This file contains hidden or bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
| 1. Introduction: The Paradigm Shift from Deterministic Integration to Probabilistic Agency | |
| The fundamental architecture of software integration is undergoing a seismic shift, moving away from the rigid, deterministic pathways that have defined the last two decades of computing and toward a new era of probabilistic, agentic workflows. For the past twenty years, the interaction between disparate software systems has been governed by the strict contract of the Application Programming Interface (API). In this traditional paradigm, the "glue" code connecting a user interface to a backend service was static and brittle; a developer was required to hard-code the specific endpoint, define the exact JSON payload structure, and pre-program distinct error handling routines for every conceivable failure state. If the API schema changed, or if a user’s request deviated even slightly from the pre-programmed path, the integration would fail. This model, while reliable for repetitive and predictable tasks, is inherently frag |
This file contains hidden or bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
| Architecture Design: Autonomous API Agent Platform | |
| 1. System Overview | |
| The system is a "ReAct" (Reasoning + Acting) Agent platform. It ingests OpenAPI specifications (Swagger) from an Admin, converts them into "Tools," and allows an LLM to orchestrate HTTP requests to fulfill natural language user intents. | |
| high-Level Context Diagram (Mermaid) | |
| Code snippet | |
| graph TD | |
| User((User)) -->|Natural Language| FE[Frontend UI] | |
| Admin((Admin)) -->|OpenAPI Specs / Prompts| FE |
This file contains hidden or bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
| Software Architecture Document: Metadata-Driven Workflow Engine | |
| Version: 1.0 | |
| Date: June 12, 2025 | |
| Author: Gemini Architect | |
| 1. Motivation | |
| The current system for executing data and machine learning pipelines involves multiple distinct workflows, each with its own dedicated codebase, repository, and CI/CD pipeline. While this approach allows for isolation, it has led to significant inefficiencies and operational challenges: |
This file contains hidden or bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
| <?xml version="1.0" encoding="UTF-8" ?> | |
| <!DOCTYPE mapper | |
| PUBLIC "-//mybatis.org//DTD Mapper 3.0//EN" | |
| "https://mybatis.org/dtd/mybatis-3-mapper.dtd"> | |
| <mapper namespace="com.example.mapper.OlympicWinnerMapper"> | |
| <sql id="selectColumns"> | |
| * | |
| </sql> |
This file contains hidden or bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
| # combined_pipeline.py | |
| import multiprocessing | |
| import time | |
| import os | |
| import pickle | |
| import pandas as pd | |
| import sys | |
| import functools # Import functools for decorators |
This file contains hidden or bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
| def __mul__(self, other): | |
| if isinstance(other, (N.ndarray, list, tuple)) : | |
| # This promotes 1-D vectors to row vectors | |
| return N.dot(self, asmatrix(other)) | |
| if isscalar(other) or not hasattr(other, '__rmul__') : | |
| return N.dot(self, other) | |
| return NotImplemented |