# SwarmLogic Roadmap

This roadmap outlines the main components and functions that need to be built to achieve our end goal of developing an AI-powered backend that evolves based on API calls and manages data persistence.

## Phase 1: Core Development & Learning

### Components
1. **AI Swarm**: A collective of AI models that interact with API calls, infer business logic, and handle data persistence.
2. **API Interpretation Module**: Functionality to understand the intent of an API call.

### Functions
1. Training the AI swarm with a variety of API calls.
2. Developing the capability to infer business logic from API calls.
3. Building the ability to manage the state of different data schemas.

## Phase 2: Advanced Data Persistence & Intelligent Error Handling

### Components
1. **Advanced Data Persistence Module**: An enhanced data management system capable of handling complex data schemas and sources.
2. **Intelligent Error Handling Module**: A mechanism to handle and learn from errors.

### Functions
1. Enhancing the data management system to cater to a variety of schemas and data sources.
2. Building an error handling mechanism that learns from previous errors to improve future performance.

## Phase 3: Enhanced Business Logic Inference & AI Swarm Refinement

### Components
1. **Advanced Business Logic Inference Module**: An improved system capable of understanding complex API calls and inferring intricate business logic.

### Functions
1. Upgrading the inference system to handle increasingly complex API calls.
2. Refining the AI swarm to improve its learning efficiency and accuracy.

## Optimization & Performance Enhancement

### Components
1. **Optimization Module**: A system dedicated to enhancing SwarmLogic's efficiency and performance.
2. **Integration Module**: A module for facilitating SwarmLogic's integration with various development platforms.

### Functions
1. Optimizing SwarmLogic to handle high-volume, complex API calls.
2. Developing integrations with major development platforms for easy adoption.
3. Expanding the range of programming languages that SwarmLogic can interact with.

## Phase 4: Industry Mastery & New Horizons

### Components
1. **Advanced Capability Module**: A system to handle complex and niche requirements.
2. **Trend Awareness Module**: A mechanism to stay updated with emerging technologies and trends.

### Functions
1. Building advanced capabilities to cater to diverse requirements.
2. Staying abreast of emerging technologies and trends to ensure SwarmLogic remains a leader in the backend development solutions space.

The above roadmap is a guideline for our development process and is subject to change based on our continuous learning and feedback from users. We invite you to join us on this journey as we revolutionize backend development with SwarmLogic.


# Plan to Create Phase 1

Phase 1 involves the development of the core functionalities of SwarmLogic. This includes building an AI Swarm and API Interpretation Module. The main components of this phase are:

1. Set up the AI Swarm: An AI Swarm is an ensemble of AI models. For this, we'll utilize OpenAI's GPT-4 model as the primary AI model for handling the API calls and interpreting the business logic.

2. Develop API Interpretation Module: This functionality will allow the system to interpret the intent of any API call. We can use the natural language understanding capabilities of GPT-4 for this purpose.

3. Set up the database state: We need to design a structure for storing the database state of various applications.

Here's an extension of the given code to implement these components. I'm assuming that we have a helper module "swarms" which provides the `Swarms` class that can be used to run the AI models and interpret the results.

```python
from fastapi import FastAPI, HTTPException
from typing import Optional

import json
import logging
from pydantic import BaseModel
from swarms import Swarms

class AppState(BaseModel):
    app_name: str
    api_call: str

# Set up logging
logging.basicConfig(filename="app.log", level=logging.INFO, format='%(asctime)s %(levelname)-8s %(message)s', datefmt='%Y-%m-%d %H:%M:%S')

# Initialize Swarms with your API key
api_key = "your-api-key-here"
swarm = Swarms(openai_api_key=api_key)

app = FastAPI()

@app.post("/{app_name}/{api_call}")
async def api(app_state: AppState):
    try:
        db = json.load(open('db.json', 'r'))
    except Exception as e:
        logging.error("Error loading database: %s", e)
        raise HTTPException(status_code=500, detail="Error loading database")

    prompt = f"""{db[app_state.app_name]["prompt"]}
    API Call (indexes are zero-indexed):
    {app_state.api_call}

    Database State:
    {db[app_state.app_name]["state"]}

    Output the API response as json prefixed with '!API response!:'. Then output the new database state as json, prefixed with '!New Database State!:'. If the API call is only requesting data, then don't change the database state, but base your 'API Response' off what's in the database.
    """

    try:
        # Update to call the swarm model
        response = swarm.run_swarms(prompt)
        new_state = response['new_database_state']
        
        if new_state:
            db[app_state.app_name]["state"] = new_state
            json.dump(db, open('db.json', 'w'), indent=4)
    except Exception as e:
        logging.error("Error running model or updating state: %s", e)
        raise HTTPException(status_code=500, detail="Error running model or updating state")
        
    return response
```

Please note, the "swarms" module and the `run_swarms()` function are placeholders and need to be replaced with the actual code that uses GPT-4 to run the AI model and interpret the results.

# Phase 2: Advanced Data Persistence & Intelligent Error Handling

Let's outline the steps we need to take to achieve the components of Phase 2:

## Advanced Data Persistence Module

To handle a wide range of data schemas, we will make use of SQLAlchemy, a SQL toolkit and Object-Relational Mapping (ORM) system for Python. SQLAlchemy provides a full suite of well-known enterprise-level persistence patterns, designed for efficient and high-performing database access.

Here's how to set up SQLAlchemy in our application:

```python
from sqlalchemy import create_engine, MetaData, Table, select
from sqlalchemy.orm import sessionmaker, scoped_session

DATABASE_URL = "sqlite:///./swarmlogic.db"  # Use your actual database url

engine = create_engine(DATABASE_URL)
metadata = MetaData()

SessionLocal = sessionmaker(autocommit=False, autoflush=False, bind=engine)
```

To make use of SQLAlchemy, we will have to replace the manual file handling for `db.json` with proper SQLAlchemy ORM models and queries.

## Intelligent Error Handling Module

Intelligent Error Handling requires us to log our errors, analyze them, and update our system accordingly. Python's in-built `logging` library will allow us to log all errors, and we can utilize our AI swarm to understand these errors and suggest improvements.

First, let's improve our logging:

```python
import traceback

def log_exception(exc):
    # Capture the traceback associated with the exception
    tb_lines = traceback.format_exception(exc.__class__, exc, exc.__traceback__)
    tb_text = ''.join(tb_lines)

    # Log the exception
    logging.error("Caught an exception:\n%s", tb_text)
```

Now, let's create a placeholder function to handle exceptions, interpret them using the AI Swarm, and take actions accordingly:

```python
def handle_exception(exc):
    log_exception(exc)

    # Placeholder for running AI Swarm on the exception
    # response = swarm.run_swarms(f"Interpret this error:\n{str(exc)}")
    
    # TODO: Handle the exception based on the Swarm's response
```

Then, in our routes, instead of just logging the error and returning a `HTTPException`, we should call our `handle_exception` function:

```python
@app.post("/{app_name}/{api_call}")
async def api(app_state: AppState):
    try:
        db = json.load(open('db.json', 'r'))
    except Exception as e:
        handle_exception(e)
        raise HTTPException(status_code=500, detail="Error loading database")

    # ...
    
    try:
        # Update to call the swarm model
        response = swarm.run_swarms(prompt)
        new_state = response['new_database_state']
        
        if new_state:
            db[app_state.app_name]["state"] = new_state
            json.dump(db, open('db.json', 'w'), indent=4)
    except Exception as e:
        handle_exception(e)
        raise HTTPException(status_code=500, detail="Error running model or updating state")
        
    return response
```

Please note that the error handling code is just a placeholder. How exactly the swarm should handle exceptions depends on the swarm's capabilities and requirements. You need to replace the placeholder code with the actual implementation.