Module_3_AI_Agent_with_Mistral_and_LangChain.ipynb

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        "<a href=\"https://colab.research.google.com/github/Yue1Harriet1/LLM-Cookbook/blob/master/Module_3_AI_Agent_with_Mistral_and_LangChain.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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        "# Module 3 - AI Agent with Mistral and LangChain.ipynb\n",
        "\n",
        "This notebook presents an example of how to use the Mistral-7B-Instruct model and LangChain to create an AI agent that can solve math problems using a calculator as a tool.\n",
        "\n",
        "An agent is a program that interacts with a user. It has a main task, which is to solve math problems, and it can use a calculator to do so.\n",
        "\n",
        "![image.png](data:image/png;base64,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)\n"
      ],
      "metadata": {
        "id": "Bl983IX144H1"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Installing required packages\n",
        "\n",
        "For this example, we will install the following libraries:\n",
        "\n",
        "**`transformers`**:\n",
        "\n",
        "Transformers is an open-source library for NLP developed by Hugging Face. It provides state-of-the-art pre-trained models for various NLP tasks, such as text classification, sentiment analysis, question-answering, named entity recognition, etc.\n",
        "\n",
        "**`langchain`**:\n",
        "\n",
        "Langchain is a library that facilitates the building of language model applications. It is designed to be used alongside models like those provided by the transformers library, and it offers tools and functionalities to streamline the integration of language models into various applications. This can include tasks like conversational agents, text generation, and more advanced NLP operations.\n",
        "\n",
        "**`accelerate`**:\n",
        "\n",
        "Accelerate is a library created by Hugging Face, designed to simplify the use of hardware acceleration (such as GPUs and TPUs) in machine learning projects. It provides an easy-to-use interface for distributing training and inference tasks across multiple devices, helping to significantly speed up the processing time for complex models. This library is particularly useful when working with large models or datasets, as it efficiently manages the hardware resources to optimize performance.\n",
        "\n",
        "**`bitsandbytes`**:\n",
        "\n",
        "BitsAndBytes is a library designed to optimize the training of neural networks on modern GPUs. It offers efficient implementations of 8-bit optimizers, which significantly reduce the memory footprint of model parameters and gradients. This reduction in memory usage enables training larger models or using larger batch sizes within the same memory constraints.\n",
        "\n"
      ],
      "metadata": {
        "id": "REBIn1nq5KdT"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install langchain\n",
        "!pip install accelerate\n",
        "!pip install transformers\n",
        "!pip install bitsandbytes"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oBvYAslKsAT1",
        "outputId": "c363b71c-7070-4883-8721-72df629cdc12"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting langchain\n",
            "  Downloading langchain-0.0.341-py3-none-any.whl (1.9 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.9/1.9 MB\u001b[0m \u001b[31m19.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: PyYAML>=5.3 in /usr/local/lib/python3.10/dist-packages (from langchain) (6.0.1)\n",
            "Requirement already satisfied: SQLAlchemy<3,>=1.4 in /usr/local/lib/python3.10/dist-packages (from langchain) (2.0.23)\n",
            "Requirement already satisfied: aiohttp<4.0.0,>=3.8.3 in /usr/local/lib/python3.10/dist-packages (from langchain) (3.8.6)\n",
            "Requirement already satisfied: anyio<4.0 in /usr/local/lib/python3.10/dist-packages (from langchain) (3.7.1)\n",
            "Requirement already satisfied: async-timeout<5.0.0,>=4.0.0 in /usr/local/lib/python3.10/dist-packages (from langchain) (4.0.3)\n",
            "Collecting dataclasses-json<0.7,>=0.5.7 (from langchain)\n",
            "  Downloading dataclasses_json-0.6.3-py3-none-any.whl (28 kB)\n",
            "Collecting jsonpatch<2.0,>=1.33 (from langchain)\n",
            "  Downloading jsonpatch-1.33-py2.py3-none-any.whl (12 kB)\n",
            "Collecting langchain-core<0.0.7,>=0.0.6 (from langchain)\n",
            "  Downloading langchain_core-0.0.6-py3-none-any.whl (174 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m174.2/174.2 kB\u001b[0m \u001b[31m20.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hCollecting langsmith<0.1.0,>=0.0.63 (from langchain)\n",
            "  Downloading langsmith-0.0.67-py3-none-any.whl (47 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m47.0/47.0 kB\u001b[0m \u001b[31m5.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: numpy<2,>=1 in /usr/local/lib/python3.10/dist-packages (from langchain) (1.23.5)\n",
            "Requirement already satisfied: pydantic<3,>=1 in /usr/local/lib/python3.10/dist-packages (from langchain) (1.10.13)\n",
            "Requirement already satisfied: requests<3,>=2 in /usr/local/lib/python3.10/dist-packages (from langchain) (2.31.0)\n",
            "Requirement already satisfied: tenacity<9.0.0,>=8.1.0 in /usr/local/lib/python3.10/dist-packages (from langchain) (8.2.3)\n",
            "Requirement already satisfied: attrs>=17.3.0 in /usr/local/lib/python3.10/dist-packages (from aiohttp<4.0.0,>=3.8.3->langchain) (23.1.0)\n",
            "Requirement already satisfied: charset-normalizer<4.0,>=2.0 in /usr/local/lib/python3.10/dist-packages (from aiohttp<4.0.0,>=3.8.3->langchain) (3.3.2)\n",
            "Requirement already satisfied: multidict<7.0,>=4.5 in /usr/local/lib/python3.10/dist-packages (from aiohttp<4.0.0,>=3.8.3->langchain) (6.0.4)\n",
            "Requirement already satisfied: yarl<2.0,>=1.0 in /usr/local/lib/python3.10/dist-packages (from aiohttp<4.0.0,>=3.8.3->langchain) (1.9.2)\n",
            "Requirement already satisfied: frozenlist>=1.1.1 in /usr/local/lib/python3.10/dist-packages (from aiohttp<4.0.0,>=3.8.3->langchain) (1.4.0)\n",
            "Requirement already satisfied: aiosignal>=1.1.2 in /usr/local/lib/python3.10/dist-packages (from aiohttp<4.0.0,>=3.8.3->langchain) (1.3.1)\n",
            "Requirement already satisfied: idna>=2.8 in /usr/local/lib/python3.10/dist-packages (from anyio<4.0->langchain) (3.4)\n",
            "Requirement already satisfied: sniffio>=1.1 in /usr/local/lib/python3.10/dist-packages (from anyio<4.0->langchain) (1.3.0)\n",
            "Requirement already satisfied: exceptiongroup in /usr/local/lib/python3.10/dist-packages (from anyio<4.0->langchain) (1.1.3)\n",
            "Collecting marshmallow<4.0.0,>=3.18.0 (from dataclasses-json<0.7,>=0.5.7->langchain)\n",
            "  Downloading marshmallow-3.20.1-py3-none-any.whl (49 kB)\n",
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            "Installing collected packages: mypy-extensions, marshmallow, jsonpointer, typing-inspect, langsmith, jsonpatch, langchain-core, dataclasses-json, langchain\n",
            "Successfully installed dataclasses-json-0.6.3 jsonpatch-1.33 jsonpointer-2.4 langchain-0.0.341 langchain-core-0.0.6 langsmith-0.0.67 marshmallow-3.20.1 mypy-extensions-1.0.0 typing-inspect-0.9.0\n",
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            "Collecting bitsandbytes\n",
            "  Downloading bitsandbytes-0.41.2.post2-py3-none-any.whl (92.6 MB)\n",
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            "\u001b[?25hInstalling collected packages: bitsandbytes\n",
            "Successfully installed bitsandbytes-0.41.2.post2\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Setting the device\n",
        "\n",
        "In this example, we will use a GPU to speed up the processing of our model. GPUs (Graphics Processing Units) are specialized processors that are optimized for performing large-scale computations in parallel. By using a GPU, we can accelerate the training and inference of a machine learning model, which can significantly reduce the time required to complete these tasks.\n",
        "\n",
        "Before we begin, we need to check whether a GPU is available and select it as the default device for our PyTorch operations. This is because PyTorch can use either a CPU or a GPU to perform computations, and by default, it will use the CPU.\n",
        "\n",
        "For using a GPU in Google Colab:\n",
        "1. Click on the \"Runtime\" menu at the top of the screen.\n",
        "2. From the dropdown menu, click on \"Change runtime type\".\n",
        "3. In the popup window that appears, select \"GPU\" as the hardware accelerator.\n",
        "4. Click on the \"Save\" button.\n",
        "\n",
        "That's it! Now you can use the GPU for faster computations in your notebook."
      ],
      "metadata": {
        "id": "pGdm9g9fkjxE"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!nvidia-smi"
      ],
      "metadata": {
        "id": "YiV9Y1ZweR2Y",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "41887b39-cd9e-433d-90fa-0fe418024696"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Tue Nov 28 14:30:45 2023       \n",
            "+-----------------------------------------------------------------------------+\n",
            "| NVIDIA-SMI 525.105.17   Driver Version: 525.105.17   CUDA Version: 12.0     |\n",
            "|-------------------------------+----------------------+----------------------+\n",
            "| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |\n",
            "| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |\n",
            "|                               |                      |               MIG M. |\n",
            "|===============================+======================+======================|\n",
            "|   0  Tesla V100-SXM2...  Off  | 00000000:00:04.0 Off |                    0 |\n",
            "| N/A   36C    P0    24W / 300W |      0MiB / 16384MiB |      0%      Default |\n",
            "|                               |                      |                  N/A |\n",
            "+-------------------------------+----------------------+----------------------+\n",
            "                                                                               \n",
            "+-----------------------------------------------------------------------------+\n",
            "| Processes:                                                                  |\n",
            "|  GPU   GI   CI        PID   Type   Process name                  GPU Memory |\n",
            "|        ID   ID                                                   Usage      |\n",
            "|=============================================================================|\n",
            "|  No running processes found                                                 |\n",
            "+-----------------------------------------------------------------------------+\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Import libraries\n",
        "\n",
        "In the following code cell we import all the libraries at once. We will use the libraries as we go along."
      ],
      "metadata": {
        "id": "Ied-Cj4Z5jPh"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import torch\n",
        "\n",
        "from textwrap import fill\n",
        "from IPython.display import Markdown, display\n",
        "\n",
        "from langchain import PromptTemplate\n",
        "from langchain import HuggingFacePipeline\n",
        "\n",
        "from langchain.agents.format_scratchpad import format_log_to_str\n",
        "from langchain.agents.output_parsers import ReActSingleInputOutputParser\n",
        "from langchain.tools.render import render_text_description\n",
        "from langchain.agents import tool\n",
        "from langchain.prompts import PromptTemplate\n",
        "from langchain.tools import  Tool\n",
        "\n",
        "from transformers import BitsAndBytesConfig, AutoModelForCausalLM, AutoTokenizer, GenerationConfig, pipeline\n",
        "\n",
        "import warnings\n",
        "warnings.filterwarnings('ignore')"
      ],
      "metadata": {
        "id": "gDpRyEg5sEOj"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Download and load Mistral-7B-Instruct\n",
        "\n",
        "In this example, we will use Mistral-7B-Instruc, a language model with 7 billion parameters. Before loading the model, we have to define the quantization configuration. The quantization configuration is a dictionary that contains the quantization parameters for each layer. The quantization parameters are defined in the `BitsAndBytesConfig` class. The `BitsAndBytesConfig` class contains the following parameters:\n",
        "\n",
        "- `load_in_4bit`: If True, the model will be loaded in 4-bit. If False, the model will be loaded in 8-bit.\n",
        "- `bnb_4bit_compute_dtype`: The compute dtype for 4-bit quantization. The default value is `torch.float16`.\n",
        "- `bnb_4bit_quant_type`: The quantization type for 4-bit quantization. The default value is `nf4`.\n",
        "- `bnb_4bit_use_double_quant`: If True, the model will be quantized twice. The first quantization is done with the `bnb_4bit_compute_dtype` and the second quantization is done with `torch.float16`. The default value is `True`.\n"
      ],
      "metadata": {
        "id": "gX2olCCMBKni"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "quantization_config = BitsAndBytesConfig(\n",
        "    load_in_4bit=True,\n",
        "    bnb_4bit_compute_dtype=torch.float16,\n",
        "    bnb_4bit_quant_type=\"nf4\",\n",
        "    bnb_4bit_use_double_quant=True,\n",
        ")"
      ],
      "metadata": {
        "id": "16XHPtb6AFD6"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Now, we can load the model and the tokenizer. The `trust_remote_code` parameter is set to `True` to allow the model to be loaded from the HuggingFace model hub. The `device_map` parameter is set to `auto` to allow the model to be loaded on the GPU if available. The `quantization_config` parameter is set to the quantization configuration defined above. We also set the `torch_dtype` parameter to `torch.float16` to load the model in 16-bit. The `use_fast` parameter is set to `True` to load the tokenizer in fast mode. Finally, we set the `pad_token` to the `eos_token` to allow the model to generate sequences of any length.\n"
      ],
      "metadata": {
        "id": "DRX-3FRNAFft"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "MODEL_NAME = \"mistralai/Mistral-7B-Instruct-v0.1\"\n",
        "\n",
        "model = AutoModelForCausalLM.from_pretrained(\n",
        "    MODEL_NAME, torch_dtype=torch.float16,\n",
        "    trust_remote_code=True,\n",
        "    device_map=\"auto\",\n",
        "    quantization_config=quantization_config\n",
        ")\n",
        "\n",
        "tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME, use_fast=True)\n",
        "tokenizer.pad_token = tokenizer.eos_token"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
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          "metadata": {}
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        {
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          "metadata": {}
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      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Then, we can define the generation configuration.\n",
        "- The `max_new_tokens` parameter is set to 1024 to allow the model to generate sequences of up to 1024 tokens.\n",
        "- The `temperature` parameter is set to 0.0001 to allow the model to generate sequences with a very low temperature.\n",
        "- The `top_p` parameter is set to 0.95 to allow the model to generate sequences with a very high top-p.\n",
        "- The `do_sample` parameter is set to `True` to allow the model to generate sequences with sampling. - The `repetition_penalty` parameter is set to 1.15 to allow the model to generate sequences with a high repetition penalty.\n",
        "- Finally, we set the `return_full_text` parameter to `True` to allow the model to return the full generated text."
      ],
      "metadata": {
        "id": "XkFT2ytTAyPj"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "generation_config = GenerationConfig.from_pretrained(MODEL_NAME)\n",
        "generation_config.max_new_tokens = 1024\n",
        "generation_config.temperature = 0.0001\n",
        "generation_config.top_p = 0.95\n",
        "generation_config.do_sample = True\n",
        "generation_config.repetition_penalty = 1.15\n",
        "\n",
        "pipeline = pipeline(\n",
        "    \"text-generation\",\n",
        "    model=model,\n",
        "    tokenizer=tokenizer,\n",
        "    return_full_text=True,\n",
        "    generation_config=generation_config,\n",
        ")"
      ],
      "metadata": {
        "id": "9kQNz87asHMK"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Testing the model\n",
        "\n",
        "Now, we can test the model. First, let's define the LLM object using the `HuggingFacePipeline` from `langchain`. We do this by passing the pipeline defined above to the `pipeline` parameter."
      ],
      "metadata": {
        "id": "RyGf5y8A7ZhM"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "llm = HuggingFacePipeline(\n",
        "    pipeline=pipeline,\n",
        ")"
      ],
      "metadata": {
        "id": "FVQv65GUsKR0"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Now, let's test the model on a simple query."
      ],
      "metadata": {
        "id": "LzwaBzp14U7T"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "query = \"Explain the difference between ChatGPT and open source LLMs in a couple of lines.\"\n",
        "result = llm(\n",
        "    query\n",
        ")\n",
        "\n",
        "display(Markdown(f\"<b>{query}</b>\"))\n",
        "display(Markdown(f\"<p>{result}</p>\"))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 104
        },
        "id": "D23oqMhHsNdM",
        "outputId": "f5d8ff55-d37b-4a87-dad4-43433c5be577"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Markdown object>"
            ],
            "text/markdown": "<b>Explain the difference between ChatGPT and open source LLMs in a couple of lines.</b>"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Markdown object>"
            ],
            "text/markdown": "<p>\nChatGPT is a proprietary model developed by OpenAI, while open source LLMs are models that are made available for anyone to use, modify, and distribute under an open-source license.</p>"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "We can see that the model is able to generate a coherent answer to the query. Now, let's test the model on a math problem."
      ],
      "metadata": {
        "id": "k2EISkxw4mcE"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "query = \"I went to the market and bought 10 apples. I gave 2 apples to the neighbor and 2 to the repairman. I then went and bought 5 more apples and ate 1. How many apples did I remain with?\"\n",
        "result = llm(\n",
        "    query\n",
        ")\n",
        "\n",
        "display(Markdown(f\"<b>{query}</b>\"))\n",
        "display(Markdown(f\"<p>{result}</p>\"))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 104
        },
        "id": "3zwDS7-ztm53",
        "outputId": "1ba62aa4-8aed-4d3c-8f5a-601a13e734a6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Markdown object>"
            ],
            "text/markdown": "<b>I went to the market and bought 10 apples. I gave 2 apples to the neighbor and 2 to the repairman. I then went and bought 5 more apples and ate 1. How many apples did I remain with?</b>"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Markdown object>"
            ],
            "text/markdown": "<p>\nA: 9</p>"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "As we can see, **the model generated a wrong answer**. This may indicate that the model needs more instuctions in the task"
      ],
      "metadata": {
        "id": "lMite7_j5I1G"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# ReAct\n",
        "\n",
        "Let's explore the application of ReAct to enhance our responses. [ReAct](https://arxiv.org/abs/2210.03629) stands out as a novel prompting framework that effectively utilizes Large Language Models (LLMs) to interleave reasoning traces with task-specific actions. This innovative method allows LLMs to articulate not only the end response but also the logical progression leading to that conclusion. The interleaving of reasoning with actionable steps significantly bolsters the model's capacity to manage intricate tasks, yielding more profound, detailed, and precise responses. The transparency in the model's reasoning process is especially beneficial in contexts where tracing the model's thought journey is crucial, enhancing both the trustworthiness and reliability of its output.\n",
        "\n",
        "To implement ReAct, we begin by integrating the LLM with the `stop` parameter. This integration is facilitated through the `bind` method of the `HuggingFacePipeline` from `langchain`. We configure the `stop` parameter as `[\"\\nObservation\"]`, enabling the LLM to pause its response generation upon encountering the `\\nObservation` sequence. In the ReAct framework, \"Observation\" functions as a prompt for the agent to incorporate external tool observations into the model's processing. While the model attempts to self-generate the observation, the introduction of `\\nObservation` signals it to halt and await external observational input from the agent. This mechanism ensures a seamless blend of model-generated insights and external data, enriching the overall response quality."
      ],
      "metadata": {
        "id": "6qWp-B6u718o"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "llm_with_stop = llm.bind(stop=[\"\\nObservation\"])"
      ],
      "metadata": {
        "id": "PBsQA2080rns"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Now, we create our calculator as a tool."
      ],
      "metadata": {
        "id": "yztjUZtwH2te"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "@tool\n",
        "def calculator(calculator_input: str):\n",
        "    \"\"\"Perform a calculation using python `eval()`\"\"\"\n",
        "    try:\n",
        "      return eval(calculator_input)\n",
        "    except:\n",
        "      return \"Invalid syntax. The only thing I can do is process python code.\""
      ],
      "metadata": {
        "id": "TSb9A16_14uF"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Now, we define the tools our agent will have. In this case, we use only the calculator as a tool."
      ],
      "metadata": {
        "id": "ih_q6_rSIVWp"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "tools = [\n",
        "    Tool.from_function(\n",
        "        func = calculator,\n",
        "        name = \"Calculator\",\n",
        "        description = \"Perform a calculation using python `eval()`\"\n",
        "    )\n",
        "]"
      ],
      "metadata": {
        "id": "hXpbGmc93Qlr"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Next, we define the prompt template. This template contains the following placeholders: `{tools}`, `{tool_names}`, `{input}`, and `{agent_scratchpad}`.\n",
        "- The `{tools}` placeholder will be replaced with the list of tools.\n",
        "- The `{tool_names}` placeholder will be replaced with the list of tool names.\n",
        "- The `{input}` placeholder will be replaced with the input query.\n",
        "- The `{agent_scratchpad}` placeholder is used in langchain to the agent put its intermediary work.\n",
        "\n",
        "**IMPORTANT**: We have to adapt ReAct prompt to be used with Mistral. To do this, we add the `[INST]` sequence to the beginning of the prompt and the `[/INST]` sequence to the end of the prompt. As Mistral uses the `[INST]` and `[/INST]` sequences to know when to start and stop generating the response, we have to better instruct the agent by giving it some examples. We do this by adding the `Question`, `Thought`, `Action`, `Action Input`, and `Observation` sequences to the prompt. We also add the `Final Answer` sequence to the prompt to indicate the agent that it has to generate the final answer. Finally, we add the `Begin!` sequence to the prompt to indicate the agent that it has to start generating the response. You can find a reference of the ReAct prompt [here](https://github.com/run-llama/llama_index/blob/a87b63fce3cc3d24dc71ae170a8d431440025565/llama_index/agent/react/prompts.py#L7).\n"
      ],
      "metadata": {
        "id": "Vluhkt5wIXui"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "template = \"\"\"[INST]You are designed to help with a variety of tasks, from answering questions to providing summaries to other types of analyses.\n",
        "\n",
        "## Tools\n",
        "You have access to a wide variety of tools. You are responsible for using the tools in any sequence you deem appropriate to complete the task at hand.\n",
        "This may require breaking the task into subtasks and using different tools to complete each subtask.\n",
        "\n",
        "You have access to the following tools:\n",
        "\n",
        "{tools}\n",
        "\n",
        "## Output Format\n",
        "To answer the question, please use the following format:\n",
        "\n",
        "```\n",
        "Thought: I need to use a tool to help me answer the question.\n",
        "Action: tool name (one of {tool_names}) if using a tool.\n",
        "Action Input: the input to the tool.\n",
        "```\n",
        "\n",
        "Please ALWAYS start with a Thought.\n",
        "\n",
        "If this format is used, the user will respond in the following format:\n",
        "\n",
        "```\n",
        "Observation: tool response\n",
        "```\n",
        "\n",
        "You MUST respond in the one of the following two formats:\n",
        "\n",
        "```\n",
        "Thought: I can answer without using any more tools.\n",
        "Final Answer: [your answer here] <s/>\n",
        "```\n",
        "\n",
        "```\n",
        "Thought: I cannot answer the question with the provided tools.\n",
        "Final Answer: Sorry, I cannot answer your query.</s>\n",
        "```\n",
        "\n",
        "## Current Conversation\n",
        "Below is the current conversation consisting of interleaving human and assistant messages.\n",
        "\n",
        "Question: {input}[/INST]\n",
        "Thought:{agent_scratchpad}\"\"\""
      ],
      "metadata": {
        "id": "Chqg73_BiYdh"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Now, we define the prompt using the `PromptTemplate` from `langchain`. We pass the template to the `template` parameter. We set the `input_variables` parameter to `[\"agent_scratchpad\", \"input\"]` to allow the template to use the `agent_scratchpad` and `input` variables.\n"
      ],
      "metadata": {
        "id": "FaDGS8X_JBQw"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "prompt = PromptTemplate(\n",
        "    input_variables=['agent_scratchpad', 'input'],\n",
        "    template=template\n",
        ")"
      ],
      "metadata": {
        "id": "gXvVqP9rJP6B"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Now, we have to fill the `{tools}` and `{tool_names}` placeholders. We do this by using the `render_text_description` function from `langchain`. We pass the list of tools to the `tools` parameter. We pass the list of tool names to the `tool_names` parameter."
      ],
      "metadata": {
        "id": "wI0nVxBAJQq-"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "prompt = prompt.partial(\n",
        "    tools=render_text_description(tools),\n",
        "    tool_names=\", \".join([t.name for t in tools]),\n",
        ")\n",
        "\n",
        "prompt"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UQVZmC3shz3K",
        "outputId": "1824b5e8-e252-482a-8026-9c1d00fe6b5a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "PromptTemplate(input_variables=['agent_scratchpad', 'input'], partial_variables={'tools': 'Calculator: Perform a calculation using python `eval()`', 'tool_names': 'Calculator'}, template='[INST]You are designed to help with a variety of tasks, from answering questions to providing summaries to other types of analyses.\\n\\n## Tools\\nYou have access to a wide variety of tools. You are responsible for using the tools in any sequence you deem appropriate to complete the task at hand.\\nThis may require breaking the task into subtasks and using different tools to complete each subtask.\\n\\nYou have access to the following tools:\\n\\n{tools}\\n\\n## Output Format\\nTo answer the question, please use the following format:\\n\\n```\\nThought: I need to use a tool to help me answer the question.\\nAction: tool name (one of {tool_names}) if using a tool.\\nAction Input: the input to the tool.\\n```\\n\\nPlease ALWAYS start with a Thought.\\n\\nIf this format is used, the user will respond in the following format:\\n\\n```\\nObservation: tool response\\n```\\n\\nYou MUST respond in the one of the following two formats:\\n\\n```\\nThought: I can answer without using any more tools.\\nFinal Answer: [your answer here] </>\\n```\\n\\n```\\nThought: I cannot answer the question with the provided tools.\\nFinal Answer: Sorry, I cannot answer your query.</s>\\n```\\n\\n## Current Conversation\\nBelow is the current conversation consisting of interleaving human and assistant messages.\\n\\nQuestion: {input}[/INST]\\nThought:{agent_scratchpad}')"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Finally, we define the agent using the `ReActSingleInputOutputParser` from `langchain`. We pass the prompt to the `prompt` parameter. We set the `input` parameter to `lambda x: x[\"input\"]` to allow the agent to use the input query. We set the `agent_scratchpad` parameter to `lambda x: format_log_to_str(x[\"intermediate_steps\"])` to allow the agent to use the intermediary work."
      ],
      "metadata": {
        "id": "D9YrWZ8vKbkq"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "agent = (\n",
        "    {\n",
        "        \"input\": lambda x: x[\"input\"],\n",
        "        \"agent_scratchpad\": lambda x: format_log_to_str(x[\"intermediate_steps\"]),\n",
        "    }\n",
        "    | prompt\n",
        "    | llm_with_stop\n",
        "    | ReActSingleInputOutputParser()\n",
        ")"
      ],
      "metadata": {
        "id": "lRItLldl0urD"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "We also define the agent executor. We do this by using the `AgentExecutor` from `langchain`. We pass the agent to the `agent` parameter. We pass the tools to the `tools` parameter. We set the `verbose` parameter to `True` to allow the agent executor to print the intermediary work. We set the `handle_parsing_errors` parameter to `True` to allow the agent executor to handle parsing errors by returning the error message to the model so that the model can generate a coherent answer."
      ],
      "metadata": {
        "id": "Sb_t1BNPKrs-"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from langchain.agents import AgentExecutor\n",
        "agent_executor = AgentExecutor(agent=agent, tools=tools, verbose=True,handle_parsing_errors=True)"
      ],
      "metadata": {
        "id": "RiON7LsK0zzT"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Let's test our agent."
      ],
      "metadata": {
        "id": "PnRYDyfhKtKP"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "answer = agent_executor.invoke(\n",
        "    {\n",
        "        \"input\": \"I went to the market and bought 10 apples. I gave 2 apples to the neighbor and 2 to the repairman. I then went and bought 5 more apples and ate 1. How many apples did I remain with?\"\n",
        "    }\n",
        ")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rG5pQaHF3YUh",
        "outputId": "3b3524b6-fa6d-47b0-adbe-d464aa0e3883"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "\n",
            "\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[32;1m\u001b[1;3m I need to calculate how many apples were left after giving some away and buying more.\n",
            "Action: Calculator\n",
            "Action Input: \"10 - 2 - 2 + 5 - 1\"\u001b[0m\u001b[36;1m\u001b[1;3m10\u001b[0m\u001b[32;1m\u001b[1;3m10 apples were initially purchased. After giving 2 to the neighbor and 2 to the repairman, there were 6 apples remaining. Then, 5 more apples were bought, bringing the total number of apples to 11. Finally, 1 apple was eaten, leaving 10 apples.\n",
            "Final Answer: 10 apples remained.\u001b[0m\n",
            "\n",
            "\u001b[1m> Finished chain.\u001b[0m\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "And check the final answer.\n",
        "\n",
        "The expected final answer is **10**."
      ],
      "metadata": {
        "id": "rBmujReZKx5K"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "display(Markdown(f\"<b>{answer['input']}</b>\"))\n",
        "display(Markdown(f\"<p>{answer['output']}</p>\"))"
      ],
      "metadata": {
        "id": "uKRNclqqushG",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 87
        },
        "outputId": "93c236b5-5800-4ca9-bbee-1d82dcc71530"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Markdown object>"
            ],
            "text/markdown": "<b>I went to the market and bought 10 apples. I gave 2 apples to the neighbor and 2 to the repairman. I then went and bought 5 more apples and ate 1. How many apples did I remain with?</b>"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Markdown object>"
            ],
            "text/markdown": "<p>10 apples remained.</p>"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "answer = agent_executor.invoke(\n",
        "    {\n",
        "        \"input\": \"A juggler can juggle 16 balls. Half of the balls are golf balls, and half of the golf balls are blue. How many blue golf balls are there?\"\n",
        "    }\n",
        ")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "b0hlZy2BTxLM",
        "outputId": "83487273-a28b-47d6-f2a5-d32bb62a87d1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "\n",
            "\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[32;1m\u001b[1;3m I need to use a calculator to determine how many golf balls are blue.\n",
            "Action: Calculator\n",
            "Action Input: \"half of 8\" (since half of the golf balls are blue)\u001b[0m\u001b[36;1m\u001b[1;3mInvalid syntax. The only thing I can do is process python code.\u001b[0m\u001b[32;1m\u001b[1;3m\n",
            "Final Answer: Sorry, I cannot understand that input. Please provide a valid input.\u001b[0m\n",
            "\n",
            "\u001b[1m> Finished chain.\u001b[0m\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "As we can see, sometimes it falls to provide a consistent function input."
      ],
      "metadata": {
        "id": "9dDbEsN5Uuqd"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "display(Markdown(f\"<b>{answer['input']}</b>\"))\n",
        "display(Markdown(f\"<p>{answer['output']}</p>\"))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 70
        },
        "id": "BE0tuwFuUCGZ",
        "outputId": "afc37830-670d-460d-9969-ec82167fe786"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Markdown object>"
            ],
            "text/markdown": "<b>A juggler can juggle 16 balls. Half of the balls are golf balls, and half of the golf balls are blue. How many blue golf balls are there?</b>"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Markdown object>"
            ],
            "text/markdown": "<p>Sorry, I cannot understand that input. Please provide a valid input.</p>"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "answer = agent_executor.invoke(\n",
        "    {\n",
        "        \"input\": \"Roger has 5 tennis balls. He buys 2 more cans of tennis balls. Each can has 3 tennis balls. How many tennis balls does he have now?\"\n",
        "    }\n",
        ")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jJ-3yjGSU0Vt",
        "outputId": "7bfe94f6-889b-4095-a3f5-d1d42143e803"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "\n",
            "\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[32;1m\u001b[1;3m I need to calculate how many tennis balls Roger has after buying more.\n",
            "Action: Calculator\n",
            "Action Input: 5 + (2 * 3)\u001b[0m\u001b[36;1m\u001b[1;3m11\u001b[0m\u001b[32;1m\u001b[1;3m11 tennis balls.\n",
            "Final Answer: Roger has 11 tennis balls.\u001b[0m\n",
            "\n",
            "\u001b[1m> Finished chain.\u001b[0m\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "display(Markdown(f\"<b>{answer['input']}</b>\"))\n",
        "display(Markdown(f\"<p>{answer['output']}</p>\"))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 70
        },
        "id": "R65wHwn9U71E",
        "outputId": "c72ba524-58e3-453a-ef24-80fcc3fe539a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Markdown object>"
            ],
            "text/markdown": "<b>Roger has 5 tennis balls. He buys 2 more cans of tennis balls. Each can has 3 tennis balls. How many tennis balls does he have now?</b>"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Markdown object>"
            ],
            "text/markdown": "<p>Roger has 11 tennis balls.</p>"
          },
          "metadata": {}
        }
      ]
    }
  ]
}