import asyncio import json # Needed for error streaming import random from fastapi import APIRouter, Depends, Request from fastapi.responses import JSONResponse, StreamingResponse from typing import List, Dict, Any # Google and OpenAI specific imports from google.genai import types from google import genai import openai from credentials_manager import _refresh_auth # Local module imports from models import OpenAIRequest, OpenAIMessage from auth import get_api_key # from main import credential_manager # Removed to prevent circular import; accessed via request.app.state import config as app_config from model_loader import get_vertex_models, get_vertex_express_models # Import from model_loader from message_processing import ( create_gemini_prompt, create_encrypted_gemini_prompt, create_encrypted_full_gemini_prompt ) from api_helpers import ( create_generation_config, create_openai_error_response, execute_gemini_call ) router = APIRouter() @router.post("/v1/chat/completions") async def chat_completions(fastapi_request: Request, request: OpenAIRequest, api_key: str = Depends(get_api_key)): try: credential_manager_instance = fastapi_request.app.state.credential_manager OPENAI_DIRECT_SUFFIX = "-openai" EXPERIMENTAL_MARKER = "-exp-" PAY_PREFIX = "[PAY]" # Model validation based on a predefined list has been removed as per user request. # The application will now attempt to use any provided model string. # We still need to fetch vertex_express_model_ids for the Express Mode logic. vertex_express_model_ids = await get_vertex_express_models() # Updated logic for is_openai_direct_model is_openai_direct_model = False if request.model.endswith(OPENAI_DIRECT_SUFFIX): temp_name_for_marker_check = request.model[:-len(OPENAI_DIRECT_SUFFIX)] if temp_name_for_marker_check.startswith(PAY_PREFIX): is_openai_direct_model = True elif EXPERIMENTAL_MARKER in temp_name_for_marker_check: is_openai_direct_model = True is_auto_model = request.model.endswith("-auto") is_grounded_search = request.model.endswith("-search") is_encrypted_model = request.model.endswith("-encrypt") is_encrypted_full_model = request.model.endswith("-encrypt-full") is_nothinking_model = request.model.endswith("-nothinking") is_max_thinking_model = request.model.endswith("-max") base_model_name = request.model # Determine base_model_name by stripping known suffixes # This order matters if a model could have multiple (e.g. -encrypt-auto, though not currently a pattern) if is_openai_direct_model: # The general PAY_PREFIX stripper later will handle if this result starts with [PAY] base_model_name = request.model[:-len(OPENAI_DIRECT_SUFFIX)] elif is_auto_model: base_model_name = request.model[:-len("-auto")] elif is_grounded_search: base_model_name = request.model[:-len("-search")] elif is_encrypted_full_model: base_model_name = request.model[:-len("-encrypt-full")] # Must be before -encrypt elif is_encrypted_model: base_model_name = request.model[:-len("-encrypt")] elif is_nothinking_model: base_model_name = request.model[:-len("-nothinking")] elif is_max_thinking_model: base_model_name = request.model[:-len("-max")] # After all suffix stripping, if PAY_PREFIX is still at the start of base_model_name, remove it. # This handles cases like "[PAY]model-id-search" correctly. if base_model_name.startswith(PAY_PREFIX): base_model_name = base_model_name[len(PAY_PREFIX):] # Specific model variant checks (if any remain exclusive and not covered dynamically) if is_nothinking_model and base_model_name != "gemini-2.5-flash-preview-04-17": return JSONResponse(status_code=400, content=create_openai_error_response(400, f"Model '{request.model}' (-nothinking) is only supported for 'gemini-2.5-flash-preview-04-17'.", "invalid_request_error")) if is_max_thinking_model and base_model_name != "gemini-2.5-flash-preview-04-17": return JSONResponse(status_code=400, content=create_openai_error_response(400, f"Model '{request.model}' (-max) is only supported for 'gemini-2.5-flash-preview-04-17'.", "invalid_request_error")) generation_config = create_generation_config(request) client_to_use = None express_api_keys_list = app_config.VERTEX_EXPRESS_API_KEY_VAL # Use dynamically fetched express models list for this check if express_api_keys_list and base_model_name in vertex_express_model_ids: # Check against base_model_name indexed_keys = list(enumerate(express_api_keys_list)) random.shuffle(indexed_keys) for original_idx, key_val in indexed_keys: try: client_to_use = genai.Client(vertexai=True, api_key=key_val) print(f"INFO: Using Vertex Express Mode for model {base_model_name} with API key (original index: {original_idx}).") break # Successfully initialized client except Exception as e: print(f"WARNING: Vertex Express Mode client init failed for API key (original index: {original_idx}): {e}. Trying next key if available.") client_to_use = None # Ensure client_to_use is None if this attempt fails if client_to_use is None: print(f"WARNING: All {len(express_api_keys_list)} Vertex Express API key(s) failed to initialize for model {base_model_name}. Falling back.") if client_to_use is None: rotated_credentials, rotated_project_id = credential_manager_instance.get_random_credentials() if rotated_credentials and rotated_project_id: try: client_to_use = genai.Client(vertexai=True, credentials=rotated_credentials, project=rotated_project_id, location="us-central1") print(f"INFO: Using rotated credential for project: {rotated_project_id}") except Exception as e: print(f"ERROR: Rotated credential client init failed: {e}. Falling back.") client_to_use = None if client_to_use is None: print("ERROR: No Vertex AI client could be initialized via Express Mode or Rotated Credentials.") return JSONResponse(status_code=500, content=create_openai_error_response(500, "Vertex AI client not available. Ensure credentials are set up correctly (env var or files).", "server_error")) encryption_instructions_placeholder = ["// Protocol Instructions Placeholder //"] # Actual instructions are in message_processing if is_openai_direct_model: print(f"INFO: Using OpenAI Direct Path for model: {request.model}") # This mode exclusively uses rotated credentials, not express keys. rotated_credentials, rotated_project_id = credential_manager_instance.get_random_credentials() if not rotated_credentials or not rotated_project_id: error_msg = "OpenAI Direct Mode requires GCP credentials, but none were available or loaded successfully." print(f"ERROR: {error_msg}") return JSONResponse(status_code=500, content=create_openai_error_response(500, error_msg, "server_error")) print(f"INFO: [OpenAI Direct Path] Using credentials for project: {rotated_project_id}") gcp_token = _refresh_auth(rotated_credentials) if not gcp_token: error_msg = f"Failed to obtain valid GCP token for OpenAI client (Source: Credential Manager, Project: {rotated_project_id})." print(f"ERROR: {error_msg}") return JSONResponse(status_code=500, content=create_openai_error_response(500, error_msg, "server_error")) PROJECT_ID = rotated_project_id LOCATION = "us-central1" # Fixed as per user confirmation VERTEX_AI_OPENAI_ENDPOINT_URL = ( f"https://{LOCATION}-aiplatform.googleapis.com/v1beta1/" f"projects/{PROJECT_ID}/locations/{LOCATION}/endpoints/openapi" ) # base_model_name is already extracted (e.g., "gemini-1.5-pro-exp-v1") UNDERLYING_MODEL_ID = f"google/{base_model_name}" openai_client = openai.AsyncOpenAI( base_url=VERTEX_AI_OPENAI_ENDPOINT_URL, api_key=gcp_token, # OAuth token ) openai_safety_settings = [ {"category": "HARM_CATEGORY_HARASSMENT", "threshold": "OFF"}, {"category": "HARM_CATEGORY_HATE_SPEECH", "threshold": "OFF"}, {"category": "HARM_CATEGORY_SEXUALLY_EXPLICIT", "threshold": "OFF"}, {"category": "HARM_CATEGORY_DANGEROUS_CONTENT", "threshold": "OFF"}, {"category": 'HARM_CATEGORY_CIVIC_INTEGRITY', "threshold": 'OFF'} ] openai_params = { "model": UNDERLYING_MODEL_ID, "messages": [msg.model_dump(exclude_unset=True) for msg in request.messages], "temperature": request.temperature, "max_tokens": request.max_tokens, "top_p": request.top_p, "stream": request.stream, "stop": request.stop, "seed": request.seed, "n": request.n, } openai_params = {k: v for k, v in openai_params.items() if v is not None} openai_extra_body = { 'google': { 'safety_settings': openai_safety_settings } } if request.stream: async def openai_stream_generator(): try: stream_response = await openai_client.chat.completions.create( **openai_params, extra_body=openai_extra_body ) async for chunk in stream_response: yield f"data: {chunk.model_dump_json()}\n\n" yield "data: [DONE]\n\n" except Exception as stream_error: error_msg_stream = f"Error during OpenAI client streaming for {request.model}: {str(stream_error)}" print(f"ERROR: {error_msg_stream}") error_response_content = create_openai_error_response(500, error_msg_stream, "server_error") yield f"data: {json.dumps(error_response_content)}\n\n" # Ensure json is imported yield "data: [DONE]\n\n" return StreamingResponse(openai_stream_generator(), media_type="text/event-stream") else: # Not streaming try: response = await openai_client.chat.completions.create( **openai_params, extra_body=openai_extra_body ) return JSONResponse(content=response.model_dump(exclude_unset=True)) except Exception as generate_error: error_msg_generate = f"Error calling OpenAI client for {request.model}: {str(generate_error)}" print(f"ERROR: {error_msg_generate}") error_response = create_openai_error_response(500, error_msg_generate, "server_error") return JSONResponse(status_code=500, content=error_response) elif is_auto_model: print(f"Processing auto model: {request.model}") attempts = [ {"name": "base", "model": base_model_name, "prompt_func": create_gemini_prompt, "config_modifier": lambda c: c}, {"name": "encrypt", "model": base_model_name, "prompt_func": create_encrypted_gemini_prompt, "config_modifier": lambda c: {**c, "system_instruction": encryption_instructions_placeholder}}, {"name": "old_format", "model": base_model_name, "prompt_func": create_encrypted_full_gemini_prompt, "config_modifier": lambda c: c} ] last_err = None for attempt in attempts: print(f"Auto-mode attempting: '{attempt['name']}' for model {attempt['model']}") current_gen_config = attempt["config_modifier"](generation_config.copy()) try: return await execute_gemini_call(client_to_use, attempt["model"], attempt["prompt_func"], current_gen_config, request) except Exception as e_auto: last_err = e_auto print(f"Auto-attempt '{attempt['name']}' for model {attempt['model']} failed: {e_auto}") await asyncio.sleep(1) print(f"All auto attempts failed. Last error: {last_err}") err_msg = f"All auto-mode attempts failed for model {request.model}. Last error: {str(last_err)}" if not request.stream and last_err: return JSONResponse(status_code=500, content=create_openai_error_response(500, err_msg, "server_error")) elif request.stream: async def final_error_stream(): err_content = create_openai_error_response(500, err_msg, "server_error") yield f"data: {json.dumps(err_content)}\n\n" yield "data: [DONE]\n\n" return StreamingResponse(final_error_stream(), media_type="text/event-stream") return JSONResponse(status_code=500, content=create_openai_error_response(500, "All auto-mode attempts failed without specific error.", "server_error")) else: # Not an auto model current_prompt_func = create_gemini_prompt # Determine the actual model string to call the API with (e.g., "gemini-1.5-pro-search") api_model_string = request.model if is_grounded_search: search_tool = types.Tool(google_search=types.GoogleSearch()) generation_config["tools"] = [search_tool] elif is_encrypted_model: generation_config["system_instruction"] = encryption_instructions_placeholder current_prompt_func = create_encrypted_gemini_prompt elif is_encrypted_full_model: generation_config["system_instruction"] = encryption_instructions_placeholder current_prompt_func = create_encrypted_full_gemini_prompt elif is_nothinking_model: generation_config["thinking_config"] = {"thinking_budget": 0} elif is_max_thinking_model: generation_config["thinking_config"] = {"thinking_budget": 24576} # For non-auto models, the 'base_model_name' might have suffix stripped. # We should use the original 'request.model' for API call if it's a suffixed one, # or 'base_model_name' if it's truly a base model without suffixes. # The current logic uses 'base_model_name' for the API call in the 'else' block. # This means if `request.model` was "gemini-1.5-pro-search", `base_model_name` becomes "gemini-1.5-pro" # but the API call might need the full "gemini-1.5-pro-search". # Let's use `request.model` for the API call here, and `base_model_name` for checks like Express eligibility. return await execute_gemini_call(client_to_use, base_model_name, current_prompt_func, generation_config, request) except Exception as e: error_msg = f"Unexpected error in chat_completions endpoint: {str(e)}" print(error_msg) return JSONResponse(status_code=500, content=create_openai_error_response(500, error_msg, "server_error"))