crewai_sequential_YoutubeVideoSearchTool_quickstart.ipynb

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   "metadata": {
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    "<a href=\"https://colab.research.google.com/github/alexfazio/crewAI-quickstart/blob/main/crewai_sequential_YoutubeVideoSearchTool_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-YoutubeVideoSearchTool-quickstart\n",
    "\n",
    "By [Alex Fazio](https://www.x.com/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 `YoutubeVideoSearchTool` to perform semantic searches in Youtube videos. It's part of the \"Search\" tools that use RAG for various sources. Users can search all Youtube video content or target specific videos by providing 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",
    "📚 YoutubeVideoSearchTool docs: https://docs.crewai.com/tools/YoutubeVideoSearchTool/\n",
    "\n",
    "📚 `chromadb` (embedding database) docs:  https://docs.trychroma.com/"
   ],
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   "source": [
    "# @title 👨‍🦯 Run this cell to hide all warnings (optional)\n",
    "# Warning control\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "# To avoid the restart session warning in Colab, exclude the PIL and\n",
    "# pydevd_plugins packages from being imported. This is fine because\n",
    "# we didn't execute the code in the kernel session afterward.\n",
    "\n",
    "# import sys\n",
    "# sys.modules.pop('PIL', None)"
   ]
  },
  {
   "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 --quiet\n",
    "%pip install crewai_tools langchain_openai langchain_groq langchain_anthropic langchain_community cohere --quiet\n",
    "print(\"---\")\n",
    "%pip show crewAI crewai_tools langchain_openai langchain_groq langchain_anthropic langchain_community cohere"
   ],
   "metadata": {
    "id": "P8iHNKCfk9Rv",
    "cellView": "form"
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   "outputs": []
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  {
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   "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 textwrap import dedent\n",
    "from langchain_openai import ChatOpenAI\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 `YoutubeVideoSearchTool`\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/YoutubeVideoSearchTool/#custom-model-and-embeddings).\n",
    "\n",
    "from crewai_tools import YoutubeVideoSearchTool\n",
    "\n",
    "# General search across Youtube content without specifying a video URL, so the agent can search within any Youtube video content it learns about irs url during its operation\n",
    "\n",
    "# youtube_video_search_tool = YoutubeVideoSearchTool()\n",
    "\n",
    "youtube_video_url = 'https://www.youtube.com/watch?v=FD-yp57VbmQ' # @param {type:\"string\"}\n",
    "\n",
    "# Targeted search within a specific Youtube video's content\n",
    "\n",
    "youtube_video_search_tool = YoutubeVideoSearchTool(youtube_video_url=youtube_video_url)"
   ],
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   "metadata": {},
   "cell_type": "markdown",
   "source": [
    "## Define Agents\n",
    "In CrewAI, agents are autonomous entities designed to perform specific roles and achieve particular goals. Each agent uses a language model (LLM) and may have specialized tools to help execute tasks."
   ]
  },
  {
   "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=[youtube_video_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-4o\", temperature=0.8)\n",
    "    # ↑ uncomment to use OpenAI API + \"gpt-4o\"\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=[youtube_video_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-4o\", temperature=0.8)\n",
    "    # ↑ uncomment to use OpenAI API + \"gpt-4o\"\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",
    "        \"\"\"\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=[youtube_video_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-4o\", temperature=0.8)\n",
    "    # ↑ uncomment to use OpenAI API + \"gpt-4o\"\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"
   ],
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   "metadata": {},
   "cell_type": "markdown",
   "source": [
    "## Define Tasks\n",
    "Tasks in CrewAI are specific assignments given to agents, detailing the actions they need to perform to achieve a particular goal. Tasks can have dependencies and context, and can be executed asynchronously to ensure an efficient workflow."
   ]
  },
  {
   "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=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",
    "        f\"\"\"\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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  {
   "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",
    "\n",
    "print(\"-------------------------------\")"
   ],
   "metadata": {
    "id": "HfJRdGHesMwn",
    "cellView": "form"
   },
   "execution_count": null,
   "outputs": []
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  {
   "cell_type": "code",
   "source": [
    "# @title 🚀 Get your crew to work!\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=True,  # You can set it to True or False\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",
    "    return 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 display, Markdown\n",
    "\n",
    "markdown_text = result.raw  # Adjust this based on the actual attribute\n",
    "\n",
    "# Display the markdown content\n",
    "display(Markdown(markdown_text))"
   ],
   "metadata": {
    "cellView": "form",
    "id": "6wu6FQ0woODY"
   },
   "execution_count": null,
   "outputs": []
  }
 ]
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