Asgi documentation (#17)

* Add devcontainer configuration for development setup

* Create setup.sh

* Add Codespaces prebuild workflow configuration

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* Config changes (#14)

* Codespaces  prebuild configs 

* Add devcontainer configuration for development setup

* Create setup.sh

* Add Codespaces prebuild workflow configuration

* Remove unnecessary cache configuration from CI workflow

* Codespaces (#13)

* Add devcontainer configuration for development setup

* Create setup.sh

* Add Codespaces prebuild workflow configuration

* Remove unnecessary cache configuration from CI workflow

* Delete style.css

* Add ASGI and Execution Model documentation for FastAPI

Author: Santhosh-p653

docs/fastapi/01-asgi-and-execution-model.md

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+# ASGI and Execution Model in FastAPI
+
+## 1. Introduction
+
+Modern backend systems require the ability to handle a large number of concurrent requests efficiently. Traditional synchronous models often become bottlenecks under high I/O workloads. To address this, asynchronous execution models have become a standard approach in modern web frameworks.
+
+FastAPI is built on top of the ASGI (Asynchronous Server Gateway Interface) specification, which enables non-blocking request handling and high concurrency.
+
+---
+
+## 2. Problem Definition
+
+Traditional Python web applications relied on WSGI (Web Server Gateway Interface), which follows a synchronous execution model.
+
+Limitations of WSGI:
+
+- Blocking request handling
+- Poor performance under I/O-bound workloads
+- Limited scalability for real-time systems
+
+This creates challenges when building:
+
+- AI inference APIs
+- agent-based systems
+- streaming or real-time applications
+
+---
+
+## 3. Core Concept: ASGI
+
+ASGI is a specification that defines how web servers communicate with Python applications asynchronously.
+
+An ASGI application operates using three core components:
+
+- scope → metadata about the request (type, headers, path)
+- receive → channel to receive incoming data
+- send → channel to send responses
+
+---
+
+## 4. ASGI Execution Flow
+
+sequenceDiagram
+    participant Client
+    participant Uvicorn (ASGI Server)
+    participant FastAPI App
+
+    Client->>Uvicorn (ASGI Server): HTTP Request
+    Uvicorn (ASGI Server)->>FastAPI App: scope
+    FastAPI App->>Uvicorn (ASGI Server): receive()
+    FastAPI App->>Uvicorn (ASGI Server): send(response)
+    Uvicorn (ASGI Server)->>Client: HTTP Response
+
+## Figure: Interaction between client, ASGI server, and FastAPI application.
+
+---
+
+## 5. FastAPI System Perspective
+
+FastAPI does not operate in isolation. It is part of a layered architecture:
+
+flowchart LR
+    A[Client] --> B[Uvicorn ASGI Server]
+    B --> C[FastAPI Application]
+    C --> D[Starlette (Routing & Middleware)]
+    C --> E[Pydantic (Validation)]
+
+Components:
+
+- Uvicorn → ASGI server handling network communication
+- FastAPI → API layer defining endpoints
+- Starlette → routing, middleware, background tasks
+- Pydantic → data validation and parsing
+
+---
+
+## 6. Request Handling Model
+
+FastAPI leverages Python’s "async" / "await" syntax:
+
+from fastapi import FastAPI
+
+app = FastAPI()
+
+@app.get("/")
+async def root():
+    return {"message": "Hello World"}
+
+Key Idea:
+
+- The function is non-blocking
+- While waiting (e.g., DB call), other requests can be processed
+
+---
+
+## 7. Trade-offs and Limitations
+
+While ASGI provides significant advantages, it introduces complexity:
+
+Advantages:
+
+- High concurrency
+- Efficient I/O handling
+- Suitable for AI/ML inference APIs
+
+Limitations:
+
+- Increased conceptual complexity
+- Debugging async code is harder
+- Not always beneficial for CPU-bound tasks
+
+---
+
+## 8. Key Takeaways
+
+- FastAPI is built on the ASGI specification
+- ASGI enables asynchronous, non-blocking execution
+- The execution model revolves around:
+  - "scope", "receive", "send"
+- FastAPI acts as a system boundary layer between clients and backend logic
+
+---
+
+## 9. Next
+
+→ Request-Response Lifecycle in FastAPI