Customer_outreach_programs.ipynb

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        "<a href=\"https://colab.research.google.com/github/ElizabethWaithera/AI-AGENTS/blob/main/Customer_outreach_programs.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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            "\u001b[?25hDownloading types_requests-2.32.0.20241016-py3-none-any.whl (15 kB)\n",
            "Building wheels for collected packages: pypika\n",
            "  Building wheel for pypika (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for pypika: filename=PyPika-0.48.9-py2.py3-none-any.whl size=53725 sha256=c95286e4055aad3d5fcefd341ced42f0a923261e8bb5e1f8450cd17b24cc03f9\n",
            "  Stored in directory: /root/.cache/pip/wheels/e1/26/51/d0bffb3d2fd82256676d7ad3003faea3bd6dddc9577af665f4\n",
            "Successfully built pypika\n",
            "Installing collected packages: sortedcontainers, ratelimiter, pypika, monotonic, durationpy, brotli, appdirs, wsproto, websockets, uvloop, uvicorn, types-requests, typer, tenacity, semver, regex, pytube, python-rapidjson, python-dotenv, pysbd, pyproject_hooks, pypdf, pycryptodomex, py, pulsar-client, protobuf, pfzy, parameterized, packaging, overrides, outcome, opentelemetry-util-http, nodeenv, mypy-extensions, mutagen, mmh3, Mako, humanfriendly, httpx-sse, httptools, grpcio, fsspec, fastavro, docstring-parser, contextlib2, chroma-hnswlib, bcrypt, backoff, asgiref, yt_dlp, youtube-transcript-api, watchfiles, typing-inspect, tritonclient, trio, tiktoken, starlette, schema, retry, pyright, pynacl, pylance, posthog, opentelemetry-proto, marshmallow, inquirerpy, gptcache, deprecation, coloredlogs, build, alembic, trio-websocket, opentelemetry-exporter-otlp-proto-common, onnxruntime, lancedb, kubernetes, fastapi, dataclasses-json, clarifai-grpc, selenium, PyGithub, opentelemetry-instrumentation, langchain-core, instructor, cohere, opentelemetry-instrumentation-asgi, opentelemetry-exporter-otlp-proto-http, opentelemetry-exporter-otlp-proto-grpc, langchain-text-splitters, langchain-openai, langchain_community, langchain-cohere, opentelemetry-instrumentation-fastapi, langchain, chromadb, clarifai-protocol, clarifai, embedchain, crewai_tools, crewai\n",
            "  Attempting uninstall: typer\n",
            "    Found existing installation: typer 0.13.0\n",
            "    Uninstalling typer-0.13.0:\n",
            "      Successfully uninstalled typer-0.13.0\n",
            "  Attempting uninstall: tenacity\n",
            "    Found existing installation: tenacity 9.0.0\n",
            "    Uninstalling tenacity-9.0.0:\n",
            "      Successfully uninstalled tenacity-9.0.0\n",
            "  Attempting uninstall: regex\n",
            "    Found existing installation: regex 2024.9.11\n",
            "    Uninstalling regex-2024.9.11:\n",
            "      Successfully uninstalled regex-2024.9.11\n",
            "  Attempting uninstall: protobuf\n",
            "    Found existing installation: protobuf 4.25.5\n",
            "    Uninstalling protobuf-4.25.5:\n",
            "      Successfully uninstalled protobuf-4.25.5\n",
            "  Attempting uninstall: packaging\n",
            "    Found existing installation: packaging 24.2\n",
            "    Uninstalling packaging-24.2:\n",
            "      Successfully uninstalled packaging-24.2\n",
            "  Attempting uninstall: grpcio\n",
            "    Found existing installation: grpcio 1.68.0\n",
            "    Uninstalling grpcio-1.68.0:\n",
            "      Successfully uninstalled grpcio-1.68.0\n",
            "  Attempting uninstall: fsspec\n",
            "    Found existing installation: fsspec 2024.10.0\n",
            "    Uninstalling fsspec-2024.10.0:\n",
            "      Successfully uninstalled fsspec-2024.10.0\n",
            "  Attempting uninstall: docstring-parser\n",
            "    Found existing installation: docstring_parser 0.16\n",
            "    Uninstalling docstring_parser-0.16:\n",
            "      Successfully uninstalled docstring_parser-0.16\n",
            "  Attempting uninstall: langchain-core\n",
            "    Found existing installation: langchain-core 0.3.19\n",
            "    Uninstalling langchain-core-0.3.19:\n",
            "      Successfully uninstalled langchain-core-0.3.19\n",
            "  Attempting uninstall: langchain-text-splitters\n",
            "    Found existing installation: langchain-text-splitters 0.3.2\n",
            "    Uninstalling langchain-text-splitters-0.3.2:\n",
            "      Successfully uninstalled langchain-text-splitters-0.3.2\n",
            "  Attempting uninstall: langchain\n",
            "    Found existing installation: langchain 0.3.7\n",
            "    Uninstalling langchain-0.3.7:\n",
            "      Successfully uninstalled langchain-0.3.7\n",
            "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
            "gcsfs 2024.10.0 requires fsspec==2024.10.0, but you have fsspec 2024.6.1 which is incompatible.\n",
            "tensorflow 2.17.1 requires protobuf!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<5.0.0dev,>=3.20.3, but you have protobuf 5.27.3 which is incompatible.\n",
            "tensorflow-metadata 1.13.1 requires protobuf<5,>=3.20.3, but you have protobuf 5.27.3 which is incompatible.\u001b[0m\u001b[31m\n",
            "\u001b[0mSuccessfully installed Mako-1.3.6 PyGithub-1.59.1 alembic-1.14.0 appdirs-1.4.4 asgiref-3.8.1 backoff-2.2.1 bcrypt-4.2.1 brotli-1.1.0 build-1.2.2.post1 chroma-hnswlib-0.7.3 chromadb-0.4.24 clarifai-10.10.1 clarifai-grpc-10.10.1 clarifai-protocol-0.0.10 cohere-5.11.4 coloredlogs-15.0.1 contextlib2-21.6.0 crewai-0.28.8 crewai_tools-0.1.6 dataclasses-json-0.6.7 deprecation-2.1.0 docstring-parser-0.15 durationpy-0.9 embedchain-0.1.113 fastapi-0.115.5 fastavro-1.9.7 fsspec-2024.6.1 gptcache-0.1.44 grpcio-1.64.3 httptools-0.6.4 httpx-sse-0.4.0 humanfriendly-10.0 inquirerpy-0.3.4 instructor-0.5.2 kubernetes-31.0.0 lancedb-0.5.7 langchain-0.1.13 langchain-cohere-0.1.5 langchain-core-0.1.53 langchain-openai-0.1.7 langchain-text-splitters-0.0.2 langchain_community-0.0.29 marshmallow-3.23.1 mmh3-5.0.1 monotonic-1.6 mutagen-1.47.0 mypy-extensions-1.0.0 nodeenv-1.9.1 onnxruntime-1.20.1 opentelemetry-exporter-otlp-proto-common-1.28.2 opentelemetry-exporter-otlp-proto-grpc-1.28.2 opentelemetry-exporter-otlp-proto-http-1.28.2 opentelemetry-instrumentation-0.49b2 opentelemetry-instrumentation-asgi-0.49b2 opentelemetry-instrumentation-fastapi-0.49b2 opentelemetry-proto-1.28.2 opentelemetry-util-http-0.49b2 outcome-1.3.0.post0 overrides-7.7.0 packaging-23.2 parameterized-0.9.0 pfzy-0.3.4 posthog-3.7.3 protobuf-5.27.3 pulsar-client-3.5.0 py-1.11.0 pycryptodomex-3.21.0 pylance-0.9.18 pynacl-1.5.0 pypdf-4.3.1 pypika-0.48.9 pyproject_hooks-1.2.0 pyright-1.1.389 pysbd-0.3.4 python-dotenv-1.0.0 python-rapidjson-1.20 pytube-15.0.0 ratelimiter-1.2.0.post0 regex-2023.12.25 retry-0.9.2 schema-0.7.5 selenium-4.27.1 semver-3.0.2 sortedcontainers-2.4.0 starlette-0.41.3 tenacity-8.5.0 tiktoken-0.7.0 trio-0.27.0 trio-websocket-0.11.1 tritonclient-2.52.0 typer-0.9.4 types-requests-2.32.0.20241016 typing-inspect-0.9.0 uvicorn-0.32.1 uvloop-0.21.0 watchfiles-1.0.0 websockets-14.1 wsproto-1.2.0 youtube-transcript-api-0.6.3 yt_dlp-2023.12.30\n"
          ]
        }
      ],
      "source": [
        "!pip install crewai==0.28.8 crewai_tools==0.1.6 langchain_community==0.0.29"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Warning control\n",
        "import warnings\n",
        "warnings.filterwarnings('ignore')"
      ],
      "metadata": {
        "id": "hQPnciTeHjSq"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from crewai import Agent, Task, Crew"
      ],
      "metadata": {
        "id": "FwQ0xWlhHxkO"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import os\n",
        "from google.colab import userdata\n",
        "\n",
        "\n",
        "openai_api_key = userdata.get('OPENAI_API_KEY')\n",
        "os.environ[\"OPENAI_API_KEY\"] = userdata.get('OPENAI_API_KEY')\n",
        "os.environ[\"SERPER_API_KEY\"] = userdata.get('SERPER_API_KEY')\n",
        "\n",
        "os.environ[\"OPENAI_MODEL_NAME\"] = 'gpt-3.5-turbo'\n"
      ],
      "metadata": {
        "id": "Op_Y1u_IH2pL"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "sales_rep_agent = Agent(\n",
        "    role=\"Sales Representative\",\n",
        "    goal=\"Identify high-value leads that match \"\n",
        "         \"our ideal customer profile\",\n",
        "    backstory=(\n",
        "        \"As a part of the dynamic sales team at CrewAI, \"\n",
        "        \"your mission is to scour \"\n",
        "        \"the digital landscape for potential leads. \"\n",
        "        \"Armed with cutting-edge tools \"\n",
        "        \"and a strategic mindset, you analyze data, \"\n",
        "        \"trends, and interactions to \"\n",
        "        \"unearth opportunities that others might overlook. \"\n",
        "        \"Your work is crucial in paving the way \"\n",
        "        \"for meaningful engagements and driving the company's growth.\"\n",
        "    ),\n",
        "    allow_delegation=False,\n",
        "    verbose=True\n",
        ")"
      ],
      "metadata": {
        "id": "QFnhHBWIIhf5"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "lead_sales_rep_agent = Agent(\n",
        "    role=\"Lead Sales Representative\",\n",
        "    goal=\"Nurture leads with personalized, compelling communications\",\n",
        "    backstory=(\n",
        "        \"Within the vibrant ecosystem of CrewAI's sales department, \"\n",
        "        \"you stand out as the bridge between potential clients \"\n",
        "        \"and the solutions they need.\"\n",
        "        \"By creating engaging, personalized messages, \"\n",
        "        \"you not only inform leads about our offerings \"\n",
        "        \"but also make them feel seen and heard.\"\n",
        "        \"Your role is pivotal in converting interest \"\n",
        "        \"into action, guiding leads through the journey \"\n",
        "        \"from curiosity to commitment.\"\n",
        "    ),\n",
        "    allow_delegation=False,\n",
        "    verbose=True\n",
        ")"
      ],
      "metadata": {
        "id": "VqJ-Vm4UIomg"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from crewai_tools import DirectoryReadTool, \\\n",
        "                         FileReadTool, \\\n",
        "                         SerperDevTool"
      ],
      "metadata": {
        "id": "6QOagnHCI5a9"
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "directory_read_tool = DirectoryReadTool(directory='./instructions')\n",
        "file_read_tool = FileReadTool()\n",
        "search_tool = SerperDevTool()"
      ],
      "metadata": {
        "id": "m7g7jsLtNBNo"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from crewai_tools import BaseTool"
      ],
      "metadata": {
        "id": "wthZQSqMNHf2"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "class SentimentAnalysisTool(BaseTool):\n",
        "    name: str =\"Sentiment Analysis Tool\"\n",
        "    description: str = (\"Analyzes the sentiment of text \"\n",
        "         \"to ensure positive and engaging communication.\")\n",
        "\n",
        "    def _run(self, text: str) -> str:\n",
        "        # Your custom code tool goes here\n",
        "        return \"positive\""
      ],
      "metadata": {
        "id": "usEol5xENL8U"
      },
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "sentiment_analysis_tool = SentimentAnalysisTool()"
      ],
      "metadata": {
        "id": "MKsuBsXNNRkl"
      },
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "lead_profiling_task = Task(\n",
        "    description=(\n",
        "        \"Conduct an in-depth analysis of {lead_name}, \"\n",
        "        \"a company in the {industry} sector \"\n",
        "        \"that recently showed interest in our solutions. \"\n",
        "        \"Utilize all available data sources \"\n",
        "        \"to compile a detailed profile, \"\n",
        "        \"focusing on key decision-makers, recent business \"\n",
        "        \"developments, and potential needs \"\n",
        "        \"that align with our offerings. \"\n",
        "        \"This task is crucial for tailoring \"\n",
        "        \"our engagement strategy effectively.\\n\"\n",
        "        \"Don't make assumptions and \"\n",
        "        \"only use information you absolutely sure about.\"\n",
        "    ),\n",
        "    expected_output=(\n",
        "        \"A comprehensive report on {lead_name}, \"\n",
        "        \"including company background, \"\n",
        "        \"key personnel, recent milestones, and identified needs. \"\n",
        "        \"Highlight potential areas where \"\n",
        "        \"our solutions can provide value, \"\n",
        "        \"and suggest personalized engagement strategies.\"\n",
        "    ),\n",
        "    tools=[directory_read_tool, file_read_tool, search_tool],\n",
        "    agent=sales_rep_agent,\n",
        ")"
      ],
      "metadata": {
        "id": "TYgwzkFGNh4B"
      },
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "personalized_outreach_task = Task(\n",
        "    description=(\n",
        "        \"Using the insights gathered from \"\n",
        "        \"the lead profiling report on {lead_name}, \"\n",
        "        \"craft a personalized outreach campaign \"\n",
        "        \"aimed at {key_decision_maker}, \"\n",
        "        \"the {position} of {lead_name}. \"\n",
        "        \"The campaign should address their recent {milestone} \"\n",
        "        \"and how our solutions can support their goals. \"\n",
        "        \"Your communication must resonate \"\n",
        "        \"with {lead_name}'s company culture and values, \"\n",
        "        \"demonstrating a deep understanding of \"\n",
        "        \"their business and needs.\\n\"\n",
        "        \"Don't make assumptions and only \"\n",
        "        \"use information you absolutely sure about.\"\n",
        "    ),\n",
        "    expected_output=(\n",
        "        \"A series of personalized email drafts \"\n",
        "        \"tailored to {lead_name}, \"\n",
        "        \"specifically targeting {key_decision_maker}.\"\n",
        "        \"Each draft should include \"\n",
        "        \"a compelling narrative that connects our solutions \"\n",
        "        \"with their recent achievements and future goals. \"\n",
        "        \"Ensure the tone is engaging, professional, \"\n",
        "        \"and aligned with {lead_name}'s corporate identity.\"\n",
        "    ),\n",
        "    tools=[sentiment_analysis_tool, search_tool],\n",
        "    agent=lead_sales_rep_agent,\n",
        ")"
      ],
      "metadata": {
        "id": "kIbON6wsNq6L"
      },
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "crew = Crew(\n",
        "    agents=[sales_rep_agent,\n",
        "            lead_sales_rep_agent],\n",
        "\n",
        "    tasks=[lead_profiling_task,\n",
        "           personalized_outreach_task],\n",
        "\n",
        "    verbose=2,\n",
        "\tmemory=True\n",
        ")"
      ],
      "metadata": {
        "id": "j8cYTQFsNzbb"
      },
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "inputs = {\n",
        "    \"lead_name\": \"daada\",\n",
        "    \"industry\": \"mobile money\",\n",
        "    \"key_decision_maker\": \"Andrew Ng\",\n",
        "    \"position\": \"CEO\",\n",
        "    \"milestone\": \"product launch\"\n",
        "}\n",
        "\n",
        "result = crew.kickoff(inputs=inputs)"
      ],
      "metadata": {
        "id": "Vv9K41S5N5Yj"
      },
      "execution_count": 24,
      "outputs": []
    }
  ]
}