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            "Collecting nvidia-cufft-cu12==11.0.2.54 (from torch>=1.11.0->sentence-transformers<3.0.0,>=2.6.1->llama_index.embeddings.huggingface)\n",
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            "Collecting nvidia-curand-cu12==10.3.2.106 (from torch>=1.11.0->sentence-transformers<3.0.0,>=2.6.1->llama_index.embeddings.huggingface)\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<3.0.0,>=2.6.1->llama_index.embeddings.huggingface)\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<3.0.0,>=2.6.1->llama_index.embeddings.huggingface)\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<3.0.0,>=2.6.1->llama_index.embeddings.huggingface)\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<3.0.0,>=2.6.1->llama_index.embeddings.huggingface)\n",
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            "Requirement already satisfied: triton==2.3.0 in /usr/local/lib/python3.10/dist-packages (from torch>=1.11.0->sentence-transformers<3.0.0,>=2.6.1->llama_index.embeddings.huggingface) (2.3.0)\n",
            "Collecting nvidia-nvjitlink-cu12 (from nvidia-cusolver-cu12==11.4.5.107->torch>=1.11.0->sentence-transformers<3.0.0,>=2.6.1->llama_index.embeddings.huggingface)\n",
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            "Collecting mypy-extensions>=0.3.0 (from typing-inspect>=0.8.0->llama-index-core<0.11.0,>=0.10.27->llama-index==0.10.27->beyondllm)\n",
            "  Downloading mypy_extensions-1.0.0-py3-none-any.whl (4.7 kB)\n",
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            "Requirement already satisfied: grpcio-status<2.0.dev0,>=1.33.2 in /usr/local/lib/python3.10/dist-packages (from google-api-core->google-generativeai<0.5.0,>=0.4.1->llama-index-embeddings-gemini==0.1.6->beyondllm) (1.48.2)\n",
            "Requirement already satisfied: marisa-trie>=0.7.7 in /usr/local/lib/python3.10/dist-packages (from language-data>=1.2->langcodes<4.0.0,>=3.2.0->spacy->llama-index-core<0.11.0,>=0.10.27->llama-index==0.10.27->beyondllm) (1.1.1)\n",
            "Requirement already satisfied: pyasn1<0.7.0,>=0.4.6 in /usr/local/lib/python3.10/dist-packages (from pyasn1-modules>=0.2.1->google-auth>=2.15.0->google-generativeai<0.5.0,>=0.4.1->llama-index-embeddings-gemini==0.1.6->beyondllm) (0.6.0)\n",
            "Installing collected packages: striprtf, ply, dirtyjson, sqlalchemy, regex, pysbd, pypdf, pydantic, 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, numpy, mypy-extensions, minijinja, marshmallow, jsonpath-ng, h11, deprecated, youtube_transcript_api, typing-inspect, tiktoken, nvidia-cusparse-cu12, nvidia-cudnn-cu12, httpcore, nvidia-cusolver-cu12, httpx, dataclasses-json, openai, llamaindex-py-client, sentence-transformers, llama-index-legacy, llama-index-core, google-ai-generativelanguage, llama-parse, llama-index-readers-youtube-transcript, llama-index-readers-file, llama-index-llms-openai, llama-index-indices-managed-llama-cloud, llama-index-embeddings-openai, llama_index.embeddings.huggingface, google-generativeai, llama-index-readers-llama-parse, llama-index-multi-modal-llms-openai, llama-index-embeddings-gemini, llama-index-cli, llama-index-agent-openai, llama-index-program-openai, llama-index-question-gen-openai, llama-index, beyondllm\n",
            "  Attempting uninstall: sqlalchemy\n",
            "    Found existing installation: SQLAlchemy 2.0.30\n",
            "    Uninstalling SQLAlchemy-2.0.30:\n",
            "      Successfully uninstalled SQLAlchemy-2.0.30\n",
            "  Attempting uninstall: regex\n",
            "    Found existing installation: regex 2023.12.25\n",
            "    Uninstalling regex-2023.12.25:\n",
            "      Successfully uninstalled regex-2023.12.25\n",
            "  Attempting uninstall: pydantic\n",
            "    Found existing installation: pydantic 2.7.1\n",
            "    Uninstalling pydantic-2.7.1:\n",
            "      Successfully uninstalled pydantic-2.7.1\n",
            "  Attempting uninstall: numpy\n",
            "    Found existing installation: numpy 1.25.2\n",
            "    Uninstalling numpy-1.25.2:\n",
            "      Successfully uninstalled numpy-1.25.2\n",
            "  Attempting uninstall: google-ai-generativelanguage\n",
            "    Found existing installation: google-ai-generativelanguage 0.6.4\n",
            "    Uninstalling google-ai-generativelanguage-0.6.4:\n",
            "      Successfully uninstalled google-ai-generativelanguage-0.6.4\n",
            "  Attempting uninstall: google-generativeai\n",
            "    Found existing installation: google-generativeai 0.5.4\n",
            "    Uninstalling google-generativeai-0.5.4:\n",
            "      Successfully uninstalled google-generativeai-0.5.4\n",
            "Successfully installed beyondllm-0.2.0 dataclasses-json-0.6.6 deprecated-1.2.14 dirtyjson-1.0.8 google-ai-generativelanguage-0.4.0 google-generativeai-0.4.1 h11-0.14.0 httpcore-1.0.5 httpx-0.27.0 jsonpath-ng-1.6.1 llama-index-0.10.27 llama-index-agent-openai-0.2.5 llama-index-cli-0.1.12 llama-index-core-0.10.38.post1 llama-index-embeddings-gemini-0.1.6 llama-index-embeddings-openai-0.1.10 llama-index-indices-managed-llama-cloud-0.1.6 llama-index-legacy-0.9.48 llama-index-llms-openai-0.1.20 llama-index-multi-modal-llms-openai-0.1.6 llama-index-program-openai-0.1.6 llama-index-question-gen-openai-0.1.3 llama-index-readers-file-0.1.22 llama-index-readers-llama-parse-0.1.4 llama-index-readers-youtube-transcript-0.1.4 llama-parse-0.4.3 llama_index.embeddings.huggingface-0.2.0 llamaindex-py-client-0.1.19 marshmallow-3.21.2 minijinja-2.0.1 mypy-extensions-1.0.0 numpy-1.26.4 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 openai-1.20.0 ply-3.11 pydantic-1.10.15 pypdf-4.2.0 pysbd-0.3.4 regex-2024.4.16 sentence-transformers-2.7.0 sqlalchemy-2.0.29 striprtf-0.0.26 tiktoken-0.6.0 typing-inspect-0.9.0 youtube_transcript_api-0.6.2\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.colab-display-data+json": {
              "pip_warning": {
                "packages": [
                  "google"
                ]
              },
              "id": "0c0963be601849cc8b7c93a566f76ee5"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "!pip install beyondllm youtube_transcript_api llama-index-readers-youtube-transcript llama_index.embeddings.huggingface"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Set Environment Variables"
      ],
      "metadata": {
        "id": "MjyijQh_WYGD"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from getpass import getpass\n",
        "import os\n",
        "\n",
        "hf_token = getpass('Enter Your HuggingfaceHub Token')\n",
        "google_api_key = getpass('Enter Your Google API Key')\n",
        "\n",
        "os.environ['HF_TOKEN'] = hf_token\n",
        "os.environ['GOOGLE_API_KEY'] = google_api_key"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_VJnpwVlQrM-",
        "outputId": "a9f0a208-9d91-48f2-9152-88b34f692e90"
      },
      "execution_count": null,
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Enter Your HuggingfaceHub Token··········\n",
            "Enter Your Google API Key··········\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from beyondllm import source,retrieve,embeddings,llms,generator"
      ],
      "metadata": {
        "id": "1KQD3DpMRHTD"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Load Data from YouTube Video"
      ],
      "metadata": {
        "id": "kQy7ihN-Wb_j"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "data = source.fit(\n",
        "    path=\"https://www.youtube.com/watch?v=ZM1bdh2mDJQ\",\n",
        "    dtype=\"youtube\",\n",
        "    chunk_size=1024,\n",
        "    chunk_overlap=0)"
      ],
      "metadata": {
        "id": "hBNYKsRCRCF1"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(data)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "collapsed": true,
        "id": "yjOgpOPBRY4y",
        "outputId": "13a2a920-df64-4cfb-eb35-bef1c659492f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[TextNode(id_='d8c9bfbf-7ff3-451c-814f-be0e2a41a7e4', embedding=None, metadata={'video_id': 'ZM1bdh2mDJQ'}, excluded_embed_metadata_keys=[], excluded_llm_metadata_keys=[], relationships={<NodeRelationship.SOURCE: '1'>: RelatedNodeInfo(node_id='250c8287-9f69-43a3-98be-0e83bf052f96', node_type=<ObjectType.TEXT: '1'>, metadata={'video_id': 'ZM1bdh2mDJQ'}, hash='615e7d33180855d5371f6b37c8848fe6cb5ae545e1ba29af48aae860c75a6437')}, text=\"hi everyone welcome to the part seven\\nvideo of building llm applications using\\ngen stack in this video we are going to\\nbuild a CSV agent using this tack so the\\nCSV file will be the data source for the\\nentire R pipeline so if you do not know\\nwhat rag is then in short it's retrieval\\naugmented generation it streamlines the\\nprocess of retrieving data from a data\\nsource then pre-processing it and\\nstoring it in a certain way in a vector\\nstore and then retri it when a query is\\npassed and then presenting it with the\\nhelp of a llm which we're going to\\nprovide and the llm will generate\\nresponse based on the query which is\\nbeing\\nasked so let's start building the rack\\npipeline first of all we need a loader\\nwhich in this case will be a CSV loader\\nsince we are using a CSV file so let's\\ndrag and drop uh now I'll be using a\\nTitanic data set which I have downloaded\\nfrom kaggle so yeah I'll be using that\\ndata ass it as the reference data source\\nof this\\npipeline so I've uploaded the data now\\nwe need to split the text into smaller\\nchunks so that it could fit into the llm\\nthat we're going to use in the future so\\nyeah let's do that right now uh here\\nI'll be using the recursive character\\ntext splitter and I'll be joining both\\nof the components so now since we have\\nour smaller chunks of text we can uh\\nconvert it to uh numerical embeddings\\nwith the help of an embedding model so\\nuh let's do that uh we need a hugging\\nface inference API embeddings and I'll\\nput my API key and also the model name\\nso I've added the API key and the model\\nname here I'll be using this model\\nmentioned in the model name now since we\\nhave our embedding model defined we also\\nneed to convert those text information\\ninto embeddings and then we have to\\nstore it in a separate Vector store here\\nI'm going to use the chroma Vector store\\nand I'll be connecting\\nthis both\\ncomponents like this so as you can see\\nuh these all are the retriever\\ncomponents so whenever we ask a query to\\nthe rack pipeline the vector store\\nfetches the most relevant information\\nand passes it to the generator component\\nwhich generates a proper answer and\\ngives to us so let's start building the\\ngenerator component for that we need an\\nllm here I'll be using the Azure chat\\nopeni so you have to put the Azure chat\\nopeni API base the API key\\nthe type and the deployment name along\\nwith the model name so I'll fill all the\\nfields and unfortunately I cannot expose\\nthem in the video now since we have\\ndefined our llm we need to connect the\\ngenerator component with the Ral\\ncomponent which you can see on your\\nscreen so for that we need chains first\\nof all I will drag and drop the combine\\ndocs chain and after that uh I need to\\nhave the retrieval qm I will connect\\nboth of the components and I will\\nconnect the chroma to the Reet component\\nso this is how we have our generator\\ncomponent ready but before that we have\\nto connect the llm to the combine do\\nstream so I'll do that right now after\\nconnecting all the components we can\\nbuild the entire rack pipeline by\\nclicking on this buildt stack\\nbutton once the stack is built you can\\nchat with your application by clicking\\non the chat with stack button which is\\npresent just below the build stack\\nbutton it will notify you once the stack\\nis ready for\\ntesting so as you can see our stack is\\nready for testing and we are going to\\nchat with our application so let's ask\\nwhat are some of the values of the field\\nP class the field P class is present in\\nthe CSV file which I have uploaded for\\nreference so it answers in the G given\\ncontext the values of class are 1 and\\nthree so it automatically recognizes\\nthat the value class refers to P class\\nso yeah as you can see building this\\nentire application took me just a few\\nminutes so you can also build your\\napplication by using a separate CSV file\\nlet's say or we also have supports for\\nPDF and other types of data so you can\\nalso experiment with the embedding model\\nand the llm so yeah you can also try\\nbuilding your own stack I hope this\\nvideo was helpful if you like this video\\nplease let us know by hitting on the\\nlike button and subscribing to the\\nchannel we will release more videos in\\nthis series to solve new use cases till\\nthen stay tuned thank you\", start_char_idx=0, end_char_idx=4190, text_template='{metadata_str}\\n\\n{content}', metadata_template='{key}: {value}', metadata_seperator='\\n')]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Set Embedding Model"
      ],
      "metadata": {
        "id": "XiIZflwhWfsA"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model_name='BAAI/bge-small-en-v1.5'\n",
        "\n",
        "embed_model = embeddings.HuggingFaceEmbeddings(\n",
        "    model_name=model_name\n",
        ")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 369,
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        "id": "mVluuEDVRvVF",
        "outputId": "768650e2-3929-46d7-c67a-c46954fada53"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "modules.json:   0%|          | 0.00/349 [00:00<?, ?B/s]"
            ],
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          "metadata": {}
        },
        {
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          "data": {
            "text/plain": [
              "config_sentence_transformers.json:   0%|          | 0.00/124 [00:00<?, ?B/s]"
            ],
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          "metadata": {}
        },
        {
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            "text/plain": [
              "README.md:   0%|          | 0.00/94.8k [00:00<?, ?B/s]"
            ],
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          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "sentence_bert_config.json:   0%|          | 0.00/52.0 [00:00<?, ?B/s]"
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            "text/plain": [
              "config.json:   0%|          | 0.00/743 [00:00<?, ?B/s]"
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              "model.safetensors:   0%|          | 0.00/133M [00:00<?, ?B/s]"
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        {
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              "tokenizer_config.json:   0%|          | 0.00/366 [00:00<?, ?B/s]"
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              "vocab.txt:   0%|          | 0.00/232k [00:00<?, ?B/s]"
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        {
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              "1_Pooling/config.json:   0%|          | 0.00/190 [00:00<?, ?B/s]"
            ],
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          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Create Retriever"
      ],
      "metadata": {
        "id": "dYmY-7vGWh-k"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "retriever = retrieve.auto_retriever(\n",
        "    data=data,\n",
        "    embed_model=embed_model,\n",
        "    type=\"hybrid\",\n",
        "    mode=\"OR\",\n",
        "    top_k=2)"
      ],
      "metadata": {
        "id": "i3CpYJAoSz3e"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "retrieved_nodes = retriever.retrieve(\"which tool is mentioned in the video?\")\n",
        "print(retrieved_nodes)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TBPy89TQVFw6",
        "outputId": "170f53f5-a8ae-4375-a5d6-72f6e0a5ccc9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "LLM is explicitly disabled. Using MockLLM.\n",
            "Embeddings have been explicitly disabled. Using MockEmbedding.\n",
            "[NodeWithScore(node=TextNode(id_='d8c9bfbf-7ff3-451c-814f-be0e2a41a7e4', embedding=None, metadata={'video_id': 'ZM1bdh2mDJQ'}, excluded_embed_metadata_keys=[], excluded_llm_metadata_keys=[], relationships={<NodeRelationship.SOURCE: '1'>: RelatedNodeInfo(node_id='250c8287-9f69-43a3-98be-0e83bf052f96', node_type=<ObjectType.TEXT: '1'>, metadata={'video_id': 'ZM1bdh2mDJQ'}, hash='615e7d33180855d5371f6b37c8848fe6cb5ae545e1ba29af48aae860c75a6437')}, text=\"hi everyone welcome to the part seven\\nvideo of building llm applications using\\ngen stack in this video we are going to\\nbuild a CSV agent using this tack so the\\nCSV file will be the data source for the\\nentire R pipeline so if you do not know\\nwhat rag is then in short it's retrieval\\naugmented generation it streamlines the\\nprocess of retrieving data from a data\\nsource then pre-processing it and\\nstoring it in a certain way in a vector\\nstore and then retri it when a query is\\npassed and then presenting it with the\\nhelp of a llm which we're going to\\nprovide and the llm will generate\\nresponse based on the query which is\\nbeing\\nasked so let's start building the rack\\npipeline first of all we need a loader\\nwhich in this case will be a CSV loader\\nsince we are using a CSV file so let's\\ndrag and drop uh now I'll be using a\\nTitanic data set which I have downloaded\\nfrom kaggle so yeah I'll be using that\\ndata ass it as the reference data source\\nof this\\npipeline so I've uploaded the data now\\nwe need to split the text into smaller\\nchunks so that it could fit into the llm\\nthat we're going to use in the future so\\nyeah let's do that right now uh here\\nI'll be using the recursive character\\ntext splitter and I'll be joining both\\nof the components so now since we have\\nour smaller chunks of text we can uh\\nconvert it to uh numerical embeddings\\nwith the help of an embedding model so\\nuh let's do that uh we need a hugging\\nface inference API embeddings and I'll\\nput my API key and also the model name\\nso I've added the API key and the model\\nname here I'll be using this model\\nmentioned in the model name now since we\\nhave our embedding model defined we also\\nneed to convert those text information\\ninto embeddings and then we have to\\nstore it in a separate Vector store here\\nI'm going to use the chroma Vector store\\nand I'll be connecting\\nthis both\\ncomponents like this so as you can see\\nuh these all are the retriever\\ncomponents so whenever we ask a query to\\nthe rack pipeline the vector store\\nfetches the most relevant information\\nand passes it to the generator component\\nwhich generates a proper answer and\\ngives to us so let's start building the\\ngenerator component for that we need an\\nllm here I'll be using the Azure chat\\nopeni so you have to put the Azure chat\\nopeni API base the API key\\nthe type and the deployment name along\\nwith the model name so I'll fill all the\\nfields and unfortunately I cannot expose\\nthem in the video now since we have\\ndefined our llm we need to connect the\\ngenerator component with the Ral\\ncomponent which you can see on your\\nscreen so for that we need chains first\\nof all I will drag and drop the combine\\ndocs chain and after that uh I need to\\nhave the retrieval qm I will connect\\nboth of the components and I will\\nconnect the chroma to the Reet component\\nso this is how we have our generator\\ncomponent ready but before that we have\\nto connect the llm to the combine do\\nstream so I'll do that right now after\\nconnecting all the components we can\\nbuild the entire rack pipeline by\\nclicking on this buildt stack\\nbutton once the stack is built you can\\nchat with your application by clicking\\non the chat with stack button which is\\npresent just below the build stack\\nbutton it will notify you once the stack\\nis ready for\\ntesting so as you can see our stack is\\nready for testing and we are going to\\nchat with our application so let's ask\\nwhat are some of the values of the field\\nP class the field P class is present in\\nthe CSV file which I have uploaded for\\nreference so it answers in the G given\\ncontext the values of class are 1 and\\nthree so it automatically recognizes\\nthat the value class refers to P class\\nso yeah as you can see building this\\nentire application took me just a few\\nminutes so you can also build your\\napplication by using a separate CSV file\\nlet's say or we also have supports for\\nPDF and other types of data so you can\\nalso experiment with the embedding model\\nand the llm so yeah you can also try\\nbuilding your own stack I hope this\\nvideo was helpful if you like this video\\nplease let us know by hitting on the\\nlike button and subscribing to the\\nchannel we will release more videos in\\nthis series to solve new use cases till\\nthen stay tuned thank you\", start_char_idx=0, end_char_idx=4190, text_template='{metadata_str}\\n\\n{content}', metadata_template='{key}: {value}', metadata_seperator='\\n'), score=0.7287942560396022)]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Set Language Model"
      ],
      "metadata": {
        "id": "3eA675CgWldE"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "llm = llms.GeminiModel(\n",
        "    model_name=\"gemini-pro\",\n",
        "    google_api_key=google_api_key)"
      ],
      "metadata": {
        "id": "xCFYs26kW9M4"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Generate Answer"
      ],
      "metadata": {
        "id": "SG5rJ8NZWoGR"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "question = \"what tool is the video mentioning about?\""
      ],
      "metadata": {
        "id": "PV_EbNNmXYtC"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "pipeline = generator.Generate(\n",
        "    question=question,\n",
        "    retriever=retriever,\n",
        "    llm=llm)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 53
        },
        "id": "SoNDH0DyXTjc",
        "outputId": "b24d2851-4872-4ec0-f082-0b23f8d89e8b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "LLM is explicitly disabled. Using MockLLM.\n",
            "Embeddings have been explicitly disabled. Using MockEmbedding.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(pipeline.call())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wiPvzG6EZo_e",
        "outputId": "7ce90054-3b18-44ad-d293-bdf1f9a9d773"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The video mentions the following tools:\n",
            "- Gen Stack\n",
            "- CSV loader\n",
            "- Recursive character text splitter\n",
            "- Hugging Face inference API embeddings\n",
            "- Chroma Vector store\n",
            "- Azure chat openi\n",
            "- Combine docs chain\n",
            "- Retrieval qm\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(pipeline.get_rag_triad_evals())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 142
        },
        "id": "Y8nFSyemdCmC",
        "outputId": "bd4b69ab-9292-4dec-ccb1-94e323469f9c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Executing RAG Triad Evaluations...\n",
            "Context relevancy Score: 1.0\n",
            "This response does not meet the evaluation threshold. Consider refining the structure and content for better clarity and effectiveness.\n",
            "Answer relevancy Score: 8.0\n",
            "This response meets the evaluation threshold. It demonstrates strong comprehension and coherence.\n",
            "Groundness score: 7.3\n",
            "This response does not meet the evaluation threshold. Consider refining the structure and content for better clarity and effectiveness.\n"
          ]
        }
      ]
    }
  ]
}