toolfront

<p align="center"> <a href="https://github.com/kruskal-labs/toolfront"> <img src="https://raw.githubusercontent.com/kruskal-labs/toolfront/main/img/logo.png" width="150" alt="ToolFront Logo"> </a> </p> <div align="center"> *Simple data retrieval for AI with unmatched control, precision, and speed.* [](https://github.com/kruskal-labs/toolfront/actions/workflows/test.yml) [](https://pypi.org/project/toolfront/) [](https://discord.gg/rRyM7zkZTf) [](https://x.com/toolfront) </div> --- **Documentation: [docs.toolfront.ai](http://docs.toolfront.ai/)** --- ## Installation Install with `pip` or your favorite PyPI package manager. ```bash pip install toolfront ``` ## Example 1: Text2SQL with ChatGPT ```python from toolfront import Database db = Database("postgres://user:pass@localhost:5432/mydb", model="openai:gpt-4o") context = "We're an e-commerce company. Sales data is in the `cust_orders` table." # Returns a string answer = db.ask("What's our best-selling product?", context=context) # >>> "Wireless Headphones Pro" ``` > **Note**: For databases, install with PyPI extras, e.g.: `pip install "toolfront[postgres]"`. See the [documentation](http://docs.toolfront.ai/) for the complete list of 10+ databases. ## Example 2: API retrieval with Claude ```python from toolfront import API api = API("http://localhost:8000/openapi.json", model="anthropic:claude-3-5-sonnet") # Returns a list of integers answer: list[int] = api.ask("Get the last 5 order IDs for user_id=42") # >>> [1001, 998, 987, 976, 965] ``` > **Note**: ToolFront supports any API with an OpenAPI (formerly Swagger) specification. Most common APIs like Slack, Discord, and GitHub have OpenAPI specs. See the [documentation](http://docs.toolfront.ai/) for more details. ## Example 3: Document information extraction with Gemini ```python from toolfront import Document from pydantic import BaseModel, Field class CompanyReport(BaseModel): company_name: str = Field(..., description="Name of the company") revenue: int | float = Field(..., description="Annual revenue in USD") is_profitable: bool = Field(..., description="Whether the company is profitable") doc = Document("/path/annual_report.pdf", model="google:gemini-pro") # Returns a structured Pydantic object answer: CompanyReport = doc.ask("Extract the key company information from this report") # >>> CompanyReport(company_name="TechCorp Inc.", revenue=2500000, is_profitable=True) ``` > **Note**: ToolFront supports OpenAI, Anthropic, Google, xAI, and 14+ AI model providers. See the [documentation](http://docs.toolfront.ai/) for the complete list. ## Example 4: Snowflake MCP Server ```json { "mcpServers": { "toolfront": { "command": "uvx", "args": [ "toolfront[snowflake]", "snowflake://user:pass@account/warehouse/database" ] } } } ``` ## Community & Contributing - **Discord**: Join our [community server](https://discord.gg/rRyM7zkZTf) for real-time help and discussions - **X**: Follow us [@toolfront](https://x.com/toolfront) for updates and news - **Issues**: Report bugs or request features on [GitHub Issues](https://github.com/kruskal-labs/toolfront/issues) ## License This project is licensed under the terms of the MIT license.