \"Final Answer\",\n",
    " \"action_input\": \"I'm good thanks, how are you?\"}}\n",
    "```\n",
    "User: I'm great, what is the square root of 4?\n",
    "Assistant: ```json\n",
    "{{\"action\": \"Calculator\",\n",
    " \"action_input\": \"sqrt(4)\"}}\n",
    "```\n",
    "User: 2.0\n",
    "Assistant: ```json\n",
    "{{\"action\": \"Final Answer\",\n",
    " \"action_input\": \"It looks like the answer is 2!\"}}\n",
    "```\n",
    "User: Thanks could you tell me what 4 to the power of 2 is?\n",
    "Assistant: ```json\n",
    "{{\"action\": \"Calculator\",\n",
    " \"action_input\": \"4**2\"}}\n",
    "```\n",
    "User: 16.0\n",
    "Assistant: ```json\n",
    "{{\"action\": \"Final Answer\",\n",
    " \"action_input\": \"It looks like the answer is 16!\"}}\n",
    "```\n",
    "\n",
    "Here is the latest conversation between Assistant and User.\"\"\" + E_SYS\n",
    "new_prompt = agent.agent.create_prompt(\n",
    "    system_message=sys_msg,\n",
    "    tools=tools\n",
    ")\n",
    "agent.agent.llm_chain.prompt = new_prompt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "FT59fuKJVEvB"
   },
   "source": [
    "In the Llama 2 paper they mentioned that it was difficult to keep Llama 2 chat models following instructions over multiple interactions. One way they found that improves this is by inserting a reminder of the instructions to each user query. We do that here:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "id": "s9HSoPJknbmI",
    "tags": []
   },
   "outputs": [],
   "source": [
    "instruction = B_INST + \" Respond to the following in JSON with 'action' and 'action_input' values \" + E_INST\n",
    "human_msg = instruction + \"\\nUser: {input}\"\n",
    "\n",
    "agent.agent.llm_chain.prompt.messages[2].prompt.template = human_msg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "KEgrOtXHEnPy",
    "outputId": "ea310ad4-3d4d-4447-eef3-4511f3772548",
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ChatPromptTemplate(input_variables=['input', 'chat_history', 'agent_scratchpad'], input_types={'chat_history': typing.List[typing.Union[langchain.schema.messages.AIMessage, langchain.schema.messages.HumanMessage, langchain.schema.messages.ChatMessage, langchain.schema.messages.SystemMessage, langchain.schema.messages.FunctionMessage]], 'agent_scratchpad': typing.List[typing.Union[langchain.schema.messages.AIMessage, langchain.schema.messages.HumanMessage, langchain.schema.messages.ChatMessage, langchain.schema.messages.SystemMessage, langchain.schema.messages.FunctionMessage]]}, messages=[SystemMessagePromptTemplate(prompt=PromptTemplate(input_variables=[], template='<<SYS>>\\nAssistant is a expert JSON builder designed to assist with a wide range of tasks.\\n\\nAssistant is able to respond to the User and use tools using JSON strings that contain \"action\" and \"action_input\" parameters.\\n\\nAll of Assistant\\'s communication is performed using this JSON format.\\n\\nAssistant can also use tools by responding to the user with tool use instructions in the same \"action\" and \"action_input\" JSON format. Tools available to Assistant are:\\n\\n- \"Calculator\": Useful for when you need to answer questions about math.\\n  - To use the calculator tool, Assistant should write like so:\\n    ```json\\n    {{\"action\": \"Calculator\",\\n      \"action_input\": \"sqrt(4)\"}}\\n    ```\\n\\nHere are some previous conversations between the Assistant and User:\\n\\nUser: Hey how are you today?\\nAssistant: ```json\\n{{\"action\": \"Final Answer\",\\n \"action_input\": \"I\\'m good thanks, how are you?\"}}\\n```\\nUser: I\\'m great, what is the square root of 4?\\nAssistant: ```json\\n{{\"action\": \"Calculator\",\\n \"action_input\": \"sqrt(4)\"}}\\n```\\nUser: 2.0\\nAssistant: ```json\\n{{\"action\": \"Final Answer\",\\n \"action_input\": \"It looks like the answer is 2!\"}}\\n```\\nUser: Thanks could you tell me what 4 to the power of 2 is?\\nAssistant: ```json\\n{{\"action\": \"Calculator\",\\n \"action_input\": \"4**2\"}}\\n```\\nUser: 16.0\\nAssistant: ```json\\n{{\"action\": \"Final Answer\",\\n \"action_input\": \"It looks like the answer is 16!\"}}\\n```\\n\\nHere is the latest conversation between Assistant and User.\\n<</SYS>>\\n\\n')), MessagesPlaceholder(variable_name='chat_history'), HumanMessagePromptTemplate(prompt=PromptTemplate(input_variables=['input'], template=\"[INST] Respond to the following in JSON with 'action' and 'action_input' values [/INST]\\nUser: {input}\")), MessagesPlaceholder(variable_name='agent_scratchpad')])"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "agent.agent.llm_chain.prompt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "_LDoIUbyVbCG"
   },
   "source": [
    "Now we can begin asking questions..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "hjnM44q76itO",
    "outputId": "26cc3c7f-74f1-46ae-a08f-3a1022146f0d",
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n"
     ]
    },
    {
     "ename": "ValueError",
     "evalue": "An output parsing error occurred. In order to pass this error back to the agent and have it try again, pass `handle_parsing_errors=True` to the AgentExecutor. This is the error: Could not parse LLM output: \nAssistant: {\n\"action\": \"Final Answer\",\n\"action_input\": \"I'm good thanks, how are you?\"\n}",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mJSONDecodeError\u001b[0m                           Traceback (most recent call last)",
      "File \u001b[0;32m/opt/conda/lib/python3.10/site-packages/langchain/agents/conversational_chat/output_parser.py:28\u001b[0m, in \u001b[0;36mConvoOutputParser.parse\u001b[0;34m(self, text)\u001b[0m\n\u001b[1;32m     25\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m     26\u001b[0m     \u001b[38;5;66;03m# Attempt to parse the text into a structured format (assumed to be JSON\u001b[39;00m\n\u001b[1;32m     27\u001b[0m     \u001b[38;5;66;03m# stored as markdown)\u001b[39;00m\n\u001b[0;32m---> 28\u001b[0m     response \u001b[38;5;241m=\u001b[39m \u001b[43mparse_json_markdown\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtext\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     30\u001b[0m     \u001b[38;5;66;03m# If the response contains an 'action' and 'action_input'\u001b[39;00m\n",
      "File \u001b[0;32m/opt/conda/lib/python3.10/site-packages/langchain/output_parsers/json.py:136\u001b[0m, in \u001b[0;36mparse_json_markdown\u001b[0;34m(json_string, parser)\u001b[0m\n\u001b[1;32m    135\u001b[0m \u001b[38;5;66;03m# Parse the JSON string into a Python dictionary\u001b[39;00m\n\u001b[0;32m--> 136\u001b[0m parsed \u001b[38;5;241m=\u001b[39m \u001b[43mparser\u001b[49m\u001b[43m(\u001b[49m\u001b[43mjson_str\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    138\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m parsed\n",
      "File \u001b[0;32m/opt/conda/lib/python3.10/json/__init__.py:346\u001b[0m, in \u001b[0;36mloads\u001b[0;34m(s, cls, object_hook, parse_float, parse_int, parse_constant, object_pairs_hook, **kw)\u001b[0m\n\u001b[1;32m    343\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m (\u001b[38;5;28mcls\u001b[39m \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mand\u001b[39;00m object_hook \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mand\u001b[39;00m\n\u001b[1;32m    344\u001b[0m         parse_int \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mand\u001b[39;00m parse_float \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mand\u001b[39;00m\n\u001b[1;32m    345\u001b[0m         parse_constant \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mand\u001b[39;00m object_pairs_hook \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m kw):\n\u001b[0;32m--> 346\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43m_default_decoder\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdecode\u001b[49m\u001b[43m(\u001b[49m\u001b[43ms\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    347\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mcls\u001b[39m \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n",
      "File \u001b[0;32m/opt/conda/lib/python3.10/json/decoder.py:337\u001b[0m, in \u001b[0;36mJSONDecoder.decode\u001b[0;34m(self, s, _w)\u001b[0m\n\u001b[1;32m    333\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"Return the Python representation of ``s`` (a ``str`` instance\u001b[39;00m\n\u001b[1;32m    334\u001b[0m \u001b[38;5;124;03mcontaining a JSON document).\u001b[39;00m\n\u001b[1;32m    335\u001b[0m \n\u001b[1;32m    336\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m--> 337\u001b[0m obj, end \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mraw_decode\u001b[49m\u001b[43m(\u001b[49m\u001b[43ms\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43midx\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m_w\u001b[49m\u001b[43m(\u001b[49m\u001b[43ms\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mend\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    338\u001b[0m end \u001b[38;5;241m=\u001b[39m _w(s, end)\u001b[38;5;241m.\u001b[39mend()\n",
      "File \u001b[0;32m/opt/conda/lib/python3.10/json/decoder.py:355\u001b[0m, in \u001b[0;36mJSONDecoder.raw_decode\u001b[0;34m(self, s, idx)\u001b[0m\n\u001b[1;32m    354\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mStopIteration\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m err:\n\u001b[0;32m--> 355\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m JSONDecodeError(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mExpecting value\u001b[39m\u001b[38;5;124m\"\u001b[39m, s, err\u001b[38;5;241m.\u001b[39mvalue) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m    356\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m obj, end\n",
      "\u001b[0;31mJSONDecodeError\u001b[0m: Expecting value: line 1 column 1 (char 0)",
      "\nThe above exception was the direct cause of the following exception:\n",
      "\u001b[0;31mOutputParserException\u001b[0m                     Traceback (most recent call last)",
      "File \u001b[0;32m/opt/conda/lib/python3.10/site-packages/langchain/agents/agent.py:928\u001b[0m, in \u001b[0;36mAgentExecutor._take_next_step\u001b[0;34m(self, name_to_tool_map, color_mapping, inputs, intermediate_steps, run_manager)\u001b[0m\n\u001b[1;32m    927\u001b[0m     \u001b[38;5;66;03m# Call the LLM to see what to do.\u001b[39;00m\n\u001b[0;32m--> 928\u001b[0m     output \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43magent\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mplan\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    929\u001b[0m \u001b[43m        \u001b[49m\u001b[43mintermediate_steps\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    930\u001b[0m \u001b[43m        \u001b[49m\u001b[43mcallbacks\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mrun_manager\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_child\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mif\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mrun_manager\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01melse\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[1;32m    931\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43minputs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    932\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    933\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m OutputParserException \u001b[38;5;28;01mas\u001b[39;00m e:\n",
      "File \u001b[0;32m/opt/conda/lib/python3.10/site-packages/langchain/agents/agent.py:542\u001b[0m, in \u001b[0;36mAgent.plan\u001b[0;34m(self, intermediate_steps, callbacks, **kwargs)\u001b[0m\n\u001b[1;32m    541\u001b[0m full_output \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mllm_chain\u001b[38;5;241m.\u001b[39mpredict(callbacks\u001b[38;5;241m=\u001b[39mcallbacks, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mfull_inputs)\n\u001b[0;32m--> 542\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43moutput_parser\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mparse\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfull_output\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m/opt/conda/lib/python3.10/site-packages/langchain/agents/conversational_chat/output_parser.py:50\u001b[0m, in \u001b[0;36mConvoOutputParser.parse\u001b[0;34m(self, text)\u001b[0m\n\u001b[1;32m     47\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[1;32m     48\u001b[0m     \u001b[38;5;66;03m# If any other exception is raised during parsing, also raise an\u001b[39;00m\n\u001b[1;32m     49\u001b[0m     \u001b[38;5;66;03m# OutputParserException\u001b[39;00m\n\u001b[0;32m---> 50\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m OutputParserException(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mCould not parse LLM output: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mtext\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01me\u001b[39;00m\n",
      "\u001b[0;31mOutputParserException\u001b[0m: Could not parse LLM output: \nAssistant: {\n\"action\": \"Final Answer\",\n\"action_input\": \"I'm good thanks, how are you?\"\n}",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[17], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43magent\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mhey how are you today?\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m/opt/conda/lib/python3.10/site-packages/langchain/chains/base.py:306\u001b[0m, in \u001b[0;36mChain.__call__\u001b[0;34m(self, inputs, return_only_outputs, callbacks, tags, metadata, run_name, include_run_info)\u001b[0m\n\u001b[1;32m    304\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mBaseException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[1;32m    305\u001b[0m     run_manager\u001b[38;5;241m.\u001b[39mon_chain_error(e)\n\u001b[0;32m--> 306\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m e\n\u001b[1;32m    307\u001b[0m run_manager\u001b[38;5;241m.\u001b[39mon_chain_end(outputs)\n\u001b[1;32m    308\u001b[0m final_outputs: Dict[\u001b[38;5;28mstr\u001b[39m, Any] \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mprep_outputs(\n\u001b[1;32m    309\u001b[0m     inputs, outputs, return_only_outputs\n\u001b[1;32m    310\u001b[0m )\n",
      "File \u001b[0;32m/opt/conda/lib/python3.10/site-packages/langchain/chains/base.py:300\u001b[0m, in \u001b[0;36mChain.__call__\u001b[0;34m(self, inputs, return_only_outputs, callbacks, tags, metadata, run_name, include_run_info)\u001b[0m\n\u001b[1;32m    293\u001b[0m run_manager \u001b[38;5;241m=\u001b[39m callback_manager\u001b[38;5;241m.\u001b[39mon_chain_start(\n\u001b[1;32m    294\u001b[0m     dumpd(\u001b[38;5;28mself\u001b[39m),\n\u001b[1;32m    295\u001b[0m     inputs,\n\u001b[1;32m    296\u001b[0m     name\u001b[38;5;241m=\u001b[39mrun_name,\n\u001b[1;32m    297\u001b[0m )\n\u001b[1;32m    298\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m    299\u001b[0m     outputs \u001b[38;5;241m=\u001b[39m (\n\u001b[0;32m--> 300\u001b[0m         \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call\u001b[49m\u001b[43m(\u001b[49m\u001b[43minputs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrun_manager\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mrun_manager\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    301\u001b[0m         \u001b[38;5;28;01mif\u001b[39;00m new_arg_supported\n\u001b[1;32m    302\u001b[0m         \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_call(inputs)\n\u001b[1;32m    303\u001b[0m     )\n\u001b[1;32m    304\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mBaseException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[1;32m    305\u001b[0m     run_manager\u001b[38;5;241m.\u001b[39mon_chain_error(e)\n",
      "File \u001b[0;32m/opt/conda/lib/python3.10/site-packages/langchain/agents/agent.py:1141\u001b[0m, in \u001b[0;36mAgentExecutor._call\u001b[0;34m(self, inputs, run_manager)\u001b[0m\n\u001b[1;32m   1139\u001b[0m \u001b[38;5;66;03m# We now enter the agent loop (until it returns something).\u001b[39;00m\n\u001b[1;32m   1140\u001b[0m \u001b[38;5;28;01mwhile\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_should_continue(iterations, time_elapsed):\n\u001b[0;32m-> 1141\u001b[0m     next_step_output \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_take_next_step\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m   1142\u001b[0m \u001b[43m        \u001b[49m\u001b[43mname_to_tool_map\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1143\u001b[0m \u001b[43m        \u001b[49m\u001b[43mcolor_mapping\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1144\u001b[0m \u001b[43m        \u001b[49m\u001b[43minputs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1145\u001b[0m \u001b[43m        \u001b[49m\u001b[43mintermediate_steps\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1146\u001b[0m \u001b[43m        \u001b[49m\u001b[43mrun_manager\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mrun_manager\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1147\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1148\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(next_step_output, AgentFinish):\n\u001b[1;32m   1149\u001b[0m         \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_return(\n\u001b[1;32m   1150\u001b[0m             next_step_output, intermediate_steps, run_manager\u001b[38;5;241m=\u001b[39mrun_manager\n\u001b[1;32m   1151\u001b[0m         )\n",
      "File \u001b[0;32m/opt/conda/lib/python3.10/site-packages/langchain/agents/agent.py:939\u001b[0m, in \u001b[0;36mAgentExecutor._take_next_step\u001b[0;34m(self, name_to_tool_map, color_mapping, inputs, intermediate_steps, run_manager)\u001b[0m\n\u001b[1;32m    937\u001b[0m     raise_error \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mFalse\u001b[39;00m\n\u001b[1;32m    938\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m raise_error:\n\u001b[0;32m--> 939\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[1;32m    940\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mAn output parsing error occurred. \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    941\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mIn order to pass this error back to the agent and have it try \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    942\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124magain, pass `handle_parsing_errors=True` to the AgentExecutor. \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    943\u001b[0m         \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mThis is the error: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mstr\u001b[39m(e)\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    944\u001b[0m     )\n\u001b[1;32m    945\u001b[0m text \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mstr\u001b[39m(e)\n\u001b[1;32m    946\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhandle_parsing_errors, \u001b[38;5;28mbool\u001b[39m):\n",
      "\u001b[0;31mValueError\u001b[0m: An output parsing error occurred. In order to pass this error back to the agent and have it try again, pass `handle_parsing_errors=True` to the AgentExecutor. This is the error: Could not parse LLM output: \nAssistant: {\n\"action\": \"Final Answer\",\n\"action_input\": \"I'm good thanks, how are you?\"\n}"
     ]
    }
   ],
   "source": [
    "agent(\"hey how are you today?\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "adBWZl1v-iwA",
    "outputId": "37ad266b-8b5b-4c9b-80e8-6d921c8b4b01",
    "tags": []
   },
   "outputs": [],
   "source": [
    "agent(\"what is 4 to the power of 2.1?\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "E3qWzs1M-sqp",
    "outputId": "5a35464c-0ce3-439c-8a27-4447dfc5ad07",
    "tags": []
   },
   "outputs": [],
   "source": [
    "agent(\"can you multiply that by 3?\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "kkO9FR7VUrZS"
   },
   "source": [
    "With that we have our **open source** conversational agent running on Colab with ~38GB of RAM.\n",
    "\n",
    "---"
   ]
  }
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