03_SelfQuery_Retriever.ipynb

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        "# Sip & Search: Crafting Your Personal Wine Expert with LangChain\n",
        "\n",
        "Indulge in the world of wines as you embark on a journey to create your very own wine expert with LangChain. In this captivating tutorial, discover the secrets of building a self-querying retriever for wine recommendations, right at your fingertips. Here's what awaits you:\n",
        "\n",
        "1. **Library Setup:** Dive into the installation of essential libraries like `langchain`, `huggingface_hub`, `google-search-results`, and more, laying the groundwork for your wine exploration adventure.\n",
        "\n",
        "2. **Setting the Scene:** Immerse yourself in the realm of wine expertise with a picturesque introduction, drawing inspiration from Sam's channel for insights into LLMs and beyond.\n",
        "\n",
        "3. **Crafting the Retriever:** Learn the art of creating a self-querying retriever tailored specifically for wine recommendations, leveraging LangChain's powerful capabilities. Explore metadata setup, model initialization, and vectorstore integration for seamless retrieval.\n",
        "\n",
        "4. **Example Data Delight:** Delve into a delectable assortment of example wine data, complete with tantalizing metadata, providing the perfect backdrop for your wine journey.\n",
        "\n",
        "5. **Querying Magic:** Unleash the magic of self-querying as you explore various query types, from simple inquiries to composite filters, unlocking a treasure trove of wine recommendations with each question.\n",
        "\n",
        "6. **Fine-Tuning with Filters:** Master the art of fine-tuning your wine search with filters, refining your results based on criteria like grape variety, country of origin, ratings, and more.\n",
        "\n",
        "7. **Exploring Limitations:** Discover how to set limits on your search results, controlling the number of documents fetched, and ensuring a tailored wine recommendation experience.\n",
        "\n",
        "8. **Expert Interaction:** Engage in expert-level wine discussions with a seamless Gradio interface, where you can ask questions and receive detailed recommendations with ease.\n",
        "\n",
        "Craft your personal wine expert and elevate your wine journey to new heights with LangChain's self-querying retriever. Cheers to a world of wine exploration at your fingertips! 🍷"
      ]
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          "base_uri": "https://localhost:8080/"
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          "output_type": "stream",
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          "text": [
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            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m341.4/341.4 kB\u001b[0m \u001b[31m27.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m3.4/3.4 MB\u001b[0m \u001b[31m58.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.2/1.2 MB\u001b[0m \u001b[31m43.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m307.7/307.7 kB\u001b[0m \u001b[31m19.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m86.8/86.8 kB\u001b[0m \u001b[31m8.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h  Building wheel for google-search-results (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Building wheel for pypika (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
            "  Building wheel for ffmpy (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",
            "spacy 3.7.4 requires typer<0.10.0,>=0.3.0, but you have typer 0.12.3 which is incompatible.\n",
            "weasel 0.3.4 requires typer<0.10.0,>=0.3.0, but you have typer 0.12.3 which is incompatible.\u001b[0m\u001b[31m\n",
            "\u001b[0m"
          ]
        }
      ],
      "source": [
        "!pip -q install langchain huggingface_hub google-search-results tiktoken chromadb lark langchain-together sentence_transformers gradio -qqq"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install langchain-community"
      ],
      "metadata": {
        "id": "nK35sAsuVF6y",
        "outputId": "2c20cdc2-8d50-4ace-80b4-72deb0d58e91",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting langchain-community\n",
            "  Downloading langchain_community-0.2.4-py3-none-any.whl (2.2 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m2.2/2.2 MB\u001b[0m \u001b[31m9.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: PyYAML>=5.3 in /usr/local/lib/python3.10/dist-packages (from langchain-community) (6.0.1)\n",
            "Requirement already satisfied: SQLAlchemy<3,>=1.4 in /usr/local/lib/python3.10/dist-packages (from langchain-community) (2.0.30)\n",
            "Requirement already satisfied: aiohttp<4.0.0,>=3.8.3 in /usr/local/lib/python3.10/dist-packages (from langchain-community) (3.9.5)\n",
            "Collecting dataclasses-json<0.7,>=0.5.7 (from langchain-community)\n",
            "  Downloading dataclasses_json-0.6.7-py3-none-any.whl (28 kB)\n",
            "Requirement already satisfied: langchain<0.3.0,>=0.2.0 in /usr/local/lib/python3.10/dist-packages (from langchain-community) (0.2.3)\n",
            "Requirement already satisfied: langchain-core<0.3.0,>=0.2.0 in /usr/local/lib/python3.10/dist-packages (from langchain-community) (0.2.5)\n",
            "Requirement already satisfied: langsmith<0.2.0,>=0.1.0 in /usr/local/lib/python3.10/dist-packages (from langchain-community) (0.1.76)\n",
            "Requirement already satisfied: numpy<2,>=1 in /usr/local/lib/python3.10/dist-packages (from langchain-community) (1.25.2)\n",
            "Requirement already satisfied: requests<3,>=2 in /usr/local/lib/python3.10/dist-packages (from langchain-community) (2.31.0)\n",
            "Requirement already satisfied: tenacity<9.0.0,>=8.1.0 in /usr/local/lib/python3.10/dist-packages (from langchain-community) (8.3.0)\n",
            "Requirement already satisfied: aiosignal>=1.1.2 in /usr/local/lib/python3.10/dist-packages (from aiohttp<4.0.0,>=3.8.3->langchain-community) (1.3.1)\n",
            "Requirement already satisfied: attrs>=17.3.0 in /usr/local/lib/python3.10/dist-packages (from aiohttp<4.0.0,>=3.8.3->langchain-community) (23.2.0)\n",
            "Requirement already satisfied: frozenlist>=1.1.1 in /usr/local/lib/python3.10/dist-packages (from aiohttp<4.0.0,>=3.8.3->langchain-community) (1.4.1)\n",
            "Requirement already satisfied: multidict<7.0,>=4.5 in /usr/local/lib/python3.10/dist-packages (from aiohttp<4.0.0,>=3.8.3->langchain-community) (6.0.5)\n",
            "Requirement already satisfied: yarl<2.0,>=1.0 in /usr/local/lib/python3.10/dist-packages (from aiohttp<4.0.0,>=3.8.3->langchain-community) (1.9.4)\n",
            "Requirement already satisfied: async-timeout<5.0,>=4.0 in /usr/local/lib/python3.10/dist-packages (from aiohttp<4.0.0,>=3.8.3->langchain-community) (4.0.3)\n",
            "Collecting marshmallow<4.0.0,>=3.18.0 (from dataclasses-json<0.7,>=0.5.7->langchain-community)\n",
            "  Downloading marshmallow-3.21.3-py3-none-any.whl (49 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m49.2/49.2 kB\u001b[0m \u001b[31m5.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hCollecting typing-inspect<1,>=0.4.0 (from dataclasses-json<0.7,>=0.5.7->langchain-community)\n",
            "  Downloading typing_inspect-0.9.0-py3-none-any.whl (8.8 kB)\n",
            "Requirement already satisfied: langchain-text-splitters<0.3.0,>=0.2.0 in /usr/local/lib/python3.10/dist-packages (from langchain<0.3.0,>=0.2.0->langchain-community) (0.2.1)\n",
            "Requirement already satisfied: pydantic<3,>=1 in /usr/local/lib/python3.10/dist-packages (from langchain<0.3.0,>=0.2.0->langchain-community) (2.7.3)\n",
            "Requirement already satisfied: jsonpatch<2.0,>=1.33 in /usr/local/lib/python3.10/dist-packages (from langchain-core<0.3.0,>=0.2.0->langchain-community) (1.33)\n",
            "Requirement already satisfied: packaging<24.0,>=23.2 in /usr/local/lib/python3.10/dist-packages (from langchain-core<0.3.0,>=0.2.0->langchain-community) (23.2)\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-community) (3.10.4)\n",
            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests<3,>=2->langchain-community) (3.3.2)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests<3,>=2->langchain-community) (3.7)\n",
            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests<3,>=2->langchain-community) (2.0.7)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests<3,>=2->langchain-community) (2024.6.2)\n",
            "Requirement already satisfied: typing-extensions>=4.6.0 in /usr/local/lib/python3.10/dist-packages (from SQLAlchemy<3,>=1.4->langchain-community) (4.12.1)\n",
            "Requirement already satisfied: greenlet!=0.4.17 in /usr/local/lib/python3.10/dist-packages (from SQLAlchemy<3,>=1.4->langchain-community) (3.0.3)\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,>=0.2.0->langchain-community) (3.0.0)\n",
            "Requirement already satisfied: annotated-types>=0.4.0 in /usr/local/lib/python3.10/dist-packages (from pydantic<3,>=1->langchain<0.3.0,>=0.2.0->langchain-community) (0.7.0)\n",
            "Requirement already satisfied: pydantic-core==2.18.4 in /usr/local/lib/python3.10/dist-packages (from pydantic<3,>=1->langchain<0.3.0,>=0.2.0->langchain-community) (2.18.4)\n",
            "Collecting mypy-extensions>=0.3.0 (from typing-inspect<1,>=0.4.0->dataclasses-json<0.7,>=0.5.7->langchain-community)\n",
            "  Downloading mypy_extensions-1.0.0-py3-none-any.whl (4.7 kB)\n",
            "Installing collected packages: mypy-extensions, marshmallow, typing-inspect, dataclasses-json, langchain-community\n",
            "Successfully installed dataclasses-json-0.6.7 langchain-community-0.2.4 marshmallow-3.21.3 mypy-extensions-1.0.0 typing-inspect-0.9.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "NHUvclI9ALOt"
      },
      "outputs": [],
      "source": [
        "from google.colab import userdata\n",
        "import textwrap\n",
        "import os\n",
        "\n",
        "os.environ[\"TOGETHER_API_KEY\"] = userdata.get('TOGETHER_API_KEY')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HqwsGJDhvAQ5"
      },
      "source": [
        "## Self-querying Retriever\n",
        "\n",
        "\n",
        "![](https://i.imgur.com/Js698XPl.jpg)\n",
        "\n",
        "This Example is adopted from https://youtu.be/f4LeWlt3T8Y?si=MCTX2AsOVNXGR97q - Follow Sam's channel for excellent content on LLMs & more\n",
        "\n",
        "\n",
        "This Jupyter Notebook focuses on creating a self-querying retriever for wine recommendations using LangChain. It starts with importing necessary libraries, setting up metadata for wine documents, and initializing a Together model from OpenHermes-2p5-Mistral-7B. The notebook then creates a SelfQueryRetriever based on the LLM and vectorstore to search for relevant wine information based on user queries. The retriever can handle various query types, including simple queries, queries with filters, or composite filters. It also demonstrates how to limit the number of returned documents (k) using the enable_limit parameter. Finally, the notebook shows how to integrate this functionality into a Gradio interface for user interaction."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "id": "IfCt8bhHNu9u",
        "outputId": "38ced3fd-884c-4c83-f2ec-982a201c867d",
        "colab": {
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          "height": 456,
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      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/langchain_core/_api/deprecation.py:119: LangChainDeprecationWarning: The class `HuggingFaceEmbeddings` was deprecated in LangChain 0.2.2 and will be removed in 0.3.0. An updated version of the class exists in the langchain-huggingface package and should be used instead. To use it run `pip install -U langchain-huggingface` and import as `from langchain_huggingface import HuggingFaceEmbeddings`.\n",
            "  warn_deprecated(\n",
            "/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%|          | 0.00/387 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "f2f5b73bbf2e4fb4a57acfaffd1f7501"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
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            "/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"
          ]
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            ],
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            ],
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              "model_id": "90a865141a534a9ea49356827c93c950"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "from langchain.schema import Document\n",
        "#from langchain.embeddings.openai import OpenAIEmbeddings\n",
        "#from langchain_together.embeddings import TogetherEmbeddings\n",
        "from langchain_community.embeddings import HuggingFaceEmbeddings\n",
        "from langchain.vectorstores import Chroma\n",
        "\n",
        "#embeddings = OpenAIEmbeddings()\n",
        "#embeddings = TogetherEmbeddings(model=\"togethercomputer/m2-bert-80M-2k-retrieval\")\n",
        "embeddings = HuggingFaceEmbeddings(model_name=\"intfloat/multilingual-e5-small\")\n",
        "\n",
        "from operator import itemgetter\n",
        "\n",
        "from langchain_core.prompts import ChatPromptTemplate\n",
        "from langchain_core.output_parsers import StrOutputParser\n",
        "from langchain_core.runnables import RunnableLambda, RunnablePassthrough"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YTDxLMs_vg8K"
      },
      "source": [
        "## Example data with metadata attached"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "id": "eAazrw6RP9Y5"
      },
      "outputs": [],
      "source": [
        "docs = [\n",
        "    Document(\n",
        "        page_content=\"Complex, layered, rich red with dark fruit flavors\",\n",
        "        metadata={\"name\": \"Opus One\", \"year\": 2018, \"rating\": 96, \"grape\": \"Cabernet Sauvignon\", \"color\": \"red\", \"country\": \"USA\", \"price_eur\": 350},\n",
        "    ),\n",
        "    Document(\n",
        "        page_content=\"Luxurious, sweet wine with flavors of honey, apricot, and peach\",\n",
        "        metadata={\"name\": \"Château d'Yquem\", \"year\": 2015, \"rating\": 98, \"grape\": \"Sémillon\", \"color\": \"white\", \"country\": \"France\", \"price_eur\": 650},\n",
        "    ),\n",
        "    Document(\n",
        "        page_content=\"Full-bodied red with notes of black fruit and spice\",\n",
        "        metadata={\"name\": \"Penfolds Grange\", \"year\": 2017, \"rating\": 97, \"grape\": \"Shiraz\", \"color\": \"red\", \"country\": \"Australia\", \"price_eur\": 500},\n",
        "    ),\n",
        "    Document(\n",
        "        page_content=\"Elegant, balanced red with herbal and berry nuances\",\n",
        "        metadata={\"name\": \"Sassicaia\", \"year\": 2016, \"rating\": 95, \"grape\": \"Cabernet Franc\", \"color\": \"red\", \"country\": \"Italy\", \"price_eur\": 225},\n",
        "    ),\n",
        "    Document(\n",
        "        page_content=\"Highly sought-after Pinot Noir with red fruit and earthy notes\",\n",
        "        metadata={\"name\": \"Domaine de la Romanée-Conti\", \"year\": 2018, \"rating\": 100, \"grape\": \"Pinot Noir\", \"color\": \"red\", \"country\": \"France\", \"price_eur\": 20000},\n",
        "    ),\n",
        "    Document(\n",
        "        page_content=\"Crisp white with tropical fruit and citrus flavors\",\n",
        "        metadata={\"name\": \"Cloudy Bay\", \"year\": 2021, \"rating\": 92, \"grape\": \"Sauvignon Blanc\", \"color\": \"white\", \"country\": \"New Zealand\", \"price_eur\": 30},\n",
        "    ),\n",
        "    Document(\n",
        "        page_content=\"Rich, complex Champagne with notes of brioche and citrus\",\n",
        "        metadata={\"name\": \"Krug Grande Cuvée\", \"year\": 2010, \"rating\": 93, \"grape\": \"Chardonnay blend\", \"color\": \"sparkling\", \"country\": \"France\", \"price_eur\": 180},\n",
        "    ),\n",
        "    Document(\n",
        "        page_content=\"Intense, dark fruit flavors with hints of chocolate\",\n",
        "        metadata={\"name\": \"Caymus Special Selection\", \"year\": 2018, \"rating\": 96, \"grape\": \"Cabernet Sauvignon\", \"color\": \"red\", \"country\": \"USA\", \"price_eur\": 160},\n",
        "    ),\n",
        "    Document(\n",
        "        page_content=\"Exotic, aromatic white with stone fruit and floral notes\",\n",
        "        metadata={\"name\": \"Jermann Vintage Tunina\", \"year\": 2020, \"rating\": 91, \"grape\": \"Sauvignon Blanc blend\", \"color\": \"white\", \"country\": \"Italy\", \"price_eur\": 60},\n",
        "    ),\n",
        "    Document(\n",
        "        page_content=\"Vibrant and fresh with a fine blend of minerality and citrus flavors, showcasing the terroir's signature\",\n",
        "        metadata={\"name\": \"Trimbach Riesling Cuvée Frédéric Emile\", \"year\": 2015, \"rating\": 95, \"grape\": \"Riesling\", \"color\": \"white\", \"country\": \"France\", \"region\": \"Alsace\", \"price_eur\": 90},\n",
        "    ),\n",
        "    Document(\n",
        "        page_content=\"Richly textured with lychee, spice, and floral notes, a true expression of Alsace's terroir\",\n",
        "        metadata={\"name\": \"Hugel & Fils Gewurztraminer Classic\", \"year\": 2018, \"rating\": 92, \"grape\": \"Gewurztraminer\", \"color\": \"white\", \"country\": \"France\", \"region\": \"Alsace\", \"price_eur\": 45},\n",
        "    ),\n",
        "    Document(\n",
        "        page_content=\"Elegant and complex, offering a seamless blend of chalky minerality and crisp apple notes\",\n",
        "        metadata={\"name\": \"Domaine Zind-Humbrecht Pinot Gris\", \"year\": 2019, \"rating\": 94, \"grape\": \"Pinot Gris\", \"color\": \"white\", \"country\": \"France\", \"region\": \"Alsace\", \"price_eur\": 50},\n",
        "    ),\n",
        "    Document(\n",
        "        page_content=\"A luxurious, full-bodied wine with deep berry flavors and a hint of earthiness, representing the pinnacle of Bordeaux excellence\",\n",
        "        metadata={\"name\": \"Château Latour\", \"year\": 2010, \"rating\": 100, \"grape\": \"Cabernet Sauvignon blend\", \"color\": \"red\", \"country\": \"France\", \"region\": \"Bordeaux\", \"price_eur\": 1200},\n",
        "    ),\n",
        "    Document(\n",
        "        page_content=\"An iconic, powerful Syrah from the Northern Rhône, offering a complex array of blackberry, smoke, and pepper notes\",\n",
        "        metadata={\"name\": \"Guigal La Mouline\", \"year\": 2015, \"rating\": 97, \"grape\": \"Syrah\", \"color\": \"red\", \"country\": \"France\", \"region\": \"Rhône\", \"price_eur\": 250},\n",
        "    ),\n",
        "    Document(\n",
        "        page_content=\"A standout Chardonnay from Burgundy, with a perfect balance of oak and vibrant acidity, featuring apple, pear, and mineral notes\",\n",
        "        metadata={\"name\": \"Louis Latour Corton-Charlemagne\", \"year\": 2018, \"rating\": 96, \"grape\": \"Chardonnay\", \"color\": \"white\", \"country\": \"France\", \"region\": \"Burgundy\", \"price_eur\": 150},\n",
        "    )\n",
        "]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "id": "aHpTW4wcNsY0"
      },
      "outputs": [],
      "source": [
        "# get the wines in the store\n",
        "vectorstore = Chroma.from_documents(docs, embeddings)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "M-DU2BD6sPJj"
      },
      "source": [
        "## Creating our self-querying retriever"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "id": "GWW_t_MFsKC8"
      },
      "outputs": [],
      "source": [
        "from langchain_together import Together\n",
        "from langchain.retrievers.self_query.base import SelfQueryRetriever\n",
        "from langchain.chains.query_constructor.base import AttributeInfo\n",
        "\n",
        "metadata_field_info = [\n",
        "    AttributeInfo(\n",
        "        name=\"grape\",\n",
        "        description=\"The grape used to make the wine\",\n",
        "        type=\"string or list[string]\",\n",
        "    ),\n",
        "    AttributeInfo(\n",
        "        name=\"name\",\n",
        "        description=\"The name of the wine\",\n",
        "        type=\"string or list[string]\",\n",
        "    ),\n",
        "    AttributeInfo(\n",
        "        name=\"color\",\n",
        "        description=\"The color of the wine\",\n",
        "        type=\"string or list[string]\",\n",
        "    ),\n",
        "    AttributeInfo(\n",
        "        name=\"year\",\n",
        "        description=\"The year the wine was released\",\n",
        "        type=\"integer\",\n",
        "    ),\n",
        "    AttributeInfo(\n",
        "        name=\"country\",\n",
        "        description=\"The name of the country the wine comes from\",\n",
        "        type=\"string\",\n",
        "    ),\n",
        "    AttributeInfo(\n",
        "        name=\"rating\", description=\"The Robert Parker rating for the wine 0-100\", type=\"integer\" #float\n",
        "    ),\n",
        "    AttributeInfo(\n",
        "    name=\"price_eur\", description=\"Average price in EUR\", type=\"integer\" #float\n",
        "    ),\n",
        "]\n",
        "document_content_description = \"Brief description of the wine\"\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "id": "Cjo9-YU5rCnv"
      },
      "outputs": [],
      "source": [
        "llm = Together(\n",
        "    model=\"teknium/OpenHermes-2p5-Mistral-7B\",\n",
        "    temperature=0.3,\n",
        "    max_tokens=256,\n",
        "    top_k=50,\n",
        "    # together_api_key=\"...\"\n",
        ")\n",
        "\n",
        "retriever = SelfQueryRetriever.from_llm(\n",
        "    llm,\n",
        "    vectorstore,\n",
        "    document_content_description,\n",
        "    metadata_field_info,\n",
        "    verbose=True\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6fNUskHNsRsL",
        "outputId": "a12f3f63-c178-477c-fb77-0d81307f9ef0"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/langchain_core/_api/deprecation.py:119: LangChainDeprecationWarning: The method `BaseRetriever.get_relevant_documents` was deprecated in langchain-core 0.1.46 and will be removed in 0.3.0. Use invoke instead.\n",
            "  warn_deprecated(\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[Document(page_content='Complex, layered, rich red with dark fruit flavors', metadata={'color': 'red', 'country': 'USA', 'grape': 'Cabernet Sauvignon', 'name': 'Opus One', 'price_eur': 350, 'rating': 96, 'year': 2018}),\n",
              " Document(page_content='Highly sought-after Pinot Noir with red fruit and earthy notes', metadata={'color': 'red', 'country': 'France', 'grape': 'Pinot Noir', 'name': 'Domaine de la Romanée-Conti', 'price_eur': 20000, 'rating': 100, 'year': 2018}),\n",
              " Document(page_content='Elegant, balanced red with herbal and berry nuances', metadata={'color': 'red', 'country': 'Italy', 'grape': 'Cabernet Franc', 'name': 'Sassicaia', 'price_eur': 225, 'rating': 95, 'year': 2016}),\n",
              " Document(page_content='Full-bodied red with notes of black fruit and spice', metadata={'color': 'red', 'country': 'Australia', 'grape': 'Shiraz', 'name': 'Penfolds Grange', 'price_eur': 500, 'rating': 97, 'year': 2017})]"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ],
      "source": [
        "# This example only specifies a relevant query\n",
        "retriever.get_relevant_documents(\"What are some red wines\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xZlJud7_s3Ng",
        "outputId": "06fdfd8b-02a9-4860-bbbc-1c49dc9dc4b8"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[Document(page_content='Crisp white with tropical fruit and citrus flavors', metadata={'color': 'white', 'country': 'New Zealand', 'grape': 'Sauvignon Blanc', 'name': 'Cloudy Bay', 'price_eur': 30, 'rating': 92, 'year': 2021}),\n",
              " Document(page_content='Intense, dark fruit flavors with hints of chocolate', metadata={'color': 'red', 'country': 'USA', 'grape': 'Cabernet Sauvignon', 'name': 'Caymus Special Selection', 'price_eur': 160, 'rating': 96, 'year': 2018}),\n",
              " Document(page_content='Complex, layered, rich red with dark fruit flavors', metadata={'color': 'red', 'country': 'USA', 'grape': 'Cabernet Sauvignon', 'name': 'Opus One', 'price_eur': 350, 'rating': 96, 'year': 2018}),\n",
              " Document(page_content='Exotic, aromatic white with stone fruit and floral notes', metadata={'color': 'white', 'country': 'Italy', 'grape': 'Sauvignon Blanc blend', 'name': 'Jermann Vintage Tunina', 'price_eur': 60, 'rating': 91, 'year': 2020})]"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ],
      "source": [
        "retriever.get_relevant_documents(\"I want a wine that has fruity notes\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fcGVyKpwtOAJ",
        "outputId": "6fedaa6c-18e3-41e7-840e-2103ad47f1ea"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[Document(page_content='Full-bodied red with notes of black fruit and spice', metadata={'color': 'red', 'country': 'Australia', 'grape': 'Shiraz', 'name': 'Penfolds Grange', 'price_eur': 500, 'rating': 97, 'year': 2017}),\n",
              " Document(page_content='An iconic, powerful Syrah from the Northern Rhône, offering a complex array of blackberry, smoke, and pepper notes', metadata={'color': 'red', 'country': 'France', 'grape': 'Syrah', 'name': 'Guigal La Mouline', 'price_eur': 250, 'rating': 97, 'region': 'Rhône', 'year': 2015})]"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ],
      "source": [
        "# This example specifies a query and a filter\n",
        "retriever.get_relevant_documents(\"I want a wine that has fruity nodes and has a rating above 97\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "C5Qw1u9FtSRm",
        "outputId": "3ec1fb1e-7729-46e4-baac-faf3383a57da"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[Document(page_content='Exotic, aromatic white with stone fruit and floral notes', metadata={'color': 'white', 'country': 'Italy', 'grape': 'Sauvignon Blanc blend', 'name': 'Jermann Vintage Tunina', 'price_eur': 60, 'rating': 91, 'year': 2020}),\n",
              " Document(page_content='Elegant, balanced red with herbal and berry nuances', metadata={'color': 'red', 'country': 'Italy', 'grape': 'Cabernet Franc', 'name': 'Sassicaia', 'price_eur': 225, 'rating': 95, 'year': 2016})]"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ],
      "source": [
        "retriever.get_relevant_documents(\n",
        "    \"What wines come from Italy?\"\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RBwo26n0taDI",
        "outputId": "bc948a4c-490a-4bba-c838-1518fbddbb1a"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[Document(page_content='Highly sought-after Pinot Noir with red fruit and earthy notes', metadata={'color': 'red', 'country': 'France', 'grape': 'Pinot Noir', 'name': 'Domaine de la Romanée-Conti', 'price_eur': 20000, 'rating': 100, 'year': 2018}),\n",
              " Document(page_content='Elegant, balanced red with herbal and berry nuances', metadata={'color': 'red', 'country': 'Italy', 'grape': 'Cabernet Franc', 'name': 'Sassicaia', 'price_eur': 225, 'rating': 95, 'year': 2016}),\n",
              " Document(page_content='Complex, layered, rich red with dark fruit flavors', metadata={'color': 'red', 'country': 'USA', 'grape': 'Cabernet Sauvignon', 'name': 'Opus One', 'price_eur': 350, 'rating': 96, 'year': 2018}),\n",
              " Document(page_content='Elegant and complex, offering a seamless blend of chalky minerality and crisp apple notes', metadata={'color': 'white', 'country': 'France', 'grape': 'Pinot Gris', 'name': 'Domaine Zind-Humbrecht Pinot Gris', 'price_eur': 50, 'rating': 94, 'region': 'Alsace', 'year': 2019})]"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ],
      "source": [
        "# This example specifies a query and composite filter\n",
        "retriever.get_relevant_documents(\n",
        "    \"What's a wine after 2015 but before 2020 that's all earthy\"\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0ucdYJ-gtoIP"
      },
      "source": [
        "## Filter K\n",
        "\n",
        "We can also use the self query retriever to specify k: the number of documents to fetch.\n",
        "\n",
        "We can do this by passing enable_limit=True to the constructor."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "id": "oH3JPClFthXq"
      },
      "outputs": [],
      "source": [
        "retriever = SelfQueryRetriever.from_llm(\n",
        "    llm,\n",
        "    vectorstore,\n",
        "    document_content_description,\n",
        "    metadata_field_info,\n",
        "    enable_limit=True,\n",
        "    verbose=True,\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0D5eUK47txEs",
        "outputId": "9e39901e-43ee-4ef2-f6ea-74ac716e0443"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[Document(page_content='Complex, layered, rich red with dark fruit flavors', metadata={'color': 'red', 'country': 'USA', 'grape': 'Cabernet Sauvignon', 'name': 'Opus One', 'price_eur': 350, 'rating': 96, 'year': 2018}),\n",
              " Document(page_content='Intense, dark fruit flavors with hints of chocolate', metadata={'color': 'red', 'country': 'USA', 'grape': 'Cabernet Sauvignon', 'name': 'Caymus Special Selection', 'price_eur': 160, 'rating': 96, 'year': 2018})]"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ],
      "source": [
        "# This example only specifies a relevant query - k= 2\n",
        "retriever.get_relevant_documents(\"what are two that have a rating above 97\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "P5ahYRXet4ka",
        "outputId": "3719bb60-0e4b-41a5-edaf-85ad76e47164"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[Document(page_content='Full-bodied red with notes of black fruit and spice', metadata={'color': 'red', 'country': 'Australia', 'grape': 'Shiraz', 'name': 'Penfolds Grange', 'price_eur': 500, 'rating': 97, 'year': 2017}),\n",
              " Document(page_content='Crisp white with tropical fruit and citrus flavors', metadata={'color': 'white', 'country': 'New Zealand', 'grape': 'Sauvignon Blanc', 'name': 'Cloudy Bay', 'price_eur': 30, 'rating': 92, 'year': 2021})]"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ],
      "source": [
        "retriever.get_relevant_documents(\"what are two wines that come from australia or New zealand\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "id": "ITvPO5y1VC1-"
      },
      "outputs": [],
      "source": [
        "# Provide a template following the LLM's original chat template.\n",
        "template = \"\"\"You are a wine expert. Answer the question in detail based only on the following context.\n",
        "Come with some over the top wine expertise:\n",
        "{context}\n",
        "\n",
        "Question: {question}\n",
        "Do not start with \"Answer:\"\n",
        "\"\"\"\n",
        "prompt = ChatPromptTemplate.from_template(template)\n",
        "\n",
        "chain = (\n",
        "    {\"context\": retriever, \"question\": RunnablePassthrough()}\n",
        "    | prompt\n",
        "    | llm\n",
        "    | StrOutputParser()\n",
        ")\n",
        "\n",
        "input_query = \"what are two wines that come from australia or New zealand?\"\n",
        "output = chain.invoke(input_query)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hSAYZ2WLVjS9",
        "outputId": "3cab475c-5283-4a06-b672-c8d494500649"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            " Answer: One wine that comes from Australia is the full-bodied red\n",
            "Penfolds Grange, which boasts rich notes of black fruit and spice.\n",
            "This exquisite wine is a product of the renowned Shiraz grape, and the\n",
            "2017 vintage is highly rated at 97 points. With a price tag of 500\n",
            "EUR, it is a true testament to the exceptional quality of Australian\n",
            "winemaking.  Another wine that hails from New Zealand is the crisp\n",
            "white Cloudy Bay, characterized by its delightful tropical fruit and\n",
            "citrus flavors. This wine is crafted from the Sauvignon Blanc grape\n",
            "and is a shining example of the exceptional wines produced in New\n",
            "Zealand. The 2021 vintage has earned a respectable rating of 92 points\n",
            "and is available for a more affordable 30 EUR, making it an excellent\n",
            "choice for wine enthusiasts seeking a refreshing and flavorful white\n",
            "wine.\n"
          ]
        }
      ],
      "source": [
        "# word wrap the output\n",
        "print(textwrap.fill(output))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
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        "outputId": "cad0bbb7-687a-461a-cb79-c9c5b51e7c77"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Setting queue=True in a Colab notebook requires sharing enabled. Setting `share=True` (you can turn this off by setting `share=False` in `launch()` explicitly).\n",
            "\n",
            "Colab notebook detected. To show errors in colab notebook, set debug=True in launch()\n",
            "Running on public URL: https://8a836cae36e5c54e26.gradio.live\n",
            "\n",
            "This share link expires in 72 hours. For free permanent hosting and GPU upgrades, run `gradio deploy` from Terminal to deploy to Spaces (https://huggingface.co/spaces)\n"
          ]
        },
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          "data": {
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              "<IPython.core.display.HTML object>"
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              "<div><iframe src=\"https://8a836cae36e5c54e26.gradio.live\" width=\"100%\" height=\"500\" allow=\"autoplay; camera; microphone; clipboard-read; clipboard-write;\" frameborder=\"0\" allowfullscreen></iframe></div>"
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      ],
      "source": [
        "import gradio as gr\n",
        "\n",
        "# Assuming your setup for retriever, prompt, llm, and StrOutputParser is already done\n",
        "\n",
        "def recommend_wines(question):\n",
        "    # Simulating the retrieval and processing chain for demonstration\n",
        "    # In practice, replace the line below with your actual chain invocation\n",
        "    # output = chain.invoke(question)\n",
        "    output = chain.invoke(question)\n",
        "    return output\n",
        "\n",
        "interface = gr.Interface(\n",
        "    fn=recommend_wines,\n",
        "    inputs=\"text\",\n",
        "    outputs=\"text\",\n",
        "    title=\"Wine Recommendation Expert\",\n",
        "    description=\"Ask me for wine recommendations or information about wines!\"\n",
        ")\n",
        "\n",
        "interface.launch()\n"
      ]
    }
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