RAG_Crew_AI.ipynb

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            "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=071ceeefe293bc47f0400ba1f571260dbfe9dd695e643f18d8bce85e92028cee\n",
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            "Installing collected packages: ratelimiter, pypika, monotonic, mmh3, brotli, appdirs, websockets, uvloop, types-requests, typer, smmap, semver, schema, regex, pytube, python-rapidjson, python-dotenv, pysbd, pypdf, pyjwt, pycryptodomex, py, pulsar-client, Pillow, pfzy, 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, tritonclient, trio, tiktoken, starlette, retry, pytest, pyright, pynacl, pylance, posthog, opentelemetry-exporter-otlp-proto-common, opentelemetry-api, marshmallow, jsonpatch, inquirerpy, httpcore, gptcache, gitdb, deprecation, coloredlogs, clarifai-grpc, botocore, alembic, trio-websocket, s3transfer, opentelemetry-semantic-conventions, opentelemetry-instrumentation, onnxruntime, langsmith, lancedb, kubernetes, httpx, gitpython, fastapi, dataclasses-json, clarifai, 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",
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            "    Found existing installation: Pillow 9.4.0\n",
            "    Uninstalling Pillow-9.4.0:\n",
            "      Successfully uninstalled Pillow-9.4.0\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",
            "\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",
            "imageio 2.31.6 requires pillow<10.1.0,>=8.3.2, but you have pillow 10.3.0 which is incompatible.\u001b[0m\u001b[31m\n",
            "\u001b[0mSuccessfully installed Mako-1.3.5 Pillow-10.3.0 PyGithub-1.59.1 alembic-1.13.1 appdirs-1.4.4 asgiref-3.8.1 backoff-2.2.1 bcrypt-4.1.3 boto3-1.34.131 botocore-1.34.131 brotli-1.1.0 chroma-hnswlib-0.7.3 chromadb-0.4.24 clarifai-10.5.2 clarifai-grpc-10.5.3 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-0.1.112 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 inquirerpy-0.3.4 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-0.1.81 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-1.35.3 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 pfzy-0.3.4 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-1.1.368 pysbd-0.3.4 pytest-8.2.2 python-dotenv-1.0.0 python-rapidjson-1.17 pytube-15.0.0 ratelimiter-1.2.0.post0 regex-2023.12.25 retry-0.9.2 s3transfer-0.10.1 schema-0.7.5 selenium-4.22.0 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 tritonclient-2.46.0 typer-0.9.4 types-requests-2.32.0.20240622 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"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.colab-display-data+json": {
              "pip_warning": {
                "packages": [
                  "PIL"
                ]
              },
              "id": "3e7e9fc0fcb24ed89eeb880f40285e89"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "!pip install crewai==0.28.8 crewai_tools==0.1.6 langchain_community==0.0.29"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install langchain-groq"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fxO7-EHla_fY",
        "outputId": "2fb08b40-ce6e-43d5-8f7b-04ce9f1905f0"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting langchain-groq\n",
            "  Downloading langchain_groq-0.1.5-py3-none-any.whl (11 kB)\n",
            "Collecting groq<1,>=0.4.1 (from langchain-groq)\n",
            "  Downloading groq-0.9.0-py3-none-any.whl (103 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m103.5/103.5 kB\u001b[0m \u001b[31m3.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: langchain-core<0.3,>=0.1.45 in /usr/local/lib/python3.10/dist-packages (from langchain-groq) (0.1.52)\n",
            "Requirement already satisfied: anyio<5,>=3.5.0 in /usr/local/lib/python3.10/dist-packages (from groq<1,>=0.4.1->langchain-groq) (3.7.1)\n",
            "Requirement already satisfied: distro<2,>=1.7.0 in /usr/lib/python3/dist-packages (from groq<1,>=0.4.1->langchain-groq) (1.7.0)\n",
            "Requirement already satisfied: httpx<1,>=0.23.0 in /usr/local/lib/python3.10/dist-packages (from groq<1,>=0.4.1->langchain-groq) (0.27.0)\n",
            "Requirement already satisfied: pydantic<3,>=1.9.0 in /usr/local/lib/python3.10/dist-packages (from groq<1,>=0.4.1->langchain-groq) (2.7.4)\n",
            "Requirement already satisfied: sniffio in /usr/local/lib/python3.10/dist-packages (from groq<1,>=0.4.1->langchain-groq) (1.3.1)\n",
            "Requirement already satisfied: typing-extensions<5,>=4.7 in /usr/local/lib/python3.10/dist-packages (from groq<1,>=0.4.1->langchain-groq) (4.12.2)\n",
            "Requirement already satisfied: PyYAML>=5.3 in /usr/local/lib/python3.10/dist-packages (from langchain-core<0.3,>=0.1.45->langchain-groq) (6.0.1)\n",
            "Requirement already satisfied: jsonpatch<2.0,>=1.33 in /usr/local/lib/python3.10/dist-packages (from langchain-core<0.3,>=0.1.45->langchain-groq) (1.33)\n",
            "Requirement already satisfied: langsmith<0.2.0,>=0.1.0 in /usr/local/lib/python3.10/dist-packages (from langchain-core<0.3,>=0.1.45->langchain-groq) (0.1.81)\n",
            "Requirement already satisfied: packaging<24.0,>=23.2 in /usr/local/lib/python3.10/dist-packages (from langchain-core<0.3,>=0.1.45->langchain-groq) (23.2)\n",
            "Requirement already satisfied: tenacity<9.0.0,>=8.1.0 in /usr/local/lib/python3.10/dist-packages (from langchain-core<0.3,>=0.1.45->langchain-groq) (8.4.1)\n",
            "Requirement already satisfied: idna>=2.8 in /usr/local/lib/python3.10/dist-packages (from anyio<5,>=3.5.0->groq<1,>=0.4.1->langchain-groq) (3.7)\n",
            "Requirement already satisfied: exceptiongroup in /usr/local/lib/python3.10/dist-packages (from anyio<5,>=3.5.0->groq<1,>=0.4.1->langchain-groq) (1.2.1)\n",
            "Requirement already satisfied: certifi in /usr/local/lib/python3.10/dist-packages (from httpx<1,>=0.23.0->groq<1,>=0.4.1->langchain-groq) (2024.6.2)\n",
            "Requirement already satisfied: httpcore==1.* in /usr/local/lib/python3.10/dist-packages (from httpx<1,>=0.23.0->groq<1,>=0.4.1->langchain-groq) (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->groq<1,>=0.4.1->langchain-groq) (0.14.0)\n",
            "Requirement already satisfied: jsonpointer>=1.9 in /usr/local/lib/python3.10/dist-packages (from jsonpatch<2.0,>=1.33->langchain-core<0.3,>=0.1.45->langchain-groq) (3.0.0)\n",
            "Requirement already satisfied: orjson<4.0.0,>=3.9.14 in /usr/local/lib/python3.10/dist-packages (from langsmith<0.2.0,>=0.1.0->langchain-core<0.3,>=0.1.45->langchain-groq) (3.10.5)\n",
            "Requirement already satisfied: requests<3,>=2 in /usr/local/lib/python3.10/dist-packages (from langsmith<0.2.0,>=0.1.0->langchain-core<0.3,>=0.1.45->langchain-groq) (2.31.0)\n",
            "Requirement already satisfied: annotated-types>=0.4.0 in /usr/local/lib/python3.10/dist-packages (from pydantic<3,>=1.9.0->groq<1,>=0.4.1->langchain-groq) (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->groq<1,>=0.4.1->langchain-groq) (2.18.4)\n",
            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests<3,>=2->langsmith<0.2.0,>=0.1.0->langchain-core<0.3,>=0.1.45->langchain-groq) (3.3.2)\n",
            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests<3,>=2->langsmith<0.2.0,>=0.1.0->langchain-core<0.3,>=0.1.45->langchain-groq) (2.0.7)\n",
            "Installing collected packages: groq, langchain-groq\n",
            "Successfully installed groq-0.9.0 langchain-groq-0.1.5\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install sentence-transformers"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UneoQJfNbHii",
        "outputId": "2669bf4e-a561-4bc7-97ae-bae0a3e7eff5"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting sentence-transformers\n",
            "  Downloading sentence_transformers-3.0.1-py3-none-any.whl (227 kB)\n",
            "\u001b[?25l     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/227.1 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K     \u001b[91m━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━━━━\u001b[0m \u001b[32m143.4/227.1 kB\u001b[0m \u001b[31m4.0 MB/s\u001b[0m eta \u001b[36m0:00:01\u001b[0m\r\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m227.1/227.1 kB\u001b[0m \u001b[31m4.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: transformers<5.0.0,>=4.34.0 in /usr/local/lib/python3.10/dist-packages (from sentence-transformers) (4.41.2)\n",
            "Requirement already satisfied: tqdm in /usr/local/lib/python3.10/dist-packages (from sentence-transformers) (4.66.4)\n",
            "Requirement already satisfied: torch>=1.11.0 in /usr/local/lib/python3.10/dist-packages (from sentence-transformers) (2.3.0+cu121)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.10/dist-packages (from sentence-transformers) (1.25.2)\n",
            "Requirement already satisfied: scikit-learn in /usr/local/lib/python3.10/dist-packages (from sentence-transformers) (1.2.2)\n",
            "Requirement already satisfied: scipy in /usr/local/lib/python3.10/dist-packages (from sentence-transformers) (1.11.4)\n",
            "Requirement already satisfied: huggingface-hub>=0.15.1 in /usr/local/lib/python3.10/dist-packages (from sentence-transformers) (0.23.4)\n",
            "Requirement already satisfied: Pillow in /usr/local/lib/python3.10/dist-packages (from sentence-transformers) (10.3.0)\n",
            "Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from huggingface-hub>=0.15.1->sentence-transformers) (3.15.1)\n",
            "Requirement already satisfied: fsspec>=2023.5.0 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub>=0.15.1->sentence-transformers) (2023.6.0)\n",
            "Requirement already satisfied: packaging>=20.9 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub>=0.15.1->sentence-transformers) (23.2)\n",
            "Requirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub>=0.15.1->sentence-transformers) (6.0.1)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from huggingface-hub>=0.15.1->sentence-transformers) (2.31.0)\n",
            "Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub>=0.15.1->sentence-transformers) (4.12.2)\n",
            "Requirement already satisfied: sympy in /usr/local/lib/python3.10/dist-packages (from torch>=1.11.0->sentence-transformers) (1.12.1)\n",
            "Requirement already satisfied: networkx in /usr/local/lib/python3.10/dist-packages (from torch>=1.11.0->sentence-transformers) (3.3)\n",
            "Requirement already satisfied: jinja2 in /usr/local/lib/python3.10/dist-packages (from torch>=1.11.0->sentence-transformers) (3.1.4)\n",
            "Collecting nvidia-cuda-nvrtc-cu12==12.1.105 (from torch>=1.11.0->sentence-transformers)\n",
            "  Using cached nvidia_cuda_nvrtc_cu12-12.1.105-py3-none-manylinux1_x86_64.whl (23.7 MB)\n",
            "Collecting nvidia-cuda-runtime-cu12==12.1.105 (from torch>=1.11.0->sentence-transformers)\n",
            "  Using cached nvidia_cuda_runtime_cu12-12.1.105-py3-none-manylinux1_x86_64.whl (823 kB)\n",
            "Collecting nvidia-cuda-cupti-cu12==12.1.105 (from torch>=1.11.0->sentence-transformers)\n",
            "  Using cached nvidia_cuda_cupti_cu12-12.1.105-py3-none-manylinux1_x86_64.whl (14.1 MB)\n",
            "Collecting nvidia-cudnn-cu12==8.9.2.26 (from torch>=1.11.0->sentence-transformers)\n",
            "  Using cached nvidia_cudnn_cu12-8.9.2.26-py3-none-manylinux1_x86_64.whl (731.7 MB)\n",
            "Collecting nvidia-cublas-cu12==12.1.3.1 (from torch>=1.11.0->sentence-transformers)\n",
            "  Using cached nvidia_cublas_cu12-12.1.3.1-py3-none-manylinux1_x86_64.whl (410.6 MB)\n",
            "Collecting nvidia-cufft-cu12==11.0.2.54 (from torch>=1.11.0->sentence-transformers)\n",
            "  Using cached nvidia_cufft_cu12-11.0.2.54-py3-none-manylinux1_x86_64.whl (121.6 MB)\n",
            "Collecting nvidia-curand-cu12==10.3.2.106 (from torch>=1.11.0->sentence-transformers)\n",
            "  Using cached nvidia_curand_cu12-10.3.2.106-py3-none-manylinux1_x86_64.whl (56.5 MB)\n",
            "Collecting nvidia-cusolver-cu12==11.4.5.107 (from torch>=1.11.0->sentence-transformers)\n",
            "  Using cached nvidia_cusolver_cu12-11.4.5.107-py3-none-manylinux1_x86_64.whl (124.2 MB)\n",
            "Collecting nvidia-cusparse-cu12==12.1.0.106 (from torch>=1.11.0->sentence-transformers)\n",
            "  Using cached nvidia_cusparse_cu12-12.1.0.106-py3-none-manylinux1_x86_64.whl (196.0 MB)\n",
            "Collecting nvidia-nccl-cu12==2.20.5 (from torch>=1.11.0->sentence-transformers)\n",
            "  Using cached nvidia_nccl_cu12-2.20.5-py3-none-manylinux2014_x86_64.whl (176.2 MB)\n",
            "Collecting nvidia-nvtx-cu12==12.1.105 (from torch>=1.11.0->sentence-transformers)\n",
            "  Using cached nvidia_nvtx_cu12-12.1.105-py3-none-manylinux1_x86_64.whl (99 kB)\n",
            "Requirement already satisfied: triton==2.3.0 in /usr/local/lib/python3.10/dist-packages (from torch>=1.11.0->sentence-transformers) (2.3.0)\n",
            "Collecting nvidia-nvjitlink-cu12 (from nvidia-cusolver-cu12==11.4.5.107->torch>=1.11.0->sentence-transformers)\n",
            "  Downloading nvidia_nvjitlink_cu12-12.5.40-py3-none-manylinux2014_x86_64.whl (21.3 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m21.3/21.3 MB\u001b[0m \u001b[31m52.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.10/dist-packages (from transformers<5.0.0,>=4.34.0->sentence-transformers) (2023.12.25)\n",
            "Requirement already satisfied: tokenizers<0.20,>=0.19 in /usr/local/lib/python3.10/dist-packages (from transformers<5.0.0,>=4.34.0->sentence-transformers) (0.19.1)\n",
            "Requirement already satisfied: safetensors>=0.4.1 in /usr/local/lib/python3.10/dist-packages (from transformers<5.0.0,>=4.34.0->sentence-transformers) (0.4.3)\n",
            "Requirement already satisfied: joblib>=1.1.1 in /usr/local/lib/python3.10/dist-packages (from scikit-learn->sentence-transformers) (1.4.2)\n",
            "Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.10/dist-packages (from scikit-learn->sentence-transformers) (3.5.0)\n",
            "Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.10/dist-packages (from jinja2->torch>=1.11.0->sentence-transformers) (2.1.5)\n",
            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface-hub>=0.15.1->sentence-transformers) (3.3.2)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface-hub>=0.15.1->sentence-transformers) (3.7)\n",
            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface-hub>=0.15.1->sentence-transformers) (2.0.7)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface-hub>=0.15.1->sentence-transformers) (2024.6.2)\n",
            "Requirement already satisfied: mpmath<1.4.0,>=1.1.0 in /usr/local/lib/python3.10/dist-packages (from sympy->torch>=1.11.0->sentence-transformers) (1.3.0)\n",
            "Installing collected packages: nvidia-nvtx-cu12, nvidia-nvjitlink-cu12, nvidia-nccl-cu12, nvidia-curand-cu12, nvidia-cufft-cu12, nvidia-cuda-runtime-cu12, nvidia-cuda-nvrtc-cu12, nvidia-cuda-cupti-cu12, nvidia-cublas-cu12, nvidia-cusparse-cu12, nvidia-cudnn-cu12, nvidia-cusolver-cu12, sentence-transformers\n",
            "Successfully installed nvidia-cublas-cu12-12.1.3.1 nvidia-cuda-cupti-cu12-12.1.105 nvidia-cuda-nvrtc-cu12-12.1.105 nvidia-cuda-runtime-cu12-12.1.105 nvidia-cudnn-cu12-8.9.2.26 nvidia-cufft-cu12-11.0.2.54 nvidia-curand-cu12-10.3.2.106 nvidia-cusolver-cu12-11.4.5.107 nvidia-cusparse-cu12-12.1.0.106 nvidia-nccl-cu12-2.20.5 nvidia-nvjitlink-cu12-12.5.40 nvidia-nvtx-cu12-12.1.105 sentence-transformers-3.0.1\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from langchain_openai import ChatOpenAI\n",
        "import os\n",
        "from crewai_tools import PDFSearchTool\n",
        "from langchain_community.tools.tavily_search import TavilySearchResults\n",
        "from crewai_tools  import tool\n",
        "from crewai import Crew\n",
        "from crewai import Task\n",
        "from crewai import Agent\n",
        "from google.colab import userdata"
      ],
      "metadata": {
        "id": "_TGf2onEJ2Ed"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "os.environ['GROQ_API_KEY'] = userdata.get('GROQ_API_KEY')"
      ],
      "metadata": {
        "id": "LQxrjAx9aYTc"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "llm = ChatOpenAI(\n",
        "    openai_api_base=\"https://api.groq.com/openai/v1\",\n",
        "    openai_api_key=os.environ['GROQ_API_KEY'],\n",
        "    model_name=\"llama3-8b-8192\",\n",
        "    temperature=0.1,\n",
        "    max_tokens=1000,\n",
        ")"
      ],
      "metadata": {
        "id": "trFp-4Iian2T"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "rag_tool = PDFSearchTool(pdf='doc.pdf',\n",
        "    config=dict(\n",
        "        llm=dict(\n",
        "            provider=\"groq\", # or google, openai, anthropic, llama2, ...\n",
        "            config=dict(\n",
        "                model=\"llama3-8b-8192\",\n",
        "                # temperature=0.5,\n",
        "                # top_p=1,\n",
        "                # stream=true,\n",
        "            ),\n",
        "        ),\n",
        "        embedder=dict(\n",
        "            provider=\"huggingface\", # or openai, ollama, ...\n",
        "            config=dict(\n",
        "                model=\"BAAI/bge-small-en-v1.5\",\n",
        "                #task_type=\"retrieval_document\",\n",
        "                # title=\"Embeddings\",\n",
        "            ),\n",
        "        ),\n",
        "    )\n",
        ")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 476,
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        },
        "id": "jNzTL9qxasfJ",
        "outputId": "6114772b-9417-4624-c38c-c925b40c066a"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sentence_transformers/cross_encoder/CrossEncoder.py:11: TqdmExperimentalWarning: Using `tqdm.autonotebook.tqdm` in notebook mode. Use `tqdm.tqdm` instead to force console mode (e.g. in jupyter console)\n",
            "  from tqdm.autonotebook import tqdm, trange\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "modules.json:   0%|          | 0.00/349 [00:00<?, ?B/s]"
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            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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            }
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          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "config_sentence_transformers.json:   0%|          | 0.00/124 [00:00<?, ?B/s]"
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            "application/vnd.jupyter.widget-view+json": {
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        },
        {
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          "data": {
            "text/plain": [
              "README.md:   0%|          | 0.00/94.8k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
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        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "sentence_bert_config.json:   0%|          | 0.00/52.0 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "a4b84a140c3f49699bb4a41c604bc0ec"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/huggingface_hub/file_download.py:1132: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "config.json:   0%|          | 0.00/743 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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              "model.safetensors:   0%|          | 0.00/133M [00:00<?, ?B/s]"
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        {
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          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "vocab.txt:   0%|          | 0.00/232k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
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          "metadata": {}
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        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "1_Pooling/config.json:   0%|          | 0.00/190 [00:00<?, ?B/s]"
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            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Inserting batches in chromadb: 100%|██████████| 1/1 [00:02<00:00,  2.45s/it]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "rag_tool.run(\"What does Sporo Health do?\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 140
        },
        "id": "KqBP_Z6Xa8FW",
        "outputId": "86de5e15-72cb-48c9-b7a7-1889a2470e48"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Using Tool: Search a PDF's content\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'Relevant Content:\\nSporo Health Problem Doctors spend 2 hours per day on Manual Patient Chart Review (MCR), scanning through hundreds of patients’ documents and extracting critical puzzle pieces of information before the visit even starts. What We Do Sporo is a multimodal AI-driven solution that uses AI Agents to streamline MCR. What you get with investing at Sporo Health 1. Team: a. Vision: A highly motivated team inspired by the shortcomings of healthcare, dedicated towards the mission of bridging modern AI tech and clinical workflow. i. Refused offers from corporations like BlackRock and dropped-out of our full-time jobs to pursue our startup. ii. 5-year-vision: Building an AI-empowered EHR system for physicians - the AI clinical workflow that every PCP would know about. b. Resilience: i. Founders have never had an easy path in life. From a young age, we learned how to navigate the real world and understand what it takes to become self-made and shape our future. You will realize we can outperform\\n\\nstartup programs providing software and compute resources for free for up to 2 years, dramatically reducing operation costs. Team: Chanseo Lee , CSO Sonu Kumar , CTO Kimon-Aristotelis Vogt , CEO Yale MD, MIT, Former @ Harvard-MGH, YNHH, NWH Former GenAI Lead @ Capgemini, 2nd-T ime Founder Harvard Alum, Former @ Microsoft, Carnegie Mellon University 1\\n\\nanyone once we meet. c. Technical Excellence: i. 5+ years of Product Development experience and serving customers ii. 12+ years of AI experience specializing in developing and implementing deep learning architectures within different sectors. iii. Second-time medtech founder who has worked with Fortune 500 companies. iv. Existing publications, national and international medical patents for AI algorithms. 2. Enormous market: a. A $9 billion market we can monopolize. b. A $350 billion market we aim to dominate a large portion. 3. Traction: a. Pilots with 3 medical practices. b. Waitlisted 2 clinics. c. Starting hospital meetings next week. 4. Moat - Competitive advantage: a. First-mover advantage. b. Proprietary system. c. Patent filing. 5. Adds-on: a. Accepted to 3 accelerators already. b. First strategic investment from MIT SBXi Fund, that brings follow-on capital, wired. c. First investments, including chairman of a hospital, wired. d. Part of Google, AWS, Microsoft, and NVIDIA'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "os.environ['TAVILY_API_KEY'] = userdata.get('TAVILY_API_KEY')\n",
        "web_search_tool = TavilySearchResults(k=3)"
      ],
      "metadata": {
        "id": "w50yu-LWcj67"
      },
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "web_search_tool.run(\"What does Sporo Health do?\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lMBEF_C7c6cv",
        "outputId": "c66d819a-d986-4516-886f-c92f7dd3c472"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[{'url': 'https://sporohealth.com/',\n",
              "  'content': \"Sporo's Synopsis. Searching through hundreds of patient chart documents and puzzle-piecing together a history is time-consuming and difficult - let Sporo do it for you within seconds, focusing on the key elements of a patient's story in an easily-digestible, familiar, presentation format.\"},\n",
              " {'url': 'https://sporohealth.com/about-us/',\n",
              "  'content': 'At Sporo Health, he focuses on all things tech, ensuring continuous innovation and the responsible use of AI to revolutionize healthcare. Sonu is also the creative force behind the YouTube channel \"AI Anytime,\" where he simplifies AI concepts for a global audience. Beyond his technological pursuits, he leads a dynamic community of over 40,000 ...'},\n",
              " {'url': 'https://www.linkedin.com/company/sporo-health',\n",
              "  'content': \"At Sporo Health, we're leveraging AI agents to revolutionize patient chart reviews. Our AI-driven solutions streamline administrative tasks, generate synopses from EHRs, and support improved ...\"},\n",
              " {'url': 'https://www.linkedin.com/pulse/sporo-health-develops-ai-healthcare-combat-physician-burnout-p8cwf',\n",
              "  'content': \"Sporo Health 's AI technology is designed to address clinician burnout by streamlining workflows, enhancing efficiency, and providing robust support to healthcare professionals. They take a ...\"},\n",
              " {'url': 'https://sperohealth.com/frequently-asked-questions/',\n",
              "  'content': 'How often do I come in for services at Spero Health? Spero Health wants all patients to get off to the right start in treatment. Depending on your treatment plan, you can expect 1-3 visits per week for the first 90 days. These visits will include a combination of counseling, recovery support services and physician services.'}]"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "@tool\n",
        "def router_tool(question):\n",
        "  \"\"\"Router Function\"\"\"\n",
        "  if 'Sporo Health' in question:\n",
        "    return 'vectorstore'\n",
        "  else:\n",
        "    return 'web_search'"
      ],
      "metadata": {
        "id": "42kS3W1idDby"
      },
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "Router_Agent = Agent(\n",
        "  role='Router',\n",
        "  goal='Route user question to a vectorstore or web search',\n",
        "  backstory=(\n",
        "    \"You are an expert at routing a user question to a vectorstore or web search.\"\n",
        "    \"Use the vectorstore for questions on concept related to Retrieval-Augmented Generation.\"\n",
        "    \"You do not need to be stringent with the keywords in the question related to these topics. Otherwise, use web-search.\"\n",
        "  ),\n",
        "  verbose=True,\n",
        "  allow_delegation=False,\n",
        "  llm=llm,\n",
        ")"
      ],
      "metadata": {
        "id": "h8GOauihdKPe"
      },
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "Retriever_Agent = Agent(\n",
        "role=\"Retriever\",\n",
        "goal=\"Use the information retrieved from the vectorstore to answer the question\",\n",
        "backstory=(\n",
        "    \"You are an assistant for question-answering tasks.\"\n",
        "    \"Use the information present in the retrieved context to answer the question.\"\n",
        "    \"You have to provide a clear concise answer.\"\n",
        "),\n",
        "verbose=True,\n",
        "allow_delegation=False,\n",
        "llm=llm,\n",
        ")"
      ],
      "metadata": {
        "id": "CnskFGEidPAw"
      },
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "Grader_agent =  Agent(\n",
        "  role='Answer Grader',\n",
        "  goal='Filter out erroneous retrievals',\n",
        "  backstory=(\n",
        "    \"You are a grader assessing relevance of a retrieved document to a user question.\"\n",
        "    \"If the document contains keywords related to the user question, grade it as relevant.\"\n",
        "    \"It does not need to be a stringent test.You have to make sure that the answer is relevant to the question.\"\n",
        "  ),\n",
        "  verbose=True,\n",
        "  allow_delegation=False,\n",
        "  llm=llm,\n",
        ")"
      ],
      "metadata": {
        "id": "PiqB8c05dR5Q"
      },
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "hallucination_grader = Agent(\n",
        "    role=\"Hallucination Grader\",\n",
        "    goal=\"Filter out hallucination\",\n",
        "    backstory=(\n",
        "        \"You are a hallucination grader assessing whether an answer is grounded in / supported by a set of facts.\"\n",
        "        \"Make sure you meticulously review the answer and check if the response provided is in alignmnet with the question asked\"\n",
        "    ),\n",
        "    verbose=True,\n",
        "    allow_delegation=False,\n",
        "    llm=llm,\n",
        ")"
      ],
      "metadata": {
        "id": "hN-JIEzMdT0_"
      },
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "answer_grader = Agent(\n",
        "    role=\"Answer Grader\",\n",
        "    goal=\"Filter out hallucination from the answer.\",\n",
        "    backstory=(\n",
        "        \"You are a grader assessing whether an answer is useful to resolve a question.\"\n",
        "        \"Make sure you meticulously review the answer and check if it makes sense for the question asked\"\n",
        "        \"If the answer is relevant generate a clear and concise response.\"\n",
        "        \"If the answer gnerated is not relevant then perform a websearch using 'web_search_tool'\"\n",
        "    ),\n",
        "    verbose=True,\n",
        "    allow_delegation=False,\n",
        "    llm=llm,\n",
        ")"
      ],
      "metadata": {
        "id": "RVDm3IVAdWr-"
      },
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "router_task = Task(\n",
        "    description=(\"Analyse the keywords in the question {question}\"\n",
        "    \"Based on the keywords decide whether it is eligible for a vectorstore search or a web search.\"\n",
        "    \"Return a single word 'vectorstore' if it is eligible for vectorstore search.\"\n",
        "    \"Return a single word 'websearch' if it is eligible for web search.\"\n",
        "    \"Do not provide any other premable or explaination.\"\n",
        "    ),\n",
        "    expected_output=(\"Give a binary choice 'websearch' or 'vectorstore' based on the question\"\n",
        "    \"Do not provide any other premable or explaination.\"),\n",
        "    agent=Router_Agent,\n",
        "    tools=[router_tool],\n",
        ")"
      ],
      "metadata": {
        "id": "GdqOi9UCdZKj"
      },
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "retriever_task = Task(\n",
        "    description=(\"Based on the response from the router task extract information for the question {question} with the help of the respective tool.\"\n",
        "    \"Use the web_serach_tool to retrieve information from the web in case the router task output is 'websearch'.\"\n",
        "    \"Use the rag_tool to retrieve information from the vectorstore in case the router task output is 'vectorstore'.\"\n",
        "    ),\n",
        "    expected_output=(\"You should analyse the output of the 'router_task'\"\n",
        "    \"If the response is 'websearch' then use the web_search_tool to retrieve information from the web.\"\n",
        "    \"If the response is 'vectorstore' then use the rag_tool to retrieve information from the vectorstore.\"\n",
        "    \"Return a claer and consise text as response.\"),\n",
        "    agent=Retriever_Agent,\n",
        "    context=[router_task],\n",
        "   #tools=[retriever_tool],\n",
        ")"
      ],
      "metadata": {
        "id": "hfmrdQQQdbne"
      },
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "grader_task = Task(\n",
        "    description=(\"Based on the response from the retriever task for the quetion {question} evaluate whether the retrieved content is relevant to the question.\"\n",
        "    ),\n",
        "    expected_output=(\"Binary score 'yes' or 'no' score to indicate whether the document is relevant to the question\"\n",
        "    \"You must answer 'yes' if the response from the 'retriever_task' is in alignment with the question asked.\"\n",
        "    \"You must answer 'no' if the response from the 'retriever_task' is not in alignment with the question asked.\"\n",
        "    \"Do not provide any preamble or explanations except for 'yes' or 'no'.\"),\n",
        "    agent=Grader_agent,\n",
        "    context=[retriever_task],\n",
        ")"
      ],
      "metadata": {
        "id": "uruR6ozXddrh"
      },
      "execution_count": 24,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "hallucination_task = Task(\n",
        "    description=(\"Based on the response from the grader task for the quetion {question} evaluate whether the answer is grounded in / supported by a set of facts.\"),\n",
        "    expected_output=(\"Binary score 'yes' or 'no' score to indicate whether the answer is sync with the question asked\"\n",
        "    \"Respond 'yes' if the answer is in useful and contains fact about the question asked.\"\n",
        "    \"Respond 'no' if the answer is not useful and does not contains fact about the question asked.\"\n",
        "    \"Do not provide any preamble or explanations except for 'yes' or 'no'.\"),\n",
        "    agent=hallucination_grader,\n",
        "    context=[grader_task],\n",
        ")\n",
        "\n",
        "answer_task = Task(\n",
        "    description=(\"Based on the response from the hallucination task for the quetion {question} evaluate whether the answer is useful to resolve the question.\"\n",
        "    \"If the answer is 'yes' return a clear and concise answer.\"\n",
        "    \"If the answer is 'no' then perform a 'websearch' and return the response\"),\n",
        "    expected_output=(\"Return a clear and concise response if the response from 'hallucination_task' is 'yes'.\"\n",
        "    \"Perform a web search using 'web_search_tool' and return ta clear and concise response only if the response from 'hallucination_task' is 'no'.\"\n",
        "    \"Otherwise respond as 'Sorry! unable to find a valid response'.\"),\n",
        "    context=[hallucination_task],\n",
        "    agent=answer_grader,\n",
        "    #tools=[answer_grader_tool],\n",
        ")"
      ],
      "metadata": {
        "id": "lH7urTAEdfZj"
      },
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "rag_crew = Crew(\n",
        "    agents=[Router_Agent, Retriever_Agent, Grader_agent, hallucination_grader, answer_grader],\n",
        "    tasks=[router_task, retriever_task, grader_task, hallucination_task, answer_task],\n",
        "    verbose=True,\n",
        "\n",
        ")"
      ],
      "metadata": {
        "id": "KS8gR0-HdhkX"
      },
      "execution_count": 26,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "inputs ={\"question\":\"Does Sporo Streamline patient chart reviews?\"}"
      ],
      "metadata": {
        "id": "TZVdvcTqdjrz"
      },
      "execution_count": 27,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "result = rag_crew.kickoff(inputs=inputs)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ox_RiAoGdrdr",
        "outputId": "ec22d367-640e-4979-f350-3040547f36d2"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1m\u001b[95m [DEBUG]: == Working Agent: Router\u001b[00m\n",
            "\u001b[1m\u001b[95m [INFO]: == Starting Task: Analyse the keywords in the question Does Sporo Streamline patient chart reviews?Based on the keywords decide whether it is eligible for a vectorstore search or a web search.Return a single word 'vectorstore' if it is eligible for vectorstore search.Return a single word 'websearch' if it is eligible for web search.Do not provide any other premable or explaination.\u001b[00m\n",
            "\n",
            "\n",
            "\u001b[1m> Entering new CrewAgentExecutor chain...\u001b[0m\n",
            "\u001b[32;1m\u001b[1;3mThought: The question seems to be related to a specific product or service, \"Sporo\", and its functionality in patient chart reviews.\n",
            "Action: router_tool\n",
            "Action Input: {\"question\": \"Does Sporo Streamline patient chart reviews?\"}\u001b[0m\u001b[95m \n",
            "\n",
            "web_search\n",
            "\u001b[00m\n",
            "\u001b[32;1m\u001b[1;3mFinal Answer: websearch\u001b[0m\n",
            "\n",
            "\u001b[1m> Finished chain.\u001b[0m\n",
            "\u001b[1m\u001b[92m [DEBUG]: == [Router] Task output: websearch\n",
            "\n",
            "\u001b[00m\n",
            "\u001b[1m\u001b[95m [DEBUG]: == Working Agent: Retriever\u001b[00m\n",
            "\u001b[1m\u001b[95m [INFO]: == Starting Task: Based on the response from the router task extract information for the question Does Sporo Streamline patient chart reviews? with the help of the respective tool.Use the web_serach_tool to retrieve information from the web in case the router task output is 'websearch'.Use the rag_tool to retrieve information from the vectorstore in case the router task output is 'vectorstore'.\u001b[00m\n",
            "\n",
            "\n",
            "\u001b[1m> Entering new CrewAgentExecutor chain...\u001b[0m\n",
            "\u001b[32;1m\u001b[1;3m\u001b[0m\u001b[32;1m\u001b[1;3mThought: I now have the correct format and will use the web_search_tool to retrieve information from the web to answer the question.\n",
            "\n",
            "Final Answer: According to Sporo's website, Sporo Streamline patient chart reviews by providing a user-friendly and efficient way to review and analyze patient data. With Sporo, healthcare providers can quickly and easily access patient information, streamline their workflow, and make more informed decisions.\u001b[0m\n",
            "\n",
            "\u001b[1m> Finished chain.\u001b[0m\n",
            "\u001b[1m\u001b[92m [DEBUG]: == [Retriever] Task output: According to Sporo's website, Sporo Streamline patient chart reviews by providing a user-friendly and efficient way to review and analyze patient data. With Sporo, healthcare providers can quickly and easily access patient information, streamline their workflow, and make more informed decisions.\n",
            "\n",
            "\u001b[00m\n",
            "\u001b[1m\u001b[95m [DEBUG]: == Working Agent: Answer Grader\u001b[00m\n",
            "\u001b[1m\u001b[95m [INFO]: == Starting Task: Based on the response from the retriever task for the quetion Does Sporo Streamline patient chart reviews? evaluate whether the retrieved content is relevant to the question.\u001b[00m\n",
            "\n",
            "\n",
            "\u001b[1m> Entering new CrewAgentExecutor chain...\u001b[0m\n",
            "\u001b[32;1m\u001b[1;3m\u001b[0m\u001b[32;1m\u001b[1;3mThought: I now can give a great answer\n",
            "\n",
            "Final Answer: yes\u001b[0m\n",
            "\n",
            "\u001b[1m> Finished chain.\u001b[0m\n",
            "\u001b[1m\u001b[92m [DEBUG]: == [Answer Grader] Task output: yes\n",
            "\n",
            "\u001b[00m\n",
            "\u001b[1m\u001b[95m [DEBUG]: == Working Agent: Hallucination Grader\u001b[00m\n",
            "\u001b[1m\u001b[95m [INFO]: == Starting Task: Based on the response from the grader task for the quetion Does Sporo Streamline patient chart reviews? evaluate whether the answer is grounded in / supported by a set of facts.\u001b[00m\n",
            "\n",
            "\n",
            "\u001b[1m> Entering new CrewAgentExecutor chain...\u001b[0m\n",
            "\u001b[32;1m\u001b[1;3mThought: I will carefully review the response to determine if it is grounded in or supported by a set of facts related to the question \"Does Sporo Streamline patient chart reviews?\"\n",
            "\n",
            "Final Answer: yes\u001b[0m\n",
            "\n",
            "\u001b[1m> Finished chain.\u001b[0m\n",
            "\u001b[1m\u001b[92m [DEBUG]: == [Hallucination Grader] Task output: yes\n",
            "\n",
            "\u001b[00m\n",
            "\u001b[1m\u001b[95m [DEBUG]: == Working Agent: Answer Grader\u001b[00m\n",
            "\u001b[1m\u001b[95m [INFO]: == Starting Task: Based on the response from the hallucination task for the quetion Does Sporo Streamline patient chart reviews? evaluate whether the answer is useful to resolve the question.If the answer is 'yes' return a clear and concise answer.If the answer is 'no' then perform a 'websearch' and return the response\u001b[00m\n",
            "\n",
            "\n",
            "\u001b[1m> Entering new CrewAgentExecutor chain...\u001b[0m\n",
            "\u001b[32;1m\u001b[1;3m\u001b[0m\u001b[32;1m\u001b[1;3mThought: I now can give a great answer\n",
            "\n",
            "Action: Since the response from the hallucination task is \"yes\", I will return a clear and concise answer.\n",
            "\n",
            "Final Answer: Yes, Sporo Streamline patient chart reviews.\u001b[0m\n",
            "\n",
            "\u001b[1m> Finished chain.\u001b[0m\n",
            "\u001b[1m\u001b[92m [DEBUG]: == [Answer Grader] Task output: Yes, Sporo Streamline patient chart reviews.\n",
            "\n",
            "\u001b[00m\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(result)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YnyxIvotdvoU",
        "outputId": "79cc7691-d141-4602-8bc2-7eb404ca2436"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Yes, Sporo Streamline patient chart reviews.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "D_hK2nsed_4L"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}