cookbooks/gorillaLLM-integration.ipynb

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      "metadata": {
        "id": "Bheeth_Dur0Y"
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      "source": [
        "<center>\n",
        "  <p style=\"text-align:center\">\n",
        "    Automating tool/function calling workflow\n",
        "    <br>\n",
        "    <a href=\"https://github.com/synacktraa/tool-parse\">Github</a>\n",
        "    |\n",
        "    <a href=\"https://pypi.org/project/tool-parse\">PyPI</a>\n",
        "    |\n",
        "    <a href=\"https://colab.research.google.com/drive/1C2WCgIZ7LnkpLt3KARL9ROh4iLwaACa6?usp=sharing\">Colab</a>\n",
        "  </p>\n",
        "</center>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
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      "source": [
        "#@title Install and import necessary libraries\n",
        "%pip install -qU tool-parse[pydantic] duckduckgo_search\n",
        "%pip install -qU sentencepiece accelerate bitsandbytes\n",
        "\n",
        "import json\n",
        "import torch\n",
        "from tool_parse import ToolRegistry\n",
        "from transformers import (\n",
        "    AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig, pipeline\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AsUbahs6immj"
      },
      "source": [
        "### Creating a registry and registering tools"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "dWJX4hvZigcM"
      },
      "outputs": [],
      "source": [
        "from tool_parse import ToolRegistry\n",
        "\n",
        "from typing import Literal\n",
        "from pydantic import BaseModel\n",
        "from duckduckgo_search import DDGS\n",
        "\n",
        "tr = ToolRegistry()\n",
        "ddgs = DDGS()\n",
        "\n",
        "@tr.register\n",
        "def search_text(\n",
        "  text: str,\n",
        "  *, # Yes it supports positional and keyword arguments\n",
        "  safe_search: bool = True,\n",
        "  backend: Literal['api', 'html', 'lite'] = 'api',\n",
        "  max_results: int = 1\n",
        "):\n",
        "    \"\"\"\n",
        "    Search for text in the web.\n",
        "    :param text: Text to search for.\n",
        "    :param safe_search: If True, enable safe search.\n",
        "    :param backend: Backend to use for retrieving results.\n",
        "    :param max_results: Max results to return.\n",
        "    \"\"\"\n",
        "    return ddgs.text(\n",
        "        keywords=text,\n",
        "        safesearch=\"on\" if safe_search else \"off\",\n",
        "        backend=backend,\n",
        "        max_results=max_results\n",
        "    )\n",
        "\n",
        "@tr.register(name='product_information')\n",
        "class ProductInfo(BaseModel): # This can be TypedDict or NamedTuple if you don't want to install pydantic package.\n",
        "    \"\"\"\n",
        "    Information about the product.\n",
        "    :param name: Name of the product.\n",
        "    :param price: Price of the product.\n",
        "    :param in_stock: If the product is in stock.\n",
        "    \"\"\"\n",
        "    name: str\n",
        "    price: float\n",
        "    in_stock: bool\n",
        "\n",
        "@tr.register\n",
        "def get_translation(\n",
        "    text: str,\n",
        "    to: Literal['en', 'ja', 'hi', 'es', 'fr', 'de', 'zh'] = 'en'\n",
        "):\n",
        "    \"\"\"\n",
        "    Translate the given text.\n",
        "    :param text: Text to translate.\n",
        "    :param to: what language to translate.\n",
        "    \"\"\"\n",
        "    return ddgs.translate(keywords=text, to=to)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
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        "id": "SP9V5lGFkeD6",
        "outputId": "c750b19a-c285-4fe6-f5d8-f3201456c5d2"
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      "outputs": [],
      "source": [
        "\"\"\"\n",
        "Lets take a look at each tool schema, we can get a particular tool schema from the registry like we're accessing a dictionary object with a key\n",
        "\"\"\"\n",
        "tr['search_text']"
      ]
    },
    {
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        "colab": {
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        "outputId": "89ffd1d1-3daf-4a13-beb9-87abc656cbdc"
      },
      "outputs": [],
      "source": [
        "print('ProductInfo' in tr) # Original name is ProductInfo but we gave it a custom name so it will return False\n",
        "tr['product_information']"
      ]
    },
    {
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      "outputs": [],
      "source": [
        "tr['get_translation']"
      ]
    },
    {
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        "id": "tCAuNolapD83"
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      "source": [
        "### Setting up Gorilla pipeline"
      ]
    },
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      "source": [
        "torch_dtype = torch.float16 if torch.cuda.is_available() else torch.float32\n",
        "\n",
        "# Quantization Setup\n",
        "# This configuration compresses the model and makes it possible to use with Colab's GPU\n",
        "bnb_config = BitsAndBytesConfig(\n",
        "    load_in_4bit=True,\n",
        "    bnb_4bit_use_double_quant=True,\n",
        "    bnb_4bit_quant_type=\"nf4\",\n",
        "    bnb_4bit_compute_dtype=torch_dtype\n",
        ")\n",
        "\n",
        "# Model and tokenizer setup\n",
        "model_id : str = \"gorilla-llm/gorilla-openfunctions-v2\"\n",
        "tokenizer = AutoTokenizer.from_pretrained(model_id)\n",
        "model = AutoModelForCausalLM.from_pretrained(\n",
        "    model_id,\n",
        "    torch_dtype=torch_dtype,\n",
        "    quantization_config=bnb_config,\n",
        "    low_cpu_mem_usage=True,\n",
        "    device_map=\"auto\"\n",
        ")\n",
        "\n",
        "# Create pipeline\n",
        "pipe = pipeline(\n",
        "    \"text-generation\",\n",
        "    model=model,\n",
        "    tokenizer=tokenizer,\n",
        "    max_new_tokens=128,\n",
        "    batch_size=16,\n",
        "    torch_dtype=torch_dtype,\n",
        ")\n",
        "\n",
        "# This function will return Gorilla's response\n",
        "def get_gorilla_expression(query: str, functions: list[dict]) -> str:\n",
        "    output = pipe(\n",
        "        f\"USER: <<question>> {query} <<function>> {json.dumps(functions)}\\nASSISTANT: \"\n",
        "    )[0]['generated_text'].splitlines()[-1]\n",
        "    return output[output.index(\":\")+1:].strip().strip('<<function>>')"
      ]
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      "source": [
        "# Getting schema of registered tools\n",
        "\n",
        "tools = tr.marshal('base')\n",
        "tools"
      ]
    },
    {
      "cell_type": "code",
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      "outputs": [],
      "source": [
        "# Search text\n",
        "expression = get_gorilla_expression(\n",
        "    query=\"Search for gorilla LLM benchmarks\", functions=tools\n",
        ")\n",
        "print(f\"{expression=}\")\n",
        "\n",
        "# Compile the expression to get the output\n",
        "output = tr.compile(expression)\n",
        "print(f\"{output=}\")"
      ]
    },
    {
      "cell_type": "code",
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        "id": "1M7Nt9yoqAL_",
        "outputId": "b7609cea-228d-4f77-bdb3-0e98e2ea79ad"
      },
      "outputs": [],
      "source": [
        "# Extracting product information\n",
        "expression = get_gorilla_expression(\n",
        "    query=\"Parse: Product RTX 4900, priced at $3.5k, is in stock.\", functions=tools\n",
        ")\n",
        "print(f\"{expression=}\")\n",
        "\n",
        "# Compile the expression to get the output\n",
        "output = tr.compile(expression)\n",
        "print(f\"{output=}\") # function/tool-calling is nothing but getting Structured output :>"
      ]
    },
    {
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      "source": [
        "# Translation\n",
        "expression = get_gorilla_expression(\n",
        "    query=\"'Tool calling is one of the best features of LLM' to hindi. Please utilize tool.\", functions=tools\n",
        ") # It keeps translating the text itself, so I had to be specific regarding tool usage.\n",
        "print(f\"{expression=}\")\n",
        "\n",
        "# Compile the expression to get the output\n",
        "output = tr.compile(expression)\n",
        "print(f\"{output=}\")"
      ]
    }
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