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"<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>"
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|
||
"\n",
|
||
"⭐️ [Github Repo](https://github.com/defog-ai/sqlcoder)\n",
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"\n",
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"🤗 [Huggingface Page](https://huggingface.co/defog/sqlcoder-7b-2)"
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],
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"metadata": {
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],
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},
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},
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]
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},
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{
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],
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"metadata": {
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"id": "BzllQomZfQnj"
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},
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},
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],
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|
||
},
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},
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"execution_count": 3,
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"outputs": [
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{
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"data": {
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"text/plain": [
|
||
"True"
|
||
]
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},
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"metadata": {},
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]
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||
],
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||
"metadata": {
|
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"id": "7jsl838clW_f"
|
||
},
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|
||
"outputs": []
|
||
},
|
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{
|
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"cell_type": "code",
|
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"source": [
|
||
"print(available_memory)"
|
||
],
|
||
"metadata": {
|
||
"colab": {
|
||
"base_uri": "https://localhost:8080/"
|
||
},
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"id": "tTQnCfsen16q",
|
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"outputId": "91c8cd58-d989-46f9-df17-1ec899fa52b4"
|
||
},
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"execution_count": 5,
|
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"outputs": [
|
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{
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"output_type": "execute_result",
|
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"data": {
|
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"text/plain": [
|
||
"15835660288"
|
||
]
|
||
},
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||
"metadata": {},
|
||
"execution_count": 5
|
||
}
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||
]
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},
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{
|
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"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"
|
||
}
|
||
},
|
||
{
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||
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|
||
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|
||
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|
||
"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",
|
||
" )"
|
||
],
|
||
"metadata": {
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"base_uri": "https://localhost:8080/",
|
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"height": 525,
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{
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"output_type": "stream",
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"name": "stderr",
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"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"
|
||
]
|
||
},
|
||
{
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||
"output_type": "display_data",
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}
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|
||
},
|
||
{
|
||
"output_type": "display_data",
|
||
"data": {
|
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|
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},
|
||
{
|
||
"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,
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"version_minor": 0,
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|
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}
|
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},
|
||
"metadata": {}
|
||
},
|
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{
|
||
"output_type": "display_data",
|
||
"data": {
|
||
"text/plain": [
|
||
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|
||
],
|
||
"application/vnd.jupyter.widget-view+json": {
|
||
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}
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},
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"metadata": {}
|
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}
|
||
]
|
||
},
|
||
{
|
||
"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": []
|
||
}
|
||
]
|
||
} |