diff --git a/.gitignore b/.gitignore index 1a091b9894..697a42a819 100644 --- a/.gitignore +++ b/.gitignore @@ -101,6 +101,7 @@ env.bak/ venv.bak/ env38/ env_eva/ +test_eva_db/ # Spyder project settings .spyderproject diff --git a/README.md b/README.md index 92b3a5e681..e628e57b07 100644 --- a/README.md +++ b/README.md @@ -42,17 +42,6 @@

-

Share EvaDB

- -

- - -Follow _superAGI -Share on Telegram - -Share on Reddit -

-

Launch EvaDB on Colab @@ -67,15 +56,144 @@ Downloads -->
- Open in Gitpod

EvaDB enables software developers to build AI apps in a few lines of code. Its powerful SQL API simplifies AI app development for both structured and unstructured data. EvaDB's benefits include: -- 🔮 Easy to connect EvaDB with your SQL database system and build AI-powered apps with SQL queries -- 🤝 Query your data with a pre-trained AI model from Hugging Face, OpenAI, YOLO, PyTorch, and other AI frameworks -- ⚡️ Faster queries thanks to AI-centric query optimization -- 💰 Save money spent on running models by efficient CPU/GPU use -- 🔧 Fine-tune your AI models to achieve better results +
+ 🔮 Easy to connect the EvaDB query engine with your data sources, such as PostgreSQL or S3 buckets, and build AI-powered apps with SQL queries. +
+ + + + + + + + + + + +
Structured Data SourcesUnstructured Data SourcesApplication Data Sources
+ +- PostgreSQL +- SQLite +- MySQL +- MariaDB +- Clickhouse +- Snowflake + + + +- Local filesystem +- AWS S3 bucket + + + +- Github + +
+ +More details on the supported data sources is [available here](https://evadb.readthedocs.io/en/latest/source/reference/databases/index.html). + +
+ +
+ 🤝 Query your connected data with a pre-trained AI model from Hugging Face, OpenAI, YOLO, Stable Diffusion, etc. +
+ + + + + + + + + + + +
Hugging FaceOpenAIYOLO
+ +- Audio Classification +- Automatic Speech Recognition +- Text Classification +- Summarization +- Text2Text Generation +- Text Generation +- Image Classification +- Image Segmentation +- Image-to-Text +- Object Detection +- Depth Estimation + + + +- gpt-4 +- gpt-4-0314 +- gpt-4-32k +- gpt-4-32k-0314 +- gpt-3.5-turbo +- gpt-3.5-turbo-0301 + + + +- yolov8n.pt +- yolov8s.pt +- yolov8m.pt +- yolov8l.pt +- yolov8x.pt + +
+ +More details on the supported AI models is [available here](https://evadb.readthedocs.io/en/latest/source/reference/ai/index.html) +
+ +
+ 🔧 Create or fine-tune AI models for regression, classification, and time series forecasting. +
+ + + + + + + + + + + +
RegressionClassificationTime Series Forecasting
+ +- Ludwig +- Sklearn +- Xgboost + + + +- Ludwig +- Xboost + + + +- Statsforecast +- Neuralforecast + +
+ +More details on the supported AutoML frameworks is [available here](https://evadb.readthedocs.io/en/latest/source/reference/ai/index.html). +
+ +
+ 💰 Faster AI queries thanks to AI-centric query optimizations such as caching, batching, and parallel processing. +
+ +- Function result caching helps reuse results of expensive AI function calls. +- LLM batching reduces token usage and dollars spent on LLM calls. +- Parallel query processing saves money and time spent on running AI models by better utilizing CPUs and/or GPUs. +- Query predicate re-ordering and predicate push-down accelerates queries over both structured and unstructured data. + +More details on the optimizations in EvaDB is [available here](https://evadb.readthedocs.io/en/latest/source/reference/optimizations.html). +
+
👋 Hey! If you're excited about our vision of bringing AI inside database systems, show some ❤️ by: +We would love to learn about your AI app. Please complete this 1-minute form: https://v0fbgcue0cm.typeform.com/to/BZHZWeZm + ## Quick Links - [Quick Links](#quick-links) @@ -210,6 +330,8 @@ EvaDB's AI-centric query optimizer takes a query as input and generates a query ## Community and Support +We would love to learn about your AI app. Please complete this 1-minute form: https://v0fbgcue0cm.typeform.com/to/BZHZWeZm + 175\u001b[0;31m \u001b[0;32myield\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0moutput\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 176\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/evadb/executor/create_database_executor.py\u001b[0m in \u001b[0;36mexec\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 41\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 42\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0mExecutorError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf\"{self.node.database_name} already exists.\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 43\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mExecutorError\u001b[0m: postgres_data already exists.", + "\nDuring handling of the above exception, another exception occurred:\n", + "\u001b[0;31mExecutorError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 7\u001b[0m }\n\u001b[1;32m 8\u001b[0m \u001b[0mquery\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34mf\"CREATE DATABASE postgres_data WITH ENGINE = 'postgres', PARAMETERS = {params};\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 9\u001b[0;31m \u001b[0mcursor\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mquery\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mquery\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/evadb/interfaces/relational/relation.py\u001b[0m in \u001b[0;36mdf\u001b[0;34m(self, drop_alias)\u001b[0m\n\u001b[1;32m 121\u001b[0m \u001b[0;36m2\u001b[0m \u001b[0;36m5\u001b[0m \u001b[0;36m6\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 122\u001b[0m \"\"\"\n\u001b[0;32m--> 123\u001b[0;31m \u001b[0mbatch\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexecute\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdrop_alias\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdrop_alias\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 124\u001b[0m \u001b[0;32massert\u001b[0m \u001b[0mbatch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mframes\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"relation execute failed\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 125\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mbatch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mframes\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/evadb/interfaces/relational/relation.py\u001b[0m in \u001b[0;36mexecute\u001b[0;34m(self, drop_alias)\u001b[0m\n\u001b[1;32m 139\u001b[0m \u001b[0;34m>>\u001b[0m\u001b[0;34m>\u001b[0m \u001b[0mbatch\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcursor\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mquery\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"SELECT * FROM MyTable;\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexecute\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 140\u001b[0m \"\"\"\n\u001b[0;32m--> 141\u001b[0;31m \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mexecute_statement\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_evadb\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_query_node\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 142\u001b[0m \u001b[0;31m# TODO: this is a dirty implementation. Ideally this should be done in the final projection.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 143\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mdrop_alias\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/evadb/server/command_handler.py\u001b[0m in \u001b[0;36mexecute_statement\u001b[0;34m(evadb, stmt, do_not_raise_exceptions, do_not_print_exceptions, **kwargs)\u001b[0m\n\u001b[1;32m 51\u001b[0m )\n\u001b[1;32m 52\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0moutput\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 53\u001b[0;31m \u001b[0mbatch_list\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0moutput\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 54\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mBatch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconcat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbatch_list\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 55\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/evadb/executor/plan_executor.py\u001b[0m in \u001b[0;36mexecute_plan\u001b[0;34m(self, do_not_raise_exceptions, do_not_print_exceptions)\u001b[0m\n\u001b[1;32m 178\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mdo_not_print_exceptions\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 179\u001b[0m \u001b[0mlogger\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexception\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 180\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0mExecutorError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[0;31mExecutorError\u001b[0m: postgres_data already exists." + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "## Training Classification Models using EVADB\n", + "\n", + "Next, we employ EvaDB to facilitate the training of Classification ML models, which will enable us to predict `leave_or_not` i.e. a variable depicting whether an employee will leave the current company or not based on several parameters." + ], + "metadata": { + "id": "ZDVPDGqjfrch" + } + }, + { + "cell_type": "markdown", + "source": [ + "### Loading Employee Data from CSV into PostgreSQL\n", + "\n", + "In this step, we will import the [Employee Data](https://www.kaggle.com/datasets/tawfikelmetwally/employee-dataset) dataset into our PostgreSQL database. If you already have the data stored in PostgreSQL and are ready to proceed with the prediction model training, feel free to skip this section and head directly to the [model training process](#train-the-prediction-model)." + ], + "metadata": { + "id": "ODq5QPC3gp5U" + } + }, + { + "cell_type": "code", + "source": [ + "!mkdir -p content\n", + "!wget -nc -O /content/Employee.csv https://drive.google.com/file/d/1R4ij5Ww6bOGwLJrbBStzcaPRhAJ-fn72/view?usp=share_link" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "MlJ4adTuiDUF", + "outputId": "e99e9500-dec6-4543-ca32-c113f488d941" + }, + "execution_count": 6, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "--2023-10-27 04:16:45-- https://drive.google.com/file/d/1R4ij5Ww6bOGwLJrbBStzcaPRhAJ-fn72/view?usp=share_link\n", + "Resolving drive.google.com (drive.google.com)... 172.253.123.139, 172.253.123.101, 172.253.123.138, ...\n", + "Connecting to drive.google.com (drive.google.com)|172.253.123.139|:443... connected.\n", + "HTTP request sent, awaiting response... 200 OK\n", + "Length: unspecified [text/html]\n", + "Saving to: ‘/content/Employee.csv’\n", + "\n", + "/content/Employee.c [ <=> ] 81.89K --.-KB/s in 0.001s \n", + "\n", + "2023-10-27 04:16:45 (60.3 MB/s) - ‘/content/Employee.csv’ saved [83856]\n", + "\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "cursor.query(\"\"\"\n", + " USE postgres_data {\n", + " CREATE TABLE IF NOT EXISTS employee_data (\n", + " education VARCHAR(128),\n", + " joining_year INTEGER,\n", + " city VARCHAR(128),\n", + " payment_tier INTEGER,\n", + " age INTEGER,\n", + " gender VARCHAR(128),\n", + " ever_benched VARCHAR(128),\n", + " experience_in_current_domain INTEGER,\n", + " leave_or_not INTEGER\n", + " )\n", + " }\n", + "\"\"\").df()" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 80 + }, + "id": "g_KW1uc2iNdv", + "outputId": "3de42b0f-ed74-4083-823f-2b4373e70e59" + }, + "execution_count": 6, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + " status\n", + "0 success" + ], + "text/html": [ + "\n", + "
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\n" + ] + }, + "metadata": {}, + "execution_count": 8 + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "### Train the prediction Model\n", + "Train the XGBoost AutoML model for classification using the `accuracy` metric" + ], + "metadata": { + "id": "1AtF1AtT4OC0" + } + }, + { + "cell_type": "code", + "source": [ + "cursor.query(\"\"\"\n", + " CREATE FUNCTION IF NOT EXISTS PredictEmployee FROM\n", + " ( SELECT payment_tier, age, gender, experience_in_current_domain, leave_or_not FROM postgres_data.employee_data )\n", + " TYPE XGBoost\n", + " PREDICT 'leave_or_not'\n", + " TIME_LIMIT 180\n", + " METRIC 'accuracy'\n", + " TASK 'classification';\n", + "\"\"\").df()" + ], + "metadata": { + "id": "NUbo47cG33cp", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1000 + }, + "outputId": "c8d957d3-7e48-47a1-f48a-ae8f285c3ada" + }, + "execution_count": 9, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "[flaml.automl.logger: 10-27 05:17:19] {1679} INFO - task = classification\n", + "[flaml.automl.logger: 10-27 05:17:19] {1690} INFO - Evaluation method: cv\n", + "[flaml.automl.logger: 10-27 05:17:19] {1788} INFO - Minimizing error metric: 1-accuracy\n", + "[flaml.automl.logger: 10-27 05:17:19] {1900} INFO - List of ML learners in AutoML Run: ['xgboost']\n", + "[flaml.automl.logger: 10-27 05:17:19] {2218} INFO - iteration 0, current learner xgboost\n", + "[flaml.automl.logger: 10-27 05:17:19] {2344} INFO - Estimated sufficient time budget=1155s. 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