diff --git a/.dockerignore b/.dockerignore new file mode 100644 index 0000000000000000000000000000000000000000..5576bd07deba57020d32f297db1fe232b3533689 --- /dev/null +++ b/.dockerignore @@ -0,0 +1,9 @@ +.git +.github +__pycache__/ +*.py[cod] +.pytest_cache/ +llmopt.db +config/.env +tests/ +.env diff --git a/.env.example b/.env.example index c8ac8479bd82159d09aace6e9008803e7218a63f..e87d377573eb49b69d18a6e5ac246e0881fafd9f 100644 --- a/.env.example +++ b/.env.example @@ -1,12 +1,42 @@ # LLMOpt Environment Variables -# OpenAI -OPENAI_API_KEY=your_openai_api_key_here +# ========================================== +# 1. Database & Redis Session Cache (Production) +# ========================================== +# PostgreSQL Database URL (e.g. Neon, Supabase, etc.) +# If not set, LLMOpt defaults to local SQLite. +DATABASE_URL=postgresql://user:password@ep-cool-fog-12345.aws.neon.tech/neondb?sslmode=require -# Anthropic -ANTHROPIC_API_KEY=your_anthropic_api_key_here +# Upstash Redis or Redis Cloud connection string (Mandatory for sessions) +REDIS_URL=redis://default:password@endpoint.upstash.io:30000 + +# 32-byte base64-encoded session key for encrypting user API keys in transit/at rest. +# Generate in python with: cryptography.fernet.Fernet.generate_key().decode() +SESSION_SECRET_KEY=generate_your_own_32_byte_base64_key_here + +# Session Time-to-Live (TTL) in seconds (default: 7200 seconds / 2 hours) +SESSION_TTL=7200 + +# ========================================== +# 2. Third-Party OAuth Sign-In (Optional) +# ========================================== +# Google OAuth +GOOGLE_CLIENT_ID=your_google_client_id.apps.googleusercontent.com +GOOGLE_CLIENT_SECRET=GOCSPX-your_google_client_secret_here -# Redis Semantic Cache (V2) -# Option 1: Local Docker -> redis://localhost:6379 -# Option 2: Redis Cloud -> redis://default:password@endpoint.redis-cloud.com:12345 -REDIS_URL=redis://localhost:6379 +# GitHub OAuth +GITHUB_CLIENT_ID=your_github_client_id_here +GITHUB_CLIENT_SECRET=your_github_client_secret_here + +# The base URL of the frontend for OAuth redirect callbacks (e.g. your Vercel URL) +REDIRECT_URI_HOST=https://your-frontend.vercel.app + +# ========================================== +# 3. Direct LLM Provider Keys (Fallback / Local run only) +# ========================================== +# In Bring Your Own Key (BYOK) mode, these are not stored on the server. +# Provide them here only if running locally or using server-wide default keys. +OPENAI_API_KEY=your_openai_api_key_here +ANTHROPIC_API_KEY=your_anthropic_api_key_here +GEMINI_API_KEY=your_gemini_api_key_here +OLLAMA_API_BASE=http://localhost:11434 diff --git a/.gitignore b/.gitignore index 0e7fce11b67b7e5f4be911ceb7041736d59e11bf..fb8560b3c6f36246f5341eb01186a70cfa6ffd24 100644 --- a/.gitignore +++ b/.gitignore @@ -51,3 +51,11 @@ coverage.xml .idea/ *.swp *.swo + +# Runtime/Database files +llmopt.db +data/runtime_stats.json + +# Local Environment secrets +/config/.env + diff --git a/Dockerfile b/Dockerfile index 90a127d0b1dfa99c4ad5bdb342ba4320c9a57e25..b083f9135a0783e522843abc95711fa44f0fad0c 100644 --- a/Dockerfile +++ b/Dockerfile @@ -2,10 +2,8 @@ FROM python:3.10-slim # Install system dependencies # build-essential is needed for some ML package wheels -# redis-server is needed for the local Semantic Caching layer RUN apt-get update && apt-get install -y \ build-essential \ - redis-server \ && rm -rf /var/lib/apt/lists/* # Set up a new user named "user" with user ID 1000 (Mandatory for Hugging Face Spaces) diff --git a/data/model_registry.json b/data/model_registry.json index d2173512fefe2c3e534090e64dbfab564fa21221..ec9ae71d724c27b0e7139900dcb14a0ce942b521 100644 --- a/data/model_registry.json +++ b/data/model_registry.json @@ -84,32 +84,32 @@ "notes": "Cheapest Anthropic model. Good for classification, summarization." }, { - "model_name": "gemini-1.5-flash", + "model_name": "gemini-2.5-flash", "provider": "google", - "input_cost_per_1k": 0.000075, - "output_cost_per_1k": 0.000300, - "context_window": 1000000, - "reasoning_score": 0.74, - "coding_score": 0.74, - "math_score": 0.70, - "instruction_following_score": 0.78, - "latency_score": 0.88, - "max_complexity": 0.72, - "notes": "Extremely cheap and fast. Long context support." + "input_cost_per_1k": 0.00015, + "output_cost_per_1k": 0.0006, + "context_window": 1048576, + "reasoning_score": 0.83, + "coding_score": 0.82, + "math_score": 0.84, + "instruction_following_score": 0.85, + "latency_score": 0.90, + "max_complexity": 0.83, + "notes": "Very cheap and fast Gemini 2.5 model." }, { - "model_name": "gemini-1.5-pro", + "model_name": "gemini-2.5-pro", "provider": "google", "input_cost_per_1k": 0.00125, - "output_cost_per_1k": 0.005, - "context_window": 2000000, - "reasoning_score": 0.88, - "coding_score": 0.87, - "math_score": 0.85, - "instruction_following_score": 0.90, - "latency_score": 0.72, + "output_cost_per_1k": 0.010, + "context_window": 1048576, + "reasoning_score": 0.94, + "coding_score": 0.92, + "math_score": 0.93, + "instruction_following_score": 0.92, + "latency_score": 0.75, "max_complexity": 0.95, - "notes": "Massive context window. Great for long-doc analysis." + "notes": "Powerful Gemini 2.5 model with massive context window." }, { "model_name": "mistral-small-latest", diff --git a/data/model_registry_v2.json b/data/model_registry_v2.json new file mode 100644 index 0000000000000000000000000000000000000000..6147cd806cbe86e8ac27ae1a0142d36b5554df82 --- /dev/null +++ b/data/model_registry_v2.json @@ -0,0 +1,507 @@ +{ + "_meta": { + "version": "2.0.0", + "description": "LLMOpt Utility-Based Model Registry. Scores sourced from LMSYS Arena, Artificial Analysis, HumanEval, MMLU-Pro, MATH, IFEval benchmarks. Pricing from provider docs + OpenRouter. Updated via hybrid fetcher.", + "last_updated": "2025-01-01T00:00:00Z", + "score_range": "All capability scores normalized 0.0–1.0", + "pricing_unit": "USD per 1000 tokens" + }, + "models": { + "gpt-4o": { + "provider": "openai", + "model_family": "gpt-4o", + "context_window": 128000, + "max_output_tokens": 16384, + "input_cost_per_1k": 0.0025, + "output_cost_per_1k": 0.010, + "avg_latency_ms": 1800, + "tokens_per_second": 80, + "capabilities": { + "reasoning": 0.92, + "coding": 0.91, + "math": 0.87, + "creativity": 0.88, + "factuality": 0.89, + "instruction_following": 0.94, + "long_context": 0.85, + "multilingual": 0.84, + "tool_use": 0.93, + "summarization": 0.90, + "conversation": 0.91 + }, + "features": { + "tool_calling": true, + "json_mode": true, + "streaming": true, + "image_input": true, + "function_calling": true + }, + "benchmark_sources": ["lmsys_arena", "mmlu_pro", "humaneval", "math_benchmark", "ifeval"], + "pricing_last_updated": "2025-01-01T00:00:00Z", + "live_patch": {} + }, + "gpt-4o-mini": { + "provider": "openai", + "model_family": "gpt-4o", + "context_window": 128000, + "max_output_tokens": 16384, + "input_cost_per_1k": 0.00015, + "output_cost_per_1k": 0.0006, + "avg_latency_ms": 900, + "tokens_per_second": 120, + "capabilities": { + "reasoning": 0.78, + "coding": 0.76, + "math": 0.72, + "creativity": 0.74, + "factuality": 0.75, + "instruction_following": 0.82, + "long_context": 0.76, + "multilingual": 0.72, + "tool_use": 0.80, + "summarization": 0.78, + "conversation": 0.82 + }, + "features": { + "tool_calling": true, + "json_mode": true, + "streaming": true, + "image_input": true, + "function_calling": true + }, + "benchmark_sources": ["lmsys_arena", "mmlu_pro", "humaneval"], + "pricing_last_updated": "2025-01-01T00:00:00Z", + "live_patch": {} + }, + "gpt-4.1": { + "provider": "openai", + "model_family": "gpt-4.1", + "context_window": 1047576, + "max_output_tokens": 32768, + "input_cost_per_1k": 0.002, + "output_cost_per_1k": 0.008, + "avg_latency_ms": 1600, + "tokens_per_second": 85, + "capabilities": { + "reasoning": 0.93, + "coding": 0.95, + "math": 0.88, + "creativity": 0.87, + "factuality": 0.90, + "instruction_following": 0.95, + "long_context": 0.97, + "multilingual": 0.85, + "tool_use": 0.95, + "summarization": 0.92, + "conversation": 0.90 + }, + "features": { + "tool_calling": true, + "json_mode": true, + "streaming": true, + "image_input": true, + "function_calling": true + }, + "benchmark_sources": ["openai_evals", "swe_bench", "humaneval"], + "pricing_last_updated": "2025-01-01T00:00:00Z", + "live_patch": {} + }, + "gpt-4.1-mini": { + "provider": "openai", + "model_family": "gpt-4.1", + "context_window": 1047576, + "max_output_tokens": 32768, + "input_cost_per_1k": 0.0004, + "output_cost_per_1k": 0.0016, + "avg_latency_ms": 750, + "tokens_per_second": 140, + "capabilities": { + "reasoning": 0.80, + "coding": 0.82, + "math": 0.75, + "creativity": 0.76, + "factuality": 0.78, + "instruction_following": 0.85, + "long_context": 0.92, + "multilingual": 0.74, + "tool_use": 0.83, + "summarization": 0.80, + "conversation": 0.83 + }, + "features": { + "tool_calling": true, + "json_mode": true, + "streaming": true, + "image_input": true, + "function_calling": true + }, + "benchmark_sources": ["openai_evals"], + "pricing_last_updated": "2025-01-01T00:00:00Z", + "live_patch": {} + }, + "claude-opus-4-5": { + "provider": "anthropic", + "model_family": "claude-4", + "context_window": 200000, + "max_output_tokens": 32000, + "input_cost_per_1k": 0.015, + "output_cost_per_1k": 0.075, + "avg_latency_ms": 2500, + "tokens_per_second": 65, + "capabilities": { + "reasoning": 0.96, + "coding": 0.95, + "math": 0.91, + "creativity": 0.95, + "factuality": 0.93, + "instruction_following": 0.96, + "long_context": 0.94, + "multilingual": 0.87, + "tool_use": 0.94, + "summarization": 0.95, + "conversation": 0.96 + }, + "features": { + "tool_calling": true, + "json_mode": true, + "streaming": true, + "image_input": true, + "function_calling": true + }, + "benchmark_sources": ["lmsys_arena", "mmlu_pro", "swe_bench", "humaneval", "math_benchmark"], + "pricing_last_updated": "2025-01-01T00:00:00Z", + "live_patch": {} + }, + "claude-sonnet-4-5": { + "provider": "anthropic", + "model_family": "claude-4", + "context_window": 200000, + "max_output_tokens": 16000, + "input_cost_per_1k": 0.003, + "output_cost_per_1k": 0.015, + "avg_latency_ms": 1400, + "tokens_per_second": 90, + "capabilities": { + "reasoning": 0.91, + "coding": 0.93, + "math": 0.86, + "creativity": 0.90, + "factuality": 0.90, + "instruction_following": 0.93, + "long_context": 0.91, + "multilingual": 0.84, + "tool_use": 0.92, + "summarization": 0.91, + "conversation": 0.92 + }, + "features": { + "tool_calling": true, + "json_mode": true, + "streaming": true, + "image_input": true, + "function_calling": true + }, + "benchmark_sources": ["lmsys_arena", "mmlu_pro", "swe_bench", "humaneval"], + "pricing_last_updated": "2025-01-01T00:00:00Z", + "live_patch": {} + }, + "claude-haiku-3-5": { + "provider": "anthropic", + "model_family": "claude-3.5", + "context_window": 200000, + "max_output_tokens": 8192, + "input_cost_per_1k": 0.0008, + "output_cost_per_1k": 0.004, + "avg_latency_ms": 700, + "tokens_per_second": 150, + "capabilities": { + "reasoning": 0.74, + "coding": 0.77, + "math": 0.68, + "creativity": 0.72, + "factuality": 0.73, + "instruction_following": 0.80, + "long_context": 0.78, + "multilingual": 0.72, + "tool_use": 0.78, + "summarization": 0.76, + "conversation": 0.80 + }, + "features": { + "tool_calling": true, + "json_mode": true, + "streaming": true, + "image_input": true, + "function_calling": true + }, + "benchmark_sources": ["lmsys_arena", "humaneval"], + "pricing_last_updated": "2025-01-01T00:00:00Z", + "live_patch": {} + }, + "gemini-2.5-pro": { + "provider": "google", + "model_family": "gemini-2.5", + "context_window": 1048576, + "max_output_tokens": 65536, + "input_cost_per_1k": 0.00125, + "output_cost_per_1k": 0.010, + "avg_latency_ms": 2000, + "tokens_per_second": 75, + "capabilities": { + "reasoning": 0.94, + "coding": 0.92, + "math": 0.93, + "creativity": 0.88, + "factuality": 0.91, + "instruction_following": 0.92, + "long_context": 0.98, + "multilingual": 0.90, + "tool_use": 0.89, + "summarization": 0.92, + "conversation": 0.89 + }, + "features": { + "tool_calling": true, + "json_mode": true, + "streaming": true, + "image_input": true, + "function_calling": true + }, + "benchmark_sources": ["lmsys_arena", "mmlu_pro", "math_benchmark", "humaneval"], + "pricing_last_updated": "2025-01-01T00:00:00Z", + "live_patch": {} + }, + "gemini-2.5-flash": { + "provider": "google", + "model_family": "gemini-2.5", + "context_window": 1048576, + "max_output_tokens": 65536, + "input_cost_per_1k": 0.00015, + "output_cost_per_1k": 0.0006, + "avg_latency_ms": 800, + "tokens_per_second": 130, + "capabilities": { + "reasoning": 0.83, + "coding": 0.82, + "math": 0.84, + "creativity": 0.80, + "factuality": 0.82, + "instruction_following": 0.85, + "long_context": 0.95, + "multilingual": 0.84, + "tool_use": 0.82, + "summarization": 0.83, + "conversation": 0.84 + }, + "features": { + "tool_calling": true, + "json_mode": true, + "streaming": true, + "image_input": true, + "function_calling": true + }, + "benchmark_sources": ["lmsys_arena", "mmlu_pro", "humaneval"], + "pricing_last_updated": "2025-01-01T00:00:00Z", + "live_patch": {} + }, + + "mistral-large-latest": { + "provider": "mistral", + "model_family": "mistral-large", + "context_window": 128000, + "max_output_tokens": 8192, + "input_cost_per_1k": 0.003, + "output_cost_per_1k": 0.009, + "avg_latency_ms": 1600, + "tokens_per_second": 75, + "capabilities": { + "reasoning": 0.82, + "coding": 0.82, + "math": 0.78, + "creativity": 0.78, + "factuality": 0.80, + "instruction_following": 0.84, + "long_context": 0.78, + "multilingual": 0.88, + "tool_use": 0.82, + "summarization": 0.82, + "conversation": 0.82 + }, + "features": { + "tool_calling": true, + "json_mode": true, + "streaming": true, + "image_input": false, + "function_calling": true + }, + "benchmark_sources": ["lmsys_arena", "mmlu_pro", "humaneval"], + "pricing_last_updated": "2025-01-01T00:00:00Z", + "live_patch": {} + }, + "mistral-small-latest": { + "provider": "mistral", + "model_family": "mistral-small", + "context_window": 32000, + "max_output_tokens": 8192, + "input_cost_per_1k": 0.0001, + "output_cost_per_1k": 0.0003, + "avg_latency_ms": 700, + "tokens_per_second": 140, + "capabilities": { + "reasoning": 0.68, + "coding": 0.68, + "math": 0.62, + "creativity": 0.66, + "factuality": 0.65, + "instruction_following": 0.72, + "long_context": 0.60, + "multilingual": 0.80, + "tool_use": 0.68, + "summarization": 0.70, + "conversation": 0.72 + }, + "features": { + "tool_calling": true, + "json_mode": true, + "streaming": true, + "image_input": false, + "function_calling": true + }, + "benchmark_sources": ["lmsys_arena"], + "pricing_last_updated": "2025-01-01T00:00:00Z", + "live_patch": {} + }, + "deepseek-chat": { + "provider": "deepseek", + "model_family": "deepseek-v3", + "context_window": 64000, + "max_output_tokens": 8192, + "input_cost_per_1k": 0.00014, + "output_cost_per_1k": 0.00028, + "avg_latency_ms": 1200, + "tokens_per_second": 95, + "capabilities": { + "reasoning": 0.87, + "coding": 0.90, + "math": 0.91, + "creativity": 0.78, + "factuality": 0.82, + "instruction_following": 0.85, + "long_context": 0.72, + "multilingual": 0.75, + "tool_use": 0.82, + "summarization": 0.82, + "conversation": 0.82 + }, + "features": { + "tool_calling": true, + "json_mode": true, + "streaming": true, + "image_input": false, + "function_calling": true + }, + "benchmark_sources": ["lmsys_arena", "humaneval", "math_benchmark", "mmlu_pro"], + "pricing_last_updated": "2025-01-01T00:00:00Z", + "live_patch": {} + }, + "deepseek-reasoner": { + "provider": "deepseek", + "model_family": "deepseek-r1", + "context_window": 64000, + "max_output_tokens": 8192, + "input_cost_per_1k": 0.00055, + "output_cost_per_1k": 0.00219, + "avg_latency_ms": 3500, + "tokens_per_second": 40, + "capabilities": { + "reasoning": 0.95, + "coding": 0.91, + "math": 0.96, + "creativity": 0.72, + "factuality": 0.88, + "instruction_following": 0.83, + "long_context": 0.70, + "multilingual": 0.72, + "tool_use": 0.75, + "summarization": 0.78, + "conversation": 0.72 + }, + "features": { + "tool_calling": false, + "json_mode": true, + "streaming": true, + "image_input": false, + "function_calling": false + }, + "benchmark_sources": ["aime", "math_benchmark", "humaneval", "mmlu_pro"], + "pricing_last_updated": "2025-01-01T00:00:00Z", + "live_patch": {} + }, + "llama3.3-70b": { + "provider": "ollama", + "model_family": "llama3", + "context_window": 128000, + "max_output_tokens": 8192, + "input_cost_per_1k": 0.0, + "output_cost_per_1k": 0.0, + "avg_latency_ms": 2000, + "tokens_per_second": 50, + "capabilities": { + "reasoning": 0.80, + "coding": 0.79, + "math": 0.74, + "creativity": 0.78, + "factuality": 0.76, + "instruction_following": 0.82, + "long_context": 0.76, + "multilingual": 0.72, + "tool_use": 0.76, + "summarization": 0.80, + "conversation": 0.82 + }, + "features": { + "tool_calling": true, + "json_mode": true, + "streaming": true, + "image_input": false, + "function_calling": true + }, + "benchmark_sources": ["lmsys_arena", "mmlu_pro", "humaneval"], + "pricing_last_updated": "2025-01-01T00:00:00Z", + "live_patch": {} + }, + "llama3.2-vision": { + "provider": "ollama", + "model_family": "llama3", + "context_window": 128000, + "max_output_tokens": 8192, + "input_cost_per_1k": 0.0, + "output_cost_per_1k": 0.0, + "avg_latency_ms": 2500, + "tokens_per_second": 40, + "capabilities": { + "reasoning": 0.70, + "coding": 0.66, + "math": 0.62, + "creativity": 0.70, + "factuality": 0.68, + "instruction_following": 0.74, + "long_context": 0.70, + "multilingual": 0.65, + "tool_use": 0.65, + "summarization": 0.72, + "conversation": 0.75 + }, + "features": { + "tool_calling": false, + "json_mode": true, + "streaming": true, + "image_input": true, + "function_calling": false + }, + "benchmark_sources": ["lmsys_arena"], + "pricing_last_updated": "2025-01-01T00:00:00Z", + "live_patch": {} + } + } +} diff --git a/docs/design.md b/docs/design.md new file mode 100644 index 0000000000000000000000000000000000000000..815f81e1845548ec78bf7985cb77c9673da15bca --- /dev/null +++ b/docs/design.md @@ -0,0 +1,361 @@ +# LLMOpt UI Design Specification + +## Project Overview +**LLMOpt** is an enterprise-grade LLM middleware that intelligently routes queries to the most cost-effective model. The UI must communicate: intelligence, efficiency, cost savings, and observability — all in real time. + +--- + +## Aesthetic Direction: "Dark Industrial Dashboard" + +**Concept**: Think Bloomberg Terminal meets cyberpunk command center. Utilitarian precision with electric accents. Every pixel earns its place. Data-dense but crystal clear. + +**Mood**: Authoritative. Efficient. Technical. Like a cockpit for LLM operations. + +**One unforgettable thing**: A real-time animated pipeline that lights up as a query flows through each stage — users *watch* the optimization happen. + +--- + +## Color Palette + +``` +--bg-base: #0A0B0E /* Near-black base */ +--bg-surface: #111318 /* Card/panel surface */ +--bg-elevated: #1A1D26 /* Elevated panels */ +--bg-border: #252A38 /* Borders */ + +--accent-cyan: #00E5FF /* Primary accent — pipeline glow */ +--accent-green: #00FF94 /* Success, savings, cache hits */ +--accent-amber: #FFB300 /* Warnings, "balanced" tier */ +--accent-red: #FF3D57 /* Errors, expensive routes */ +--accent-purple: #7C4DFF /* ML / AI stage indicators */ + +--text-primary: #E8ECF4 /* Main text */ +--text-secondary: #7A8299 /* Labels, metadata */ +--text-muted: #3D4357 /* Disabled / placeholder */ + +--gradient-glow: linear-gradient(135deg, #00E5FF22, #7C4DFF11) +``` + +--- + +## Typography + +``` +Display / Headers : "JetBrains Mono" (monospace — fits the terminal DNA) +Body / UI Labels : "DM Sans" (clean, readable, modern) +Data / Numbers : "JetBrains Mono" (monospace alignment for metrics) +Code Blocks : "Fira Code" with ligatures + +Sizes: + --text-xs: 11px + --text-sm: 13px + --text-base: 15px + --text-lg: 18px + --text-xl: 24px + --text-2xl: 32px + --text-3xl: 48px +``` + +--- + +## Layout Structure + +``` +┌─────────────────────────────────────────────────────┐ +│ TOPBAR: Logo | Nav Tabs | Status Indicators │ +├──────────────┬──────────────────────────────────────┤ +│ │ │ +│ LEFT PANEL │ MAIN CONTENT AREA │ +│ (280px) │ │ +│ │ [Query Input + Pipeline Visualizer] │ +│ • Config │ [Response Output] │ +│ • Budget │ [Explainability Card] │ +│ • Providers │ │ +│ • History │ │ +│ ├──────────────────────────────────────┤ +│ │ METRICS STRIP (bottom) │ +│ │ Cost | Tokens | Latency | Savings │ +└──────────────┴──────────────────────────────────────┘ +``` + +--- + +## Page / View Breakdown + +### 1. `/` — Playground (Main View) + +The core query interface. This is what users interact with daily. + +**Components:** + +#### Query Input Box +- Large dark textarea with subtle cyan border-glow on focus +- Font: JetBrains Mono +- Placeholder: `// Enter your query...` +- Right side: Budget Mode selector (3 pills: `CHEAP` / `BALANCED` / `QUALITY`) +- Bottom bar inside textarea: token count estimate, `[RUN]` button (cyan, full-right) + +#### Pipeline Visualizer (HERO COMPONENT) +A horizontal animated flow diagram that activates on query submission: + +``` +[CACHE] ──► [NLI ANALYZE] ──► [GBR ESTIMATE] ──► [BAYESIAN OPT] ──► [COMPRESS] ──► [ROUTE] ──► [LLM] +``` + +- Each stage is a pill/node with icon + label +- Inactive: `--bg-elevated` fill, `--text-muted` text +- Active (processing): Cyan pulsing border + glow, animated spinner inside +- Complete: Green fill, checkmark icon, latency badge underneath (e.g., `12ms`) +- Skipped (cache hit): Amber fill with "CACHED" label — flow skips to end +- Connecting lines animate left-to-right as each stage completes + +**Stage Icons:** +| Stage | Icon | +|-------|------| +| Cache | ⚡ (lightning) | +| NLI Analyze | 🔍 | +| GBR Estimate | 📊 | +| Bayesian Opt | ⚙️ | +| Compress | 🗜️ | +| Route | 🔀 | +| LLM | 🤖 | + +#### Response Panel +- Appears below pipeline after completion +- Markdown rendering with syntax highlighting (dark theme) +- Header strip: `Model: claude-3-5-haiku` | `Provider: Anthropic` | copy button +- Subtle fade-in animation on arrival + +#### Explainability Card (collapsible) +- Monospace font block styled like a terminal output +- Cyan `>` prefix on each line +- Shows: complexity score, domain, selected model, scoring rationale, cost saved +- Toggle with `[EXPLAIN]` button next to Run + +--- + +### 2. `/analytics` — Observability Dashboard + +**Components:** + +#### KPI Row (top 4 cards) +``` +┌──────────────┐ ┌──────────────┐ ┌──────────────┐ ┌──────────────┐ +│ Total Saved │ │ Avg Latency │ │ Cache Hit % │ │ Total Queries│ +│ $12.48 │ │ 840ms │ │ 34% │ │ 1,204 │ +│ ↑ 18% today │ │ ↓ 12% │ │ ↑ 5% │ │ │ +└──────────────┘ └──────────────┘ └──────────────┘ └──────────────┘ +``` +- Micro sparklines inside each card (7-day trend) +- Green arrows = good, Red = bad + +#### Model Usage Breakdown +- Horizontal stacked bar chart +- Each provider has a distinct color segment +- Hover shows: model name, % of queries, avg cost + +#### Cost Over Time +- Area chart, cyan fill with glow +- X-axis: time (last 7 days / 30 days toggle) +- Y-axis: USD +- Dotted line showing "cost if all GPT-4o" — dramatic visual of savings + +#### Query Log Table +``` +Timestamp | Query Preview | Complexity | Model Used | Cost | Latency | Score +``` +- Zebra striping with `--bg-surface` / `--bg-elevated` +- Complexity shown as colored bar (green → amber → red) +- Clickable rows expand to show full explainability output + +--- + +### 3. `/models` — Model Registry + +**Components:** + +#### Model Cards Grid (2-col) +Each card: +- Model name (large, monospace) +- Provider badge (colored pill) +- Capability score as radial gauge (0–1) +- Pricing: Input / Output per 1k tokens +- "Best For" tag +- Toggle: Enable / Disable this model + +#### Comparison Table +- Sortable columns: Capability, Input Cost, Output Cost, Best For +- Highlight the "Best Value" row with cyan left border + +--- + +### 4. `/settings` — Configuration + +**Components:** +- API Key inputs per provider (masked, with test button) +- Redis URL config +- Budget weight sliders (α Cost / β Tokens / γ Quality) with live formula display +- Compression toggle + threshold slider +- Evaluation (LLM-as-Judge) toggle + +--- + +## Component Design Details + +### Sidebar Navigation +``` +┌─────────────────┐ +│ ⚡ LLMOpt │ ← Logo: monospace, cyan accent +├─────────────────┤ +│ ▸ Playground │ ← Active: cyan left border + bg highlight +│ ▸ Analytics │ +│ ▸ Models │ +│ ▸ Settings │ +├─────────────────┤ +│ SYSTEM STATUS │ +│ ● Redis OK │ ← Green dot +│ ● ML Deps OK │ +│ ● Cache 34% │ +└─────────────────┘ +``` + +### Budget Mode Pills +``` +[ CHEAP ] [ BALANCED ] [ QUALITY ] +``` +- Inactive: `--bg-elevated` + `--text-secondary` +- Active CHEAP: Green fill +- Active BALANCED: Amber fill +- Active QUALITY: Cyan fill + +### Metric Cards +```css +.metric-card { + background: var(--bg-surface); + border: 1px solid var(--bg-border); + border-radius: 8px; + padding: 20px 24px; + position: relative; + overflow: hidden; +} +.metric-card::before { + content: ''; + position: absolute; + top: 0; left: 0; right: 0; + height: 2px; + background: var(--accent-cyan); /* or green/amber/purple per card */ +} +``` + +### Status Dots +```css +.dot-live { + width: 8px; height: 8px; + border-radius: 50%; + background: var(--accent-green); + box-shadow: 0 0 8px var(--accent-green); + animation: pulse 2s infinite; +} +``` + +--- + +## Animation Spec + +### Pipeline Stage Activation +``` +Trigger: query submitted +Sequence: + t=0ms → CACHE node: border glows cyan, spinner starts + t=~200ms → CACHE completes (hit/miss), NLI node activates + t=~400ms → NLI completes, GBR node activates + ...and so on until ROUTE + Final → Response panel fades in (opacity 0→1, translateY 8px→0, 300ms ease) +``` + +### Page Load +- Sidebar slides in from left (translateX -100% → 0, 400ms ease-out) +- KPI cards stagger in with 80ms delay each (opacity 0→1, translateY 16px→0) +- Chart areas draw from left (width 0→100%, 600ms ease-in-out) + +### Hover States +- Cards: `border-color` transitions to `--accent-cyan` at 30% opacity +- Buttons: subtle scale(1.02) + glow intensification +- Table rows: `--bg-elevated` background fill + +--- + +## Responsive Breakpoints + +``` +Desktop (≥1280px) : Full 2-panel layout as described +Tablet (≥768px) : Sidebar collapses to icon rail (48px) +Mobile (<768px) : Full-screen single column, bottom tab nav +``` + +--- + +## Tech Stack Recommendation + +``` +Framework : React 18 + TypeScript +Styling : Tailwind CSS + CSS custom properties for theming +Charts : Recharts (area, bar, sparklines) +Animation : Framer Motion (pipeline, page transitions) +Markdown : react-markdown + react-syntax-highlighter +Icons : Lucide React +API Client : axios / fetch with React Query for caching +State : Zustand (lightweight global state) +``` + +--- + +## Key UX Principles + +1. **Show, don't tell** — the pipeline animation IS the explainability +2. **Every number has context** — cost shown alongside "vs GPT-4o baseline" +3. **Progressive disclosure** — simple by default, deep data on demand +4. **Zero loading skeletons** — use optimistic UI and instant local feedback +5. **Error states are designed** — not afterthoughts. Red glow on failed stages, clear recovery path. + +--- + +## Sample Data / Placeholders + +Use these for mockups: + +```json +{ + "query": "Write a recursive Fibonacci function in Rust", + "model_used": "claude-3-5-haiku-20241022", + "provider": "anthropic", + "complexity_score": 0.62, + "complexity_tier": "hard", + "estimated_cost": 0.001452, + "tokens_saved": 28, + "compression_ratio": 0.21, + "latency_ms": 1140, + "evaluation": { + "overall": 9.5, + "accuracy": 10.0, + "feedback": "The code is idiomatic and correctly implements recursion." + } +} +``` + +--- + +## Deliverables Checklist for Agent + +- [ ] `App.tsx` — root layout with sidebar + router +- [ ] `Playground.tsx` — main query interface +- [ ] `PipelineVisualizer.tsx` — animated stage flow +- [ ] `ResponsePanel.tsx` — markdown response display +- [ ] `ExplainCard.tsx` — monospace terminal-style explanation +- [ ] `Analytics.tsx` — dashboard with charts +- [ ] `ModelRegistry.tsx` — model cards + table +- [ ] `Settings.tsx` — config form +- [ ] `theme.css` — all CSS variables +- [ ] `components/MetricCard.tsx` +- [ ] `components/BudgetPills.tsx` +- [ ] `components/StatusDot.tsx` \ No newline at end of file diff --git a/frontend/.gitignore b/frontend/.gitignore new file mode 100644 index 0000000000000000000000000000000000000000..a547bf36d8d11a4f89c59c144f24795749086dd1 --- /dev/null +++ b/frontend/.gitignore @@ -0,0 +1,24 @@ +# Logs +logs +*.log +npm-debug.log* +yarn-debug.log* +yarn-error.log* +pnpm-debug.log* +lerna-debug.log* + +node_modules +dist +dist-ssr +*.local + +# Editor directories and files +.vscode/* +!.vscode/extensions.json +.idea +.DS_Store +*.suo +*.ntvs* +*.njsproj +*.sln +*.sw? diff --git a/frontend/README.md b/frontend/README.md new file mode 100644 index 0000000000000000000000000000000000000000..74872fd4af60fb8d6cdb7d27e6c587ee0b6e1df7 --- /dev/null +++ b/frontend/README.md @@ -0,0 +1,50 @@ +# React + TypeScript + Vite + +This template provides a minimal setup to get React working in Vite with HMR and some ESLint rules. + +Currently, two official plugins are available: + +- [@vitejs/plugin-react](https://github.com/vitejs/vite-plugin-react/blob/main/packages/plugin-react/README.md) uses [Babel](https://babeljs.io/) for Fast Refresh +- [@vitejs/plugin-react-swc](https://github.com/vitejs/vite-plugin-react-swc) uses [SWC](https://swc.rs/) for Fast Refresh + +## Expanding the ESLint configuration + +If you are developing a production application, we recommend updating the configuration to enable type aware lint rules: + +- Configure the top-level `parserOptions` property like this: + +```js +export default tseslint.config({ + languageOptions: { + // other options... + parserOptions: { + project: ['./tsconfig.node.json', './tsconfig.app.json'], + tsconfigRootDir: import.meta.dirname, + }, + }, +}) +``` + +- Replace `tseslint.configs.recommended` to `tseslint.configs.recommendedTypeChecked` or `tseslint.configs.strictTypeChecked` +- Optionally add `...tseslint.configs.stylisticTypeChecked` +- Install [eslint-plugin-react](https://github.com/jsx-eslint/eslint-plugin-react) and update the config: + +```js +// eslint.config.js +import react from 'eslint-plugin-react' + +export default tseslint.config({ + // Set the react version + settings: { react: { version: '18.3' } }, + plugins: { + // Add the react plugin + react, + }, + rules: { + // other rules... + // Enable its recommended rules + ...react.configs.recommended.rules, + ...react.configs['jsx-runtime'].rules, + }, +}) +``` diff --git a/frontend/eslint.config.js b/frontend/eslint.config.js new file mode 100644 index 0000000000000000000000000000000000000000..092408a9f09eae19150818b4f0db5d1b70744828 --- /dev/null +++ b/frontend/eslint.config.js @@ -0,0 +1,28 @@ +import js from '@eslint/js' +import globals from 'globals' +import reactHooks from 'eslint-plugin-react-hooks' +import reactRefresh from 'eslint-plugin-react-refresh' +import tseslint from 'typescript-eslint' + +export default tseslint.config( + { ignores: ['dist'] }, + { + extends: [js.configs.recommended, ...tseslint.configs.recommended], + files: ['**/*.{ts,tsx}'], + languageOptions: { + ecmaVersion: 2020, + globals: globals.browser, + }, + plugins: { + 'react-hooks': reactHooks, + 'react-refresh': reactRefresh, + }, + rules: { + ...reactHooks.configs.recommended.rules, + 'react-refresh/only-export-components': [ + 'warn', + { allowConstantExport: true }, + ], + }, + }, +) diff --git a/frontend/index.html b/frontend/index.html new file mode 100644 index 0000000000000000000000000000000000000000..2db32c842e122f88c2488344ef003b3ed982904c --- /dev/null +++ b/frontend/index.html @@ -0,0 +1,16 @@ + + + + + + LLMOpt — Adaptive LLM Inference Optimization + + + + + + +
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/dev/null +++ b/frontend/package.json @@ -0,0 +1,38 @@ +{ + "name": "frontend", + "private": true, + "version": "0.0.0", + "type": "module", + "scripts": { + "dev": "vite", + "build": "tsc -b && vite build", + "lint": "eslint .", + "preview": "vite preview" + }, + "dependencies": { + "@types/react-syntax-highlighter": "^15.5.13", + "framer-motion": "^12.40.0", + "lucide-react": "^1.16.0", + "react": "^18.3.1", + "react-dom": "^18.3.1", + "react-markdown": "^10.1.0", + "react-router-dom": "^7.15.1", + "react-syntax-highlighter": "^16.1.1", + "recharts": "^3.8.1", + "zustand": "^5.0.13" + }, + "devDependencies": { + "@eslint/js": "^9.13.0", + "@types/node": "^25.9.1", + "@types/react": "^18.3.12", + "@types/react-dom": "^18.3.1", + "@vitejs/plugin-react": "^4.3.3", + "eslint": "^9.13.0", + "eslint-plugin-react-hooks": "^5.0.0", + "eslint-plugin-react-refresh": "^0.4.14", + "globals": "^15.11.0", + "typescript": "~5.6.2", + "typescript-eslint": "^8.11.0", + "vite": "^5.4.10" + } +} diff --git a/frontend/public/vite.svg b/frontend/public/vite.svg new file mode 100644 index 0000000000000000000000000000000000000000..e7b8dfb1b2a60bd50538bec9f876511b9cac21e3 --- /dev/null +++ b/frontend/public/vite.svg @@ -0,0 +1 @@ + \ No newline at end of file diff --git a/frontend/src/App.css b/frontend/src/App.css new file mode 100644 index 0000000000000000000000000000000000000000..b9d355df2a5956b526c004531b7b0ffe412461e0 --- /dev/null +++ b/frontend/src/App.css @@ -0,0 +1,42 @@ +#root { + max-width: 1280px; + margin: 0 auto; + padding: 2rem; + text-align: center; +} + +.logo { + height: 6em; + padding: 1.5em; + will-change: filter; + transition: filter 300ms; +} +.logo:hover { + filter: drop-shadow(0 0 2em #646cffaa); +} +.logo.react:hover { + filter: drop-shadow(0 0 2em #61dafbaa); +} + +@keyframes logo-spin { + from { + transform: rotate(0deg); + } + to { + transform: rotate(360deg); + } +} + +@media (prefers-reduced-motion: no-preference) { + a:nth-of-type(2) .logo { + animation: logo-spin infinite 20s linear; + } +} + +.card { + padding: 2em; +} + +.read-the-docs { + color: #888; +} diff --git a/frontend/src/App.tsx b/frontend/src/App.tsx new file mode 100644 index 0000000000000000000000000000000000000000..1fd93983bd816f341c1752920a035eecda78c156 --- /dev/null +++ b/frontend/src/App.tsx @@ -0,0 +1,217 @@ +import { BrowserRouter, Routes, Route, NavLink, useLocation } from 'react-router-dom'; +import { motion, AnimatePresence } from 'framer-motion'; +import { useEffect } from 'react'; +import { Zap, LayoutDashboard, BarChart3, Database, Settings, LogOut, ChevronLeft, ChevronRight } from 'lucide-react'; +import { useAppStore } from './store'; +import { api, getStoredSession, setStoredSession } from './api'; + +import Playground from './pages/Playground'; +import Analytics from './pages/Analytics'; +import ModelRegistry from './pages/ModelRegistry'; +import SettingsPage from './pages/Settings'; +import LoginPage from './pages/Login'; + +function Sidebar({ collapsed, setCollapsed }: { collapsed: boolean; setCollapsed: (v: boolean) => void }) { + const { health, auth, setAuth } = useAppStore(); + const location = useLocation(); + + const navItems = [ + { to: '/', icon: , label: 'Playground', exact: true }, + { to: '/analytics', icon: , label: 'Analytics' }, + { to: '/models', icon: , label: 'Models' }, + { to: '/settings', icon: , label: 'Settings' }, + ]; + + const handleLogout = async () => { + try { + await api.logout(); + } catch (_) { /* ignore */ } + setStoredSession(null); + setAuth({ isLoggedIn: false, sessionId: null }); + }; + + const dotClass = (s: string) => + s === 'ok' ? 'dot dot-live' : s === 'error' ? 'dot dot-error' : 'dot dot-muted'; + + return ( + + {/* Logo */} +
+
+ {!collapsed && ( +
LLMOpt
+ )} + +
+ + {/* Nav */} + + + {/* Status */} + {!collapsed && ( +
+
System Status
+
+ + API + + {health.api.toUpperCase()} + +
+
+ + Redis + + {health.redis.toUpperCase()} + +
+
+ + ML Deps + + {health.ml_deps.toUpperCase()} + +
+
+ )} + + ); +} + +function AppShell() { + const { auth, setAuth, sidebarCollapsed, setSidebarCollapsed, setConnectedProviders } = useAppStore(); + const location = useLocation(); + + // Restore session from localStorage on startup + useEffect(() => { + const stored = getStoredSession(); + if (stored) { + setAuth({ isLoggedIn: true, sessionId: stored }); + } + }, []); + + // Poll health + useEffect(() => { + const check = async () => { + try { + await api.health(); + } catch { /* ignore */ } + }; + check(); + const t = setInterval(check, 30000); + return () => clearInterval(t); + }, []); + + // Poll connected providers if logged in + useEffect(() => { + if (!auth.isLoggedIn) return; + const check = async () => { + try { + const data = await api.getKeys(); + setConnectedProviders(data.connected_providers); + } catch (_) {} + }; + check(); + }, [auth.isLoggedIn, setConnectedProviders]); + + if (!auth.isLoggedIn) { + return ( + + + + + + ); + } + + return ( +
+ +
+ + + + } /> + } /> + } /> + } /> + + + +
+
+ ); +} + +export default function App() { + return ( + + + + ); +} diff --git a/frontend/src/api.ts b/frontend/src/api.ts new file mode 100644 index 0000000000000000000000000000000000000000..fc0dd55f62309848ee28f077886d8a6eebbd9fa9 --- /dev/null +++ b/frontend/src/api.ts @@ -0,0 +1,153 @@ +// API client for LLMOpt backend +// Session ID is stored in localStorage and sent as Authorization: Bearer +// This avoids httponly cookie issues on localhost +import type { + GenerateRequest, + GenerateResponse, + ExplainResponse, + HistoryItem, + DashboardStats, + ModelSpec, +} from './types'; + +const BASE = ''; // same-origin (served by FastAPI or proxied by Vite) + +// Get session token from localStorage +export function getStoredSession(): string | null { + return localStorage.getItem('llmopt_session'); +} + +export function setStoredSession(id: string | null) { + if (id) localStorage.setItem('llmopt_session', id); + else localStorage.removeItem('llmopt_session'); +} + +async function request( + path: string, + options: RequestInit = {}, + requiresAuth = true, +): Promise { + const headers: Record = { + 'Content-Type': 'application/json', + ...(options.headers as Record || {}), + }; + + // Inject session token as Bearer header + if (requiresAuth) { + const session = getStoredSession(); + if (session) { + headers['Authorization'] = `Bearer ${session}`; + } + } + + const res = await fetch(`${BASE}${path}`, { + credentials: 'include', + ...options, + headers, + }); + + if (!res.ok) { + const body = await res.json().catch(() => ({ detail: res.statusText })); + const err = new Error(body.detail || `HTTP ${res.status}`); + (err as any).status = res.status; + throw err; + } + + return res.json(); +} + +export const api = { + health: () => request<{ status: string; version: string }>('/health', {}, false), + + generate: (req: GenerateRequest) => + request('/generate', { + method: 'POST', + body: JSON.stringify(req), + }), + + explain: ( + query: string, + budget_mode: string, + params?: { + alpha?: number; + beta?: number; + gamma?: number; + compression_enabled?: boolean; + exclude_providers?: string[]; + only_providers?: string[]; + } + ) => + request('/explain', { + method: 'POST', + body: JSON.stringify({ query, budget_mode, ...params }), + }), // explain requires auth to access session keys + + models: () => + request<{ models: ModelSpec[] }>('/models', {}, false), + + // Auth + register: (email: string, password: string) => + request<{ message: string }>('/auth/register', { + method: 'POST', + body: JSON.stringify({ email, password }), + }, false), + + login: (email: string, password: string) => + request<{ message: string; session_id: string }>('/auth/login', { + method: 'POST', + body: JSON.stringify({ email, password }), + }, false), + + logout: () => + request<{ message: string }>('/auth/logout', { method: 'POST' }), + + getKeys: () => + request<{ connected_providers: string[] }>('/auth/keys'), + + updateKeys: (api_keys: Record) => + request<{ message: string }>('/auth/keys', { + method: 'POST', + body: JSON.stringify({ api_keys }), + }), + + deleteKey: (provider: string) => + request<{ message: string }>(`/auth/keys/${provider}`, { + method: 'DELETE', + }), + + + getDashboardStats: () => + request('/auth/dashboard-stats'), + + getHistory: () => + request('/auth/history'), + + // Streaming + stream: async (req: GenerateRequest, onChunk: (chunk: string) => void) => { + const session = getStoredSession(); + const headers: Record = { 'Content-Type': 'application/json' }; + if (session) headers['Authorization'] = `Bearer ${session}`; + + const res = await fetch('/stream', { + method: 'POST', + credentials: 'include', + headers, + body: JSON.stringify(req), + }); + + if (!res.ok) { + const body = await res.json().catch(() => ({ detail: res.statusText })); + throw new Error(body.detail || `HTTP ${res.status}`); + } + + const reader = res.body?.getReader(); + const decoder = new TextDecoder(); + if (!reader) return; + + while (true) { + const { done, value } = await reader.read(); + if (done) break; + onChunk(decoder.decode(value, { stream: true })); + } + }, +}; diff --git a/frontend/src/assets/react.svg b/frontend/src/assets/react.svg new file mode 100644 index 0000000000000000000000000000000000000000..6c87de9bb3358469122cc991d5cf578927246184 --- /dev/null +++ b/frontend/src/assets/react.svg @@ -0,0 +1 @@ + \ No newline at end of file diff --git a/frontend/src/index.css b/frontend/src/index.css new file mode 100644 index 0000000000000000000000000000000000000000..6119ad9a8faaa5073a454f67b50fb98a25972fd2 --- /dev/null +++ b/frontend/src/index.css @@ -0,0 +1,68 @@ +:root { + font-family: Inter, system-ui, Avenir, Helvetica, Arial, sans-serif; + line-height: 1.5; + font-weight: 400; + + color-scheme: light dark; + color: rgba(255, 255, 255, 0.87); + background-color: #242424; + + font-synthesis: none; + text-rendering: optimizeLegibility; + -webkit-font-smoothing: antialiased; + -moz-osx-font-smoothing: grayscale; +} + +a { + font-weight: 500; + color: #646cff; + text-decoration: inherit; +} +a:hover { + color: #535bf2; +} + +body { + margin: 0; + display: flex; + place-items: center; + min-width: 320px; + min-height: 100vh; +} + +h1 { + font-size: 3.2em; + line-height: 1.1; +} + +button { + border-radius: 8px; + border: 1px solid transparent; + padding: 0.6em 1.2em; + font-size: 1em; + font-weight: 500; + font-family: inherit; + background-color: #1a1a1a; + cursor: pointer; + transition: border-color 0.25s; +} +button:hover { + border-color: #646cff; +} +button:focus, +button:focus-visible { + outline: 4px auto -webkit-focus-ring-color; +} + +@media (prefers-color-scheme: light) { + :root { + color: #213547; + background-color: #ffffff; + } + a:hover { + color: #747bff; + } + button { + background-color: #f9f9f9; + } +} diff --git a/frontend/src/main.tsx b/frontend/src/main.tsx new file mode 100644 index 0000000000000000000000000000000000000000..dc68443564f17b81bdff3a38ff748d2f7c9d620b --- /dev/null +++ b/frontend/src/main.tsx @@ -0,0 +1,10 @@ +import { StrictMode } from 'react' +import { createRoot } from 'react-dom/client' +import './theme.css' +import App from './App.tsx' + +createRoot(document.getElementById('root')!).render( + + + , +) diff --git a/frontend/src/pages/Analytics.tsx b/frontend/src/pages/Analytics.tsx new file mode 100644 index 0000000000000000000000000000000000000000..3632a6df9c3fd1f5b2cecbe2efc1bf5614a85ce7 --- /dev/null +++ b/frontend/src/pages/Analytics.tsx @@ -0,0 +1,385 @@ +import { useEffect, useState } from 'react'; +import { motion } from 'framer-motion'; +import { BarChart3, TrendingUp, TrendingDown } from 'lucide-react'; +import { + AreaChart, + Area, + BarChart, + Bar, + XAxis, + YAxis, + CartesianGrid, + Tooltip, + ResponsiveContainer, + Cell, + Legend, +} from 'recharts'; +import { api } from '../api'; +import type { DashboardStats, HistoryItem } from '../types'; + +// ─── Mock time-series data (since backend doesn't expose it yet) ────────────── + +function generateDailyData(days: number, base: number, variance: number) { + const now = new Date(); + return Array.from({ length: days }, (_, i) => { + const d = new Date(now); + d.setDate(d.getDate() - (days - 1 - i)); + return { + date: d.toLocaleDateString('en-US', { month: 'short', day: 'numeric' }), + actual: Math.max(0, base + (Math.random() - 0.5) * variance), + baseline: base * 8, + }; + }); +} + +const PROVIDER_COLORS: Record = { + openai: '#00E5FF', + anthropic: '#7C4DFF', + google: '#00FF94', + ollama: '#FFB300', + cohere: '#FF3D57', + other: '#7A8299', +}; + +// ─── Custom Tooltip ─────────────────────────────────────────────────────────── + +const CustomTooltip = ({ active, payload, label }: any) => { + if (!active || !payload?.length) return null; + return ( +
+
{label}
+ {payload.map((p: any) => ( +
+ {p.name}: ${typeof p.value === 'number' ? p.value.toFixed(4) : p.value} +
+ ))} +
+ ); +}; + +// ─── KPI Card ──────────────────────────────────────────────────────────────── + +function KPICard({ + label, + value, + delta, + deltaPositive, + color, + delay, +}: { + label: string; + value: string; + delta?: string; + deltaPositive?: boolean; + color: string; + delay: number; +}) { + return ( + +
{label}
+
{value}
+ {delta && ( +
+ {deltaPositive ? : } + {delta} +
+ )} + + ); +} + +// ─── Query Log Table ────────────────────────────────────────────────────────── + +function ComplexityBar({ score }: { score: number }) { + const color = + score < 0.4 ? 'var(--accent-green)' : + score < 0.7 ? 'var(--accent-amber)' : + 'var(--accent-red)'; + + return ( +
+
+
+
+ + {(score * 100).toFixed(0)} + +
+ ); +} + + + +function QueryLogTable({ items }: { items: HistoryItem[] }) { + const [expanded, setExpanded] = useState(null); + + if (items.length === 0) { + return ( +
+
📋
+
No Query History
+
Run queries in the Playground to see them here.
+
+ ); + } + + return ( +
+ + + + + + + + + + + + + + {items.map((item) => ( + <> + setExpanded(expanded === item.id ? null : item.id)}> + + + + + + + + + {expanded === item.id && ( + + + + )} + + ))} + +
TimeQueryComplexityModelCostLatencyTier
+ + {item.time_ago} + + +
+ {item.query} +
+ 		+
+ {item.model_used?.split('-').slice(-2).join('-') || '—'} +
+ 		+ + ${(item.estimated_cost || 0).toFixed(6)} + + + + {item.latency_ms?.toFixed(0) || '—'}ms + + + + {item.complexity_tier || 'std'} + +
+
+
{'>'} Full query:
+
{item.query}
+
{'>'} Response snippet:
+
+ {(item.response || '').slice(0, 400)}{item.response?.length > 400 ? '...' : ''} +
+
+ Tokens in: {item.input_tokens} + Tokens out: {item.output_tokens} + Saved: {item.tokens_saved} + Cost saved: ${(item.cost_saved || 0).toFixed(6)} +
+
+
+
+ ); +} + +// ─── Analytics Page ─────────────────────────────────────────────────────────── + +export default function Analytics() { + const [stats, setStats] = useState(null); + const [history, setHistory] = useState([]); + const [loading, setLoading] = useState(true); + const [error, setError] = useState(''); + const [chartRange, setChartRange] = useState<7 | 30>(7); + + const costData = generateDailyData(chartRange, 0.05, 0.08); + const modelData = stats + ? Object.entries(stats.distribution).map(([name, pct]) => ({ + name: name.charAt(0).toUpperCase() + name.slice(1), + value: pct, + fill: PROVIDER_COLORS[name] || PROVIDER_COLORS.other, + })) + : []; + + useEffect(() => { + const load = async () => { + try { + const [s, h] = await Promise.all([ + api.getDashboardStats(), + api.getHistory(), + ]); + setStats(s); + setHistory(h); + } catch (e: any) { + setError(e.message || 'Failed to load analytics'); + } finally { + setLoading(false); + } + }; + load(); + }, []); + + if (loading) { + return ( +
+ +
+ ); + } + + return ( +
+
+
+ + Analytics + / Observability Dashboard +
+
+ +
+ {error &&
⚠ {error}
} + + {/* KPI Row */} +
+ + + + +
+ + {/* Cost Over Time + Model Distribution */} +
+
+
+
+
Cost Over Time
+
Actual vs GPT-4o baseline (USD)
+
+
+ {([7, 30] as const).map((d) => ( + + ))} +
+
+
+ + + + + + + + + + + + + + + `$${v.toFixed(2)}`} /> + } /> + + + + + +
+
+ +
+
+
+
Model Usage
+
Distribution by provider (%)
+
+
+
+ {modelData.length > 0 ? ( + + + + `${v}%`} /> + + [`${v}%`, 'Share']} + contentStyle={{ background: 'var(--bg-elevated)', border: '1px solid var(--bg-border)', borderRadius: 8, fontSize: 12 }} + /> + + {modelData.map((entry, i) => ( + + ))} + + + + ) : ( +
+
No data yet
+
Run queries to see model distribution.
+
+ )} +
+
+
+ + {/* Query Log */} +
+
+
+
Query Log
+
Last 20 requests — click to expand
+
+
+ +
+
+
+ ); +} diff --git a/frontend/src/pages/Login.tsx b/frontend/src/pages/Login.tsx new file mode 100644 index 0000000000000000000000000000000000000000..2463d635ed2a0076ca79b2a7a0bf372f222e517c --- /dev/null +++ b/frontend/src/pages/Login.tsx @@ -0,0 +1,209 @@ +import { useState } from 'react'; +import { motion, AnimatePresence } from 'framer-motion'; +import { Zap } from 'lucide-react'; +import { api, setStoredSession } from '../api'; +import { useAppStore } from '../store'; + +type Mode = 'login' | 'register'; + +export default function LoginPage() { + const { setAuth, setConnectedProviders } = useAppStore(); + const [mode, setMode] = useState('login'); + const [email, setEmail] = useState(''); + const [password, setPassword] = useState(''); + const [loading, setLoading] = useState(false); + const [error, setError] = useState(''); + + const handleSubmit = async (e: React.FormEvent) => { + e.preventDefault(); + if (!email.trim() || !password.trim()) return; + setLoading(true); + setError(''); + + try { + if (mode === 'register') { + await api.register(email, password); + // Auto-login after register + } + const data = await api.login(email, password); + setStoredSession(data.session_id); + setAuth({ isLoggedIn: true, sessionId: data.session_id }); + + // Fetch connected providers + try { + const keys = await api.getKeys(); + setConnectedProviders(keys.connected_providers); + } catch (_) {} + } catch (e: any) { + setError(e.message || 'Authentication failed'); + } finally { + setLoading(false); + } + }; + + const handleGoogleLogin = () => { + window.location.href = '/auth/login/google'; + }; + + const handleGithubLogin = () => { + window.location.href = '/auth/login/github'; + }; + + return ( +
+ {/* Background */} +
+
+
+ + {/* Floating particles */} + {[...Array(6)].map((_, i) => ( + + ))} + + + + {/* Logo */} +
+
+
LLMOpt
+
+ +
+ {mode === 'login' ? 'Welcome back' : 'Create account'} +
+
+ {mode === 'login' + ? 'Sign in to your LLMOpt workspace' + : 'Start optimizing your LLM costs today'} +
+ + {/* OAuth */} +
+ + +
+ +
+
+
or
+
+
+ + {/* Form */} +
+
+ + setEmail(e.target.value)} + placeholder="you@company.com" + autoComplete="email" + required + /> +
+
+ + setPassword(e.target.value)} + placeholder="••••••••" + autoComplete={mode === 'login' ? 'current-password' : 'new-password'} + required + /> +
+ + {error && ( + + {error} + + )} + + +
+ +
+ {mode === 'login' ? ( + <> + Don't have an account?{' '} + { setMode('register'); setError(''); }}> + Sign up + + + ) : ( + <> + Already have an account?{' '} + { setMode('login'); setError(''); }}> + Sign in + + + )} +
+ + +
+ ); +} diff --git a/frontend/src/pages/ModelRegistry.tsx b/frontend/src/pages/ModelRegistry.tsx new file mode 100644 index 0000000000000000000000000000000000000000..48e2721baa7cd5b54872cc9f0fc88c64c26c14c9 --- /dev/null +++ b/frontend/src/pages/ModelRegistry.tsx @@ -0,0 +1,354 @@ +import { useEffect, useState } from 'react'; +import { motion } from 'framer-motion'; +import { Database, Star, ArrowUpDown } from 'lucide-react'; +import { api } from '../api'; +import type { ModelSpec } from '../types'; + +const PROVIDER_COLORS: Record = { + openai: 'badge-cyan', + anthropic: 'badge-purple', + google: 'badge-green', + ollama: 'badge-amber', + mistral: 'badge-muted', + deepseek: 'badge-red', + cohere: 'badge-muted', +}; + +function providerBadge(provider: string) { + const p = provider?.toLowerCase(); + for (const key of Object.keys(PROVIDER_COLORS)) { + if (p?.includes(key)) return PROVIDER_COLORS[key]; + } + return 'badge-muted'; +} + +function CapabilityGauge({ score }: { score: number }) { + const r = 24; + const circ = 2 * Math.PI * r; + const dash = circ * score; + const color = score >= 0.8 ? 'var(--accent-cyan)' : score >= 0.6 ? 'var(--accent-amber)' : 'var(--accent-red)'; + return ( + + + + + {(score * 100).toFixed(0)} + + + ); +} + +function ModelCard({ model, index }: { model: ModelSpec; index: number }) { + // Best value: high capability + low cost + const isBestValue = model.capability_score > 0.7 && model.input_cost_per_1k < 0.002; + const isLocalFree = model.provider === 'ollama'; + + return ( + + {isBestValue && ( +
+ + + BEST VALUE + +
+ )} + +
+
+
{model.model_name}
+
+ {model.provider} + {isLocalFree && FREE LOCAL} +
+
+ +
+ +
+
+
Input /1k
+
+ {isLocalFree ? FREE : `$${model.input_cost_per_1k.toFixed(5)}`} +
+
+
+
Output /1k
+
+ {isLocalFree ? FREE : `$${model.output_cost_per_1k.toFixed(5)}`} +
+
+
+
Context
+
+ {model.context_window >= 1000000 + ? `${(model.context_window / 1000000).toFixed(0)}M` + : `${(model.context_window / 1000).toFixed(0)}k`} +
+
+
+ + {/* Capability breakdown bars */} +
+ {[ + { label: 'Reasoning', value: model.reasoning_score, color: 'var(--accent-cyan)' }, + { label: 'Coding', value: model.coding_score, color: 'var(--accent-purple)' }, + { label: 'Math', value: model.math_score, color: 'var(--accent-amber)' }, + ].map(({ label, value, color }) => ( +
+ {label} +
+
+
+ + {(value * 100).toFixed(0)} + +
+ ))} +
+ + {model.notes && ( +
+ {model.notes} +
+ )} + + ); +} + +type SortKey = 'capability_score' | 'input_cost_per_1k' | 'output_cost_per_1k' | 'max_complexity'; + +export default function ModelRegistry() { + const [models, setModels] = useState([]); + const [loading, setLoading] = useState(true); + const [error, setError] = useState(''); + const [sortKey, setSortKey] = useState('capability_score'); + const [sortAsc, setSortAsc] = useState(false); + const [view, setView] = useState<'grid' | 'table'>('grid'); + const [filter, setFilter] = useState(''); + + useEffect(() => { + const load = async () => { + try { + const data = await api.models(); + setModels(data.models || []); + } catch (e: any) { + setError(e.message || 'Failed to load models'); + } finally { + setLoading(false); + } + }; + load(); + }, []); + + const filtered = models.filter( + (m) => + m.model_name.toLowerCase().includes(filter.toLowerCase()) || + m.provider.toLowerCase().includes(filter.toLowerCase()) + ); + + const sorted = [...filtered].sort((a, b) => { + const d = a[sortKey] - b[sortKey]; + return sortAsc ? d : -d; + }); + + const toggleSort = (k: SortKey) => { + if (sortKey === k) setSortAsc((v) => !v); + else { setSortKey(k); setSortAsc(false); } + }; + + if (loading) { + return ( +
+ +
+ ); + } + + return ( +
+
+
+ + Model Registry + / {models.length} models registered +
+
+ setFilter(e.target.value)} + placeholder="Filter models..." + style={{ width: 180, padding: '6px 12px', fontSize: 'var(--text-xs)' }} + /> + + +
+
+ +
+ {error &&
⚠ {error}
} + + {sorted.length === 0 && !error ? ( +
+
+
No Models Found
+
+ {filter ? `No models match "${filter}"` : 'The model registry is empty.'} +
+
+ ) : view === 'grid' ? ( +
+ {sorted.map((m, i) => )} +
+ ) : ( +
+
+ + + + + + + + + + + + + + + {sorted.map((m) => { + const isBest = m.capability_score > 0.7 && m.input_cost_per_1k < 0.002; + return ( + + + + + + + + + + + ); + })} + +
ModelProvider toggleSort('capability_score')} + > +
+ Capability +
+ 		toggleSort('input_cost_per_1k')} + > +
+ Input /1k +
+ 		toggleSort('output_cost_per_1k')} + > +
+ Output /1k +
+ 		toggleSort('max_complexity')} + > +
+ Max Complexity +
+ 		ContextNotes
+
+ {isBest && } + {m.model_name} +
+ 		+ {m.provider} + +
+
+
+
+ + {(m.capability_score * 100).toFixed(0)} + +
+
+ + {m.input_cost_per_1k === 0 ? 'FREE' : `$${m.input_cost_per_1k.toFixed(5)}`} + + + + {m.output_cost_per_1k === 0 ? 'FREE' : `$${m.output_cost_per_1k.toFixed(5)}`} + + +
+
+
+
+ + {(m.max_complexity * 100).toFixed(0)} + +
+
+ + {m.context_window >= 1000000 + ? `${(m.context_window / 1000000).toFixed(0)}M` + : `${(m.context_window / 1000).toFixed(0)}k`} + + + + {m.notes?.length > 50 ? m.notes.slice(0, 50) + '…' : m.notes} + +
+
+
+ )} +
+
+ ); +} diff --git a/frontend/src/pages/Playground.tsx b/frontend/src/pages/Playground.tsx new file mode 100644 index 0000000000000000000000000000000000000000..20f5e2ce1d4932dcc32086fd336a61ca4dc70078 --- /dev/null +++ b/frontend/src/pages/Playground.tsx @@ -0,0 +1,606 @@ +import { useState, useCallback } from 'react'; +import { motion, AnimatePresence } from 'framer-motion'; +import { Play, Zap, Eye, EyeOff, Copy, Check } from 'lucide-react'; +import { useAppStore } from '../store'; +import { api } from '../api'; +import type { GenerateResponse, ExplainResponse, PipelineStage, PipelineStageStatus, ComplexityTier } from '../types'; +import ReactMarkdown from 'react-markdown'; + +// ─── Tier Badge ──────────────────────────────────────────────────────────────── + +const TIER_COLORS: Record = { + trivial: '#00ff94', + easy: '#00e5ff', + medium: '#ffc700', + hard: '#ff6b35', + expert: '#e040fb', +}; + +function TierBadge({ tier }: { tier: ComplexityTier }) { + const color = TIER_COLORS[tier] || 'var(--text-muted)'; + return ( + + {tier} + + ); +} + +// ─── Pipeline Visualizer ───────────────────────────────────────────────────── + +const PIPELINE_STAGES: { id: string; label: string; icon: string; desc: string }[] = [ + { id: 'analyze', label: 'Query Analyzer', icon: '🔍', desc: 'Detecting domain, complexity signals, token count' }, + { id: 'estimate', label: 'Complexity Estimator', icon: '🧠', desc: 'ML model scoring query complexity C(q) ∈ [0,1]' }, + { id: 'optimize', label: 'Optimization Engine', icon: '⚡', desc: 'Selecting optimal model via budget constraints' }, + { id: 'compress', label: 'Prompt Optimizer', icon: '📦', desc: 'Compressing tokens and injecting system prompt' }, + { id: 'route', label: 'Model Router', icon: '🚀', desc: 'Routing request to provider API' }, +]; + +function PipelineVisualizer({ + stages, + explainData, +}: { + stages: PipelineStage[]; + explainData?: ExplainResponse | null; +}) { + return ( +
+ {PIPELINE_STAGES.map((def, i) => { + const stage = stages.find((s) => s.id === def.id); + const status: PipelineStageStatus = stage?.status || 'idle'; + + // Extract real detail from explain data + let detail = def.desc; + if (explainData) { + if (def.id === 'analyze') { + detail = `Domain: ${explainData.features.primary_domain} | Tokens: ${explainData.features.token_count} | Output: ${explainData.features.estimated_output_length}`; + } else if (def.id === 'estimate') { + detail = `Score: ${explainData.complexity.score.toFixed(3)} | Tier: ${explainData.complexity.tier} | Reasoning req: ${explainData.complexity.required_reasoning.toFixed(2)}`; + } else if (def.id === 'optimize') { + const opt = explainData.optimization; + detail = `Selected: ${opt.selected_model} | Budget: ${opt.budget_mode} | Compression: ${opt.compression_enabled ? 'yes' : 'no'}`; + } else if (def.id === 'compress') { + const p = explainData.optimized_prompt; + const saved = p.tokens_saved || 0; + detail = `Tokens before: ${p.tokens_before} → after: ${p.tokens_after} | Saved: ${saved} tokens`; + } else if (def.id === 'route') { + const opt = explainData.optimization; + detail = `Provider: ${opt.provider} | Max tokens: ${opt.estimated_output_tokens} | Style: ${opt.system_prompt_style}`; + } + } + + return ( +
+
+ {status === 'active' ? ( + + ) : status === 'complete' ? ( + ✓ + ) : status === 'skipped' ? ( + ⊘ + ) : status === 'error' ? ( + ✕ + ) : ( + {def.icon} + )} +
+
+
{def.label}
+
{detail}
+
+ {i < PIPELINE_STAGES.length - 1 && ( +
+ )} +
+ ); + })} +
+ ); +} + +// ─── Rationale Card ────────────────────────────────────────────────────────── + +function RationaleCard({ explain }: { explain: ExplainResponse }) { + const { complexity, optimization, optimized_prompt, features } = explain; + + return ( +
+
+ + LLMOpt Decision Rationale +
+ + {/* Complexity breakdown */} +
+
Complexity Analysis
+
+ + + Score: {complexity.score.toFixed(3)} + +
+
+ {[ + { label: 'Reasoning req.', value: complexity.required_reasoning, color: 'var(--accent-cyan)' }, + { label: 'Coding req.', value: complexity.required_coding, color: 'var(--accent-purple)' }, + { label: 'Math req.', value: complexity.required_math, color: 'var(--accent-amber)' }, + ].map(({ label, value, color }) => ( +
+ {label} +
+
+
+ + {(value * 100).toFixed(0)} + +
+ ))} +
+
+ + {/* Feature flags */} +
+
Detected Features
+
+ {[ + { key: 'domain_code', label: 'Code' }, + { key: 'domain_math', label: 'Math' }, + { key: 'domain_science', label: 'Science' }, + { key: 'domain_reasoning', label: 'Reasoning' }, + { key: 'domain_creative', label: 'Creative' }, + { key: 'multi_step', label: 'Multi-step' }, + { key: 'requires_comparison', label: 'Comparison' }, + { key: 'requires_analysis', label: 'Analysis' }, + { key: 'has_math_notation', label: 'Math notation' }, + { key: 'has_code_block', label: 'Code block' }, + ].filter((f) => features[f.key as keyof typeof features] === true).map((f) => ( + + {f.label} + + ))} + {Object.entries(features).filter(([k]) => + k.startsWith('domain_') || k.startsWith('requires_') || k.startsWith('has_') + ).every(([, v]) => v !== true) && ( + No special features detected + )} +
+
+ + {/* Routing decision */} +
+
Routing Decision
+
+ {[ + { label: 'Selected Model', value: optimization.selected_model, mono: true }, + { label: 'Provider', value: optimization.provider, mono: true }, + { label: 'Budget Mode', value: optimization.budget_mode, mono: false }, + { label: 'System Prompt', value: optimization.system_prompt_style, mono: false }, + { label: 'Compression', value: optimization.compression_enabled ? 'enabled' : 'disabled', mono: false }, + { label: 'Fallback', value: optimization.fallback_model || 'N/A', mono: true }, + ].map(({ label, value, mono }) => ( +
+
{label}
+
+ {value} +
+
+ ))} +
+
+ + {/* Compression */} + {optimized_prompt.tokens_saved > 0 && ( +
+
Prompt Compression
+
+ + {optimized_prompt.tokens_before} + + → + + {optimized_prompt.tokens_after} tokens + + + -{optimized_prompt.tokens_saved} saved ({(optimized_prompt.compression_ratio * 100).toFixed(1)}%) + +
+
+ )} + + {/* Rationale bullets */} + {optimization.rationale?.length > 0 && ( +
+
Optimizer Rationale
+
    + {optimization.rationale.map((r, i) => ( +
  • {r}
    • + ))} +
+
+ )} +
+ ); +} + +// ─── Metrics Bar ───────────────────────────────────────────────────────────── + +function MetricsBar({ result }: { result: GenerateResponse }) { + return ( +
+
+
Model
+
{result.model_used}
+
+
+
+
Tokens
+
{result.total_tokens.toLocaleString()}
+
+
+
+
Cost
+
${result.estimated_cost.toFixed(6)}
+
+
+
+
Saved
+
${result.cost_saved.toFixed(6)}
+
+
+
+
Latency
+
{result.latency_ms.toFixed(0)}ms
+
+
+
+
Complexity
+
+ +
+
+ {result.tokens_saved > 0 && ( + <> +
+
+
Tokens Compressed
+
-{result.tokens_saved}
+
+ + )} +
+ ); +} + +// ─── Main Playground Page ──────────────────────────────────────────────────── + +export default function Playground() { + const { + budgetMode, + alphaWeight, + betaWeight, + gammaWeight, + compressionEnabled, + evaluationEnabled, + connectedProviders, + } = useAppStore(); + const [query, setQuery] = useState(''); + const [selectedProviders, setSelectedProviders] = useState([]); + const [loading, setLoading] = useState(false); + const [error, setError] = useState(''); + const [result, setResult] = useState(null); + const [explain, setExplain] = useState(null); + const [showExplain, setShowExplain] = useState(false); + const [copied, setCopied] = useState(false); + const [stages, setStages] = useState([]); + + const setStageStatus = useCallback((id: string, status: PipelineStageStatus, detail?: string) => { + setStages((prev) => { + const existing = prev.find((s) => s.id === id); + if (existing) { + return prev.map((s) => s.id === id ? { ...s, status, detail: detail ?? s.detail } : s); + } + return [...prev, { id, label: id, icon: '', status, detail }]; + }); + }, []); + + const resetStages = useCallback(() => { + setStages(PIPELINE_STAGES.map((s) => ({ id: s.id, label: s.label, icon: s.icon, status: 'idle' as PipelineStageStatus }))); + }, []); + + // Animate stages based on real pipeline latency + const runPipelineAnimation = useCallback(async () => { + const timings = [300, 600, 400, 300, 0]; // ms per stage (route waits for real API) + const ids = ['analyze', 'estimate', 'optimize', 'compress', 'route']; + + for (let i = 0; i < ids.length - 1; i++) { + setStageStatus(ids[i], 'active'); + await new Promise((r) => setTimeout(r, timings[i])); + setStageStatus(ids[i], 'complete'); + } + setStageStatus('route', 'active'); + }, [setStageStatus]); + + const handleSubmit = async () => { + if (!query.trim() || loading) return; + setLoading(true); + setError(''); + setResult(null); + setExplain(null); + resetStages(); + + const providerConstraints = selectedProviders.length > 0 ? selectedProviders : undefined; + + try { + // 1. Run explain in parallel with pipeline animation to get real routing data + const explainPromise = api.explain(query, budgetMode, { + alpha: alphaWeight, + beta: betaWeight, + gamma: gammaWeight, + compression_enabled: compressionEnabled, + only_providers: providerConstraints, + }); + + // 2. Start animation + await runPipelineAnimation(); + + // 3. Get explain data (ready by now or wait a bit more) + const explainData = await explainPromise; + setExplain(explainData); + setShowExplain(false); + + // 4. Complete route stage after getting result + setStageStatus('route', 'active'); + + // 5. Generate with real API + const genResult = await api.generate({ + query, + budget_mode: budgetMode, + alpha: alphaWeight, + beta: betaWeight, + gamma: gammaWeight, + compression_enabled: compressionEnabled, + evaluate: evaluationEnabled, + only_providers: providerConstraints, + }); + + setStageStatus('route', 'complete'); + setResult(genResult); + } catch (e: any) { + const status = (e as any).status; + if (status === 401) { + setError('Please add your API keys in Settings to generate responses.'); + } else if (status === 503) { + setError('Redis is unavailable — sessions require Redis. Try adding ?session= to the request or run Redis locally.'); + } else { + setError(e.message || 'Generation failed'); + } + setStageStatus('route', 'error'); + } finally { + setLoading(false); + } + }; + + const handleCopy = () => { + if (result?.response) { + navigator.clipboard.writeText(result.response); + setCopied(true); + setTimeout(() => setCopied(false), 2000); + } + }; + + const examplePrompts = [ + 'Explain quicksort with Python code and time complexity analysis', + 'What is the derivative of x²·sin(x)?', + 'Write a haiku about machine learning', + 'Design a distributed rate limiter for 1M RPS', + 'Summarize the French Revolution in 3 bullet points', + ]; + + return ( +
+
+
+ + Playground + / Query Optimizer +
+
+ {connectedProviders.length > 0 && ( +
+ Routing Pool: + {connectedProviders.map((prov) => { + const isSelected = selectedProviders.includes(prov); + const isActive = selectedProviders.length === 0 || isSelected; + return ( + + ); + })} +
+ )} + Budget: + {(['cheap', 'balanced', 'quality'] as const).map((m) => ( + + ))} +
+
+ + +
+ {/* Left panel: Input + Pipeline + Examples */} +
+ {/* Query input */} +
+
Query
+