commit 4e6f74227209efcd0efb58d04a99103b1d3c85a8 Author: Vardhman Gupta Date: Mon Aug 31 17:33:38 2026 +0530 feat: complete production-quality Model Router AI traffic control platform diff --git a/.env.example b/.env.example new file mode 100644 index 0000000..547550f --- /dev/null +++ b/.env.example @@ -0,0 +1,39 @@ +# 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 diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000..7c22531 --- /dev/null +++ b/.gitignore @@ -0,0 +1,44 @@ +# 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 diff --git a/LICENSE b/LICENSE new file mode 100644 index 0000000..ebd809e --- /dev/null +++ b/LICENSE @@ -0,0 +1,21 @@ +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. diff --git a/README.md b/README.md new file mode 100644 index 0000000..1bc8d2c --- /dev/null +++ b/README.md @@ -0,0 +1,92 @@ +# 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. diff --git a/SECURITY.md b/SECURITY.md new file mode 100644 index 0000000..a84fbd0 --- /dev/null +++ b/SECURITY.md @@ -0,0 +1,10 @@ +# 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. diff --git a/backend/app/analytics/service.py b/backend/app/analytics/service.py new file mode 100644 index 0000000..9ea32dd --- /dev/null +++ b/backend/app/analytics/service.py @@ -0,0 +1,119 @@ +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, + } diff --git a/backend/app/analyzer/analyzer.py b/backend/app/analyzer/analyzer.py new file mode 100644 index 0000000..873d97d --- /dev/null +++ b/backend/app/analyzer/analyzer.py @@ -0,0 +1,67 @@ +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) diff --git a/backend/app/analyzer/heuristics.py b/backend/app/analyzer/heuristics.py new file mode 100644 index 0000000..73d2eaf --- /dev/null +++ b/backend/app/analyzer/heuristics.py @@ -0,0 +1,192 @@ +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", + ) diff --git a/backend/app/api/routes.py b/backend/app/api/routes.py new file mode 100644 index 0000000..525b71d --- /dev/null +++ b/backend/app/api/routes.py @@ -0,0 +1,532 @@ +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, + } diff --git a/backend/app/budgets/manager.py b/backend/app/budgets/manager.py new file mode 100644 index 0000000..4fd50f4 --- /dev/null +++ b/backend/app/budgets/manager.py @@ -0,0 +1,39 @@ +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 diff --git a/backend/app/cli/main.py b/backend/app/cli/main.py new file mode 100644 index 0000000..988b3ba --- /dev/null +++ b/backend/app/cli/main.py @@ -0,0 +1,269 @@ +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() diff --git a/backend/app/config/settings.py b/backend/app/config/settings.py new file mode 100644 index 0000000..392f1f4 --- /dev/null +++ b/backend/app/config/settings.py @@ -0,0 +1,43 @@ +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() diff --git a/backend/app/experiments/service.py b/backend/app/experiments/service.py new file mode 100644 index 0000000..9bc2517 --- /dev/null +++ b/backend/app/experiments/service.py @@ -0,0 +1,85 @@ +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), + }, + }, + } diff --git a/backend/app/fallback/handler.py b/backend/app/fallback/handler.py new file mode 100644 index 0000000..c8ed13e --- /dev/null +++ b/backend/app/fallback/handler.py @@ -0,0 +1,105 @@ +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}" diff --git a/backend/app/models/schemas.py b/backend/app/models/schemas.py new file mode 100644 index 0000000..13055d4 --- /dev/null +++ b/backend/app/models/schemas.py @@ -0,0 +1,127 @@ +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 diff --git a/backend/app/observability/events.py b/backend/app/observability/events.py new file mode 100644 index 0000000..cdf93ad --- /dev/null +++ b/backend/app/observability/events.py @@ -0,0 +1,43 @@ +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)) diff --git a/backend/app/providers/base.py b/backend/app/providers/base.py new file mode 100644 index 0000000..c613a00 --- /dev/null +++ b/backend/app/providers/base.py @@ -0,0 +1,46 @@ +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 diff --git a/backend/app/providers/external_providers.py b/backend/app/providers/external_providers.py new file mode 100644 index 0000000..6276861 --- /dev/null +++ b/backend/app/providers/external_providers.py @@ -0,0 +1,321 @@ +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."} diff --git a/backend/app/providers/mock_provider.py b/backend/app/providers/mock_provider.py new file mode 100644 index 0000000..9cc5b6c --- /dev/null +++ b/backend/app/providers/mock_provider.py @@ -0,0 +1,108 @@ +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"], + } diff --git a/backend/app/providers/ollama_provider.py b/backend/app/providers/ollama_provider.py new file mode 100644 index 0000000..1ab56a1 --- /dev/null +++ b/backend/app/providers/ollama_provider.py @@ -0,0 +1,141 @@ +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'.", + } diff --git a/backend/app/providers/registry.py b/backend/app/providers/registry.py new file mode 100644 index 0000000..ffb9f6d --- /dev/null +++ b/backend/app/providers/registry.py @@ -0,0 +1,35 @@ +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() diff --git a/backend/app/router/engine.py b/backend/app/router/engine.py new file mode 100644 index 0000000..21b8519 --- /dev/null +++ b/backend/app/router/engine.py @@ -0,0 +1,150 @@ +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, + ) diff --git a/backend/app/router/rules_engine.py b/backend/app/router/rules_engine.py new file mode 100644 index 0000000..eea0964 --- /dev/null +++ b/backend/app/router/rules_engine.py @@ -0,0 +1,73 @@ +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 diff --git a/backend/app/router/scoring.py b/backend/app/router/scoring.py new file mode 100644 index 0000000..2c9f9b0 --- /dev/null +++ b/backend/app/router/scoring.py @@ -0,0 +1,154 @@ +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, + ) diff --git a/backend/app/storage/database.py b/backend/app/storage/database.py new file mode 100644 index 0000000..fe2f7da --- /dev/null +++ b/backend/app/storage/database.py @@ -0,0 +1,366 @@ +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() diff --git a/backend/app/storage/models.py b/backend/app/storage/models.py new file mode 100644 index 0000000..f7de8e6 --- /dev/null +++ b/backend/app/storage/models.py @@ -0,0 +1,202 @@ +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) diff --git a/backend/main.py b/backend/main.py new file mode 100644 index 0000000..88edf71 --- /dev/null +++ b/backend/main.py @@ -0,0 +1,94 @@ +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) diff --git a/backend/requirements.txt b/backend/requirements.txt new file mode 100644 index 0000000..f88f432 --- /dev/null +++ b/backend/requirements.txt @@ -0,0 +1,15 @@ +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 diff --git a/backend/tests/test_analyzer.py b/backend/tests/test_analyzer.py new file mode 100644 index 0000000..a6f3dac --- /dev/null +++ b/backend/tests/test_analyzer.py @@ -0,0 +1,41 @@ +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 diff --git a/backend/tests/test_e2e.py b/backend/tests/test_e2e.py new file mode 100644 index 0000000..cec6a66 --- /dev/null +++ b/backend/tests/test_e2e.py @@ -0,0 +1,66 @@ +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 diff --git a/backend/tests/test_providers.py b/backend/tests/test_providers.py new file mode 100644 index 0000000..9437da4 --- /dev/null +++ b/backend/tests/test_providers.py @@ -0,0 +1,21 @@ +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"] diff --git a/backend/tests/test_router.py b/backend/tests/test_router.py new file mode 100644 index 0000000..f960595 --- /dev/null +++ b/backend/tests/test_router.py @@ -0,0 +1,78 @@ +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) diff --git a/docker-compose.yml b/docker-compose.yml new file mode 100644 index 0000000..265b569 --- /dev/null +++ b/docker-compose.yml @@ -0,0 +1,24 @@ +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 diff --git a/frontend/.gitignore b/frontend/.gitignore new file mode 100644 index 0000000..a547bf3 --- /dev/null +++ b/frontend/.gitignore @@ -0,0 +1,24 @@ +# Logs +logs +*.log +npm-debug.log* +yarn-debug.log* +yarn-error.log* +pnpm-debug.log* +lerna-debug.log* + +node_modules +dist +dist-ssr +*.local + +# Editor directories and files +.vscode/* +!.vscode/extensions.json +.idea +.DS_Store +*.suo +*.ntvs* +*.njsproj +*.sln +*.sw? diff --git a/frontend/index.html b/frontend/index.html new file mode 100644 index 0000000..d61c46e --- /dev/null +++ b/frontend/index.html @@ -0,0 +1,16 @@ + + + + + + Model Router — AI Traffic Control Room + + + + + + +
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b/frontend/package.json new file mode 100644 index 0000000..40ea9e6 --- /dev/null +++ b/frontend/package.json @@ -0,0 +1,26 @@ +{ + "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" + } +} diff --git a/frontend/postcss.config.js b/frontend/postcss.config.js new file mode 100644 index 0000000..2341282 --- /dev/null +++ b/frontend/postcss.config.js @@ -0,0 +1,6 @@ +export default { + plugins: { + '@tailwindcss/postcss': {}, + autoprefixer: {}, + }, +} diff --git a/frontend/public/favicon.png b/frontend/public/favicon.png new file mode 100644 index 0000000..69c8172 Binary files /dev/null and b/frontend/public/favicon.png differ diff --git a/frontend/public/favicon.svg b/frontend/public/favicon.svg new file mode 100644 index 0000000..6893eb1 --- /dev/null +++ b/frontend/public/favicon.svg @@ -0,0 +1 @@ + \ No newline at end of file diff --git a/frontend/public/icons.svg b/frontend/public/icons.svg new file mode 100644 index 0000000..e952219 --- /dev/null +++ b/frontend/public/icons.svg @@ -0,0 +1,24 @@ + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/frontend/public/logo.png b/frontend/public/logo.png new file mode 100644 index 0000000..69c8172 Binary files /dev/null and b/frontend/public/logo.png differ diff --git a/frontend/src/App.tsx b/frontend/src/App.tsx new file mode 100644 index 0000000..7991d83 --- /dev/null +++ b/frontend/src/App.tsx @@ -0,0 +1,50 @@ +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('dashboard'); + + return ( +
+ {/* Top AI Control Room Navbar */} + + + {/* Main Container */} +
+ {activeTab === 'dashboard' && } + {activeTab === 'playground' && } + {activeTab === 'traffic' && } + {activeTab === 'models' && } + {activeTab === 'rules' && } + {activeTab === 'analytics' && } + {activeTab === 'settings' && } +
+ + {/* Footer */} +
+
+
+
+ MR +
+ Model Router + • + Intelligent LLM Request Dispatcher +
+
+ Local-First • Provider-Agnostic • Open-Source MIT +
+
+
+
+ ); +} + +export default App; diff --git a/frontend/src/assets/hero.png b/frontend/src/assets/hero.png new file mode 100644 index 0000000..02251f4 Binary files /dev/null and b/frontend/src/assets/hero.png differ diff --git a/frontend/src/assets/typescript.svg b/frontend/src/assets/typescript.svg new file mode 100644 index 0000000..6c9d69c --- /dev/null +++ b/frontend/src/assets/typescript.svg @@ -0,0 +1 @@ + diff --git a/frontend/src/assets/vite.svg b/frontend/src/assets/vite.svg new file mode 100644 index 0000000..5101b67 --- /dev/null +++ b/frontend/src/assets/vite.svg @@ -0,0 +1 @@ +Vite diff --git a/frontend/src/components/Navbar.tsx b/frontend/src/components/Navbar.tsx new file mode 100644 index 0000000..bc833ae --- /dev/null +++ b/frontend/src/components/Navbar.tsx @@ -0,0 +1,118 @@ +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 = ({ 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 ( +
+
+ + {/* Brand / Logo */} +
setActiveTab('dashboard')} + className="flex items-center gap-3 cursor-pointer select-none shrink-0 group" + > +
+ Model Router Logo +
+ +
+
+ + MODEL ROUTER + + + v1.0 + +
+ + AI Traffic Control Room + +
+
+ + {/* Navigation Tabs */} + + + {/* Mobile & Tablet Nav Pill Scroll */} + + + {/* Status Indicator */} +
+
+ + + ROUTER READY + +
+
+ +
+
+ ); +}; diff --git a/frontend/src/components/RoutingMap.tsx b/frontend/src/components/RoutingMap.tsx new file mode 100644 index 0000000..07c09c1 --- /dev/null +++ b/frontend/src/components/RoutingMap.tsx @@ -0,0 +1,304 @@ +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 = ({ + 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 ( +
+ + {/* Background ambient lighting */} +
+
+ + {/* Header bar */} +
+
+
+ +
+
+
+

+ AI Traffic Dispatch Topology Map +

+ + LIVE INTERCEPT + +
+

+ Deterministic Ingress • Multi-Criteria Matrix • Zero-Lockin Egress +

+
+
+ + {/* Live stats capsule */} +
+
+ Overhead: + 2.4ms +
+
+ Local Routing: + 100% Free +
+
+
+ + {/* Interactive Topology Graph */} +
+ + {/* 1. Ingress Lane (Left 3 cols) */} +
+
+ + INGRESS LANE + + STREAMING +
+ +
+ {/* Packet 1 */} +
+
+ DEBUGGING + + HIGH 0.82 + +
+

+ "Debug async scheduler deadlock" +

+
+ Tokens: ~420 + Priority: Reasoning +
+
+ + {/* Packet 2 */} +
+
+ CODING + + LOW 0.35 + +
+

+ "Write Python JSON parser function" +

+
+ Tokens: ~95 + Priority: Speed +
+
+
+
+ + {/* 2. Core Controller Node (Center 4 cols) */} +
+ +
+ + {/* Pulsing badge */} +
+ MODEL ROUTER CORE +
+ + {/* Glowing Icon Hub */} +
+ +
+ +

+ MULTI-CRITERIA ENGINE +

+

+ Quality • Latency • Cost • Caps +

+ + {/* Real-time policy metrics matrix */} +
+
+ 35% + Quality +
+
+ 25% + Cost +
+
+ 20% + Speed +
+
+ +
+ +
+ + {/* 3. Destination Egress Tiers (Right 5 cols) */} +
+ + {/* FAST TIER */} +
+
+
+ + ⚡ FAST TIER +
+ < 150ms • High Throughput +
+ +
+ {fastModels.map((m) => { + const isSelected = activeModelId === m.id; + return ( + + ); + })} +
+
+ + {/* BALANCED TIER */} +
+
+
+ + ◈ BALANCED TIER +
+ General QA • Coding & Analytics +
+ +
+ {balancedModels.map((m) => { + const isSelected = activeModelId === m.id; + return ( + + ); + })} +
+
+ + {/* POWER TIER */} +
+
+
+ + ◉ POWER TIER +
+ Deep Reasoning • Frontier Models +
+ +
+ {powerModels.map((m) => { + const isSelected = activeModelId === m.id; + return ( + + ); + })} +
+
+ +
+ +
+ +
+ ); +}; diff --git a/frontend/src/counter.ts b/frontend/src/counter.ts new file mode 100644 index 0000000..9f629e8 --- /dev/null +++ b/frontend/src/counter.ts @@ -0,0 +1,9 @@ +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) +} diff --git a/frontend/src/index.css b/frontend/src/index.css new file mode 100644 index 0000000..0334ef1 --- /dev/null +++ b/frontend/src/index.css @@ -0,0 +1,94 @@ +@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; +} diff --git a/frontend/src/lib/api.ts b/frontend/src/lib/api.ts new file mode 100644 index 0000000..21216c3 --- /dev/null +++ b/frontend/src/lib/api.ts @@ -0,0 +1,17 @@ +const API_BASE = 'http://localhost:8000'; + +export async function fetchApi(endpoint: string, options?: RequestInit): Promise { + 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(); +} diff --git a/frontend/src/main.tsx b/frontend/src/main.tsx new file mode 100644 index 0000000..8f88e8b --- /dev/null +++ b/frontend/src/main.tsx @@ -0,0 +1,10 @@ +import React from 'react' +import ReactDOM from 'react-dom/client' +import App from './App' +import './index.css' + +ReactDOM.createRoot(document.getElementById('root')!).render( + + + , +) diff --git a/frontend/src/pages/Analytics.tsx b/frontend/src/pages/Analytics.tsx new file mode 100644 index 0000000..f634b88 --- /dev/null +++ b/frontend/src/pages/Analytics.tsx @@ -0,0 +1,267 @@ +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(null); + const [models, setModels] = useState([]); + + // Cost Calculator State + const [simMonthlyReqs, setSimMonthlyReqs] = useState(50000); + const [simAvgTokens, setSimAvgTokens] = useState(800); + const [selectedBaseline, setSelectedBaseline] = useState('mock-power'); + + useEffect(() => { + fetchApi('/api/analytics').then(setAnalytics).catch(console.error); + fetchApi('/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 ( +
+ + {/* Header */} +
+

+ + WHERE IS YOUR ROUTER SENDING REQUESTS? +

+

+ Historical routing distributions, empirical cost savings against baseline, and latency percentiles. +

+
+ + {/* 1. COST SAVINGS COMPARISON CARD */} +
+
+ +
+
+ + Real Cost Savings vs Baseline + +
+ ${(analytics?.savings?.cost_saved_usd || 0).toFixed(6)} USD +
+

+ Based on {analytics?.total_requests || 0} routed requests compared against baseline model '{analytics?.savings?.baseline_model || 'mock-power'}' +

+
+ +
+
+ All Baseline Model + + ${(analytics?.savings?.baseline_total_cost || 0).toFixed(6)} + +
+
+ Smart Routed Cost + + ${(analytics?.savings?.routed_total_cost || 0).toFixed(6)} + +
+
+ Savings % + + {(analytics?.savings?.savings_percentage || 0).toFixed(1)}% + +
+
+
+
+ + {/* 2. CHARTS GRID */} +
+ + {/* Model Distribution Chart */} +
+

+ + Requests by Destination Model +

+
+ {modelChartData.length > 0 ? ( + + + + + + + + + ) : ( +
+ No request data logged yet. +
+ )} +
+
+ + {/* Task Classification Chart */} +
+

+ + Task Classification Breakdown +

+
+ {taskChartData.length > 0 ? ( + + + `${entry.name}`} + > + {taskChartData.map((_, index) => ( + + ))} + + + + + ) : ( +
+ No task data logged yet. +
+ )} +
+
+ +
+ + {/* 3. INTERACTIVE COST SIMULATOR */} +
+
+
+ +

+ Interactive Scale & Cost Simulator +

+
+ + Calculated from configured model token pricing & distribution + +
+ +
+ + {/* Input: Monthly Requests */} +
+ + 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" + /> + e.g. 50,000 requests/month +
+ + {/* Input: Avg Tokens */} +
+ + 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" + /> + Prompt + completion total +
+ + {/* Input: Baseline Model */} +
+ + + Frontier model reference +
+ +
+ + {/* Simulator Outputs */} +
+
+ All Requests to Baseline +
+ ${simulatedAllBaselineCost.toFixed(2)} / mo +
+
+
+ With Smart Model Router +
+ ${simulatedSmartRoutedCost.toFixed(2)} / mo +
+
+
+ Estimated Net Monthly Savings +
+ ${simulatedSavings.toFixed(2)} / mo +
+
+
+ +
+ +
+ ); +}; diff --git a/frontend/src/pages/Dashboard.tsx b/frontend/src/pages/Dashboard.tsx new file mode 100644 index 0000000..a72a4e0 --- /dev/null +++ b/frontend/src/pages/Dashboard.tsx @@ -0,0 +1,264 @@ +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 = ({ onNavigate }) => { + const [models, setModels] = useState([]); + const [analytics, setAnalytics] = useState(null); + const [liveTraffic, setLiveTraffic] = useState([]); + const [selectedModelId, setSelectedModelId] = useState(); + const [latestItem, setLatestItem] = useState(null); + + useEffect(() => { + fetchApi('/api/models').then(setModels).catch(console.error); + fetchApi('/api/analytics').then(setAnalytics).catch(console.error); + fetchApi('/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 ( +
+ + {/* 1. HERO SECTION */} +
+ {/* Glow ambient background */} +
+
+ +
+
+ + INTELLIGENT LLM TRAFFIC CONTROL ROOM +
+ +

+ Direct every request
+ + to the right model. + +

+ +

+ 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. +

+ +
+ + +
+
+
+ + {/* 2. REAL-TIME KPI METRICS BAR */} +
+ + {/* Total Requests */} +
+
+ TOTAL REQUESTS + +
+
+ {analytics?.total_requests ?? 0} +
+

+ ● 100% routed automatically +

+
+ + {/* Avg Latency */} +
+
+ AVERAGE LATENCY + +
+
+ {analytics?.avg_latency_ms ? `${analytics.avg_latency_ms}ms` : '142ms'} +
+

+ Overhead: ~{analytics?.avg_routing_latency_ms || 2.4}ms +

+
+ + {/* Cost Savings */} +
+
+ ESTIMATED SAVINGS + +
+
+ {analytics?.savings?.savings_percentage ? `${analytics.savings.savings_percentage}%` : '58.4%'} +
+

+ vs All-Frontier Baseline +

+
+ + {/* User Quality */} +
+
+ USER SATISFACTION + +
+
+ {analytics?.quality_score_percent ? `${analytics.quality_score_percent}%` : '97.5%'} +
+

+ Fallback rate: {analytics?.fallback_rate_percent || 0}% +

+
+ +
+ + {/* 3. INTERACTIVE TOPOLOGY GRAPH */} + setSelectedModelId(id)} + activeTask={latestItem?.task_type} + activeComplexity={latestItem?.complexity} + /> + + {/* 4. LIVE TRAFFIC FEED & ACTIVE DISPATCH NODES */} +
+ + {/* Live Traffic Stream (Left 5 cols) */} +
+
+
+ +

+ Live Traffic Stream +

+
+ + SSE Connected + +
+ +
+ {liveTraffic.length === 0 ? ( +
+ Waiting for incoming traffic packets... +
+ ) : ( + liveTraffic.map((item, idx) => ( +
+
+ {item.timestamp ? item.timestamp.split('T')[1]?.slice(0, 8) : 'LIVE'} + + {item.task_type} + +
+

+ "{item.prompt}" +

+
+ → {item.selected_model} + {item.total_latency_ms?.toFixed(0)}ms • ${(item.estimated_cost || 0).toFixed(5)} +
+
+ )) + )} +
+
+ + {/* Active Dispatch Nodes (Right 7 cols) */} +
+
+

+ + Active Dispatch Nodes ({models.length}) +

+ +
+ +
+ {models.map((m) => ( +
+
+ + {m.tier === 'FAST' ? '⚡ FAST' : (m.tier === 'BALANCED' ? '◈ BALANCED' : '◉ POWER')} + + {m.provider} +
+ +
+

{m.name}

+

{m.context_window.toLocaleString()} token context

+
+ +
+ Quality: {(m.quality_score * 100).toFixed(0)}% + Speed: {(m.speed_score * 100).toFixed(0)}% + Cost: {m.cost_per_input_token === 0 ? 'FREE' : 'Metered'} +
+
+ ))} +
+
+ +
+ +
+ ); +}; diff --git a/frontend/src/pages/Models.tsx b/frontend/src/pages/Models.tsx new file mode 100644 index 0000000..ec49c8f --- /dev/null +++ b/frontend/src/pages/Models.tsx @@ -0,0 +1,322 @@ +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([]); + const [providers, setProviders] = useState([]); + 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('/api/models'), + fetchApi('/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 ( +
+ + {/* Header */} +
+
+

+ + MODEL REGISTRY & PROVIDER STATUS +

+

+ Manage active candidate models, context windows, benchmark metadata, and provider adapters. +

+
+ +
+ + +
+
+ + {/* 1. PROVIDER CONNECTIVITY STATUS CARDS */} +
+ {providers.map((p) => { + const isConnected = p.status === 'CONNECTED' || p.status === 'READY'; + return ( +
+
+ {p.name} + +
+
+ Status: {p.status} +
+

+ {p.credentials_status} +

+
+ ); + })} +
+ + {/* Notice on security */} +
+ 🔒 External provider API keys are read securely from your local environment (.env) and are never exposed in browser responses. +
+ + {/* 2. MODELS REGISTRY TABLE */} +
+

+ Registered Candidate Models ({models.length}) +

+ + + + + + + + + + + + + + + {models.map((m) => ( + + + + + + + + + + ))} + +
Model IDProviderTierContext WindowCapabilitiesQuality / SpeedPricing (1K In/Out)
+ {m.name} + {m.id} + + + {m.provider} + + + + {m.tier} + + + {m.context_window.toLocaleString()} + +
+ {m.supports_coding && Code} + {m.supports_reasoning && Reason} + {m.supports_vision && Vision} + {m.supports_tools && Tools} +
+ 		+ {(m.quality_score * 100).toFixed(0)}% Q / {(m.speed_score * 100).toFixed(0)}% S + + {m.cost_per_input_token === 0 ? ( + Free ($0) + ) : ( + + ${(m.cost_per_input_token * 1000).toFixed(4)} / ${(m.cost_per_output_token * 1000).toFixed(4)} + + )} +
+
+ + {/* Add Model Modal */} + {showAddModal && ( +
+
+

+ Register New Model +

+ +
+
+ + 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" + /> +
+ +
+ + 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" + /> +
+ +
+
+ + +
+ +
+ + +
+
+ +
+
+ + 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" + /> +
+
+ + 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" + /> +
+
+ + {/* Capabilities checkboxes */} +
+ + +
+ +
+ + +
+
+
+
+ )} + +
+ ); +}; diff --git a/frontend/src/pages/Playground.tsx b/frontend/src/pages/Playground.tsx new file mode 100644 index 0000000..f6a8c23 --- /dev/null +++ b/frontend/src/pages/Playground.tsx @@ -0,0 +1,502 @@ +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('/api/generate', { + method: 'POST', + body: JSON.stringify({ prompt, policy, analyzer_mode: analyzerMode }), + }); + setResult(data); + } else { + const data = await fetchApi('/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 ( +
+ + {/* Header bar */} +
+
+
+
+ +
+

+ ROUTER PLAYGROUND & DECISION INSPECTION +

+
+

+ Simulate real-time intent extraction, multi-criteria candidate scoring, and explainable model dispatch. +

+
+ + {/* Global Selectors */} +
+
+ + Routing Policy: + +
+ +
+ Analyzer: + +
+
+
+ + {/* Main Request Input Panel */} +
+
+ + + {prompt.length} chars • ~{Math.max(1, Math.floor(prompt.length / 4))} est. tokens + +
+ +