{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8Ksv005GbN2w"
      },
      "source": [
        "# 🧪🐦‍⬛ Needle in a Jungle - Information Extraction via LLMs\n",
        "\n",
        "<img src=\"https://haystack.deepset.ai/images/haystack-ogimage.png\" width=\"430\" style=\"display:inline;\">&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;<img src=\"https://huggingface.co/Nexusflow/NexusRaven-V2-13B/resolve/main/NexusRaven.png\" width=\"250\" style=\"display:inline;\">\n",
        "\n",
        "*Notebook by [Stefano Fiorucci](https://github.com/anakin87)*\n",
        "\n",
        "In this experiment, we will use Large Language Models to perform Information Extraction from textual data.\n",
        "\n",
        "🎯 Goal: create an application that, given a URL and a specific structure provided by the user, extracts information from the source.\n",
        "\n",
        "The \"**function calling**\" capabilities of OpenAI models unlock this task: the user can describe a structure, by defining a mock up function with all its typed and specific parameters. The LLM will prepare the data in this specific form and send it back to the user.\n",
        "\n",
        "A nice example of using OpenAI Function Calling for information extraction is this [gist by Kyle McDonald](https://gist.github.com/kylemcdonald/dbac21de2d7855633689f5526225154c).\n",
        "\n",
        "**What is changing now is that open models such as NexusRaven are emerging, with function calling capabilities...**\n",
        "\n",
        "*This is an improved version of an older experiment, using Gorilla Open Functions*\n",
        "\n",
        "\n",
        "**Stack**\n",
        "- **[NexusRaven](https://huggingface.co/Nexusflow/NexusRaven-V2-13B)**: an open-source and commercially viable function calling model that surpasses the state-of-the-art in function calling capabilities.\n",
        "- **[Haystack](https://haystack.deepset.ai/)**: open-source LLM orchestration framework that streamlines the development of your LLM applications."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Qd4cBgege7PW"
      },
      "source": [
        "## Install the dependencies"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "UBhGlKTXesIX"
      },
      "outputs": [],
      "source": [
        "%%capture\n",
        "! pip install haystack-ai \"huggingface_hub>=0.22.0\" trafilatura"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-3GvBu0Ffp9I"
      },
      "source": [
        "## Load and try the model\n",
        "We use the [`HuggingFaceAPIGenerator`](https://docs.haystack.deepset.ai/docs/huggingfaceapigenerator), which allows to use models hosted on Hugging Face endpoints.\n",
        "In particular, we use a paid endpoint kindly provided by Nexusflow to test the LLM.\n",
        "\n",
        "Alternative inference options:\n",
        "- load the model on Colab using the HuggingFaceLocalGenerator. This is a bit impractical because the model is quite big (13B parameters) and even using quantization, there would be few GPU resources left for inference.\n",
        "- local inference via TGI or vLLM: this is a good option if you have GPU avalaible.\n",
        "- local inference via Ollama/llama.cpp: this is suitable for machines with few resources and no GPU. Keep in mind that in this case a quantized GGUF version of the model would be used, with lower quality than the original model."
      ]
    },
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        "id": "-_V9RbxafpdY",
        "outputId": "01dcea83-e09a-4394-93e2-5d1966695a58"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_token.py:80: UserWarning: \n",
            "Access to the secret `HF_TOKEN` has not been granted on this notebook.\n",
            "You will not be requested again.\n",
            "Please restart the session if you want to be prompted again.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "0bb4b3b110a44ee0be148a43d7f49818",
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            },
            "text/plain": [
              "tokenizer_config.json:   0%|          | 0.00/985 [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "42afbbf9b75b4369861ff68483e6f365",
              "version_major": 2,
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            "text/plain": [
              "tokenizer.model:   0%|          | 0.00/500k [00:00<?, ?B/s]"
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        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "caa2b76f499f42478c26deeb5620a96f",
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            "text/plain": [
              "tokenizer.json:   0%|          | 0.00/1.85M [00:00<?, ?B/s]"
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          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "da7b6373712640119114cddbec57d5c1",
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              "version_minor": 0
            },
            "text/plain": [
              "added_tokens.json:   0%|          | 0.00/195 [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "dbf0a606810a4dad8bd316ccbf52776d",
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            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "from haystack.components.generators import HuggingFaceAPIGenerator\n",
        "\n",
        "generator = HuggingFaceAPIGenerator(\n",
        "    api_type=\"inference_endpoints\",\n",
        "    api_params={\"url\": \"http://38.142.9.20:10240\"},\n",
        "    stop_words=[\"<bot_end>\"],\n",
        "    generation_kwargs={\"temperature\":0.001,\n",
        "                    \"do_sample\" : False,\n",
        "                    \"max_new_tokens\" : 1000})"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cp4hJ34JivkB"
      },
      "source": [
        "To understand how to prompt the model, give a look at the [Prompting notebook](https://github.com/nexusflowai/NexusRaven-V2/blob/master/How-To-Prompt.ipynb).\n",
        "Later we will see how to better organize the prompt for our purpose."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "MdU98plyfdZ1"
      },
      "outputs": [],
      "source": [
        "prompt='''\n",
        "Function:\n",
        "def get_weather_data(coordinates):\n",
        "    \"\"\"\n",
        "    Fetches weather data from the Open-Meteo API for the given latitude and longitude.\n",
        "\n",
        "    Args:\n",
        "    coordinates (tuple): The latitude of the location.\n",
        "\n",
        "    Returns:\n",
        "    float: The current temperature in the coordinates you've asked for\n",
        "    \"\"\"\n",
        "\n",
        "Function:\n",
        "def get_coordinates_from_city(city_name):\n",
        "    \"\"\"\n",
        "    Fetches the latitude and longitude of a given city name using the Maps.co Geocoding API.\n",
        "\n",
        "    Args:\n",
        "    city_name (str): The name of the city.\n",
        "\n",
        "    Returns:\n",
        "    tuple: The latitude and longitude of the city.\n",
        "    \"\"\"\n",
        "\n",
        "User Query: What's the weather like in Seattle right now?<human_end>\n",
        "\n",
        "'''"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iPIqKQe8j2wX",
        "outputId": "5dc92919-26ab-412b-a1a9-db1ff88629bd"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "{'replies': [\"Call: get_weather_data(coordinates=get_coordinates_from_city(city_name='Seattle'))\"], 'meta': [{'model': 'http://38.142.9.20:10240', 'index': 0, 'finish_reason': 'stop_sequence', 'usage': {'completion_tokens': 29, 'prompt_tokens': 188, 'total_tokens': 217}}]}\n"
          ]
        }
      ],
      "source": [
        "print(generator.run(prompt=prompt))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4zvaVXFcmy2v"
      },
      "source": [
        "All good! ✅"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uXiUvn0dsNj6"
      },
      "source": [
        "## Prompt template and Prompt Builder\n",
        "\n",
        "- The Prompt template to apply is model specific. In our case, we customize a bit the original prompt which is available on [Prompting notebook](https://github.com/nexusflowai/NexusRaven-V2/blob/master/How-To-Prompt.ipynb).\n",
        "- In Haystack, the prompt template is rendered using the [Prompt Builder component](https://docs.haystack.deepset.ai/docs/promptbuilder)."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "mhcao8wYj9ew"
      },
      "outputs": [],
      "source": [
        "from haystack.components.builders import PromptBuilder\n",
        "\n",
        "prompt_template = '''\n",
        "Function:\n",
        "{{function}}\n",
        "User Query: Save data from the provided text. START TEXT:{{docs[0].content|replace(\"\\n\",\" \")|truncate(10000)}} END TEXT\n",
        "<human_end>'''\n",
        "\n",
        "prompt_builder = PromptBuilder(template=prompt_template)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dNA4VHAtxsQR",
        "outputId": "bda1e81b-2b11-45af-9208-7bc8fd490d90"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "{'prompt': '\\nFunction:\\nmy fake function definition\\nUser Query: Save data from the provided text. START TEXT:my fake document END TEXT\\n<human_end>'}\n"
          ]
        }
      ],
      "source": [
        "# let's see if the Prompt Builder works properly\n",
        "\n",
        "from haystack import Document\n",
        "print(prompt_builder.run(docs=[Document(content=\"my fake document\")], function=\"my fake function definition\"))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iOonBACL9eue"
      },
      "source": [
        "Nice ✅"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vlqZUbLqyTtT"
      },
      "source": [
        "## Other Components"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VFU0J3_nzdxz"
      },
      "source": [
        "The following Components are required for the Pipeline we are about to create. However, they are simple and there is no need to customize and try them, so we can instantiate them directly during Pipeline creation.\n",
        "\n",
        "- [LinkContentFetcher](https://docs.haystack.deepset.ai/docs/linkcontentfetcher): fetches the contents of the URLs you give it and returns a list of content streams.\n",
        "- [HTMLToDocument](https://docs.haystack.deepset.ai/docs/htmltodocument): converts HTML files to Documents.\n",
        "- [DocumentCleaner](https://docs.haystack.deepset.ai/docs/documentcleaner): make text documents more readable.\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ASq0gDuZ1Vn_"
      },
      "source": [
        "## Define a custom Component to parse and visualize the result\n",
        "\n",
        "The output of the model generation is a function call string.\n",
        "\n",
        "We are going to create a simple Haystack Component to appropriately parse this string and create a nice HTML visualization.\n",
        "\n",
        "For more information on Creating custom Components, see the [docs](https://docs.haystack.deepset.ai/docs/custom-components)."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "MWIXo9gOyTP6"
      },
      "outputs": [],
      "source": [
        "from haystack import component\n",
        "from typing import List, Optional\n",
        "import ast\n",
        "import re\n",
        "\n",
        "def val_to_color(val):\n",
        "  \"\"\"\n",
        "  Helper function to return a color based on the type/value of a variable\n",
        "  \"\"\"\n",
        "  if isinstance(val, list):\n",
        "    return \"#FFFEE0\"\n",
        "  if val is True:\n",
        "    return \"#90EE90\"\n",
        "  if val is False:\n",
        "    return \"#FFCCCB\"\n",
        "  return \"\"\n",
        "\n",
        "@component\n",
        "class FunctionCallParser:\n",
        "  \"\"\"\n",
        "  A component that parses the function call string and creates a HTML visualization\n",
        "  \"\"\"\n",
        "  @component.output_types(html_visualization=str)\n",
        "  def run(self, replies:List[str]):\n",
        "\n",
        "    print(replies)\n",
        "\n",
        "    func_call_str = replies[0].replace(\"Call:\", \"\").strip()\n",
        "\n",
        "    # sometimes the model output contains wrong expressions like \"'date=[...]\" or \"date'=...\"\n",
        "    # that can't be correctly parsed, so we remove these substrings\n",
        "    func_call_str=func_call_str.replace(\"'=\",\"=\")\n",
        "    func_call_str=re.sub(\"'([a-z]+)=\", \"\\g<1>=\", func_call_str)\n",
        "\n",
        "    func_call=ast.parse(func_call_str).body[0].value\n",
        "    kwargs = {arg.arg: ast.literal_eval(arg.value) for arg in func_call.keywords}\n",
        "\n",
        "    # Convert data to HTML format\n",
        "    html_content = '<div style=\"border: 1px solid #ccc; padding: 10px; border-radius: 5px; background-color: #f9f9f9;\">'\n",
        "    for key, value in kwargs.items():\n",
        "        html_content += f'<p><span style=\"font-family: Cursive; font-size: 30px;\">{key}:</span>'\n",
        "        html_content += f'&emsp;<span style=\"background-color:{val_to_color(value)}; font-family: Cursive; font-size: 20px;\">{value}</span></p>'\n",
        "    html_content += '</div>'\n",
        "\n",
        "    return {\"html_visualization\": html_content}"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pAMBYA7ZFEk0"
      },
      "source": [
        "## Create an Information Extraction Pipeline\n",
        "\n",
        "To combine the Components in an appropriate and reproducible way, we resort to Haystack Pipelines.\n",
        "The syntax should be easily understood. You can find more infomation [in the docs](https://docs.haystack.deepset.ai/docs/pipelines).\n",
        "\n",
        "This pipeline will extract the information from the given URL following the provided structure."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "B9W73HI3yBHU",
        "outputId": "ddf8921c-a1ec-4584-d2e5-46427d020840"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "text/plain": []
          },
          "execution_count": 8,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "from haystack import Pipeline\n",
        "from haystack.components.fetchers import LinkContentFetcher\n",
        "from haystack.components.converters import HTMLToDocument\n",
        "from haystack.components.preprocessors import DocumentCleaner\n",
        "\n",
        "pipe = Pipeline()\n",
        "\n",
        "pipe.add_component(\"fetcher\", LinkContentFetcher())\n",
        "pipe.add_component(\"converter\", HTMLToDocument(extractor_type=\"DefaultExtractor\"))\n",
        "pipe.add_component(\"cleaner\", DocumentCleaner())\n",
        "pipe.add_component(\"prompt_builder\", prompt_builder)\n",
        "pipe.add_component(\"generator\", generator)\n",
        "pipe.add_component(\"parser\", FunctionCallParser())\n",
        "\n",
        "pipe.connect(\"fetcher\", \"converter\")\n",
        "pipe.connect(\"converter\", \"cleaner\")\n",
        "pipe.connect(\"cleaner.documents\", \"prompt_builder.docs\")\n",
        "pipe.connect(\"prompt_builder\", \"generator\")\n",
        "pipe.connect(\"generator\", \"parser\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5fCLeNtVLJLm"
      },
      "source": [
        "Now we create an `extract` function that wraps the Pipeline and displays the result in the HTML format.\n",
        "This will accept:\n",
        "- a `function` dict, containing the structure definition of the information we want to extract\n",
        "- a `url`, to use as data source"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "r0fsBycdFxzV"
      },
      "outputs": [],
      "source": [
        "from IPython.display import display, HTML\n",
        "\n",
        "def extract(function:str, url:str) -> dict:\n",
        "  if not function:\n",
        "    raise ValueError(\"function definition is needed\")\n",
        "  if not url:\n",
        "    raise ValueError(\"URL is needed\")\n",
        "\n",
        "  data_for_pipeline = {\"fetcher\":{\"urls\":[url]},\n",
        "                       \"prompt_builder\":{\"function\":function}}\n",
        "\n",
        "  html_visualization = pipe.run(data=data_for_pipeline)['parser']['html_visualization']\n",
        "  display(HTML(html_visualization))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "furABTbcV57u"
      },
      "source": [
        "## 🕹️ Try our application!"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_zgm1yj6WHf7"
      },
      "source": [
        "Let's first define the structure to extract.\n",
        "\n",
        "We are going to parse some news articles about animals... 🦆🐻🦌"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "dWtLYYacGGht"
      },
      "outputs": [],
      "source": [
        "function = '''def save_data(about_animals: bool, about_ai: bool, habitat:List[string], predators:List[string], diet:List[string]):\n",
        "    \"\"\"\n",
        "    Save data extracted from source text\n",
        "\n",
        "    Args:\n",
        "    about_animals (bool): Is the article about animals?\n",
        "    about_ai (bool): Is the article about artificial intelligence?\n",
        "    habitat (List[string]): List of places where the animal lives\n",
        "    predators (List[string]): What are the animals that threaten them?\n",
        "    diet (List[string]): What does the animal eat?\n",
        "    \"\"\"'''"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bm5ikRkUXEl2"
      },
      "source": [
        "Let's start with an article about **Capybaras**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 316
        },
        "id": "8JWzR5ZsGP_F",
        "outputId": "ac889316-71e8-4b10-acec-193e9fcdbad5"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "INFO:haystack.core.pipeline.pipeline:Warming up component generator...\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[\"\\nCall: save_data(about_animals=True, about_ai=False, habitat=['Panama', 'Colombia', 'Venezuela', 'Guyana', 'Peru', 'Brazil', 'Paraguay', 'Northeast Argentina', 'Uruguay'], predators=['jaguars', 'caimans', 'anacondas', 'ocelots', 'harpy eagles'], diet=['vegetation', 'grass', 'grains', 'melons', 'reeds', 'squashes'])\"]\n"
          ]
        },
        {
          "data": {
            "text/html": [
              "<div style=\"border: 1px solid #ccc; padding: 10px; border-radius: 5px; background-color: #f9f9f9;\"><p><span style=\"font-family: Cursive; font-size: 30px;\">about_animals:</span>&emsp;<span style=\"background-color:#90EE90; font-family: Cursive; font-size: 20px;\">True</span></p><p><span style=\"font-family: Cursive; font-size: 30px;\">about_ai:</span>&emsp;<span style=\"background-color:#FFCCCB; font-family: Cursive; font-size: 20px;\">False</span></p><p><span style=\"font-family: Cursive; font-size: 30px;\">habitat:</span>&emsp;<span style=\"background-color:#FFFEE0; font-family: Cursive; font-size: 20px;\">['Panama', 'Colombia', 'Venezuela', 'Guyana', 'Peru', 'Brazil', 'Paraguay', 'Northeast Argentina', 'Uruguay']</span></p><p><span style=\"font-family: Cursive; font-size: 30px;\">predators:</span>&emsp;<span style=\"background-color:#FFFEE0; font-family: Cursive; font-size: 20px;\">['jaguars', 'caimans', 'anacondas', 'ocelots', 'harpy eagles']</span></p><p><span style=\"font-family: Cursive; font-size: 30px;\">diet:</span>&emsp;<span style=\"background-color:#FFFEE0; font-family: Cursive; font-size: 20px;\">['vegetation', 'grass', 'grains', 'melons', 'reeds', 'squashes']</span></p></div>"
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      "source": [
        "extract(function=function, url=\"https://www.rainforest-alliance.org/species/capybara/\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "brMOC5q3XjeD"
      },
      "source": [
        "Now let's try with an article about the **Andean cock of the rock**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
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        "id": "5qPN4yB3gwMh",
        "outputId": "94c6d2e4-dca9-42fb-8e32-306f98a9a484"
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      "outputs": [
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          "output_type": "stream",
          "text": [
            "INFO:haystack.core.pipeline.pipeline:Warming up component generator...\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[\"\\nCall: save_data(about_animals=True, about_ai=False, habitat=['Andes'], predators=['birds of prey', 'puma', 'jaguars', 'boa constrictors'], diet=['fruit', 'insects', 'small vertebrates'])\"]\n"
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              "<div style=\"border: 1px solid #ccc; padding: 10px; border-radius: 5px; background-color: #f9f9f9;\"><p><span style=\"font-family: Cursive; font-size: 30px;\">about_animals:</span>&emsp;<span style=\"background-color:#90EE90; font-family: Cursive; font-size: 20px;\">True</span></p><p><span style=\"font-family: Cursive; font-size: 30px;\">about_ai:</span>&emsp;<span style=\"background-color:#FFCCCB; font-family: Cursive; font-size: 20px;\">False</span></p><p><span style=\"font-family: Cursive; font-size: 30px;\">habitat:</span>&emsp;<span style=\"background-color:#FFFEE0; font-family: Cursive; font-size: 20px;\">['Andes']</span></p><p><span style=\"font-family: Cursive; font-size: 30px;\">predators:</span>&emsp;<span style=\"background-color:#FFFEE0; font-family: Cursive; font-size: 20px;\">['birds of prey', 'puma', 'jaguars', 'boa constrictors']</span></p><p><span style=\"font-family: Cursive; font-size: 30px;\">diet:</span>&emsp;<span style=\"background-color:#FFFEE0; font-family: Cursive; font-size: 20px;\">['fruit', 'insects', 'small vertebrates']</span></p></div>"
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              "<IPython.core.display.HTML object>"
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        "extract(function=function, url=\"https://www.rainforest-alliance.org/species/cock-rock/\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_rQhVWTkaSkw"
      },
      "source": [
        "Now, the **Yucatan Deer**!"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "id": "L3SCmn5IkOFE",
        "outputId": "0dfc66c9-de59-4892-fa36-521693ff3ba1"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "INFO:haystack.core.pipeline.pipeline:Warming up component generator...\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[\"\\nCall: save_data(about_animals=True, about_ai=False, habitat=['forests'], predators=['cougar', 'jaguar'], diet=['grass', 'leaves', 'sprouts', 'lichens', 'mosses', 'tree bark', 'fruit'])\"]\n"
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              "<div style=\"border: 1px solid #ccc; padding: 10px; border-radius: 5px; background-color: #f9f9f9;\"><p><span style=\"font-family: Cursive; font-size: 30px;\">about_animals:</span>&emsp;<span style=\"background-color:#90EE90; font-family: Cursive; font-size: 20px;\">True</span></p><p><span style=\"font-family: Cursive; font-size: 30px;\">about_ai:</span>&emsp;<span style=\"background-color:#FFCCCB; font-family: Cursive; font-size: 20px;\">False</span></p><p><span style=\"font-family: Cursive; font-size: 30px;\">habitat:</span>&emsp;<span style=\"background-color:#FFFEE0; font-family: Cursive; font-size: 20px;\">['forests']</span></p><p><span style=\"font-family: Cursive; font-size: 30px;\">predators:</span>&emsp;<span style=\"background-color:#FFFEE0; font-family: Cursive; font-size: 20px;\">['cougar', 'jaguar']</span></p><p><span style=\"font-family: Cursive; font-size: 30px;\">diet:</span>&emsp;<span style=\"background-color:#FFFEE0; font-family: Cursive; font-size: 20px;\">['grass', 'leaves', 'sprouts', 'lichens', 'mosses', 'tree bark', 'fruit']</span></p></div>"
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        "extract(function=function, url=\"https://www.rainforest-alliance.org/species/yucatan-deer/\")"
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      "source": [
        "A completely different example, about AI..."
      ]
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    {
      "cell_type": "code",
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      "metadata": {
        "id": "BbK-q_6NcvrT"
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      "source": [
        "function='''def save_data(people:List[string], companies:List[string], summary:string, topics:List[string], about_animals: bool, about_ai: bool):\n",
        "    \"\"\"\n",
        "    Save data extracted from source text\n",
        "\n",
        "    Args:\n",
        "    people (List[string]): List of the mentioned people\n",
        "    companies (List[string]): List of the mentioned companies.\n",
        "    summary (string): Summarize briefly what happened in one sentence of max 15 words.\n",
        "    topics (List[string]): what are the five most important topics?\n",
        "    about_animals (bool): Is the article about animals?\n",
        "    about_ai (bool): Is the article about artificial intelligence?\n",
        "    \"\"\"'''\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
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        "id": "BU1ehEqtKEui",
        "outputId": "613452f8-423b-4578-ad2d-63d83e73ed47"
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      "outputs": [
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          "name": "stderr",
          "output_type": "stream",
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            "INFO:haystack.core.pipeline.pipeline:Warming up component generator...\n"
          ]
        },
        {
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          "output_type": "stream",
          "text": [
            "[\"\\nCall: save_data(people=['Sam Altman', 'Greg Brockman', 'Bret Taylor', 'Larry Summers', 'Adam D’Angelo', 'Ilya Sutskever', 'Emmett Shear'], companies=['OpenAI', 'Microsoft', 'Thrive Capital'], summary='Sam Altman will return as CEO of OpenAI, overcoming an attempted boardroom coup that sent the company into chaos over the past several days.', topics=['OpenAI', 'Artificial intelligence', 'Machine learning', 'Computer vision', 'Natural language processing'], about_animals=False, about_ai=True)\"]\n"
          ]
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              "<div style=\"border: 1px solid #ccc; padding: 10px; border-radius: 5px; background-color: #f9f9f9;\"><p><span style=\"font-family: Cursive; font-size: 30px;\">people:</span>&emsp;<span style=\"background-color:#FFFEE0; font-family: Cursive; font-size: 20px;\">['Sam Altman', 'Greg Brockman', 'Bret Taylor', 'Larry Summers', 'Adam D’Angelo', 'Ilya Sutskever', 'Emmett Shear']</span></p><p><span style=\"font-family: Cursive; font-size: 30px;\">companies:</span>&emsp;<span style=\"background-color:#FFFEE0; font-family: Cursive; font-size: 20px;\">['OpenAI', 'Microsoft', 'Thrive Capital']</span></p><p><span style=\"font-family: Cursive; font-size: 30px;\">summary:</span>&emsp;<span style=\"background-color:; font-family: Cursive; font-size: 20px;\">Sam Altman will return as CEO of OpenAI, overcoming an attempted boardroom coup that sent the company into chaos over the past several days.</span></p><p><span style=\"font-family: Cursive; font-size: 30px;\">topics:</span>&emsp;<span style=\"background-color:#FFFEE0; font-family: Cursive; font-size: 20px;\">['OpenAI', 'Artificial intelligence', 'Machine learning', 'Computer vision', 'Natural language processing']</span></p><p><span style=\"font-family: Cursive; font-size: 30px;\">about_animals:</span>&emsp;<span style=\"background-color:#FFCCCB; font-family: Cursive; font-size: 20px;\">False</span></p><p><span style=\"font-family: Cursive; font-size: 30px;\">about_ai:</span>&emsp;<span style=\"background-color:#90EE90; font-family: Cursive; font-size: 20px;\">True</span></p></div>"
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            "[\"\\nCall: save_data(people=['Sam Bankman-Fried'], companies=['FTX'], summary='Sam Bankman-Fried will not face second trial after multibillion-dollar crypto fraud conviction', topics=['crypto fraud', 'FTX', 'cryptocurrency exchange'], about_animals=False, about_ai=False)\"]\n"
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              "<div style=\"border: 1px solid #ccc; padding: 10px; border-radius: 5px; background-color: #f9f9f9;\"><p><span style=\"font-family: Cursive; font-size: 30px;\">people:</span>&emsp;<span style=\"background-color:#FFFEE0; font-family: Cursive; font-size: 20px;\">['Sam Bankman-Fried']</span></p><p><span style=\"font-family: Cursive; font-size: 30px;\">companies:</span>&emsp;<span style=\"background-color:#FFFEE0; font-family: Cursive; font-size: 20px;\">['FTX']</span></p><p><span style=\"font-family: Cursive; font-size: 30px;\">summary:</span>&emsp;<span style=\"background-color:; font-family: Cursive; font-size: 20px;\">Sam Bankman-Fried will not face second trial after multibillion-dollar crypto fraud conviction</span></p><p><span style=\"font-family: Cursive; font-size: 30px;\">topics:</span>&emsp;<span style=\"background-color:#FFFEE0; font-family: Cursive; font-size: 20px;\">['crypto fraud', 'FTX', 'cryptocurrency exchange']</span></p><p><span style=\"font-family: Cursive; font-size: 30px;\">about_animals:</span>&emsp;<span style=\"background-color:#FFCCCB; font-family: Cursive; font-size: 20px;\">False</span></p><p><span style=\"font-family: Cursive; font-size: 30px;\">about_ai:</span>&emsp;<span style=\"background-color:#FFCCCB; font-family: Cursive; font-size: 20px;\">False</span></p></div>"
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        "extract(function=function, url=\"https://www.theguardian.com/business/2023/dec/30/sam-bankman-fried-will-not-face-second-trial-after-multibillion-dollar-crypto-conviction\")"
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            "[\"\\nCall: save_data(people=['Michelle Toh', 'Wayne Chang', 'Jensen Huang', 'Lisa Su'], companies=['Nvidia', 'AMD'], summary='The Taiwanese American cousins going head-to-head in the global AI race', topics=['chip industry', 'global AI chip industry', 'Taiwanese descent', 'semiconductors', 'generative AI'], about_animals=False, about_ai=True)\"]\n"
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              "<div style=\"border: 1px solid #ccc; padding: 10px; border-radius: 5px; background-color: #f9f9f9;\"><p><span style=\"font-family: Cursive; font-size: 30px;\">people:</span>&emsp;<span style=\"background-color:#FFFEE0; font-family: Cursive; font-size: 20px;\">['Michelle Toh', 'Wayne Chang', 'Jensen Huang', 'Lisa Su']</span></p><p><span style=\"font-family: Cursive; font-size: 30px;\">companies:</span>&emsp;<span style=\"background-color:#FFFEE0; font-family: Cursive; font-size: 20px;\">['Nvidia', 'AMD']</span></p><p><span style=\"font-family: Cursive; font-size: 30px;\">summary:</span>&emsp;<span style=\"background-color:; font-family: Cursive; font-size: 20px;\">The Taiwanese American cousins going head-to-head in the global AI race</span></p><p><span style=\"font-family: Cursive; font-size: 30px;\">topics:</span>&emsp;<span style=\"background-color:#FFFEE0; font-family: Cursive; font-size: 20px;\">['chip industry', 'global AI chip industry', 'Taiwanese descent', 'semiconductors', 'generative AI']</span></p><p><span style=\"font-family: Cursive; font-size: 30px;\">about_animals:</span>&emsp;<span style=\"background-color:#FFCCCB; font-family: Cursive; font-size: 20px;\">False</span></p><p><span style=\"font-family: Cursive; font-size: 30px;\">about_ai:</span>&emsp;<span style=\"background-color:#90EE90; font-family: Cursive; font-size: 20px;\">True</span></p></div>"
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        "extract(function=function, url=\"https://lite.cnn.com/2023/11/05/tech/nvidia-amd-ceos-taiwan-intl-hnk/index.html\")"
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      "source": [
        "## ✨ Conclusions and caveats\n",
        "- Nexus Raven seems to work much better than Gorilla Open Functions (v0) for this use case.\n",
        "- I would also expect it to work significantly better than generic models to which grammars are added to make them produce JSON.\n",
        "- ⚠️ When the content of the web page is cluttered with extraneous information such as advertisements and interruptions, the model encounters difficulty in extracting relevant information, leading to occasional instances where it returns empty responses.\n",
        "- ⚠️ As a statistical model, the LLM is highly responsive to prompts. For instance, modifying the order and description of the specified arguments can yield different extraction results."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xNAXyTefgpRX"
      },
      "source": [
        "## 📚 References\n",
        "*Related to the experiment*\n",
        "- [Haystack LLM framework](https://haystack.deepset.ai/)\n",
        "- [Using OpenAI Function Calling for Information Extraction: gist by Kyle McDonald](https://gist.github.com/kylemcdonald/dbac21de2d7855633689f5526225154c)\n",
        "- [NexusRaven-V2: Surpassing GPT-4 for Zero-shot Function Calling](https://nexusflow.ai/blogs/ravenv2)"
      ]
    }
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