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  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/defog-ai/sqlcoder/blob/main/defog_sqlcoder_colab.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#SQLCoder-7b-2\n",
        "Run the cells below to run inference on our text-to-SQL LLM: SQLCoder-7b-2.\n",
        "\n",
        "⭐️ [Github Repo](https://github.com/defog-ai/sqlcoder)\n",
        "\n",
        "🤗 [Huggingface Page](https://huggingface.co/defog/sqlcoder-7b-2)"
      ],
      "metadata": {
        "id": "Hnhvqa6ZRscb"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Setup"
      ],
      "metadata": {
        "id": "ZKYJQvVmSEWs"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "8bAMjQKJfG3d"
      },
      "outputs": [],
      "source": [
        "!pip install torch transformers bitsandbytes accelerate sqlparse"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import torch\n",
        "from transformers import AutoTokenizer, AutoModelForCausalLM"
      ],
      "metadata": {
        "id": "BzllQomZfQnj"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "torch.cuda.is_available()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hztT0MXkfRs1",
        "outputId": "3730e876-3885-4945-e0a1-8ce0f28daa60"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "available_memory = torch.cuda.get_device_properties(0).total_memory"
      ],
      "metadata": {
        "id": "7jsl838clW_f"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(available_memory)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tTQnCfsen16q",
        "outputId": "91c8cd58-d989-46f9-df17-1ec899fa52b4"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "15835660288"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Download the Model\n",
        "Use any model on Colab (or any system with >30GB VRAM on your own machine) to load this in f16. If unavailable, use a GPU with minimum 8GB VRAM to load this in 8bit, or with minimum 5GB of VRAM to load in 4bit.\n",
        "\n",
        "This step can take around 5 minutes the first time. So please be patient :)"
      ],
      "metadata": {
        "id": "Tt4ilTkpTMoL"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model_name = \"defog/sqlcoder-7b-2\"\n",
        "tokenizer = AutoTokenizer.from_pretrained(model_name)\n",
        "if available_memory > 15e9:\n",
        "    # if you have atleast 15GB of GPU memory, run load the model in float16\n",
        "    model = AutoModelForCausalLM.from_pretrained(\n",
        "        model_name,\n",
        "        trust_remote_code=True,\n",
        "        torch_dtype=torch.float16,\n",
        "        device_map=\"auto\",\n",
        "        use_cache=True,\n",
        "    )\n",
        "else:\n",
        "    # else, load in 8 bits – this is a bit slower\n",
        "    model = AutoModelForCausalLM.from_pretrained(\n",
        "        model_name,\n",
        "        trust_remote_code=True,\n",
        "        # torch_dtype=torch.float16,\n",
        "        load_in_8bit=True,\n",
        "        device_map=\"auto\",\n",
        "        use_cache=True,\n",
        "    )"
      ],
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        },
        "id": "_mNT25UOfSMw",
        "outputId": "6474339d-2a52-4698-98fe-5a1275d4354a"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_token.py:88: UserWarning: \n",
            "The secret `HF_TOKEN` does not exist in your Colab secrets.\n",
            "To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n",
            "You will be able to reuse this secret in all of your notebooks.\n",
            "Please note that authentication is recommended but still optional to access public models or datasets.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "tokenizer_config.json:   0%|          | 0.00/1.84k [00:00<?, ?B/s]"
            ],
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        },
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          "output_type": "display_data",
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              "tokenizer.model:   0%|          | 0.00/500k [00:00<?, ?B/s]"
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              "tokenizer.json:   0%|          | 0.00/1.84M [00:00<?, ?B/s]"
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          "data": {
            "text/plain": [
              "special_tokens_map.json:   0%|          | 0.00/515 [00:00<?, ?B/s]"
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        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "model.safetensors.index.json:   0%|          | 0.00/23.9k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading shards:   0%|          | 0/3 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "fc72454624b146db8960c6115586fdb9"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "model-00001-of-00003.safetensors:   0%|          | 0.00/4.94G [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "9621aa8087164b808d155ccd9d1f2e91"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "model-00002-of-00003.safetensors:   0%|          | 0.00/4.95G [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "246d8b8228b7463dae2d809228c654a0"
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "model-00003-of-00003.safetensors:   0%|          | 0.00/3.59G [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "9f26da483e8248fb8d66f2b596c3bc00"
            }
          },
          "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": "35b9604694a943fd890e17550eadb55f"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "generation_config.json:   0%|          | 0.00/111 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "d9d9f5dedf58491c9294a98677a3fb4e"
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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Set the Question & Prompt and Tokenize\n",
        "Feel free to change the schema in the prompt below to your own schema"
      ],
      "metadata": {
        "id": "TzLcpXyzTkHM"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "prompt = \"\"\"### Task\n",
        "Generate a SQL query to answer [QUESTION]{question}[/QUESTION]\n",
        "\n",
        "### Instructions\n",
        "- If you cannot answer the question with the available database schema, return 'I do not know'\n",
        "- Remember that revenue is price multiplied by quantity\n",
        "- Remember that cost is supply_price multiplied by quantity\n",
        "\n",
        "### Database Schema\n",
        "This query will run on a database whose schema is represented in this string:\n",
        "CREATE TABLE products (\n",
        "  product_id INTEGER PRIMARY KEY, -- Unique ID for each product\n",
        "  name VARCHAR(50), -- Name of the product\n",
        "  price DECIMAL(10,2), -- Price of each unit of the product\n",
        "  quantity INTEGER  -- Current quantity in stock\n",
        ");\n",
        "\n",
        "CREATE TABLE customers (\n",
        "   customer_id INTEGER PRIMARY KEY, -- Unique ID for each customer\n",
        "   name VARCHAR(50), -- Name of the customer\n",
        "   address VARCHAR(100) -- Mailing address of the customer\n",
        ");\n",
        "\n",
        "CREATE TABLE salespeople (\n",
        "  salesperson_id INTEGER PRIMARY KEY, -- Unique ID for each salesperson\n",
        "  name VARCHAR(50), -- Name of the salesperson\n",
        "  region VARCHAR(50) -- Geographic sales region\n",
        ");\n",
        "\n",
        "CREATE TABLE sales (\n",
        "  sale_id INTEGER PRIMARY KEY, -- Unique ID for each sale\n",
        "  product_id INTEGER, -- ID of product sold\n",
        "  customer_id INTEGER,  -- ID of customer who made purchase\n",
        "  salesperson_id INTEGER, -- ID of salesperson who made the sale\n",
        "  sale_date DATE, -- Date the sale occurred\n",
        "  quantity INTEGER -- Quantity of product sold\n",
        ");\n",
        "\n",
        "CREATE TABLE product_suppliers (\n",
        "  supplier_id INTEGER PRIMARY KEY, -- Unique ID for each supplier\n",
        "  product_id INTEGER, -- Product ID supplied\n",
        "  supply_price DECIMAL(10,2) -- Unit price charged by supplier\n",
        ");\n",
        "\n",
        "-- sales.product_id can be joined with products.product_id\n",
        "-- sales.customer_id can be joined with customers.customer_id\n",
        "-- sales.salesperson_id can be joined with salespeople.salesperson_id\n",
        "-- product_suppliers.product_id can be joined with products.product_id\n",
        "\n",
        "### Answer\n",
        "Given the database schema, here is the SQL query that answers [QUESTION]{question}[/QUESTION]\n",
        "[SQL]\n",
        "\"\"\""
      ],
      "metadata": {
        "id": "0eI-VpCkf-fN"
      },
      "execution_count": 24,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Generate the SQL\n",
        "This can be excruciatingly slow on a T4 in Colab, and can take 10-20 seconds per query. On faster GPUs, this will take ~1-2 seconds\n",
        "\n",
        "Ideally, you should use `num_beams`=4 for best results. But because of memory constraints, we will stick to just 1 for now."
      ],
      "metadata": {
        "id": "I2Lyi9VcVoTA"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import sqlparse\n",
        "\n",
        "def generate_query(question):\n",
        "    updated_prompt = prompt.format(question=question)\n",
        "    inputs = tokenizer(updated_prompt, return_tensors=\"pt\").to(\"cuda\")\n",
        "    generated_ids = model.generate(\n",
        "        **inputs,\n",
        "        num_return_sequences=1,\n",
        "        eos_token_id=tokenizer.eos_token_id,\n",
        "        pad_token_id=tokenizer.eos_token_id,\n",
        "        max_new_tokens=400,\n",
        "        do_sample=False,\n",
        "        num_beams=1,\n",
        "    )\n",
        "    outputs = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)\n",
        "\n",
        "    torch.cuda.empty_cache()\n",
        "    torch.cuda.synchronize()\n",
        "    # empty cache so that you do generate more results w/o memory crashing\n",
        "    # particularly important on Colab – memory management is much more straightforward\n",
        "    # when running on an inference service\n",
        "    return sqlparse.format(outputs[0].split(\"[SQL]\")[-1], reindent=True)"
      ],
      "metadata": {
        "id": "4iBKg6Rmmpnw"
      },
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "question = \"What was our revenue by product in the New York region last month?\"\n",
        "generated_sql = generate_query(question)"
      ],
      "metadata": {
        "id": "jCJvcurqlP2_"
      },
      "execution_count": 26,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(generated_sql)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "flEJuo1OpYQ8",
        "outputId": "6afa2baa-570b-4e31-98b3-3532d603307a"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "SELECT p.product_id,\n",
            "       SUM(s.quantity * p.price) AS revenue\n",
            "FROM sales s\n",
            "JOIN salespeople sp ON s.salesperson_id = sp.salesperson_id\n",
            "JOIN products p ON s.product_id = p.product_id\n",
            "WHERE sp.region = 'New York'\n",
            "  AND s.sale_date >= (CURRENT_DATE - INTERVAL '1 month')\n",
            "GROUP BY p.product_id\n",
            "ORDER BY revenue DESC NULLS LAST;\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "09ysYJbsnKUh"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}