5. Evaluation.ipynb

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    {
      "cell_type": "code",
      "source": [
        "!pip install chromadb -q\n",
        "!pip install sentence-transformers -q\n",
        "!pip install unstructured -q\n",
        "\n",
        "! pip install \"unstructured[all-docs]\" pillow pydantic lxml pillow matplotlib chromadb tiktoken -q\n",
        "!pip install unstructured[local-inference] -q"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PwnfNi--gaMI",
        "outputId": "570ea370-7fd6-4326-a2f2-a6adf3ec683d"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m19.2/19.2 MB\u001b[0m \u001b[31m71.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m5.9/5.9 MB\u001b[0m \u001b[31m99.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m159.9/159.9 kB\u001b[0m \u001b[31m20.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m114.6/114.6 kB\u001b[0m \u001b[31m14.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m4.5/4.5 MB\u001b[0m \u001b[31m18.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m42.2/42.2 kB\u001b[0m \u001b[31m4.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m56.4/56.4 kB\u001b[0m \u001b[31m7.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m112.5/112.5 kB\u001b[0m \u001b[31m12.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m2.2/2.2 MB\u001b[0m \u001b[31m68.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m79.5/79.5 kB\u001b[0m \u001b[31m9.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m2.8/2.8 MB\u001b[0m \u001b[31m71.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m117.0/117.0 kB\u001b[0m \u001b[31m17.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Building wheel for iopath (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Building wheel for antlr4-python3-runtime (setup.py) ... \u001b[?25l\u001b[?25hdone\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[0m"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5qoxnFxAfgCW",
        "outputId": "1a983555-f39d-4ddc-8707-931abf05aad6"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m817.7/817.7 kB\u001b[0m \u001b[31m6.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m292.8/292.8 kB\u001b[0m \u001b[31m7.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.9/1.9 MB\u001b[0m \u001b[31m17.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m289.1/289.1 kB\u001b[0m \u001b[31m14.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m113.7/113.7 kB\u001b[0m \u001b[31m8.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m75.6/75.6 kB\u001b[0m \u001b[31m2.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m49.4/49.4 kB\u001b[0m \u001b[31m3.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m77.9/77.9 kB\u001b[0m \u001b[31m7.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m58.3/58.3 kB\u001b[0m \u001b[31m5.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m53.0/53.0 kB\u001b[0m \u001b[31m3.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m141.1/141.1 kB\u001b[0m \u001b[31m2.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h"
          ]
        }
      ],
      "source": [
        "!pip install --upgrade langchain openai  -q"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install -q accelerate -U\n",
        "!pip install transformers -q\n",
        "!pip install -q bitsandbytes==0.40.2"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Fs4-uddogXW1",
        "outputId": "1db5fbc0-080e-46d0-9edd-bbb132ae487f"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m297.4/297.4 kB\u001b[0m \u001b[31m6.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m92.5/92.5 MB\u001b[0m \u001b[31m9.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##**Langchain: Evaluation**"
      ],
      "metadata": {
        "id": "KrQcB4QdpAMB"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Utilizing 1hr course LangChain for LLM Application Development,, I'm adapting similar methodologies for Llama2 from Hugging Face. Given the limited resources available, this endeavor aims to provide valuable insights and practical applications for individuals who wants to use different styles than chat_models module in langchain**"
      ],
      "metadata": {
        "id": "6ctiZp8cpP0G"
      }
    },
    {
      "cell_type": "markdown",
      "source": [],
      "metadata": {
        "id": "DFFWRGdBozmO"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import torch\n",
        "from torch import cuda, bfloat16\n",
        "import transformers\n",
        "\n",
        "model_id = 'meta-llama/Llama-2-13b-chat-hf'\n",
        "\n",
        "device = f'cuda:{cuda.current_device()}' if cuda.is_available() else 'cpu'\n",
        "\n",
        "# set quantization configuration to load large model with less GPU memory\n",
        "# this requires the `bitsandbytes` library\n",
        "bnb_config = transformers.BitsAndBytesConfig(\n",
        "    load_in_4bit=True,\n",
        "    bnb_4bit_quant_type='nf4',\n",
        "    bnb_4bit_use_double_quant=True,\n",
        "    bnb_4bit_compute_dtype=bfloat16\n",
        ")\n",
        "\n",
        "# begin initializing HF items, need auth token for these\n",
        "hf_auth = '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'\n",
        "model_config = transformers.AutoConfig.from_pretrained(\n",
        "    model_id,\n",
        "    use_auth_token=hf_auth\n",
        ")\n",
        "\n",
        "model = transformers.AutoModelForCausalLM.from_pretrained(\n",
        "    model_id,\n",
        "    trust_remote_code=True,\n",
        "    config=model_config,\n",
        "    quantization_config=bnb_config,\n",
        "    device_map= {\"\": 0},\n",
        "    use_auth_token=hf_auth\n",
        ")\n",
        "model.eval()\n",
        "print(f\"Model loaded on {device}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 397,
          "referenced_widgets": [
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            "a2aec9903e844667a0878dee443226a6",
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            "9b1b9a2a4d68453db4cedee0c3de7485",
            "dd37022b3d66406caebf9ec370b81205",
            "619b9f49255842789a5a1e091000e314"
          ]
        },
        "id": "8HRAI1aOgd5d",
        "outputId": "3bbc87a2-72b4-4436-a4cd-f4281f1cbc9c"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/transformers/models/auto/configuration_auto.py:1096: FutureWarning: The `use_auth_token` argument is deprecated and will be removed in v5 of Transformers. Please use `token` instead.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "config.json:   0%|          | 0.00/587 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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              "model_id": "c401ca321e0744449502e7507ce93d57"
            }
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        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/transformers/models/auto/auto_factory.py:466: FutureWarning: The `use_auth_token` argument is deprecated and will be removed in v5 of Transformers. Please use `token` instead.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "display_data",
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        },
        {
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            ],
            "application/vnd.jupyter.widget-view+json": {
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            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Loading checkpoint shards:   0%|          | 0/3 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "91da01561cc34df8b193a3c1fadfbd36"
            }
          },
          "metadata": {}
        },
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            ],
            "application/vnd.jupyter.widget-view+json": {
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              "version_minor": 0,
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            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "You are calling `save_pretrained` to a 4-bit converted model, but your `bitsandbytes` version doesn't support it. If you want to save 4-bit models, make sure to have `bitsandbytes>=0.41.3` installed.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model loaded on cuda:0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "tokenizer = transformers.AutoTokenizer.from_pretrained(\n",
        "    model_id,\n",
        "    use_auth_token=hf_auth,\n",
        "     padding_side='left'\n",
        ")\n",
        "tokenizer.add_special_tokens({'pad_token': '[PAD]'})\n",
        "\n",
        "class MyPipeline:\n",
        "    def __init__(self, model, tokenizer):\n",
        "        self.model = model\n",
        "        self.tokenizer = tokenizer\n",
        "        self.task = \"text-generation\"\n",
        "        self.device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n",
        "    def __call__(self, input_text):\n",
        "        # If input_text is a single string, convert it to a list with one element\n",
        "        if isinstance(input_text, str):\n",
        "            input_text = [input_text]\n",
        "\n",
        "        # Tokenize the input text\n",
        "        input_ids = self.tokenizer(input_text, return_tensors='pt', truncation=True,padding=True)['input_ids'].to(self.device)\n",
        "\n",
        "        # Generate text\n",
        "        generated_output = self.model.generate(\n",
        "            input_ids,  # Generate a bit more than the input\n",
        "            temperature=0.1,\n",
        "            repetition_penalty=1.1,\n",
        "            pad_token_id=self.tokenizer.pad_token_id,\n",
        "            eos_token_id=self.tokenizer.eos_token_id\n",
        "        )\n",
        "\n",
        "        # Decode the generated output\n",
        "        generated_texts = []\n",
        "        for i in range(len(input_text)):\n",
        "            generated_text = self.tokenizer.decode(generated_output[i], skip_special_tokens=True)\n",
        "            # Remove the portion of generated text that matches the input length\n",
        "            generated_text = generated_text[len(input_text[i]):]\n",
        "            generated_texts.append({'input_text': input_text[i], 'generated_text': generated_text})\n",
        "\n",
        "        return generated_texts\n",
        "\n",
        "\n",
        "\n",
        "text_generator = MyPipeline(model=model, tokenizer=tokenizer)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 200,
          "referenced_widgets": [
            "58ff41e754d94fa1a171ff643629c7cb",
            "f4b24b8d12f34365833a9924dc01f1de",
            "0013a57052b942e181f248ef8b6009e2",
            "e4898712a87b4e21b35546ab0560e5f6",
            "5cfc8b5d9409456c805e5bd4ffbbf306",
            "6877d35e8f604713a2d943302b837d54",
            "553e9635bddf46508b7548cc2786115b",
            "64ce8330fa424bb7ad968f3276e21dad",
            "c3c367f026984abd8e43c2f2c62e3ba3",
            "dc7bc5f0e7984e938198e52aade5bb5d",
            "693e193501a84865b95d29a9674021a4",
            "07f5d6c688df44d4bec177e3ff394df5",
            "0526c99fa9114af0a6e1d59ffdb250dd",
            "9c8dc56491f74c988c505b8e564a86cc",
            "10137ad292024746b3783cf2c51c0a8e",
            "cdc92a99b81544bfb4c655b59d568ad3",
            "e3b41056d5c541f6b3040a2db70ef06a",
            "108b2a8e017641f7b0dab67e85243616",
            "304506610b3140fd97e599b8c6cfa2cd",
            "8e949573e4104b4a90c96af4b0fec097",
            "e15b88ce84884f41992fae452a37e64d",
            "4f3a19b21ef5434bab2db618db6ac115",
            "eb61a151233e4a878ee3db0299c5edd5",
            "84c43a943182465e8cfde6a06d413a8e",
            "2da309972c7a44d1a1146b4d50eefd49",
            "24e86d6f5a2544c6a7b8a0098b330f16",
            "b8a88aa21c2243938e0dd0c8cb923947",
            "6fe6ce1239a247fa9fc98637c7d9141d",
            "293f55b20f9840ee932795c8a983bfa4",
            "7238a0a0532a4463ab6b0add1afded75",
            "8e1cb49dab8f489c9de3230603606566",
            "6d192521b5ef4811bdb17cb4acb3d478",
            "aaab370aa3e9495f9dcada9934e382ad",
            "5e91459b262a4e5ca3c9e5f4914362a5",
            "ea1f06edc7a54e7eabcaba09f7b4326b",
            "754e4be8cc59456ba44ac1c50ae5d0fc",
            "fee485766c07421aaa5a0ab6cd2036f6",
            "18c33e61a1744a8cbcef7c1e286c7655",
            "d77d40bdbe65417fb650cf0caf400578",
            "8c458adf72c548149f813fcc265d05e4",
            "c6aefc678d0348fdbc4ef04b2cba6b10",
            "8af168ff2535459bab5924c7dc303f37",
            "3e21cb50e4a54612b444ff91627d4e90",
            "3555b3030383486c9fa5efc730d48386"
          ]
        },
        "id": "EL3nrfWrggWs",
        "outputId": "04076919-4f14-4859-d6f6-929d1c316688"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/transformers/models/auto/tokenization_auto.py:720: FutureWarning: The `use_auth_token` argument is deprecated and will be removed in v5 of Transformers. Please use `token` instead.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "tokenizer_config.json:   0%|          | 0.00/1.62k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "58ff41e754d94fa1a171ff643629c7cb"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "tokenizer.model:   0%|          | 0.00/500k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "07f5d6c688df44d4bec177e3ff394df5"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "tokenizer.json:   0%|          | 0.00/1.84M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "eb61a151233e4a878ee3db0299c5edd5"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "special_tokens_map.json:   0%|          | 0.00/414 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "5e91459b262a4e5ca3c9e5f4914362a5"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from langchain.llms import HuggingFacePipeline\n",
        "\n",
        "llm = HuggingFacePipeline(pipeline=text_generator)"
      ],
      "metadata": {
        "id": "onaRr1Lugi7u"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from langchain.chains import RetrievalQA\n",
        "from langchain.document_loaders import CSVLoader\n",
        "from langchain.vectorstores import Chroma\n",
        "from langchain.embeddings import SentenceTransformerEmbeddings\n",
        "\n",
        "embeddings = SentenceTransformerEmbeddings(model_name=\"all-MiniLM-L6-v2\")\n",
        "file = '/content/OutdoorClothingCatalog_1000.csv'\n",
        "loader = CSVLoader(file_path=file, encoding='utf-8')\n",
        "data = loader.load()"
      ],
      "metadata": {
        "id": "Hx0_Z611glk4"
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data[10]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mtQ2zNgskFvk",
        "outputId": "7a041391-a1df-46e8-81b4-763bad8aab30"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Document(page_content=\": 10\\nname: Cozy Comfort Pullover Set, Stripe\\ndescription: Perfect for lounging, this striped knit set lives up to its name. We used ultrasoft fabric and an easy design that's as comfortable at bedtime as it is when we have to make a quick run out.\\n\\nSize & Fit\\n- Pants are Favorite Fit: Sits lower on the waist.\\n- Relaxed Fit: Our most generous fit sits farthest from the body.\\n\\nFabric & Care\\n- In the softest blend of 63% polyester, 35% rayon and 2% spandex.\\n\\nAdditional Features\\n- Relaxed fit top with raglan sleeves and rounded hem.\\n- Pull-on pants have a wide elastic waistband and drawstring, side pockets and a modern slim leg.\\n\\nImported.\", metadata={'source': '/content/OutdoorClothingCatalog_1000.csv', 'row': 10})"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "vectorstore = Chroma(\"clothing\", embeddings)\n",
        "vectorstore.from_documents(data,embeddings,collection_name=\"clothing\")\n",
        "qa = RetrievalQA.from_chain_type(\n",
        "    llm=llm,\n",
        "    chain_type=\"stuff\",\n",
        "    retriever=vectorstore.as_retriever(),\n",
        "    verbose=True,\n",
        "    chain_type_kwargs = {\n",
        "        \"document_separator\": \"<<<<>>>>>\"\n",
        "    }\n",
        ")\n"
      ],
      "metadata": {
        "id": "1xom6YEegn1C"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "examples = [\n",
        "    {\n",
        "        \"query\": \"[INST] Do the Cozy Comfort Pullover Set\\\n",
        "        have side pockets? FYI there is mention of side pockets[/INST]\",\n",
        "        \"answer\": \"Yes\"\n",
        "    },\n",
        "    {\n",
        "        \"query\": \"What collection is the Ultra-Lofty \\\n",
        "        850 Stretch Down Hooded Jacket from?\",\n",
        "        \"answer\": \"The DownTek collection\"\n",
        "    }\n",
        "]"
      ],
      "metadata": {
        "id": "SNo_VaXogq1F"
      },
      "execution_count": 26,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from langchain.evaluation.qa import QAGenerateChain\n",
        "\n",
        "example_gen_chain = QAGenerateChain.from_llm(HuggingFacePipeline(pipeline=text_generator))"
      ],
      "metadata": {
        "id": "2mv0XYncgthA"
      },
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "qa.run(examples[1][\"query\"])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 802
        },
        "id": "2XJMJpOVjfAu",
        "outputId": "573774ba-8e3d-45d7-9a74-8d1def4c3d86"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[32;1m\u001b[1;3m[chain/start]\u001b[0m \u001b[1m[1:chain:RetrievalQA] Entering Chain run with input:\n",
            "\u001b[0m{\n",
            "  \"query\": \"What collection is the Ultra-Lofty         850 Stretch Down Hooded Jacket from?\"\n",
            "}\n",
            "\u001b[32;1m\u001b[1;3m[chain/start]\u001b[0m \u001b[1m[1:chain:RetrievalQA > 3:chain:StuffDocumentsChain] Entering Chain run with input:\n",
            "\u001b[0m[inputs]\n",
            "\u001b[32;1m\u001b[1;3m[chain/start]\u001b[0m \u001b[1m[1:chain:RetrievalQA > 3:chain:StuffDocumentsChain > 4:chain:LLMChain] Entering Chain run with input:\n",
            "\u001b[0m{\n",
            "  \"question\": \"What collection is the Ultra-Lofty         850 Stretch Down Hooded Jacket from?\",\n",
            "  \"context\": \": 11\\nname: Ultra-Lofty 850 Stretch Down Hooded Jacket\\ndescription: This technical stretch down jacket from our DownTek collection is sure to keep you warm and comfortable with its full-stretch construction providing exceptional range of motion. With a slightly fitted style that falls at the hip and best with a midweight layer, this jacket is suitable for light activity up to 20° and moderate activity up to -30°. The soft and durable 100% polyester shell offers complete windproof protection and is insulated with warm, lofty goose down. Other features include welded baffles for a no-stitch construction and excellent stretch, an adjustable hood, an interior media port and mesh stash pocket and a hem drawcord. Machine wash and dry. Imported.<<<<>>>>>: 364\\nname: Ultra-Loft 850 Big Baffle Hooded Jacket\\ndescription: Our Ultralight 850 Down Jacket is designed with bigger baffles to provide you with unparalleled comfort and style. It's suitable for light activity up to 15° and moderate activity to -35°. Its slightly fitted cut falls below the hips and best with midweight layer, making sure you stay warm and cozy. The shell is made from Pertex Y-shaped yarn technology, a tightly interlocked weave that resists moisture and keeps down feathers inside. It's filled with premium 850-fill-power goose down, one of the highest on the market, and treated with DownTek for water repellency. It's also responsibly sourced down. Plus, it features adjustable hood, two zippered hand pockets, one zippered chest pocket, low-profile inner cuff construction and a hem drawcord for additional coverage and wind protection. Machine wash and dry for easy care. Imported.<<<<>>>>>: 241\\nname: Women's Ultra-Loft Down Sweater Hooded Jacket\\ndescription: Our revolutionary 850 jacket is designed with the best outerwear technology to ensure unbeatable performance. With premium 850 down for more warmth for its weight and DownTek treatment for water resistance, this jacket is light, lofty and warm. The tightly woven Pertex Quantum nylon shell is wind and weather-resistant for added protection. Comfort ratings for light activity is up to 30° and moderate activity up to -20°. With a slightly fitted shape that falls at the hip and best with a midweight layer, this jacket will keep you warm in any condition. It is virtually weightless and packs away into its own pocket. Certified by the Responsible Down Standard, it is a jacket of its own class.<<<<>>>>>: 578\\nname: Women's Ultralight 850 Featherweight Hooded Jacket\\ndescription: Our revolutionary 850 jacket is unparalleled in its performance. We combined the best of outerwear technology with innovative DownTek fill, 850-fill-power and a Pertex Quantum nylon shell for unbeatable warmth and protection. Light activity: to 30°  Moderate activity: to -20°. Its slightly fitted design looks great and falls at the hip for comfortable wear. \\n\\nWe start with premium 850 down, which provides more warmth for its weight, and then treat it with revolutionary DownTek that allows our jackets to do what many others can’t – keep you warm, even in wet conditions. Unlike ordinary down that soaks up moisture, ours absorbs 33% less moisture and dries 66% faster so it stays light, lofty and warm. On the outside, the game-changing Pertex Quantum nylon shell is tightly woven to resist the elements. \\n\\nThis virtually weightless style easily packs away into its own pocket and features narrowly quilted channels of down for a streamlined appearance. An adjustable drawcord at the hem helps you get the perfect fit. Certified by the Responsible Down Standard, you can trust that the goose down used was sourced humanely. \\n\\n is the official supplier to the\"\n",
            "}\n",
            "\u001b[32;1m\u001b[1;3m[llm/start]\u001b[0m \u001b[1m[1:chain:RetrievalQA > 3:chain:StuffDocumentsChain > 4:chain:LLMChain > 5:llm:HuggingFacePipeline] Entering LLM run with input:\n",
            "\u001b[0m{\n",
            "  \"prompts\": [\n",
            "    \"Use the following pieces of context to answer the question at the end. If you don't know the answer, just say that you don't know, don't try to make up an answer.\\n\\n: 11\\nname: Ultra-Lofty 850 Stretch Down Hooded Jacket\\ndescription: This technical stretch down jacket from our DownTek collection is sure to keep you warm and comfortable with its full-stretch construction providing exceptional range of motion. With a slightly fitted style that falls at the hip and best with a midweight layer, this jacket is suitable for light activity up to 20° and moderate activity up to -30°. The soft and durable 100% polyester shell offers complete windproof protection and is insulated with warm, lofty goose down. Other features include welded baffles for a no-stitch construction and excellent stretch, an adjustable hood, an interior media port and mesh stash pocket and a hem drawcord. Machine wash and dry. Imported.<<<<>>>>>: 364\\nname: Ultra-Loft 850 Big Baffle Hooded Jacket\\ndescription: Our Ultralight 850 Down Jacket is designed with bigger baffles to provide you with unparalleled comfort and style. It's suitable for light activity up to 15° and moderate activity to -35°. Its slightly fitted cut falls below the hips and best with midweight layer, making sure you stay warm and cozy. The shell is made from Pertex Y-shaped yarn technology, a tightly interlocked weave that resists moisture and keeps down feathers inside. It's filled with premium 850-fill-power goose down, one of the highest on the market, and treated with DownTek for water repellency. It's also responsibly sourced down. Plus, it features adjustable hood, two zippered hand pockets, one zippered chest pocket, low-profile inner cuff construction and a hem drawcord for additional coverage and wind protection. Machine wash and dry for easy care. Imported.<<<<>>>>>: 241\\nname: Women's Ultra-Loft Down Sweater Hooded Jacket\\ndescription: Our revolutionary 850 jacket is designed with the best outerwear technology to ensure unbeatable performance. With premium 850 down for more warmth for its weight and DownTek treatment for water resistance, this jacket is light, lofty and warm. The tightly woven Pertex Quantum nylon shell is wind and weather-resistant for added protection. Comfort ratings for light activity is up to 30° and moderate activity up to -20°. With a slightly fitted shape that falls at the hip and best with a midweight layer, this jacket will keep you warm in any condition. It is virtually weightless and packs away into its own pocket. Certified by the Responsible Down Standard, it is a jacket of its own class.<<<<>>>>>: 578\\nname: Women's Ultralight 850 Featherweight Hooded Jacket\\ndescription: Our revolutionary 850 jacket is unparalleled in its performance. We combined the best of outerwear technology with innovative DownTek fill, 850-fill-power and a Pertex Quantum nylon shell for unbeatable warmth and protection. Light activity: to 30°  Moderate activity: to -20°. Its slightly fitted design looks great and falls at the hip for comfortable wear. \\n\\nWe start with premium 850 down, which provides more warmth for its weight, and then treat it with revolutionary DownTek that allows our jackets to do what many others can’t – keep you warm, even in wet conditions. Unlike ordinary down that soaks up moisture, ours absorbs 33% less moisture and dries 66% faster so it stays light, lofty and warm. On the outside, the game-changing Pertex Quantum nylon shell is tightly woven to resist the elements. \\n\\nThis virtually weightless style easily packs away into its own pocket and features narrowly quilted channels of down for a streamlined appearance. An adjustable drawcord at the hem helps you get the perfect fit. Certified by the Responsible Down Standard, you can trust that the goose down used was sourced humanely. \\n\\n is the official supplier to the\\n\\nQuestion: What collection is the Ultra-Lofty         850 Stretch Down Hooded Jacket from?\\nHelpful Answer:\"\n",
            "  ]\n",
            "}\n",
            "\u001b[36;1m\u001b[1;3m[llm/end]\u001b[0m \u001b[1m[1:chain:RetrievalQA > 3:chain:StuffDocumentsChain > 4:chain:LLMChain > 5:llm:HuggingFacePipeline] [15.91s] Exiting LLM run with output:\n",
            "\u001b[0m{\n",
            "  \"generations\": [\n",
            "    [\n",
            "      {\n",
            "        \"text\": \" The Ultra-Lofty 850 Stretch Down Hooded Jacket is from the DownTek collection.\",\n",
            "        \"generation_info\": null,\n",
            "        \"type\": \"Generation\"\n",
            "      }\n",
            "    ]\n",
            "  ],\n",
            "  \"llm_output\": null,\n",
            "  \"run\": null\n",
            "}\n",
            "\u001b[36;1m\u001b[1;3m[chain/end]\u001b[0m \u001b[1m[1:chain:RetrievalQA > 3:chain:StuffDocumentsChain > 4:chain:LLMChain] [15.91s] Exiting Chain run with output:\n",
            "\u001b[0m{\n",
            "  \"text\": \" The Ultra-Lofty 850 Stretch Down Hooded Jacket is from the DownTek collection.\"\n",
            "}\n",
            "\u001b[36;1m\u001b[1;3m[chain/end]\u001b[0m \u001b[1m[1:chain:RetrievalQA > 3:chain:StuffDocumentsChain] [15.92s] Exiting Chain run with output:\n",
            "\u001b[0m{\n",
            "  \"output_text\": \" The Ultra-Lofty 850 Stretch Down Hooded Jacket is from the DownTek collection.\"\n",
            "}\n",
            "\u001b[36;1m\u001b[1;3m[chain/end]\u001b[0m \u001b[1m[1:chain:RetrievalQA] [15.99s] Exiting Chain run with output:\n",
            "\u001b[0m{\n",
            "  \"result\": \" The Ultra-Lofty 850 Stretch Down Hooded Jacket is from the DownTek collection.\"\n",
            "}\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "' The Ultra-Lofty 850 Stretch Down Hooded Jacket is from the DownTek collection.'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import langchain\n",
        "langchain.debug = True"
      ],
      "metadata": {
        "id": "Ol1t0TAqmQ9a"
      },
      "execution_count": 24,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "qa.run(examples[1][\"query\"])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 802
        },
        "id": "RDK5pkTumSJH",
        "outputId": "e17833f6-34e2-4146-92f6-140b420b9138"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[32;1m\u001b[1;3m[chain/start]\u001b[0m \u001b[1m[1:chain:RetrievalQA] Entering Chain run with input:\n",
            "\u001b[0m{\n",
            "  \"query\": \"What collection is the Ultra-Lofty         850 Stretch Down Hooded Jacket from?\"\n",
            "}\n",
            "\u001b[32;1m\u001b[1;3m[chain/start]\u001b[0m \u001b[1m[1:chain:RetrievalQA > 3:chain:StuffDocumentsChain] Entering Chain run with input:\n",
            "\u001b[0m[inputs]\n",
            "\u001b[32;1m\u001b[1;3m[chain/start]\u001b[0m \u001b[1m[1:chain:RetrievalQA > 3:chain:StuffDocumentsChain > 4:chain:LLMChain] Entering Chain run with input:\n",
            "\u001b[0m{\n",
            "  \"question\": \"What collection is the Ultra-Lofty         850 Stretch Down Hooded Jacket from?\",\n",
            "  \"context\": \": 11\\nname: Ultra-Lofty 850 Stretch Down Hooded Jacket\\ndescription: This technical stretch down jacket from our DownTek collection is sure to keep you warm and comfortable with its full-stretch construction providing exceptional range of motion. With a slightly fitted style that falls at the hip and best with a midweight layer, this jacket is suitable for light activity up to 20° and moderate activity up to -30°. The soft and durable 100% polyester shell offers complete windproof protection and is insulated with warm, lofty goose down. Other features include welded baffles for a no-stitch construction and excellent stretch, an adjustable hood, an interior media port and mesh stash pocket and a hem drawcord. Machine wash and dry. Imported.<<<<>>>>>: 364\\nname: Ultra-Loft 850 Big Baffle Hooded Jacket\\ndescription: Our Ultralight 850 Down Jacket is designed with bigger baffles to provide you with unparalleled comfort and style. It's suitable for light activity up to 15° and moderate activity to -35°. Its slightly fitted cut falls below the hips and best with midweight layer, making sure you stay warm and cozy. The shell is made from Pertex Y-shaped yarn technology, a tightly interlocked weave that resists moisture and keeps down feathers inside. It's filled with premium 850-fill-power goose down, one of the highest on the market, and treated with DownTek for water repellency. It's also responsibly sourced down. Plus, it features adjustable hood, two zippered hand pockets, one zippered chest pocket, low-profile inner cuff construction and a hem drawcord for additional coverage and wind protection. Machine wash and dry for easy care. Imported.<<<<>>>>>: 241\\nname: Women's Ultra-Loft Down Sweater Hooded Jacket\\ndescription: Our revolutionary 850 jacket is designed with the best outerwear technology to ensure unbeatable performance. With premium 850 down for more warmth for its weight and DownTek treatment for water resistance, this jacket is light, lofty and warm. The tightly woven Pertex Quantum nylon shell is wind and weather-resistant for added protection. Comfort ratings for light activity is up to 30° and moderate activity up to -20°. With a slightly fitted shape that falls at the hip and best with a midweight layer, this jacket will keep you warm in any condition. It is virtually weightless and packs away into its own pocket. Certified by the Responsible Down Standard, it is a jacket of its own class.<<<<>>>>>: 578\\nname: Women's Ultralight 850 Featherweight Hooded Jacket\\ndescription: Our revolutionary 850 jacket is unparalleled in its performance. We combined the best of outerwear technology with innovative DownTek fill, 850-fill-power and a Pertex Quantum nylon shell for unbeatable warmth and protection. Light activity: to 30°  Moderate activity: to -20°. Its slightly fitted design looks great and falls at the hip for comfortable wear. \\n\\nWe start with premium 850 down, which provides more warmth for its weight, and then treat it with revolutionary DownTek that allows our jackets to do what many others can’t – keep you warm, even in wet conditions. Unlike ordinary down that soaks up moisture, ours absorbs 33% less moisture and dries 66% faster so it stays light, lofty and warm. On the outside, the game-changing Pertex Quantum nylon shell is tightly woven to resist the elements. \\n\\nThis virtually weightless style easily packs away into its own pocket and features narrowly quilted channels of down for a streamlined appearance. An adjustable drawcord at the hem helps you get the perfect fit. Certified by the Responsible Down Standard, you can trust that the goose down used was sourced humanely. \\n\\n is the official supplier to the\"\n",
            "}\n",
            "\u001b[32;1m\u001b[1;3m[llm/start]\u001b[0m \u001b[1m[1:chain:RetrievalQA > 3:chain:StuffDocumentsChain > 4:chain:LLMChain > 5:llm:HuggingFacePipeline] Entering LLM run with input:\n",
            "\u001b[0m{\n",
            "  \"prompts\": [\n",
            "    \"Use the following pieces of context to answer the question at the end. If you don't know the answer, just say that you don't know, don't try to make up an answer.\\n\\n: 11\\nname: Ultra-Lofty 850 Stretch Down Hooded Jacket\\ndescription: This technical stretch down jacket from our DownTek collection is sure to keep you warm and comfortable with its full-stretch construction providing exceptional range of motion. With a slightly fitted style that falls at the hip and best with a midweight layer, this jacket is suitable for light activity up to 20° and moderate activity up to -30°. The soft and durable 100% polyester shell offers complete windproof protection and is insulated with warm, lofty goose down. Other features include welded baffles for a no-stitch construction and excellent stretch, an adjustable hood, an interior media port and mesh stash pocket and a hem drawcord. Machine wash and dry. Imported.<<<<>>>>>: 364\\nname: Ultra-Loft 850 Big Baffle Hooded Jacket\\ndescription: Our Ultralight 850 Down Jacket is designed with bigger baffles to provide you with unparalleled comfort and style. It's suitable for light activity up to 15° and moderate activity to -35°. Its slightly fitted cut falls below the hips and best with midweight layer, making sure you stay warm and cozy. The shell is made from Pertex Y-shaped yarn technology, a tightly interlocked weave that resists moisture and keeps down feathers inside. It's filled with premium 850-fill-power goose down, one of the highest on the market, and treated with DownTek for water repellency. It's also responsibly sourced down. Plus, it features adjustable hood, two zippered hand pockets, one zippered chest pocket, low-profile inner cuff construction and a hem drawcord for additional coverage and wind protection. Machine wash and dry for easy care. Imported.<<<<>>>>>: 241\\nname: Women's Ultra-Loft Down Sweater Hooded Jacket\\ndescription: Our revolutionary 850 jacket is designed with the best outerwear technology to ensure unbeatable performance. With premium 850 down for more warmth for its weight and DownTek treatment for water resistance, this jacket is light, lofty and warm. The tightly woven Pertex Quantum nylon shell is wind and weather-resistant for added protection. Comfort ratings for light activity is up to 30° and moderate activity up to -20°. With a slightly fitted shape that falls at the hip and best with a midweight layer, this jacket will keep you warm in any condition. It is virtually weightless and packs away into its own pocket. Certified by the Responsible Down Standard, it is a jacket of its own class.<<<<>>>>>: 578\\nname: Women's Ultralight 850 Featherweight Hooded Jacket\\ndescription: Our revolutionary 850 jacket is unparalleled in its performance. We combined the best of outerwear technology with innovative DownTek fill, 850-fill-power and a Pertex Quantum nylon shell for unbeatable warmth and protection. Light activity: to 30°  Moderate activity: to -20°. Its slightly fitted design looks great and falls at the hip for comfortable wear. \\n\\nWe start with premium 850 down, which provides more warmth for its weight, and then treat it with revolutionary DownTek that allows our jackets to do what many others can’t – keep you warm, even in wet conditions. Unlike ordinary down that soaks up moisture, ours absorbs 33% less moisture and dries 66% faster so it stays light, lofty and warm. On the outside, the game-changing Pertex Quantum nylon shell is tightly woven to resist the elements. \\n\\nThis virtually weightless style easily packs away into its own pocket and features narrowly quilted channels of down for a streamlined appearance. An adjustable drawcord at the hem helps you get the perfect fit. Certified by the Responsible Down Standard, you can trust that the goose down used was sourced humanely. \\n\\n is the official supplier to the\\n\\nQuestion: What collection is the Ultra-Lofty         850 Stretch Down Hooded Jacket from?\\nHelpful Answer:\"\n",
            "  ]\n",
            "}\n",
            "\u001b[36;1m\u001b[1;3m[llm/end]\u001b[0m \u001b[1m[1:chain:RetrievalQA > 3:chain:StuffDocumentsChain > 4:chain:LLMChain > 5:llm:HuggingFacePipeline] [16.21s] Exiting LLM run with output:\n",
            "\u001b[0m{\n",
            "  \"generations\": [\n",
            "    [\n",
            "      {\n",
            "        \"text\": \" The Ultra-Lofty 850 Stretch Down Hooded Jacket is from the DownTek collection.\",\n",
            "        \"generation_info\": null,\n",
            "        \"type\": \"Generation\"\n",
            "      }\n",
            "    ]\n",
            "  ],\n",
            "  \"llm_output\": null,\n",
            "  \"run\": null\n",
            "}\n",
            "\u001b[36;1m\u001b[1;3m[chain/end]\u001b[0m \u001b[1m[1:chain:RetrievalQA > 3:chain:StuffDocumentsChain > 4:chain:LLMChain] [16.21s] Exiting Chain run with output:\n",
            "\u001b[0m{\n",
            "  \"text\": \" The Ultra-Lofty 850 Stretch Down Hooded Jacket is from the DownTek collection.\"\n",
            "}\n",
            "\u001b[36;1m\u001b[1;3m[chain/end]\u001b[0m \u001b[1m[1:chain:RetrievalQA > 3:chain:StuffDocumentsChain] [16.21s] Exiting Chain run with output:\n",
            "\u001b[0m{\n",
            "  \"output_text\": \" The Ultra-Lofty 850 Stretch Down Hooded Jacket is from the DownTek collection.\"\n",
            "}\n",
            "\u001b[36;1m\u001b[1;3m[chain/end]\u001b[0m \u001b[1m[1:chain:RetrievalQA] [16.23s] Exiting Chain run with output:\n",
            "\u001b[0m{\n",
            "  \"result\": \" The Ultra-Lofty 850 Stretch Down Hooded Jacket is from the DownTek collection.\"\n",
            "}\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "' The Ultra-Lofty 850 Stretch Down Hooded Jacket is from the DownTek collection.'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "langchain.debug = False"
      ],
      "metadata": {
        "id": "JYp53Czxn0-o"
      },
      "execution_count": 30,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "predictions = qa.apply(examples)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0iLjYKRMn8g2",
        "outputId": "e525bdec-d437-4b02-98eb-53a32a1ac73f"
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/langchain_core/_api/deprecation.py:117: LangChainDeprecationWarning: The function `apply` was deprecated in LangChain 0.1.0 and will be removed in 0.2.0. Use batch instead.\n",
            "  warn_deprecated(\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "\n",
            "\u001b[1m> Entering new RetrievalQA chain...\u001b[0m\n",
            "\n",
            "\u001b[1m> Finished chain.\u001b[0m\n",
            "\n",
            "\n",
            "\u001b[1m> Entering new RetrievalQA chain...\u001b[0m\n",
            "\n",
            "\u001b[1m> Finished chain.\u001b[0m\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from langchain.evaluation.qa import QAEvalChain\n",
        "\n",
        "eval_chain = QAEvalChain.from_llm(llm)"
      ],
      "metadata": {
        "id": "sEjSSdffn_jJ"
      },
      "execution_count": 33,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "eU1Ly2T5oRsy"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}