crewai_sequential_WebsiteSearchTool_quickstart.ipynb

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    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
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      "source": [
        "<a href=\"https://colab.research.google.com/github/alexfazio/crewAI-quickstart/blob/main/crewai_sequential_WebsiteSearchTool_quickstart.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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    {
      "cell_type": "markdown",
      "source": [
        "# crewai-sequential-WebsiteSearchTool-quickstart\n",
        "\n",
        "By [Alex Fazio](https://www.linkedin.com/in/alxfazio)\n",
        "\n",
        "📂 Github repo: https://github.com/alexfazio/crewai-quickstart\n",
        "\n",
        "This is a simplified and tested template of a **sequential** CrewAI crew.\n",
        "\n",
        "This team uses the WebsiteSearchTool to perform semantic searches (RAG), efficiently extracting information from designated URLs.\n",
        "\n",
        "Check the folder named `./output-files` for detailed results from each task or agent.\n",
        "\n",
        "Extra Requirements:\n",
        "- [OpenAI](https://platform.openai.com/playground)/[Groq](https://console.groq.com/settings/organization)/[Anthropic](https://console.anthropic.com/dashboard) API Key\n",
        "\n",
        "📚 CrewAI docs: https://docs.crewai.com/\n",
        "\n",
        "📚 WebsiteSearchTool docs: https://docs.crewai.com/tools/WebsiteSearchTool/\n",
        "\n",
        "📚 `chromadb` (embedding database) docs:  https://docs.trychroma.com/"
      ],
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      "cell_type": "code",
      "outputs": [],
      "execution_count": null,
      "source": [
        "# @title 👨‍🦯 Run this cell to hide all warnings (optional)\n",
        "# Warning control\n",
        "import warnings\n",
        "warnings.filterwarnings('ignore')"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# @title 🛠️ Install the Project Dependencies by Executing This Cell\n",
        "# @markdown  🔄 Restart the session and rerun the cell **if Colab requires it**.\n",
        "\n",
        "!pip install git+https://github.com/joaomdmoura/crewAI.git\n",
        "!pip install crewai_tools langchain_openai langchain_groq langchain_anthropic langchain_community cohere"
      ],
      "metadata": {
        "id": "P8iHNKCfk9Rv",
        "cellView": "form"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "BhAt-unGk4kA",
        "cellView": "form"
      },
      "outputs": [],
      "source": [
        "# @title 🔑 Input API Key by running this cell\n",
        "\n",
        "import os\n",
        "from getpass import getpass\n",
        "from crewai import Agent, Task, Crew, Process\n",
        "from langchain_openai import ChatOpenAI\n",
        "from textwrap import dedent\n",
        "# ↑ uncomment to use OpenAI's API\n",
        "# from langchain_groq import ChatGroq\n",
        "# ↑ uncomment to use Groq's API\n",
        "# from langchain_anthropic import ChatAnthropic\n",
        "# ↑ uncomment to use Antrhopic's API\n",
        "# from langchain_community.chat_models import ChatCohere\n",
        "# ↑ uncomment to use ChatCohere API\n",
        "\n",
        "os.environ[\"OPENAI_API_KEY\"] = getpass(\"Enter OPENAI_API_KEY: \")\n",
        "# ↑ uncomment to use OpenAI's API\n",
        "# os.environ[\"GROQ_API_KEY\"] = getpass(\"Enter GROQ_API_KEY: \")\n",
        "# ↑ uncomment to use Groq's API\n",
        "# os.environ[\"ANTHROPIC_API_KEY\"] = getpass(\"Enter ANTHROPIC_API_KEY: \")\n",
        "# ↑ uncomment to use Anthropic's API\n",
        "# os.environ[\"COHERE_API_KEY\"] = getpass(\"Enter COHERE_API_KEY: \")\n",
        "# ↑ uncomment to use Cohere's API\n",
        "\n",
        "# Check if the 'output-files' directory exists, and create it if it doesn't\n",
        "if not os.path.exists('output-files'):\n",
        "    os.makedirs('output-files')"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# @title 🌐 Instantiate `WebsiteSearchTool`\n",
        "\n",
        "# @markdown By default, the tool uses OpenAI for both embeddings and summarization.\n",
        "\n",
        "# @markdown To customize the model, you can use a config dictionary. See how [here](https://docs.crewai.com/tools/WebsiteSearchTool/#custom-model-and-embeddings).\n",
        "\n",
        "from crewai_tools import WebsiteSearchTool\n",
        "\n",
        "# Example of initiating tool that agents can use to search across any discovered websites\n",
        "\n",
        "# website_search_tool = WebsiteSearchTool()\n",
        "\n",
        "# Example of limiting the search to the content of a specific website, so now agents can only search within that website\n",
        "\n",
        "website = 'https://www.theverge.com/2024/5/2/24146989/peloton-announces-new-round-of-layoffs-as-ceo-quits' # @param {type:\"string\"}\n",
        "\n",
        "website_search_tool = WebsiteSearchTool(website)"
      ],
      "metadata": {
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      "cell_type": "code",
      "source": [
        "# @title 🕵🏻 Define your agents\n",
        "\n",
        "from langchain_community.chat_models import ChatOpenAI\n",
        "from crewai import Agent\n",
        "\n",
        "# from langchain_groq import ChatGroq\n",
        "# ↑ Uncomment to use Groq's API\n",
        "# from langchain_anthropic import ChatAnthropic\n",
        "# ↑ Uncomment to use Anthropic's API\n",
        "\n",
        "agent_1 = Agent(\n",
        "    role=dedent((\n",
        "        \"\"\"\n",
        "        Defines the agent's function within the crew. It determines the kind of tasks the agent is best suited for.\n",
        "        \"\"\")), # Think of this as the job title\n",
        "    backstory=dedent((\n",
        "        \"\"\"\n",
        "        Provides context to the agent's role and goal, enriching the interaction and collaboration dynamics.\n",
        "        \"\"\")), # This is the backstory of the agent, this helps the agent to understand the context of the task\n",
        "    goal=dedent((\n",
        "        \"\"\"\n",
        "        The individual objective that the agent aims to achieve. It guides the agent's decision-making process.\n",
        "        \"\"\")), # This is the goal that the agent is trying to achieve\n",
        "    tools=[website_search_tool],\n",
        "    allow_delegation=False,\n",
        "    verbose=True,\n",
        "    # ↑ Whether the agent execution should be in verbose mode\n",
        "    max_iter=3,\n",
        "    # ↑ maximum number of iterations the agent can perform before being forced to give its best answer (generate the output)\n",
        "    max_rpm=100, # This is the maximum number of requests per minute that the agent can make to the language model\n",
        "    llm=ChatOpenAI(model_name=\"gpt-4\", temperature=0.8)\n",
        "    # ↑ uncomment to use OpenAI API + \"gpt-4\"\n",
        "    # llm=ChatGroq(temperature=0.8, model_name=\"mixtral-8x7b-32768\"),\n",
        "    # ↑ uncomment to use Groq's API + \"llama3-70b-8192\"\n",
        "    # llm=ChatGroq(temperature=0.6, model_name=\"llama3-70b-8192\"),\n",
        "    # ↑ uncomment to use Groq's API + \"mixtral-8x7b-32768\"\n",
        "    # llm = ChatAnthropic(model='claude-3-opus-20240229', temperature=0.8),\n",
        "    # ↑ uncomment to use Anthropic's API + \"claude-3-opus-20240229\"\n",
        ")\n",
        "\n",
        "agent_2 = Agent(\n",
        "    role=dedent((\n",
        "        \"\"\"\n",
        "        Defines the agent's function within the crew. It determines the kind of tasks the agent is best suited for.\n",
        "        \"\"\")), # Think of this as the job title\n",
        "    backstory=dedent((\n",
        "        \"\"\"\n",
        "        Provides context to the agent's role and goal, enriching the interaction and collaboration dynamics.\n",
        "        \"\"\")), # This is the backstory of the agent, this helps the agent to understand the context of the task\n",
        "    goal=dedent((\n",
        "        \"\"\"\n",
        "        The individual objective that the agent aims to achieve. It guides the agent's decision-making process.\n",
        "        \"\"\")), # This is the goal that the agent is trying to achieve\n",
        "    tools=[website_search_tool],\n",
        "    allow_delegation=False,\n",
        "    verbose=True,\n",
        "    # ↑ Whether the agent execution should be in verbose mode\n",
        "    max_iter=3,\n",
        "    # ↑ maximum number of iterations the agent can perform before being forced to give its best answer (generate the output)\n",
        "    max_rpm=100, # This is the maximum number of requests per minute that the agent can make to the language model\n",
        "    llm=ChatOpenAI(model_name=\"gpt-4\", temperature=0.8)\n",
        "    # ↑ uncomment to use OpenAI API + \"gpt-4\"\n",
        "    # llm=ChatGroq(temperature=0.8, model_name=\"mixtral-8x7b-32768\"),\n",
        "    # ↑ uncomment to use Groq's API + \"llama3-70b-8192\"\n",
        "    # llm=ChatGroq(temperature=0.6, model_name=\"llama3-70b-8192\"),\n",
        "    # ↑ uncomment to use Groq's API + \"mixtral-8x7b-32768\"\n",
        "    # llm = ChatAnthropic(model='claude-3-opus-20240229', temperature=0.8),\n",
        "    # ↑ uncomment to use Anthropic's API + \"claude-3-opus-20240229\"\n",
        ")\n",
        "\n",
        "agent_3 = Agent(\n",
        "    role=dedent((\n",
        "        \"\"\"\n",
        "        Defines the agent's function within the crew. It determines the kind of tasks the agent is best suited for.\n",
        "        \"\"\")), # Think of this as the job title\n",
        "    backstory=dedent((\n",
        "        \"\"\"\n",
        "        Provides context to the agent's role and goal, enriching the interaction and collaboration dynamics.\n",
        "        \"\"\")), # This is the backstory of the agent, this helps the agent to understand the context of the task\n",
        "    goal=dedent((\n",
        "        f\"\"\"\n",
        "        The individual objective that the agent aims to achieve. It guides the agent's decision-making process.\n",
        "        \"\"\")), # This is the goal that the agent is trying to achieve\n",
        "    tools=[website_search_tool],\n",
        "    allow_delegation=False,\n",
        "    verbose=True,\n",
        "    # ↑ Whether the agent execution should be in verbose mode\n",
        "    max_iter=3,\n",
        "    # ↑ maximum number of iterations the agent can perform before being forced to give its best answer (generate the output)\n",
        "    max_rpm=100, # This is the maximum number of requests per minute that the agent can make to the language model\n",
        "    llm=ChatOpenAI(model_name=\"gpt-4\", temperature=0.8)\n",
        "    # ↑ uncomment to use OpenAI API + \"gpt-4\"\n",
        "    # llm=ChatGroq(temperature=0.8, model_name=\"mixtral-8x7b-32768\"),\n",
        "    # ↑ uncomment to use Groq's API + \"llama3-70b-8192\"\n",
        "    # llm=ChatGroq(temperature=0.6, model_name=\"llama3-70b-8192\"),\n",
        "    # ↑ uncomment to use Groq's API + \"mixtral-8x7b-32768\"\n",
        "    # llm = ChatAnthropic(model='claude-3-opus-20240229', temperature=0.8),\n",
        "    # ↑ uncomment to use Anthropic's API + \"claude-3-opus-20240229\"\n",
        ")\n"
      ],
      "metadata": {
        "id": "hZJwUoXasrhx",
        "cellView": "form"
      },
      "execution_count": null,
      "outputs": []
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    {
      "cell_type": "code",
      "source": [
        "# @title 📝 Define your tasks\n",
        "\n",
        "import datetime\n",
        "from crewai import Task\n",
        "\n",
        "task_1 = Task(\n",
        "    description=dedent((\n",
        "        \"\"\"\n",
        "        A clear, concise statement of what the task entails.\n",
        "        ---\n",
        "        VARIABLE 1: \"{var_1}\"\n",
        "        VARIABLE 2: \"{var_2}\"\n",
        "        VARIABLE 3: \"{var_3}\"\n",
        "        Add more variables if needed...\n",
        "        \"\"\")),\n",
        "    expected_output=dedent((\n",
        "        \"\"\"\n",
        "        A detailed description of what the task's completion looks like.\n",
        "        \"\"\")),\n",
        "    agent=agent_1,\n",
        "    # output_file='blog-posts/new_post.md'  # The final blog post will be saved here\n",
        "    output_file=f'output-files/agent_1-output_{datetime.datetime.now().strftime(\"%Y%m%d_%H%M%S\")}.md'\n",
        "    # ↑ The output of each task iteration will be saved here\n",
        ")\n",
        "\n",
        "task_2 = Task(\n",
        "    description=dedent((\n",
        "        \"\"\"\n",
        "        A clear, concise statement of what the task entails.\n",
        "        ---\n",
        "        VARIABLE 1: \"{var_1}\"\n",
        "        VARIABLE 2: \"{var_2}\"\n",
        "        VARIABLE 3: \"{var_3}\"\n",
        "        Add more variables if needed...\n",
        "        \"\"\")),\n",
        "    expected_output=dedent((\n",
        "        \"\"\"\n",
        "        A detailed description of what the task's completion looks like.\n",
        "        \"\"\")),\n",
        "    agent=agent_2,\n",
        "    context=[task_1],\n",
        "    # ↑ specify which task's output should be used as context for subsequent tasks\n",
        "    output_file=f'output-files/agent_2-output_{datetime.datetime.now().strftime(\"%Y%m%d_%H%M%S\")}.md'\n",
        "    # ↑ The output of each task iteration will be saved here\n",
        ")\n",
        "\n",
        "task_3 = Task(\n",
        "    description=dedent((\n",
        "        \"\"\"\n",
        "        A clear, concise statement of what the task entails.\n",
        "        ---\n",
        "        VARIABLE 1: \"{var_1}\"\n",
        "        VARIABLE 2: \"{var_2}\"\n",
        "        VARIABLE 3: \"{var_3}\"\n",
        "        Add more variables if needed...\n",
        "        \"\"\")),\n",
        "    expected_output=dedent((\n",
        "        \"\"\"\n",
        "        A detailed description of what the task's completion looks like.\n",
        "        \"\"\")),\n",
        "    agent=agent_3,\n",
        "    context=[task_2],\n",
        "    # ↑ specify which task's output should be used as context for subsequent tasks\n",
        "    output_file=f'output-files/agent_3-output_{datetime.datetime.now().strftime(\"%Y%m%d_%H%M%S\")}.md'\n",
        "    # ↑ The output of each task iteration will be saved here\n",
        ")"
      ],
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        "id": "dqtn3w1qs-Bu",
        "cellView": "form"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# @title ⌨️ Define any variables you have and input them\n",
        "print(\"## Welcome to the YOUR_CREW_NAME\")\n",
        "print('-------------------------------------------')\n",
        "var_1 = input(\"What is the  to pass to your crew?\\n\"),\n",
        "var_2 = input(\"What is the  to pass to your crew?\\n\"),\n",
        "var_3 = input(\"What is the  to pass to your crew?\\n\"),\n",
        "print(\"-------------------------------\")"
      ],
      "metadata": {
        "id": "HfJRdGHesMwn",
        "cellView": "form"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# @title 🚀 Get your crew to work!\n",
        "\n",
        "from crewai import Crew, Process\n",
        "\n",
        "def main():\n",
        "    # Instantiate your crew with a sequential process\n",
        "    crew = Crew(\n",
        "        agents=[agent_1, agent_2, agent_3],\n",
        "        tasks=[task_1, task_2, task_3],\n",
        "        verbose=2,  # You can set it to 1 or 2 to different logging levels\n",
        "        # ↑ indicates the verbosity level for logging during execution.\n",
        "        process=Process.sequential\n",
        "        # ↑ the process flow that the crew will follow (e.g., sequential, hierarchical).\n",
        "    )\n",
        "\n",
        "    inputs = {\n",
        "    \"var_1\": var_1,\n",
        "    \"var_2\": var_2,\n",
        "    \"var_3\": var_3\n",
        "    }\n",
        "\n",
        "    result = crew.kickoff(inputs=inputs)\n",
        "    print(\"\\n\\n########################\")\n",
        "    print(\"## Here is your custom crew run result:\")\n",
        "    print(\"########################\\n\")\n",
        "    print(result)\n",
        "\n",
        "if __name__ == \"__main__\":\n",
        "  result = main()"
      ],
      "metadata": {
        "id": "nrBn8dMlxfCn",
        "cellView": "form"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# @title 🖥️ Display the results of your crew as markdown\n",
        "from IPython.display import Markdown\n",
        "Markdown(result)"
      ],
      "metadata": {
        "cellView": "form",
        "id": "TmT35fG9xD7A"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}