diff --git a/.gitignore b/.gitignore index 52b89d7..f8135bf 100644 --- a/.gitignore +++ b/.gitignore @@ -208,3 +208,5 @@ __marimo__/ notes.txt testing_base + +settings.json diff --git a/README.md b/README.md index b9de557..1a12571 100644 --- a/README.md +++ b/README.md @@ -1,142 +1,114 @@ -# Data Tools: Automated Data Understanding and Integration +# Data-Tools -Data Tools is a Python library designed to automate the complex process of understanding and connecting siloed datasets. In modern data environments, tables are often disconnected and poorly documented. This library tackles that challenge head-on by providing two primary capabilities: +[![Release](https://img.shields.io/github/release/Intugle/data-tools)](https://github.com/Intugle/data-tools/releases/tag/v0.1.0) +[![Made with Python](https://img.shields.io/badge/Made_with-Python-blue?logo=python&logoColor=white)](https://www.python.org/) +![contributions - welcome](https://img.shields.io/badge/contributions-welcome-blue) +[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT) +[![Open Issues](https://img.shields.io/github/issues-raw/Intugle/data-tools)](https://github.com/Intugle/data-tools/issues) +[![GitHub star chart](https://img.shields.io/github/stars/Intugle/data-tools?style=social)](https://github.com/Intugle/data-tools/stargazers) -1. **Automated Link Prediction**: Its flagship feature, the `LinkPredictor`, analyzes multiple datasets, identifies primary keys, and automatically predicts relationships (foreign key links) between them. -2. **In-Depth Data Profiling**: A flexible, multi-step analysis pipeline that creates a rich profile for each dataset, including data types, column statistics, and generating a business glossary. +*Automated Data Profiling, Link Prediction, and Semantic Layer Generation* -By combining these features, Data Tools helps you move from a collection of separate tables to a fully documented and interconnected data model, ready for integration or analysis. +## Overview -## Core Features +Intugle's Data-Tools is a GenAI-powered Python library that simplifies and accelerates the journey from raw data to insights. It empowers data and business teams to build an intelligent semantic layer over their data, enabling self-serve analytics and natural language queries. By automating data profiling, link prediction, and SQL generation, Data-Tools helps you build data products faster and more efficiently than traditional methods. -- **Automated Link Prediction**: Intelligently discovers potential foreign key relationships across multiple datasets. -- **In-Depth Data Profiling**: A multi-step pipeline that identifies keys, profiles columns, and determines data types. -- **Business Glossary Generation**: Automatically generates business-friendly descriptions and tags for columns and tables based on a provided domain. -- **Extensible Pipeline Architecture**: Easily add custom analysis steps to the pipeline. -- **DataFrame Agnostic**: Uses a factory pattern to seamlessly handle different dataframe types (e.g., pandas). +## Who is this for? -## Installation and Setup +This tool is designed for both **data teams** and **business teams**. -### Installation +* **Data teams** can use it to automate data profiling, schema discovery, and documentation, significantly accelerating their workflow. +* **Business teams** can use it to gain a better understanding of their data and to perform self-service analytics without needing to write complex SQL queries. + +## Features -To install the library and its dependencies, run the following command: +* **Automated Data Profiling:** Generate detailed statistics for each column in your dataset, including distinct count, uniqueness, completeness, and more. +* **Datatype Identification:** Automatically identify the data type of each column (e.g., integer, string, datetime). +* **Key Identification:** Identify potential primary keys in your tables. +* **LLM-Powered Link Prediction:** Use GenAI to automatically discover relationships (foreign keys) between tables. +* **Business Glossary Generation:** Generate a business glossary for each column, with support for industry-specific domains. +* **Semantic Layer Generation:** Create YAML files that defines your semantic layer, including models (tables) and their relationships. +* **SQL Generation:** Generate SQL queries from the semantic layer, allowing you to query your data using business-friendly terms. + +## Getting Started + +### Installation ```bash -pip install data_tools +pip install data-tools ``` -### LLM Configuration - -This library uses LLMs for features like Business Glossary Generation. It supports any LLM provider compatible with LangChain's `init_chat_model` function. To configure your LLM provider, you need to set environment variables. +### Configuration -The `LLM_CONFIG` environment variable should be set to your desired model, optionally including the provider, in the format `provider:model_name`. If the provider is omitted, it will try to infer the provider. +Before running the project, you need to configure a LLM. This is used for tasks like generating business glossaries and predicting links between tables. -**For OpenAI:** +You can configure the LLM by setting the following environment variables: -```bash -# Provider is optional -export LLM_CONFIG="gpt-4" -export OPENAI_API_KEY="your-super-secret-key" -``` +* `LLM_PROVIDER`: The LLM provider and model to use (e.g., `openai:gpt-3.5-turbo`). +* `OPENAI_API_KEY`: Your API key for the LLM provider. -**For Google GenAI:** +Here's an example of how to set these variables in your environment: ```bash -export LLM_CONFIG="google_genai:gemini-pro" -export GOOGLE_API_KEY="your-google-api-key" +export LLM_PROVIDER="openai:gpt-3.5-turbo" +export OPENAI_API_KEY="your-openai-api-key" ``` +## Quickstart -## Usage Examples - -### Example 1: Automated Link Prediction (Primary Use Case) +For a detailed, hands-on introduction to the project, please see the [`quickstart.ipynb`](notebooks/quickstart.ipynb) notebook. It will walk you through the entire process of profiling your data, predicting links, generating a semantic layer, and querying your data. -This is the most direct way to use the library. Provide a dictionary of named dataframes, and the `LinkPredictor` will automatically run all prerequisite analysis steps and predict the links. +## Usage -```python -import pandas as pd -from data_tools.link_predictor.predictor import LinkPredictor +The core workflow of the project involves the following steps: -# 1. Prepare your collection of dataframes -customers_df = pd.DataFrame({"id": [1, 2, 3], "name": ["A", "B", "C"]}) -orders_df = pd.DataFrame({"order_id": [101, 102], "customer_id": [1, 3]}) +1. **Load your data:** Load your data into a DataSet object. +2. **Run the analysis pipeline:** Use the `run()` method to profile your data and generate a business glossary. +3. **Predict links:** Use the `LinkPredictor` to discover relationships between your tables. -datasets = { - "customers": customers_df, - "orders": orders_df -} + ```python + from data_tools import LinkPredictor -# 2. Initialize the predictor -# This automatically runs profiling, key identification, etc., for you. -link_predictor = LinkPredictor(datasets) + # Initialize the predictor + predictor = LinkPredictor(datasets) -# 3. Predict the links -# (This example assumes you have implemented the logic in _predict_for_pair) -prediction_results = link_predictor.predict() + # Run the prediction + results = predictor.predict() + results.show_graph() + ``` -# 4. Review the results -print(prediction_results.model_dump_json(indent=2)) -``` +5. **Generate SQL:** Use the `SqlGenerator` to generate SQL queries from the semantic layer. -### Example 2: In-Depth Analysis with the Pipeline - -If you want to perform a deep analysis on a single dataset, you can use the pipeline directly. This gives you fine-grained control over the analysis steps. - -```python -import pandas as pd -from data_tools.analysis.pipeline import Pipeline -from data_tools.analysis.steps import ( - TableProfiler, - KeyIdentifier, - BusinessGlossaryGenerator -) - -# 1. Define your analysis pipeline -pipeline = Pipeline([ - TableProfiler(), - KeyIdentifier(), - BusinessGlossaryGenerator(domain="e-commerce") # Provide context for the glossary -]) - -# 2. Prepare your dataframe -products_df = pd.DataFrame({ - "product_id": [10, 20, 30], - "name": ["Laptop", "Mouse", "Keyboard"], - "unit_price": [1200, 25, 75] -}) - -# 3. Run the pipeline -# The pipeline creates and returns a DataSet object -product_dataset = pipeline.run(df=products_df, name="products") - -# 4. Access the rich analysis results -print(f"Identified Key: {product_dataset.results['key'].column_name}") -print("\n--- Table Glossary ---") -print(product_dataset.results['table_glossary']) -``` + ```python + from data_tools import SqlGenerator -## Available Analysis Steps + # Create a SqlGenerator + sql_generator = SqlGenerator() -You can construct a custom `Pipeline` using any combination of the following steps: + # Create an ETL model + etl = { + name": "test_etl", + fields": [ + {"id": "patients.first", "name": "first_name"}, + {"id": "patients.last", "name": "last_name"}, + {"id": "allergies.start", "name": "start_date"}, + , + filter": { + "selections": [{"id": "claims.departmentid", "values": ["3", "20"]}], + , + } -- `TableProfiler`: Gathers basic table-level statistics (row count, column names). -- `ColumnProfiler`: Runs detailed profiling for each column (null counts, distinct counts, samples). -- `DataTypeIdentifierL1` & `L2`: Determines the logical data type for each column through multiple levels of analysis. -- `KeyIdentifier`: Analyzes the profiled data to predict the primary key of the dataset. -- `BusinessGlossaryGenerator(domain: str)`: Generates business-friendly descriptions and tags for all columns and the table itself, using the provided `domain` for context. + # Generate the query + sql_query = sql_generator.generate_query(etl_model) + print(sql_query) + ``` -## Running Tests +For detailed code examples and a complete walkthrough, please refer to the [`quickstart.ipynb`](quickstart.ipynb) notebook. -To run the test suite, first install the testing dependencies and then execute `pytest` via `uv`. +## Contributing -```bash -# Install testing library -uv pip install pytest - -# Run tests -uv run pytest -``` +Contributions are welcome! Please see the [`CONTRIBUTING.md`](CONTRIBUTING.md) file for guidelines. ## License -This project is licensed under the terms of the LICENSE file. +This project is licensed under the MIT License. See the [`LICENSE`](LICENSE) file for details. \ No newline at end of file diff --git a/notebooks/quickstart_content.txt b/notebooks/quickstart_content.txt new file mode 100644 index 0000000..fc85d55 --- /dev/null +++ b/notebooks/quickstart_content.txt @@ -0,0 +1,231 @@ +--- +type: markdown +--- +# Quickstart: Building a Semantic Layer with Data-Tools + +This notebook provides a quick introduction to the `data-tools` project. You'll learn how to use its key features to automatically build a semantic layer over your data. + +**What is a Semantic Layer?** + +A semantic layer is a business-friendly representation of your data. It hides the complexity of the underlying data sources and provides a unified view of your data using familiar business terms. This makes it easier for business users to understand and query the data without needing to be SQL experts. + +**Who is this for?** + +This tool is designed for both **data teams** and **business teams**. + +* **Data teams** can use it to automate data profiling, schema discovery, and documentation, significantly accelerating their workflow. +* **Business teams** can use it to gain a better understanding of their data and to perform self-service analytics without needing to write complex SQL queries. + +**In this notebook, you will learn how to:** + +1. **Configure your LLM Provider:** Set up the Large Language Model that will power the automated features. +2. **Profile your data:** Analyze your data sources to understand their structure, data types, and other characteristics. +3. **Automatically predict links:** Use a Large Language Model (LLM) to automatically discover relationships (foreign keys) between tables. +4. **Generate a semantic layer:** Create a `manifest.json` file that defines your semantic layer. +5. **Generate SQL queries:** Use the semantic layer to generate SQL queries and retrieve data. +--- +type: markdown +--- +## 1. Setup + +First, let's install the necessary libraries. This project uses `uv` for package management. +--- +type: code +--- +!pip install uv +!uv pip install -r ../requirements.txt +--- +type: markdown +--- +## 2. LLM Configuration + +Before running the project, you need to configure a Large Language Model (LLM). This is used for tasks like generating business glossaries and predicting links between tables. + +You can configure the LLM by setting the following environment variables: + +* `LLM_PROVIDER`: The LLM provider and model to use (e.g., `openai:gpt-3.5-turbo`). +* `OPENAI_API_KEY`: Your API key for the LLM provider. + +Here's an example of how to set these variables in your environment: + +```bash +export LLM_PROVIDER="openai:gpt-3.5-turbo" +export OPENAI_API_KEY="your-openai-api-key" +``` + +Alternatively, you can set them in the notebook like this: +--- +type: code +--- +import os + +os.environ["LLM_PROVIDER"] = "openai:gpt-3.5-turbo" +os.environ["OPENAI_API_KEY"] = "your-openai-api-key" # Replace with your actual key + +--- +type: markdown +--- +## 3. Data Profiling + +The first step in building a semantic layer is to profile your data. This involves analyzing your data sources to understand their structure, data types, and other characteristics. The `data-tools` project provides a pipeline for this purpose. +--- +type: code +--- +import pandas as pd +from data_tools.analysis.models import DataSet +--- +type: code +--- +# Load the sample data +allergies_df = pd.read_csv('../sample_data/healthcare/allergies.csv') +allergies_df.head() +--- +type: code +--- +# Create a DataSet object and run the profiling pipeline +dataset_allergies = DataSet(allergies_df, "allergies") +dataset_allergies.run(domain="Healthcare") +--- +type: markdown +--- +The `run()` method performs a series of analysis steps, including: + +* **Profiling:** Calculates statistics for each column, such as distinct count, uniqueness, and completeness. +* **Datatype Identification:** Identifies the data type of each column (e.g., integer, string, datetime). +* **Key Identification:** Identifies potential primary keys. +* **Glossary Generation:** Generates a business glossary for each column using an LLM. + +**The `domain` parameter** + +The `domain` parameter helps the LLM generate a more contextual business glossary. It specifies the industry domain that the dataset belongs to (e.g., "Healthcare", "Finance", "E-commerce"). + +Let's take a look at the profiling results: +--- +type: code +--- +dataset_allergies.profile_df.head() +--- +type: markdown +--- +## 4. Automated Link Prediction + +Now that we've profiled our data, let's discover the relationships between tables. The `data-tools` project uses a Large Language Model (LLM) to predict links (foreign keys) between tables. + +First, we'll load a few more tables from the sample dataset. +--- +type: code +--- +table_names = ["patients", "claims", "careplans", "claims_transactions", "medications"] +datasets = [dataset_allergies] + +for table_name in table_names: + df = pd.read_csv(f'../sample_data/healthcare/{table_name}.csv') + dataset = DataSet(df, table_name) + dataset.run(domain="Healthcare") + datasets.append(dataset) +--- +type: markdown +--- +Now, let's run the link prediction pipeline. +--- +type: code +--- +from data_tools.link_predictor import LinkPredictor + +# Initialize the predictor +predictor = LinkPredictor(datasets) + +# Run the prediction +results = predictor.predict() +results.links +--- +type: markdown +--- +The `results` object contains the predicted links between the tables. You can also visualize the relationships as a graph. +--- +type: code +--- +results.show_graph() +--- +type: markdown +--- +## 5. The Semantic Layer (Manifest) + +The profiling and link prediction results are used to generate a `manifest.json` file. This file defines the semantic layer, including the models (tables) and their relationships. + +Let's save the datasets and the predicted links to YAML files. By default, these files are saved in a `sources` directory in the current working directory. You can configure this path by setting the `PROJECT_BASE` environment variable. +--- +type: code +--- +for ds in datasets: + ds.save_yaml() + +results.save_yaml("relationships.yml") +--- +type: markdown +--- +Now, we can load the YAML files and create a manifest. +--- +type: code +--- +from data_tools.parser.manifest import ManifestLoader + +# Load the manifest +manifest_loader = ManifestLoader('.') +manifest_loader.load() +manifest = manifest_loader.manifest + +# Print the manifest +print(manifest.json(indent=4)) +--- +type: markdown +--- +## 6. SQL Generation + +Once you have a semantic layer, you can use the `SqlGenerator` to generate SQL queries. This allows you to query the data using business-friendly terms, without having to write complex SQL. + +Let's create an ETL model to define the query we want to generate. +--- +type: code +--- +from data_tools.libs.smart_query_generator.models.models import ETLModel, FieldModel + +# Create an ETL model +etl_model = ETLModel( + fields=[ + FieldModel(id="patients.FIRST"), + FieldModel(id="patients.LAST"), + FieldModel(id="claims.TOTAL_CLAIM_COST"), + ] +) +--- +type: markdown +--- +Now, let's use the `SqlGenerator` to generate the SQL query. +--- +type: code +--- +from data_tools.sql_generator import SqlGenerator + +# Create a SqlGenerator +sql_generator = SqlGenerator('.') + +# Generate the query +sql_query = sql_generator.generate_query(etl_model) + +# Print the query +print(sql_query) +--- +type: markdown +--- +## Conclusion + +This notebook has provided a brief overview of the `data-tools` project. You've learned how to: + +* Configure your LLM provider. +* Profile your data to understand its characteristics. +* Use an LLM to automatically predict links between tables. +* Generate a semantic layer (`manifest.json`). +* Use the semantic layer to generate SQL queries. + +This is just a starting point. The `data-tools` project has many other features to explore. We encourage you to try it with your own data and see how it can help you build a powerful semantic layer. \ No newline at end of file diff --git a/pyproject.toml b/pyproject.toml index f6f905b..c66d003 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -52,6 +52,7 @@ test = [ ] lint = ["ruff"] dev = [ + "ipykernel>=6.30.1", "pytest>=8.4.1", "pytest-asyncio>=1.1.0", "pytest-cov>=6.2.1", diff --git a/quickstart.ipynb b/quickstart.ipynb new file mode 100644 index 0000000..78589cf --- /dev/null +++ b/quickstart.ipynb @@ -0,0 +1,342 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "757096f5", + "metadata": {}, + "source": [ + "# Quickstart: Building a Semantic Layer with Data-Tools\n", + "\n", + "This notebook provides a quick introduction to the `data-tools` project. You'll learn how to use its key features to automatically build a semantic layer over your data.\n", + "\n", + "**What is a Semantic Layer?**\n", + "\n", + "A semantic layer is a business-friendly representation of your data. It hides the complexity of the underlying data sources and provides a unified view of your data using familiar business terms. This makes it easier for both business users and data teams to understand and query the data, accelerating data-driven insights.\n", + "\n", + "**Who is this for?**\n", + "\n", + "This tool is designed for both **data teams** and **business teams**. \n", + "\n", + "* **Data teams** can use it to automate data profiling, schema discovery, and documentation, significantly accelerating their workflow.\n", + "* **Business teams** can use it to gain a better understanding of their data and to perform self-service analytics without needing to write complex SQL queries.\n", + "\n", + "**In this notebook, you will learn how to:**\n", + "\n", + "1. **Profile your data:** Analyze your data sources to understand their structure, data types, and other characteristics.\n", + "2. **Business Glossary Generation:** Generate a business glossary for each column, with support for industry-specific domains.\n", + "2. **Automatically predict links:** Use a Large Language Model (LLM) to automatically discover relationships (foreign keys) between tables.\n", + "3. **Generate a semantic layer:** Create YAML files file that defines your semantic layer.\n", + "4. **Generate SQL queries:** Use the semantic layer to generate SQL queries and retrieve data." + ] + }, + { + "cell_type": "markdown", + "id": "a2a1b244", + "metadata": {}, + "source": [ + "## 1. LLM Configuration\n", + "\n", + "Before running the project, you need to configure a Large Language Model (LLM). This is used for tasks like generating business glossaries and predicting links between tables.\n", + "\n", + "You can configure the LLM by setting the following environment variables:\n", + "\n", + "* `LLM_PROVIDER`: The LLM provider and model to use (e.g., `openai:gpt-3.5-turbo`).\n", + "* `OPENAI_API_KEY`: Your API key for the LLM provider.\n", + "\n", + "Here's an example of how to set these variables in your environment:\n", + "\n", + "```bash\n", + "export LLM_PROVIDER=\"openai:gpt-3.5-turbo\"\n", + "export OPENAI_API_KEY=\"your-openai-api-key\"\n", + "```\n", + "\n", + "Alternatively, you can set them in the notebook like this:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "36454d44", + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "\n", + "os.environ[\"LLM_PROVIDER\"] = \"openai:gpt-3.5-turbo\"\n", + "os.environ[\"OPENAI_API_KEY\"] = \"your-openai-api-key\" # Replace with your actual key" + ] + }, + { + "cell_type": "markdown", + "id": "a3dad9d8", + "metadata": {}, + "source": [ + "## 2. Data Profiling\n", + "\n", + "The first step in building a semantic layer is to profile your data. This involves analyzing your data sources to understand their structure, data types, and other characteristics. The `data-tools` project provides a pipeline for this purpose.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "7f0e28d7", + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "\n", + "from data_tools import DataSet" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "0e8971bb", + "metadata": {}, + "outputs": [], + "source": [ + "# Load the sample data\n", + "allergies_df = pd.read_csv('https://raw.githubusercontent.com/Intugle/data-tools/refs/heads/main/sample_data/healthcare/allergies.csv')\n", + "allergies_df.head()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8d3e8299", + "metadata": {}, + "outputs": [], + "source": [ + "# Create a DataSet object and run the profiling pipeline\n", + "dataset_allergies = DataSet(allergies_df, \"allergies\")\n", + "dataset_allergies.run(domain=\"Healthcare\")" + ] + }, + { + "cell_type": "markdown", + "id": "0f4ea58f", + "metadata": {}, + "source": [ + "The `run()` method performs a series of analysis steps, including:\n", + "\n", + "* **Profiling:** Calculates statistics for each column, such as distinct count, uniqueness, and completeness.\n", + "* **Datatype Identification:** Identifies the data type of each column (e.g., integer, string, datetime).\n", + "* **Key Identification:** Identifies potential primary keys.\n", + "* **Glossary Generation:** Generates a business glossary for each column using an LLM.\n", + "\n", + "> The `domain` parameter helps the LLM generate a more contextual business glossary. It specifies the industry domain that the dataset belongs to (e.g., \"Healthcare\", \"Finance\", \"E-commerce\").\n" + ] + }, + { + "cell_type": "markdown", + "id": "eb562acd", + "metadata": {}, + "source": [ + "## 3. Automated Link Prediction\n", + "\n", + "Now that we've profiled our data, let's discover the relationships between tables. The `data-tools` project uses a LLM to predict links (foreign keys) between tables.\n", + "\n", + "First, we'll load a few more tables from the sample dataset." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "0aa894eb", + "metadata": {}, + "outputs": [], + "source": [ + "table_names = [\"patients\", \"claims\", \"careplans\", \"claims_transactions\", \"medications\"]\n", + "datasets = [dataset_allergies]\n", + "\n", + "\n", + "def generate_table_url(table_name: str) -> str:\n", + " \"\"\"Append the base URL to the table name.\"\"\"\n", + " return f\"https://raw.githubusercontent.com/Intugle/data-tools/refs/heads/main/sample_data/healthcare/{table_name}.csv\"\n", + "\n", + "\n", + "for table_name in table_names:\n", + " table_url = generate_table_url(table_name)\n", + " df = pd.read_csv(table_url)\n", + " dataset = DataSet(df, table_name)\n", + " dataset.run(domain=\"Healthcare\")\n", + " datasets.append(dataset)" + ] + }, + { + "cell_type": "markdown", + "id": "e083a8d8", + "metadata": {}, + "source": [ + "Now, let's run the link prediction pipeline." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "fe4cd56d", + "metadata": {}, + "outputs": [], + "source": [ + "from data_tools import LinkPredictor\n", + "\n", + "# Initialize the predictor\n", + "predictor = LinkPredictor(datasets)\n", + "\n", + "# Run the prediction\n", + "results = predictor.predict()\n", + "results.links" + ] + }, + { + "cell_type": "markdown", + "id": "d18dc421", + "metadata": {}, + "source": [ + "The `results` object contains the predicted links between the tables. You can also visualize the relationships as a graph.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a59704ba", + "metadata": {}, + "outputs": [], + "source": [ + "results.show_graph()\n" + ] + }, + { + "cell_type": "markdown", + "id": "32fd4917", + "metadata": {}, + "source": [ + "## 4. The Semantic Layer (Manifest)\n", + "\n", + "The profiling and link prediction results are used to generate YAML files which are saved automatically. These files defines the semantic layer, including the models (tables) and their relationships. \n", + "\n", + "By default, these files are saved in the current working directory. You can configure this path by setting the `PROJECT_BASE` environment variable. They can also be saved manually as shown below:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a0daf854", + "metadata": {}, + "outputs": [], + "source": [ + "for ds in datasets:\n", + " ds.save_yaml()\n", + "\n", + "results.save_yaml(\"relationships.yml\")" + ] + }, + { + "cell_type": "markdown", + "id": "8cf08923", + "metadata": {}, + "source": [ + "Now, we can load the YAML files and create a manifest." + ] + }, + { + "cell_type": "markdown", + "id": "3d086f45", + "metadata": {}, + "source": [ + "## 5. SQL Generation\n", + "\n", + "Once you have a semantic layer, you can use the `SqlGenerator` to generate SQL queries. This allows you to query the data using business-friendly terms, without having to write complex SQL.\n", + "\n", + "Let's create an ETL model to define the query we want to generate." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2570af46", + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "etl = {\n", + " \"name\": \"test_etl\",\n", + " \"fields\": [\n", + " {\"id\": \"patients.first\", \"name\": \"first_name\"},\n", + " {\"id\": \"patients.last\", \"name\": \"last_name\"},\n", + " {\"id\": \"allergies.start\", \"name\": \"start_date\"},\n", + " ],\n", + " \"filter\": {\n", + " \"selections\": [{\"id\": \"claims.departmentid\", \"values\": [\"3\", \"20\"]}],\n", + " },\n", + "}" + ] + }, + { + "cell_type": "markdown", + "id": "2e4ff6be", + "metadata": {}, + "source": [ + "Now, let's use the `SqlGenerator` to generate the SQL query." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f0432e77", + "metadata": {}, + "outputs": [], + "source": [ + "from data_tools.sql_generator import SqlGenerator\n", + "\n", + "# Create a SqlGenerator\n", + "sql_generator = SqlGenerator()\n", + "\n", + "# Generate the query\n", + "sql_query = sql_generator.generate_query(etl)\n", + "\n", + "# Print the query\n", + "print(sql_query)" + ] + }, + { + "cell_type": "markdown", + "id": "c29d33de", + "metadata": {}, + "source": [ + "## Conclusion\n", + "\n", + "This notebook has provided a brief overview of the `data-tools` project. You've learned how to:\n", + "\n", + "* Configure your LLM provider.\n", + "* Profile your data to understand its characteristics.\n", + "* Use an LLM to automatically predict links between tables.\n", + "* Generate a semantic layer.\n", + "* Use the semantic layer to generate SQL queries.\n", + "\n", + "This is just a starting point. The `data-tools` project has many other features to explore. We encourage you to try it with your own data and see how it can help you build a powerful semantic layer.\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "data-tools", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/src/data_tools/__init__.py b/src/data_tools/__init__.py index e69de29..71a6ddd 100644 --- a/src/data_tools/__init__.py +++ b/src/data_tools/__init__.py @@ -0,0 +1,3 @@ +from data_tools.analysis.models import DataSet +from data_tools.link_predictor.predictor import LinkPredictor +from data_tools.sql_generator import SqlGenerator diff --git a/src/data_tools/analysis/models.py b/src/data_tools/analysis/models.py index 4e61880..f735906 100644 --- a/src/data_tools/analysis/models.py +++ b/src/data_tools/analysis/models.py @@ -157,13 +157,16 @@ def generate_glossary(self, domain: str = "") -> Self: self.results["table_glossary"] = glossary_output.table_glossary return self - def run(self, domain: str) -> Self: + def run(self, domain: str, save: bool = True) -> Self: """Run all stages """ self.profile()\ .identify_datatypes()\ .identify_keys()\ .generate_glossary(domain=domain) + + if save: + self.save_yaml() return self diff --git a/src/data_tools/core/pipeline/business_glossary/utils.py b/src/data_tools/core/pipeline/business_glossary/utils.py index cf8225b..e2f9656 100644 --- a/src/data_tools/core/pipeline/business_glossary/utils.py +++ b/src/data_tools/core/pipeline/business_glossary/utils.py @@ -39,7 +39,7 @@ def manual_parsing(keys_name: list, text: str, **kwargs): except Exception: log.warning("[!] Failed llm parsing also") - return "" + return [] def get_additional_context(table_name: str, global_additional_context: str = "", additional_table_context: str = ""): diff --git a/src/data_tools/core/settings.py b/src/data_tools/core/settings.py index 5987f35..8796a8f 100644 --- a/src/data_tools/core/settings.py +++ b/src/data_tools/core/settings.py @@ -30,7 +30,7 @@ class Settings(BaseSettings): DI_MODEL_VERSION: str = "13052023" - PROJECT_BASE: str + PROJECT_BASE: str = str(Path(os.getenv("VSCODE_WORKSPACE", os.getcwd()))) MCP_SERVER_NAME: str = "data-tools" MCP_SERVER_DESCRIPTION: str = "Data Tools for MCP" diff --git a/src/data_tools/link_predictor/predictor.py b/src/data_tools/link_predictor/predictor.py index 0f62533..7f65af5 100644 --- a/src/data_tools/link_predictor/predictor.py +++ b/src/data_tools/link_predictor/predictor.py @@ -143,7 +143,7 @@ def _predict_for_pair( ] return pair_links - def predict(self) -> Self: + def predict(self, filename='relationships.yml') -> Self: """ Iterates through all unique pairs of datasets, predicts the links for each pair, and returns the aggregated results. @@ -165,6 +165,7 @@ def predict(self) -> Self: print("No links found for this pair.") self.links = all_links + self.save_yaml(file_path=filename) return self def get_links_df(self): diff --git a/src/data_tools/models/resources/model.py b/src/data_tools/models/resources/model.py index 14671df..caa9ab1 100644 --- a/src/data_tools/models/resources/model.py +++ b/src/data_tools/models/resources/model.py @@ -17,7 +17,7 @@ class Column(SchemaBase): name: str type: Optional[str] = None category: Literal["dimension", "measure"] = "dimension" - description: str + description: Optional[str] = None tags: Optional[List[str]] = Field(default_factory=list) profiling_metrics: Optional[ColumnProfilingMetrics] = None diff --git a/src/data_tools/sql_generator.py b/src/data_tools/sql_generator.py index 28b9ceb..4c886d1 100644 --- a/src/data_tools/sql_generator.py +++ b/src/data_tools/sql_generator.py @@ -1,5 +1,6 @@ from typing import TYPE_CHECKING +from data_tools.core import settings from 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