From ef54ca233b76760ef22de9e7d1072c98adf71a31 Mon Sep 17 00:00:00 2001 From: "patched.codes[bot]" <298395+patched.codes[bot]@users.noreply.github.com> Date: Mon, 14 Apr 2025 07:53:13 +0000 Subject: [PATCH] Patched patchwork/steps/DatabaseAgent/README.md --- patchwork/steps/DatabaseAgent/README.md | 55 +++++++++++++++++++++++++ 1 file changed, 55 insertions(+) create mode 100644 patchwork/steps/DatabaseAgent/README.md diff --git a/patchwork/steps/DatabaseAgent/README.md b/patchwork/steps/DatabaseAgent/README.md new file mode 100644 index 000000000..3c931e687 --- /dev/null +++ b/patchwork/steps/DatabaseAgent/README.md @@ -0,0 +1,55 @@ +# DatabaseAgent Module Documentation + +The `DatabaseAgent` module is designed to execute database query operations utilizing an agentic strategy involving large language models and tools. This module primarily serves as an interface between a database and a language model to facilitate data retrieval and task execution. + +## Overview + +The module contains several key components: + +- **DatabaseAgent**: The main class that configures and executes the database agent strategy. +- **DatabaseAgentInputs & DatabaseAgentOutputs**: Typed dictionaries that define the expected input and output structures for the `DatabaseAgent`. +- **Initialization**: An empty `__init__.py` making the module importable. + +## Code Components + +### File: DatabaseAgent.py + +#### Primary Class: `DatabaseAgent` + +The `DatabaseAgent` class inherits from the `Step` class and is responsible for executing an agentic strategy using language models to perform database queries. + +#### Inputs + +- **prompt_value**: A dictionary for dynamic content in the prompt. +- **task**: A string template rendered with `prompt_value` to define tasks. +- **db_dialect**: The database dialect/engine to use. +- **llm_client**: Initializes a language model client using the asynchronous `AioLlmClient`. +- **task configuration**: Configures the reasoning strategy of the agent, leveraging an AgentConfig. + +#### Outputs + +- Returns a dictionary containing the execution result and usage metrics of the agentic strategy. Specifically, it provides: + - **request_tokens**: The number of tokens sent in the request. + - **response_tokens**: The number of tokens received from the response. + +#### Usage + +This class would be instantiated with specific database and task parameters and then execute its `run()` method to perform operations, logging the data execution tasks and associated metrics. + +### File: typed.py + +#### Classes: `DatabaseAgentInputs` and `DatabaseAgentOutputs` + +- **DatabaseAgentInputs**: Defines possible inputs with required and optional fields, including database connection details, API keys, and example data. + +- **DatabaseAgentOutputs**: Structures the expected output, primarily focusing on token usage during the data execution process. + +### File: __init__.py + +This file is an empty initializer to allow importing of the module components. + +## Use Cases + +The `DatabaseAgent` is intended for integration in larger systems where automated and intelligent database querying is required. Researchers and developers could employ this tool for tasks demanding dynamic, context-aware interactions with databases, possibly for analytics, reporting, or data mining functions. + +Overall, this module simplifies the process of querying databases by employing a smart agent that abstracts interactions through structured prompts and agent strategies.