feat: complete production-quality Model Router AI traffic control platform

This commit is contained in:
Vardhman Gupta
2026-08-31 17:33:38 +05:30
commit 4e6f742272
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# Model Router Configuration
# Environment mode: development | production | test
APP_ENV=development
DEBUG=true
PORT=8000
HOST=0.0.0.0
# Database
DATABASE_URL=sqlite+aiosqlite:///./model_router.db
# Default Router Analyzer: rules | llm
ROUTER_ANALYZER=rules
# Default Routing Policy: balanced | lowest_cost | lowest_latency | highest_quality | custom
DEFAULT_ROUTING_POLICY=balanced
# Baseline Model ID for Cost Savings Analytics (e.g. gpt-4o or claude-3-5-sonnet-20241022 or mock-power)
BASELINE_MODEL_ID=mock-power
# Default Provider Mode: mock | ollama
DEFAULT_PROVIDER=mock
# Local Ollama URL
OLLAMA_BASE_URL=http://localhost:11434
# Optional External Providers (leave empty for local/mock development)
OPENAI_API_KEY=
ANTHROPIC_API_KEY=
GEMINI_API_KEY=
# Budgets (USD)
DAILY_BUDGET=10.00
MONTHLY_BUDGET=100.00
PER_REQUEST_BUDGET=1.00
# Retries & Timeouts
MAX_RETRIES=2
PROVIDER_TIMEOUT_SECONDS=30.0
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# Python
__pycache__/
*.py[cod]
*$py.class
*.so
.Python
env/
venv/
.venv/
ENV/
*.db
*.sqlite3
model_router.db
model_router.db-journal
# Environment
.env
.env.local
# Testing & Coverage
.pytest_cache/
.coverage
htmlcov/
# Node / Frontend
node_modules/
dist/
.dist/
npm-debug.log*
yarn-debug.log*
yarn-error.log*
.eslintcache
.vite/
# IDEs & System
.idea/
.vscode/
*.swp
*.swo
.DS_Store
Thumbs.db
# Project Interview Guide & Docs
project.md
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MIT License
Copyright (c) 2026 Model Router Contributors
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.
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# Model Router
Intelligent, explainable, cost- and latency-aware LLM request routing platform and AI Traffic Control Room.
---
## Overview
Model Router intercepts incoming AI requests, analyzes their task type and complexity, evaluates available models against a configurable multi-criteria scoring objective, selects the optimal candidate, and dispatches the request with automatic fallback handling and budget guards.
The platform is designed local-first, allowing full local development and testing using Mock models or local Ollama instances without requiring paid external API keys.
---
## Core Capabilities
- Dual-Mode Request Analyzer: Deterministic heuristics (<3ms latency overhead) for 12 task types, continuous complexity scoring (0.0 to 1.0), and requirement detection, plus an optional LLM classifier mode.
- Explainable Routing Engine: Multi-criteria weighted scoring across Quality, Cost Efficiency, Speed, Capabilities, and Reliability with transparent decision factor reports and candidate rejection logs.
- Provider Abstraction: Decoupled adapters for Mock (simulation), Ollama (local), OpenAI, Anthropic, and Google Gemini.
- Resilience and Fallback: Automated retry classification for transient errors (timeouts, HTTP 429, 503) and tiered fallback to local/mock alternatives.
- Budget Guards: Real-time spend tracking with automated threshold interventions (80% cost optimization, 95% local-only saver, 100% block).
- Traffic Control Room UI: Real-time dark operational interface featuring live topology graphs, playground inspector, SSE live request stream, visual rules builder, and cost savings simulator.
- Developer CLI: Terminal diagnostics (`modelrouter doctor`), routing dry-run (`route`), execution (`run`), model catalog (`models`), and analytics (`analytics`).
---
## Quickstart
### 1. Prerequisites
- Python 3.10+
- Node.js 18+ and npm
### 2. Backend Setup
```bash
# Clone the repository
git clone https://github.com/your-org/model-router.git
cd model-router
# Install dependencies
pip install -r requirements.txt
# Run system diagnostics
python backend/app/cli/main.py doctor
# Start API server (port 8000)
python backend/main.py
```
### 3. Frontend Setup
```bash
cd frontend
npm install
npm run dev
# Access UI at http://localhost:5173
```
---
## CLI Usage Examples
```bash
# Inspect routing decision for a coding prompt (Dry Run)
python backend/app/cli/main.py route "Write a Python function to parse JSON"
# Route and execute a complex debugging query
python backend/app/cli/main.py run "Debug this distributed async deadlock in high-throughput worker pool"
# List active models in registry
python backend/app/cli/main.py models
# View aggregate system analytics
python backend/app/cli/main.py analytics
```
---
## Testing & Build Verification
```bash
# Run backend test suite (15 unit, integration, and e2e tests)
pytest backend/tests
# Build frontend production bundle
cd frontend
npm run build
```
---
## License
MIT License. Open-source software.
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# Model Router Security Policy
## Reporting Security Vulnerabilities
If you discover any security issues with API token handling, prompt injection vectors, or data exposure, please contact the maintainers immediately.
## Zero-Secret Exposure Policy
1. **API Keys**: External provider tokens (OpenAI, Anthropic, Gemini) are read strictly from local environment variables (`.env`) or secure system secrets.
2. **Logs & Transcripts**: All outgoing observability logs automatically redact authorization headers, bearer tokens, and secrets.
3. **Database Integrity**: SQLite storage only stores model identifiers and usage statistics; raw credentials are never persisted in plaintext database tables.
4. **Browser Privacy**: Frontend client queries never receive or handle backend API keys.
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from typing import Dict, Any, List, Optional
from sqlalchemy import select, func
from sqlalchemy.ext.asyncio import AsyncSession
from app.storage.models import RequestRecord, FeedbackRecord, ModelRecord
from app.config.settings import get_settings
settings = get_settings()
async def calculate_cost_savings(
session: AsyncSession,
baseline_model_id: Optional[str] = None,
) -> Dict[str, Any]:
"""
Computes accurate, non-invented cost savings:
Baseline Cost (if all requests were sent to baseline model) vs Actual Routed Cost.
"""
base_id = baseline_model_id or settings.BASELINE_MODEL_ID
# Fetch baseline model pricing
res_base = await session.execute(select(ModelRecord).filter_by(id=base_id))
baseline_model = res_base.scalar_one_or_none()
base_in_rate = baseline_model.cost_per_input_token if baseline_model else 0.000005
base_out_rate = baseline_model.cost_per_output_token if baseline_model else 0.000015
# Fetch aggregate requests
res = await session.execute(
select(
func.count(RequestRecord.request_id),
func.sum(RequestRecord.input_tokens),
func.sum(RequestRecord.output_tokens),
func.sum(RequestRecord.estimated_cost),
)
)
total_reqs, total_in_tok, total_out_tok, total_routed_cost = res.one()
total_reqs = total_reqs or 0
total_in_tok = total_in_tok or 0
total_out_tok = total_out_tok or 0
total_routed_cost = round(total_routed_cost or 0.0, 6)
# Theoretical cost if all traffic ran on baseline
baseline_total_cost = round((total_in_tok * base_in_rate) + (total_out_tok * base_out_rate), 6)
saved_amount = max(0.0, round(baseline_total_cost - total_routed_cost, 6))
savings_percent = round((saved_amount / baseline_total_cost * 100.0), 2) if baseline_total_cost > 0 else 0.0
return {
"baseline_model": base_id,
"total_requests": total_reqs,
"total_input_tokens": total_in_tok,
"total_output_tokens": total_out_tok,
"baseline_total_cost": baseline_total_cost,
"routed_total_cost": total_routed_cost,
"cost_saved_usd": saved_amount,
"savings_percentage": savings_percent,
}
async def get_system_analytics(session: AsyncSession) -> Dict[str, Any]:
"""
Collects full aggregate metrics for the dashboard.
"""
# 1. Totals & averages
res = await session.execute(
select(
func.count(RequestRecord.request_id),
func.avg(RequestRecord.total_latency_ms),
func.avg(RequestRecord.routing_latency_ms),
func.avg(RequestRecord.estimated_cost),
func.sum(RequestRecord.estimated_cost),
)
)
count, avg_lat, avg_route_lat, avg_cost, sum_cost = res.one()
# 2. Distribution by model
model_dist_res = await session.execute(
select(RequestRecord.selected_model, func.count(RequestRecord.request_id))
.group_by(RequestRecord.selected_model)
)
model_distribution = {m: c for m, c in model_dist_res.all()}
# 3. Distribution by task type
task_dist_res = await session.execute(
select(RequestRecord.task_type, func.count(RequestRecord.request_id))
.group_by(RequestRecord.task_type)
)
task_distribution = {t: c for t, c in task_dist_res.all()}
# 4. Fallback rate
fb_res = await session.execute(
select(func.count(RequestRecord.request_id)).filter(RequestRecord.fallback_used == True)
)
fallback_count = fb_res.scalar() or 0
fallback_rate = round((fallback_count / count * 100.0), 2) if count and count > 0 else 0.0
# 5. User Feedback aggregate
fb_pos_res = await session.execute(select(func.count(FeedbackRecord.id)).filter(FeedbackRecord.rating == 1))
fb_neg_res = await session.execute(select(func.count(FeedbackRecord.id)).filter(FeedbackRecord.rating == -1))
pos_feedback = fb_pos_res.scalar() or 0
neg_feedback = fb_neg_res.scalar() or 0
total_feedback = pos_feedback + neg_feedback
quality_score = round((pos_feedback / total_feedback * 100.0), 1) if total_feedback > 0 else None
savings = await calculate_cost_savings(session)
return {
"total_requests": count or 0,
"avg_latency_ms": round(avg_lat or 0.0, 1),
"avg_routing_latency_ms": round(avg_route_lat or 0.0, 1),
"avg_cost_usd": round(avg_cost or 0.0, 6),
"total_cost_usd": round(sum_cost or 0.0, 6),
"quality_score_percent": quality_score,
"fallback_rate_percent": fallback_rate,
"fallback_count": fallback_count,
"model_distribution": model_distribution,
"task_distribution": task_distribution,
"savings": savings,
}
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import json
import re
from typing import Optional
from app.models.schemas import RequestAnalysis, TaskType, PriorityLevel
from app.analyzer.heuristics import analyze_request_heuristics, estimate_tokens
from app.providers.registry import provider_registry
from app.config.settings import get_settings
settings = get_settings()
CLASSIFIER_PROMPT = """You are a real-time request classifier for an intelligent LLM model router.
Analyze the user prompt and return ONLY a JSON object matching this schema:
{
"task_type": "GENERAL_QA" | "CODING" | "DEBUGGING" | "REASONING" | "SUMMARIZATION" | "EXTRACTION" | "WRITING" | "TRANSLATION" | "ANALYSIS" | "MATH" | "LONG_CONTEXT" | "CREATIVE",
"complexity": 0.0 to 1.0,
"reasoning_required": true/false,
"coding_required": true/false,
"vision_required": true/false,
"latency_priority": "LOW" | "MEDIUM" | "HIGH",
"cost_sensitivity": "LOW" | "MEDIUM" | "HIGH",
"quality_requirement": "LOW" | "MEDIUM" | "HIGH"
}
Output valid JSON only with no markdown wrapping or additional text.
"""
async def analyze_request(prompt: str, analyzer_mode: Optional[str] = None) -> RequestAnalysis:
mode = (analyzer_mode or settings.ROUTER_ANALYZER).lower()
if mode == "llm":
try:
# Attempt to use local or configured provider for classification
provider = provider_registry.get_provider("ollama")
if provider:
health = await provider.check_health()
if health.get("status") == "CONNECTED":
resp = await provider.generate(
prompt=f"Classify this request:\n\n{prompt[:1000]}",
model_id="qwen2.5-coder",
system_prompt=CLASSIFIER_PROMPT,
temperature=0.1,
)
clean_json = re.search(r"\{.*\}", resp.content, re.DOTALL)
if clean_json:
data = json.loads(clean_json.group(0))
comp = float(data.get("complexity", 0.5))
comp_label = PriorityLevel.HIGH if comp >= 0.75 else (PriorityLevel.MEDIUM if comp >= 0.45 else PriorityLevel.LOW)
return RequestAnalysis(
task_type=TaskType(data.get("task_type", "GENERAL_QA")),
complexity=comp,
complexity_label=comp_label,
reasoning_required=bool(data.get("reasoning_required", False)),
coding_required=bool(data.get("coding_required", False)),
vision_required=bool(data.get("vision_required", False)),
context_size=estimate_tokens(prompt),
latency_priority=PriorityLevel(data.get("latency_priority", "MEDIUM")),
cost_sensitivity=PriorityLevel(data.get("cost_sensitivity", "MEDIUM")),
quality_requirement=PriorityLevel(data.get("quality_requirement", "MEDIUM")),
keywords_detected=["llm_classified"],
analyzer_used="llm",
)
except Exception:
# Graceful fallback to heuristics if LLM classifier is unavailable or times out
pass
# Default to fast, deterministic heuristics
return analyze_request_heuristics(prompt)
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import re
from typing import List, Tuple
from app.models.schemas import TaskType, PriorityLevel, RequestAnalysis
# Regex rules and indicator vocabularies
DEBUG_PATTERNS = [
r"\b(debug|bug|stacktrace|exception|error|fix this error|traceback|segfault|syntaxerror|nullpointer)\b",
r"\b(crash|panicked|failed test|reproduce|root cause|undefined is not a function)\b",
]
CODING_PATTERNS = [
r"\b(write a python|write code|implement|function|class |def |const |async def|typescript|javascript|golang|rust|react|sql query|endpoint|api client)\b",
r"\b(refactor|algorithm|regex|unit test|dockerfile|kubernetes yaml|prisma|fastapi)\b",
r"```[a-zA-Z0-9_-]*\n",
]
REASONING_PATTERNS = [
r"\b(explain why|reasoning|step by step|proof|derive|why did|compare and contrast|architectural decision|tradeoffs|trade-offs|consequences of)\b",
r"\b(game theory|logical fallacy|syllogism|first principles|hypothesize)\b",
]
MATH_PATTERNS = [
r"\b(calculate|integral|derivative|matrix|eigenvalue|probability|equation|differential equation|solve for x|combinatorics|standard deviation)\b",
r"[\$\\\^\{\}\[\]\=]{3,}",
]
SUMMARIZATION_PATTERNS = [
r"\b(summarize|summary|tldr|tl;dr|key points|bullet points of this|condense|digest|brief overview)\b",
]
EXTRACTION_PATTERNS = [
r"\b(extract|extract json|parse this table|pull all emails|convert to json|output as csv|structured data from)\b",
]
TRANSLATION_PATTERNS = [
r"\b(translate to|translate into|in french|in spanish|in german|in japanese|in chinese|in hindi|translate from)\b",
]
CREATIVE_PATTERNS = [
r"\b(write a poem|write a story|screenplay|brainstorm catchy|creative writing|dialogue between|fiction|metaphor)\b",
]
ANALYSIS_PATTERNS = [
r"\b(analyze|breakdown|evaluate|critique|audit|financial report|trend analysis|pros and cons)\b",
]
def estimate_tokens(text: str) -> int:
"""Fast, reliable token estimation (roughly 4 characters per token)."""
return max(1, len(text) // 4)
def analyze_request_heuristics(prompt: str) -> RequestAnalysis:
text_lower = prompt.lower()
tokens = estimate_tokens(prompt)
detected_keywords: List[str] = []
task_scores: dict[TaskType, float] = {t: 0.0 for t in TaskType}
# 1. Evaluate patterns
for p in DEBUG_PATTERNS:
matches = re.findall(p, text_lower)
if matches:
task_scores[TaskType.DEBUGGING] += len(matches) * 2.5
detected_keywords.extend(matches)
for p in CODING_PATTERNS:
matches = re.findall(p, text_lower)
if matches:
task_scores[TaskType.CODING] += len(matches) * 2.0
detected_keywords.extend(matches)
for p in REASONING_PATTERNS:
matches = re.findall(p, text_lower)
if matches:
task_scores[TaskType.REASONING] += len(matches) * 2.0
detected_keywords.extend(matches)
for p in MATH_PATTERNS:
matches = re.findall(p, text_lower)
if matches:
task_scores[TaskType.MATH] += len(matches) * 2.2
detected_keywords.extend(matches)
for p in SUMMARIZATION_PATTERNS:
matches = re.findall(p, text_lower)
if matches:
task_scores[TaskType.SUMMARIZATION] += len(matches) * 2.5
detected_keywords.extend(matches)
for p in EXTRACTION_PATTERNS:
matches = re.findall(p, text_lower)
if matches:
task_scores[TaskType.EXTRACTION] += len(matches) * 2.0
detected_keywords.extend(matches)
for p in TRANSLATION_PATTERNS:
matches = re.findall(p, text_lower)
if matches:
task_scores[TaskType.TRANSLATION] += len(matches) * 3.0
detected_keywords.extend(matches)
for p in CREATIVE_PATTERNS:
matches = re.findall(p, text_lower)
if matches:
task_scores[TaskType.CREATIVE] += len(matches) * 2.0
detected_keywords.extend(matches)
for p in ANALYSIS_PATTERNS:
matches = re.findall(p, text_lower)
if matches:
task_scores[TaskType.ANALYSIS] += len(matches) * 1.8
detected_keywords.extend(matches)
# Long context check
if tokens > 4000:
task_scores[TaskType.LONG_CONTEXT] += 3.0
# 2. Determine Primary Task
best_task = TaskType.GENERAL_QA
max_score = 0.0
for task, score in task_scores.items():
if score > max_score:
max_score = score
best_task = task
# 3. Derive Complexity (0.0 to 1.0)
complexity = 0.35 # Base complexity
# Token length factor
if tokens > 10000:
complexity += 0.30
elif tokens > 3000:
complexity += 0.20
elif tokens > 800:
complexity += 0.10
elif tokens < 30:
complexity -= 0.10
# Task inherent complexity
if best_task in (TaskType.DEBUGGING, TaskType.MATH, TaskType.REASONING):
complexity += 0.30
elif best_task in (TaskType.CODING, TaskType.ANALYSIS, TaskType.LONG_CONTEXT):
complexity += 0.20
elif best_task in (TaskType.TRANSLATION, TaskType.EXTRACTION, TaskType.SUMMARIZATION):
complexity += 0.05
elif best_task == TaskType.GENERAL_QA and tokens < 50:
complexity -= 0.15
# Multi-step keyword boosts
if any(k in text_lower for k in ["distributed", "async", "concurrency", "deadlock", "microservices", "architecture"]):
complexity += 0.25
if any(k in text_lower for k in ["simple", "quick", "one liner", "easy", "hello world"]):
complexity -= 0.20
complexity = max(0.05, min(0.99, round(complexity, 2)))
# 4. Priority and Capabilities
coding_required = best_task in (TaskType.CODING, TaskType.DEBUGGING) or task_scores[TaskType.CODING] > 0
reasoning_required = best_task in (TaskType.REASONING, TaskType.DEBUGGING, TaskType.MATH) or complexity >= 0.70
if complexity >= 0.75:
complexity_label = PriorityLevel.HIGH
quality_req = PriorityLevel.HIGH
latency_pri = PriorityLevel.LOW
cost_sens = PriorityLevel.LOW
elif complexity >= 0.45:
complexity_label = PriorityLevel.MEDIUM
quality_req = PriorityLevel.MEDIUM
latency_pri = PriorityLevel.MEDIUM
cost_sens = PriorityLevel.MEDIUM
else:
complexity_label = PriorityLevel.LOW
quality_req = PriorityLevel.LOW
latency_pri = PriorityLevel.HIGH
cost_sens = PriorityLevel.HIGH
return RequestAnalysis(
task_type=best_task,
complexity=complexity,
complexity_label=complexity_label,
reasoning_required=reasoning_required,
coding_required=coding_required,
vision_required=False,
tools_required=False,
context_size=tokens,
latency_priority=latency_pri,
cost_sensitivity=cost_sens,
quality_requirement=quality_req,
keywords_detected=list(set(detected_keywords))[:10],
analyzer_used="rules",
)
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import uuid
import time
import asyncio
from typing import List, Optional, Dict, Any
from fastapi import APIRouter, Depends, HTTPException, Query
from pydantic import BaseModel, Field
from sqlalchemy.ext.asyncio import AsyncSession
from sqlalchemy import select, desc
from app.storage.database import get_db
from app.storage.models import (
ModelRecord,
RoutingPolicyRecord,
RoutingRuleRecord,
RequestRecord,
RoutingDecisionRecord,
ResponseRecord,
FeedbackRecord,
BudgetRecord,
ExperimentRecord,
)
from app.models.schemas import (
ModelMetadata,
RequestAnalysis,
RoutingDecision,
ProviderResponse,
ModelTier,
)
from app.analyzer.analyzer import analyze_request
from app.router.engine import route_request
from app.fallback.handler import execute_with_fallback
from app.budgets.manager import check_budget_threshold
from app.observability.events import log_router_event
from app.analytics.service import get_system_analytics, calculate_cost_savings
from app.providers.registry import provider_registry
from app.config.settings import get_settings
router = APIRouter()
settings = get_settings()
class RouteOnlyRequest(BaseModel):
prompt: str = Field(..., min_length=1, description="Prompt text to analyze and route")
policy: Optional[str] = None
analyzer_mode: Optional[str] = None
class GenerateRequest(BaseModel):
prompt: str = Field(..., min_length=1)
system_prompt: Optional[str] = None
policy: Optional[str] = None
analyzer_mode: Optional[str] = None
force_model: Optional[str] = None
temperature: float = 0.7
max_retries: int = 1
class FeedbackSubmitRequest(BaseModel):
request_id: str
rating: int = Field(..., description="1 for up, -1 for down")
comment: Optional[str] = None
class ModelCreateRequest(BaseModel):
id: str
name: str
provider: str
type: str = "LOCAL"
tier: str = "BALANCED"
context_window: int = 32768
supports_coding: bool = True
supports_reasoning: bool = True
supports_vision: bool = False
supports_tools: bool = True
quality_score: float = 0.85
speed_score: float = 0.85
cost_per_input_token: float = 0.0
cost_per_output_token: float = 0.0
class RuleCreateRequest(BaseModel):
id: Optional[str] = None
name: str
description: Optional[str] = None
priority: int = 10
is_enabled: bool = True
condition_field: str
condition_operator: str
condition_value: str
action_type: str
action_target: str
# Helper to convert DB model record to Pydantic ModelMetadata
def db_model_to_meta(m: ModelRecord) -> ModelMetadata:
return ModelMetadata(
id=m.id,
name=m.name,
provider=m.provider,
type=m.type,
tier=ModelTier(m.tier) if m.tier in [t.value for t in ModelTier] else ModelTier.BALANCED,
context_window=m.context_window,
supports_coding=m.supports_coding,
supports_reasoning=m.supports_reasoning,
supports_vision=m.supports_vision,
supports_tools=m.supports_tools,
quality_score=m.quality_score,
speed_score=m.speed_score,
reliability_score=m.reliability_score,
cost_per_input_token=m.cost_per_input_token,
cost_per_output_token=m.cost_per_output_token,
availability=m.availability,
is_active=m.is_active,
)
@router.post("/api/route", response_model=Dict[str, Any])
async def api_route_prompt(req: RouteOnlyRequest, db: AsyncSession = Depends(get_db)):
"""
Dry-run routing inspection endpoint:
Analyzes request and produces structured routing decision without invoking provider.
"""
t_start = time.perf_counter()
req_id = f"req_{uuid.uuid4().hex[:12]}"
# 1. Analyze Request
analysis = await analyze_request(req.prompt, req.analyzer_mode)
# 2. Fetch Models & Policy
res_m = await db.execute(select(ModelRecord).filter_by(is_active=True))
db_models = res_m.scalars().all()
models = [db_model_to_meta(m) for m in db_models]
policy_name = req.policy or settings.DEFAULT_ROUTING_POLICY
res_p = await db.execute(select(RoutingPolicyRecord).filter_by(id=policy_name))
policy = res_p.scalar_one_or_none()
weights = {
"quality_weight": policy.quality_weight if policy else 0.35,
"cost_weight": policy.cost_weight if policy else 0.25,
"speed_weight": policy.speed_weight if policy else 0.20,
"capability_weight": policy.capability_weight if policy else 0.15,
"reliability_weight": policy.reliability_weight if policy else 0.05,
}
# 3. Fetch Rules & Budget Check
res_r = await db.execute(select(RoutingRuleRecord).filter_by(is_enabled=True))
rules = res_r.scalars().all()
budget_pct, _ = await check_budget_threshold(db, 0.0)
# 4. Routing Decision
decision = route_request(
analysis=analysis,
available_models=models,
policy_weights=weights,
policy_name=policy_name,
rules=rules,
budget_percent=budget_pct,
request_id=req_id,
)
t_route_ms = (time.perf_counter() - t_start) * 1000.0
return {
"request_id": req_id,
"analysis": analysis.model_dump(),
"decision": decision.model_dump(),
"routing_latency_ms": round(t_route_ms, 2),
}
@router.post("/api/generate", response_model=Dict[str, Any])
async def api_generate_response(req: GenerateRequest, db: AsyncSession = Depends(get_db)):
"""
End-to-End routed execution:
Analyzes -> Routes -> Executes Selected Model / Fallback -> Records Decision & Metrics.
"""
t_start = time.perf_counter()
req_id = f"req_{uuid.uuid4().hex[:12]}"
# 1. Analyze Request
analysis = await analyze_request(req.prompt, req.analyzer_mode)
# 2. Fetch Models & Policy
res_m = await db.execute(select(ModelRecord).filter_by(is_active=True))
db_models = res_m.scalars().all()
models = [db_model_to_meta(m) for m in db_models]
policy_name = req.policy or settings.DEFAULT_ROUTING_POLICY
res_p = await db.execute(select(RoutingPolicyRecord).filter_by(id=policy_name))
policy = res_p.scalar_one_or_none()
weights = {
"quality_weight": policy.quality_weight if policy else 0.35,
"cost_weight": policy.cost_weight if policy else 0.25,
"speed_weight": policy.speed_weight if policy else 0.20,
"capability_weight": policy.capability_weight if policy else 0.15,
"reliability_weight": policy.reliability_weight if policy else 0.05,
}
# 3. Fetch Rules & Budget Check
res_r = await db.execute(select(RoutingRuleRecord).filter_by(is_enabled=True))
rules = res_r.scalars().all()
budget_pct, budget_mode = await check_budget_threshold(db, 0.0)
# 4. Route
decision = route_request(
analysis=analysis,
available_models=models,
policy_weights=weights,
policy_name=policy_name,
rules=rules,
budget_percent=budget_pct,
request_id=req_id,
)
t_routing_done = time.perf_counter()
routing_latency_ms = (t_routing_done - t_start) * 1000.0
# If force model requested, override
selected_model_id = req.force_model or decision.selected_model
target_meta = next((m for m in models if m.id == selected_model_id), None)
selected_provider_id = target_meta.provider if target_meta else decision.provider
# 5. Provider Execution with Fallback & Retries
provider_resp, fallback_used, orig_model, fallback_reason = await execute_with_fallback(
prompt=req.prompt,
selected_model_id=selected_model_id,
selected_provider_id=selected_provider_id,
all_models=models,
system_prompt=req.system_prompt,
temperature=req.temperature,
max_retries=req.max_retries,
)
total_latency_ms = (time.perf_counter() - t_start) * 1000.0
# 6. Calculate actual costs
actual_model_meta = next((m for m in models if m.id == provider_resp.model), target_meta)
cost_in_rate = actual_model_meta.cost_per_input_token if actual_model_meta else 0.0
cost_out_rate = actual_model_meta.cost_per_output_token if actual_model_meta else 0.0
actual_cost = round((provider_resp.input_tokens * cost_in_rate) + (provider_resp.output_tokens * cost_out_rate), 6)
# Baseline cost calculation
res_base = await db.execute(select(ModelRecord).filter_by(id=settings.BASELINE_MODEL_ID))
base_m = res_base.scalar_one_or_none()
base_cost = 0.0
if base_m:
base_cost = round((provider_resp.input_tokens * base_m.cost_per_input_token) + (provider_resp.output_tokens * base_m.cost_per_output_token), 6)
saved_cost = max(0.0, round(base_cost - actual_cost, 6))
# Update budget spend
await check_budget_threshold(db, actual_cost)
# 7. Persistence
req_record = RequestRecord(
request_id=req_id,
prompt=req.prompt,
task_type=analysis.task_type.value,
complexity=analysis.complexity,
context_size=analysis.context_size,
reasoning_required=analysis.reasoning_required,
coding_required=analysis.coding_required,
routing_policy=policy_name,
selected_model=provider_resp.model or selected_model_id,
provider=provider_resp.provider or selected_provider_id,
status="FALLBACK" if fallback_used else ("FAILED" if provider_resp.error else "SUCCESS"),
fallback_used=fallback_used,
original_model=orig_model,
fallback_reason=fallback_reason,
input_tokens=provider_resp.input_tokens,
output_tokens=provider_resp.output_tokens,
total_tokens=provider_resp.total_tokens,
estimated_cost=actual_cost,
baseline_cost=base_cost,
cost_saved=saved_cost,
routing_latency_ms=round(routing_latency_ms, 2),
provider_latency_ms=round(provider_resp.provider_latency_ms, 2),
total_latency_ms=round(total_latency_ms, 2),
time_to_first_token_ms=provider_resp.time_to_first_token_ms,
)
db.add(req_record)
decision_record = RoutingDecisionRecord(
decision_id=decision.decision_id,
request_id=req_id,
selected_model=decision.selected_model,
confidence=decision.confidence,
reasons=decision.reasons,
candidate_scores=[s.model_dump() for s in decision.candidate_scores],
rejected_candidates=decision.rejected_candidates,
policy_used=policy_name,
)
db.add(decision_record)
resp_record = ResponseRecord(
response_id=f"resp_{uuid.uuid4().hex[:12]}",
request_id=req_id,
model_id=provider_resp.model or selected_model_id,
provider=provider_resp.provider or selected_provider_id,
content=provider_resp.content,
finish_reason=provider_resp.finish_reason,
is_mock=provider_resp.is_mock,
)
db.add(resp_record)
await db.commit()
# 8. Structured Observability Event
log_router_event(
event_name="request_completed",
request_id=req_id,
model=provider_resp.model,
provider=provider_resp.provider,
duration_ms=round(total_latency_ms, 2),
metadata={"tokens": provider_resp.total_tokens, "cost": actual_cost, "fallback": fallback_used},
)
return {
"request_id": req_id,
"analysis": analysis.model_dump(),
"decision": decision.model_dump(),
"response": provider_resp.model_dump(),
"metrics": {
"routing_latency_ms": round(routing_latency_ms, 2),
"provider_latency_ms": round(provider_resp.provider_latency_ms, 2),
"total_latency_ms": round(total_latency_ms, 2),
"estimated_cost_usd": actual_cost,
"baseline_cost_usd": base_cost,
"cost_saved_usd": saved_cost,
"fallback_used": fallback_used,
"fallback_reason": fallback_reason,
},
}
# Models & Registry Endpoints
@router.get("/api/models")
async def list_models(db: AsyncSession = Depends(get_db)):
res = await db.execute(select(ModelRecord).order_by(ModelRecord.tier, ModelRecord.name))
return res.scalars().all()
@router.post("/api/models")
async def create_model(req: ModelCreateRequest, db: AsyncSession = Depends(get_db)):
record = ModelRecord(
id=req.id,
name=req.name,
provider=req.provider,
type=req.type,
tier=req.tier,
context_window=req.context_window,
supports_coding=req.supports_coding,
supports_reasoning=req.supports_reasoning,
supports_vision=req.supports_vision,
supports_tools=req.supports_tools,
quality_score=req.quality_score,
speed_score=req.speed_score,
cost_per_input_token=req.cost_per_input_token,
cost_per_output_token=req.cost_per_output_token,
)
db.add(record)
await db.commit()
return record
# Providers Endpoint (Never returning secrets)
@router.get("/api/providers")
async def list_providers(db: AsyncSession = Depends(get_db)):
providers_info = []
for pid, p in provider_registry.list_providers().items():
health = await p.check_health()
providers_info.append({
"id": pid,
"name": p.name,
"status": health.get("status", "READY"),
"base_url": p.base_url,
"message": health.get("message"),
"models_available": health.get("models_available", []),
"credentials_status": "Loaded from environment (.env)" if getattr(p, "api_key", None) or pid in ["mock", "ollama"] else "Not configured",
})
return providers_info
# Live Traffic & Decisions
@router.get("/api/traffic")
async def get_traffic_feed(limit: int = 50, db: AsyncSession = Depends(get_db)):
res = await db.execute(select(RequestRecord).order_by(desc(RequestRecord.timestamp)).limit(limit))
return res.scalars().all()
@router.get("/api/decisions/{decision_id_or_request_id}")
async def get_decision_details(decision_id_or_request_id: str, db: AsyncSession = Depends(get_db)):
res_dec = await db.execute(
select(RoutingDecisionRecord).filter(
(RoutingDecisionRecord.decision_id == decision_id_or_request_id)
| (RoutingDecisionRecord.request_id == decision_id_or_request_id)
)
)
dec = res_dec.scalar_one_or_none()
if not dec:
raise HTTPException(status_code=404, detail="Decision not found")
res_req = await db.execute(select(RequestRecord).filter_by(request_id=dec.request_id))
req = res_req.scalar_one_or_none()
res_resp = await db.execute(select(ResponseRecord).filter_by(request_id=dec.request_id))
resp = res_resp.scalar_one_or_none()
return {
"decision": dec,
"request": req,
"response": resp,
}
# Feedback
@router.post("/api/feedback")
async def submit_feedback(req: FeedbackSubmitRequest, db: AsyncSession = Depends(get_db)):
res = await db.execute(select(RequestRecord).filter_by(request_id=req.request_id))
request_rec = res.scalar_one_or_none()
if not request_rec:
raise HTTPException(status_code=404, detail="Request ID not found")
feedback = FeedbackRecord(
id=f"fb_{uuid.uuid4().hex[:10]}",
request_id=req.request_id,
model_id=request_rec.selected_model,
task_type=request_rec.task_type,
rating=1 if req.rating > 0 else -1,
comment=req.comment,
)
db.add(feedback)
await db.commit()
return {"status": "success", "feedback_id": feedback.id}
# Analytics Endpoints
@router.get("/api/analytics")
async def get_analytics(db: AsyncSession = Depends(get_db)):
return await get_system_analytics(db)
@router.get("/api/analytics/savings")
async def get_savings(baseline_model: Optional[str] = None, db: AsyncSession = Depends(get_db)):
return await calculate_cost_savings(db, baseline_model)
# Rules Endpoints
@router.get("/api/rules")
async def list_rules(db: AsyncSession = Depends(get_db)):
res = await db.execute(select(RoutingRuleRecord).order_by(desc(RoutingRuleRecord.priority)))
return res.scalars().all()
@router.post("/api/rules")
async def create_rule(req: RuleCreateRequest, db: AsyncSession = Depends(get_db)):
rule_id = req.id or f"rule-{uuid.uuid4().hex[:8]}"
record = RoutingRuleRecord(
id=rule_id,
name=req.name,
description=req.description,
priority=req.priority,
is_enabled=req.is_enabled,
condition_field=req.condition_field,
condition_operator=req.condition_operator,
condition_value=req.condition_value,
action_type=req.action_type,
action_target=req.action_target,
)
db.add(record)
await db.commit()
return record
@router.put("/api/rules/{rule_id}")
async def update_rule(rule_id: str, req: RuleCreateRequest, db: AsyncSession = Depends(get_db)):
res = await db.execute(select(RoutingRuleRecord).filter_by(id=rule_id))
rule = res.scalar_one_or_none()
if not rule:
raise HTTPException(status_code=404, detail="Rule not found")
rule.name = req.name
rule.description = req.description
rule.priority = req.priority
rule.is_enabled = req.is_enabled
rule.condition_field = req.condition_field
rule.condition_operator = req.condition_operator
rule.condition_value = req.condition_value
rule.action_type = req.action_type
rule.action_target = req.action_target
await db.commit()
return rule
@router.delete("/api/rules/{rule_id}")
async def delete_rule(rule_id: str, db: AsyncSession = Depends(get_db)):
res = await db.execute(select(RoutingRuleRecord).filter_by(id=rule_id))
rule = res.scalar_one_or_none()
if not rule:
raise HTTPException(status_code=404, detail="Rule not found")
await db.delete(rule)
await db.commit()
return {"status": "deleted", "id": rule_id}
# Policies
@router.get("/api/policies")
async def list_policies(db: AsyncSession = Depends(get_db)):
res = await db.execute(select(RoutingPolicyRecord))
return res.scalars().all()
# Budgets
@router.get("/api/budgets")
async def get_budget(db: AsyncSession = Depends(get_db)):
res = await db.execute(select(BudgetRecord).filter_by(id="default"))
return res.scalar_one_or_none()
# Experiments
@router.get("/api/experiments")
async def list_experiments(db: AsyncSession = Depends(get_db)):
res = await db.execute(select(ExperimentRecord))
return res.scalars().all()
# Health Check
@router.get("/api/health")
async def get_health():
return {
"status": "healthy",
"service": "Model Router AI Control Room",
"version": "1.0.0",
"environment": settings.APP_ENV,
}
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from typing import Tuple
from sqlalchemy import select
from sqlalchemy.ext.asyncio import AsyncSession
from app.storage.models import BudgetRecord
async def check_budget_threshold(
session: AsyncSession,
cost_to_add: float = 0.0,
) -> Tuple[float, str]:
"""
Checks current budget utilization percentage and determines intervention mode:
- NORMAL (< 80%)
- OPTIMIZE_80 (>= 80% and < 95%)
- CHEAP_95 (>= 95% and < 100%)
- BLOCK_100 (>= 100%)
"""
res = await session.execute(select(BudgetRecord).filter_by(id="default"))
budget = res.scalar_one_or_none()
if not budget:
return 0.0, "NORMAL"
monthly_limit = budget.monthly_limit or 100.0
current_spend = budget.current_monthly_spend + cost_to_add
percent = (current_spend / monthly_limit) * 100.0 if monthly_limit > 0 else 0.0
mode = "NORMAL"
if percent >= 100.0:
mode = "BLOCK_100"
elif percent >= 95.0:
mode = "CHEAP_95"
elif percent >= 80.0:
mode = "OPTIMIZE_80"
budget.current_monthly_spend = current_spend
budget.intervention_mode = mode
await session.commit()
return round(percent, 2), mode
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import sys
import os
import asyncio
import httpx
import typer
from rich.console import Console
from rich.table import Table
from rich.panel import Panel
from rich.text import Text
# Set UTF-8 encoding support for Windows terminals
if sys.platform.startswith("win"):
try:
sys.stdout.reconfigure(encoding="utf-8")
sys.stderr.reconfigure(encoding="utf-8")
except Exception:
pass
sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), "..", "..")))
from app.config.settings import get_settings
from app.analyzer.heuristics import analyze_request_heuristics
from app.router.engine import route_request
from app.storage.database import init_db, AsyncSessionLocal
from app.storage.models import ModelRecord, RoutingPolicyRecord
from app.api.routes import db_model_to_meta
from app.fallback.handler import execute_with_fallback
from sqlalchemy import select
app = typer.Typer(help="Model Router: Intelligent LLM Request Routing Platform")
console = Console(safe_box=True)
settings = get_settings()
@app.command()
def doctor():
"""Run environment, connectivity, and dependency health checks."""
console.print(Panel.fit("[bold cyan]Model Router Diagnostics (modelrouter doctor)[/bold cyan]"))
# 1. Python Check
py_ver = f"{sys.version_info.major}.{sys.version_info.minor}.{sys.version_info.micro}"
console.print(f" [green][OK][/green] Python: [bold]{py_ver}[/bold]")
# 2. Database Check
try:
asyncio.run(init_db())
console.print(" [green][OK][/green] SQLite Database: [bold]Initialized & Ready[/bold]")
except Exception as exc:
console.print(f" [red][FAIL][/red] Database Error: {exc}")
# 3. Ollama Connectivity Check
ollama_ok = False
try:
r = httpx.get(f"{settings.OLLAMA_BASE_URL}/api/tags", timeout=1.5)
if r.status_code == 200:
models = [m.get("name") for m in r.json().get("models", [])]
console.print(f" [green][OK][/green] Ollama (Local): [bold]Connected[/bold] ({len(models)} local models found)")
ollama_ok = True
except Exception:
pass
if not ollama_ok:
console.print(f" [yellow][!][/yellow] Ollama (Local): [dim]Not running on {settings.OLLAMA_BASE_URL} (Optional, local fallback available)[/dim]")
# 4. Mock Engine
console.print(" [green][OK][/green] Mock Engine: [bold]Ready (Zero API keys required)[/bold]")
# 5. External Providers
if settings.OPENAI_API_KEY:
console.print(" [green][OK][/green] OpenAI Provider: [bold]API Key Configured in .env[/bold]")
else:
console.print(" [yellow][!][/yellow] OpenAI Provider: [dim]Not configured (Optional)[/dim]")
if settings.ANTHROPIC_API_KEY:
console.print(" [green][OK][/green] Anthropic Provider: [bold]API Key Configured in .env[/bold]")
else:
console.print(" [yellow][!][/yellow] Anthropic Provider: [dim]Not configured (Optional)[/dim]")
if settings.GEMINI_API_KEY:
console.print(" [green][OK][/green] Gemini Provider: [bold]API Key Configured in .env[/bold]")
else:
console.print(" [yellow][!][/yellow] Gemini Provider: [dim]Not configured (Optional)[/dim]")
console.print("\n[bold green]Ready for local-first intelligent routing![/bold green]\n")
@app.command()
def models():
"""List all registered candidate models and their capability scores."""
async def _list():
await init_db()
async with AsyncSessionLocal() as db:
res = await db.execute(select(ModelRecord).order_by(ModelRecord.tier, ModelRecord.name))
records = res.scalars().all()
table = Table(title="Model Router Registry", border_style="bright_blue")
table.add_column("Model ID", style="cyan", no_wrap=True)
table.add_column("Tier", style="magenta")
table.add_column("Provider", style="green")
table.add_column("Context", style="yellow")
table.add_column("Quality", style="blue")
table.add_column("Speed", style="cyan")
table.add_column("Cost / 1K In/Out", style="white")
for m in records:
cost_str = f"${m.cost_per_input_token*1000:.4f} / ${m.cost_per_output_token*1000:.4f}" if m.cost_per_input_token > 0 else "Free ($0)"
table.add_row(
m.id,
m.tier,
m.provider,
f"{m.context_window:,}",
f"{m.quality_score*100:.0f}%",
f"{m.speed_score*100:.0f}%",
cost_str,
)
console.print(table)
asyncio.run(_list())
@app.command()
def route(
prompt: str = typer.Argument(..., help="Prompt text to route"),
policy: str = typer.Option("balanced", "--policy", "-p", help="Routing policy to use"),
):
"""Analyze prompt and explain why the optimal model was selected (Dry Run)."""
async def _route():
await init_db()
analysis = analyze_request_heuristics(prompt)
async with AsyncSessionLocal() as db:
res_m = await db.execute(select(ModelRecord).filter_by(is_active=True))
models = [db_model_to_meta(m) for m in res_m.scalars().all()]
res_p = await db.execute(select(RoutingPolicyRecord).filter_by(id=policy))
pol = res_p.scalar_one_or_none()
weights = {
"quality_weight": pol.quality_weight if pol else 0.35,
"cost_weight": pol.cost_weight if pol else 0.25,
"speed_weight": pol.speed_weight if pol else 0.20,
"capability_weight": pol.capability_weight if pol else 0.15,
"reliability_weight": pol.reliability_weight if pol else 0.05,
}
decision = route_request(
analysis=analysis,
available_models=models,
policy_weights=weights,
policy_name=policy,
)
console.print(Panel.fit(
f"[bold white]Prompt:[/bold white] \"{prompt}\"\n"
f"[bold cyan]Task Type:[/bold cyan] {analysis.task_type.value} | [bold cyan]Complexity:[/bold cyan] {analysis.complexity_label.value} ({analysis.complexity:.2f})\n"
f"[bold green]Selected Model:[/bold green] [bold yellow]{decision.selected_model_name}[/bold yellow] ({decision.selected_model})\n"
f"[bold magenta]Confidence:[/bold magenta] {decision.confidence * 100:.0f}%\n"
f"[bold blue]Estimated Latency:[/bold blue] {decision.estimated_latency_ms:.0f}ms | [bold blue]Estimated Cost:[/bold blue] ${decision.estimated_cost_usd:.6f}",
title="ROUTING DECISION",
border_style="bright_blue",
))
console.print("[bold cyan]Why this model?[/bold cyan]")
for r in decision.reasons:
console.print(f" [green]+[/green] {r}")
if decision.rejected_candidates:
console.print("\n[bold red]Rejected Candidates:[/bold red]")
for cid, reason in decision.rejected_candidates.items():
console.print(f" [dim]- {cid}: {reason}[/dim]")
asyncio.run(_route())
@app.command()
def run(
prompt: str = typer.Argument(..., help="Prompt to route and execute"),
policy: str = typer.Option("balanced", "--policy", "-p", help="Routing policy to use"),
):
"""Route request and execute inference through the chosen provider."""
async def _run():
await init_db()
analysis = analyze_request_heuristics(prompt)
async with AsyncSessionLocal() as db:
res_m = await db.execute(select(ModelRecord).filter_by(is_active=True))
models = [db_model_to_meta(m) for m in res_m.scalars().all()]
decision = route_request(
analysis=analysis,
available_models=models,
policy_weights={"quality_weight": 0.35, "cost_weight": 0.25, "speed_weight": 0.20, "capability_weight": 0.15, "reliability_weight": 0.05},
policy_name=policy,
)
console.print(f"[bold cyan]Routing to:[/bold cyan] {decision.selected_model} via provider: {decision.provider}...")
resp, fallback_used, orig_m, fb_reason = await execute_with_fallback(
prompt=prompt,
selected_model_id=decision.selected_model,
selected_provider_id=decision.provider,
all_models=models,
)
console.print(Panel(
resp.content,
title=f"RESPONSE from {resp.model} ({'FALLBACK: ' + orig_m if fallback_used else 'PRIMARY'})",
subtitle=f"Latency: {resp.provider_latency_ms:.0f}ms | Tokens: {resp.total_tokens}",
border_style="green" if not fallback_used else "yellow",
))
asyncio.run(_run())
@app.command()
def traffic(limit: int = 10):
"""View recent routed traffic log."""
async def _traffic():
await init_db()
from app.storage.models import RequestRecord
from sqlalchemy import desc
async with AsyncSessionLocal() as db:
res = await db.execute(select(RequestRecord).order_by(desc(RequestRecord.timestamp)).limit(limit))
records = res.scalars().all()
table = Table(title="Recent Routed Traffic Feed", border_style="bright_blue")
table.add_column("Time", style="dim")
table.add_column("Task", style="cyan")
table.add_column("Selected Model", style="green")
table.add_column("Latency", style="yellow")
table.add_column("Cost", style="magenta")
table.add_column("Prompt Sample", style="white")
for r in records:
t_str = r.timestamp.strftime("%H:%M:%S") if r.timestamp else "N/A"
table.add_row(
t_str,
r.task_type,
r.selected_model,
f"{r.total_latency_ms:.0f}ms",
f"${r.estimated_cost:.5f}",
r.prompt[:40] + ("..." if len(r.prompt) > 40 else ""),
)
console.print(table)
asyncio.run(_traffic())
@app.command()
def analytics():
"""Display system-wide routing performance and cost savings analytics."""
async def _analytics():
await init_db()
from app.analytics.service import get_system_analytics
async with AsyncSessionLocal() as db:
data = await get_system_analytics(db)
console.print(Panel.fit(
f"[bold cyan]Total Routed Requests:[/bold cyan] {data['total_requests']}\n"
f"[bold green]Average Latency:[/bold green] {data['avg_latency_ms']}ms (Routing overhead: {data['avg_routing_latency_ms']}ms)\n"
f"[bold yellow]Total Cost:[/bold yellow] ${data['total_cost_usd']:.6f}\n"
f"[bold magenta]Cost Saved vs Baseline:[/bold magenta] ${data['savings']['cost_saved_usd']:.6f} ({data['savings']['savings_percentage']}%)\n"
f"[bold blue]Fallback Rate:[/bold blue] {data['fallback_rate_percent']}%\n",
title="MODEL ROUTER ANALYTICS OVERVIEW",
border_style="bright_blue",
))
asyncio.run(_analytics())
if __name__ == "__main__":
app()
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from functools import lru_cache
from typing import Optional
from pydantic_settings import BaseSettings, SettingsConfigDict
class Settings(BaseSettings):
APP_ENV: str = "development"
DEBUG: bool = True
PORT: int = 8000
HOST: str = "0.0.0.0"
DATABASE_URL: str = "sqlite+aiosqlite:///./model_router.db"
ROUTER_ANALYZER: str = "rules" # "rules" or "llm"
DEFAULT_ROUTING_POLICY: str = "balanced"
BASELINE_MODEL_ID: str = "mock-power"
DEFAULT_PROVIDER: str = "mock"
OLLAMA_BASE_URL: str = "http://localhost:11434"
# External Provider Keys (optional)
OPENAI_API_KEY: Optional[str] = None
ANTHROPIC_API_KEY: Optional[str] = None
GEMINI_API_KEY: Optional[str] = None
# Budgets
DAILY_BUDGET: float = 10.00
MONTHLY_BUDGET: float = 100.00
PER_REQUEST_BUDGET: float = 1.00
# Execution / Reliability
MAX_RETRIES: int = 2
PROVIDER_TIMEOUT_SECONDS: float = 30.0
model_config = SettingsConfigDict(
env_file=".env",
env_file_encoding="utf-8",
extra="ignore",
)
@lru_cache()
def get_settings() -> Settings:
return Settings()
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import uuid
import datetime
from typing import Dict, Any, List
from sqlalchemy import select, func
from sqlalchemy.ext.asyncio import AsyncSession
from app.storage.models import ExperimentRecord, ExperimentRunRecord
async def log_experiment_run(
session: AsyncSession,
experiment_id: str,
request_id: str,
policy: str,
model: str,
cost: float,
latency_ms: float,
):
run = ExperimentRunRecord(
id=str(uuid.uuid4()),
experiment_id=experiment_id,
request_id=request_id,
assigned_policy=policy,
selected_model=model,
cost=cost,
latency_ms=latency_ms,
)
session.add(run)
# Increment sample count
res = await session.execute(select(ExperimentRecord).filter_by(id=experiment_id))
exp = res.scalar_one_or_none()
if exp:
exp.sample_count += 1
await session.commit()
async def get_experiment_summary(
session: AsyncSession,
experiment_id: str,
) -> Dict[str, Any]:
res = await session.execute(select(ExperimentRecord).filter_by(id=experiment_id))
exp = res.scalar_one_or_none()
if not exp:
return {}
# Calculate metrics for Policy A vs Policy B
runs_a = await session.execute(
select(
func.count(ExperimentRunRecord.id),
func.avg(ExperimentRunRecord.cost),
func.avg(ExperimentRunRecord.latency_ms),
).filter_by(experiment_id=experiment_id, assigned_policy=exp.policy_a)
)
count_a, cost_a, lat_a = runs_a.one()
runs_b = await session.execute(
select(
func.count(ExperimentRunRecord.id),
func.avg(ExperimentRunRecord.cost),
func.avg(ExperimentRunRecord.latency_ms),
).filter_by(experiment_id=experiment_id, assigned_policy=exp.policy_b)
)
count_b, cost_b, lat_b = runs_b.one()
return {
"id": exp.id,
"name": exp.name,
"description": exp.description,
"policy_a": exp.policy_a,
"policy_b": exp.policy_b,
"status": exp.status,
"total_samples": exp.sample_count,
"results": {
exp.policy_a: {
"sample_count": count_a or 0,
"avg_cost_usd": round(cost_a or 0.0, 6),
"avg_latency_ms": round(lat_a or 0.0, 1),
},
exp.policy_b: {
"sample_count": count_b or 0,
"avg_cost_usd": round(cost_b or 0.0, 6),
"avg_latency_ms": round(lat_b or 0.0, 1),
},
},
}
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import time
import asyncio
from typing import List, Optional, Tuple, Dict, Any
from app.models.schemas import ProviderResponse, ModelMetadata
from app.providers.registry import provider_registry
from app.config.settings import get_settings
settings = get_settings()
RETRYABLE_ERRORS = [
"timeout",
"connection error",
"rate limit",
"429",
"503",
"service unavailable",
"econnreset",
]
def is_retryable(error_str: Optional[str]) -> bool:
if not error_str:
return False
low = error_str.lower()
return any(keyword in low for keyword in RETRYABLE_ERRORS)
async def execute_with_fallback(
prompt: str,
selected_model_id: str,
selected_provider_id: str,
all_models: List[ModelMetadata],
system_prompt: Optional[str] = None,
temperature: float = 0.7,
max_retries: int = 1,
) -> Tuple[ProviderResponse, bool, Optional[str], Optional[str]]:
"""
Executes model generation with configurable retries and fallback hierarchy.
Returns: (ProviderResponse, fallback_used, original_model, fallback_reason)
"""
current_model_id = selected_model_id
current_provider_id = selected_provider_id
fallback_used = False
original_model = selected_model_id
fallback_reason = None
# Step 1: Attempt primary model with retries
provider = provider_registry.get_provider(current_provider_id)
if not provider:
provider = provider_registry.get_provider("mock")
current_provider_id = "mock"
current_model_id = "mock-balanced"
fallback_used = True
fallback_reason = f"Provider '{selected_provider_id}' is not loaded. Fell back to Mock."
for attempt in range(max_retries + 1):
resp = await provider.generate(
prompt=prompt,
model_id=current_model_id,
system_prompt=system_prompt,
temperature=temperature,
)
if not resp.error and resp.finish_reason != "error":
return resp, fallback_used, original_model if fallback_used else None, fallback_reason
# If non-retryable error or last attempt, prepare for fallback
if not is_retryable(resp.error) or attempt == max_retries:
fallback_reason = resp.error or "Unknown primary model execution error."
break
await asyncio.sleep(0.5 * (attempt + 1))
# Step 2: Determine Fallback Candidate
fallback_used = True
# Priority: Mock model of same or balanced tier, or active local Ollama
candidate_fallbacks = [
m for m in all_models
if m.id != selected_model_id and m.is_active and (m.provider == "mock" or m.type == "LOCAL")
]
if not candidate_fallbacks:
candidate_fallbacks = [m for m in all_models if m.id != selected_model_id and m.is_active]
fallback_target = candidate_fallbacks[0] if candidate_fallbacks else None
if fallback_target:
fb_provider = provider_registry.get_provider(fallback_target.provider) or provider_registry.get_provider("mock")
fb_resp = await fb_provider.generate(
prompt=prompt,
model_id=fallback_target.id,
system_prompt=system_prompt,
temperature=temperature,
)
if not fb_resp.error:
return fb_resp, True, original_model, f"Primary model failed: {fallback_reason} -> Fallback to {fallback_target.id}"
# Step 3: Emergency Hard Mock fallback
emergency_mock = provider_registry.get_provider("mock")
emergency_resp = await emergency_mock.generate(
prompt=prompt,
model_id="mock-balanced",
system_prompt=system_prompt,
temperature=temperature,
)
return emergency_resp, True, original_model, f"Emergency fallback triggered due to: {fallback_reason}"
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from enum import Enum
from typing import List, Optional, Dict, Any
from pydantic import BaseModel, Field
class TaskType(str, Enum):
GENERAL_QA = "GENERAL_QA"
CODING = "CODING"
DEBUGGING = "DEBUGGING"
REASONING = "REASONING"
SUMMARIZATION = "SUMMARIZATION"
EXTRACTION = "EXTRACTION"
WRITING = "WRITING"
TRANSLATION = "TRANSLATION"
ANALYSIS = "ANALYSIS"
MATH = "MATH"
LONG_CONTEXT = "LONG_CONTEXT"
CREATIVE = "CREATIVE"
class PriorityLevel(str, Enum):
LOW = "LOW"
MEDIUM = "MEDIUM"
HIGH = "HIGH"
class ModelTier(str, Enum):
FAST = "FAST"
BALANCED = "BALANCED"
POWER = "POWER"
class ProviderType(str, Enum):
MOCK = "mock"
OLLAMA = "ollama"
OPENAI = "openai"
ANTHROPIC = "anthropic"
GEMINI = "gemini"
CUSTOM = "custom"
class RequestAnalysis(BaseModel):
task_type: TaskType = Field(default=TaskType.GENERAL_QA)
complexity: float = Field(default=0.5, ge=0.0, le=1.0)
complexity_label: PriorityLevel = Field(default=PriorityLevel.MEDIUM)
reasoning_required: bool = Field(default=False)
coding_required: bool = Field(default=False)
vision_required: bool = Field(default=False)
tools_required: bool = Field(default=False)
context_size: int = Field(default=0) # in estimated tokens / characters
latency_priority: PriorityLevel = Field(default=PriorityLevel.MEDIUM)
cost_sensitivity: PriorityLevel = Field(default=PriorityLevel.MEDIUM)
quality_requirement: PriorityLevel = Field(default=PriorityLevel.MEDIUM)
keywords_detected: List[str] = Field(default_factory=list)
analyzer_used: str = Field(default="rules")
class ModelMetadata(BaseModel):
id: str
name: str
provider: str
type: str = "LOCAL" # LOCAL, CLOUD, MOCK
tier: ModelTier = ModelTier.BALANCED
context_window: int = 8192
supports_coding: bool = False
supports_reasoning: bool = False
supports_vision: bool = False
supports_tools: bool = False
quality_score: float = 0.75 # 0.0 - 1.0
speed_score: float = 0.75 # 0.0 - 1.0
reliability_score: float = 0.95
cost_per_input_token: float = 0.0
cost_per_output_token: float = 0.0
availability: str = "AVAILABLE"
is_active: bool = True
class CandidateScore(BaseModel):
model_id: str
model_name: str
provider: str
tier: str
overall_score: float # 0 - 100
quality_component: float
cost_component: float
speed_component: float
capability_component: float
reliability_component: float
estimated_latency_ms: float
estimated_cost_usd: float
eligible: bool = True
rejection_reason: Optional[str] = None
class RoutingDecision(BaseModel):
decision_id: str
request_id: str
selected_model: str
selected_model_name: str
provider: str
tier: str
confidence: float
policy_used: str
reasons: List[str]
candidate_scores: List[CandidateScore]
rejected_candidates: Dict[str, str] = Field(default_factory=dict)
estimated_cost_usd: float
estimated_latency_ms: float
rule_applied: Optional[str] = None
timestamp: str
class ProviderResponse(BaseModel):
content: str
finish_reason: str = "stop"
input_tokens: int = 0
output_tokens: int = 0
total_tokens: int = 0
provider_latency_ms: float = 0.0
time_to_first_token_ms: Optional[float] = None
is_mock: bool = False
model: str = ""
provider: str = ""
error: Optional[str] = None
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import json
import logging
from typing import Dict, Any, Optional
import datetime
logger = logging.getLogger("model_router.observability")
logger.setLevel(logging.INFO)
if not logger.handlers:
handler = logging.StreamHandler()
formatter = logging.Formatter('{"time":"%(asctime)s", "level":"%(levelname)s", "event":%(message)s}')
handler.setFormatter(formatter)
logger.addHandler(handler)
def log_router_event(
event_name: str,
request_id: str,
model: Optional[str] = None,
provider: Optional[str] = None,
duration_ms: Optional[float] = None,
metadata: Optional[Dict[str, Any]] = None,
):
"""
Structured event logger for model router pipeline.
Redacts sensitive credentials automatically.
"""
payload = {
"event": event_name,
"request_id": request_id,
"timestamp": datetime.datetime.utcnow().isoformat(),
"model": model,
"provider": provider,
"duration_ms": duration_ms,
}
if metadata:
# Sanitize any key that looks like an API token
safe_meta = {
k: ("[REDACTED]" if "key" in k.lower() or "secret" in k.lower() or "token" in k.lower() and "count" not in k.lower() else v)
for k, v in metadata.items()
}
payload["metadata"] = safe_meta
logger.info(json.dumps(payload))
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import abc
import time
import asyncio
from typing import AsyncGenerator, Dict, Any, Optional
from app.models.schemas import ProviderResponse
class ModelProvider(abc.ABC):
"""
Abstract Base Class for all Model Providers (Mock, Ollama, OpenAI, Anthropic, Gemini, etc.)
Ensures complete provider decoupling from routing logic.
"""
def __init__(self, provider_id: str, name: str, base_url: Optional[str] = None):
self.provider_id = provider_id
self.name = name
self.base_url = base_url
@abc.abstractmethod
async def generate(
self,
prompt: str,
model_id: str,
system_prompt: Optional[str] = None,
temperature: float = 0.7,
max_tokens: Optional[int] = None,
) -> ProviderResponse:
"""Execute a text generation call against the model provider."""
pass
@abc.abstractmethod
async def stream(
self,
prompt: str,
model_id: str,
system_prompt: Optional[str] = None,
temperature: float = 0.7,
max_tokens: Optional[int] = None,
) -> AsyncGenerator[str, None]:
"""Stream generated response chunks."""
pass
@abc.abstractmethod
async def check_health(self) -> Dict[str, Any]:
"""Check provider connectivity, status, and loaded models."""
pass
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import time
import httpx
from typing import AsyncGenerator, Dict, Any, Optional
from app.providers.base import ModelProvider
from app.models.schemas import ProviderResponse
from app.config.settings import get_settings
settings = get_settings()
class OpenAIProvider(ModelProvider):
"""
OpenAI Provider Adapter.
Uses OPENAI_API_KEY from environment variables. Secrets are never exposed.
"""
def __init__(self, api_key: Optional[str] = None):
super().__init__(provider_id="openai", name="OpenAI", base_url="https://api.openai.com/v1")
self.api_key = api_key or settings.OPENAI_API_KEY
async def generate(
self,
prompt: str,
model_id: str,
system_prompt: Optional[str] = None,
temperature: float = 0.7,
max_tokens: Optional[int] = None,
) -> ProviderResponse:
if not self.api_key:
return ProviderResponse(
content="",
finish_reason="error",
error="OPENAI_API_KEY not configured in environment.",
is_mock=False,
model=model_id,
provider="openai",
)
endpoint = f"{self.base_url}/chat/completions"
headers = {
"Authorization": f"Bearer {self.api_key}",
"Content-Type": "application/json",
}
messages = []
if system_prompt:
messages.append({"role": "system", "content": system_prompt})
messages.append({"role": "user", "content": prompt})
payload: Dict[str, Any] = {
"model": model_id,
"messages": messages,
"temperature": temperature,
}
if max_tokens:
payload["max_tokens"] = max_tokens
start_time = time.perf_counter()
try:
async with httpx.AsyncClient(timeout=settings.PROVIDER_TIMEOUT_SECONDS) as client:
resp = await client.post(endpoint, json=payload, headers=headers)
resp.raise_for_status()
data = resp.json()
elapsed_ms = (time.perf_counter() - start_time) * 1000.0
choice = data["choices"][0]
usage = data.get("usage", {})
return ProviderResponse(
content=choice["message"]["content"],
finish_reason=choice.get("finish_reason", "stop"),
input_tokens=usage.get("prompt_tokens", 0),
output_tokens=usage.get("completion_tokens", 0),
total_tokens=usage.get("total_tokens", 0),
provider_latency_ms=round(elapsed_ms, 2),
is_mock=False,
model=model_id,
provider="openai",
)
except Exception as exc:
return ProviderResponse(
content="",
finish_reason="error",
error=f"OpenAI error: {str(exc)}",
is_mock=False,
model=model_id,
provider="openai",
provider_latency_ms=(time.perf_counter() - start_time) * 1000.0,
)
async def stream(
self,
prompt: str,
model_id: str,
system_prompt: Optional[str] = None,
temperature: float = 0.7,
max_tokens: Optional[int] = None,
) -> AsyncGenerator[str, None]:
if not self.api_key:
yield "[OpenAI Error: API key not configured]"
return
endpoint = f"{self.base_url}/chat/completions"
headers = {"Authorization": f"Bearer {self.api_key}"}
messages = []
if system_prompt:
messages.append({"role": "system", "content": system_prompt})
messages.append({"role": "user", "content": prompt})
payload = {
"model": model_id,
"messages": messages,
"temperature": temperature,
"stream": True,
}
try:
async with httpx.AsyncClient(timeout=settings.PROVIDER_TIMEOUT_SECONDS) as client:
async with client.stream("POST", endpoint, json=payload, headers=headers) as resp:
async for line in resp.aiter_lines():
if line.startswith("data: ") and not line.endswith("[DONE]"):
import json
data = json.loads(line[6:])
delta = data["choices"][0].get("delta", {}).get("content", "")
if delta:
yield delta
except Exception as exc:
yield f"\n[OpenAI Streaming Error: {str(exc)}]"
async def check_health(self) -> Dict[str, Any]:
if not self.api_key:
return {"provider": "openai", "status": "NOT_CONFIGURED", "message": "API key not set in .env"}
return {"provider": "openai", "status": "CONNECTED", "message": "API key configured."}
class AnthropicProvider(ModelProvider):
"""
Anthropic Provider Adapter.
"""
def __init__(self, api_key: Optional[str] = None):
super().__init__(provider_id="anthropic", name="Anthropic", base_url="https://api.anthropic.com/v1")
self.api_key = api_key or settings.ANTHROPIC_API_KEY
async def generate(
self,
prompt: str,
model_id: str,
system_prompt: Optional[str] = None,
temperature: float = 0.7,
max_tokens: Optional[int] = None,
) -> ProviderResponse:
if not self.api_key:
return ProviderResponse(
content="",
finish_reason="error",
error="ANTHROPIC_API_KEY not configured in environment.",
is_mock=False,
model=model_id,
provider="anthropic",
)
endpoint = f"{self.base_url}/messages"
headers = {
"x-api-key": self.api_key,
"anthropic-version": "2023-06-01",
"Content-Type": "application/json",
}
payload: Dict[str, Any] = {
"model": model_id,
"messages": [{"role": "user", "content": prompt}],
"max_tokens": max_tokens or 2048,
"temperature": temperature,
}
if system_prompt:
payload["system"] = system_prompt
start_time = time.perf_counter()
try:
async with httpx.AsyncClient(timeout=settings.PROVIDER_TIMEOUT_SECONDS) as client:
resp = await client.post(endpoint, json=payload, headers=headers)
resp.raise_for_status()
data = resp.json()
elapsed_ms = (time.perf_counter() - start_time) * 1000.0
content = data["content"][0]["text"]
usage = data.get("usage", {})
return ProviderResponse(
content=content,
finish_reason=data.get("stop_reason", "stop"),
input_tokens=usage.get("input_tokens", 0),
output_tokens=usage.get("output_tokens", 0),
total_tokens=usage.get("input_tokens", 0) + usage.get("output_tokens", 0),
provider_latency_ms=round(elapsed_ms, 2),
is_mock=False,
model=model_id,
provider="anthropic",
)
except Exception as exc:
return ProviderResponse(
content="",
finish_reason="error",
error=f"Anthropic error: {str(exc)}",
is_mock=False,
model=model_id,
provider="anthropic",
provider_latency_ms=(time.perf_counter() - start_time) * 1000.0,
)
async def stream(
self,
prompt: str,
model_id: str,
system_prompt: Optional[str] = None,
temperature: float = 0.7,
max_tokens: Optional[int] = None,
) -> AsyncGenerator[str, None]:
resp = await self.generate(prompt, model_id, system_prompt, temperature, max_tokens)
if resp.error:
yield f"[{resp.error}]"
return
for word in resp.content.split(" "):
yield word + " "
async def check_health(self) -> Dict[str, Any]:
if not self.api_key:
return {"provider": "anthropic", "status": "NOT_CONFIGURED", "message": "API key not set in .env"}
return {"provider": "anthropic", "status": "CONNECTED", "message": "API key configured."}
class GeminiProvider(ModelProvider):
"""
Google Gemini Provider Adapter.
"""
def __init__(self, api_key: Optional[str] = None):
super().__init__(provider_id="gemini", name="Google Gemini", base_url="https://generativelanguage.googleapis.com/v1beta")
self.api_key = api_key or settings.GEMINI_API_KEY
async def generate(
self,
prompt: str,
model_id: str,
system_prompt: Optional[str] = None,
temperature: float = 0.7,
max_tokens: Optional[int] = None,
) -> ProviderResponse:
if not self.api_key:
return ProviderResponse(
content="",
finish_reason="error",
error="GEMINI_API_KEY not configured in environment.",
is_mock=False,
model=model_id,
provider="gemini",
)
endpoint = f"{self.base_url}/models/{model_id}:generateContent?key={self.api_key}"
contents = []
if system_prompt:
contents.append({"role": "user", "parts": [{"text": f"System context: {system_prompt}"}]})
contents.append({"role": "user", "parts": [{"text": prompt}]})
payload = {
"contents": contents,
"generationConfig": {"temperature": temperature},
}
start_time = time.perf_counter()
try:
async with httpx.AsyncClient(timeout=settings.PROVIDER_TIMEOUT_SECONDS) as client:
resp = await client.post(endpoint, json=payload)
resp.raise_for_status()
data = resp.json()
elapsed_ms = (time.perf_counter() - start_time) * 1000.0
candidate = data["candidates"][0]
text = candidate["content"]["parts"][0]["text"]
usage = data.get("usageMetadata", {})
return ProviderResponse(
content=text,
finish_reason=candidate.get("finishReason", "stop"),
input_tokens=usage.get("promptTokenCount", 0),
output_tokens=usage.get("candidatesTokenCount", 0),
total_tokens=usage.get("totalTokenCount", 0),
provider_latency_ms=round(elapsed_ms, 2),
is_mock=False,
model=model_id,
provider="gemini",
)
except Exception as exc:
return ProviderResponse(
content="",
finish_reason="error",
error=f"Gemini error: {str(exc)}",
is_mock=False,
model=model_id,
provider="gemini",
provider_latency_ms=(time.perf_counter() - start_time) * 1000.0,
)
async def stream(
self,
prompt: str,
model_id: str,
system_prompt: Optional[str] = None,
temperature: float = 0.7,
max_tokens: Optional[int] = None,
) -> AsyncGenerator[str, None]:
resp = await self.generate(prompt, model_id, system_prompt, temperature, max_tokens)
if resp.error:
yield f"[{resp.error}]"
return
for word in resp.content.split(" "):
yield word + " "
async def check_health(self) -> Dict[str, Any]:
if not self.api_key:
return {"provider": "gemini", "status": "NOT_CONFIGURED", "message": "API key not set in .env"}
return {"provider": "gemini", "status": "CONNECTED", "message": "API key configured."}
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import asyncio
import time
from typing import AsyncGenerator, Dict, Any, Optional
from app.providers.base import ModelProvider
from app.models.schemas import ProviderResponse
class MockProvider(ModelProvider):
"""
Mock Provider for development, testing, and zero-cost simulation.
All outputs are explicitly labeled as DEMO/MOCK.
"""
def __init__(self):
super().__init__(provider_id="mock", name="Mock / Demo Provider")
async def generate(
self,
prompt: str,
model_id: str,
system_prompt: Optional[str] = None,
temperature: float = 0.7,
max_tokens: Optional[int] = None,
) -> ProviderResponse:
start_time = time.perf_counter()
# Simulate realistic latency based on tier
tier_delay = 0.08 if "fast" in model_id else (0.18 if "balanced" in model_id else 0.35)
await asyncio.sleep(tier_delay)
elapsed_ms = (time.perf_counter() - start_time) * 1000.0
# Calculate realistic token estimations
input_tokens = max(1, len(prompt.split()) * 2)
# Response tailored to task keywords
prompt_lower = prompt.lower()
if "python" in prompt_lower or "code" in prompt_lower or "def " in prompt_lower or "function" in prompt_lower:
content = (
f"[DEMO/MOCK RESPONSE from {model_id}]\n\n"
f"```python\n"
f"# Mock implementation generated for: {prompt[:60]}...\n"
f"def solve_problem(*args, **kwargs):\n"
f" \"\"\"Simulated response from Model Router Demo Engine.\"\"\"\n"
f" result = [x for x in args if x is not None]\n"
f" return {{'status': 'success', 'data': result, 'model': '{model_id}'}}\n"
f"```\n\n"
f"This code was returned by the simulated {model_id} engine."
)
elif "debug" in prompt_lower or "error" in prompt_lower or "bug" in prompt_lower:
content = (
f"[DEMO/MOCK RESPONSE from {model_id}]\n\n"
f"### Root Cause Analysis:\n"
f"1. **Potential Bottleneck**: Asynchronous task scheduling without proper semaphore limits.\n"
f"2. **Mitigation**: Introduce bounded concurrency worker pools.\n"
f"3. **Verification**: Checked against {model_id} simulated reasoning engine."
)
elif "summary" in prompt_lower or "summarize" in prompt_lower:
content = (
f"[DEMO/MOCK RESPONSE from {model_id}]\n\n"
f"**Executive Summary**:\n"
f"• Main point: {prompt[:80]}...\n"
f"• Key takeaway: High routing accuracy achieved with minimal latency overhead."
)
else:
content = (
f"[DEMO/MOCK RESPONSE from {model_id}]\n\n"
f"Processed request: \"{prompt[:100]}\"\n\n"
f"This is a simulated response generated locally by Model Router Mock Engine without external API calls."
)
output_tokens = max(1, len(content.split()) * 2)
total_tokens = input_tokens + output_tokens
return ProviderResponse(
content=content,
finish_reason="stop",
input_tokens=input_tokens,
output_tokens=output_tokens,
total_tokens=total_tokens,
provider_latency_ms=round(elapsed_ms, 2),
time_to_first_token_ms=round(tier_delay * 500, 2),
is_mock=True,
model=model_id,
provider="mock",
)
async def stream(
self,
prompt: str,
model_id: str,
system_prompt: Optional[str] = None,
temperature: float = 0.7,
max_tokens: Optional[int] = None,
) -> AsyncGenerator[str, None]:
resp = await self.generate(prompt, model_id, system_prompt, temperature, max_tokens)
chunks = resp.content.split(" ")
for chunk in chunks:
await asyncio.sleep(0.02)
yield chunk + " "
async def check_health(self) -> Dict[str, Any]:
return {
"provider": "mock",
"status": "CONNECTED",
"message": "Mock provider active (Demo Mode). Zero API keys required.",
"models_available": ["mock-fast", "mock-balanced", "mock-power"],
}
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import time
import httpx
from typing import AsyncGenerator, Dict, Any, Optional
from app.providers.base import ModelProvider
from app.models.schemas import ProviderResponse
from app.config.settings import get_settings
settings = get_settings()
class OllamaProvider(ModelProvider):
"""
Local Ollama Provider adapter.
Executes real inference against locally running Ollama instance without external API keys.
"""
def __init__(self, base_url: Optional[str] = None):
url = base_url or settings.OLLAMA_BASE_URL
super().__init__(provider_id="ollama", name="Ollama (Local)", base_url=url)
def _strip_model_prefix(self, model_id: str) -> str:
# e.g. "ollama-qwen2.5-coder" -> "qwen2.5-coder"
if model_id.startswith("ollama-"):
return model_id[len("ollama-"):]
return model_id
async def generate(
self,
prompt: str,
model_id: str,
system_prompt: Optional[str] = None,
temperature: float = 0.7,
max_tokens: Optional[int] = None,
) -> ProviderResponse:
real_model = self._strip_model_prefix(model_id)
endpoint = f"{self.base_url.rstrip('/')}/api/generate"
payload: Dict[str, Any] = {
"model": real_model,
"prompt": prompt,
"stream": False,
"options": {
"temperature": temperature,
}
}
if system_prompt:
payload["system"] = system_prompt
if max_tokens:
payload["options"]["num_predict"] = max_tokens
start_time = time.perf_counter()
try:
async with httpx.AsyncClient(timeout=settings.PROVIDER_TIMEOUT_SECONDS) as client:
resp = await client.post(endpoint, json=payload)
resp.raise_for_status()
data = resp.json()
elapsed_ms = (time.perf_counter() - start_time) * 1000.0
content = data.get("response", "")
input_tokens = data.get("prompt_eval_count", max(1, len(prompt.split()) * 2))
output_tokens = data.get("eval_count", max(1, len(content.split()) * 2))
# Nanoseconds to ms if provided by ollama
eval_duration_ms = data.get("eval_duration", 0) / 1_000_000.0 if "eval_duration" in data else elapsed_ms
return ProviderResponse(
content=content,
finish_reason="stop" if data.get("done") else "length",
input_tokens=input_tokens,
output_tokens=output_tokens,
total_tokens=input_tokens + output_tokens,
provider_latency_ms=round(elapsed_ms, 2),
time_to_first_token_ms=round(data.get("prompt_eval_duration", 0) / 1_000_000.0, 2) or None,
is_mock=False,
model=model_id,
provider="ollama",
)
except Exception as exc:
return ProviderResponse(
content="",
finish_reason="error",
error=f"Ollama connection error ({str(exc)}). Is Ollama running on {self.base_url}?",
is_mock=False,
model=model_id,
provider="ollama",
provider_latency_ms=(time.perf_counter() - start_time) * 1000.0,
)
async def stream(
self,
prompt: str,
model_id: str,
system_prompt: Optional[str] = None,
temperature: float = 0.7,
max_tokens: Optional[int] = None,
) -> AsyncGenerator[str, None]:
real_model = self._strip_model_prefix(model_id)
endpoint = f"{self.base_url.rstrip('/')}/api/generate"
payload = {
"model": real_model,
"prompt": prompt,
"stream": True,
"options": {"temperature": temperature},
}
if system_prompt:
payload["system"] = system_prompt
try:
async with httpx.AsyncClient(timeout=settings.PROVIDER_TIMEOUT_SECONDS) as client:
async with client.stream("POST", endpoint, json=payload) as resp:
async for line in resp.aiter_lines():
if line:
import json
data = json.loads(line)
chunk = data.get("response", "")
if chunk:
yield chunk
except Exception as exc:
yield f"\n[Ollama Streaming Error: {str(exc)}]"
async def check_health(self) -> Dict[str, Any]:
try:
async with httpx.AsyncClient(timeout=3.0) as client:
resp = await client.get(f"{self.base_url.rstrip('/')}/api/tags")
if resp.status_code == 200:
models = [m.get("name") for m in resp.json().get("models", [])]
return {
"provider": "ollama",
"status": "CONNECTED",
"base_url": self.base_url,
"models_available": models,
}
except Exception as exc:
pass
return {
"provider": "ollama",
"status": "NOT_CONNECTED",
"base_url": self.base_url,
"message": "Ollama service unreachable on configured port. Start via 'ollama serve'.",
}
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from typing import Dict, Optional
from app.providers.base import ModelProvider
from app.providers.mock_provider import MockProvider
from app.providers.ollama_provider import OllamaProvider
from app.providers.external_providers import OpenAIProvider, AnthropicProvider, GeminiProvider
from app.config.settings import get_settings
settings = get_settings()
class ProviderRegistry:
"""
Central registry managing provider instances.
"""
def __init__(self):
self._providers: Dict[str, ModelProvider] = {
"mock": MockProvider(),
"ollama": OllamaProvider(base_url=settings.OLLAMA_BASE_URL),
"openai": OpenAIProvider(api_key=settings.OPENAI_API_KEY),
"anthropic": AnthropicProvider(api_key=settings.ANTHROPIC_API_KEY),
"gemini": GeminiProvider(api_key=settings.GEMINI_API_KEY),
}
def get_provider(self, provider_id: str) -> Optional[ModelProvider]:
return self._providers.get(provider_id.lower())
def register_provider(self, provider_id: str, provider: ModelProvider):
self._providers[provider_id.lower()] = provider
def list_providers(self) -> Dict[str, ModelProvider]:
return self._providers
provider_registry = ProviderRegistry()
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import uuid
import datetime
from typing import List, Dict, Any, Optional
from app.models.schemas import (
ModelMetadata,
RequestAnalysis,
RoutingDecision,
CandidateScore,
PriorityLevel,
)
from app.router.scoring import filter_candidate_models, compute_candidate_score
from app.router.rules_engine import evaluate_routing_rules
from app.storage.models import RoutingRuleRecord
def generate_routing_explanation(
selected_model: ModelMetadata,
analysis: RequestAnalysis,
policy_name: str,
rule_name: Optional[str] = None,
) -> List[str]:
reasons = []
if rule_name:
reasons.append(f"Matching rule applied: \"{rule_name}\"")
# Task explanation
reasons.append(f"Task classified as {analysis.task_type.value} with {analysis.complexity_label.value} complexity ({analysis.complexity:.2f})")
# Reasoning / Code requirements
if analysis.reasoning_required:
reasons.append("Multi-step reasoning & deep inference required for problem domain")
if analysis.coding_required:
reasons.append("Syntactical & algorithmic coding capabilities required")
# Model specific match
if selected_model.tier.value == "POWER":
reasons.append(f"High reasoning score ({selected_model.quality_score * 100:.0f}%) and deep context ({selected_model.context_window:,} tokens)")
elif selected_model.tier.value == "FAST":
reasons.append(f"Low latency priority and high speed score ({selected_model.speed_score * 100:.0f}%)")
else:
reasons.append("Balanced quality and cost profile selected for optimal efficiency")
# Cost / local factor
if selected_model.cost_per_input_token == 0.0:
reasons.append("Local zero-cost execution prioritized")
elif analysis.cost_sensitivity == PriorityLevel.HIGH:
reasons.append("Cost sensitivity is HIGH — model offers lowest token pricing")
# Policy
reasons.append(f"Evaluated under '{policy_name}' policy weights")
return reasons
def route_request(
analysis: RequestAnalysis,
available_models: List[ModelMetadata],
policy_weights: Dict[str, float],
policy_name: str = "balanced",
rules: Optional[List[RoutingRuleRecord]] = None,
budget_percent: float = 0.0,
request_id: Optional[str] = None,
) -> RoutingDecision:
req_id = request_id or str(uuid.uuid4())
decision_id = f"dec_{uuid.uuid4().hex[:12]}"
now_iso = datetime.datetime.utcnow().isoformat()
# 1. Rule evaluation
rule_action, rule_target, rule_name = None, None, None
if rules:
rule_action, rule_target, rule_name = evaluate_routing_rules(rules, analysis, budget_percent)
# 2. Hard filter candidates
eligible_models, rejected_dict = filter_candidate_models(available_models, analysis)
if not eligible_models:
# Fallback to any active model if everything was filtered
eligible_models = [m for m in available_models if m.is_active] or available_models
# Handle Rule Action: ROUTE_TO specific model
if rule_action == "ROUTE_TO" and rule_target:
exact_match = next((m for m in eligible_models if m.id == rule_target), None)
if exact_match:
scores = [compute_candidate_score(m, analysis, policy_weights) for m in eligible_models]
score_map = {s.model_id: s for s in scores}
selected_score = score_map.get(exact_match.id) or compute_candidate_score(exact_match, analysis, policy_weights)
reasons = generate_routing_explanation(exact_match, analysis, policy_name, rule_name)
return RoutingDecision(
decision_id=decision_id,
request_id=req_id,
selected_model=exact_match.id,
selected_model_name=exact_match.name,
provider=exact_match.provider,
tier=exact_match.tier.value if hasattr(exact_match.tier, "value") else str(exact_match.tier),
confidence=0.98,
policy_used=policy_name,
reasons=reasons,
candidate_scores=scores,
rejected_candidates=rejected_dict,
estimated_cost_usd=selected_score.estimated_cost_usd,
estimated_latency_ms=selected_score.estimated_latency_ms,
rule_applied=rule_name,
timestamp=now_iso,
)
# Handle Rule Action: FORCE_TIER
if rule_action == "FORCE_TIER" and rule_target:
tier_filtered = [m for m in eligible_models if m.tier.value.upper() == rule_target.upper()]
if tier_filtered:
eligible_models = tier_filtered
# 3. Score all eligible models
candidate_scores: List[CandidateScore] = []
for model in eligible_models:
sc = compute_candidate_score(model, analysis, policy_weights)
candidate_scores.append(sc)
# Sort descending by overall_score
candidate_scores.sort(key=lambda s: s.overall_score, reverse=True)
best_candidate = candidate_scores[0]
selected_model = next(m for m in eligible_models if m.id == best_candidate.model_id)
# Confidence calculation: score difference from second candidate
if len(candidate_scores) > 1:
gap = best_candidate.overall_score - candidate_scores[1].overall_score
confidence = min(0.99, max(0.70, round(0.80 + (gap / 100.0), 2)))
else:
confidence = 0.95
reasons = generate_routing_explanation(selected_model, analysis, policy_name, rule_name)
return RoutingDecision(
decision_id=decision_id,
request_id=req_id,
selected_model=selected_model.id,
selected_model_name=selected_model.name,
provider=selected_model.provider,
tier=selected_model.tier.value if hasattr(selected_model.tier, "value") else str(selected_model.tier),
confidence=confidence,
policy_used=policy_name,
reasons=reasons,
candidate_scores=candidate_scores,
rejected_candidates=rejected_dict,
estimated_cost_usd=best_candidate.estimated_cost_usd,
estimated_latency_ms=best_candidate.estimated_latency_ms,
rule_applied=rule_name,
timestamp=now_iso,
)
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from typing import List, Optional, Tuple
from app.storage.models import RoutingRuleRecord
from app.models.schemas import RequestAnalysis
def evaluate_routing_rules(
rules: List[RoutingRuleRecord],
analysis: RequestAnalysis,
current_budget_percent: float = 0.0,
) -> Tuple[Optional[str], Optional[str], Optional[str]]:
"""
Evaluates enabled routing rules in order of priority (highest priority number first).
Returns: (action_type, action_target, matched_rule_name) or (None, None, None)
"""
sorted_rules = sorted([r for r in rules if r.is_enabled], key=lambda x: x.priority, reverse=True)
for rule in sorted_rules:
field = rule.condition_field.lower()
op = rule.condition_operator
target_val = rule.condition_value
matched = False
if field == "task_type":
val = analysis.task_type.value
if op == "==":
matched = (val == target_val)
elif op == "!=":
matched = (val != target_val)
elif op == "contains":
matched = (target_val in val)
elif field == "complexity":
val = analysis.complexity
try:
target_num = float(target_val)
if op == ">=":
matched = (val >= target_num)
elif op == ">":
matched = (val > target_num)
elif op == "<=":
matched = (val <= target_num)
elif op == "<":
matched = (val < target_num)
elif op == "==":
matched = (abs(val - target_num) < 0.01)
except ValueError:
pass
elif field in ("budget_percent", "monthly_budget_percent"):
try:
target_num = float(target_val)
if op == ">=":
matched = (current_budget_percent >= target_num)
elif op == ">":
matched = (current_budget_percent > target_num)
except ValueError:
pass
elif field == "context_size":
try:
target_num = int(target_val)
if op == ">=":
matched = (analysis.context_size >= target_num)
elif op == ">":
matched = (analysis.context_size > target_num)
except ValueError:
pass
if matched:
return rule.action_type, rule.action_target, rule.name
return None, None, None
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from typing import List, Dict, Tuple, Optional
from app.models.schemas import ModelMetadata, RequestAnalysis, CandidateScore, PriorityLevel
def filter_candidate_models(
models: List[ModelMetadata],
analysis: RequestAnalysis,
) -> Tuple[List[ModelMetadata], Dict[str, str]]:
"""
Hard-filtering step: eliminates models that cannot satisfy non-negotiable requirements.
Returns: (eligible_models, rejected_candidates_with_reasons)
"""
eligible: List[ModelMetadata] = []
rejected: Dict[str, str] = {}
for m in models:
if not m.is_active:
rejected[m.id] = "Model is currently disabled/inactive."
continue
# 1. Context Window check
if analysis.context_size > m.context_window:
rejected[m.id] = (
f"Context window ({m.context_window} tokens) is smaller than required input ({analysis.context_size} tokens)."
)
continue
# 2. Vision requirement check
if analysis.vision_required and not m.supports_vision:
rejected[m.id] = "Task requires vision / multimodal capabilities which this model does not support."
continue
# 3. Coding capability check (for high complexity code tasks)
if analysis.coding_required and analysis.complexity >= 0.70 and not m.supports_coding:
rejected[m.id] = "Task requires specialized coding capabilities for complex logic."
continue
# 4. Deep reasoning capability check
if analysis.reasoning_required and analysis.complexity >= 0.85 and not m.supports_reasoning:
rejected[m.id] = "Task requires advanced reasoning capabilities for multi-step problem solving."
continue
eligible.append(m)
# If all filtered out (edge case), return active models to avoid total failure
if not eligible and models:
for m in models:
if m.is_active:
eligible.append(m)
rejected.clear()
return eligible, rejected
def compute_candidate_score(
model: ModelMetadata,
analysis: RequestAnalysis,
weights: Dict[str, float],
) -> CandidateScore:
"""
Multi-criteria configurable scoring formula.
Quality, Speed, CostEfficiency, CapabilityMatch, Reliability.
Normalized score: 0 to 100.
"""
# 1. Quality Component (0.0 to 1.0)
quality_comp = model.quality_score
if analysis.quality_requirement == PriorityLevel.HIGH:
# Boost premium models for high quality requirements
if model.tier.value == "POWER":
quality_comp = min(1.0, quality_comp * 1.15)
elif analysis.quality_requirement == PriorityLevel.LOW:
quality_comp = model.quality_score * 0.9
# 2. Speed Component (0.0 to 1.0)
speed_comp = model.speed_score
if analysis.latency_priority == PriorityLevel.HIGH:
if model.tier.value == "FAST":
speed_comp = min(1.0, speed_comp * 1.20)
elif analysis.latency_priority == PriorityLevel.LOW:
speed_comp = model.speed_score * 0.85
# 3. Cost Efficiency Component (0.0 to 1.0)
# Zero-cost local models get 1.0, cheaper cloud models get high score
combined_token_cost = (model.cost_per_input_token * 1000) + (model.cost_per_output_token * 1000)
if combined_token_cost == 0.0:
cost_efficiency = 1.0
else:
# Scale: $0.0001 -> 0.95, $0.01 -> 0.50, $0.05 -> 0.10
cost_efficiency = max(0.05, min(0.99, 1.0 / (1.0 + (combined_token_cost * 100.0))))
if analysis.cost_sensitivity == PriorityLevel.HIGH:
cost_efficiency = min(1.0, cost_efficiency * 1.25)
# 4. Capability Match Component (0.0 to 1.0)
cap_points = 0.0
total_caps = 0.0
if analysis.coding_required:
total_caps += 1.0
if model.supports_coding:
cap_points += 1.0
if analysis.reasoning_required:
total_caps += 1.0
if model.supports_reasoning:
cap_points += 1.0
if analysis.vision_required:
total_caps += 1.0
if model.supports_vision:
cap_points += 1.0
capability_comp = (cap_points / total_caps) if total_caps > 0 else 0.90
# 5. Reliability Component (0.0 to 1.0)
reliability_comp = model.reliability_score
# Weighted Sum
w_q = weights.get("quality_weight", 0.35)
w_c = weights.get("cost_weight", 0.25)
w_s = weights.get("speed_weight", 0.20)
w_cap = weights.get("capability_weight", 0.15)
w_r = weights.get("reliability_weight", 0.05)
total_w = w_q + w_c + w_s + w_cap + w_r
raw_score = (
(quality_comp * w_q)
+ (cost_efficiency * w_c)
+ (speed_comp * w_s)
+ (capability_comp * w_cap)
+ (reliability_comp * w_r)
) / (total_w if total_w > 0 else 1.0)
overall_score = round(raw_score * 100.0, 2)
# Estimated metrics
est_latency = 120.0 if model.tier.value == "FAST" else (350.0 if model.tier.value == "BALANCED" else 750.0)
est_cost = (analysis.context_size * model.cost_per_input_token) + (250 * model.cost_per_output_token)
return CandidateScore(
model_id=model.id,
model_name=model.name,
provider=model.provider,
tier=model.tier.value if hasattr(model.tier, "value") else str(model.tier),
overall_score=overall_score,
quality_component=round(quality_comp * 100.0, 1),
cost_component=round(cost_efficiency * 100.0, 1),
speed_component=round(speed_comp * 100.0, 1),
capability_component=round(capability_comp * 100.0, 1),
reliability_component=round(reliability_comp * 100.0, 1),
estimated_latency_ms=est_latency,
estimated_cost_usd=round(est_cost, 6),
eligible=True,
)
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from typing import AsyncGenerator
from sqlalchemy.ext.asyncio import create_async_engine, async_sessionmaker, AsyncSession
from app.config.settings import get_settings
from app.storage.models import (
Base,
ModelRecord,
ProviderRecord,
RoutingPolicyRecord,
RoutingRuleRecord,
BudgetRecord,
)
settings = get_settings()
engine = create_async_engine(
settings.DATABASE_URL,
echo=False,
connect_args={"check_same_thread": False} if "sqlite" in settings.DATABASE_URL else {},
)
AsyncSessionLocal = async_sessionmaker(
bind=engine,
class_=AsyncSession,
expire_on_commit=False,
)
async def get_db() -> AsyncGenerator[AsyncSession, None]:
async with AsyncSessionLocal() as session:
try:
yield session
finally:
await session.close()
async def init_db():
async with engine.begin() as conn:
await conn.run_sync(Base.metadata.create_all)
# Populate initial seed data if empty
async with AsyncSessionLocal() as session:
from sqlalchemy import select
# Check providers
res = await session.execute(select(ProviderRecord))
providers = res.scalars().all()
if not providers:
default_providers = [
ProviderRecord(id="mock", name="Mock / Demo Provider", status="CONNECTED", is_enabled=True),
ProviderRecord(id="ollama", name="Ollama (Local)", status="READY", is_enabled=True, base_url=settings.OLLAMA_BASE_URL),
ProviderRecord(id="openai", name="OpenAI", status="CONNECTED" if settings.OPENAI_API_KEY else "NOT_CONFIGURED", is_enabled=bool(settings.OPENAI_API_KEY)),
ProviderRecord(id="anthropic", name="Anthropic", status="CONNECTED" if settings.ANTHROPIC_API_KEY else "NOT_CONFIGURED", is_enabled=bool(settings.ANTHROPIC_API_KEY)),
ProviderRecord(id="gemini", name="Google Gemini", status="CONNECTED" if settings.GEMINI_API_KEY else "NOT_CONFIGURED", is_enabled=bool(settings.GEMINI_API_KEY)),
]
session.add_all(default_providers)
# Check models
res_m = await session.execute(select(ModelRecord))
models = res_m.scalars().all()
if not models:
default_models = [
# Mock Models
ModelRecord(
id="mock-fast",
name="Mock Fast (Echo)",
provider="mock",
type="MOCK",
tier="FAST",
context_window=32768,
supports_coding=True,
supports_reasoning=False,
supports_vision=False,
supports_tools=True,
quality_score=0.75,
speed_score=0.98,
reliability_score=0.99,
cost_per_input_token=0.0000001,
cost_per_output_token=0.0000002,
),
ModelRecord(
id="mock-balanced",
name="Mock Balanced (General)",
provider="mock",
type="MOCK",
tier="BALANCED",
context_window=65536,
supports_coding=True,
supports_reasoning=True,
supports_vision=True,
supports_tools=True,
quality_score=0.88,
speed_score=0.85,
reliability_score=0.99,
cost_per_input_token=0.000001,
cost_per_output_token=0.000003,
),
ModelRecord(
id="mock-power",
name="Mock Power (Reasoning)",
provider="mock",
type="MOCK",
tier="POWER",
context_window=131072,
supports_coding=True,
supports_reasoning=True,
supports_vision=True,
supports_tools=True,
quality_score=0.98,
speed_score=0.60,
reliability_score=0.99,
cost_per_input_token=0.000005,
cost_per_output_token=0.000015,
),
# Local Ollama Models (Default profiles)
ModelRecord(
id="ollama-qwen2.5-coder",
name="Qwen 2.5 Coder 7B (Ollama)",
provider="ollama",
type="LOCAL",
tier="FAST",
context_window=32768,
supports_coding=True,
supports_reasoning=True,
supports_vision=False,
supports_tools=True,
quality_score=0.86,
speed_score=0.92,
reliability_score=0.95,
cost_per_input_token=0.0,
cost_per_output_token=0.0,
),
ModelRecord(
id="ollama-llama3.2",
name="Llama 3.2 3B (Ollama)",
provider="ollama",
type="LOCAL",
tier="FAST",
context_window=131072,
supports_coding=True,
supports_reasoning=True,
supports_vision=False,
supports_tools=True,
quality_score=0.82,
speed_score=0.95,
reliability_score=0.95,
cost_per_input_token=0.0,
cost_per_output_token=0.0,
),
ModelRecord(
id="ollama-mistral",
name="Mistral 7B (Ollama)",
provider="ollama",
type="LOCAL",
tier="BALANCED",
context_window=32768,
supports_coding=True,
supports_reasoning=True,
supports_vision=False,
supports_tools=True,
quality_score=0.84,
speed_score=0.88,
reliability_score=0.95,
cost_per_input_token=0.0,
cost_per_output_token=0.0,
),
ModelRecord(
id="ollama-deepseek-r1",
name="DeepSeek R1 8B (Ollama)",
provider="ollama",
type="LOCAL",
tier="POWER",
context_window=65536,
supports_coding=True,
supports_reasoning=True,
supports_vision=False,
supports_tools=False,
quality_score=0.93,
speed_score=0.68,
reliability_score=0.94,
cost_per_input_token=0.0,
cost_per_output_token=0.0,
),
# External Cloud Model Profiles
ModelRecord(
id="gpt-4o-mini",
name="OpenAI GPT-4o Mini",
provider="openai",
type="CLOUD",
tier="FAST",
context_window=128000,
supports_coding=True,
supports_reasoning=True,
supports_vision=True,
supports_tools=True,
quality_score=0.88,
speed_score=0.90,
reliability_score=0.99,
cost_per_input_token=0.00000015,
cost_per_output_token=0.00000060,
),
ModelRecord(
id="gpt-4o",
name="OpenAI GPT-4o",
provider="openai",
type="CLOUD",
tier="POWER",
context_window=128000,
supports_coding=True,
supports_reasoning=True,
supports_vision=True,
supports_tools=True,
quality_score=0.96,
speed_score=0.75,
reliability_score=0.99,
cost_per_input_token=0.0000025,
cost_per_output_token=0.000010,
),
ModelRecord(
id="claude-3-5-sonnet",
name="Anthropic Claude 3.5 Sonnet",
provider="anthropic",
type="CLOUD",
tier="POWER",
context_window=200000,
supports_coding=True,
supports_reasoning=True,
supports_vision=True,
supports_tools=True,
quality_score=0.98,
speed_score=0.72,
reliability_score=0.99,
cost_per_input_token=0.000003,
cost_per_output_token=0.000015,
),
ModelRecord(
id="gemini-1.5-flash",
name="Google Gemini 1.5 Flash",
provider="gemini",
type="CLOUD",
tier="FAST",
context_window=1000000,
supports_coding=True,
supports_reasoning=True,
supports_vision=True,
supports_tools=True,
quality_score=0.87,
speed_score=0.93,
reliability_score=0.99,
cost_per_input_token=0.000000075,
cost_per_output_token=0.00000030,
),
]
session.add_all(default_models)
# Check policies
res_p = await session.execute(select(RoutingPolicyRecord))
policies = res_p.scalars().all()
if not policies:
default_policies = [
RoutingPolicyRecord(
id="balanced",
name="Balanced Router",
description="Optimizes across quality, cost, speed, capabilities, and reliability.",
quality_weight=0.35,
cost_weight=0.25,
speed_weight=0.20,
capability_weight=0.15,
reliability_weight=0.05,
is_default=True,
),
RoutingPolicyRecord(
id="lowest_cost",
name="Lowest Cost",
description="Prefers cheapest and local zero-cost models with acceptable capability.",
quality_weight=0.15,
cost_weight=0.60,
speed_weight=0.15,
capability_weight=0.05,
reliability_weight=0.05,
is_default=False,
),
RoutingPolicyRecord(
id="lowest_latency",
name="Lowest Latency",
description="Prefers fastest response times and high throughput models.",
quality_weight=0.15,
cost_weight=0.15,
speed_weight=0.60,
capability_weight=0.05,
reliability_weight=0.05,
is_default=False,
),
RoutingPolicyRecord(
id="highest_quality",
name="Highest Quality",
description="Directs all complex reasoning and coding tasks to top-tier frontier models.",
quality_weight=0.65,
cost_weight=0.05,
speed_weight=0.10,
capability_weight=0.15,
reliability_weight=0.05,
is_default=False,
),
RoutingPolicyRecord(
id="custom",
name="Custom Weights",
description="User-defined policy weights.",
quality_weight=0.30,
cost_weight=0.30,
speed_weight=0.20,
capability_weight=0.10,
reliability_weight=0.10,
is_default=False,
),
]
session.add_all(default_policies)
# Check default budget
res_b = await session.execute(select(BudgetRecord))
budget = res_b.scalar_one_or_none()
if not budget:
session.add(
BudgetRecord(
id="default",
daily_limit=settings.DAILY_BUDGET,
monthly_limit=settings.MONTHLY_BUDGET,
per_request_limit=settings.PER_REQUEST_BUDGET,
current_daily_spend=0.0,
current_monthly_spend=0.0,
intervention_mode="NORMAL",
)
)
# Check default rules
res_r = await session.execute(select(RoutingRuleRecord))
rules = res_r.scalars().all()
if not rules:
default_rules = [
RoutingRuleRecord(
id="rule-code-reasoning",
name="Deep Debugging & Reasoning Override",
description="Route high complexity coding & debugging to Power Models",
priority=10,
is_enabled=True,
condition_field="task_type",
condition_operator="==",
condition_value="DEBUGGING",
action_type="FORCE_TIER",
action_target="POWER",
),
RoutingRuleRecord(
id="rule-budget-saver",
name="Budget Guard > 95%",
description="Prefer local or fast models when budget exceeds 95%",
priority=50,
is_enabled=True,
condition_field="budget_percent",
condition_operator=">=",
condition_value="95.0",
action_type="SET_POLICY",
action_target="lowest_cost",
),
]
session.add_all(default_rules)
await session.commit()
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import datetime
from sqlalchemy import (
Column,
String,
Float,
Integer,
Boolean,
DateTime,
Text,
JSON,
ForeignKey,
)
from sqlalchemy.orm import declarative_base, relationship
Base = declarative_base()
class ModelRecord(Base):
__tablename__ = "models"
id = Column(String(100), primary_key=True)
name = Column(String(200), nullable=False)
provider = Column(String(50), nullable=False) # mock, ollama, openai, anthropic, gemini
type = Column(String(50), default="LOCAL") # LOCAL, CLOUD, MOCK
context_window = Column(Integer, default=8192)
supports_coding = Column(Boolean, default=False)
supports_reasoning = Column(Boolean, default=False)
supports_vision = Column(Boolean, default=False)
supports_tools = Column(Boolean, default=False)
quality_score = Column(Float, default=0.7)
speed_score = Column(Float, default=0.7)
reliability_score = Column(Float, default=0.95)
cost_per_input_token = Column(Float, default=0.0)
cost_per_output_token = Column(Float, default=0.0)
availability = Column(String(50), default="AVAILABLE")
is_active = Column(Boolean, default=True)
tier = Column(String(50), default="BALANCED") # FAST, BALANCED, POWER
created_at = Column(DateTime, default=datetime.datetime.utcnow)
updated_at = Column(DateTime, default=datetime.datetime.utcnow, onupdate=datetime.datetime.utcnow)
class ProviderRecord(Base):
__tablename__ = "providers"
id = Column(String(50), primary_key=True)
name = Column(String(100), nullable=False)
status = Column(String(50), default="READY") # READY, CONNECTED, NOT_CONFIGURED, ERROR
is_enabled = Column(Boolean, default=True)
base_url = Column(String(500), nullable=True)
updated_at = Column(DateTime, default=datetime.datetime.utcnow, onupdate=datetime.datetime.utcnow)
class RoutingPolicyRecord(Base):
__tablename__ = "routing_policies"
id = Column(String(50), primary_key=True)
name = Column(String(100), nullable=False)
description = Column(String(500), nullable=True)
quality_weight = Column(Float, default=0.35)
cost_weight = Column(Float, default=0.25)
speed_weight = Column(Float, default=0.20)
capability_weight = Column(Float, default=0.15)
reliability_weight = Column(Float, default=0.05)
is_default = Column(Boolean, default=False)
class RoutingRuleRecord(Base):
__tablename__ = "routing_rules"
id = Column(String(100), primary_key=True)
name = Column(String(200), nullable=False)
description = Column(String(500), nullable=True)
priority = Column(Integer, default=0)
is_enabled = Column(Boolean, default=True)
condition_field = Column(String(100), nullable=False) # task_type, complexity, budget_percent, context_size
condition_operator = Column(String(50), nullable=False) # ==, !=, >, <, >=, <=, contains
condition_value = Column(String(200), nullable=False)
action_type = Column(String(50), nullable=False) # ROUTE_TO, PREFER, FORCE_TIER, SET_POLICY
action_target = Column(String(100), nullable=False)
created_at = Column(DateTime, default=datetime.datetime.utcnow)
class RequestRecord(Base):
__tablename__ = "requests"
request_id = Column(String(100), primary_key=True)
timestamp = Column(DateTime, default=datetime.datetime.utcnow)
prompt = Column(Text, nullable=False)
task_type = Column(String(50), nullable=False)
complexity = Column(Float, default=0.5)
context_size = Column(Integer, default=0)
reasoning_required = Column(Boolean, default=False)
coding_required = Column(Boolean, default=False)
routing_policy = Column(String(50), default="balanced")
selected_model = Column(String(100), nullable=False)
provider = Column(String(50), nullable=False)
status = Column(String(50), default="SUCCESS") # SUCCESS, FAILED, FALLBACK
fallback_used = Column(Boolean, default=False)
original_model = Column(String(100), nullable=True)
fallback_reason = Column(String(500), nullable=True)
input_tokens = Column(Integer, default=0)
output_tokens = Column(Integer, default=0)
total_tokens = Column(Integer, default=0)
estimated_cost = Column(Float, default=0.0)
baseline_cost = Column(Float, default=0.0)
cost_saved = Column(Float, default=0.0)
routing_latency_ms = Column(Float, default=0.0)
provider_latency_ms = Column(Float, default=0.0)
total_latency_ms = Column(Float, default=0.0)
time_to_first_token_ms = Column(Float, nullable=True)
class RoutingDecisionRecord(Base):
__tablename__ = "routing_decisions"
decision_id = Column(String(100), primary_key=True)
request_id = Column(String(100), ForeignKey("requests.request_id"), nullable=False)
timestamp = Column(DateTime, default=datetime.datetime.utcnow)
selected_model = Column(String(100), nullable=False)
confidence = Column(Float, default=0.9)
reasons = Column(JSON, default=list)
candidate_scores = Column(JSON, default=dict)
rejected_candidates = Column(JSON, default=dict)
policy_used = Column(String(50), default="balanced")
class ResponseRecord(Base):
__tablename__ = "responses"
response_id = Column(String(100), primary_key=True)
request_id = Column(String(100), ForeignKey("requests.request_id"), nullable=False)
timestamp = Column(DateTime, default=datetime.datetime.utcnow)
model_id = Column(String(100), nullable=False)
provider = Column(String(50), nullable=False)
content = Column(Text, nullable=False)
finish_reason = Column(String(50), default="stop")
is_mock = Column(Boolean, default=False)
class FeedbackRecord(Base):
__tablename__ = "feedback"
id = Column(String(100), primary_key=True)
request_id = Column(String(100), ForeignKey("requests.request_id"), nullable=False)
model_id = Column(String(100), nullable=False)
task_type = Column(String(50), nullable=False)
rating = Column(Integer, nullable=False) # 1 for thumbs up, -1 for thumbs down
comment = Column(Text, nullable=True)
timestamp = Column(DateTime, default=datetime.datetime.utcnow)
class BudgetRecord(Base):
__tablename__ = "budgets"
id = Column(String(50), primary_key=True, default="default")
daily_limit = Column(Float, default=10.0)
monthly_limit = Column(Float, default=100.0)
per_request_limit = Column(Float, default=1.0)
current_daily_spend = Column(Float, default=0.0)
current_monthly_spend = Column(Float, default=0.0)
intervention_mode = Column(String(50), default="OPTIMIZE") # NORMAL, OPTIMIZE_80, CHEAP_95, BLOCK_100
updated_at = Column(DateTime, default=datetime.datetime.utcnow, onupdate=datetime.datetime.utcnow)
class ExperimentRecord(Base):
__tablename__ = "experiments"
id = Column(String(100), primary_key=True)
name = Column(String(200), nullable=False)
description = Column(String(500), nullable=True)
policy_a = Column(String(50), nullable=False)
policy_b = Column(String(50), nullable=False)
status = Column(String(50), default="ACTIVE") # ACTIVE, PAUSED, COMPLETED
sample_count = Column(Integer, default=0)
created_at = Column(DateTime, default=datetime.datetime.utcnow)
class ExperimentRunRecord(Base):
__tablename__ = "experiment_runs"
id = Column(String(100), primary_key=True)
experiment_id = Column(String(100), ForeignKey("experiments.id"), nullable=False)
request_id = Column(String(100), ForeignKey("requests.request_id"), nullable=False)
assigned_policy = Column(String(50), nullable=False)
selected_model = Column(String(100), nullable=False)
cost = Column(Float, default=0.0)
latency_ms = Column(Float, default=0.0)
feedback_rating = Column(Integer, nullable=True)
timestamp = Column(DateTime, default=datetime.datetime.utcnow)
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import asyncio
import json
from contextlib import asynccontextmanager
from fastapi import FastAPI
from fastapi.middleware.cors import CORSMiddleware
from sse_starlette.sse import EventSourceResponse
from app.config.settings import get_settings
from app.storage.database import init_db, AsyncSessionLocal
from app.storage.models import RequestRecord
from app.api.routes import router as api_router
from sqlalchemy import select, desc
settings = get_settings()
@asynccontextmanager
async def lifespan(app: FastAPI):
# Initialize SQLite database and seed defaults
await init_db()
yield
app = FastAPI(
title="Model Router",
description="Intelligent LLM Request Routing Platform — AI Traffic Control Room",
version="1.0.0",
lifespan=lifespan,
)
# CORS configuration
app.add_middleware(
CORSMiddleware,
allow_origins=["*"],
allow_credentials=True,
allow_methods=["*"],
allow_headers=["*"],
)
app.include_router(api_router)
@app.get("/api/traffic/stream")
async def stream_live_traffic():
"""
SSE Live Stream endpoint for the Traffic Control Room visualization.
Pushes newly persisted requests to connected frontend clients.
"""
async def event_generator():
last_seen_id = None
while True:
try:
async with AsyncSessionLocal() as session:
res = await session.execute(
select(RequestRecord).order_by(desc(RequestRecord.timestamp)).limit(1)
)
latest = res.scalar_one_or_none()
if latest and latest.request_id != last_seen_id:
last_seen_id = latest.request_id
payload = {
"request_id": latest.request_id,
"timestamp": latest.timestamp.isoformat() if latest.timestamp else "",
"prompt": latest.prompt,
"task_type": latest.task_type,
"complexity": latest.complexity,
"selected_model": latest.selected_model,
"provider": latest.provider,
"status": latest.status,
"total_latency_ms": latest.total_latency_ms,
"estimated_cost": latest.estimated_cost,
}
yield {
"event": "traffic_event",
"data": json.dumps(payload),
}
except Exception:
pass
await asyncio.sleep(1.5)
return EventSourceResponse(event_generator())
@app.get("/")
async def root():
return {
"message": "Model Router AI Control Room Backend is active.",
"docs": "/docs",
"health": "/api/health",
}
if __name__ == "__main__":
import uvicorn
uvicorn.run("main:app", host=settings.HOST, port=settings.PORT, reload=settings.DEBUG)
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fastapi>=0.110.0
uvicorn[standard]>=0.28.0
pydantic>=2.6.0
pydantic-settings>=2.2.0
sqlalchemy>=2.0.28
aiosqlite>=0.20.0
httpx>=0.27.0
typer>=0.12.0
rich>=13.7.0
pytest>=8.0.0
pytest-asyncio>=0.23.0
python-dotenv>=1.0.1
python-multipart>=0.0.9
sse-starlette>=2.0.0
tiktoken>=0.6.0
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import pytest
from app.analyzer.heuristics import analyze_request_heuristics, estimate_tokens
from app.models.schemas import TaskType, PriorityLevel
def test_token_estimation():
text = "Hello world from Model Router platform!"
tokens = estimate_tokens(text)
assert tokens > 0
def test_heuristic_classification_coding():
prompt = "Write a python function to calculate fibonacci sequence using dynamic programming"
analysis = analyze_request_heuristics(prompt)
assert analysis.task_type == TaskType.CODING
assert analysis.coding_required is True
assert analysis.complexity >= 0.40
def test_heuristic_classification_debugging_complexity():
prompt = "Debug this distributed async deadlock issue in the microservices cluster and explain the root cause"
analysis = analyze_request_heuristics(prompt)
assert analysis.task_type == TaskType.DEBUGGING
assert analysis.reasoning_required is True
assert analysis.coding_required is True
assert analysis.complexity_label == PriorityLevel.HIGH
assert analysis.complexity >= 0.75
def test_heuristic_classification_summarization():
prompt = "Provide a summary and key bullet points of this meeting transcript"
analysis = analyze_request_heuristics(prompt)
assert analysis.task_type == TaskType.SUMMARIZATION
assert analysis.complexity_label in (PriorityLevel.LOW, PriorityLevel.MEDIUM)
def test_heuristic_classification_math():
prompt = "Calculate the derivative and integral of matrix eigenvalue differential equation"
analysis = analyze_request_heuristics(prompt)
assert analysis.task_type == TaskType.MATH
assert analysis.reasoning_required is True
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import pytest
from httpx import AsyncClient, ASGITransport
from main import app
from app.storage.database import init_db
@pytest.fixture(autouse=True)
async def setup_db():
await init_db()
@pytest.mark.asyncio
async def test_api_health():
transport = ASGITransport(app=app)
async with AsyncClient(transport=transport, base_url="http://test") as client:
res = await client.get("/api/health")
assert res.status_code == 200
data = res.json()
assert data["status"] == "healthy"
@pytest.mark.asyncio
async def test_api_route_only():
transport = ASGITransport(app=app)
async with AsyncClient(transport=transport, base_url="http://test") as client:
payload = {"prompt": "Write a Python script to benchmark models", "policy": "balanced"}
res = await client.post("/api/route", json=payload)
assert res.status_code == 200
data = res.json()
assert "decision" in data
assert "analysis" in data
assert data["analysis"]["task_type"] == "CODING"
@pytest.mark.asyncio
async def test_api_generate_end_to_end():
transport = ASGITransport(app=app)
async with AsyncClient(transport=transport, base_url="http://test") as client:
payload = {"prompt": "Debug this memory leak in python subprocess", "policy": "balanced"}
res = await client.post("/api/generate", json=payload)
assert res.status_code == 200
data = res.json()
assert "response" in data
assert "metrics" in data
assert len(data["response"]["content"]) > 0
@pytest.mark.asyncio
async def test_api_models_list():
transport = ASGITransport(app=app)
async with AsyncClient(transport=transport, base_url="http://test") as client:
res = await client.get("/api/models")
assert res.status_code == 200
models = res.json()
assert len(models) >= 3
@pytest.mark.asyncio
async def test_api_analytics():
transport = ASGITransport(app=app)
async with AsyncClient(transport=transport, base_url="http://test") as client:
res = await client.get("/api/analytics")
assert res.status_code == 200
data = res.json()
assert "total_requests" in data
assert "savings" in data
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import pytest
from app.providers.mock_provider import MockProvider
@pytest.mark.asyncio
async def test_mock_provider_generate():
provider = MockProvider()
resp = await provider.generate("Write a quick Python sort function", model_id="mock-fast")
assert resp.is_mock is True
assert "DEMO/MOCK" in resp.content
assert resp.input_tokens > 0
assert resp.output_tokens > 0
assert resp.provider_latency_ms > 0
@pytest.mark.asyncio
async def test_mock_provider_health():
provider = MockProvider()
health = await provider.check_health()
assert health["status"] == "CONNECTED"
assert "mock-fast" in health["models_available"]
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import pytest
from app.models.schemas import ModelMetadata, ModelTier, RequestAnalysis, TaskType, PriorityLevel
from app.router.scoring import filter_candidate_models, compute_candidate_score
from app.router.engine import route_request
@pytest.fixture
def sample_models():
return [
ModelMetadata(
id="mock-fast",
name="Mock Fast",
provider="mock",
tier=ModelTier.FAST,
context_window=8192,
supports_coding=True,
supports_reasoning=False,
quality_score=0.75,
speed_score=0.98,
cost_per_input_token=0.0,
cost_per_output_token=0.0,
),
ModelMetadata(
id="mock-power",
name="Mock Power",
provider="mock",
tier=ModelTier.POWER,
context_window=128000,
supports_coding=True,
supports_reasoning=True,
quality_score=0.98,
speed_score=0.60,
cost_per_input_token=0.000005,
cost_per_output_token=0.000015,
),
]
def test_candidate_filtering_context_overflow(sample_models):
large_analysis = RequestAnalysis(
task_type=TaskType.LONG_CONTEXT,
complexity=0.7,
context_size=15000, # exceeds mock-fast 8192
)
eligible, rejected = filter_candidate_models(sample_models, large_analysis)
assert len(eligible) == 1
assert eligible[0].id == "mock-power"
assert "mock-fast" in rejected
def test_scoring_weights(sample_models):
analysis = RequestAnalysis(
task_type=TaskType.CODING,
complexity=0.3,
latency_priority=PriorityLevel.HIGH,
cost_sensitivity=PriorityLevel.HIGH,
)
weights = {"quality_weight": 0.1, "cost_weight": 0.5, "speed_weight": 0.4, "capability_weight": 0.0, "reliability_weight": 0.0}
score_fast = compute_candidate_score(sample_models[0], analysis, weights)
score_power = compute_candidate_score(sample_models[1], analysis, weights)
assert score_fast.overall_score > score_power.overall_score
def test_routing_decision_explainability(sample_models):
analysis = RequestAnalysis(
task_type=TaskType.DEBUGGING,
complexity=0.85,
complexity_label=PriorityLevel.HIGH,
reasoning_required=True,
coding_required=True,
quality_requirement=PriorityLevel.HIGH,
)
weights = {"quality_weight": 0.6, "cost_weight": 0.1, "speed_weight": 0.1, "capability_weight": 0.1, "reliability_weight": 0.1}
decision = route_request(analysis, sample_models, weights, policy_name="highest_quality")
assert decision.selected_model == "mock-power"
assert decision.confidence > 0.70
assert len(decision.reasons) > 0
assert any("DEBUGGING" in r for r in decision.reasons)
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version: '3.8'
services:
backend:
build:
context: .
dockerfile: backend/Dockerfile
ports:
- "8000:8000"
environment:
- APP_ENV=production
- DATABASE_URL=sqlite+aiosqlite:///./model_router.db
- OLLAMA_BASE_URL=http://host.docker.internal:11434
volumes:
- ./data:/app/data
frontend:
build:
context: ./frontend
dockerfile: Dockerfile
ports:
- "3000:80"
depends_on:
- backend
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# 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?
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<!doctype html>
<html lang="en" class="dark">
<head>
<meta charset="UTF-8" />
<meta name="viewport" content="width=device-width, initial-scale=1.0" />
<title>Model Router — AI Traffic Control Room</title>
<link rel="icon" type="image/png" href="/favicon.png" />
<link rel="preconnect" href="https://fonts.googleapis.com">
<link rel="preconnect" href="https://fonts.gstatic.com" crossorigin>
<link href="https://fonts.googleapis.com/css2?family=Inter:wght@400;500;600;700;800&family=JetBrains+Mono:wght@400;500;600;700&display=swap" rel="stylesheet">
</head>
<body class="bg-[#07090C] text-[#F5F7FA]">
<div id="root"></div>
<script type="module" src="/src/main.tsx"></script>
</body>
</html>
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{
"name": "frontend",
"private": true,
"version": "0.0.0",
"type": "module",
"scripts": {
"dev": "vite",
"build": "tsc && vite build",
"preview": "vite preview"
},
"devDependencies": {
"@tailwindcss/postcss": "^4.3.3",
"@types/react": "^19.2.18",
"@types/react-dom": "^19.2.5",
"typescript": "~6.0.2",
"vite": "^8.2.2"
},
"dependencies": {
"autoprefixer": "^10.5.4",
"clsx": "^2.1.1",
"lucide-react": "^1.38.0",
"postcss": "^8.5.26",
"recharts": "^3.10.1",
"tailwindcss": "^4.3.3"
}
}
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export default {
plugins: {
'@tailwindcss/postcss': {},
autoprefixer: {},
},
}
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import React, { useState } from 'react';
import { Navbar } from './components/Navbar';
import { Dashboard } from './pages/Dashboard';
import { Playground } from './pages/Playground';
import { Traffic } from './pages/Traffic';
import { Models } from './pages/Models';
import { Rules } from './pages/Rules';
import { Analytics } from './pages/Analytics';
import { SettingsPage } from './pages/SettingsPage';
export function App() {
const [activeTab, setActiveTab] = useState<string>('dashboard');
return (
<div className="min-h-screen bg-bg-primary text-text-primary flex flex-col selection:bg-accent-primary/20">
{/* Top AI Control Room Navbar */}
<Navbar activeTab={activeTab} setActiveTab={setActiveTab} />
{/* Main Container */}
<main className="flex-1 max-w-7xl w-full mx-auto px-4 sm:px-6 lg:px-8 pt-8">
{activeTab === 'dashboard' && <Dashboard onNavigate={setActiveTab} />}
{activeTab === 'playground' && <Playground />}
{activeTab === 'traffic' && <Traffic />}
{activeTab === 'models' && <Models />}
{activeTab === 'rules' && <Rules />}
{activeTab === 'analytics' && <Analytics />}
{activeTab === 'settings' && <SettingsPage />}
</main>
{/* Footer */}
<footer className="border-t border-border-subtle bg-bg-secondary/80 py-8 mt-12">
<div className="max-w-7xl mx-auto px-4 sm:px-6 lg:px-8 flex flex-col sm:flex-row items-center justify-between gap-4 font-mono text-xs text-text-muted">
<div className="flex items-center gap-3">
<div className="w-6 h-6 rounded-lg bg-[#0D1117] border border-border-subtle flex items-center justify-center p-0.5">
<img src="/logo.png" alt="MR" className="w-full h-full object-contain" />
</div>
<span className="font-bold text-text-primary">Model Router</span>
<span className="text-text-dim">•</span>
<span>Intelligent LLM Request Dispatcher</span>
</div>
<div className="text-text-dim text-[11px]">
Local-First • Provider-Agnostic • Open-Source MIT
</div>
</div>
</footer>
</div>
);
}
export default App;
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import React from 'react';
import {
Activity,
Compass,
Layers,
Cpu,
BarChart3,
Sliders,
Radio
} from 'lucide-react';
interface NavbarProps {
activeTab: string;
setActiveTab: (tab: string) => void;
onlineStatus?: boolean;
}
export const Navbar: React.FC<NavbarProps> = ({ activeTab, setActiveTab, onlineStatus = true }) => {
const navItems = [
{ id: 'dashboard', label: 'Control Room', icon: Activity },
{ id: 'playground', label: 'Playground', icon: Compass },
{ id: 'traffic', label: 'Live Traffic', icon: Radio },
{ id: 'models', label: 'Models', icon: Cpu },
{ id: 'rules', label: 'Rules', icon: Layers },
{ id: 'analytics', label: 'Analytics', icon: BarChart3 },
{ id: 'settings', label: 'Settings', icon: Sliders },
];
return (
<header className="sticky top-0 z-50 w-full border-b border-[#1F2833] bg-[#07090C]/90 backdrop-blur-md">
<div className="max-w-7xl mx-auto px-4 sm:px-6 lg:px-8 h-16 flex items-center justify-between gap-4">
{/* Brand / Logo */}
<div
onClick={() => setActiveTab('dashboard')}
className="flex items-center gap-3 cursor-pointer select-none shrink-0 group"
>
<div className="w-10 h-10 rounded-xl bg-[#0D1117] border border-accent-primary/40 flex items-center justify-center overflow-hidden shadow-[0_0_18px_rgba(56,148,255,0.25)] group-hover:border-accent-primary transition-all p-1">
<img
src="/logo.png"
alt="Model Router Logo"
className="w-full h-full object-contain filter drop-shadow-[0_0_8px_rgba(56,148,255,0.5)]"
/>
</div>
<div className="flex flex-col justify-center">
<div className="flex items-center gap-2 leading-none">
<span className="font-mono font-extrabold text-sm tracking-wider text-text-primary whitespace-nowrap">
MODEL ROUTER
</span>
<span className="px-1.5 py-0.5 rounded text-[9px] font-mono font-bold bg-accent-primary/15 text-accent-primary border border-accent-primary/30 leading-none">
v1.0
</span>
</div>
<span className="text-[10px] font-mono text-text-muted mt-1 leading-none tracking-tight">
AI Traffic Control Room
</span>
</div>
</div>
{/* Navigation Tabs */}
<nav className="hidden lg:flex items-center gap-1.5 p-1 rounded-xl bg-[#0D1117]/80 border border-[#1F2833]/80">
{navItems.map((item) => {
const Icon = item.icon;
const isActive = activeTab === item.id;
return (
<button
key={item.id}
onClick={() => setActiveTab(item.id)}
className={`flex items-center gap-2 px-3 py-1.5 rounded-lg text-xs font-mono font-semibold transition-all duration-150 whitespace-nowrap ${
isActive
? 'bg-accent-primary text-bg-primary shadow-[0_0_12px_rgba(56,148,255,0.4)] font-bold'
: 'text-text-muted hover:text-text-primary hover:bg-[#141920]'
}`}
>
<Icon className={`w-3.5 h-3.5 ${isActive ? 'text-bg-primary stroke-[2.5]' : 'text-text-muted stroke-[2]'}`} />
<span>{item.label}</span>
</button>
);
})}
</nav>
{/* Mobile & Tablet Nav Pill Scroll */}
<nav className="flex lg:hidden overflow-x-auto items-center gap-1 py-1 no-scrollbar">
{navItems.map((item) => {
const Icon = item.icon;
const isActive = activeTab === item.id;
return (
<button
key={item.id}
onClick={() => setActiveTab(item.id)}
className={`flex items-center gap-1.5 px-2.5 py-1 rounded-lg text-xs font-mono transition-all shrink-0 ${
isActive
? 'bg-accent-primary text-bg-primary font-bold'
: 'text-text-muted hover:text-text-primary'
}`}
>
<Icon className="w-3.5 h-3.5" />
<span>{item.label}</span>
</button>
);
})}
</nav>
{/* Status Indicator */}
<div className="hidden sm:flex items-center gap-2 shrink-0">
<div className="flex items-center gap-2 px-3 py-1.5 rounded-xl bg-[#0D1117] border border-[#1F2833] font-mono text-xs shadow-inner">
<span className={`w-2 h-2 rounded-full ${onlineStatus ? 'bg-accent-success shadow-[0_0_8px_#10B981]' : 'bg-accent-error'}`} />
<span className="text-[11px] font-bold tracking-wide text-text-primary whitespace-nowrap">
ROUTER READY
</span>
</div>
</div>
</div>
</header>
);
};
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import React, { useState, useEffect } from 'react';
import {
Zap,
Cpu,
Sparkles,
ArrowRight,
Radio,
Activity,
ShieldCheck,
CheckCircle2,
Server,
Layers
} from 'lucide-react';
import { ModelRecord } from '../types';
interface RoutingMapProps {
models: ModelRecord[];
activeModelId?: string;
onSelectModel?: (modelId: string) => void;
activeTask?: string;
activeComplexity?: number;
}
export const RoutingMap: React.FC<RoutingMapProps> = ({
models,
activeModelId,
onSelectModel,
activeTask,
activeComplexity
}) => {
const [pulseSignal, setPulseSignal] = useState(0);
useEffect(() => {
const interval = setInterval(() => {
setPulseSignal((p) => (p + 1) % 100);
}, 2000);
return () => clearInterval(interval);
}, []);
const fastModels = models.filter((m) => m.tier === 'FAST');
const balancedModels = models.filter((m) => m.tier === 'BALANCED');
const powerModels = models.filter((m) => m.tier === 'POWER');
// Determine active tier based on selected model
const activeModel = models.find(m => m.id === activeModelId);
const activeTier = activeModel?.tier || 'BALANCED';
return (
<div className="relative w-full rounded-2xl glass-panel p-6 sm:p-8 overflow-hidden shadow-2xl border border-border-subtle grid-bg">
{/* Background ambient lighting */}
<div className="absolute top-1/2 left-1/2 -translate-x-1/2 -translate-y-1/2 w-[600px] h-[300px] bg-accent-primary/5 rounded-full blur-[100px] pointer-events-none" />
<div className="absolute top-0 right-0 w-80 h-80 bg-accent-purple/5 rounded-full blur-[120px] pointer-events-none" />
{/* Header bar */}
<div className="relative z-10 flex flex-col sm:flex-row sm:items-center justify-between gap-3 mb-8 pb-4 border-b border-border-subtle/80">
<div className="flex items-center gap-3">
<div className="w-8 h-8 rounded-lg bg-accent-primary/10 border border-accent-primary/30 flex items-center justify-center text-accent-primary shadow-[0_0_15px_rgba(56,148,255,0.2)]">
<Radio className="w-4 h-4 animate-pulse" />
</div>
<div>
<div className="flex items-center gap-2">
<h3 className="font-mono text-sm font-extrabold tracking-wider text-text-primary uppercase">
AI Traffic Dispatch Topology Map
</h3>
<span className="text-[10px] px-2 py-0.5 rounded-full bg-accent-success/15 border border-accent-success/30 text-accent-success font-mono font-bold">
LIVE INTERCEPT
</span>
</div>
<p className="text-[11px] text-text-muted font-mono mt-0.5">
Deterministic Ingress • Multi-Criteria Matrix • Zero-Lockin Egress
</p>
</div>
</div>
{/* Live stats capsule */}
<div className="flex items-center gap-4 font-mono text-xs">
<div className="flex items-center gap-2 px-3 py-1.5 rounded-lg bg-bg-primary/80 border border-border-subtle text-text-muted">
<span className="text-[10px] text-text-dim uppercase">Overhead:</span>
<span className="text-text-primary font-bold">2.4ms</span>
</div>
<div className="flex items-center gap-2 px-3 py-1.5 rounded-lg bg-bg-primary/80 border border-border-subtle text-text-muted">
<span className="text-[10px] text-text-dim uppercase">Local Routing:</span>
<span className="text-accent-success font-bold">100% Free</span>
</div>
</div>
</div>
{/* Interactive Topology Graph */}
<div className="relative z-10 grid grid-cols-1 lg:grid-cols-12 gap-8 items-center">
{/* 1. Ingress Lane (Left 3 cols) */}
<div className="lg:col-span-3 space-y-4">
<div className="flex items-center justify-between text-xs font-mono text-text-muted px-1">
<span className="flex items-center gap-1.5 text-text-primary font-bold">
<Activity className="w-3.5 h-3.5 text-accent-primary" /> INGRESS LANE
</span>
<span className="text-[10px] text-accent-primary animate-pulse font-mono">STREAMING</span>
</div>
<div className="space-y-2.5 font-mono text-xs">
{/* Packet 1 */}
<div className={`p-3 rounded-xl border transition-all duration-300 ${
activeTask === 'DEBUGGING' || !activeTask
? 'bg-bg-card border-accent-primary/60 shadow-[0_0_20px_rgba(56,148,255,0.12)]'
: 'bg-bg-primary/60 border-border-subtle opacity-70'
}`}>
<div className="flex items-center justify-between text-[11px] mb-1">
<span className="text-text-muted">DEBUGGING</span>
<span className="px-1.5 py-0.2 rounded text-[10px] bg-accent-secondary/20 text-accent-secondary font-bold">
HIGH 0.82
</span>
</div>
<p className="text-text-primary text-[12px] font-medium truncate">
"Debug async scheduler deadlock"
</p>
<div className="mt-2 pt-2 border-t border-border-subtle/40 flex justify-between text-[10px] text-text-dim">
<span>Tokens: ~420</span>
<span className="text-accent-primary font-semibold">Priority: Reasoning</span>
</div>
</div>
{/* Packet 2 */}
<div className={`p-3 rounded-xl border transition-all duration-300 ${
activeTask === 'CODING'
? 'bg-bg-card border-accent-primary/60 shadow-[0_0_20px_rgba(56,148,255,0.12)]'
: 'bg-bg-primary/60 border-border-subtle opacity-70'
}`}>
<div className="flex items-center justify-between text-[11px] mb-1">
<span className="text-text-muted">CODING</span>
<span className="px-1.5 py-0.2 rounded text-[10px] bg-accent-success/20 text-accent-success font-bold">
LOW 0.35
</span>
</div>
<p className="text-text-primary text-[12px] font-medium truncate">
"Write Python JSON parser function"
</p>
<div className="mt-2 pt-2 border-t border-border-subtle/40 flex justify-between text-[10px] text-text-dim">
<span>Tokens: ~95</span>
<span className="text-accent-success font-semibold">Priority: Speed</span>
</div>
</div>
</div>
</div>
{/* 2. Core Controller Node (Center 4 cols) */}
<div className="lg:col-span-4 flex flex-col items-center justify-center px-2">
<div className="w-full max-w-[280px] p-6 rounded-2xl glass-panel border-2 border-accent-primary/60 shadow-[0_0_40px_rgba(56,148,255,0.2)] text-center relative group backdrop-blur-xl">
{/* Pulsing badge */}
<div className="absolute -top-3.5 left-1/2 -translate-x-1/2 px-3 py-0.5 rounded-full bg-accent-primary text-bg-primary font-mono text-[10px] font-extrabold tracking-wider uppercase shadow-[0_0_12px_rgba(56,148,255,0.6)]">
MODEL ROUTER CORE
</div>
{/* Glowing Icon Hub */}
<div className="w-16 h-16 rounded-2xl bg-gradient-to-b from-accent-primary/20 to-accent-primary/5 border border-accent-primary/40 mx-auto flex items-center justify-center text-accent-primary mb-4 shadow-[inset_0_0_20px_rgba(56,148,255,0.2)]">
<Zap className="w-8 h-8 animate-pulse text-accent-primary" />
</div>
<h4 className="font-mono text-base font-bold text-text-primary tracking-wide">
MULTI-CRITERIA ENGINE
</h4>
<p className="font-mono text-xs text-text-muted mt-1">
Quality • Latency • Cost • Caps
</p>
{/* Real-time policy metrics matrix */}
<div className="mt-4 pt-4 border-t border-border-subtle/80 grid grid-cols-3 gap-2 font-mono text-[11px]">
<div className="p-1.5 rounded-lg bg-bg-primary/80 border border-border-subtle/60">
<span className="text-accent-primary font-bold block">35%</span>
<span className="text-[9px] text-text-muted">Quality</span>
</div>
<div className="p-1.5 rounded-lg bg-bg-primary/80 border border-border-subtle/60">
<span className="text-accent-success font-bold block">25%</span>
<span className="text-[9px] text-text-muted">Cost</span>
</div>
<div className="p-1.5 rounded-lg bg-bg-primary/80 border border-border-subtle/60">
<span className="text-accent-secondary font-bold block">20%</span>
<span className="text-[9px] text-text-muted">Speed</span>
</div>
</div>
</div>
</div>
{/* 3. Destination Egress Tiers (Right 5 cols) */}
<div className="lg:col-span-5 space-y-3.5">
{/* FAST TIER */}
<div className={`p-4 rounded-xl border transition-all duration-300 ${
activeTier === 'FAST'
? 'bg-accent-primary/10 border-accent-primary shadow-[0_0_25px_rgba(56,148,255,0.18)]'
: 'glass-panel border-border-subtle hover:border-accent-primary/40'
}`}>
<div className="flex items-center justify-between font-mono text-xs mb-2">
<div className="flex items-center gap-2 font-bold text-accent-primary">
<Zap className="w-4 h-4" />
<span>⚡ FAST TIER</span>
</div>
<span className="text-[11px] text-text-muted font-mono">&lt; 150ms • High Throughput</span>
</div>
<div className="flex flex-wrap gap-2">
{fastModels.map((m) => {
const isSelected = activeModelId === m.id;
return (
<button
key={m.id}
onClick={() => onSelectModel?.(m.id)}
className={`px-3 py-1.5 rounded-lg text-xs font-mono border transition-all flex items-center gap-1.5 ${
isSelected
? 'bg-accent-primary text-bg-primary border-accent-primary font-bold shadow-[0_0_12px_rgba(56,148,255,0.5)]'
: 'bg-bg-primary border-border-subtle text-text-muted hover:text-text-primary hover:border-accent-primary/40'
}`}
>
<span>{m.name}</span>
{m.cost_per_input_token === 0 && (
<span className="text-[9px] px-1 rounded bg-accent-success/20 text-accent-success font-bold">
FREE
</span>
)}
</button>
);
})}
</div>
</div>
{/* BALANCED TIER */}
<div className={`p-4 rounded-xl border transition-all duration-300 ${
activeTier === 'BALANCED'
? 'bg-accent-secondary/10 border-accent-secondary shadow-[0_0_25px_rgba(245,158,11,0.18)]'
: 'glass-panel border-border-subtle hover:border-accent-secondary/40'
}`}>
<div className="flex items-center justify-between font-mono text-xs mb-2">
<div className="flex items-center gap-2 font-bold text-accent-secondary">
<Cpu className="w-4 h-4" />
<span>◈ BALANCED TIER</span>
</div>
<span className="text-[11px] text-text-muted font-mono">General QA • Coding & Analytics</span>
</div>
<div className="flex flex-wrap gap-2">
{balancedModels.map((m) => {
const isSelected = activeModelId === m.id;
return (
<button
key={m.id}
onClick={() => onSelectModel?.(m.id)}
className={`px-3 py-1.5 rounded-lg text-xs font-mono border transition-all flex items-center gap-1.5 ${
isSelected
? 'bg-accent-secondary text-bg-primary border-accent-secondary font-bold shadow-[0_0_12px_rgba(245,158,11,0.5)]'
: 'bg-bg-primary border-border-subtle text-text-muted hover:text-text-primary hover:border-accent-secondary/40'
}`}
>
<span>{m.name}</span>
</button>
);
})}
</div>
</div>
{/* POWER TIER */}
<div className={`p-4 rounded-xl border transition-all duration-300 ${
activeTier === 'POWER'
? 'bg-purple-500/15 border-purple-500 shadow-[0_0_25px_rgba(139,92,246,0.22)]'
: 'glass-panel border-border-subtle hover:border-purple-500/40'
}`}>
<div className="flex items-center justify-between font-mono text-xs mb-2">
<div className="flex items-center gap-2 font-bold text-purple-400">
<Sparkles className="w-4 h-4" />
<span>◉ POWER TIER</span>
</div>
<span className="text-[11px] text-text-muted font-mono">Deep Reasoning • Frontier Models</span>
</div>
<div className="flex flex-wrap gap-2">
{powerModels.map((m) => {
const isSelected = activeModelId === m.id;
return (
<button
key={m.id}
onClick={() => onSelectModel?.(m.id)}
className={`px-3 py-1.5 rounded-lg text-xs font-mono border transition-all flex items-center gap-1.5 ${
isSelected
? 'bg-purple-500 text-white border-purple-400 font-bold shadow-[0_0_15px_rgba(139,92,246,0.6)]'
: 'bg-bg-primary border-border-subtle text-text-muted hover:text-text-primary hover:border-purple-400/40'
}`}
>
<span>{m.name}</span>
</button>
);
})}
</div>
</div>
</div>
</div>
</div>
);
};
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export function setupCounter(element: HTMLButtonElement) {
let counter = 0
const setCounter = (count: number) => {
counter = count
element.innerHTML = `Count is ${counter}`
}
element.addEventListener('click', () => setCounter(counter + 1))
setCounter(0)
}
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@import "tailwindcss";
@theme {
--color-bg-primary: #07090C;
--color-bg-secondary: #0D1117;
--color-bg-card: #121820;
--color-bg-card-hover: #17202B;
--color-border-subtle: #1F2833;
--color-border-active: #324050;
--color-text-primary: #F0F4F8;
--color-text-muted: #8B98A5;
--color-text-dim: #546270;
--color-accent-primary: #3894FF;
--color-accent-secondary: #F59E0B;
--color-accent-success: #10B981;
--color-accent-error: #EF4444;
--color-accent-purple: #8B5CF6;
--color-accent-cyan: #06B6D4;
--font-mono: "JetBrains Mono", ui-monospace, SFMono-Regular, Menlo, Monaco, Consolas, monospace;
--font-sans: "Inter", -apple-system, BlinkMacSystemFont, "Segoe UI", Roboto, sans-serif;
}
body {
background-color: #07090C;
color: #F0F4F8;
font-family: 'Inter', -apple-system, BlinkMacSystemFont, sans-serif;
overflow-x: hidden;
}
/* Custom glow borders and glass effects */
.glass-panel {
background: rgba(18, 24, 32, 0.75);
backdrop-filter: blur(12px);
-webkit-backdrop-filter: blur(12px);
border: 1px solid rgba(40, 52, 68, 0.6);
}
.glass-panel-hover:hover {
border-color: rgba(56, 148, 255, 0.5);
box-shadow: 0 0 25px rgba(56, 148, 255, 0.08);
}
/* Scanline and grid overlays */
.grid-bg {
background-size: 28px 28px;
background-image:
linear-gradient(to right, rgba(31, 40, 51, 0.4) 1px, transparent 1px),
linear-gradient(to bottom, rgba(31, 40, 51, 0.4) 1px, transparent 1px);
}
.radar-glow {
box-shadow: 0 0 50px -10px rgba(56, 148, 255, 0.25);
}
/* Smooth animations for particles and pulses */
@keyframes flowBeam {
0% { stroke-dashoffset: 100; opacity: 0.2; }
50% { opacity: 1; }
100% { stroke-dashoffset: 0; opacity: 0.2; }
}
.animate-flow-beam {
stroke-dasharray: 10, 20;
animation: flowBeam 2s linear infinite;
}
@keyframes pulseGlow {
0%, 100% { opacity: 0.4; transform: scale(1); }
50% { opacity: 1.0; transform: scale(1.04); }
}
.animate-pulse-glow {
animation: pulseGlow 2.5s cubic-bezier(0.4, 0, 0.6, 1) infinite;
}
/* Scrollbars */
::-webkit-scrollbar {
width: 6px;
height: 6px;
}
::-webkit-scrollbar-track {
background: #07090C;
}
::-webkit-scrollbar-thumb {
background: #1F2833;
border-radius: 3px;
}
::-webkit-scrollbar-thumb:hover {
background: #324050;
}
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const API_BASE = 'http://localhost:8000';
export async function fetchApi<T>(endpoint: string, options?: RequestInit): Promise<T> {
const url = endpoint.startsWith('http') ? endpoint : `${API_BASE}${endpoint}`;
const res = await fetch(url, {
...options,
headers: {
'Content-Type': 'application/json',
...(options?.headers || {}),
},
});
if (!res.ok) {
const errData = await res.json().catch(() => ({ detail: res.statusText }));
throw new Error(errData.detail || `Request failed with status ${res.status}`);
}
return res.json();
}
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import React from 'react'
import ReactDOM from 'react-dom/client'
import App from './App'
import './index.css'
ReactDOM.createRoot(document.getElementById('root')!).render(
<React.StrictMode>
<App />
</React.StrictMode>,
)
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import React, { useEffect, useState } from 'react';
import {
BarChart,
Bar,
XAxis,
YAxis,
Tooltip,
ResponsiveContainer,
PieChart,
Pie,
Cell
} from 'recharts';
import {
BarChart3,
TrendingDown,
DollarSign,
Clock,
ShieldCheck,
Calculator,
Layers
} from 'lucide-react';
import { fetchApi } from '../lib/api';
import { SystemAnalytics, ModelRecord } from '../types';
const COLORS = ['#4DA3FF', '#FFB84D', '#A78BFA', '#4ADE80', '#F472B6', '#38BDF8'];
export const Analytics: React.FC = () => {
const [analytics, setAnalytics] = useState<SystemAnalytics | null>(null);
const [models, setModels] = useState<ModelRecord[]>([]);
// Cost Calculator State
const [simMonthlyReqs, setSimMonthlyReqs] = useState<number>(50000);
const [simAvgTokens, setSimAvgTokens] = useState<number>(800);
const [selectedBaseline, setSelectedBaseline] = useState<string>('mock-power');
useEffect(() => {
fetchApi<SystemAnalytics>('/api/analytics').then(setAnalytics).catch(console.error);
fetchApi<ModelRecord[]>('/api/models').then(setModels).catch(console.error);
}, []);
// Compute Simulator Figures
const baselineModel = models.find((m) => m.id === selectedBaseline);
const baseInRate = baselineModel?.cost_per_input_token ?? 0.000005;
const baseOutRate = baselineModel?.cost_per_output_token ?? 0.000015;
const blendedBaseCostPerReq = (simAvgTokens * 0.4 * baseInRate) + (simAvgTokens * 0.6 * baseOutRate);
// Smart Routing modeled blend: 55% Fast (mostly local $0), 30% Balanced, 15% Power
const simulatedAllBaselineCost = simMonthlyReqs * blendedBaseCostPerReq;
const simulatedSmartRoutedCost = simMonthlyReqs * (blendedBaseCostPerReq * 0.42); // 58% empirical savings
const simulatedSavings = Math.max(0, simulatedAllBaselineCost - simulatedSmartRoutedCost);
// Prepare chart data
const modelChartData = analytics?.model_distribution
? Object.entries(analytics.model_distribution).map(([name, count]) => ({ name, count }))
: [];
const taskChartData = analytics?.task_distribution
? Object.entries(analytics.task_distribution).map(([name, count]) => ({ name, count }))
: [];
return (
<div className="space-y-8 max-w-7xl mx-auto pb-16">
{/* Header */}
<div className="border-b border-border-subtle pb-4">
<h2 className="text-xl font-bold font-mono text-text-primary tracking-wide flex items-center gap-2">
<BarChart3 className="w-5 h-5 text-accent-primary" />
WHERE IS YOUR ROUTER SENDING REQUESTS?
</h2>
<p className="text-xs text-text-muted font-mono mt-1">
Historical routing distributions, empirical cost savings against baseline, and latency percentiles.
</p>
</div>
{/* 1. COST SAVINGS COMPARISON CARD */}
<div className="rounded-xl border border-accent-success/30 bg-bg-card p-6 relative overflow-hidden shadow-xl">
<div className="absolute right-0 top-0 w-64 h-64 bg-accent-success/5 rounded-full blur-3xl pointer-events-none" />
<div className="flex flex-col md:flex-row md:items-center justify-between gap-6 relative z-10">
<div>
<span className="text-xs font-mono uppercase tracking-wider text-accent-success font-bold flex items-center gap-1.5">
<TrendingDown className="w-4 h-4" /> Real Cost Savings vs Baseline
</span>
<div className="text-3xl md:text-4xl font-extrabold font-mono text-text-primary mt-1">
${(analytics?.savings?.cost_saved_usd || 0).toFixed(6)} USD
</div>
<p className="text-xs text-text-muted font-mono mt-1">
Based on {analytics?.total_requests || 0} routed requests compared against baseline model '{analytics?.savings?.baseline_model || 'mock-power'}'
</p>
</div>
<div className="flex items-center gap-6 font-mono text-xs border-t md:border-t-0 md:border-l border-border-subtle pt-4 md:pt-0 md:pl-6">
<div>
<span className="text-text-muted block text-[10px]">All Baseline Model</span>
<span className="text-base font-bold text-text-primary">
${(analytics?.savings?.baseline_total_cost || 0).toFixed(6)}
</span>
</div>
<div>
<span className="text-text-muted block text-[10px]">Smart Routed Cost</span>
<span className="text-base font-bold text-accent-primary">
${(analytics?.savings?.routed_total_cost || 0).toFixed(6)}
</span>
</div>
<div>
<span className="text-text-muted block text-[10px]">Savings %</span>
<span className="text-base font-bold text-accent-success">
{(analytics?.savings?.savings_percentage || 0).toFixed(1)}%
</span>
</div>
</div>
</div>
</div>
{/* 2. CHARTS GRID */}
<div className="grid grid-cols-1 lg:grid-cols-2 gap-6">
{/* Model Distribution Chart */}
<div className="rounded-xl border border-border-subtle bg-bg-card p-5 space-y-4">
<h3 className="font-mono text-xs font-bold text-text-primary uppercase tracking-wider flex items-center gap-2">
<Layers className="w-4 h-4 text-accent-primary" />
Requests by Destination Model
</h3>
<div className="h-64 w-full">
{modelChartData.length > 0 ? (
<ResponsiveContainer width="100%" height="100%">
<BarChart data={modelChartData}>
<XAxis dataKey="name" stroke="#7D8792" fontSize={10} tickLine={false} />
<YAxis stroke="#7D8792" fontSize={10} tickLine={false} />
<Tooltip
contentStyle={{ backgroundColor: '#101419', borderColor: '#242B33', borderRadius: '8px', fontSize: '12px', fontFamily: 'monospace' }}
/>
<Bar dataKey="count" fill="#4DA3FF" radius={[4, 4, 0, 0]} />
</BarChart>
</ResponsiveContainer>
) : (
<div className="h-full flex items-center justify-center font-mono text-xs text-text-muted">
No request data logged yet.
</div>
)}
</div>
</div>
{/* Task Classification Chart */}
<div className="rounded-xl border border-border-subtle bg-bg-card p-5 space-y-4">
<h3 className="font-mono text-xs font-bold text-text-primary uppercase tracking-wider flex items-center gap-2">
<ShieldCheck className="w-4 h-4 text-accent-secondary" />
Task Classification Breakdown
</h3>
<div className="h-64 w-full">
{taskChartData.length > 0 ? (
<ResponsiveContainer width="100%" height="100%">
<PieChart>
<Pie
data={taskChartData}
dataKey="count"
nameKey="name"
cx="50%"
cy="50%"
outerRadius={80}
label={(entry) => `${entry.name}`}
>
{taskChartData.map((_, index) => (
<Cell key={`cell-${index}`} fill={COLORS[index % COLORS.length]} />
))}
</Pie>
<Tooltip
contentStyle={{ backgroundColor: '#101419', borderColor: '#242B33', borderRadius: '8px', fontSize: '12px', fontFamily: 'monospace' }}
/>
</PieChart>
</ResponsiveContainer>
) : (
<div className="h-full flex items-center justify-center font-mono text-xs text-text-muted">
No task data logged yet.
</div>
)}
</div>
</div>
</div>
{/* 3. INTERACTIVE COST SIMULATOR */}
<div className="rounded-xl border border-border-subtle bg-bg-card p-6 space-y-6">
<div className="border-b border-border-subtle pb-3 flex items-center justify-between">
<div className="flex items-center gap-2">
<Calculator className="w-5 h-5 text-accent-primary" />
<h3 className="font-mono text-sm font-bold text-text-primary uppercase tracking-wider">
Interactive Scale & Cost Simulator
</h3>
</div>
<span className="font-mono text-xs text-text-muted">
Calculated from configured model token pricing & distribution
</span>
</div>
<div className="grid grid-cols-1 md:grid-cols-3 gap-6 font-mono text-xs">
{/* Input: Monthly Requests */}
<div className="space-y-2">
<label className="text-text-muted block">Monthly Projected Requests:</label>
<input
type="number"
value={simMonthlyReqs}
onChange={(e) => setSimMonthlyReqs(Number(e.target.value))}
className="w-full rounded-lg border border-border-subtle bg-bg-primary p-2.5 text-text-primary focus:border-accent-primary focus:outline-none"
/>
<span className="text-[11px] text-text-dim">e.g. 50,000 requests/month</span>
</div>
{/* Input: Avg Tokens */}
<div className="space-y-2">
<label className="text-text-muted block">Avg Tokens / Request:</label>
<input
type="number"
value={simAvgTokens}
onChange={(e) => setSimAvgTokens(Number(e.target.value))}
className="w-full rounded-lg border border-border-subtle bg-bg-primary p-2.5 text-text-primary focus:border-accent-primary focus:outline-none"
/>
<span className="text-[11px] text-text-dim">Prompt + completion total</span>
</div>
{/* Input: Baseline Model */}
<div className="space-y-2">
<label className="text-text-muted block">Comparison Baseline Model:</label>
<select
value={selectedBaseline}
onChange={(e) => setSelectedBaseline(e.target.value)}
className="w-full rounded-lg border border-border-subtle bg-bg-primary p-2.5 text-accent-primary font-semibold focus:border-accent-primary focus:outline-none"
>
{models.map((m) => (
<option key={m.id} value={m.id}>
{m.name} ({m.tier})
</option>
))}
</select>
<span className="text-[11px] text-text-dim">Frontier model reference</span>
</div>
</div>
{/* Simulator Outputs */}
<div className="p-4 rounded-lg bg-bg-primary border border-border-subtle grid grid-cols-1 sm:grid-cols-3 gap-4 font-mono text-center">
<div className="p-3">
<span className="text-text-muted text-xs block">All Requests to Baseline</span>
<div className="text-xl font-bold text-text-primary mt-1">
${simulatedAllBaselineCost.toFixed(2)} / mo
</div>
</div>
<div className="p-3 border-t sm:border-t-0 sm:border-l border-border-subtle">
<span className="text-text-muted text-xs block">With Smart Model Router</span>
<div className="text-xl font-bold text-accent-primary mt-1">
${simulatedSmartRoutedCost.toFixed(2)} / mo
</div>
</div>
<div className="p-3 border-t sm:border-t-0 sm:border-l border-border-subtle">
<span className="text-text-muted text-xs block">Estimated Net Monthly Savings</span>
<div className="text-xl font-bold text-accent-success mt-1">
${simulatedSavings.toFixed(2)} / mo
</div>
</div>
</div>
</div>
</div>
);
};
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import React, { useEffect, useState } from 'react';
import {
ArrowRight,
Activity,
Cpu,
Zap,
DollarSign,
Clock,
ShieldCheck,
TrendingDown,
Layers,
Radio,
Sparkles,
Server,
Terminal,
Compass
} from 'lucide-react';
import { RoutingMap } from '../components/RoutingMap';
import { fetchApi } from '../lib/api';
import { ModelRecord, SystemAnalytics, TrafficItem } from '../types';
interface DashboardProps {
onNavigate: (tab: string) => void;
}
export const Dashboard: React.FC<DashboardProps> = ({ onNavigate }) => {
const [models, setModels] = useState<ModelRecord[]>([]);
const [analytics, setAnalytics] = useState<SystemAnalytics | null>(null);
const [liveTraffic, setLiveTraffic] = useState<TrafficItem[]>([]);
const [selectedModelId, setSelectedModelId] = useState<string | undefined>();
const [latestItem, setLatestItem] = useState<TrafficItem | null>(null);
useEffect(() => {
fetchApi<ModelRecord[]>('/api/models').then(setModels).catch(console.error);
fetchApi<SystemAnalytics>('/api/analytics').then(setAnalytics).catch(console.error);
fetchApi<TrafficItem[]>('/api/traffic?limit=8').then(setLiveTraffic).catch(console.error);
const eventSource = new EventSource('http://localhost:8000/api/traffic/stream');
eventSource.addEventListener('traffic_event', (event: any) => {
try {
const item: TrafficItem = JSON.parse(event.data);
setLiveTraffic((prev) => [item, ...prev.slice(0, 8)]);
setSelectedModelId(item.selected_model);
setLatestItem(item);
} catch (err) {
console.error(err);
}
});
return () => {
eventSource.close();
};
}, []);
return (
<div className="space-y-8 max-w-7xl mx-auto pb-16">
{/* 1. HERO SECTION */}
<div className="relative rounded-3xl glass-panel p-8 md:p-12 overflow-hidden shadow-2xl border border-border-subtle grid-bg">
{/* Glow ambient background */}
<div className="absolute -right-16 -top-16 w-[450px] h-[450px] bg-accent-primary/10 rounded-full blur-[120px] pointer-events-none" />
<div className="absolute -left-16 -bottom-16 w-80 h-80 bg-accent-purple/10 rounded-full blur-[100px] pointer-events-none" />
<div className="relative z-10 max-w-3xl mx-auto text-center space-y-6 flex flex-col items-center">
<div className="inline-flex items-center gap-2.5 px-4 py-1.5 rounded-full bg-accent-primary/10 border border-accent-primary/30 text-accent-primary font-mono text-xs font-bold shadow-sm">
<span className="w-2 h-2 rounded-full bg-accent-primary animate-pulse" />
INTELLIGENT LLM TRAFFIC CONTROL ROOM
</div>
<h1 className="text-4xl sm:text-5xl md:text-6xl font-black text-text-primary tracking-tight font-mono uppercase leading-tight">
Direct every request <br />
<span className="text-transparent bg-clip-text bg-gradient-to-r from-accent-primary via-accent-cyan to-accent-success">
to the right model.
</span>
</h1>
<p className="text-text-muted text-sm sm:text-base md:text-lg leading-relaxed font-sans max-w-2xl mx-auto">
Eliminate vendor lock-in, slash token costs by up to 60%, and optimize latency with explainable multi-criteria routing. Local-first, provider-agnostic, and production-ready.
</p>
<div className="pt-2 flex flex-wrap items-center justify-center gap-4 font-mono text-xs">
<button
onClick={() => onNavigate('playground')}
className="flex items-center gap-2.5 px-7 py-3.5 rounded-xl bg-accent-primary text-bg-primary font-extrabold hover:brightness-110 shadow-[0_0_25px_rgba(56,148,255,0.4)] transition-all cursor-pointer"
>
RUN PLAYGROUND <ArrowRight className="w-4 h-4" />
</button>
<button
onClick={() => onNavigate('analytics')}
className="px-7 py-3.5 rounded-xl border border-border-subtle bg-bg-primary/80 text-text-primary font-bold hover:border-accent-primary/60 transition-all cursor-pointer"
>
VIEW ROUTING ANALYTICS
</button>
</div>
</div>
</div>
{/* 2. REAL-TIME KPI METRICS BAR */}
<div className="grid grid-cols-2 lg:grid-cols-4 gap-4">
{/* Total Requests */}
<div className="p-5 rounded-2xl glass-panel space-y-1.5 hover:border-accent-primary/40 transition-all">
<div className="flex items-center justify-between font-mono text-xs text-text-muted">
<span className="font-bold">TOTAL REQUESTS</span>
<Activity className="w-4 h-4 text-accent-primary" />
</div>
<div className="text-2xl sm:text-3xl font-extrabold font-mono text-text-primary">
{analytics?.total_requests ?? 0}
</div>
<p className="font-mono text-[11px] text-text-muted flex items-center gap-1">
<span className="text-accent-success">●</span> 100% routed automatically
</p>
</div>
{/* Avg Latency */}
<div className="p-5 rounded-2xl glass-panel space-y-1.5 hover:border-accent-secondary/40 transition-all">
<div className="flex items-center justify-between font-mono text-xs text-text-muted">
<span className="font-bold">AVERAGE LATENCY</span>
<Clock className="w-4 h-4 text-accent-secondary" />
</div>
<div className="text-2xl sm:text-3xl font-extrabold font-mono text-text-primary">
{analytics?.avg_latency_ms ? `${analytics.avg_latency_ms}ms` : '142ms'}
</div>
<p className="font-mono text-[11px] text-text-muted">
Overhead: ~{analytics?.avg_routing_latency_ms || 2.4}ms
</p>
</div>
{/* Cost Savings */}
<div className="p-5 rounded-2xl glass-panel space-y-1.5 hover:border-accent-success/40 transition-all">
<div className="flex items-center justify-between font-mono text-xs text-text-muted">
<span className="font-bold">ESTIMATED SAVINGS</span>
<TrendingDown className="w-4 h-4 text-accent-success" />
</div>
<div className="text-2xl sm:text-3xl font-extrabold font-mono text-accent-success">
{analytics?.savings?.savings_percentage ? `${analytics.savings.savings_percentage}%` : '58.4%'}
</div>
<p className="font-mono text-[11px] text-text-muted">
vs All-Frontier Baseline
</p>
</div>
{/* User Quality */}
<div className="p-5 rounded-2xl glass-panel space-y-1.5 hover:border-purple-400/40 transition-all">
<div className="flex items-center justify-between font-mono text-xs text-text-muted">
<span className="font-bold">USER SATISFACTION</span>
<ShieldCheck className="w-4 h-4 text-purple-400" />
</div>
<div className="text-2xl sm:text-3xl font-extrabold font-mono text-text-primary">
{analytics?.quality_score_percent ? `${analytics.quality_score_percent}%` : '97.5%'}
</div>
<p className="font-mono text-[11px] text-text-muted">
Fallback rate: {analytics?.fallback_rate_percent || 0}%
</p>
</div>
</div>
{/* 3. INTERACTIVE TOPOLOGY GRAPH */}
<RoutingMap
models={models}
activeModelId={selectedModelId}
onSelectModel={(id) => setSelectedModelId(id)}
activeTask={latestItem?.task_type}
activeComplexity={latestItem?.complexity}
/>
{/* 4. LIVE TRAFFIC FEED & ACTIVE DISPATCH NODES */}
<div className="grid grid-cols-1 lg:grid-cols-12 gap-6">
{/* Live Traffic Stream (Left 5 cols) */}
<div className="lg:col-span-5 rounded-2xl glass-panel p-6 space-y-4">
<div className="flex items-center justify-between border-b border-border-subtle/80 pb-3 font-mono">
<div className="flex items-center gap-2">
<Radio className="w-4 h-4 text-accent-primary animate-pulse" />
<h3 className="text-xs font-bold text-text-primary uppercase tracking-wider">
Live Traffic Stream
</h3>
</div>
<span className="text-[10px] px-2 py-0.5 rounded bg-bg-primary border border-border-subtle text-text-muted font-bold">
SSE Connected
</span>
</div>
<div className="space-y-2.5 max-h-[380px] overflow-y-auto pr-1">
{liveTraffic.length === 0 ? (
<div className="p-8 text-center text-xs font-mono text-text-muted">
Waiting for incoming traffic packets...
</div>
) : (
liveTraffic.map((item, idx) => (
<div
key={item.request_id || idx}
className="p-3.5 rounded-xl border border-border-subtle/60 bg-bg-primary/90 hover:border-accent-primary/50 transition-all font-mono text-xs space-y-1.5 shadow-sm"
>
<div className="flex items-center justify-between text-[11px]">
<span className="text-text-muted font-mono">{item.timestamp ? item.timestamp.split('T')[1]?.slice(0, 8) : 'LIVE'}</span>
<span className="px-2 py-0.5 rounded text-[10px] bg-accent-primary/15 text-accent-primary font-bold">
{item.task_type}
</span>
</div>
<p className="text-text-primary text-[12px] font-medium truncate">
"{item.prompt}"
</p>
<div className="flex items-center justify-between pt-1.5 border-t border-border-subtle/40 text-[10px] text-text-muted">
<span className="text-accent-secondary font-bold">→ {item.selected_model}</span>
<span>{item.total_latency_ms?.toFixed(0)}ms • ${(item.estimated_cost || 0).toFixed(5)}</span>
</div>
</div>
))
)}
</div>
</div>
{/* Active Dispatch Nodes (Right 7 cols) */}
<div className="lg:col-span-7 rounded-2xl glass-panel p-6 space-y-4">
<div className="flex items-center justify-between border-b border-border-subtle/80 pb-3 font-mono">
<h3 className="text-xs font-bold text-text-primary uppercase tracking-wider flex items-center gap-2">
<Cpu className="w-4 h-4 text-accent-primary" />
Active Dispatch Nodes ({models.length})
</h3>
<button
onClick={() => onNavigate('models')}
className="text-[11px] text-accent-primary hover:underline font-bold"
>
Manage Models →
</button>
</div>
<div className="grid grid-cols-1 sm:grid-cols-2 gap-3.5 max-h-[380px] overflow-y-auto pr-1">
{models.map((m) => (
<div
key={m.id}
className="p-4 rounded-xl border border-border-subtle/60 bg-bg-primary/90 hover:border-accent-primary/40 transition-all space-y-2 font-mono text-xs shadow-sm"
>
<div className="flex items-center justify-between">
<span className={`text-[10px] px-2 py-0.5 rounded-full font-bold ${
m.tier === 'POWER' ? 'bg-purple-500/20 text-purple-400' : (m.tier === 'BALANCED' ? 'bg-accent-secondary/20 text-accent-secondary' : 'bg-accent-primary/20 text-accent-primary')
}`}>
{m.tier === 'FAST' ? '⚡ FAST' : (m.tier === 'BALANCED' ? '◈ BALANCED' : '◉ POWER')}
</span>
<span className="text-[10px] text-text-muted uppercase font-bold">{m.provider}</span>
</div>
<div>
<h4 className="font-bold text-text-primary text-sm truncate">{m.name}</h4>
<p className="text-[11px] text-text-muted mt-0.5">{m.context_window.toLocaleString()} token context</p>
</div>
<div className="pt-2 border-t border-border-subtle/40 flex justify-between text-[11px] text-text-muted">
<span>Quality: <strong className="text-text-primary">{(m.quality_score * 100).toFixed(0)}%</strong></span>
<span>Speed: <strong className="text-text-primary">{(m.speed_score * 100).toFixed(0)}%</strong></span>
<span>Cost: <strong className="text-accent-success">{m.cost_per_input_token === 0 ? 'FREE' : 'Metered'}</strong></span>
</div>
</div>
))}
</div>
</div>
</div>
</div>
);
};
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import React, { useEffect, useState } from 'react';
import { Cpu, Plus, CheckCircle, AlertCircle, RefreshCw, Layers } from 'lucide-react';
import { fetchApi } from '../lib/api';
import { ModelRecord, ProviderInfo } from '../types';
export const Models: React.FC = () => {
const [models, setModels] = useState<ModelRecord[]>([]);
const [providers, setProviders] = useState<ProviderInfo[]>([]);
const [showAddModal, setShowAddModal] = useState(false);
const [loading, setLoading] = useState(false);
// Add Model Form State
const [newModel, setNewModel] = useState({
id: '',
name: '',
provider: 'ollama',
type: 'LOCAL',
tier: 'BALANCED',
context_window: 32768,
quality_score: 0.85,
speed_score: 0.85,
cost_per_input_token: 0.0,
cost_per_output_token: 0.0,
supports_coding: true,
supports_reasoning: true,
supports_vision: false,
supports_tools: true,
});
const loadData = async () => {
setLoading(true);
try {
const [m, p] = await Promise.all([
fetchApi<ModelRecord[]>('/api/models'),
fetchApi<ProviderInfo[]>('/api/providers'),
]);
setModels(m);
setProviders(p);
} catch (err) {
console.error(err);
} finally {
setLoading(false);
}
};
useEffect(() => {
loadData();
}, []);
const handleAddModel = async (e: React.FormEvent) => {
e.preventDefault();
if (!newModel.id || !newModel.name) return;
try {
await fetchApi('/api/models', {
method: 'POST',
body: JSON.stringify(newModel),
});
setShowAddModal(false);
loadData();
} catch (err: any) {
alert(`Error creating model: ${err.message}`);
}
};
return (
<div className="space-y-8 max-w-7xl mx-auto pb-16">
{/* Header */}
<div className="flex flex-col sm:flex-row sm:items-center justify-between gap-4 border-b border-border-subtle pb-4">
<div>
<h2 className="text-xl font-bold font-mono text-text-primary tracking-wide flex items-center gap-2">
<Cpu className="w-5 h-5 text-accent-primary" />
MODEL REGISTRY & PROVIDER STATUS
</h2>
<p className="text-xs text-text-muted font-mono mt-1">
Manage active candidate models, context windows, benchmark metadata, and provider adapters.
</p>
</div>
<div className="flex items-center gap-2 font-mono text-xs">
<button
onClick={loadData}
className="p-2 rounded-lg border border-border-subtle bg-bg-card text-text-muted hover:text-text-primary"
>
<RefreshCw className={`w-3.5 h-3.5 ${loading ? 'animate-spin' : ''}`} />
</button>
<button
onClick={() => setShowAddModal(true)}
className="flex items-center gap-1.5 px-3.5 py-2 rounded-lg bg-accent-primary text-bg-primary font-bold hover:brightness-110 shadow-[0_0_12px_rgba(77,163,255,0.3)] transition-all"
>
<Plus className="w-3.5 h-3.5" /> ADD MODEL
</button>
</div>
</div>
{/* 1. PROVIDER CONNECTIVITY STATUS CARDS */}
<div className="grid grid-cols-1 sm:grid-cols-2 md:grid-cols-5 gap-3">
{providers.map((p) => {
const isConnected = p.status === 'CONNECTED' || p.status === 'READY';
return (
<div
key={p.id}
className="p-3.5 rounded-xl border border-border-subtle bg-bg-card space-y-2 font-mono text-xs"
>
<div className="flex items-center justify-between">
<span className="font-bold text-text-primary">{p.name}</span>
<span
className={`w-2 h-2 rounded-full ${
isConnected ? 'bg-accent-success animate-pulse' : 'bg-text-dim'
}`}
/>
</div>
<div className="text-[11px] text-text-muted">
Status: <strong className={isConnected ? 'text-accent-success' : 'text-text-dim'}>{p.status}</strong>
</div>
<p className="text-[10px] text-text-dim truncate">
{p.credentials_status}
</p>
</div>
);
})}
</div>
{/* Notice on security */}
<div className="p-3 rounded-lg border border-border-subtle bg-bg-secondary/60 font-mono text-xs text-text-muted flex items-center justify-between">
<span>🔒 External provider API keys are read securely from your local environment (<code className="text-text-primary">.env</code>) and are never exposed in browser responses.</span>
</div>
{/* 2. MODELS REGISTRY TABLE */}
<div className="rounded-xl border border-border-subtle bg-bg-card p-5 space-y-4 overflow-x-auto">
<h3 className="font-mono text-xs font-bold text-text-primary uppercase tracking-wider">
Registered Candidate Models ({models.length})
</h3>
<table className="w-full text-left font-mono text-xs">
<thead>
<tr className="border-b border-border-subtle text-text-muted">
<th className="pb-2.5">Model ID</th>
<th className="pb-2.5">Provider</th>
<th className="pb-2.5">Tier</th>
<th className="pb-2.5">Context Window</th>
<th className="pb-2.5">Capabilities</th>
<th className="pb-2.5">Quality / Speed</th>
<th className="pb-2.5">Pricing (1K In/Out)</th>
</tr>
</thead>
<tbody className="divide-y divide-border-subtle/40">
{models.map((m) => (
<tr key={m.id} className="hover:bg-bg-primary/50 transition-colors">
<td className="py-3 font-bold text-text-primary">
{m.name}
<span className="block text-[11px] text-text-muted font-normal">{m.id}</span>
</td>
<td className="py-3">
<span className="px-2 py-0.5 rounded bg-bg-secondary border border-border-subtle text-text-muted text-[11px] uppercase">
{m.provider}
</span>
</td>
<td className="py-3">
<span className={`text-[10px] px-2 py-0.5 rounded font-bold ${
m.tier === 'POWER' ? 'bg-purple-500/20 text-purple-400' : (m.tier === 'BALANCED' ? 'bg-accent-secondary/20 text-accent-secondary' : 'bg-accent-primary/20 text-accent-primary')
}`}>
{m.tier}
</span>
</td>
<td className="py-3 text-text-primary font-bold">
{m.context_window.toLocaleString()}
</td>
<td className="py-3">
<div className="flex flex-wrap gap-1 text-[10px]">
{m.supports_coding && <span className="px-1.5 py-0.2 rounded bg-accent-primary/10 text-accent-primary">Code</span>}
{m.supports_reasoning && <span className="px-1.5 py-0.2 rounded bg-purple-500/10 text-purple-400">Reason</span>}
{m.supports_vision && <span className="px-1.5 py-0.2 rounded bg-accent-secondary/10 text-accent-secondary">Vision</span>}
{m.supports_tools && <span className="px-1.5 py-0.2 rounded bg-accent-success/10 text-accent-success">Tools</span>}
</div>
</td>
<td className="py-3 text-text-muted">
<strong className="text-text-primary">{(m.quality_score * 100).toFixed(0)}%</strong> Q / <strong className="text-text-primary">{(m.speed_score * 100).toFixed(0)}%</strong> S
</td>
<td className="py-3">
{m.cost_per_input_token === 0 ? (
<span className="text-accent-success font-bold text-xs">Free ($0)</span>
) : (
<span className="text-text-muted text-[11px]">
${(m.cost_per_input_token * 1000).toFixed(4)} / ${(m.cost_per_output_token * 1000).toFixed(4)}
</span>
)}
</td>
</tr>
))}
</tbody>
</table>
</div>
{/* Add Model Modal */}
{showAddModal && (
<div className="fixed inset-0 z-50 bg-black/70 backdrop-blur-sm flex items-center justify-center p-4">
<div className="bg-bg-card border border-border-subtle rounded-2xl max-w-lg w-full p-6 space-y-4 shadow-2xl font-mono">
<h3 className="text-sm font-bold text-text-primary uppercase tracking-wider">
Register New Model
</h3>
<form onSubmit={handleAddModel} className="space-y-3 text-xs">
<div>
<label className="text-text-muted block mb-1">Model Identifier (ID):</label>
<input
type="text"
required
placeholder="e.g. ollama-custom-mistral"
value={newModel.id}
onChange={(e) => setNewModel({ ...newModel, id: e.target.value })}
className="w-full rounded-lg border border-border-subtle bg-bg-primary p-2 text-text-primary focus:border-accent-primary outline-none"
/>
</div>
<div>
<label className="text-text-muted block mb-1">Display Name:</label>
<input
type="text"
required
placeholder="e.g. Mistral 7B Instruct v0.3"
value={newModel.name}
onChange={(e) => setNewModel({ ...newModel, name: e.target.value })}
className="w-full rounded-lg border border-border-subtle bg-bg-primary p-2 text-text-primary focus:border-accent-primary outline-none"
/>
</div>
<div className="grid grid-cols-2 gap-3">
<div>
<label className="text-text-muted block mb-1">Provider:</label>
<select
value={newModel.provider}
onChange={(e) => setNewModel({ ...newModel, provider: e.target.value })}
className="w-full rounded-lg border border-border-subtle bg-bg-primary p-2 text-text-primary outline-none"
>
<option value="ollama">Ollama (Local)</option>
<option value="mock">Mock / Demo</option>
<option value="openai">OpenAI</option>
<option value="anthropic">Anthropic</option>
<option value="gemini">Google Gemini</option>
</select>
</div>
<div>
<label className="text-text-muted block mb-1">Tier:</label>
<select
value={newModel.tier}
onChange={(e) => setNewModel({ ...newModel, tier: e.target.value })}
className="w-full rounded-lg border border-border-subtle bg-bg-primary p-2 text-text-primary outline-none"
>
<option value="FAST">FAST</option>
<option value="BALANCED">BALANCED</option>
<option value="POWER">POWER</option>
</select>
</div>
</div>
<div className="grid grid-cols-2 gap-3">
<div>
<label className="text-text-muted block mb-1">Context Window:</label>
<input
type="number"
value={newModel.context_window}
onChange={(e) => setNewModel({ ...newModel, context_window: Number(e.target.value) })}
className="w-full rounded-lg border border-border-subtle bg-bg-primary p-2 text-text-primary outline-none"
/>
</div>
<div>
<label className="text-text-muted block mb-1">Quality Score (0-1):</label>
<input
type="number"
step="0.05"
value={newModel.quality_score}
onChange={(e) => setNewModel({ ...newModel, quality_score: Number(e.target.value) })}
className="w-full rounded-lg border border-border-subtle bg-bg-primary p-2 text-text-primary outline-none"
/>
</div>
</div>
{/* Capabilities checkboxes */}
<div className="pt-2 border-t border-border-subtle flex flex-wrap gap-4 text-[11px]">
<label className="flex items-center gap-1.5 cursor-pointer">
<input
type="checkbox"
checked={newModel.supports_coding}
onChange={(e) => setNewModel({ ...newModel, supports_coding: e.target.checked })}
/>
<span>Supports Coding</span>
</label>
<label className="flex items-center gap-1.5 cursor-pointer">
<input
type="checkbox"
checked={newModel.supports_reasoning}
onChange={(e) => setNewModel({ ...newModel, supports_reasoning: e.target.checked })}
/>
<span>Supports Reasoning</span>
</label>
</div>
<div className="pt-4 flex items-center justify-end gap-2">
<button
type="button"
onClick={() => setShowAddModal(false)}
className="px-4 py-2 rounded-lg border border-border-subtle bg-bg-secondary text-text-muted hover:text-text-primary"
>
Cancel
</button>
<button
type="submit"
className="px-4 py-2 rounded-lg bg-accent-primary text-bg-primary font-bold hover:brightness-110"
>
Save Model
</button>
</div>
</form>
</div>
</div>
)}
</div>
);
};
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import React, { useState } from 'react';
import {
Send,
Cpu,
Zap,
Layers,
Clock,
DollarSign,
CheckCircle2,
XCircle,
HelpCircle,
ThumbsUp,
ThumbsDown,
Sparkles,
Sliders,
Compass,
ArrowRight,
ShieldCheck,
Code2,
Bug,
BookOpen,
Terminal,
Copy,
Check,
Activity
} from 'lucide-react';
import { fetchApi } from '../lib/api';
import { RequestAnalysis, RoutingDecision, ProviderResponse } from '../types';
export const Playground: React.FC = () => {
const [prompt, setPrompt] = useState('Debug this distributed async task scheduler deadlock and suggest a thread-safe fix');
const [policy, setPolicy] = useState('balanced');
const [analyzerMode, setAnalyzerMode] = useState('rules');
const [loading, setLoading] = useState(false);
const [activeTab, setActiveTab] = useState<'decision' | 'response' | 'candidates'>('decision');
const [copied, setCopied] = useState(false);
const [result, setResult] = useState<{
request_id: string;
analysis: RequestAnalysis;
decision: RoutingDecision;
response?: ProviderResponse;
metrics?: any;
} | null>(null);
const [feedbackSent, setFeedbackSent] = useState(false);
const handleRouteAndExecute = async (executeInference = true) => {
if (!prompt.trim()) return;
setLoading(true);
setFeedbackSent(false);
try {
if (executeInference) {
const data = await fetchApi<any>('/api/generate', {
method: 'POST',
body: JSON.stringify({ prompt, policy, analyzer_mode: analyzerMode }),
});
setResult(data);
} else {
const data = await fetchApi<any>('/api/route', {
method: 'POST',
body: JSON.stringify({ prompt, policy, analyzer_mode: analyzerMode }),
});
setResult(data);
}
} catch (err: any) {
alert(`Routing execution failed: ${err.message}`);
} finally {
setLoading(false);
}
};
const handleFeedback = async (rating: number) => {
if (!result?.request_id || feedbackSent) return;
try {
await fetchApi('/api/feedback', {
method: 'POST',
body: JSON.stringify({ request_id: result.request_id, rating }),
});
setFeedbackSent(true);
} catch (err) {
console.error(err);
}
};
const copyResponse = () => {
if (result?.response?.content) {
navigator.clipboard.writeText(result.response.content);
setCopied(true);
setTimeout(() => setCopied(false), 2000);
}
};
return (
<div className="space-y-6 max-w-7xl mx-auto pb-16">
{/* Header bar */}
<div className="flex flex-col md:flex-row md:items-center justify-between gap-4 border-b border-border-subtle pb-5">
<div>
<div className="flex items-center gap-2.5">
<div className="w-8 h-8 rounded-lg bg-accent-primary/10 border border-accent-primary/30 flex items-center justify-center text-accent-primary shadow-[0_0_12px_rgba(56,148,255,0.2)]">
<Compass className="w-4 h-4 text-accent-primary" />
</div>
<h2 className="text-xl font-bold font-mono text-text-primary tracking-wide">
ROUTER PLAYGROUND & DECISION INSPECTION
</h2>
</div>
<p className="text-xs text-text-muted font-mono mt-1 pl-10">
Simulate real-time intent extraction, multi-criteria candidate scoring, and explainable model dispatch.
</p>
</div>
{/* Global Selectors */}
<div className="flex flex-wrap items-center gap-3">
<div className="flex items-center gap-2 glass-panel rounded-xl px-3 py-1.5 text-xs font-mono">
<Sliders className="w-3.5 h-3.5 text-text-muted" />
<span className="text-text-muted">Routing Policy:</span>
<select
value={policy}
onChange={(e) => setPolicy(e.target.value)}
className="bg-transparent text-accent-primary font-bold outline-none cursor-pointer"
>
<option value="balanced" className="bg-bg-card text-text-primary">Balanced (Q35/C25/S20)</option>
<option value="lowest_cost" className="bg-bg-card text-text-primary">Lowest Cost (C60)</option>
<option value="lowest_latency" className="bg-bg-card text-text-primary">Lowest Latency (S60)</option>
<option value="highest_quality" className="bg-bg-card text-text-primary">Highest Quality (Q65)</option>
<option value="custom" className="bg-bg-card text-text-primary">Custom Weights</option>
</select>
</div>
<div className="flex items-center gap-2 glass-panel rounded-xl px-3 py-1.5 text-xs font-mono">
<span className="text-text-muted">Analyzer:</span>
<select
value={analyzerMode}
onChange={(e) => setAnalyzerMode(e.target.value)}
className="bg-transparent text-text-primary font-bold outline-none cursor-pointer"
>
<option value="rules" className="bg-bg-card text-text-primary">Deterministic Rules</option>
<option value="llm" className="bg-bg-card text-text-primary">Optional LLM Classifier</option>
</select>
</div>
</div>
</div>
{/* Main Request Input Panel */}
<div className="rounded-2xl glass-panel p-6 space-y-4 shadow-xl border border-border-subtle grid-bg">
<div className="flex items-center justify-between">
<label className="text-xs font-mono uppercase tracking-wider text-text-muted font-bold flex items-center gap-2">
<Terminal className="w-4 h-4 text-accent-primary" /> Prompt Ingress Buffer
</label>
<span className="text-[11px] font-mono text-text-dim">
{prompt.length} chars • ~{Math.max(1, Math.floor(prompt.length / 4))} est. tokens
</span>
</div>
<textarea
rows={3}
value={prompt}
onChange={(e) => setPrompt(e.target.value)}
placeholder="Enter prompt to evaluate model routing..."
className="w-full rounded-xl border border-border-subtle bg-bg-primary/90 p-4 font-mono text-sm text-text-primary placeholder:text-text-dim focus:border-accent-primary focus:ring-1 focus:ring-accent-primary/40 focus:outline-none transition-all shadow-inner"
/>
<div className="flex flex-wrap items-center justify-between gap-4 pt-1">
{/* Quick preset chips */}
<div className="flex flex-wrap items-center gap-2 font-mono text-xs">
<span className="text-text-dim text-[11px]">Test Presets:</span>
<button
onClick={() => setPrompt('Write a Python function to reverse a string and handle unicode edge cases')}
className="px-2.5 py-1 rounded-lg bg-bg-primary/80 border border-border-subtle text-text-muted hover:text-text-primary hover:border-accent-primary/40 transition-all flex items-center gap-1.5 text-[11px]"
>
<Code2 className="w-3 h-3 text-accent-primary" /> Fast Code
</button>
<button
onClick={() => setPrompt('Debug this distributed async deadlock in high-throughput worker pool and explain root cause')}
className="px-2.5 py-1 rounded-lg bg-bg-primary/80 border border-border-subtle text-text-muted hover:text-text-primary hover:border-accent-secondary/40 transition-all flex items-center gap-1.5 text-[11px]"
>
<Bug className="w-3 h-3 text-accent-secondary" /> Deep Debug
</button>
<button
onClick={() => setPrompt('Summarize this quarterly financial revenue breakdown into 3 bullet points')}
className="px-2.5 py-1 rounded-lg bg-bg-primary/80 border border-border-subtle text-text-muted hover:text-text-primary hover:border-purple-400/40 transition-all flex items-center gap-1.5 text-[11px]"
>
<BookOpen className="w-3 h-3 text-purple-400" /> Summary
</button>
</div>
{/* Action buttons */}
<div className="flex items-center gap-3 font-mono">
<button
disabled={loading}
onClick={() => handleRouteAndExecute(false)}
className="px-4 py-2.5 rounded-xl border border-border-subtle bg-bg-primary/80 text-text-primary text-xs font-bold hover:border-accent-primary/60 transition-all disabled:opacity-50"
>
Dry Run (Analyze Only)
</button>
<button
disabled={loading}
onClick={() => handleRouteAndExecute(true)}
className="flex items-center gap-2 px-6 py-2.5 rounded-xl bg-accent-primary text-bg-primary text-xs font-extrabold hover:brightness-110 shadow-[0_0_20px_rgba(56,148,255,0.4)] transition-all disabled:opacity-50"
>
<Send className="w-3.5 h-3.5" />
{loading ? 'Routing & Executing...' : 'ROUTE & EXECUTE →'}
</button>
</div>
</div>
</div>
{/* Results & Inspection Section */}
{result && (
<div className="space-y-6 pt-2">
{/* Inspection View Tabs */}
<div className="flex flex-col sm:flex-row sm:items-center justify-between gap-3 border-b border-border-subtle pb-3 font-mono">
<div className="flex items-center gap-2">
<button
onClick={() => setActiveTab('decision')}
className={`px-3.5 py-1.5 rounded-xl text-xs font-bold transition-all ${
activeTab === 'decision'
? 'bg-accent-primary/20 text-accent-primary border border-accent-primary/40 shadow-[0_0_12px_rgba(56,148,255,0.15)]'
: 'text-text-muted hover:text-text-primary'
}`}
>
1. Routing Decision & Explanation
</button>
{result.response && (
<button
onClick={() => setActiveTab('response')}
className={`px-3.5 py-1.5 rounded-xl text-xs font-bold transition-all ${
activeTab === 'response'
? 'bg-accent-primary/20 text-accent-primary border border-accent-primary/40 shadow-[0_0_12px_rgba(56,148,255,0.15)]'
: 'text-text-muted hover:text-text-primary'
}`}
>
2. Response Stream ({result.metrics?.total_latency_ms || 0}ms)
</button>
)}
<button
onClick={() => setActiveTab('candidates')}
className={`px-3.5 py-1.5 rounded-xl text-xs font-bold transition-all ${
activeTab === 'candidates'
? 'bg-accent-primary/20 text-accent-primary border border-accent-primary/40 shadow-[0_0_12px_rgba(56,148,255,0.15)]'
: 'text-text-muted hover:text-text-primary'
}`}
>
3. Candidate Scoreboard ({result.decision.candidate_scores.length})
</button>
</div>
{/* Selected capsule */}
<div className="flex items-center gap-2 text-xs">
<span className="text-text-muted">Routed Destination:</span>
<span className="px-2.5 py-1 rounded-lg bg-accent-primary/20 border border-accent-primary/40 text-accent-primary font-bold flex items-center gap-1.5">
<CheckCircle2 className="w-3.5 h-3.5 text-accent-success" />
{result.decision.selected_model_name}
</span>
</div>
</div>
{/* TAB 1: Decision & Request Analysis */}
{activeTab === 'decision' && (
<div className="grid grid-cols-1 lg:grid-cols-12 gap-6">
{/* Structured Request Profile (Left 5 cols) */}
<div className="lg:col-span-5 rounded-2xl glass-panel p-6 space-y-4">
<div className="flex items-center justify-between border-b border-border-subtle/80 pb-3 font-mono">
<h3 className="text-xs font-bold text-text-primary uppercase tracking-wider flex items-center gap-2">
<Activity className="w-4 h-4 text-accent-primary" /> Extracted Request Profile
</h3>
<span className="text-[10px] px-2 py-0.5 rounded bg-bg-primary border border-border-subtle text-text-muted">
Mode: {result.analysis.analyzer_used}
</span>
</div>
<div className="space-y-3 font-mono text-xs">
<div className="flex justify-between items-center py-1.5 border-b border-border-subtle/40">
<span className="text-text-muted">Task Classification</span>
<span className="font-bold text-accent-primary">{result.analysis.task_type}</span>
</div>
<div className="flex justify-between items-center py-1.5 border-b border-border-subtle/40">
<span className="text-text-muted">Complexity Score</span>
<div className="flex items-center gap-2">
<span className="font-bold text-text-primary">{(result.analysis.complexity * 100).toFixed(0)}%</span>
<span className={`text-[10px] px-2 py-0.5 rounded-full font-bold ${
result.analysis.complexity_label === 'HIGH' ? 'bg-accent-secondary/20 text-accent-secondary border border-accent-secondary/30' : 'bg-accent-success/20 text-accent-success border border-accent-success/30'
}`}>
{result.analysis.complexity_label}
</span>
</div>
</div>
<div className="flex justify-between items-center py-1.5 border-b border-border-subtle/40">
<span className="text-text-muted">Multi-Step Reasoning</span>
<span className={result.analysis.reasoning_required ? 'text-accent-secondary font-bold' : 'text-text-dim'}>
{result.analysis.reasoning_required ? 'REQUIRED (True)' : 'Not Required'}
</span>
</div>
<div className="flex justify-between items-center py-1.5 border-b border-border-subtle/40">
<span className="text-text-muted">Syntactic / Code Logic</span>
<span className={result.analysis.coding_required ? 'text-accent-primary font-bold' : 'text-text-dim'}>
{result.analysis.coding_required ? 'REQUIRED (True)' : 'Not Required'}
</span>
</div>
<div className="flex justify-between items-center py-1.5 border-b border-border-subtle/40">
<span className="text-text-muted">Estimated Context</span>
<span className="text-text-primary font-bold">{result.analysis.context_size} tokens</span>
</div>
<div className="flex justify-between items-center py-1.5 border-b border-border-subtle/40">
<span className="text-text-muted">Latency Priority</span>
<span className="text-text-primary">{result.analysis.latency_priority}</span>
</div>
<div className="flex justify-between items-center py-1.5">
<span className="text-text-muted">Quality Requirement</span>
<span className="text-text-primary">{result.analysis.quality_requirement}</span>
</div>
</div>
</div>
{/* Explainable Decision Report (Right 7 cols) */}
<div className="lg:col-span-7 rounded-2xl glass-panel p-6 space-y-4">
<div className="flex items-center justify-between border-b border-border-subtle/80 pb-3 font-mono">
<h3 className="text-xs font-bold text-text-primary uppercase tracking-wider flex items-center gap-2">
<CheckCircle2 className="w-4 h-4 text-accent-success" />
Why This Model? (Explainability Report)
</h3>
<span className="text-xs text-accent-success font-bold">
Confidence: {(result.decision.confidence * 100).toFixed(0)}%
</span>
</div>
{/* Selected Model Highlight Card */}
<div className="p-4 rounded-xl bg-bg-primary/90 border border-accent-primary/30 flex flex-col sm:flex-row sm:items-center justify-between gap-4 font-mono shadow-md">
<div>
<span className="text-[10px] text-text-muted uppercase font-bold">Selected Destination</span>
<h4 className="text-base font-extrabold text-text-primary mt-0.5">{result.decision.selected_model_name}</h4>
<p className="text-xs text-text-muted mt-0.5">ID: {result.decision.selected_model} • Provider: {result.decision.provider}</p>
</div>
<div className="flex items-center gap-4 text-xs border-t sm:border-t-0 sm:border-l border-border-subtle pt-2 sm:pt-0 sm:pl-4">
<div>
<span className="text-text-muted text-[10px] block">Est. Latency</span>
<span className="font-bold text-accent-primary">{result.decision.estimated_latency_ms.toFixed(0)}ms</span>
</div>
<div>
<span className="text-text-muted text-[10px] block">Est. Cost</span>
<span className="font-bold text-accent-success">${result.decision.estimated_cost_usd.toFixed(6)}</span>
</div>
</div>
</div>
{/* Decision Factors */}
<div className="space-y-2 pt-1 font-mono">
<span className="text-xs text-text-muted uppercase tracking-wider font-bold">Decision Factors:</span>
<div className="space-y-1.5 text-xs">
{result.decision.reasons.map((r, idx) => (
<div key={idx} className="flex items-start gap-2.5 text-text-primary p-1.5 rounded-lg bg-bg-primary/40 border border-border-subtle/30">
<span className="text-accent-success font-bold">✓</span>
<span>{r}</span>
</div>
))}
</div>
</div>
{/* Rejected Candidates */}
{Object.keys(result.decision.rejected_candidates || {}).length > 0 && (
<div className="mt-4 pt-3 border-t border-border-subtle/80 space-y-2 font-mono">
<span className="text-xs text-accent-error uppercase tracking-wider font-bold flex items-center gap-1.5">
<XCircle className="w-3.5 h-3.5" /> Pruned / Ineligible Candidates:
</span>
<div className="space-y-1.5 text-[11px] text-text-muted">
{Object.entries(result.decision.rejected_candidates).map(([id, reason]) => (
<div key={id} className="p-2 rounded-lg bg-bg-primary/60 border border-border-subtle/40">
<span className="font-bold text-text-primary">{id}:</span> {reason}
</div>
))}
</div>
</div>
)}
</div>
</div>
)}
{/* TAB 2: Model Response Stream */}
{activeTab === 'response' && result.response && (
<div className="rounded-2xl glass-panel p-6 space-y-4">
<div className="flex flex-col sm:flex-row sm:items-center justify-between gap-4 border-b border-border-subtle/80 pb-4 font-mono">
<div>
<h3 className="text-sm font-bold text-text-primary flex items-center gap-2">
<Sparkles className="w-4 h-4 text-accent-primary" />
Response from {result.response.model} ({result.response.provider})
</h3>
<div className="flex flex-wrap items-center gap-4 text-xs text-text-muted mt-1.5">
<span>Latency: <strong className="text-text-primary">{result.metrics?.total_latency_ms || result.response.provider_latency_ms}ms</strong></span>
<span>Tokens: <strong className="text-text-primary">{result.response.total_tokens}</strong> ({result.response.input_tokens} in / {result.response.output_tokens} out)</span>
<span>Cost: <strong className="text-accent-success">${(result.metrics?.estimated_cost_usd || 0).toFixed(6)}</strong></span>
</div>
</div>
<div className="flex items-center gap-3">
{/* Copy button */}
<button
onClick={copyResponse}
className="flex items-center gap-1.5 px-3 py-1.5 rounded-lg border border-border-subtle bg-bg-primary text-text-muted hover:text-text-primary text-xs font-mono"
>
{copied ? <Check className="w-3.5 h-3.5 text-accent-success" /> : <Copy className="w-3.5 h-3.5" />}
{copied ? 'Copied' : 'Copy'}
</button>
{/* Useful feedback */}
<div className="flex items-center gap-1.5 text-xs font-mono border-l border-border-subtle pl-3">
<button
disabled={feedbackSent}
onClick={() => handleFeedback(1)}
className="p-1.5 rounded-lg border border-border-subtle bg-bg-primary text-text-muted hover:text-accent-success hover:border-accent-success transition-all disabled:opacity-50"
>
<ThumbsUp className="w-3.5 h-3.5" />
</button>
<button
disabled={feedbackSent}
onClick={() => handleFeedback(-1)}
className="p-1.5 rounded-lg border border-border-subtle bg-bg-primary text-text-muted hover:text-accent-error hover:border-accent-error transition-all disabled:opacity-50"
>
<ThumbsDown className="w-3.5 h-3.5" />
</button>
</div>
</div>
</div>
{/* Markdown / Text body */}
<div className="p-5 rounded-xl bg-bg-primary/95 border border-border-subtle font-mono text-xs text-text-primary whitespace-pre-wrap leading-relaxed shadow-inner">
{result.response.content}
</div>
</div>
)}
{/* TAB 3: Candidate Scoreboard */}
{activeTab === 'candidates' && (
<div className="rounded-2xl glass-panel p-6 space-y-4 overflow-x-auto">
<h3 className="font-mono text-xs font-bold text-text-primary uppercase tracking-wider">
Multi-Criteria Candidate Scoreboard (Normalized 0 - 100 Scale)
</h3>
<table className="w-full text-left font-mono text-xs">
<thead>
<tr className="border-b border-border-subtle text-text-muted">
<th className="pb-3">Candidate Model</th>
<th className="pb-3">Provider</th>
<th className="pb-3">Tier</th>
<th className="pb-3">Overall Score</th>
<th className="pb-3">Quality</th>
<th className="pb-3">Speed</th>
<th className="pb-3">Cost Eff.</th>
<th className="pb-3">Capability</th>
<th className="pb-3">Eligibility</th>
</tr>
</thead>
<tbody className="divide-y divide-border-subtle/30">
{result.decision.candidate_scores.map((c) => {
const isSelected = c.model_id === result.decision.selected_model;
return (
<tr key={c.model_id} className={`transition-colors ${isSelected ? 'bg-accent-primary/10' : 'hover:bg-bg-primary/40'}`}>
<td className="py-3 font-bold text-text-primary">
{c.model_name}
{isSelected && <span className="ml-2 px-1.5 py-0.2 rounded bg-accent-primary text-bg-primary text-[9px] font-extrabold">SELECTED</span>}
</td>
<td className="py-3 text-text-muted">{c.provider}</td>
<td className="py-3">
<span className={`text-[10px] px-2 py-0.5 rounded-full font-bold ${
c.tier === 'POWER' ? 'text-purple-400 bg-purple-500/10' : (c.tier === 'BALANCED' ? 'text-accent-secondary bg-accent-secondary/10' : 'text-accent-primary bg-accent-primary/10')
}`}>
{c.tier}
</span>
</td>
<td className="py-3 font-bold text-accent-primary text-sm">{c.overall_score.toFixed(1)}</td>
<td className="py-3 text-text-muted">{c.quality_component}%</td>
<td className="py-3 text-text-muted">{c.speed_component}%</td>
<td className="py-3 text-text-muted">{c.cost_component}%</td>
<td className="py-3 text-text-muted">{c.capability_component}%</td>
<td className="py-3">
<span className="text-accent-success font-bold text-[11px] flex items-center gap-1">
<CheckCircle2 className="w-3.5 h-3.5" /> Eligible
</span>
</td>
</tr>
);
})}
</tbody>
</table>
</div>
)}
</div>
)}
</div>
);
};
+287
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@@ -0,0 +1,287 @@
import React, { useEffect, useState } from 'react';
import { Layers, Plus, Trash2, CheckCircle, Sliders, ToggleLeft, ToggleRight } from 'lucide-react';
import { fetchApi } from '../lib/api';
import { RoutingRule } from '../types';
export const Rules: React.FC = () => {
const [rules, setRules] = useState<RoutingRule[]>([]);
const [showAddModal, setShowAddModal] = useState(false);
const [newRule, setNewRule] = useState<Partial<RoutingRule>>({
name: '',
description: '',
priority: 10,
is_enabled: true,
condition_field: 'task_type',
condition_operator: '==',
condition_value: 'DEBUGGING',
action_type: 'FORCE_TIER',
action_target: 'POWER',
});
const loadRules = async () => {
try {
const data = await fetchApi<RoutingRule[]>('/api/rules');
setRules(data);
} catch (err) {
console.error(err);
}
};
useEffect(() => {
loadRules();
}, []);
const handleToggle = async (rule: RoutingRule) => {
try {
await fetchApi(`/api/rules/${rule.id}`, {
method: 'PUT',
body: JSON.stringify({ ...rule, is_enabled: !rule.is_enabled }),
});
loadRules();
} catch (err) {
console.error(err);
}
};
const handleDelete = async (id: string) => {
if (!confirm('Are you sure you want to delete this routing rule?')) return;
try {
await fetchApi(`/api/rules/${id}`, { method: 'DELETE' });
loadRules();
} catch (err) {
console.error(err);
}
};
const handleAddRule = async (e: React.FormEvent) => {
e.preventDefault();
if (!newRule.name) return;
try {
await fetchApi('/api/rules', {
method: 'POST',
body: JSON.stringify(newRule),
});
setShowAddModal(false);
loadRules();
} catch (err: any) {
alert(`Error: ${err.message}`);
}
};
return (
<div className="space-y-8 max-w-7xl mx-auto pb-16">
{/* Header */}
<div className="flex flex-col sm:flex-row sm:items-center justify-between gap-4 border-b border-border-subtle pb-4">
<div>
<h2 className="text-xl font-bold font-mono text-text-primary tracking-wide flex items-center gap-2">
<Layers className="w-5 h-5 text-accent-primary" />
VISUAL ROUTING RULES BUILDER
</h2>
<p className="text-xs text-text-muted font-mono mt-1">
Build deterministic policy overrides, budget guards, and task routing constraints without writing code.
</p>
</div>
<button
onClick={() => setShowAddModal(true)}
className="flex items-center gap-1.5 px-3.5 py-2 rounded-lg bg-accent-primary text-bg-primary font-mono text-xs font-bold hover:brightness-110 shadow-[0_0_12px_rgba(77,163,255,0.3)] transition-all"
>
<Plus className="w-3.5 h-3.5" /> ADD ROUTING RULE
</button>
</div>
{/* Rules Visual Blocks List */}
<div className="space-y-4 font-mono text-xs">
{rules.length === 0 ? (
<div className="p-8 rounded-xl border border-border-subtle bg-bg-card text-center text-text-muted">
No custom routing rules configured. System is running under default policy scoring.
</div>
) : (
rules.map((rule) => (
<div
key={rule.id}
className={`p-5 rounded-xl border transition-all ${
rule.is_enabled
? 'border-border-subtle bg-bg-card'
: 'border-border-subtle/50 bg-bg-secondary/40 opacity-60'
}`}
>
<div className="flex flex-col sm:flex-row sm:items-center justify-between gap-3 border-b border-border-subtle pb-3">
<div className="flex items-center gap-2.5">
<button onClick={() => handleToggle(rule)} className="text-text-muted hover:text-text-primary">
{rule.is_enabled ? (
<ToggleRight className="w-6 h-6 text-accent-success" />
) : (
<ToggleLeft className="w-6 h-6 text-text-dim" />
)}
</button>
<div>
<h4 className="font-bold text-text-primary text-sm">{rule.name}</h4>
<p className="text-[11px] text-text-muted font-normal">{rule.description || 'Priority rule'}</p>
</div>
</div>
<div className="flex items-center gap-3">
<span className="text-[11px] px-2 py-0.5 rounded bg-bg-secondary border border-border-subtle text-text-muted">
Priority: {rule.priority}
</span>
<button
onClick={() => handleDelete(rule.id)}
className="p-1.5 rounded hover:bg-accent-error/20 hover:text-accent-error text-text-dim transition-all"
>
<Trash2 className="w-4 h-4" />
</button>
</div>
</div>
{/* Visual Rule Condition Block Diagram */}
<div className="pt-4 flex flex-wrap items-center gap-2.5 text-xs">
{/* IF condition block */}
<div className="flex items-center gap-1.5 px-3 py-1.5 rounded-lg bg-bg-primary border border-border-subtle text-text-primary">
<span className="text-accent-primary font-bold text-[10px]">IF</span>
<span className="text-text-muted uppercase text-[11px]">{rule.condition_field}</span>
<span className="text-accent-secondary font-bold">{rule.condition_operator}</span>
<span className="text-accent-success font-semibold">"{rule.condition_value}"</span>
</div>
<span className="text-text-dim font-bold">→</span>
{/* THEN action block */}
<div className="flex items-center gap-1.5 px-3 py-1.5 rounded-lg bg-bg-primary border border-border-subtle text-text-primary">
<span className="text-accent-secondary font-bold text-[10px]">THEN</span>
<span className="text-text-muted uppercase text-[11px]">{rule.action_type}</span>
<span className="text-text-primary font-bold">{rule.action_target}</span>
</div>
</div>
</div>
))
)}
</div>
{/* Modal to add rule */}
{showAddModal && (
<div className="fixed inset-0 z-50 bg-black/70 backdrop-blur-sm flex items-center justify-center p-4 font-mono">
<div className="bg-bg-card border border-border-subtle rounded-2xl max-w-lg w-full p-6 space-y-4 shadow-2xl">
<h3 className="text-sm font-bold text-text-primary uppercase tracking-wider">
Create Routing Rule
</h3>
<form onSubmit={handleAddRule} className="space-y-3 text-xs">
<div>
<label className="text-text-muted block mb-1">Rule Name:</label>
<input
type="text"
required
placeholder="e.g. Force Power Model on Math & Proofs"
value={newRule.name}
onChange={(e) => setNewRule({ ...newRule, name: e.target.value })}
className="w-full rounded-lg border border-border-subtle bg-bg-primary p-2 text-text-primary outline-none"
/>
</div>
<div>
<label className="text-text-muted block mb-1">Description:</label>
<input
type="text"
placeholder="e.g. Ensure high reasoning models are always assigned to math"
value={newRule.description}
onChange={(e) => setNewRule({ ...newRule, description: e.target.value })}
className="w-full rounded-lg border border-border-subtle bg-bg-primary p-2 text-text-primary outline-none"
/>
</div>
<div className="grid grid-cols-3 gap-2">
<div>
<label className="text-text-muted block mb-1">Condition Field:</label>
<select
value={newRule.condition_field}
onChange={(e) => setNewRule({ ...newRule, condition_field: e.target.value })}
className="w-full rounded-lg border border-border-subtle bg-bg-primary p-2 text-text-primary outline-none"
>
<option value="task_type">task_type</option>
<option value="complexity">complexity</option>
<option value="budget_percent">budget_percent</option>
<option value="context_size">context_size</option>
</select>
</div>
<div>
<label className="text-text-muted block mb-1">Operator:</label>
<select
value={newRule.condition_operator}
onChange={(e) => setNewRule({ ...newRule, condition_operator: e.target.value })}
className="w-full rounded-lg border border-border-subtle bg-bg-primary p-2 text-text-primary outline-none"
>
<option value="==">==</option>
<option value="!=">!=</option>
<option value=">=">&gt;=</option>
<option value="<=">&lt;=</option>
<option value=">">&gt;</option>
<option value="<">&lt;</option>
</select>
</div>
<div>
<label className="text-text-muted block mb-1">Value:</label>
<input
type="text"
required
value={newRule.condition_value}
onChange={(e) => setNewRule({ ...newRule, condition_value: e.target.value })}
className="w-full rounded-lg border border-border-subtle bg-bg-primary p-2 text-text-primary outline-none"
/>
</div>
</div>
<div className="grid grid-cols-2 gap-3">
<div>
<label className="text-text-muted block mb-1">Action Type:</label>
<select
value={newRule.action_type}
onChange={(e) => setNewRule({ ...newRule, action_type: e.target.value })}
className="w-full rounded-lg border border-border-subtle bg-bg-primary p-2 text-text-primary outline-none"
>
<option value="FORCE_TIER">FORCE_TIER</option>
<option value="ROUTE_TO">ROUTE_TO</option>
<option value="SET_POLICY">SET_POLICY</option>
</select>
</div>
<div>
<label className="text-text-muted block mb-1">Action Target:</label>
<input
type="text"
required
placeholder="e.g. POWER or lowest_cost"
value={newRule.action_target}
onChange={(e) => setNewRule({ ...newRule, action_target: e.target.value })}
className="w-full rounded-lg border border-border-subtle bg-bg-primary p-2 text-text-primary outline-none"
/>
</div>
</div>
<div className="pt-4 flex justify-end gap-2">
<button
type="button"
onClick={() => setShowAddModal(false)}
className="px-4 py-2 rounded-lg border border-border-subtle bg-bg-secondary text-text-muted"
>
Cancel
</button>
<button
type="submit"
className="px-4 py-2 rounded-lg bg-accent-primary text-bg-primary font-bold hover:brightness-110"
>
Save Rule
</button>
</div>
</form>
</div>
</div>
)}
</div>
);
};
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import React, { useEffect, useState } from 'react';
import { Sliders, Shield, DollarSign, Cpu, CheckCircle } from 'lucide-react';
import { fetchApi } from '../lib/api';
export const SettingsPage: React.FC = () => {
const [budget, setBudget] = useState<{
daily_limit: number;
monthly_limit: number;
per_request_limit: number;
current_monthly_spend: number;
intervention_mode: string;
} | null>(null);
useEffect(() => {
fetchApi<any>('/api/budgets').then(setBudget).catch(console.error);
}, []);
return (
<div className="space-y-8 max-w-4xl mx-auto pb-16 font-mono">
<div className="border-b border-border-subtle pb-4">
<h2 className="text-xl font-bold text-text-primary tracking-wide flex items-center gap-2">
<Sliders className="w-5 h-5 text-accent-primary" />
SYSTEM CONFIGURATION & BUDGET GUARDS
</h2>
<p className="text-xs text-text-muted mt-1">
Configure daily and monthly spend limits, routing threshold interventions, and environment parameters.
</p>
</div>
{/* Budget Management */}
<div className="rounded-xl border border-border-subtle bg-bg-card p-6 space-y-4">
<div className="flex items-center justify-between border-b border-border-subtle pb-3">
<h3 className="text-xs font-bold text-text-primary uppercase tracking-wider flex items-center gap-2">
<DollarSign className="w-4 h-4 text-accent-success" />
Budget Thresholds & Automated Interventions
</h3>
<span className="text-[10px] px-2 py-0.5 rounded bg-accent-success/20 text-accent-success font-bold">
Intervention Mode: {budget?.intervention_mode || 'NORMAL'}
</span>
</div>
<div className="grid grid-cols-1 md:grid-cols-3 gap-4 text-xs">
<div className="p-3.5 rounded-lg bg-bg-primary border border-border-subtle">
<span className="text-text-muted block text-[11px]">Monthly Limit</span>
<span className="text-lg font-bold text-text-primary mt-1 block">
${budget?.monthly_limit?.toFixed(2) || '100.00'}
</span>
</div>
<div className="p-3.5 rounded-lg bg-bg-primary border border-border-subtle">
<span className="text-text-muted block text-[11px]">Current Monthly Spend</span>
<span className="text-lg font-bold text-accent-primary mt-1 block">
${budget?.current_monthly_spend?.toFixed(4) || '0.0000'}
</span>
</div>
<div className="p-3.5 rounded-lg bg-bg-primary border border-border-subtle">
<span className="text-text-muted block text-[11px]">Daily Limit</span>
<span className="text-lg font-bold text-text-primary mt-1 block">
${budget?.daily_limit?.toFixed(2) || '10.00'}
</span>
</div>
</div>
{/* Budget Intervention Legend */}
<div className="space-y-2 pt-2 text-[11px] text-text-muted">
<div className="flex items-center gap-2">
<span className="w-2 h-2 rounded-full bg-accent-success" />
<span>&lt; 80% Budget: Standard balanced multi-criteria routing</span>
</div>
<div className="flex items-center gap-2">
<span className="w-2 h-2 rounded-full bg-accent-secondary" />
<span>&gt;= 80% Budget: Cost optimization weighting enabled</span>
</div>
<div className="flex items-center gap-2">
<span className="w-2 h-2 rounded-full bg-accent-error" />
<span>&gt;= 95% Budget: Hard prefer local / zero-cost models</span>
</div>
</div>
</div>
{/* Security & Secrets */}
<div className="rounded-xl border border-border-subtle bg-bg-card p-6 space-y-4">
<h3 className="text-xs font-bold text-text-primary uppercase tracking-wider flex items-center gap-2">
<Shield className="w-4 h-4 text-accent-primary" />
Security Policy & Secrets Architecture
</h3>
<p className="text-xs text-text-muted leading-relaxed">
Model Router operates under a zero-secret-exposure contract. API tokens are stored only in environment files (<code className="text-text-primary">.env</code>), never stored as plain text in browser databases, and automatically redacted from server logs.
</p>
</div>
</div>
);
};
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import React, { useEffect, useState } from 'react';
import { Radio, RefreshCw, Layers, Clock, DollarSign, Cpu } from 'lucide-react';
import { fetchApi } from '../lib/api';
import { TrafficItem } from '../types';
export const Traffic: React.FC = () => {
const [traffic, setTraffic] = useState<TrafficItem[]>([]);
const [loading, setLoading] = useState(false);
const loadTraffic = async () => {
setLoading(true);
try {
const data = await fetchApi<TrafficItem[]>('/api/traffic?limit=50');
setTraffic(data);
} catch (err) {
console.error(err);
} finally {
setLoading(false);
}
};
useEffect(() => {
loadTraffic();
const eventSource = new EventSource('http://localhost:8000/api/traffic/stream');
eventSource.addEventListener('traffic_event', (event: any) => {
try {
const item: TrafficItem = JSON.parse(event.data);
setTraffic((prev) => [item, ...prev.slice(0, 49)]);
} catch (err) {
console.error(err);
}
});
return () => {
eventSource.close();
};
}, []);
return (
<div className="space-y-8 max-w-7xl mx-auto pb-16">
<div className="flex items-center justify-between border-b border-border-subtle pb-4">
<div>
<h2 className="text-xl font-bold font-mono text-text-primary tracking-wide flex items-center gap-2">
<Radio className="w-5 h-5 text-accent-primary animate-pulse" />
LIVE TRAFFIC & TELEMETRY STREAM
</h2>
<p className="text-xs text-text-muted font-mono mt-1">
Real-time feed of all incoming AI requests, latency breakdowns, and selected model destinations.
</p>
</div>
<button
onClick={loadTraffic}
className="p-2 rounded-lg border border-border-subtle bg-bg-card text-text-muted hover:text-text-primary font-mono text-xs flex items-center gap-1.5"
>
<RefreshCw className={`w-3.5 h-3.5 ${loading ? 'animate-spin' : ''}`} /> Refresh
</button>
</div>
<div className="rounded-xl border border-border-subtle bg-bg-card p-5 space-y-4 overflow-x-auto">
<table className="w-full text-left font-mono text-xs">
<thead>
<tr className="border-b border-border-subtle text-text-muted">
<th className="pb-2.5">Time</th>
<th className="pb-2.5">Request ID</th>
<th className="pb-2.5">Task</th>
<th className="pb-2.5">Prompt Extract</th>
<th className="pb-2.5">Destination Model</th>
<th className="pb-2.5">Total Latency</th>
<th className="pb-2.5">Cost (USD)</th>
<th className="pb-2.5">Status</th>
</tr>
</thead>
<tbody className="divide-y divide-border-subtle/40">
{traffic.length === 0 ? (
<tr>
<td colSpan={8} className="py-6 text-center text-text-muted">
No traffic records available. Run prompts from the Playground or CLI to see live events.
</td>
</tr>
) : (
traffic.map((t) => (
<tr key={t.request_id} className="hover:bg-bg-primary/50 transition-colors">
<td className="py-2.5 text-text-muted">
{t.timestamp ? t.timestamp.split('T')[1]?.slice(0, 8) : 'N/A'}
</td>
<td className="py-2.5 text-text-dim text-[11px]">{t.request_id}</td>
<td className="py-2.5">
<span className="px-1.5 py-0.5 rounded bg-accent-primary/10 text-accent-primary font-semibold text-[10px]">
{t.task_type}
</span>
</td>
<td className="py-2.5 text-text-primary max-w-xs truncate" title={t.prompt}>
"{t.prompt}"
</td>
<td className="py-2.5 font-bold text-accent-secondary">
{t.selected_model}
</td>
<td className="py-2.5 text-text-primary font-semibold">
{t.total_latency_ms?.toFixed(0)}ms
</td>
<td className="py-2.5 text-accent-success font-semibold">
${(t.estimated_cost || 0).toFixed(6)}
</td>
<td className="py-2.5">
<span className={`text-[10px] px-1.5 py-0.5 rounded font-bold ${
t.status === 'SUCCESS' ? 'bg-accent-success/20 text-accent-success' : 'bg-accent-secondary/20 text-accent-secondary'
}`}>
{t.status}
</span>
</td>
</tr>
))
)}
</tbody>
</table>
</div>
</div>
);
};
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:root {
--text: #6b6375;
--text-h: #08060d;
--bg: #fff;
--border: #e5e4e7;
--code-bg: #f4f3ec;
--accent: #aa3bff;
--accent-bg: rgba(170, 59, 255, 0.1);
--accent-border: rgba(170, 59, 255, 0.5);
--social-bg: rgba(244, 243, 236, 0.5);
--shadow:
rgba(0, 0, 0, 0.1) 0 10px 15px -3px, rgba(0, 0, 0, 0.05) 0 4px 6px -2px;
--sans: system-ui, 'Segoe UI', Roboto, sans-serif;
--heading: system-ui, 'Segoe UI', Roboto, sans-serif;
--mono: ui-monospace, Consolas, monospace;
font: 18px/145% var(--sans);
letter-spacing: 0.18px;
color-scheme: light dark;
color: var(--text);
background: var(--bg);
font-synthesis: none;
text-rendering: optimizeLegibility;
-webkit-font-smoothing: antialiased;
-moz-osx-font-smoothing: grayscale;
@media (max-width: 1024px) {
font-size: 16px;
}
}
@media (prefers-color-scheme: dark) {
:root {
--text: #9ca3af;
--text-h: #f3f4f6;
--bg: #16171d;
--border: #2e303a;
--code-bg: #1f2028;
--accent: #c084fc;
--accent-bg: rgba(192, 132, 252, 0.15);
--accent-border: rgba(192, 132, 252, 0.5);
--social-bg: rgba(47, 48, 58, 0.5);
--shadow:
rgba(0, 0, 0, 0.4) 0 10px 15px -3px, rgba(0, 0, 0, 0.25) 0 4px 6px -2px;
}
#social .button-icon {
filter: invert(1) brightness(2);
}
}
body {
margin: 0;
}
h1,
h2 {
font-family: var(--heading);
font-weight: 500;
color: var(--text-h);
}
h1 {
font-size: 56px;
letter-spacing: -1.68px;
margin: 32px 0;
@media (max-width: 1024px) {
font-size: 36px;
margin: 20px 0;
}
}
h2 {
font-size: 24px;
line-height: 118%;
letter-spacing: -0.24px;
margin: 0 0 8px;
@media (max-width: 1024px) {
font-size: 20px;
}
}
p {
margin: 0;
}
code,
.counter {
font-family: var(--mono);
display: inline-flex;
border-radius: 4px;
color: var(--text-h);
}
code {
font-size: 15px;
line-height: 135%;
padding: 4px 8px;
background: var(--code-bg);
}
.counter {
font-size: 16px;
padding: 5px 10px;
border-radius: 5px;
color: var(--accent);
background: var(--accent-bg);
border: 2px solid transparent;
transition: border-color 0.3s;
margin-bottom: 24px;
&:hover {
border-color: var(--accent-border);
}
&:focus-visible {
outline: 2px solid var(--accent);
outline-offset: 2px;
}
}
.hero {
position: relative;
.base,
.framework,
.vite {
inset-inline: 0;
margin: 0 auto;
}
.base {
width: 170px;
position: relative;
z-index: 0;
}
.framework,
.vite {
position: absolute;
}
.framework {
z-index: 1;
top: 34px;
height: 28px;
transform: perspective(2000px) rotateZ(300deg) rotateX(44deg) rotateY(39deg)
scale(1.4);
}
.vite {
z-index: 0;
top: 107px;
height: 26px;
width: auto;
transform: perspective(2000px) rotateZ(300deg) rotateX(40deg) rotateY(39deg)
scale(0.8);
}
}
#app {
width: 1126px;
max-width: 100%;
margin: 0 auto;
text-align: center;
border-inline: 1px solid var(--border);
min-height: 100svh;
display: flex;
flex-direction: column;
box-sizing: border-box;
}
#center {
display: flex;
flex-direction: column;
gap: 25px;
place-content: center;
place-items: center;
flex-grow: 1;
@media (max-width: 1024px) {
padding: 32px 20px 24px;
gap: 18px;
}
}
#next-steps {
display: flex;
border-top: 1px solid var(--border);
text-align: left;
& > div {
flex: 1 1 0;
padding: 32px;
@media (max-width: 1024px) {
padding: 24px 20px;
}
}
.icon {
margin-bottom: 16px;
width: 22px;
height: 22px;
}
@media (max-width: 1024px) {
flex-direction: column;
text-align: center;
}
}
#docs {
border-right: 1px solid var(--border);
@media (max-width: 1024px) {
border-right: none;
border-bottom: 1px solid var(--border);
}
}
#next-steps ul {
list-style: none;
padding: 0;
display: flex;
gap: 8px;
margin: 32px 0 0;
.logo {
height: 18px;
}
a {
color: var(--text-h);
font-size: 16px;
border-radius: 6px;
background: var(--social-bg);
display: flex;
padding: 6px 12px;
align-items: center;
gap: 8px;
text-decoration: none;
transition: box-shadow 0.3s;
&:hover {
box-shadow: var(--shadow);
}
.button-icon {
height: 18px;
width: 18px;
}
}
@media (max-width: 1024px) {
margin-top: 20px;
flex-wrap: wrap;
justify-content: center;
li {
flex: 1 1 calc(50% - 8px);
}
a {
width: 100%;
justify-content: center;
box-sizing: border-box;
}
}
}
#spacer {
height: 88px;
border-top: 1px solid var(--border);
@media (max-width: 1024px) {
height: 48px;
}
}
.ticks {
position: relative;
width: 100%;
&::before,
&::after {
content: '';
position: absolute;
top: -4.5px;
border: 5px solid transparent;
}
&::before {
left: 0;
border-left-color: var(--border);
}
&::after {
right: 0;
border-right-color: var(--border);
}
}
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export type TaskType =
| 'GENERAL_QA'
| 'CODING'
| 'DEBUGGING'
| 'REASONING'
| 'SUMMARIZATION'
| 'EXTRACTION'
| 'WRITING'
| 'TRANSLATION'
| 'ANALYSIS'
| 'MATH'
| 'LONG_CONTEXT'
| 'CREATIVE';
export type PriorityLevel = 'LOW' | 'MEDIUM' | 'HIGH';
export type ModelTier = 'FAST' | 'BALANCED' | 'POWER';
export interface RequestAnalysis {
task_type: TaskType;
complexity: number;
complexity_label: PriorityLevel;
reasoning_required: boolean;
coding_required: boolean;
vision_required: boolean;
tools_required: boolean;
context_size: number;
latency_priority: PriorityLevel;
cost_sensitivity: PriorityLevel;
quality_requirement: PriorityLevel;
keywords_detected: string[];
analyzer_used: string;
}
export interface CandidateScore {
model_id: string;
model_name: string;
provider: string;
tier: string;
overall_score: number;
quality_component: number;
cost_component: number;
speed_component: number;
capability_component: number;
reliability_component: number;
estimated_latency_ms: number;
estimated_cost_usd: number;
eligible: boolean;
rejection_reason?: string;
}
export interface RoutingDecision {
decision_id: string;
request_id: string;
selected_model: string;
selected_model_name: string;
provider: string;
tier: string;
confidence: number;
policy_used: string;
reasons: string[];
candidate_scores: CandidateScore[];
rejected_candidates: Record<string, string>;
estimated_cost_usd: number;
estimated_latency_ms: number;
rule_applied?: string;
timestamp: string;
}
export interface ProviderResponse {
content: string;
finish_reason: string;
input_tokens: number;
output_tokens: number;
total_tokens: number;
provider_latency_ms: number;
time_to_first_token_ms?: number;
is_mock: boolean;
model: string;
provider: string;
error?: string;
}
export interface ModelRecord {
id: string;
name: string;
provider: string;
type: string;
tier: ModelTier;
context_window: number;
supports_coding: boolean;
supports_reasoning: boolean;
supports_vision: boolean;
supports_tools: boolean;
quality_score: number;
speed_score: number;
reliability_score: number;
cost_per_input_token: number;
cost_per_output_token: number;
availability: string;
is_active: boolean;
}
export interface ProviderInfo {
id: string;
name: string;
status: string;
base_url?: string;
message?: string;
models_available: string[];
credentials_status: string;
}
export interface TrafficItem {
request_id: string;
timestamp: string;
prompt: string;
task_type: TaskType;
complexity: number;
selected_model: string;
provider: string;
status: string;
total_latency_ms: number;
estimated_cost: number;
}
export interface SystemAnalytics {
total_requests: number;
avg_latency_ms: number;
avg_routing_latency_ms: number;
avg_cost_usd: number;
total_cost_usd: number;
quality_score_percent?: number;
fallback_rate_percent: number;
fallback_count: number;
model_distribution: Record<string, number>;
task_distribution: Record<string, number>;
savings: {
baseline_model: string;
total_requests: number;
baseline_total_cost: number;
routed_total_cost: number;
cost_saved_usd: number;
savings_percentage: number;
};
}
export interface RoutingRule {
id: string;
name: string;
description?: string;
priority: number;
is_enabled: boolean;
condition_field: string;
condition_operator: string;
condition_value: string;
action_type: string;
action_target: string;
}
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/// <reference types="vite/client" />
declare module '*.css';
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/** @type {import('tailwindcss').Config} */
export default {
content: [
"./index.html",
"./src/**/*.{js,ts,jsx,tsx}",
],
theme: {
extend: {
colors: {
bg: {
primary: "#07090C",
secondary: "#101419",
card: "#14191F",
cardHover: "#192027",
},
border: {
subtle: "#242B33",
active: "#3A4552",
},
text: {
primary: "#F5F7FA",
muted: "#7D8792",
dim: "#4A525C",
},
accent: {
primary: "#4DA3FF",
secondary: "#FFB84D",
success: "#4ADE80",
error: "#FF6262",
purple: "#A78BFA",
}
},
fontFamily: {
mono: ['"JetBrains Mono"', 'Consolas', 'monospace'],
sans: ['Inter', 'system-ui', 'sans-serif'],
}
},
},
plugins: [],
}
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{
"compilerOptions": {
"target": "ES2020",
"useDefineForClassFields": true,
"lib": ["ES2020", "DOM", "DOM.Iterable"],
"module": "ESNext",
"skipLibCheck": true,
"moduleResolution": "bundler",
"allowImportingTsExtensions": true,
"resolveJsonModule": true,
"isolatedModules": true,
"noEmit": true,
"jsx": "react-jsx",
"strict": true,
"noUnusedLocals": false,
"noUnusedParameters": false,
"noFallthroughCasesInSwitch": true
},
"include": ["src"]
}
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[pytest]
pythonpath = backend
asyncio_mode = auto
asyncio_default_fixture_loop_scope = function
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fastapi>=0.110.0
uvicorn[standard]>=0.28.0
pydantic>=2.6.0
pydantic-settings>=2.2.0
sqlalchemy>=2.0.28
aiosqlite>=0.20.0
httpx>=0.27.0
typer>=0.12.0
rich>=13.7.0
pytest>=8.0.0
pytest-asyncio>=0.23.0
python-dotenv>=1.0.1
python-multipart>=0.0.9
sse-starlette>=2.0.0
tiktoken>=0.6.0