| from typing import Any, cast |
|
|
| from fastapi import APIRouter, HTTPException, Query |
|
|
| from ...config.logfire_config import get_logger |
| from ...services.ollama.embedding_router import embedding_router |
| from ...services.ollama.model_discovery_service import model_discovery_service |
| from ...services.ollama.routing.vector_normalization import VectorNormalization |
| from .schemas import EmbeddingRouteRequest, EmbeddingRouteResponse |
|
|
| logger = get_logger(__name__) |
| router = APIRouter() |
|
|
|
|
| @router.post("/embedding/route", response_model=EmbeddingRouteResponse) |
| async def analyze_embedding_route_endpoint(request: EmbeddingRouteRequest) -> EmbeddingRouteResponse: |
| """Analyze optimal routing for embedding operations.""" |
| try: |
| logger.info(f"Analyzing embedding route for {request.model_name} on {request.instance_url}") |
| routing_decision = await embedding_router.route_embedding( |
| model_name=request.model_name, instance_url=request.instance_url, text_content=request.text_sample |
| ) |
| performance_score = VectorNormalization.calculate_performance_score(routing_decision.dimensions) |
| return EmbeddingRouteResponse( |
| target_column=routing_decision.target_column, |
| model_name=routing_decision.model_name, |
| instance_url=routing_decision.instance_url, |
| dimensions=routing_decision.dimensions, |
| confidence=routing_decision.confidence, |
| fallback_applied=routing_decision.fallback_applied, |
| routing_strategy=routing_decision.routing_strategy, |
| performance_score=performance_score, |
| ) |
| except Exception as e: |
| logger.error(f"Error analyzing embedding route: {e}") |
| raise HTTPException(status_code=500, detail=f"Embedding route analysis failed: {str(e)}") from e |
|
|
|
|
| @router.get("/embedding/routes") |
| async def get_available_embedding_routes_endpoint( |
| instance_urls: list[str] = Query(..., description="Ollama instance URLs"), |
| sort_by_performance: bool = Query(True, description="Sort by performance score"), |
| ) -> dict[str, Any]: |
| """Get all available embedding routes across multiple instances.""" |
| try: |
| logger.info(f"Getting embedding routes for {len(instance_urls)} instances") |
| routes = await embedding_router.get_available_embedding_routes(instance_urls) |
| route_data = [] |
| for route in routes: |
| route_data.append( |
| { |
| "model_name": route.model_name, |
| "instance_url": route.instance_url, |
| "dimensions": route.dimensions, |
| "column_name": route.column_name, |
| "performance_score": route.performance_score, |
| "index_type": embedding_router.get_optimal_index_type(route.dimensions), |
| } |
| ) |
|
|
| dimension_stats: dict[int, dict[str, Any]] = {} |
| for route in routes: |
| dim = route.dimensions |
| if dim not in dimension_stats: |
| dimension_stats[dim] = {"count": 0, "models": [], "avg_performance": 0.0} |
| stats_entry = dimension_stats[dim] |
| stats_entry["count"] += 1 |
| cast(list[str], stats_entry["models"]).append(route.model_name) |
| stats_entry["avg_performance"] += float(route.performance_score) |
|
|
| for dim_data in dimension_stats.values(): |
| if dim_data["count"] > 0: |
| dim_data["avg_performance"] /= dim_data["count"] |
|
|
| return { |
| "total_routes": len(routes), |
| "routes": route_data, |
| "dimension_analysis": dimension_stats, |
| "routing_statistics": embedding_router.get_routing_statistics(), |
| } |
| except Exception as e: |
| logger.error(f"Error getting embedding routes: {e}") |
| raise HTTPException(status_code=500, detail=f"Failed to get embedding routes: {str(e)}") from e |
|
|
|
|
| @router.delete("/cache") |
| async def clear_ollama_cache_endpoint() -> dict[str, str]: |
| """Clear all Ollama-related caches.""" |
| try: |
| logger.info("Clearing Ollama caches") |
| model_discovery_service.model_cache.clear() |
| model_discovery_service.capability_cache.clear() |
| model_discovery_service.health_cache.clear() |
| embedding_router.clear_routing_cache() |
| return {"message": "All Ollama caches cleared successfully"} |
| except Exception as e: |
| logger.error(f"Error clearing caches: {e}") |
| raise HTTPException(status_code=500, detail=f"Failed to clear caches: {str(e)}") from e |
|
|
