Multi_AI_Agent_Systems_with_crewAI.ipynb

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      "source": [
        "# L3: Multi-agent Customer Support Automation\n",
        "\n",
        "In this lesson, you will learn about the six key elements which help make Agents perform even better:\n",
        "- Role Playing\n",
        "- Focus\n",
        "- Tools\n",
        "- Cooperation\n",
        "- Guardrails\n",
        "- Memory"
      ],
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      }
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        "id": "h1uKqWqrSP3U",
        "outputId": "ff4b4584-cb24-4173-8a3f-5f625dd28473"
      },
      "outputs": [
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            "  Stored in directory: /root/.cache/pip/wheels/e1/26/51/d0bffb3d2fd82256676d7ad3003faea3bd6dddc9577af665f4\n",
            "Successfully built pypika\n",
            "Installing collected packages: schema, ratelimiter, pypika, monotonic, mmh3, brotli, appdirs, websockets, uvloop, types-requests, typer, smmap, semver, regex, pytube, python-dotenv, pysbd, pypdf, pyjwt, pycryptodomex, py, pulsar-client, parameterized, packaging, overrides, outcome, orjson, opentelemetry-util-http, opentelemetry-proto, nodeenv, mypy-extensions, mutagen, Mako, jsonpointer, jmespath, humanfriendly, httpx-sse, httptools, h11, fastavro, docstring-parser, deprecated, chroma-hnswlib, bcrypt, backoff, asgiref, yt_dlp, youtube-transcript-api, wsproto, watchfiles, uvicorn, typing-inspect, trio, tiktoken, starlette, retry, pytest, pyright, pynacl, pylance, posthog, opentelemetry-exporter-otlp-proto-common, opentelemetry-api, marshmallow, jsonpatch, httpcore, gptcache, gitdb, deprecation, coloredlogs, botocore, alembic, trio-websocket, s3transfer, opentelemetry-semantic-conventions, opentelemetry-instrumentation, onnxruntime, langsmith, lancedb, kubernetes, httpx, gitpython, fastapi, dataclasses-json, selenium, PyGithub, opentelemetry-sdk, opentelemetry-instrumentation-asgi, openai, langchain-core, boto3, opentelemetry-instrumentation-fastapi, opentelemetry-exporter-otlp-proto-http, opentelemetry-exporter-otlp-proto-grpc, langchain-text-splitters, langchain-openai, langchain_community, instructor, cohere, langchain-cohere, langchain, chromadb, embedchain, crewai, crewai_tools\n",
            "  Attempting uninstall: typer\n",
            "    Found existing installation: typer 0.12.3\n",
            "    Uninstalling typer-0.12.3:\n",
            "      Successfully uninstalled typer-0.12.3\n",
            "  Attempting uninstall: regex\n",
            "    Found existing installation: regex 2024.5.15\n",
            "    Uninstalling regex-2024.5.15:\n",
            "      Successfully uninstalled regex-2024.5.15\n",
            "  Attempting uninstall: pyjwt\n",
            "    Found existing installation: PyJWT 2.3.0\n",
            "    Uninstalling PyJWT-2.3.0:\n",
            "      Successfully uninstalled PyJWT-2.3.0\n",
            "  Attempting uninstall: packaging\n",
            "    Found existing installation: packaging 24.1\n",
            "    Uninstalling packaging-24.1:\n",
            "      Successfully uninstalled packaging-24.1\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: pytest\n",
            "    Found existing installation: pytest 7.4.4\n",
            "    Uninstalling pytest-7.4.4:\n",
            "      Successfully uninstalled pytest-7.4.4\n",
            "Successfully installed Mako-1.3.5 PyGithub-1.59.1 alembic-1.13.1 appdirs-1.4.4 asgiref-3.8.1 backoff-2.2.1 bcrypt-4.1.3 boto3-1.34.128 botocore-1.34.128 brotli-1.1.0 chroma-hnswlib-0.7.3 chromadb-0.4.24 cohere-5.5.8 coloredlogs-15.0.1 crewai-0.28.8 crewai_tools-0.1.6 dataclasses-json-0.6.7 deprecated-1.2.14 deprecation-2.1.0 docstring-parser-0.15 embedchain-0.1.110 fastapi-0.110.3 fastavro-1.9.4 gitdb-4.0.11 gitpython-3.1.43 gptcache-0.1.43 h11-0.14.0 httpcore-1.0.5 httptools-0.6.1 httpx-0.27.0 httpx-sse-0.4.0 humanfriendly-10.0 instructor-0.5.2 jmespath-1.0.1 jsonpatch-1.33 jsonpointer-3.0.0 kubernetes-30.1.0 lancedb-0.5.7 langchain-0.1.13 langchain-cohere-0.1.5 langchain-core-0.1.52 langchain-openai-0.1.7 langchain-text-splitters-0.0.2 langchain_community-0.0.29 langsmith-0.1.79 marshmallow-3.21.3 mmh3-4.1.0 monotonic-1.6 mutagen-1.47.0 mypy-extensions-1.0.0 nodeenv-1.9.1 onnxruntime-1.18.0 openai-1.34.0 opentelemetry-api-1.25.0 opentelemetry-exporter-otlp-proto-common-1.25.0 opentelemetry-exporter-otlp-proto-grpc-1.25.0 opentelemetry-exporter-otlp-proto-http-1.25.0 opentelemetry-instrumentation-0.46b0 opentelemetry-instrumentation-asgi-0.46b0 opentelemetry-instrumentation-fastapi-0.46b0 opentelemetry-proto-1.25.0 opentelemetry-sdk-1.25.0 opentelemetry-semantic-conventions-0.46b0 opentelemetry-util-http-0.46b0 orjson-3.10.5 outcome-1.3.0.post0 overrides-7.7.0 packaging-23.2 parameterized-0.9.0 posthog-3.5.0 pulsar-client-3.5.0 py-1.11.0 pycryptodomex-3.20.0 pyjwt-2.8.0 pylance-0.9.18 pynacl-1.5.0 pypdf-4.2.0 pypika-0.48.9 pyright-1.1.367 pysbd-0.3.4 pytest-8.2.2 python-dotenv-1.0.0 pytube-15.0.0 ratelimiter-1.2.0.post0 regex-2023.12.25 retry-0.9.2 s3transfer-0.10.1 schema-0.7.7 selenium-4.21.0 semver-3.0.2 smmap-5.0.1 starlette-0.37.2 tiktoken-0.7.0 trio-0.25.1 trio-websocket-0.11.1 typer-0.9.4 types-requests-2.32.0.20240602 typing-inspect-0.9.0 uvicorn-0.30.1 uvloop-0.19.0 watchfiles-0.22.0 websockets-12.0 wsproto-1.2.0 youtube-transcript-api-0.6.2 yt_dlp-2023.12.30\n"
          ]
        }
      ],
      "source": [
        "!pip install crewai==0.28.8 crewai_tools==0.1.6 langchain_community==0.0.29\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Warning control\n",
        "import warnings\n",
        "warnings.filterwarnings('ignore')"
      ],
      "metadata": {
        "id": "WSDRu2eRSltd"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install openai\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5xfeTQl9Xy-x",
        "outputId": "47a323db-495c-4e6e-ad19-35502e9fc1f4"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: openai in /usr/local/lib/python3.10/dist-packages (1.34.0)\n",
            "Requirement already satisfied: anyio<5,>=3.5.0 in /usr/local/lib/python3.10/dist-packages (from openai) (3.7.1)\n",
            "Requirement already satisfied: distro<2,>=1.7.0 in /usr/lib/python3/dist-packages (from openai) (1.7.0)\n",
            "Requirement already satisfied: httpx<1,>=0.23.0 in /usr/local/lib/python3.10/dist-packages (from openai) (0.27.0)\n",
            "Requirement already satisfied: pydantic<3,>=1.9.0 in /usr/local/lib/python3.10/dist-packages (from openai) (2.7.3)\n",
            "Requirement already satisfied: sniffio in /usr/local/lib/python3.10/dist-packages (from openai) (1.3.1)\n",
            "Requirement already satisfied: tqdm>4 in /usr/local/lib/python3.10/dist-packages (from openai) (4.66.4)\n",
            "Requirement already satisfied: typing-extensions<5,>=4.7 in /usr/local/lib/python3.10/dist-packages (from openai) (4.12.2)\n",
            "Requirement already satisfied: idna>=2.8 in /usr/local/lib/python3.10/dist-packages (from anyio<5,>=3.5.0->openai) (3.7)\n",
            "Requirement already satisfied: exceptiongroup in /usr/local/lib/python3.10/dist-packages (from anyio<5,>=3.5.0->openai) (1.2.1)\n",
            "Requirement already satisfied: certifi in /usr/local/lib/python3.10/dist-packages (from httpx<1,>=0.23.0->openai) (2024.6.2)\n",
            "Requirement already satisfied: httpcore==1.* in /usr/local/lib/python3.10/dist-packages (from httpx<1,>=0.23.0->openai) (1.0.5)\n",
            "Requirement already satisfied: h11<0.15,>=0.13 in /usr/local/lib/python3.10/dist-packages (from httpcore==1.*->httpx<1,>=0.23.0->openai) (0.14.0)\n",
            "Requirement already satisfied: annotated-types>=0.4.0 in /usr/local/lib/python3.10/dist-packages (from pydantic<3,>=1.9.0->openai) (0.7.0)\n",
            "Requirement already satisfied: pydantic-core==2.18.4 in /usr/local/lib/python3.10/dist-packages (from pydantic<3,>=1.9.0->openai) (2.18.4)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "- Import libraries, API and LLM"
      ],
      "metadata": {
        "id": "aiScUFkWS9-2"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from crewai import Agent, Task, Crew"
      ],
      "metadata": {
        "id": "O68fdg2wS_RD"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import os\n",
        "import openai\n",
        "\n",
        "# from utils import get_openai_api_key\n",
        "# Set the API key as an environment variable\n",
        "os.environ['OPENAI_API_KEY'] = 'Your-API-Key-Here'\n",
        "\n",
        "openai.api_key = os.environ['OPENAI_API_KEY']\n",
        "\n",
        "os.environ[\"OPENAI_MODEL_NAME\"] = 'gpt-3.5-turbo'"
      ],
      "metadata": {
        "id": "a4CqVH3nTG1H"
      },
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Role Playing, Focus and Cooperation\n"
      ],
      "metadata": {
        "id": "M8QLf2qUTQVb"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "support_agent = Agent(\n",
        "    role=\"Senior Support Representative\",\n",
        "\tgoal=\"Be the most friendly and helpful \"\n",
        "        \"support representative in your team\",\n",
        "\tbackstory=(\n",
        "\t\t\"You work at crewAI (https://crewai.com) and \"\n",
        "        \" are now working on providing \"\n",
        "\t\t\"support to {customer}, a super important customer \"\n",
        "        \" for your company.\"\n",
        "\t\t\"You need to make sure that you provide the best support!\"\n",
        "\t\t\"Make sure to provide full complete answers, \"\n",
        "        \" and make no assumptions.\"\n",
        "\t),\n",
        "\tallow_delegation=False,\n",
        "\tverbose=True\n",
        ")"
      ],
      "metadata": {
        "id": "UYcZd-ZsTQ9V"
      },
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "- By not setting `allow_delegation=False`, `allow_delegation` takes its default value of being `True`.\n",
        "- This means the agent _can_ delegate its work to another agent which is better suited to do a particular task."
      ],
      "metadata": {
        "id": "POlIIrY4Tsuc"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "support_quality_assurance_agent = Agent(\n",
        "\trole=\"Support Quality Assurance Specialist\",\n",
        "\tgoal=\"Get recognition for providing the \"\n",
        "    \"best support quality assurance in your team\",\n",
        "\tbackstory=(\n",
        "\t\t\"You work at crewAI (https://crewai.com) and \"\n",
        "        \"are now working with your team \"\n",
        "\t\t\"on a request from {customer} ensuring that \"\n",
        "        \"the support representative is \"\n",
        "\t\t\"providing the best support possible.\\n\"\n",
        "\t\t\"You need to make sure that the support representative \"\n",
        "        \"is providing full\"\n",
        "\t\t\"complete answers, and make no assumptions.\"\n",
        "\t),\n",
        "\tverbose=True\n",
        ")"
      ],
      "metadata": {
        "id": "KpkntqfRTva3"
      },
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "* **Role Playing**: Both agents have been given a role, goal and backstory.\n",
        "* **Focus**: Both agents have been prompted to get into the character of the roles they are playing.\n",
        "* **Cooperation**: Support Quality Assurance Agent can delegate work back to the Support Agent, allowing for these agents to work together."
      ],
      "metadata": {
        "id": "KxPUKCAzT1na"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "- Import CrewAI tools"
      ],
      "metadata": {
        "id": "-6JD4hl5T40w"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from crewai_tools import SerperDevTool, \\\n",
        "                         ScrapeWebsiteTool, \\\n",
        "                         WebsiteSearchTool"
      ],
      "metadata": {
        "id": "17ZomLkxT7e8"
      },
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Possible Custom Tools\n",
        "- Load customer data\n",
        "- Tap into previous conversations\n",
        "- Load data from a CRM\n",
        "- Checking existing bug reports\n",
        "- Checking existing feature requests\n",
        "- Checking ongoing tickets\n",
        "- ... and more"
      ],
      "metadata": {
        "id": "MYvSxUiST_Rr"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "docs_scrape_tool = ScrapeWebsiteTool(\n",
        "    website_url=\"https://docs.crewai.com/how-to/Creating-a-Crew-and-kick-it-off/\"\n",
        ")"
      ],
      "metadata": {
        "id": "oR6PmO5IUHjI"
      },
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Creating Tasks\n",
        "- You are passing the Tool on the Task Level."
      ],
      "metadata": {
        "id": "TpP02hjDUNvu"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "inquiry_resolution = Task(\n",
        "    description=(\n",
        "        \"{customer} just reached out with a super important ask:\\n\"\n",
        "\t    \"{inquiry}\\n\\n\"\n",
        "        \"{person} from {customer} is the one that reached out. \"\n",
        "\t\t\"Make sure to use everything you know \"\n",
        "        \"to provide the best support possible.\"\n",
        "\t\t\"You must strive to provide a complete \"\n",
        "        \"and accurate response to the customer's inquiry.\"\n",
        "    ),\n",
        "    expected_output=(\n",
        "\t    \"A detailed, informative response to the \"\n",
        "        \"customer's inquiry that addresses \"\n",
        "        \"all aspects of their question.\\n\"\n",
        "        \"The response should include references \"\n",
        "        \"to everything you used to find the answer, \"\n",
        "        \"including external data or solutions. \"\n",
        "        \"Ensure the answer is complete, \"\n",
        "\t\t\"leaving no questions unanswered, and maintain a helpful and friendly \"\n",
        "\t\t\"tone throughout.\"\n",
        "    ),\n",
        "\ttools=[docs_scrape_tool],\n",
        "    agent=support_agent,\n",
        ")"
      ],
      "metadata": {
        "id": "BbDbsccnUUKE"
      },
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "- `quality_assurance_review` is not using any Tool(s)\n",
        "- Here the QA Agent will only review the work of the Support Agent"
      ],
      "metadata": {
        "id": "F9SftepfUGcK"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "quality_assurance_review = Task(\n",
        "    description=(\n",
        "        \"Review the response drafted by the Senior Support Representative for {customer}'s inquiry. \"\n",
        "        \"Ensure that the answer is comprehensive, accurate, and adheres to the \"\n",
        "\t\t\"high-quality standards expected for customer support.\\n\"\n",
        "        \"Verify that all parts of the customer's inquiry \"\n",
        "        \"have been addressed \"\n",
        "\t\t\"thoroughly, with a helpful and friendly tone.\\n\"\n",
        "        \"Check for references and sources used to \"\n",
        "        \" find the information, \"\n",
        "\t\t\"ensuring the response is well-supported and \"\n",
        "        \"leaves no questions unanswered.\"\n",
        "    ),\n",
        "    expected_output=(\n",
        "        \"A final, detailed, and informative response \"\n",
        "        \"ready to be sent to the customer.\\n\"\n",
        "        \"This response should fully address the \"\n",
        "        \"customer's inquiry, incorporating all \"\n",
        "\t\t\"relevant feedback and improvements.\\n\"\n",
        "\t\t\"Don't be too formal, we are a chill and cool company \"\n",
        "\t    \"but maintain a professional and friendly tone throughout.\"\n",
        "    ),\n",
        "    agent=support_quality_assurance_agent,\n",
        ")\n"
      ],
      "metadata": {
        "id": "p9VBfmPIUY2h"
      },
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "crew = Crew(\n",
        "  agents=[support_agent, support_quality_assurance_agent],\n",
        "  tasks=[inquiry_resolution, quality_assurance_review],\n",
        "  verbose=2,\n",
        "  memory=True\n",
        ")"
      ],
      "metadata": {
        "id": "F0noLUk4Trzr"
      },
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "inputs = {\n",
        "    \"customer\": \"DeepLearningAI\",\n",
        "    \"person\": \"Andrew Ng\",\n",
        "    \"inquiry\": \"I need help with setting up a Crew \"\n",
        "               \"and kicking it off, specifically \"\n",
        "               \"how can I add memory to my crew? \"\n",
        "               \"Can you provide guidance?\"\n",
        "}\n",
        "result = crew.kickoff(inputs=inputs)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 463
        },
        "id": "ysz7pyfiUfB1",
        "outputId": "e5fe78e3-4216-419a-eb65-e16fd6a631ab"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1m\u001b[95m [DEBUG]: == Working Agent: Senior Support Representative\u001b[00m\n",
            "\u001b[1m\u001b[95m [INFO]: == Starting Task: DeepLearningAI just reached out with a super important ask:\n",
            "I need help with setting up a Crew and kicking it off, specifically how can I add memory to my crew? Can you provide guidance?\n",
            "\n",
            "Andrew Ng from DeepLearningAI is the one that reached out. Make sure to use everything you know to provide the best support possible.You must strive to provide a complete and accurate response to the customer's inquiry.\u001b[00m\n"
          ]
        },
        {
          "output_type": "error",
          "ename": "RateLimitError",
          "evalue": "Error code: 429 - {'error': {'message': 'You exceeded your current quota, please check your plan and billing details. For more information on this error, read the docs: https://platform.openai.com/docs/guides/error-codes/api-errors.', 'type': 'insufficient_quota', 'param': None, 'code': 'insufficient_quota'}}",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mRateLimitError\u001b[0m                            Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-20-b2bf29913abe>\u001b[0m in \u001b[0;36m<cell line: 9>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      7\u001b[0m                \u001b[0;34m\"Can you provide guidance?\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      8\u001b[0m }\n\u001b[0;32m----> 9\u001b[0;31m \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcrew\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mkickoff\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minputs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0minputs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/crewai/crew.py\u001b[0m in \u001b[0;36mkickoff\u001b[0;34m(self, inputs)\u001b[0m\n\u001b[1;32m    250\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    251\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprocess\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mProcess\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msequential\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 252\u001b[0;31m             \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_run_sequential_process\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    253\u001b[0m         \u001b[0;32melif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprocess\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mProcess\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhierarchical\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    254\u001b[0m             \u001b[0mresult\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmanager_metrics\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_run_hierarchical_process\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/crewai/crew.py\u001b[0m in \u001b[0;36m_run_sequential_process\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    291\u001b[0m                 )\n\u001b[1;32m    292\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 293\u001b[0;31m             \u001b[0moutput\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtask\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexecute\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcontext\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtask_output\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    294\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mtask\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0masync_execution\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    295\u001b[0m                 \u001b[0mtask_output\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0moutput\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/crewai/task.py\u001b[0m in \u001b[0;36mexecute\u001b[0;34m(self, agent, context, tools)\u001b[0m\n\u001b[1;32m    171\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mthread\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstart\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    172\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 173\u001b[0;31m             result = self._execute(\n\u001b[0m\u001b[1;32m    174\u001b[0m                 \u001b[0mtask\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    175\u001b[0m                 \u001b[0magent\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0magent\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/crewai/task.py\u001b[0m in \u001b[0;36m_execute\u001b[0;34m(self, agent, task, context, tools)\u001b[0m\n\u001b[1;32m    180\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    181\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_execute\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0magent\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtask\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcontext\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtools\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 182\u001b[0;31m         result = agent.execute_task(\n\u001b[0m\u001b[1;32m    183\u001b[0m             \u001b[0mtask\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtask\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    184\u001b[0m             \u001b[0mcontext\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcontext\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/crewai/agent.py\u001b[0m in \u001b[0;36mexecute_task\u001b[0;34m(self, task, context, tools)\u001b[0m\n\u001b[1;32m    205\u001b[0m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcrew\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_entity_memory\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    206\u001b[0m             )\n\u001b[0;32m--> 207\u001b[0;31m             \u001b[0mmemory\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcontextual_memory\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbuild_context_for_task\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtask\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcontext\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    208\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mmemory\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstrip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0;34m\"\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    209\u001b[0m                 \u001b[0mtask_prompt\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mi18n\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mslice\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"memory\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmemory\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmemory\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/crewai/memory/contextual/contextual_memory.py\u001b[0m in \u001b[0;36mbuild_context_for_task\u001b[0;34m(self, task, context)\u001b[0m\n\u001b[1;32m     20\u001b[0m         \u001b[0mcontext\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     21\u001b[0m         \u001b[0mcontext\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_fetch_ltm_context\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtask\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdescription\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 22\u001b[0;31m         \u001b[0mcontext\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_fetch_stm_context\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mquery\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     23\u001b[0m         \u001b[0mcontext\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_fetch_entity_context\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mquery\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     24\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0;34m\"\\n\"\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mjoin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcontext\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/crewai/memory/contextual/contextual_memory.py\u001b[0m in \u001b[0;36m_fetch_stm_context\u001b[0;34m(self, query)\u001b[0m\n\u001b[1;32m     29\u001b[0m         \u001b[0mformatted\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mbullet\u001b[0m \u001b[0mpoints\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     30\u001b[0m         \"\"\"\n\u001b[0;32m---> 31\u001b[0;31m         \u001b[0mstm_results\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstm\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msearch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mquery\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     32\u001b[0m         \u001b[0mformatted_results\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"\\n\"\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mjoin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34mf\"- {result}\"\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mresult\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mstm_results\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     33\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0;34mf\"Recent Insights:\\n{formatted_results}\"\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mstm_results\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0;34m\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/crewai/memory/short_term/short_term_memory.py\u001b[0m in \u001b[0;36msearch\u001b[0;34m(self, query, score_threshold)\u001b[0m\n\u001b[1;32m     21\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     22\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0msearch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mquery\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mstr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mscore_threshold\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mfloat\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m0.35\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 23\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstorage\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msearch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mquery\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mquery\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mscore_threshold\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mscore_threshold\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/crewai/memory/storage/rag_storage.py\u001b[0m in \u001b[0;36msearch\u001b[0;34m(self, query, limit, filter, score_threshold)\u001b[0m\n\u001b[1;32m     88\u001b[0m                     \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msearch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mquery\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlimit\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mwhere\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfilter\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     89\u001b[0m                     \u001b[0;32mif\u001b[0m \u001b[0mfilter\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 90\u001b[0;31m                     \u001b[0;32melse\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msearch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mquery\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlimit\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     91\u001b[0m                 )\n\u001b[1;32m     92\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mInvalidDimensionException\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/embedchain/embedchain.py\u001b[0m in \u001b[0;36msearch\u001b[0;34m(self, query, num_documents, where, raw_filter, namespace)\u001b[0m\n\u001b[1;32m    648\u001b[0m         }\n\u001b[1;32m    649\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 650\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m{\u001b[0m\u001b[0;34m\"context\"\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mc\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"metadata\"\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mc\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m}\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mc\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdb\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mquery\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0mparams\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    651\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    652\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mset_collection_name\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mstr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/embedchain/vectordb/chroma.py\u001b[0m in \u001b[0;36mquery\u001b[0;34m(self, input_query, n_results, where, raw_filter, citations, **kwargs)\u001b[0m\n\u001b[1;32m    218\u001b[0m             \u001b[0mwhere_clause\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_generate_where_clause\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mwhere\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    219\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 220\u001b[0;31m             result = self.collection.query(\n\u001b[0m\u001b[1;32m    221\u001b[0m                 query_texts=[\n\u001b[1;32m    222\u001b[0m                     \u001b[0minput_query\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/chromadb/api/models/Collection.py\u001b[0m in \u001b[0;36mquery\u001b[0;34m(self, query_embeddings, query_texts, query_images, query_uris, n_results, where, where_document, include)\u001b[0m\n\u001b[1;32m    325\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mvalid_query_embeddings\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    326\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mquery_texts\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 327\u001b[0;31m                 \u001b[0mvalid_query_embeddings\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_embed\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mvalid_query_texts\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    328\u001b[0m             \u001b[0;32melif\u001b[0m \u001b[0mquery_images\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    329\u001b[0m                 \u001b[0mvalid_query_embeddings\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_embed\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mvalid_query_images\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/chromadb/api/models/Collection.py\u001b[0m in \u001b[0;36m_embed\u001b[0;34m(self, input)\u001b[0m\n\u001b[1;32m    631\u001b[0m                 \u001b[0;34m\"https://docs.trychroma.com/embeddings\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    632\u001b[0m             )\n\u001b[0;32m--> 633\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_embedding_function\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/chromadb/api/types.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, input)\u001b[0m\n\u001b[1;32m    191\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    192\u001b[0m         \u001b[0;32mdef\u001b[0m \u001b[0m__call__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mEmbeddingFunction\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mD\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minput\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mD\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mEmbeddings\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 193\u001b[0;31m             \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcall\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minput\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    194\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mvalidate_embeddings\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmaybe_cast_one_to_many_embedding\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mresult\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    195\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/chromadb/utils/embedding_functions.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, input)\u001b[0m\n\u001b[1;32m    186\u001b[0m         \u001b[0;31m# Call the OpenAI Embedding API\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    187\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_v1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 188\u001b[0;31m             embeddings = self._client.create(\n\u001b[0m\u001b[1;32m    189\u001b[0m                 \u001b[0minput\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_deployment_id\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_model_name\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    190\u001b[0m             ).data\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/openai/resources/embeddings.py\u001b[0m in \u001b[0;36mcreate\u001b[0;34m(self, input, model, dimensions, encoding_format, user, extra_headers, extra_query, extra_body, timeout)\u001b[0m\n\u001b[1;32m    112\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mobj\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    113\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 114\u001b[0;31m         return self._post(\n\u001b[0m\u001b[1;32m    115\u001b[0m             \u001b[0;34m\"/embeddings\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    116\u001b[0m             \u001b[0mbody\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmaybe_transform\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mparams\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0membedding_create_params\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mEmbeddingCreateParams\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/openai/_base_client.py\u001b[0m in \u001b[0;36mpost\u001b[0;34m(self, path, cast_to, body, options, files, stream, stream_cls)\u001b[0m\n\u001b[1;32m   1238\u001b[0m             \u001b[0mmethod\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"post\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0murl\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mjson_data\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mbody\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfiles\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mto_httpx_files\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfiles\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0moptions\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1239\u001b[0m         )\n\u001b[0;32m-> 1240\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mcast\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mResponseT\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrequest\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcast_to\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mopts\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstream\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mstream\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstream_cls\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mstream_cls\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1241\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1242\u001b[0m     def patch(\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/openai/_base_client.py\u001b[0m in \u001b[0;36mrequest\u001b[0;34m(self, cast_to, options, remaining_retries, stream, stream_cls)\u001b[0m\n\u001b[1;32m    919\u001b[0m         \u001b[0mstream_cls\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mtype\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0m_StreamT\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m|\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    920\u001b[0m     ) -> ResponseT | _StreamT:\n\u001b[0;32m--> 921\u001b[0;31m         return self._request(\n\u001b[0m\u001b[1;32m    922\u001b[0m             \u001b[0mcast_to\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcast_to\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    923\u001b[0m             \u001b[0moptions\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0moptions\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/openai/_base_client.py\u001b[0m in \u001b[0;36m_request\u001b[0;34m(self, cast_to, options, remaining_retries, stream, stream_cls)\u001b[0m\n\u001b[1;32m   1003\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mretries\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m0\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_should_retry\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0merr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mresponse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1004\u001b[0m                 \u001b[0merr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mresponse\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclose\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1005\u001b[0;31m                 return self._retry_request(\n\u001b[0m\u001b[1;32m   1006\u001b[0m                     \u001b[0moptions\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1007\u001b[0m                     \u001b[0mcast_to\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/openai/_base_client.py\u001b[0m in \u001b[0;36m_retry_request\u001b[0;34m(self, options, cast_to, remaining_retries, response_headers, stream, stream_cls)\u001b[0m\n\u001b[1;32m   1051\u001b[0m         \u001b[0mtime\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msleep\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1052\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1053\u001b[0;31m         return self._request(\n\u001b[0m\u001b[1;32m   1054\u001b[0m             \u001b[0moptions\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0moptions\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1055\u001b[0m             \u001b[0mcast_to\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcast_to\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/openai/_base_client.py\u001b[0m in \u001b[0;36m_request\u001b[0;34m(self, cast_to, options, remaining_retries, stream, stream_cls)\u001b[0m\n\u001b[1;32m   1003\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mretries\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m0\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_should_retry\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0merr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mresponse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1004\u001b[0m                 \u001b[0merr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mresponse\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclose\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1005\u001b[0;31m                 return self._retry_request(\n\u001b[0m\u001b[1;32m   1006\u001b[0m                     \u001b[0moptions\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1007\u001b[0m                     \u001b[0mcast_to\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/openai/_base_client.py\u001b[0m in \u001b[0;36m_retry_request\u001b[0;34m(self, options, cast_to, remaining_retries, response_headers, stream, stream_cls)\u001b[0m\n\u001b[1;32m   1051\u001b[0m         \u001b[0mtime\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msleep\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1052\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1053\u001b[0;31m         return self._request(\n\u001b[0m\u001b[1;32m   1054\u001b[0m             \u001b[0moptions\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0moptions\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1055\u001b[0m             \u001b[0mcast_to\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcast_to\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/openai/_base_client.py\u001b[0m in \u001b[0;36m_request\u001b[0;34m(self, cast_to, options, remaining_retries, stream, stream_cls)\u001b[0m\n\u001b[1;32m   1018\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1019\u001b[0m             \u001b[0mlog\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdebug\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Re-raising status error\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1020\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_make_status_error_from_response\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0merr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mresponse\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1021\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1022\u001b[0m         return self._process_response(\n",
            "\u001b[0;31mRateLimitError\u001b[0m: Error code: 429 - {'error': {'message': 'You exceeded your current quota, please check your plan and billing details. For more information on this error, read the docs: https://platform.openai.com/docs/guides/error-codes/api-errors.', 'type': 'insufficient_quota', 'param': None, 'code': 'insufficient_quota'}}"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from IPython.display import Markdown\n",
        "Markdown(result)"
      ],
      "metadata": {
        "id": "CEjUS9rAUkVB"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "iCy5tIL-UfNC"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "I2fvWT7IUfUh"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}