diff --git a/.gitignore b/.gitignore index c02218b..a94ed77 100644 --- a/.gitignore +++ b/.gitignore @@ -211,3 +211,4 @@ testing_base models settings.json +archived/ diff --git a/README.md b/README.md index 21db03a..444fb1b 100644 --- a/README.md +++ b/README.md @@ -10,37 +10,55 @@ [![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) -*Automated Data Profiling, Link Prediction, and Semantic Layer Generation* +*Transform Fragmented Data into Connected Semantic Layer* ## Overview -Intugle provides a set of GenAI-powered Python tools to simplify and accelerate the journey from raw data to insights. This library 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, Intugle helps you build data products faster and more efficiently than traditional methods. +Intugle’s GenAI-powered open-source Python library builds an intelligent semantic layer over your existing data systems. At its core, it discovers meaningful links and relationships across data assets — enriching them with profiles, classifications, and business glossaries. With this connected knowledge layer, you can enable semantic search and auto-generate queries to create unified data products, making data integration and exploration faster, more accurate, and far less manual. ## 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. +* **Data Engineers & Architects** often spend weeks manually profiling, classifying, and stitching together fragmented data assets. With Intugle, they can automate this process end-to-end, uncovering meaningful links and relationships to instantly generate a connected semantic layer. +* **Data Analysts & Scientists** spend endless hours on data readiness and preparation before they can even start the real analysis. Intugle accelerates this by providing contextual intelligence, automatically generating SQL and reusable data products enriched with relationships and business meaning. +* **Business Analysts & Decision Makers** are slowed down by constant dependence on technical teams for answers. Intugle removes this bottleneck by enabling natural language queries and semantic search, giving them trusted insights on demand. ## Features -* **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. +* **Semantic Intelligence:** Transform raw, fragmented datasets into an intelligent semantic graph that captures entities, relationships, and context — the foundation for connected intelligence. +* **Business Glossary & Semantic Search:** Auto-generate a business glossary and enable search that understands meaning, not just keywords — making data more accessible across technical and business users. +* **Smart SQL & Data Products:** Instantly generate SQL and reusable data products enriched with context, eliminating manual pipelines and accelerating data-to-insight. ## Getting Started ### Installation +Before installing, it is recommended to create a virtual environment: + +```bash +python -m venv .venv +source .venv/bin/activate +``` + +Then, install the package: + ```bash pip install intugle ``` +#### macOS + +For macOS users, you may need to install the `libomp` library: + +```bash +brew install libomp +``` + +If you installed Python using the official installer from python.org, you may also need to install SSL certificates by running the following command in your terminal. Please replace `3.XX` with your specific Python version. This step is not necessary if you installed Python using Homebrew. + +```bash +/Applications/Python\ 3.XX/Install\ Certificates.command +``` + ### Configuration Before running the project, you need to configure a LLM. This is used for tasks like generating business glossaries and predicting links between tables. @@ -48,7 +66,7 @@ Before running the project, you need to configure a LLM. This is used for tasks 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`) following LangChain's [conventions](https://python.langchain.com/docs/integrations/chat/) -* `OPENAI_API_KEY`: Your API key for the LLM provider. +* `API_KEY`: Your API key for the LLM provider. The exact name of the variable may vary from provider to provider. Here's an example of how to set these variables in your environment: @@ -59,66 +77,53 @@ export OPENAI_API_KEY="your-openai-api-key" ## Quickstart -For a detailed, hands-on introduction to the project, please see the [`quickstart.ipynb`](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. - -## Usage - -The core workflow of the project involves the following steps: - -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. - - ```python - from intugle import LinkPredictor +For a detailed, hands-on introduction to the project, please see the [`quickstart.ipynb`](quickstart.ipynb) notebook. It will walk you through the entire process of building a semantic layer, including: - # Initialize the predictor - predictor = LinkPredictor(datasets) +* **Building a Knowledge Base:** Use the `KnowledgeBuilder` to automatically profile your data, generate a business glossary, and predict links between tables. +* **Accessing Enriched Metadata:** Learn how to access the profiling results and business glossary for each dataset. +* **Visualizing Relationships:** Visualize the predicted links between your tables. +* **Generating Data Products:** Use the semantic layer to generate data products and retrieve data. +* **Serving the Semantic Layer:** Learn how to start the MCP server to interact with your semantic layer using natural language. - # Run the prediction - results = predictor.predict() - results.show_graph() - ``` - -5. **Generate SQL:** Use the `SqlGenerator` to generate SQL queries from the semantic layer. - - ```python - from intugle import SqlGenerator +## Usage - # Create a SqlGenerator - sql_generator = SqlGenerator() +The core workflow of the project involves using the `KnowledgeBuilder` to build a semantic layer, and then using the `DataProductBuilder` to generate data products from that layer. - # 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"]}], - , - } +```python +from intugle import KnowledgeBuilder, DataProductBuilder - # Generate the query - sql_query = sql_generator.generate_query(etl_model) - print(sql_query) - ``` +# Define your datasets +datasets = { + "allergies": {"path": "path/to/allergies.csv", "type": "csv"}, + "patients": {"path": "path/to/patients.csv", "type": "csv"}, + # ... add other datasets +} -For detailed code examples and a complete walkthrough, please refer to the [`quickstart.ipynb`](quickstart.ipynb) notebook. +# Build the knowledge base +kb = KnowledgeBuilder(datasets, domain="Healthcare") +kb.build() -### MCP Server +# Create a DataProductBuilder +dp_builder = DataProductBuilder() -This tool also includes an MCP server that exposes your semantic layer as a set of tools that can be used by an LLM client. This enables you to interact with your semantic layer using natural language to generate SQL queries, discover data, and more. +# Define an ETL model +etl = { + "name": "patient_allergies", + "fields": [ + {"id": "patients.first", "name": "first_name"}, + {"id": "patients.last", "name": "last_name"}, + {"id": "allergies.description", "name": "allergy"}, + ], +} -To start the MCP server, run the following command: +# Generate the data product +data_product = dp_builder.build(etl) -```bash -intugle-mcp +# View the data product as a DataFrame +print(data_product.to_df()) ``` -You can then connect to the server from any MCP client, such as Claude Desktop or Gemini CLI, at `http://localhost:8000/semantic_layer/mcp`. +For detailed code examples and a complete walkthrough, please refer to the [`quickstart.ipynb`](quickstart.ipynb) notebook. ## Contributing diff --git a/notebooks/quickstart_content.txt b/notebooks/quickstart_content.txt index 5d50c21..74601e7 100644 --- a/notebooks/quickstart_content.txt +++ b/notebooks/quickstart_content.txt @@ -1,50 +1,39 @@ --- type: markdown --- -# Quickstart: Building a Semantic Layer with Data-Tools +# Quickstart: Building a Semantic Layer with Intugle -This notebook provides a quick introduction to the `intugle` project. You'll learn how to use its key features to automatically build a semantic layer over your data. +This notebook provides a quick introduction to this 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. +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. **Who is this for?** -This tool is designed for both **data teams** and **business teams**. +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. +* **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 +1. **Build a Knowledge Base:** Use the `KnowledgeBuilder` to automatically profile your data, generate a business glossary, and predict links between tables. +2. **Generate Data Products:** Use the semantic layer to generate data products and retrieve data. +3. **Serve the Semantic Layer:** Learn how to start the MCP server to interact with your semantic layer using natural language. -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 + +## 1. 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. +* `LLM_PROVIDER`: The LLM provider and model to use (e.g., `openai:gpt-3.5-turbo`). The format follows langchain's format for initializing chat models. Checkout how to specify your model [here](https://python.langchain.com/docs/integrations/chat/) +* `OPENAI_API_KEY`: Your API key for the LLM provider. Here's an example of how to set these variables in your environment: @@ -54,171 +43,200 @@ 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 +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 `intugle` project provides a pipeline for this purpose. ---- -type: code ---- -import pandas as pd -from intugle.analysis.models import DataSet +> Currently the langchain packages for OpenAI, Anthropic and Gemini is installed by default. For additional models, make sure you have the integration packages installed. E.g. you should have langchain-deepseek installed to use a DeepSeek model. You can get these packages here: [LangChain Chat Models](https://python.langchain.com/docs/integrations/chat/) + --- -type: code +type: markdown --- -# Load the sample data -allergies_df = pd.read_csv('../sample_data/healthcare/allergies.csv') -allergies_df.head() + +## 2. Building the Knowledge Base + +The `KnowledgeBuilder` is the entry point for building your semantic layer. It takes a dictionary of datasets as input and performs the following steps: + +1. **Data Profiling:** Calculates statistics for each column, such as distinct count, uniqueness, and completeness. +2. **Datatype Identification:** Identifies the data type of each column (e.g., integer, string, datetime). +3. **Key Identification:** Identifies potential primary keys. +4. **Glossary Generation:** Generates a business glossary for each column using an LLM. +5. **Link Prediction:** Predicts the relationships (foreign keys) between tables. + +Let's start by defining the datasets we want to use: + --- type: code --- -# Create a DataSet object and run the profiling pipeline -dataset_allergies = DataSet(allergies_df, "allergies") -dataset_allergies.run(domain="Healthcare") + +def generate_config(table_name: str) -> str: + """Append the base URL to the table name.""" + return { + "path": f"https://raw.githubusercontent.com/Intugle/data-tools/refs/heads/main/sample_data/healthcare/{table_name}.csv", + "type": "csv" + } + + +table_names = ["allergies", "patients", "claims", "careplans"] +datasets = {table: generate_config(table) for table in table_names} + --- 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. +Now, let's use the `KnowledgeBuilder` to build our semantic layer: -**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() + +from intugle import KnowledgeBuilder + +# Initialize the knowledge builder +kb = KnowledgeBuilder(datasets, domain="Healthcare") + +# Run the prediction +kb.build() + --- type: markdown --- -## 4. Automated Link Prediction -Now that we've profiled our data, let's discover the relationships between tables. The `intugle` project uses a Large Language Model (LLM) to predict links (foreign keys) between tables. +## 3. Accessing Enriched Metadata + +Now that the knowledge base is built, you can easily access the enriched metadata for each dataset. + +### Accessing a Dataset + +You can access a specific dataset by its name from the `kb.datasets` dictionary: -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) +allergies_dataset = kb.datasets['allergies'] + --- type: markdown --- -Now, let's run the link prediction pipeline. + +### Viewing Profiling Results + +The profiling results can be accessed through the `profiling_df` property of the `DataSet` object. It's a pandas DataFrame that you can easily explore: +> The business glossary is also available in the `profile_df`: --- type: code --- -from intugle.link_predictor import LinkPredictor -# Initialize the predictor -predictor = LinkPredictor(datasets) +allergies_dataset.profiling_df -# 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. + +### Visualizing Relationships + +The `KnowledgeBuilder` automatically discovers the relationships between your tables. You can access the predicted links as a list of `PredictedLink` objects: + --- type: code --- -results.show_graph() + +kb.links + --- 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. +You can visualize these relationships as a graph: --- type: code --- -for ds in datasets: - ds.save_yaml() -results.save_yaml("relationships.yml") +kb.visualize() + --- type: markdown --- -Now, we can load the YAML files and create a manifest. + +## 4. The Semantic Layer + +The KnowledgeBuilder results are used to generate YAML files which are saved automatically. These files defines the semantic layer, including the models (tables) and their relationships. + +By default, these files are saved in the current working directory. You can configure this path by setting the `PROJECT_BASE` environment variable. + +## 5. Data Product Creation + +The semantic layer serves as a foundation for the DataProductBuilder, which streamlines the creation of reusable data products. This allows you +to encapsulate business logic and create standardized, trustworthy data assets that can be easily shared and reused across different teams and +applications. + +Let's define the model for the data product we want to build: + --- type: code --- -from intugle.parser.manifest import ManifestLoader -# Load the manifest -manifest_loader = ManifestLoader('.') -manifest_loader.load() -manifest = manifest_loader.manifest +etl = { + "name": "patient_names", + "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"]}], + }, +} -# 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. +Now, let's use the `DataProductBuilder` to generate the data product: -Let's create an ETL model to define the query we want to generate. --- type: code --- -from intugle.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"), - ] -) +from intugle.dp_builder import DataProductBuilder + +# Create a DataProductBuilder +dp_builder = DataProductBuilder() + +# Generate the data product +data_product = dp_builder.build(etl) + +# Print the generated SQL query +print(data_product.data['path']) + --- type: markdown --- -Now, let's use the `SqlGenerator` to generate the SQL query. + +The `generate_product` function returns a `DataSet` object. You can use the `to_df()` method to view the data product as a pandas DataFrame: + --- type: code --- -from intugle.sql_generator import SqlGenerator - -# Create a SqlGenerator -sql_generator = SqlGenerator('.') -# Generate the query -sql_query = sql_generator.generate_query(etl_model) +data_product.to_df() -# Print the query -print(sql_query) --- type: markdown --- -## 7. MCP Server: Interacting with Your Semantic Layer + +## 6. MCP Server: Interacting with Your Semantic Layer Now that you have a semantic layer, you can serve it as a MCP server to interact with it using natural language. The MCP server exposes your semantic layer as a set of tools that can be used by any MCP client. @@ -244,23 +262,21 @@ You can use a variety of MCP clients to connect to the server, such as Claude De Once connected, you can interact with your semantic layer using natural language. Here are some exciting applications: -* **Generate SQL Queries:** Ask questions in natural language and have the MCP server generate the corresponding SQL query. +* **Generate and Execute SQL Queries:** Ask questions in natural language and have the MCP server generate and execute the corresponding SQL query. * **Data Discovery:** Ask questions about the tables and columns in your semantic layer to better understand your data. -> To execute the generated SQL queries, you can also connect your database instance as a MCP tool to your client. - --- type: markdown --- + ## Conclusion -This notebook has provided a brief overview of the `intugle` project. You've learned how to: +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. -* Interact with your semantic layer using the MCP server. +* Configure your LLM provider. +* Build a knowledge base using the `KnowledgeBuilder`. +* Access enriched metadata, business glossaries and visualize the relationships between your tables. +* Generate data products from the semantic layer using the `DataProductBuilder`. +* Interact with your semantic layer using the MCP server. -This is just a starting point. The `intugle` 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 +This is just a starting point. This 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. diff --git a/notebooks/quickstart_duckdb.ipynb b/notebooks/quickstart_duckdb.ipynb new file mode 100644 index 0000000..0328c28 --- /dev/null +++ b/notebooks/quickstart_duckdb.ipynb @@ -0,0 +1,443 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "757096f5", + "metadata": {}, + "source": [ + "# Quickstart: Building a Semantic Layer with Intugle\n", + "\n", + "This notebook provides a quick introduction to this 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`). The format follows langchain's format for initializing chat models. Checkout how to specify your model [here](https://python.langchain.com/docs/integrations/chat/) \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\n" + ] + }, + { + "cell_type": "markdown", + "id": "fa9fda69", + "metadata": {}, + "source": [ + "> Currently the langchain packages for OpenAI, Anthropic and Gemini is installed by default. For additional models, make sure you have the integration packages installed. E.g. you should have langchain-deepseek installed to use a DeepSeek model. You can get these packages here: [LangChain Chat Models](https://python.langchain.com/docs/integrations/chat/)" + ] + }, + { + "cell_type": "markdown", + "id": "a3dad9d8", + "metadata": {}, + "source": [ + "## 2. Data Profiling and Glossary Generation\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. This tool provides a pipeline for this purpose. It can also generate business glossary for your data.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "7f0e28d7", + "metadata": {}, + "outputs": [], + "source": [ + "from intugle import DataSet" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8d3e8299", + "metadata": {}, + "outputs": [], + "source": [ + "allergies_data = {\n", + " \"path\": \"https://raw.githubusercontent.com/Intugle/data-tools/refs/heads/main/sample_data/healthcare/allergies.csv\",\n", + " \"type\": \"csv\"\n", + "}\n", + "# Create a DataSet object and run the profiling pipeline\n", + "dataset_allergies = DataSet(allergies_data, \"allergies\")\n", + "dataset_allergies.run(domain=\"Healthcare\")\n", + "\n", + "# Or you can run each step manually:\n", + "# dataset_allergies.profile().identify_datatypes().identify_keys().generate_glossary(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": "code", + "execution_count": null, + "id": "ac8a78fa", + "metadata": {}, + "outputs": [], + "source": [ + "dataset_allergies.profiling_df[[\"business_glossary\"]]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "86d162c1", + "metadata": {}, + "outputs": [], + "source": [ + "dataset_allergies.to_df()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "83a72c04", + "metadata": {}, + "outputs": [], + "source": [ + "from intugle.core.settings import settings\n", + "\n", + "settings.PROJECT_BASE" + ] + }, + { + "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. This tool uses an 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_config(table_name: str) -> str:\n", + " \"\"\"Append the base URL to the table name.\"\"\"\n", + " return {\n", + " \"path\": f\"https://raw.githubusercontent.com/Intugle/data-tools/refs/heads/main/sample_data/healthcare/{table_name}.csv\",\n", + " \"type\": \"csv\"\n", + " }\n", + "\n", + "\n", + "for table_name in table_names:\n", + " config = generate_config(table_name)\n", + " dataset = DataSet({**config}, table_name)\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 intugle import LinkPredictor\n", + "\n", + "# Initialize the predictor\n", + "predictor = LinkPredictor(datasets)\n", + "\n", + "# Run the prediction\n", + "results = predictor.predict(save=True)\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\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\": \"patient_names\",\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": "e0f9a090", + "metadata": {}, + "outputs": [], + "source": [ + "from intugle.dp_builder import DataProductBuilder\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f0432e77", + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "# Create a SqlGenerator\n", + "sql_generator = DataProductBuilder()\n", + "\n", + "# Generate the query\n", + "test_etl = sql_generator.build(etl)\n", + "\n", + "# Print the query\n", + "test_etl.data" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "95d2e49d", + "metadata": {}, + "outputs": [], + "source": [ + "test_etl.to_df()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "fa02b661", + "metadata": {}, + "outputs": [], + "source": [ + "sql_generator.plot_sources_graph()" + ] + }, + { + "cell_type": "markdown", + "id": "bfde8c96", + "metadata": {}, + "source": [ + "## 6. MCP Server: Interacting with Your Semantic Layer\n", + "\n", + "Now that you have a semantic layer, you can serve it as a MCP server to interact with it using natural language. The MCP server exposes your semantic layer as a set of tools that can be used by any MCP client.\n", + "\n", + "### Starting the MCP Server\n", + "\n", + "To start the MCP server, run the following command in your terminal:\n", + "\n", + "```bash\n", + "intugle-mcp\n", + "```\n", + "\n", + "This will start a server on `localhost:8000`.\n", + "\n", + "### Connecting to the MCP Server\n", + "\n", + "Once the server is running, you can connect to it from any MCP client. The endpoint for the MCP server is:\n", + "\n", + "`http://localhost:8000/semantic_layer/mcp`\n", + "\n", + "You can use a variety of MCP clients to connect to the server, such as Claude Desktop, Gemini CLI etc.\n", + "\n", + "### Use Cases\n", + "\n", + "Once connected, you can interact with your semantic layer using natural language. Here are some exciting applications:\n", + "\n", + "* **Generate SQL Queries:** Ask questions in natural language and have the MCP server generate the corresponding SQL query.\n", + "* **Data Discovery:** Ask questions about the tables and columns in your semantic layer to better understand your data.\n", + "\n", + "> To execute the generated SQL queries, you can also connect your database instance as a MCP tool to your client." + ] + }, + { + "cell_type": "markdown", + "id": "c29d33de", + "metadata": {}, + "source": [ + "## Conclusion\n", + "\n", + "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", + "* Interact with your semantic layer using the MCP server.\n", + "\n", + "This is just a starting point. This 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/pyproject.toml b/pyproject.toml index 55c8c7e..7f9df3e 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -4,7 +4,7 @@ build-backend = "setuptools.build_meta" [project] name = "intugle" -version = "0.1.1" +version = "0.1.2" authors = [ { name="Intugle", email="hello@intugle.ai" }, ] @@ -29,13 +29,13 @@ dependencies = [ "langchain-openai>=0.3.28", "langgraph>=0.6.4", "nltk>=3.9.1", - "numpy==1.26.2", + "numpy<=2.3.0", "asyncpg>=0.30.0", "fastapi[standard]>=0.116.1", "matplotlib>=3.10.5", "mcp[cli]>=1.12.4", "networkx>=3.4.2", - "pandas>=2.3.1", + "pandas>=2.3.0", "pyaml>=25.7.0", "pydantic>=2.11.7", "pydantic-settings>=2.10.1", @@ -43,14 +43,14 @@ dependencies = [ "python-dotenv>=1.1.1", "symspellpy>=6.9.0", "trieregex>=1.0.0", - "xgboost==1.7.5", + "xgboost<=3.0.4", "pyyaml>=6.0.2", "langchain-deepseek>=0.1.4", "langchain-nvidia-ai-endpoints>=0.3.16", "langchain-xai>=0.2.5", "langchain-perplexity>=0.1.2", "duckdb>=1.3.2", - "scikit-learn==1.2.2", + "scikit-learn<=1.7.1", "langchain[anthropic,google-genai,openai]>=0.3.27", ] diff --git a/quickstart.ipynb b/quickstart.ipynb index 669d62f..7bda4a5 100644 --- a/quickstart.ipynb +++ b/quickstart.ipynb @@ -5,28 +5,27 @@ "id": "757096f5", "metadata": {}, "source": [ - "# Quickstart: Building a Semantic Layer with Intugle\n", + "# Quickstart: Building a GenAI powered Semantic Intelligence with Intugle\n", "\n", - "This notebook provides a quick introduction to this project. You'll learn how to use its key features to automatically build a semantic layer over your data.\n", + "This quickstart shows you how to use Intugle’s open-source library to transform fragmented datasets into a connected semantic layer. The semantic layer captures profiling, relationships, and business meaning of your data — making it instantly usable for exploration, search, and data product creation.\n", "\n", - "**What is a Semantic Layer?**\n", + "**What is Semantic Intelligence?**\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", + "A semantic layer is an intelligent knowledge graph of your data. It connects tables, discovers relationships, and enriches them with business glossaries — so both data teams and business users can query with clarity, not complexity.\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", + "* **Data Engineers & Architects** often spend weeks manually profiling, classifying, and stitching together fragmented data assets. With Intugle, they can automate this process end-to-end, uncovering meaningful links and relationships to instantly generate a connected semantic layer.\n", + "* **Data Analysts & Scientists** spend endless hours on data readiness and preparation before they can even start the real analysis. Intugle accelerates this by providing contextual intelligence, automatically generating SQL and reusable data products enriched with relationships and business meaning.\n", + "* **Business Analysts & Decision Makers** are slowed down by constant dependence on technical teams for answers. Intugle removes this bottleneck by enabling natural language queries and semantic search, giving them trusted insights on demand.\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." + "- **Profile and classify data** : Analyze your data sources to understand their structure, data types, and other characteristics.\n", + "- **Discover links & relationships among data** : Reveal meaningful connections (PK & FK) across fragmented tables.\n", + "- **Generate a business glossary & enable semantic search** : Create business-friendly terms and use them to query data with context.\n", + "- **Create a semantic intelligence layer** : Build a unified model (YAML) of your data and relationships.\n", + "- **Build unified data products** : Generate reusable SQL queries powered by the semantic layer." ] }, { @@ -34,14 +33,15 @@ "id": "a2a1b244", "metadata": {}, "source": [ + "\n", "## 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`). The format follows langchain's format for initializing chat models. Checkout how to specify your model [here](https://python.langchain.com/docs/integrations/chat/) \n", - "* `OPENAI_API_KEY`: Your API key for the LLM provider.\n", + "* `LLM_PROVIDER`: The LLM provider and model to use (e.g., `openai:gpt-3.5-turbo`). The format follows langchain's format for initializing chat models. Checkout how to specify your model [here](https://python.langchain.com/docs/integrations/chat/)\n", + "* `API_KEY`: Your API key for the LLM provider. The exact name of the variable may vary from provider to provider\n", "\n", "Here's an example of how to set these variables in your environment:\n", "\n", @@ -50,7 +50,7 @@ "export OPENAI_API_KEY=\"your-openai-api-key\"\n", "```\n", "\n", - "Alternatively, you can set them in the notebook like this:" + "Alternatively, you can set them in the notebook like this:\n" ] }, { @@ -63,7 +63,7 @@ "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\n" + "os.environ[\"OPENAI_API_KEY\"] = \"your-openai-api-key\" # Replace with your actual key\n" ] }, { @@ -79,9 +79,17 @@ "id": "a3dad9d8", "metadata": {}, "source": [ - "## 2. Data Profiling and Glossary Generation\n", + "## 2. Building the Knowledge Base\n", + "\n", + "The `KnowledgeBuilder` is the entry point for building your semantic layer. It takes a dictionary of datasets as input and performs the following steps:\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. This tool provides a pipeline for this purpose. It can also generate business glossary for your data.\n" + "1. **Data Profiling:** Calculates statistics for each column, such as distinct count, uniqueness, and completeness.\n", + "2. **Datatype Identification:** Identifies the data type of each column (e.g., integer, string, datetime).\n", + "3. **Key Identification:** Identifies potential primary keys.\n", + "4. **Glossary Generation:** Generates a business glossary for each column using an LLM.\n", + "5. **Link Prediction:** Predicts the relationships (foreign keys) between tables.\n", + "\n", + "Let's start by defining the datasets we want to use:" ] }, { @@ -91,51 +99,62 @@ "metadata": {}, "outputs": [], "source": [ - "import pandas as pd\n", - "\n", - "from intugle import DataSet" + "def generate_config(table_name: str) -> str:\n", + " \"\"\"Append the base URL to the table name.\"\"\"\n", + " return {\n", + " \"path\": f\"https://raw.githubusercontent.com/Intugle/data-tools/refs/heads/main/sample_data/healthcare/{table_name}.csv\",\n", + " \"type\": \"csv\"\n", + " }\n", + "\n", + "\n", + "table_names = [\n", + " \"allergies\",\n", + " \"careplans\",\n", + " \"claims\",\n", + " \"claims_transactions\",\n", + " \"conditions\",\n", + " \"devices\",\n", + " \"encounters\",\n", + " \"imaging_studies\",\n", + " \"immunizations\",\n", + " \"medications\",\n", + " \"observations\",\n", + " \"organizations\",\n", + " \"patients\",\n", + " \"payers\",\n", + " \"payer_transitions\",\n", + " \"procedures\",\n", + " \"providers\",\n", + " \"supplies\",\n", + "]\n", + "\n", + "datasets = {table: generate_config(table) for table in table_names}" ] }, { - "cell_type": "code", - "execution_count": null, - "id": "0e8971bb", + "cell_type": "markdown", + "id": "fffe1d34", "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()" + "Now, let's use the `KnowledgeBuilder` to build our semantic layer:\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\")." ] }, { "cell_type": "code", "execution_count": null, - "id": "8d3e8299", + "id": "ac8a78fa", "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\")\n", - "\n", - "# Or you can run each step manually:\n", - "#dataset_allergies.profile().identify_datatypes().identify_keys().generate_glossary(domain=\"Healthcare\")" - ] - }, - { - "cell_type": "markdown", - "id": "0f4ea58f", - "metadata": {}, - "source": [ - "The `run()` method performs a series of analysis steps, including:\n", + "from intugle import KnowledgeBuilder\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", + "# Initialize the knowledge builder\n", + "kb = KnowledgeBuilder(datasets, domain=\"Healthcare\")\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" + "# Run the prediction\n", + "kb.build()" ] }, { @@ -143,11 +162,13 @@ "id": "eb562acd", "metadata": {}, "source": [ - "## 3. Automated Link Prediction\n", + "## 3. Accessing Enriched Metadata\n", + "\n", + "Now that the knowledge base is built, you can easily access the enriched metadata for each dataset.\n", "\n", - "Now that we've profiled our data, let's discover the relationships between tables. This tool uses an LLM to predict links (foreign keys) between tables.\n", + "### Accessing a Dataset\n", "\n", - "First, we'll load a few more tables from the sample dataset." + "You can access a specific dataset by its name from the `kb.datasets` dictionary:" ] }, { @@ -157,96 +178,68 @@ "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", - " df = pd.read_csv(generate_table_url(table_name))\n", - " dataset = DataSet(df, table_name)\n", - " dataset.run(domain=\"Healthcare\")\n", - " datasets.append(dataset)" + "allergies_dataset = kb.datasets['patients']\n", + "allergies_dataset.profiling_df\n" ] }, { "cell_type": "markdown", - "id": "e083a8d8", + "id": "6b3e8bfd", "metadata": {}, "source": [ - "Now, let's run the link prediction pipeline." + "The profiling results can be accessed through the `profiling_df` property of the `DataSet` object. It's a pandas DataFrame that you can easily explore. \n", + "> The business glossary is also available in the `profiling_df`:" ] }, { - "cell_type": "code", - "execution_count": null, - "id": "fe4cd56d", + "cell_type": "markdown", + "id": "e083a8d8", "metadata": {}, - "outputs": [], "source": [ - "from intugle import LinkPredictor\n", "\n", - "# Initialize the predictor\n", - "predictor = LinkPredictor(datasets)\n", + "### Visualizing Relationships\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" + "The `KnowledgeBuilder` automatically discovers the relationships between your tables. You can access the predicted links as a list of `PredictedLink` objects:\n" ] }, { "cell_type": "code", "execution_count": null, - "id": "a59704ba", + "id": "fe4cd56d", "metadata": {}, "outputs": [], "source": [ - "results.show_graph()\n" + "kb.links" ] }, { "cell_type": "markdown", - "id": "32fd4917", + "id": "9c8aa361", "metadata": {}, "source": [ - "## 4. The Semantic Layer\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:" + "You can also visualize these relationships as a graph:\n" ] }, { "cell_type": "code", "execution_count": null, - "id": "a0daf854", + "id": "a59704ba", "metadata": {}, "outputs": [], "source": [ - "for ds in datasets:\n", - " ds.save_yaml()\n", - "\n", - "results.save_yaml(\"relationships.yml\")" + "kb.visualize()\n" ] }, { "cell_type": "markdown", - "id": "8cf08923", + "id": "32fd4917", "metadata": {}, "source": [ - "Now, we can load the YAML files and create a manifest." + "## 4. The Semantic Layer\n", + "\n", + "The KnowledgeBuilder 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.\n" ] }, { @@ -254,31 +247,50 @@ "id": "3d086f45", "metadata": {}, "source": [ - "## 5. SQL Generation\n", + "## 5. Data Product Creation\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", + "The semantic layer serves as a foundation for the DataProductBuilder, which streamlines the creation of reusable data products. This allows you\n", + "to encapsulate business logic and create standardized, trustworthy data assets that can be easily shared and reused across different teams and \n", + "applications.\n", "\n", - "Let's create an ETL model to define the query we want to generate." + "Let's define the model for the data product we want to build:" ] }, { "cell_type": "code", "execution_count": null, - "id": "2570af46", + "id": "4b3ca934", "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", + " \"name\": \"top_patients_by_claim_count\",\n", + " \"fields\": [\n", + " {\n", + " \"id\": \"patients.first\",\n", + " \"name\": \"first_name\",\n", + " },\n", + " {\n", + " \"id\": \"patients.last\",\n", + " \"name\": \"last_name\",\n", " },\n", + " {\n", + " \"id\": \"claims.id\",\n", + " \"name\": \"number_of_claims\",\n", + " \"category\": \"measure\",\n", + " \"measure_func\": \"count\"\n", + " }\n", + " ],\n", + " \"filter\": {\n", + " \"sort_by\": [\n", + " {\n", + " \"id\": \"claims.id\",\n", + " \"alias\": \"number_of_claims\",\n", + " \"direction\": \"desc\"\n", + " }\n", + " ],\n", + " \"limit\": 10\n", + " }\n", "}" ] }, @@ -287,63 +299,45 @@ "id": "2e4ff6be", "metadata": {}, "source": [ - "Now, let's use the `SqlGenerator` to generate the SQL query." + "Now, let's use the `DataProductBuilder` to generate the data product:" ] }, { "cell_type": "code", "execution_count": null, - "id": "f0432e77", + "id": "e0f9a090", "metadata": {}, "outputs": [], "source": [ - "from intugle.sql_generator import SqlGenerator\n", + "from intugle.dp_builder import DataProductBuilder\n", "\n", - "# Create a SqlGenerator\n", - "sql_generator = SqlGenerator()\n", + "# Create a DataProductBuilder\n", + "dp_builder = DataProductBuilder()\n", "\n", - "# Generate the query\n", - "sql_query = sql_generator.generate_query(etl)\n", + "# Generate the data product\n", + "data_product = dp_builder.build(etl)\n", "\n", - "# Print the query\n", - "print(sql_query)" + "data_product.to_df()\n" ] }, { "cell_type": "markdown", - "id": "bfde8c96", + "id": "e235b222", "metadata": {}, "source": [ - "## 6. MCP Server: Interacting with Your Semantic Layer\n", - "\n", - "Now that you have a semantic layer, you can serve it as a MCP server to interact with it using natural language. The MCP server exposes your semantic layer as a set of tools that can be used by any MCP client.\n", - "\n", - "### Starting the MCP Server\n", - "\n", - "To start the MCP server, run the following command in your terminal:\n", - "\n", - "```bash\n", - "intugle-mcp\n", - "```\n", - "\n", - "This will start a server on `localhost:8000`.\n", - "\n", - "### Connecting to the MCP Server\n", - "\n", - "Once the server is running, you can connect to it from any MCP client. The endpoint for the MCP server is:\n", - "\n", - "`http://localhost:8000/semantic_layer/mcp`\n", - "\n", - "You can use a variety of MCP clients to connect to the server, such as Claude Desktop, Gemini CLI etc.\n", - "\n", - "### Use Cases\n", - "\n", - "Once connected, you can interact with your semantic layer using natural language. Here are some exciting applications:\n", - "\n", - "* **Generate SQL Queries:** Ask questions in natural language and have the MCP server generate the corresponding SQL query.\n", - "* **Data Discovery:** Ask questions about the tables and columns in your semantic layer to better understand your data.\n", - "\n", - "> To execute the generated SQL queries, you can also connect your database instance as a MCP tool to your client." + "The `generate_product` function returns a `DataSet` object. You can also view the generated SQL query used for creating the data product:\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "31a3e32a", + "metadata": {}, + "outputs": [], + "source": [ + "# The generated SQL query\n", + "data_product.sql_query\n", + "\n" ] }, { @@ -355,12 +349,10 @@ "\n", "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", - "* Interact with your semantic layer using the MCP server.\n", + "* Configure your LLM provider.\n", + "* Build a knowledge base using the `KnowledgeBuilder`.\n", + "* Access enriched metadata, business glossaries and visualize the relationships between your tables.\n", + "* Generate data products from the semantic layer using the `DataProductBuilder`.\n", "\n", "This is just a starting point. This 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" ] @@ -368,7 +360,7 @@ ], "metadata": { "kernelspec": { - "display_name": "data-tools", + "display_name": "intugle", "language": "python", "name": "python3" }, @@ -382,7 +374,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.12.3" + "version": "3.10.17" } }, "nbformat": 4, diff --git a/src/intugle/__init__.py b/src/intugle/__init__.py index 76422fa..67af0a4 100644 --- a/src/intugle/__init__.py +++ b/src/intugle/__init__.py @@ -1,3 +1,3 @@ -from intugle.analysis.models import DataSet -from intugle.link_predictor.predictor import LinkPredictor -from intugle.sql_generator import SqlGenerator +from intugle.analysis.models import DataSet as DataSet +from intugle.knowledge_builder import KnowledgeBuilder as KnowledgeBuilder +from intugle.dp_builder import DataProductBuilder as DataProductBuilder diff --git a/src/intugle/adapters/adapter.py b/src/intugle/adapters/adapter.py index 786e0c8..864b555 100644 --- a/src/intugle/adapters/adapter.py +++ b/src/intugle/adapters/adapter.py @@ -3,13 +3,14 @@ from intugle.adapters.models import ( ColumnProfile, + DataSetData, ProfilingOutput, ) class Adapter(ABC): @abstractmethod - def profile(self, data: Any) -> ProfilingOutput: + def profile(self, data: Any, table_name: str) -> ProfilingOutput: pass @abstractmethod @@ -31,3 +32,10 @@ def load(): @abstractmethod def execute(): raise NotImplementedError() + + @abstractmethod + def to_df(self: DataSetData, date, table_name: str): + raise NotImplementedError() + + def get_details(self, _: DataSetData): + return None diff --git a/src/intugle/adapters/models.py b/src/intugle/adapters/models.py index 0592390..2de485f 100644 --- a/src/intugle/adapters/models.py +++ b/src/intugle/adapters/models.py @@ -1,8 +1,15 @@ from enum import Enum from typing import Any, List, Optional +import pandas as pd + from pydantic import BaseModel, Field +from intugle.adapters.types.duckdb.models import DuckdbConfig + +# FIXME load dynamically +DataSetData = pd.DataFrame | DuckdbConfig + class ProfilingOutput(BaseModel): count: int diff --git a/src/intugle/adapters/types/duckdb/duckdb.py b/src/intugle/adapters/types/duckdb/duckdb.py index 9f12f12..eeea2bd 100644 --- a/src/intugle/adapters/types/duckdb/duckdb.py +++ b/src/intugle/adapters/types/duckdb/duckdb.py @@ -5,7 +5,6 @@ import duckdb import numpy as np import pandas as pd -import pandas.api.types as ptypes from intugle.adapters.adapter import Adapter from intugle.adapters.factory import AdapterFactory @@ -14,13 +13,26 @@ ProfilingOutput, ) from intugle.adapters.types.duckdb.models import DuckdbConfig -from intugle.adapters.types.pandas.utils import convert_to_native +from intugle.adapters.utils import convert_to_native from intugle.common.exception import errors from intugle.core.utilities.processing import string_standardization class DuckdbAdapter(Adapter): - def profile(self, data: DuckdbConfig) -> ProfilingOutput: + + def __init__(self): + duckdb.install_extension('httpfs') + duckdb.load_extension('httpfs') + + @staticmethod + def check_data(data: Any) -> DuckdbConfig: + try: + data = DuckdbConfig.model_validate(data) + except Exception: + raise TypeError("Input must be a duckdb config.") + return data + + def profile(self, data: DuckdbConfig, table_name: str) -> ProfilingOutput: """ Generates a profile of a file. @@ -33,8 +45,7 @@ def profile(self, data: DuckdbConfig) -> ProfilingOutput: - "columns": List of column names. - "dtypes": A dictionary mapping column names to generalized data types. """ - if not isinstance(data, DuckdbConfig): - raise TypeError("Input must be a duckdb config.") + data = self.check_data(data) def __format_dtype__(dtype: Any) -> str: """Maps dtype to a generalized type string.""" @@ -47,7 +58,6 @@ def __format_dtype__(dtype: Any) -> str: } return type_map.get(dtype, "string") - table_name = "__profile_table__" self.load(data, table_name) # Fetching total count of table @@ -101,9 +111,8 @@ def column_profile( A dictionary containing the profile for the column, or None if the column does not exist. """ - if not isinstance(data, DuckdbConfig): - raise TypeError("Input must be a duckdb config.") - + data = self.check_data(data) + self.load(data, table_name) start_ts = time.time() @@ -111,10 +120,10 @@ def column_profile( # --- Calculations --- # query = f""" SELECT - COUNT(DISTINCT {column_name}) AS distinct_count, - SUM(CASE WHEN {column_name} IS NULL THEN 1 ELSE 0 END) AS null_count + COUNT(DISTINCT "{column_name}") AS distinct_count, + SUM(CASE WHEN "{column_name}" IS NULL THEN 1 ELSE 0 END) AS null_count FROM - {table_name} + "{table_name}" """ distinct_null_data = duckdb.execute(query).fetchall() @@ -125,11 +134,11 @@ def column_profile( # 1. Get a sample of distinct values. sample_query = f""" SELECT - DISTINCT CAST( {column_name} AS VARCHAR) AS sample_values + DISTINCT CAST( "{column_name}" AS VARCHAR) AS sample_values FROM - {table_name} + "{table_name}" WHERE - {column_name} IS NOT NULL LIMIT {dtype_sample_limit} + "{column_name}" IS NOT NULL LIMIT {dtype_sample_limit} """ data = duckdb.execute(sample_query).fetchall() distinct_values = [d[0] for d in data] @@ -193,18 +202,54 @@ def _get_load_func(data: DuckdbConfig): return f"{ld_func}('{data.path}')" - def load(self, data: DuckdbConfig, table_name: str): - ld_func = self._get_load_func(data) + def load_view(self, data: DuckdbConfig, table_name: str): + query = f"""CREATE OR REPLACE VIEW {table_name} AS {data.path}""" + duckdb.execute(query) + def load_file(self, data: DuckdbConfig, table_name: str): + ld_func = self._get_load_func(data) query = f"""CREATE TABLE IF NOT EXISTS {table_name} AS SELECT * FROM {ld_func};""" duckdb.execute(query) - def execute(): ... + def load(self, data: DuckdbConfig, table_name: str): + data = self.check_data(data) + if data.type == "query": + self.load_view(data, table_name) + return + self.load_file(data, table_name) + + def execute_df(self, query): + df = duckdb.sql(query).to_df() + return df + + def to_df(self, _: DuckdbConfig, table_name: str): + query = f"SELECT * from {table_name}" + df = self.execute_df(query) + return df + + def execute(self, query): + df = self.execute_df(query) + data = df.to_dict(orient="records") + return data + + async def aexecute(self, query): + result = duckdb.sql(query).fetchnumpy() + df = pd.DataFrame(result) + data = df.to_dict(orient="records") + return data + + def get_details(self, data: DuckdbConfig): + data = self.check_data(data) + return data.model_dump() def can_handle_pandas(df: Any) -> bool: - return isinstance(df, DuckdbConfig) + try: + df = DuckdbAdapter.check_data(df) + except Exception: + return False + return True def register(factory: AdapterFactory): diff --git a/src/intugle/adapters/types/pandas/pandas.py b/src/intugle/adapters/types/pandas/pandas.py index 95933ff..f1a7c38 100644 --- a/src/intugle/adapters/types/pandas/pandas.py +++ b/src/intugle/adapters/types/pandas/pandas.py @@ -12,13 +12,12 @@ ColumnProfile, ProfilingOutput, ) +from intugle.adapters.utils import convert_to_native from intugle.core.utilities.processing import string_standardization -from .utils import convert_to_native - class PandasAdapter(Adapter): - def profile(self, data: pd.DataFrame) -> ProfilingOutput: + def profile(self, data: pd.DataFrame, _: str) -> ProfilingOutput: """ Generates a profile of a pandas DataFrame. @@ -148,11 +147,15 @@ def column_profile( ts=time.time() - start_ts, ) - def load(): + def load(self, data: pd.DataFrame, table_name: str): ... + # duckdb.sql(f"CREATE TABLE {table_name} AS SELECT * FROM data") def execute(): ... + + def to_df(self, data): + return data def can_handle_pandas(data: Any) -> bool: diff --git a/src/intugle/adapters/types/pandas/utils.py b/src/intugle/adapters/utils.py similarity index 100% rename from src/intugle/adapters/types/pandas/utils.py rename to src/intugle/adapters/utils.py diff --git a/src/intugle/analysis/models.py b/src/intugle/analysis/models.py index b7b95bb..cce55bb 100644 --- a/src/intugle/analysis/models.py +++ b/src/intugle/analysis/models.py @@ -12,6 +12,7 @@ BusinessGlossaryOutput, ColumnGlossary, ColumnProfile, + DataSetData, DataTypeIdentificationL1Output, DataTypeIdentificationL2Input, DataTypeIdentificationL2Output, @@ -27,9 +28,6 @@ from intugle.models.resources.model import Column, ColumnProfilingMetrics from intugle.models.resources.source import Source, SourceTables -# FIXME add type -DataSetData = pd.DataFrame - class DataSet: """ @@ -49,11 +47,27 @@ def __init__(self, data: DataSetData, name: str): # A dictionary to store the results of each analysis step self.results: Dict[str, Any] = {} + self.load() + + @property + def sql_query(self): + if 'type' in self.data and self.data['type'] == 'query': + return self.data['path'] + return None + + def load(self): + try: + self.adapter.load(self.data, self.name) + print(f"{self.name} loaded") + except Exception as e: + print("eee", e) + ... + def profile_table(self) -> Self: """ Profiles the table and stores the result in the 'results' dictionary. """ - self.results["table_profile"] = self.adapter.profile(self.data) + self.results["table_profile"] = self.adapter.profile(self.data, self.name) return self def profile_columns(self) -> Self: @@ -138,6 +152,8 @@ def identify_keys(self) -> Self: if key is not None: self.results["key"] = key + else: + self.results['key'] = None return self def profile(self) -> Self: @@ -145,8 +161,6 @@ def profile(self) -> Self: Profiles the dataset including table and columns and stores the result in the 'results' dictionary. This is a convenience method to run profiling on the raw dataframe. """ - if self.data.empty: - raise ValueError("The raw dataframe is empty. Cannot perform profiling.") self.profile_table().profile_columns() return self @@ -155,8 +169,6 @@ def identify_datatypes(self) -> Self: Identifies the data types for the dataset and stores the result in the 'results' dictionary. This is a convenience method to run data type identification on the raw dataframe. """ - if self.data.empty: - raise ValueError("The raw dataframe is empty. Cannot perform data type identification.") self.identify_datatypes_l1().identify_datatypes_l2() return self @@ -194,15 +206,11 @@ def generate_glossary(self, domain: str = "") -> Self: self.results["table_glossary"] = glossary_output.table_glossary return self - def run(self, domain: str, save: bool = True) -> Self: - """Run all stages """ + """Run all stages""" + + self.profile().identify_datatypes().identify_keys().generate_glossary(domain=domain) - self.profile()\ - .identify_datatypes()\ - .identify_keys()\ - .generate_glossary(domain=domain) - if save: self.save_yaml() @@ -244,7 +252,9 @@ def save_yaml(self, file_path: Optional[str] = None) -> None: ) columns.append(column) - table = SourceTables(name=self.name, description=table_description, columns=columns) + details = self.adapter.get_details(self.data) + + table = SourceTables(name=self.name, description=table_description, columns=columns, details=details) source = Source(name="healthcare", description=table_description, schema="public", database="", table=table) @@ -253,8 +263,12 @@ def save_yaml(self, file_path: Optional[str] = None) -> None: # Save the YAML representation of the sources with open(file_path, "w") as file: yaml.dump(sources, file, sort_keys=False, default_flow_style=False) + + def to_df(self): + return self.adapter.to_df(self.data, self.name) - def result_to_pandas(self): + @property + def profiling_df(self): column_profiles = self.results.get("column_profiles") if column_profiles is None: return "

No column profiles available.

" @@ -262,5 +276,5 @@ def result_to_pandas(self): return df def _repr_html_(self): - df = self.result_to_pandas().head() + df = self.profiling_df.head() return df._repr_html_() diff --git a/src/intugle/artifacts/model_objects/datatype_l1_identification/di_l1_classifier_encoder_13052023.pkl b/src/intugle/artifacts/model_objects/datatype_l1_identification/di_l1_classifier_encoder_13052023.pkl index 7a84d3e..19a7d30 100644 Binary files a/src/intugle/artifacts/model_objects/datatype_l1_identification/di_l1_classifier_encoder_13052023.pkl and b/src/intugle/artifacts/model_objects/datatype_l1_identification/di_l1_classifier_encoder_13052023.pkl differ diff --git a/src/intugle/artifacts/model_objects/datatype_l1_identification/di_l1_classifier_xgb_13052023.pkl b/src/intugle/artifacts/model_objects/datatype_l1_identification/di_l1_classifier_xgb_13052023.pkl index d895693..9abfdb1 100644 Binary files a/src/intugle/artifacts/model_objects/datatype_l1_identification/di_l1_classifier_xgb_13052023.pkl and b/src/intugle/artifacts/model_objects/datatype_l1_identification/di_l1_classifier_xgb_13052023.pkl differ diff --git a/src/intugle/core/llms/chat.py b/src/intugle/core/llms/chat.py index cc00393..e212127 100644 --- a/src/intugle/core/llms/chat.py +++ b/src/intugle/core/llms/chat.py @@ -3,7 +3,11 @@ from typing import TYPE_CHECKING from langchain.chat_models import init_chat_model -from langchain.output_parsers import ResponseSchema, StructuredOutputParser +from langchain.output_parsers import ( + ResponseSchema, + RetryWithErrorOutputParser, + StructuredOutputParser, +) from langchain.prompts import BaseChatPromptTemplate, ChatPromptTemplate from langchain_core.rate_limiters import InMemoryRateLimiter @@ -44,7 +48,9 @@ def __init__( self.prompt_template: BaseChatPromptTemplate = prompt_template # prompt template self.output_parser = ( - self.__output_parser_builder__(response_schemas=response_schemas) if response_schemas is not None else None + self.__output_parser_builder__(response_schemas=response_schemas) + if response_schemas is not None + else None ) # the built output parser self.format_instructions = ( @@ -62,8 +68,11 @@ def __output_parser_builder__(self, response_schemas: list[ResponseSchema] = Non """ for building the corresponding output paraser from the given ResponseSchema """ - output_parser = self.parser.from_response_schemas(response_schemas=response_schemas) - return output_parser + parser = self.parser.from_response_schemas(response_schemas=response_schemas) + retry_parser = RetryWithErrorOutputParser.from_llm( + parser=parser, llm=self.model, max_retries=self.MAX_RETRIES + ) + return retry_parser @classmethod def _get_rate_limiter(cls): @@ -85,7 +94,10 @@ def invoke(self, *args, **kwargs): sucessfull_parsing = False - messages = self.llm_prompt.format(format_instructions=self.format_instructions, **kwargs) + prompt_value = self.llm_prompt.format_prompt( + format_instructions=self.format_instructions, **kwargs + ) + messages = prompt_value.to_messages() _message = messages response = "" @@ -112,7 +124,7 @@ def invoke(self, *args, **kwargs): if self.output_parser is not None: # try to parse the content as dict raw_response = response - response = self.output_parser.parse(response) + response = self.output_parser.parse_with_prompt(response, prompt_value) sucessfull_parsing = True return response, sucessfull_parsing, raw_response except Exception as ex: diff --git a/src/intugle/core/pipeline/datatype_identification/custom_features.py b/src/intugle/core/pipeline/datatype_identification/custom_features.py index f609986..7643830 100644 --- a/src/intugle/core/pipeline/datatype_identification/custom_features.py +++ b/src/intugle/core/pipeline/datatype_identification/custom_features.py @@ -17,7 +17,7 @@ from trieregex import TrieRegEx as TRE from intugle.core.settings import settings -from intugle.core.utilities.processing import compute_stats +from intugle.core.utilities.processing import compute_stats, to_high_precision_array from .initialize import di_initalizer @@ -613,7 +613,8 @@ def additional_features(col_values: list, date_samples: int = 1000) -> list: if val == 1 ] # int_type = list(int_type) - int_type = np.array(int_type, dtype=np.float128) + int_type = to_high_precision_array(int_type) + # Iterate through each elements and get the float type data for feature creation log.info("Custom feature creation checkFloat started:%s", datetime.now()) float_data = pseq( @@ -625,7 +626,7 @@ def additional_features(col_values: list, date_samples: int = 1000) -> list: if val == 1 ] # float_type = list(float_type) - float_type = np.array(float_type, dtype=np.float128) + float_type = to_high_precision_array(float_type) # Iterate through each elements and get the range type data for feature creation log.info("Custom feature creation checkRange started:%s", datetime.now()) diff --git a/src/intugle/core/pipeline/datatype_identification/l1_model.py b/src/intugle/core/pipeline/datatype_identification/l1_model.py index cc6d931..7ab22b5 100644 --- a/src/intugle/core/pipeline/datatype_identification/l1_model.py +++ b/src/intugle/core/pipeline/datatype_identification/l1_model.py @@ -1,9 +1,9 @@ import logging import os -import pickle from datetime import datetime +import joblib import numpy as np import pandas as pd @@ -84,11 +84,11 @@ def model_prediction(self, test_df: pd.DataFrame, model_version=datetime.now().s model_path = os.path.join(self.model_objects_directory, 'di_l1_classifier_xgb_' + model_version + '.pkl') encoder_path = os.path.join(self.model_objects_directory, 'di_l1_classifier_encoder_' + model_version + '.pkl') with open(model_path, 'rb') as f: - clf_model = pickle.load(f) + clf_model = joblib.load(f) log.info("[*] Loaded L1 classifier model") # Loading the encoder objects for model prediction with open(encoder_path, 'rb') as f: - encoder = pickle.load(f) + encoder = joblib.load(f) log.info("[*] Loaded L1 Encoder") # Model prediction on the test dataset and inverse transform to get the labels for the encoder diff --git a/src/intugle/core/utilities/processing.py b/src/intugle/core/utilities/processing.py index 95e38a7..1f20fc1 100644 --- a/src/intugle/core/utilities/processing.py +++ b/src/intugle/core/utilities/processing.py @@ -9,34 +9,33 @@ log = logging.getLogger(__name__) -SPECIAL_PATTERN = r'[^a-zA-Z0-9\s]' -WHITESPACE_PATTERN = r'\s{2,}' -ASCII_PATTERN = r'[^\x00-\x7F]' +SPECIAL_PATTERN = r"[^a-zA-Z0-9\s]" +WHITESPACE_PATTERN = r"\s{2,}" +ASCII_PATTERN = r"[^\x00-\x7F]" def remove_ascii(strs) -> str: - return ''.join([char for word in str(strs) for char in word if ord(char) < 128]) + return "".join([char for word in str(strs) for char in word if ord(char) < 128]) def string_standardization(uncleaned_data: str): cleaned_data = remove_ascii(uncleaned_data) - cleaned_data = re.sub(SPECIAL_PATTERN, ' ', cleaned_data) - cleaned_data = re.sub(WHITESPACE_PATTERN, ' ', cleaned_data.strip()) + cleaned_data = re.sub(SPECIAL_PATTERN, " ", cleaned_data) + cleaned_data = re.sub(WHITESPACE_PATTERN, " ", cleaned_data.strip()) cleaned_data = cleaned_data.replace(" ", "_") cleaned_data = cleaned_data.strip().lower() return cleaned_data def compute_stats(values): - - # Converting the values to array format + # Converting the values to array format values = np.array(values) if not isinstance(values, np.ndarray) else values # Calculate the statistical results from the values _min = np.min(values) _max = np.max(values) _sum = np.sum(values) _mean = np.mean(values) - + x = values - _mean _variance = np.mean(x * x) @@ -45,50 +44,44 @@ def compute_stats(values): _skew = 0 _kurtosis = -3 else: - _skew = np.mean(x ** 3) / _variance ** 1.5 - _kurtosis = np.mean(x ** 4) / _variance ** 2 - 3 - + _skew = np.mean(x**3) / _variance**1.5 + _kurtosis = np.mean(x**4) / _variance**2 - 3 + return _mean, _variance, _skew, _kurtosis, _min, _max, _sum def adjust_sample(sample_data, expected_size, sample=True, distinct=False, empty_return_na: bool = True): - if not isinstance(sample_data, list): try: sample_data = eval(sample_data) except Exception: log.error("[!] Error when evaluating sample_data") return [np.nan] * 2 - + sample_size = len(sample_data) - + if sample_size == 0: if empty_return_na: return [np.nan] * expected_size else: return [] - - if distinct: + if distinct: sample_data = list(set(sample_data)) - + if not sample: - return sample_data[:expected_size] - - if sample_size / expected_size <= 0.3: + if sample_size / expected_size <= 0.3: sample_data = sample_data + list(np.random.choice(sample_data, expected_size - sample_size)) - - else: + else: sample_data = sample_data[:expected_size] return sample_data DATE_TIME_GROUPS = { - "YYYY-MM-DD": r"\b(?:20\d{2}|19\d{2}|\d{2})[-./_](0[1-9]|1[0-2])[-./_](0[1-9]|[12]\d|3[01])\b", "YYYY-DD-MM": r"\b(?:20\d{2}|19\d{2}|\d{2})[-./_](0[1-9]|[12]\d|3[01])[-./_](0[1-9]|1[0-2])\b", "MM-DD-YYYY": r"\b(0[1-9]|1[0-2])[-./_](0[1-9]|[12]\d|3[01])[-./_](?:20\d{2}|19\d{2}|\d{2})\b", @@ -130,7 +123,7 @@ def adjust_sample(sample_data, expected_size, sample=True, distinct=False, empty "YYYY-DD-MMTHH:MM:SS±HHMM": r"\b(?:20\d{2}|19\d{2}|\d{2})[-/._](0[1-9]|[12]\d|3[01])[-/._](0[1-9]|1[0-2])T([01]\d|2[0-4])[:,.](0[0-9]|[1-5]\d)[:,.](0[0-9]|[1-5]\d)[+-](0[0-9]|1[0-2])(?::|\.|,)?(0[0-9]|[1-5]\d)\b", "YYYY-MM-DDTHH:MM AM/PM±HHMM": r"\b(?:20\d{2}|19\d{2}|\d{2})[-/._](0[1-9]|1[0-2])[-/._](0[1-9]|[12]\d|3[01])T([01]\d|2[0-4])[:,.](0[0-9]|[1-5]\d)\s*?( ?[APap][Mm])[+-](0[0-9]|1[0-2])(?::|\.|,)?(0[0-9]|[1-5]\d)\b", "YYYY-DD-MMTHH:MM AM/PM±HHMM": r"\b(?:20\d{2}|19\d{2}|\d{2})[-/._](0[1-9]|[12]\d|3[01])[-/._](0[1-9]|1[0-2])T([01]\d|2[0-4])[:,.](0[0-9]|[1-5]\d)\s*?( ?[APap][Mm])[+-](0[0-9]|1[0-2])(?::|\.|,)?(0[0-9]|[1-5]\d)\b", - "YYYY-MM-DD HH:MM:SS": r"^\d{4}-\d{2}-\d{2} \d{2}:\d{2}:\d{2}$" + "YYYY-MM-DD HH:MM:SS": r"^\d{4}-\d{2}-\d{2} \d{2}:\d{2}:\d{2}$", } @@ -153,8 +146,8 @@ def classify_datetime_format(sampled_values: list) -> list | str: sampled_values = eval(sampled_values) except Exception: return DATETIME_TYPE - - sampled_values = sampled_values[:settings.DATE_TIME_FORMAT_LIMIT] + + sampled_values = sampled_values[: settings.DATE_TIME_FORMAT_LIMIT] format_counters = dict.fromkeys(DATE_TIME_GROUPS.keys(), 0) @@ -189,7 +182,6 @@ def __fraction_calculate__(strata_counts): strata_counts = strata_counts[:n_strata] total_count = sum([row["count"] for row in strata_counts]) if len(strata_counts) <= 1: - sizes[strata_counts[0]["length"]] = min(strata_counts[0]["count"], n_samples) else: for row in strata_counts: @@ -200,50 +192,76 @@ def __fraction_calculate__(strata_counts): sizes[length] = sample_size return sizes - + strata_counts = df.groupby("length").agg(count=("data", "count")).reset_index().to_dict(orient="records") sizes = __fraction_calculate__(strata_counts=strata_counts) samples = [] for length, d in df.groupby("length", group_keys=False): if length in sizes: - samples += sorted(d.data.values)[:sizes[length]] + samples += sorted(d.data.values)[: sizes[length]] return samples -def preprocess_profiling_data(profiling_data: pd.DataFrame, - sample_limit: int = 5, - dtypes_to_filter=["dimension",], - truncate_sample_data: bool = False, - ) -> pd.DataFrame: +def preprocess_profiling_data( + profiling_data: pd.DataFrame, + sample_limit: int = 5, + dtypes_to_filter=[ + "dimension", + ], + truncate_sample_data: bool = False, +) -> pd.DataFrame: """ - get the required profiling data with processed sample data + get the required profiling data with processed sample data """ if dtypes_to_filter: - profiling_data = profiling_data.loc[profiling_data.datatype_l2.isin(dtypes_to_filter)].reset_index(drop=True) - def __sample_process__(sample_data, limit=5): + def __sample_process__(sample_data, limit=5): try: if isinstance(sample_data, str): - sample_data = eval(sample_data) if truncate_sample_data: - - sample_data = [str(sample)[:20]for sample in sample_data] + sample_data = [str(sample)[:20] for sample in sample_data] except Exception as ex: - log.error(f"[!] Error while sampling: {ex}") - + if len(sample_data) != 0: - sample_data = character_length_based_stratified_sampling(samples=sample_data, n_strata=limit, n_samples=int(settings.LLM_SAMPLE_LIMIT)) + sample_data = character_length_based_stratified_sampling( + samples=sample_data, n_strata=limit, n_samples=int(settings.LLM_SAMPLE_LIMIT) + ) return sample_data - + profiling_data["sample_data"] = profiling_data["sample_data"].apply(__sample_process__, limit=sample_limit) - + profiling_data["sample_data"] = profiling_data["sample_data"].astype(str) + + return profiling_data + + +def to_high_precision_array(data): + """ + Converts input data to a NumPy array with the highest available floating-point precision. + + Priority: float128 > longdouble > float64 + + Parameters: + data: array-like + The data to convert. + + Returns: + np.ndarray + A NumPy array with the highest available float precision. + """ + + if hasattr(np, "float128"): # Works on most Unix-like systems + dtype = np.float128 + elif hasattr(np, "longdouble"): # Often higher precision than float64 + dtype = np.longdouble + else: + dtype = np.float64 # Fallback - return profiling_data \ No newline at end of file + return np.array(data, dtype=dtype) diff --git a/src/intugle/sql_generator.py b/src/intugle/dp_builder.py similarity index 84% rename from src/intugle/sql_generator.py rename to src/intugle/dp_builder.py index a4a801e..cd41d43 100644 --- a/src/intugle/sql_generator.py +++ b/src/intugle/dp_builder.py @@ -1,5 +1,6 @@ from typing import TYPE_CHECKING +from intugle.analysis.models import DataSet from intugle.core import settings from intugle.libs.smart_query_generator import SmartQueryGenerator from intugle.libs.smart_query_generator.models.models import ETLModel, FieldDetailsModel, LinkModel @@ -10,8 +11,8 @@ from intugle.models.resources.model import Column -class SqlGenerator: - """Generates SQL queries based on the manifest and ETL configurations.""" +class DataProductBuilder: + """Generates data products based on the manifest and ETL configurations.""" def __init__(self, project_base: str = settings.PROJECT_BASE): self.manifest_loader = ManifestLoader(project_base) @@ -28,6 +29,15 @@ def __init__(self, project_base: str = settings.PROJECT_BASE): selected_fields = set(self.field_details.keys()) self.join = Join(self.links, selected_fields) + self.load_all() + + def load_all(self): + sources = self.manifest.sources + for source in sources.values(): + table_name = source.table.name + details = source.table.details + DataSet(data=details, name=table_name) + def generate_query(self, etl: ETLModel) -> str: """Generates a SQL query based on the ETL model. @@ -37,6 +47,8 @@ def generate_query(self, etl: ETLModel) -> str: Returns: str: The generated SQL query. """ + etl = ETLModel.model_validate(etl) + # get the field details fetcher function (Not required now as we are getting all field details) # and all field details field_details_fetcher = self.get_field_details_fetcher() @@ -45,8 +57,28 @@ def generate_query(self, etl: ETLModel) -> str: sql_builder = SmartQueryGenerator(etl, field_details_fetcher, self.links, self.field_details) sql_builder.build() sql_query = sql_builder.get_query() - return sql_query + + def build(self, etl: ETLModel) -> DataSet: + """Generates a dataset on the ETL model. + + Args: + etl (ETLModel): The ETL model containing the configuration for the query. + + Returns: + Dataset: The dataset. + """ + etl = ETLModel.model_validate(etl) + + sql_query = self.generate_query(etl) + + data = { + "path": sql_query, + "type": "query" + } + print(etl.name) + dataset = DataSet(data, etl.name) + return dataset def get_all_field_details(self) -> dict[str, FieldDetailsModel]: """Fetches all field details from the manifest.""" @@ -149,3 +181,4 @@ def plot_sources_graph(self): graph = self.join.generate_graph(list(assets), only_connected=False) self.plot_graph(graph) + diff --git a/src/intugle/knowledge_builder.py b/src/intugle/knowledge_builder.py new file mode 100644 index 0000000..0588c37 --- /dev/null +++ b/src/intugle/knowledge_builder.py @@ -0,0 +1,54 @@ +from typing import TYPE_CHECKING, Any, Dict, List + +from intugle.analysis.models import DataSet +from intugle.link_predictor.predictor import LinkPredictor + +if TYPE_CHECKING: + from intugle.link_predictor.models import PredictedLink + + +class KnowledgeBuilder: + + def __init__(self, data_input: Dict[str, Any] | List[DataSet], domain: str = ""): + + self.datasets: Dict[str, DataSet] = {} + self.links: list[PredictedLink] = [] + self.domain = domain + + if isinstance(data_input, dict): + self._initialize_from_dict(data_input) + elif isinstance(data_input, list): + self._initialize_from_list(data_input) + else: + raise TypeError("Input must be a dictionary of named dataframes or a list of DataSet objects.") + + def _initialize_from_dict(self, data_dict: Dict[str, Any]): + """Creates and processes DataSet objects from a dictionary of raw dataframes.""" + for name, df in data_dict.items(): + dataset = DataSet(df, name=name) + self.datasets[name] = dataset + + def _initialize_from_list(self, data_list: List[DataSet]): + """Processes a list of existing DataSet objects""" + for dataset in data_list: + if not dataset.name: + raise ValueError("DataSet objects provided in a list must have a 'name' attribute.") + self.datasets[dataset.name] = dataset + + def build(self): + + # run analysis on all datasets + for dataset in self.datasets.values(): + dataset.run(domain=self.domain, save=True) + + # Initialize the predictor + self.link_predictor = LinkPredictor(list(self.datasets.values())) + + # Run the prediction + self.link_predictor.predict(save=True) + self.links: list[PredictedLink] = self.link_predictor.links + return self + + def visualize(self): + return self.link_predictor.show_graph() + diff --git a/src/intugle/libs/query_generator/operators.py b/src/intugle/libs/query_generator/operators.py index 1a34d63..4511e70 100644 --- a/src/intugle/libs/query_generator/operators.py +++ b/src/intugle/libs/query_generator/operators.py @@ -5,11 +5,11 @@ class Operators: @classmethod def _in(cls, field, value, **_): - return "{} IN {}".format(field, value) + return "{} IN {}".format(field, tuple(value)) @classmethod def _not_in(cls, field, value, **_): - return "{} NOT IN {}".format(field, value) + return "{} NOT IN {}".format(field, tuple(value)) @classmethod def _like(cls, field, value, **_): diff --git a/src/intugle/libs/query_generator/query_generator.py b/src/intugle/libs/query_generator/query_generator.py index 7e8f532..7240fbc 100644 --- a/src/intugle/libs/query_generator/query_generator.py +++ b/src/intugle/libs/query_generator/query_generator.py @@ -4,7 +4,7 @@ from .operators import Operators from .transformation import Transformations -from .utils import get_formatted_dataset_field +from .utils import get_formatted_dataset_field, get_formatted_name class QueryGeneratorModel(BaseModel): @@ -53,7 +53,7 @@ def get_select_fields(cls, selectFields): renameCol = field.get("renameCol", None) if renameCol: - expr = f"{expr} as `{renameCol}`" + expr = f"{expr} as {get_formatted_name(renameCol)}" selectedFields.append(expr) selectedFieldsExpr = ", ".join(selectedFields) @@ -70,7 +70,7 @@ def get_join_expr(cls, joinFields): for _i in range(tbl_cnt): i = str(_i) if _i == 0: - join_query += f"`{joinFields[i]['dataset']}`" + join_query += f"{get_formatted_name(joinFields[i]['dataset'])}" else: joining_cndns = [] for join_col in joinFields[i]["fields"]: @@ -92,7 +92,7 @@ def get_join_expr(cls, joinFields): " " + joinFields[i].get("join_type", "").upper() + " JOIN " - + f"`{joinFields[i]['dataset']}`" + + f"{get_formatted_name(joinFields[i]['dataset'])}" + " ON " + str(joining_cndn) ) @@ -132,7 +132,7 @@ def get_groupby_expr(cls, groupbyFields): # if _dataset is not None: # _dataset_field = get_formatted_dataset_field(_dataset, _field) # else: - # _dataset_field = f"`{_field}`" + # _dataset_field = f"{_field}" # _func = field.get("function", None) # if _func is not None: @@ -140,7 +140,7 @@ def get_groupby_expr(cls, groupbyFields): # _dataset_field = f"{_func}({_dataset_field})" renameCol = field.get("renameCol", None) if renameCol is not None: - _dataset_field = f"`{renameCol}`" + _dataset_field = get_formatted_name(renameCol) else: _dataset_field = field["sql_code"] groupbyCols.append(_dataset_field) @@ -163,7 +163,7 @@ def get_sort_expr(cls, sortFields): for sortField in sortFields: renameCol = sortField.get("renameCol", None) if renameCol is not None: - sortCol = f"`{renameCol}`" + sortCol = get_formatted_name(renameCol) else: sortCol = get_formatted_dataset_field( sortField["dataset"], sortField["field"] @@ -200,7 +200,7 @@ def getQuery(cls, config: QueryGeneratorModel): # From dataset # if "from" in config: - # sqlQuery = sqlQuery + " FROM " + f"`{config['from']}`" + # sqlQuery = sqlQuery + " FROM " + f"{config['from']}" if config.join != {}: sqlQuery += cls.get_join_expr(config.join) diff --git a/src/intugle/libs/query_generator/utils.py b/src/intugle/libs/query_generator/utils.py index 3f0ae86..15853eb 100644 --- a/src/intugle/libs/query_generator/utils.py +++ b/src/intugle/libs/query_generator/utils.py @@ -1,2 +1,6 @@ def get_formatted_dataset_field(dataset: str, field: str) -> str: - return f"`{dataset}`.`{field}`" + return f"{dataset}.{field}" + + +def get_formatted_name(name: str) -> str: + return name \ No newline at end of file diff --git a/src/intugle/libs/smart_query_generator/utils/filter.py b/src/intugle/libs/smart_query_generator/utils/filter.py index 614fd0d..5a2b8b3 100644 --- a/src/intugle/libs/smart_query_generator/utils/filter.py +++ b/src/intugle/libs/smart_query_generator/utils/filter.py @@ -62,7 +62,7 @@ def build_selection(self): "sql_code": field.sql_code, "operator": "not_in" if selection.exclude else "in", "md5_hash": field.is_pii and not self._can_view_pii, - "params": {"value": selection.values}, + "params": {"value": selection.values, "type": "list"}, } if selection.dimFunc: sel["isFunction"] = True @@ -76,7 +76,7 @@ def build_selection(self): "sql_code": field.sql_code, "operator": "is_not_null" if selection.exclude else "is_null", "md5_hash": field.is_pii and not self._can_view_pii, - "params": {"value": ""}, + "params": {"value": "", "type": "list"}, } if selection.dimFunc: sel_null["isFunction"] = True diff --git a/src/intugle/link_predictor/predictor.py b/src/intugle/link_predictor/predictor.py index aa01b2d..c10410e 100644 --- a/src/intugle/link_predictor/predictor.py +++ b/src/intugle/link_predictor/predictor.py @@ -143,7 +143,7 @@ def _predict_for_pair( ] return pair_links - def predict(self, filename='relationships.yml') -> Self: + def predict(self, filename='relationships.yml', save: bool = False) -> Self: """ Iterates through all unique pairs of datasets, predicts the links for each pair, and returns the aggregated results. @@ -165,7 +165,9 @@ def predict(self, filename='relationships.yml') -> Self: print("No links found for this pair.") self.links = all_links - self.save_yaml(file_path=filename) + if save: + self.save_yaml(file_path=filename) + return self def get_links_df(self): diff --git a/src/intugle/mcp/adapter/service.py b/src/intugle/mcp/adapter/service.py index 9e37fd3..55e5646 100644 --- a/src/intugle/mcp/adapter/service.py +++ b/src/intugle/mcp/adapter/service.py @@ -1,7 +1,7 @@ -from intugle.core.settings import settings -from intugle.mcp.adapter.psql import PsqlPool - -# from intugle.parser.manifest import ManifestLoader +# from intugle.adapters.factory import AdapterFactory +from intugle.adapters.types.duckdb.duckdb import DuckdbAdapter +from intugle.analysis.models import DataSet +from intugle.mcp.manifest import manifest_loader class AdapterService: @@ -9,16 +9,19 @@ class AdapterService: Adapter service for executing queries. """ - def __init__(self): - # self.manifest = ManifestLoader.manifest - self.psql = PsqlPool( - user=settings.POSTGRES_USER, - password=settings.POSTGRES_PASSWORD, - host=settings.POSTGRES_HOST, - database=settings.POSTGRES_DB, - port=settings.POSTGRES_PORT, - schema=settings.POSTGRES_SCHEMA, - ) + # Not good way to do it Need to create extandable and properly couple with adapter + def __init__(self, adapter: str = "duckdb"): + self.manifest = manifest_loader.manifest + self.adapter = DuckdbAdapter() + self.load_all() + + def load_all(self): + sources = self.manifest.sources + for source in sources.values(): + table_name = source.table.name + details = source.table.details + + DataSet(data=details, name=table_name) async def execute_query(self, sql_query: str) -> list[dict]: """ @@ -31,11 +34,11 @@ async def execute_query(self, sql_query: str) -> list[dict]: list[dict]: The result of the query execution. """ - data = await self.psql.fetch(sql_query) + data = self.adapter.execute(sql_query) data = [dict(record) for record in data] if data else [] return data -adapter_service = AdapterService() \ No newline at end of file +adapter_service = AdapterService() diff --git a/src/intugle/mcp/semantic_layer/router.py b/src/intugle/mcp/semantic_layer/router.py index 25239a8..1fb7965 100644 --- a/src/intugle/mcp/semantic_layer/router.py +++ b/src/intugle/mcp/semantic_layer/router.py @@ -50,13 +50,13 @@ async def get_schema(table_names: list[str]) -> dict[str, str]: return schemas -@semantic_layer_mcp.prompt(name="explore_data", title="Executor Agent Prompt") -async def prompt() -> str: - print("Using prompt from semantic layer") - return Prompts.raw_executor_prompt(settings.SQL_DIALECT, settings.DOMAIN, settings.UNIVERSAL_INSTRUCTIONS) +# @semantic_layer_mcp.prompt(name="explore_data", title="Executor Agent Prompt") +# async def prompt() -> str: +# print("Using prompt from semantic layer") +# return Prompts.raw_executor_prompt(settings.SQL_DIALECT, settings.DOMAIN, settings.UNIVERSAL_INSTRUCTIONS) -# @semantic_layer_mcp.tool(name="execute_query", description="Return the result of a query execution") -# async def execute_query(sql_query: str) -> list[dict]: -# data = await adapter_service.execute_query(sql_query) -# return data \ No newline at end of file +@semantic_layer_mcp.tool(name="execute_query", description="Return the result of a query execution") +async def execute_query(sql_query: str) -> list[dict]: + data = await adapter_service.execute_query(sql_query) + return data \ No newline at end of file diff --git a/src/intugle/mcp/semantic_layer/service.py b/src/intugle/mcp/semantic_layer/service.py index fe29a26..1c9d28a 100644 --- a/src/intugle/mcp/semantic_layer/service.py +++ b/src/intugle/mcp/semantic_layer/service.py @@ -1,15 +1,15 @@ from intugle.common.exception import errors +from intugle.mcp.manifest import manifest_loader -# from intugle.mcp.manifest import manifest_loader -from intugle.core import settings -from intugle.parser.manifest import ManifestLoader +# from intugle.core import settings +# from intugle.parser.manifest import ManifestLoader from intugle.parser.table_schema import TableSchema class SemanticLayerService: def __init__(self): - manifest_loader = ManifestLoader(settings.PROJECT_BASE) - manifest_loader.load() + # manifest_loader = ManifestLoader(settings.PROJECT_BASE) + # manifest_loader.load() self.manifest = manifest_loader.manifest self.table_schema = TableSchema(self.manifest) diff --git a/src/intugle/mcp/server.py b/src/intugle/mcp/server.py index f9fc431..23bca17 100644 --- a/src/intugle/mcp/server.py +++ b/src/intugle/mcp/server.py @@ -30,7 +30,7 @@ async def lifespan(app: Starlette): routes=[ # Using settings-based configuration Mount("/semantic_layer", app=semantic_layer_mcp.streamable_http_app()), - # Mount("/adapter", app=adapter_mcp.streamable_http_app()), + Mount("/adapter", app=adapter_mcp.streamable_http_app()), ], lifespan=lifespan ) diff --git a/src/intugle/models/resources/source.py b/src/intugle/models/resources/source.py index 6cf0694..39ce193 100644 --- a/src/intugle/models/resources/source.py +++ b/src/intugle/models/resources/source.py @@ -11,7 +11,7 @@ class SourceTables(SchemaBase): name: str description: str tags: Optional[List[str]] = Field(default_factory=list) - # details: dict + details: Optional[dict] columns: List[Column] = Field(default_factory=list) profiling_metrics: Optional[ModelProfilingMetrics] = None diff --git a/tests/analysis/test_profile.py b/tests/analysis/test_profile.py index 9d1bb16..7004250 100644 --- a/tests/analysis/test_profile.py +++ b/tests/analysis/test_profile.py @@ -1,22 +1,22 @@ -# from intugle.adapters.types.duckdb.models import DuckdbConfig -# from intugle.analysis.models import DataSet +from intugle.adapters.types.duckdb.models import DuckdbConfig +from intugle.analysis.models import DataSet -# def test_profile(): -# """ -# Tests the profile analysis step. -# """ -# # 1. Prepare dummy data -# data = DuckdbConfig.model_validate({ -# "path": "", # noqa: E501 -# "type": "csv" -# }) -# table_name = "allergies" +def test_profile(): + """ + Tests the profile analysis step. + """ + # 1. Prepare dummy data + data = DuckdbConfig.model_validate({ + "path": "https://raw.githubusercontent.com/Intugle/data-tools/refs/heads/main/sample_data/healthcare/allergies.csv", # noqa: E501 + "type": "csv" + }) + table_name = "allergies" -# # 2. Initialize DataSet -# dataset = DataSet(data, name=table_name) + # 2. Initialize DataSet + dataset = DataSet(data, name=table_name) -# # 3. Run profiling -# dataset.profile_table() -# dataset.profile_columns() + # 3. Run profiling + dataset.profile_table() + dataset.profile_columns() diff --git a/tests/mcp/test_semantic_layer.py b/tests/mcp/test_semantic_layer.py index d49fe2a..2ecbff4 100644 --- a/tests/mcp/test_semantic_layer.py +++ b/tests/mcp/test_semantic_layer.py @@ -62,8 +62,8 @@ async def get_schema(self, session: ClientSession = None): @connection_decorator() async def execute_query(self, session: ClientSession = None): - await session.call_tool( - name=Tools.EXECUTE_QUERY, arguments={"sql_query": "SELECT * FROM orders LIMIT 10"} + data = await session.call_tool( + name=Tools.EXECUTE_QUERY, arguments={"sql_query": "SELECT 1"} ) # assert isinstance(schemas.structuredContent, dict) diff --git a/tests/query_generator/test_query_generator.py b/tests/query_generator/test_query_generator.py index a49634f..516ae66 100644 --- a/tests/query_generator/test_query_generator.py +++ b/tests/query_generator/test_query_generator.py @@ -8,9 +8,7 @@ SelectionModel, SortByModel, ) -from intugle.sql_generator 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