01. Build a ChatGPT Clone

Introduction

Build a production-grade conversational AI platform like ChatGPT — with streaming responses, conversation memory, tool/function calling, file uploads, multi-model selection, and a polished UI.

ChatGPT redefined how users interact with AI. This project teaches you to build every feature that makes ChatGPT great — from real-time token streaming to conversation management, tool calling, and markdown rendering.

Problem Statement

Users need a conversational AI interface that provides real-time responses, maintains context across conversations, supports multiple AI models, handles file uploads, and enables tools/plugins — all with a polished, responsive UX.

Business Use Case

A startup building an AI chat platform for customer support teams needs a ChatGPT-like interface that:

Handles 10K+ conversations/day
Supports multiple LLM providers (OpenAI, Anthropic, Gemini)
Provides real-time streaming responses
Includes conversation history and search
Supports file uploads and tool calling
Deploys cost-effectively on cloud infrastructure

Requirements

Functional Requirements

#	Feature	Description
FR1	User authentication	Sign up, login, OAuth (Google, GitHub)
FR2	Chat sessions	Create, rename, delete conversations
FR3	Streaming responses	Real-time token display via SSE
FR4	Model selection	Switch between GPT-4o, Claude, Gemini
FR5	Conversation memory	Maintain context across turns
FR6	Message history	Persist and retrieve past conversations
FR7	File upload	Upload images, PDFs, documents
FR8	Tool calling	Calculator, web search, code execution
FR9	Markdown rendering	Code syntax highlighting, tables, math
FR10	Conversation search	Full-text search across history
FR11	Export chat	Download as PDF, Markdown, JSON
FR12	Dark mode	Light/dark theme toggle

Non-Functional Requirements

#	Requirement	Target
NFR1	Response latency	TTFT < 500ms, streaming at 30+ tokens/s
NFR2	Availability	99.9% uptime
NFR3	Scalability	Support 10K concurrent users
NFR4	Security	End-to-end encryption, SOC2 compliance
NFR5	Cost efficiency	$0.02 per avg conversation

Technology Stack

Layer	Technology	Purpose
Frontend	Next.js 14 + React + Tailwind CSS	Web UI, SSR, streaming
Backend	FastAPI (Python)	API server, WebSocket management
Database	PostgreSQL + pgvector	Conversations, user data, embeddings
Cache	Redis	Session cache, rate limiting
Vector DB	Qdrant / pgvector	Message embeddings for search
AI	OpenAI, Anthropic, Gemini APIs	Multi-model LLM support
Streaming	Server-Sent Events (SSE)	Real-time token streaming
Auth	NextAuth.js / JWT	Authentication
File Storage	S3 / GCS	File upload storage
Deployment	Docker + Kubernetes + AWS	Production infrastructure

Folder Structure

chatgpt-clone/
├── frontend/
│   ├── app/
│   │   ├── (auth)/          # Login, signup pages
│   │   ├── chat/            # Main chat interface
│   │   │   ├── [id]/        # Individual chat session
│   │   │   └── layout.tsx   # Chat layout with sidebar
│   │   └── api/             # Next.js API routes
│   ├── components/
│   │   ├── chat/
│   │   │   ├── ChatMessage.tsx    # Message bubble
│   │   │   ├── ChatInput.tsx      # Input with file upload
│   │   │   ├── StreamingText.tsx  # Real-time token display
│   │   │   └── ModelSelector.tsx  # Model picker dropdown
│   │   ├── ui/
│   │   │   ├── Markdown.tsx       # Rendered markdown
│   │   │   └── CodeBlock.tsx      # Syntax highlighted code
│   │   └── layout/
│   │       ├── Sidebar.tsx        # Conversation list
│   │       └── Header.tsx         # User menu, settings
│   ├── lib/
│   │   ├── api.ts            # API client
│   │   ├── streaming.ts      # SSE handler
│   │   └── utils.ts
│   └── store/
│       └── chat.ts           # Zustand state
├── backend/
│   ├── app/
│   │   ├── routes/
│   │   │   ├── auth.py       # Auth endpoints
│   │   │   ├── chat.py       # Chat CRUD + streaming
│   │   │   ├── conversations.py
│   │   │   └── files.py      # File upload
│   │   ├── services/
│   │   │   ├── llm_service.py     # Multi-provider LLM
│   │   │   ├── memory_service.py  # Conversation memory
│   │   │   ├── tool_service.py    # Tool execution
│   │   │   └── search_service.py  # Conversation search
│   │   ├── models/
│   │   │   └── conversation.py    # DB models
│   │   └── core/
│   │       ├── config.py
│   │       └── security.py
│   └── requirements.txt
├── ai/
│   ├── prompts/
│   │   ├── system-chat.yaml
│   │   └── tools/
│   └── embeddings/
├── deployment/
│   ├── Dockerfile
│   ├── docker-compose.yml
│   └── k8s/
└── docs/
    └── architecture.md

API Design

sequenceDiagram
    participant U as User
    participant F as Frontend
    participant API as API Gateway
    participant Chat as Chat Service
    participant LLM as LLM Service
    participant DB as Database

    U->>F: Type message
    F->>API: POST /api/chat/{id}/messages
    API->>Chat: Process message
    Chat->>DB: Save user message
    Chat->>Chat: Build context (history + system prompt)
    Chat->>LLM: Stream completion
    LLM-->>Chat: Token stream (SSE)
    Chat-->>API: Stream tokens
    API-->>F: SSE events
    F-->>U: Render tokens in real-time
    Note over F: User sees tokens appearing
    LLM-->>Chat: Complete response
    Chat->>DB: Save assistant response
    API-->>F: Stream complete event

Key Endpoints

Method	Endpoint	Purpose
POST	`/api/auth/login`	User login
POST	`/api/auth/signup`	User registration
GET	`/api/conversations`	List user conversations
POST	`/api/conversations`	Create new conversation
GET	`/api/conversations/{id}`	Get conversation history
DELETE	`/api/conversations/{id}`	Delete conversation
POST	`/api/chat/{id}/messages`	Send message + stream response
POST	`/api/chat/{id}/stream`	SSE streaming endpoint
POST	`/api/files/upload`	Upload file for context
GET	`/api/search?q=`	Search conversations
GET	`/api/models`	List available models

Architecture

flowchart TD
    subgraph FRONTEND["Frontend (Next.js)"]
        UI["Chat UI\nReact Components"]
        STATE["State Management\nZustand"]
        SSE["SSE Client\nEventSource"]
    end
    subgraph API["API Layer"]
        GW["NextAuth\nAuthentication"]
        ROUTER["API Routes\nEdge Functions"]
        WS["WebSocket\nStreaming"]
    end
    subgraph SERVICES["Backend Services"]
        CHAT_SVC["Chat Service\nConversation management"]
        LLM_SVC["LLM Service\nMulti-provider routing"]
        TOOL_SVC["Tool Service\nFunction execution"]
        MEM_SVC["Memory Service\nContext window"]
    end
    subgraph DATA["Data Layer"]
        PG["PostgreSQL\n+ pgvector"]
        REDIS["Redis\nCache + Sessions"]
        S3["Object Store\nFiles"]
    end
    subgraph AI["AI Providers"]
        OPENAI["OpenAI\nGPT-4o"]
        ANTHRO["Anthropic\nClaude"]
        GEMINI["Google\nGemini"]
    end

    UI --> GW
    UI --> SSE
    SSE --> ROUTER
    ROUTER --> CHAT_SVC
    CHAT_SVC --> LLM_SVC
    LLM_SVC --> TOOL_SVC
    LLM_SVC --> MEM_SVC
    CHAT_SVC --> PG
    LLM_SVC --> REDIS
    LLM_SVC --> OPENAI
    LLM_SVC --> ANTHRO
    LLM_SVC --> GEMINI
    CHAT_SVC --> S3

    style FRONTEND fill:#3b82f6,color:#fff
    style API fill:#8b5cf6,color:#fff
    style SERVICES fill:#22c55e,color:#fff
    style DATA fill:#f59e0b,color:#fff
    style AI fill:#ef4444,color:#fff

Authentication & Authorization

sequenceDiagram
    participant U as User
    participant F as Frontend
    participant Auth as NextAuth
    participant DB as Database

    U->>F: Click "Sign in with Google"
    F->>Auth: OAuth request
    Auth->>U: Google login page
    U->>Auth: Grant access
    Auth->>F: JWT token
    F->>F: Store token (httpOnly cookie)
    Note over F: All subsequent requests include JWT

    F->>Auth: GET /api/conversations
    Auth->>Auth: Verify JWT signature
    Auth->>DB: Fetch user's conversations
    DB-->>Auth: Filtered by user_id
    Auth-->>F: Return conversations

Authorization rules:

Users can only access their own conversations
Admin users can view system-wide analytics
API keys are scoped to specific features

Streaming Implementation

flowchart LR
    USER["User sends message"] --> QUEUE["Queue message"]
    QUEUE --> CONTEXT["Build context\nHistory + System prompt + Files"]
    CONTEXT --> MODEL{"Model\nSelection"}
    MODEL -->|"GPT-4o"| GPT["OpenAI Stream"]
    MODEL -->|"Claude"| CLAUDE["Anthropic Stream"]
    MODEL -->|"Gemini"| GEMINI["Google Stream"]

    GPT --> SSE["SSE Connection"]
    CLAUDE --> SSE
    GEMINI --> SSE

    SSE --> PARSE["Parse tokens\nMarkdown rendering"]
    PARSE --> UI["Update UI\nReal-time display"]

    style SSE fill:#3b82f6,color:#fff
    style UI fill:#22c55e,color:#fff

Streaming Event Format

event: token
data: {"token": "Hello", "index": 0}

event: token
data: {"token": " world", "index": 1}

event: done
data: {"conversation_id": "abc123", "usage": {"prompt_tokens": 150, "completion_tokens": 42}}

event: error
data: {"code": "rate_limit", "message": "Rate limit exceeded"}

Deployment Architecture

flowchart TD
    subgraph CI_CD["CI/CD Pipeline"]
        GIT["Git Push"] --> BUILD["Docker Build"]
        BUILD --> TEST["Run Tests"]
        TEST --> REGISTRY["Push to ECR"]
    end
    subgraph PROD["Production (AWS)"]
        subgraph NET["Networking"]
            CF["CloudFront CDN"]
            ALB["Application Load Balancer"]
        end
        subgraph K8S["EKS Cluster"]
            FE["Frontend Pods\nNext.js"]
            API_PODS["API Pods\nFastAPI"]
            WORKER["Background Workers"]
        end
        subgraph DATA_LAYER["Data"]
            RDS["RDS PostgreSQL"]
            ELASTICACHE["ElastiCache Redis"]
            S3_BUCKET["S3 Files"]
        end
    end

    REGISTRY --> K8S
    USER["Users"] --> CF
    CF --> ALB
    ALB --> FE
    ALB --> API_PODS
    FE --> API_PODS
    API_PODS --> RDS
    API_PODS --> ELASTICACHE
    API_PODS --> S3_BUCKET

    style PROD fill:#1e293b,color:#fff
    style K8S fill:#3b82f6,color:#fff
    style DATA_LAYER fill:#f59e0b,color:#fff

Security

Concern	Implementation
Authentication	NextAuth.js with OAuth + JWT
Authorization	Row-level security on conversations
Encryption	TLS 1.3, AES-256 at rest
Rate limiting	Redis-based token bucket (100 req/min per user)
Input validation	Sanitize all user input before LLM calls
Output safety	Guardrails on LLM output (toxicity, PII)
Secret management	AWS Secrets Manager / HashiCorp Vault

Monitoring

flowchart LR
    subgraph METRICS["Key Metrics"]
        LATENCY["TTFT, TPOT, Total Latency"]
        TOKENS["Tokens per Conversation"]
        COST["Cost per User per Day"]
        ERRORS["Error Rate by Endpoint"]
        USAGE["Active Users, Conversations"]
    end
    subgraph TOOLS["Tools"]
        GRAFANA["Grafana Dashboards"]
        DATADOG["Datadog APM"]
        LANGSMITH["LangSmith Traces"]
        PAGERDUTY["Alerting"]
    end

    METRICS --> TOOLS

    style METRICS fill:#3b82f6,color:#fff
    style TOOLS fill:#22c55e,color:#fff

Evaluation

Dimension	Method	Target
Response quality	LLM-as-a-Judge	≥ 90%
User satisfaction	Thumbs up/down	≥ 85%
Task completion	Rate of conversations with resolution	≥ 80%
Latency P95	Monitor	≤ 3s
Cost per conversation	Token tracking	≤ $0.05

Scaling

Component	Strategy
Frontend	Next.js CDN caching, ISR for static pages
API servers	Horizontal autoscaling (HPA), target 70% CPU
Database	Read replicas for conversation history, connection pooling
Cache	Redis cluster with read replicas
LLM API	Multi-key rotation, fallback models, request queuing
File storage	S3 with CDN for uploaded files

Future Improvements

Feature	Priority	Complexity
Voice input/output	High	Medium
Image generation (DALL-E)	Medium	Low
Custom GPTs/prompt library	High	Medium
Team workspaces	Medium	High
Offline mode (PWA)	Low	High
Plugin ecosystem	Medium	High

Interview Questions

Architecture

Q: How would you design the streaming architecture for a ChatGPT clone handling 10K concurrent users?

Use SSE (Server-Sent Events) from FastAPI to stream tokens. Each user gets a persistent SSE connection. Use async generators to yield tokens from the LLM API. Implement backpressure — if the client is slow, buffer tokens or drop older ones. Use Redis pub/sub to handle horizontal scaling: any API pod can stream to any user.

Q: How do you manage conversation context within token limits?

Strategy: (1) Track total tokens per conversation, (2) When approaching limit, summarize older messages, (3) Keep system prompt + last N messages in full, summarize everything before that, (4) Use a sliding window — discard oldest messages first, (5) For very long conversations, create “checkpoints” that summarize the conversation so far.

System Design

Q: Design the database schema for a ChatGPT clone.

Tables: users (id, email, name, auth_provider), conversations (id, user_id, title, model, created_at, updated_at), messages (id, conversation_id, role, content, tokens, created_at), message_files (id, message_id, file_url, file_type). Use pgvector for message embeddings to enable semantic search across conversations.

Coding

Q: Implement an SSE streaming endpoint for chat.

See the streaming implementation above. Key: use StreamingResponse with an async generator that yields tokens as they arrive from the LLM provider.

Summary

Feature	Implementation
Chat UI	Next.js + Tailwind + React Server Components
Streaming	SSE from FastAPI to Next.js
Multi-model	OpenAI, Anthropic, Gemini through unified interface
Memory	Sliding window + summarization
Files	S3 upload + context injection
Search	pgvector embeddings
Auth	NextAuth.js + JWT
Deployment	Docker + Kubernetes + AWS

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Related Projects:

01. Build a ChatGPT Clone

Introduction

Problem Statement

Business Use Case

Requirements

Functional Requirements

Non-Functional Requirements

Technology Stack

Folder Structure

API Design

Key Endpoints

Architecture

Authentication & Authorization

Streaming Implementation

Streaming Event Format

Deployment Architecture

Security

Monitoring

Evaluation

Scaling

Future Improvements

Interview Questions

Architecture

System Design

Coding

Summary

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