819 lines
34 KiB
Python
819 lines
34 KiB
Python
# api.py — OCI GenAI + OpenAI v1 Compatibility (files + images + multimodal)
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# -----------------------------------------------------------------------------
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# Requisitos:
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# pip install flask oci requests pillow
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# Execução:
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# export API_KEY="minha-chave"
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# export GENAI_BUCKET="lohmann-ai-br"
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# export GENAI_UPLOAD_PREFIX="genai-uploads/"
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# # opcional: onde está o JSON dos modelos
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# export LLM_CONFIG_PATH="/home/app/llm_models.json"
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# python api.py # porta 8000
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# -----------------------------------------------------------------------------
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from flask import Flask, request, jsonify, abort, Response, stream_with_context, send_file
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import oci
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import requests
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import os
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import io
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import json
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import uuid
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import base64
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import time
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import mimetypes
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from datetime import datetime, timedelta
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from typing import Any, Dict, List, Optional, Generator
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app = Flask(__name__)
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# ==========================
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# Configuração e Autenticação OCI
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# ==========================
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def load_config(config_file="/home/app/credentials.conf"):
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config = {}
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try:
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with open(config_file, 'r') as f:
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for line in f:
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line = line.strip()
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if line and not line.startswith('#'):
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key, value = line.split('=', 1)
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config[key.strip()] = value.strip()
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return config
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except FileNotFoundError:
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raise FileNotFoundError(f"Arquivo de configuração '{config_file}' não encontrado")
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except Exception as e:
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raise Exception(f"Erro ao carregar configuração: {str(e)}")
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config = load_config()
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TEST_MODE = config.get("test_mode", "false").lower() == "true"
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signer = None
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if not TEST_MODE:
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try:
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signer = oci.signer.Signer(
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tenancy=config.get("tenancy"),
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user=config.get("user"),
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fingerprint=config.get("fingerprint"),
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private_key_file_location=config.get("key_file"),
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pass_phrase=config.get("pass_phrase"),
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private_key_content=config.get("key_content"),
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)
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except Exception as e:
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print(f"Erro ao inicializar signer OCI: {e}")
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print("Executando em modo de teste...")
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TEST_MODE = True
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# ==========================
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# Segurança API
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# ==========================
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def check_api_key():
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expected_key = os.environ.get("API_KEY")
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if not expected_key:
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print("AVISO: API_KEY não configurada nas variáveis de ambiente.")
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return
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provided_key = request.headers.get("X-API-Key")
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if provided_key != expected_key:
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abort(401, description="Chave de API inválida ou ausente.")
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@app.before_request
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def before_all_requests():
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check_api_key()
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# ==========================
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# Variáveis de Bucket / Uploads
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# ==========================
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BUCKET_NAME = os.environ.get("GENAI_BUCKET", "lohmann-ai-br")
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UPLOAD_PREFIX = os.environ.get("GENAI_UPLOAD_PREFIX", "genai-uploads/")
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if UPLOAD_PREFIX and not UPLOAD_PREFIX.endswith("/"):
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UPLOAD_PREFIX += "/"
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object_client = None
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namespace = None
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region = config.get("region") or os.environ.get("OCI_REGION", "us-chicago-1")
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if not TEST_MODE:
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try:
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object_client = oci.object_storage.ObjectStorageClient(config)
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namespace = object_client.get_namespace().data
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except Exception as e:
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print(f"Erro ao inicializar ObjectStorageClient: {e}")
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TEST_MODE = True
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# Session store para mapear file_id -> object_name (para fallback de download)
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FILE_INDEX: Dict[str, str] = {}
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# ==========================
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# Helpers: Signed URL (PAR) + Upload
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# ==========================
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def guess_mime(filename: str, default: str = "application/octet-stream") -> str:
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mt, _ = mimetypes.guess_type(filename)
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return mt or default
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def create_par_for_object(object_name: str, hours_valid: int = 1) -> str:
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"""
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Cria um Pre-Authenticated Request (PAR) para leitura do objeto.
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Retorna a URL completa (https://objectstorage.region.oraclecloud.com{accessUri})
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"""
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if TEST_MODE:
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return f"https://objectstorage.{region}.oraclecloud.com/test/{object_name}"
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expires = datetime.utcnow() + timedelta(hours=hours_valid)
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details = oci.object_storage.models.CreatePreauthenticatedRequestDetails(
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name=f"par-{uuid.uuid4().hex[:8]}",
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access_type="ObjectRead",
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time_expires=expires,
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bucket_listing_action=None,
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object_name=object_name
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)
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par = object_client.create_preauthenticated_request(
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namespace_name=namespace,
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bucket_name=BUCKET_NAME,
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create_preauthenticated_request_details=details
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).data
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base = f"https://objectstorage.{region}.oraclecloud.com"
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return base + par.access_uri
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def upload_file_to_bucket(file_storage, filename: str) -> Dict[str, Any]:
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"""
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Faz upload do arquivo para o bucket e retorna metadata + signed URL (PAR).
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"""
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file_storage.stream.seek(0)
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content = file_storage.read()
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size = len(content)
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if TEST_MODE:
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file_id = f"file-{uuid.uuid4().hex[:12]}"
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url = f"https://objectstorage.{region}.oraclecloud.com/test/{UPLOAD_PREFIX}{file_id}_{filename}"
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FILE_INDEX[file_id] = f"{UPLOAD_PREFIX}{file_id}_{filename}"
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return {"id": file_id, "object": "file", "filename": filename, "bytes": size, "url": url}
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object_name = f"{UPLOAD_PREFIX}{uuid.uuid4().hex}_{filename}"
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object_client.put_object(
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namespace,
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BUCKET_NAME,
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object_name,
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content,
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content_type=guess_mime(filename, "application/octet-stream")
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)
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url = create_par_for_object(object_name, hours_valid=24)
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file_id = f"file-{uuid.uuid4().hex[:12]}"
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FILE_INDEX[file_id] = object_name
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return {
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"id": file_id,
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"object": "file",
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"filename": filename,
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"bytes": size,
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"url": url
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}
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def get_signed_url_from_file_id(file_id: str, hours_valid: int = 24) -> Optional[str]:
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if file_id in FILE_INDEX:
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obj = FILE_INDEX[file_id]
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return create_par_for_object(obj, hours_valid=hours_valid) if not TEST_MODE else f"https://test/{obj}"
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return None
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# ==========================
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# Modelos — defaults e JSON externo (hot-reload)
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# ==========================
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# Defaults embutidos (fallback)
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SUPPORTED_MODELS_DEFAULTS: Dict[str, Dict[str, Any]] = {
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"gpt5": { # OpenAI GPT-5
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"id": "ocid1.generativeaimodel.oc1.us-chicago-1.amaaaaaask7dceyasebknceb4ekbiaiisjtu3fj5i7s4io3ignvg4ip2uyma",
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"params": {"max_completion_tokens": 2048, "reasoning_effort": "MEDIUM", "verbosity": "MEDIUM"}
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},
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"grok3mini": { # xAI Grok-3 Mini
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"id": "ocid1.generativeaimodel.oc1.us-chicago-1.amaaaaaask7dceyavwbgai5nlntsd5hngaileroifuoec5qxttmydhq7mykq",
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"params": {"temperature": 1, "top_p": 1, "max_tokens": 600}
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},
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"llama4maverick": { # Meta Llama-4 Maverick
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"id": "ocid1.generativeaimodel.oc1.us-chicago-1.amaaaaaask7dceyayjawvuonfkw2ua4bob4rlnnlhs522pafbglivtwlfzta",
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"params": {"temperature": 1, "top_p": 0.75, "max_tokens": 600, "frequency_penalty": 0, "presence_penalty": 0}
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},
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"grokcode": { # xAI Grok-Code-Fast 1
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"id": "ocid1.generativeaimodel.oc1.us-chicago-1.amaaaaaask7dceyasw26b5macw3kkrm5czk7ziblk5m7axkgnzrtrtp7ytqa",
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"params": {"temperature": 1, "top_p": 1, "top_k": 0, "max_tokens": 600}
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},
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"commandrplus": { # Cohere Command-R-Plus
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"id": "ocid1.generativeaimodel.oc1.us-chicago-1.amaaaaaask7dceyaodm6rdyxmdzlddweh4amobzoo4fatlao2pwnekexmosq",
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"params": {"temperature": 1, "top_p": 0.75, "top_k": 0, "max_tokens": 600, "frequency_penalty": 0}
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},
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"gptoss120": { # OpenAI GPT-OSS 120B
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"id": "ocid1.generativeaimodel.oc1.us-chicago-1.amaaaaaask7dceya3eub3uksacl5q35mrigancv6rbppihlg7ihhjofyc22q",
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"params": {"temperature": 1, "top_p": 1, "top_k": 0, "max_tokens": 2048, "frequency_penalty": 0, "presence_penalty": 0}
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},
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"grok4": { # xAI Grok-4
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"id": "ocid1.generativeaimodel.oc1.us-chicago-1.amaaaaaask7dceya3bsfz4ogiuv3yc7gcnlry7gi3zzx6tnikg6jltqszm2q",
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"params": {"temperature": 1, "top_p": 1, "top_k": 0, "max_tokens": 20000}
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}
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}
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LLM_CONFIG_PATH = os.environ.get("LLM_CONFIG_PATH", "/home/app/llm_models.json")
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def get_supported_models() -> Dict[str, Dict[str, Any]]:
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"""
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Lê SEMPRE o JSON de modelos (hot-reload). Se ausente/ inválido, usa defaults embutidos.
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Estrutura esperada:
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{
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"models": {
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"apelido": { "id": "ocid1....", "params": {...} },
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...
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}
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}
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"""
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try:
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with open(LLM_CONFIG_PATH, "r", encoding="utf-8") as f:
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data = json.load(f)
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models = data.get("models", {})
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# Validação simples: precisa ter 'id' em cada modelo
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valid = {k: v for k, v in models.items() if isinstance(v, dict) and v.get("id")}
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if not valid:
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raise ValueError("Arquivo de modelos não contém 'models' válidos.")
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return valid
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except Exception as e:
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# fallback nos defaults embutidos
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print(f"[warn] Usando SUPPORTED_MODELS_DEFAULTS (motivo: {e})")
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return SUPPORTED_MODELS_DEFAULTS
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# ==========================
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# Session Store (Agente)
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# ==========================
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SESSION_STORE = {}
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SESSION_TTL = timedelta(hours=2)
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def session_controller(region, agent_endpoint_id, channel, cuid):
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"""
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Controla sessões com o agente, reaproveitando se estiver dentro do TTL (2h).
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A cada interação, a sessão é renovada (sliding TTL).
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"""
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session_key = f"{channel}:{cuid}"
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now = datetime.utcnow()
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existing = SESSION_STORE.get(session_key)
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if existing:
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last_used = existing["lastUsedAt"]
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if now - last_used < SESSION_TTL:
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existing["lastUsedAt"] = now
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return {"id": existing["sessionId"], "sessionKey": session_key, "reused": True}
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# Sessão expirada ou inexistente → cria nova
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if TEST_MODE:
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new_session_id = f"test_session_{agent_endpoint_id[:8]}_{int(now.timestamp())}"
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SESSION_STORE[session_key] = {
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"sessionId": new_session_id, "createdAt": now, "lastUsedAt": now, "sessionKey": session_key
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}
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return {"id": new_session_id, "sessionKey": session_key, "reused": False}
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try:
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session = requests.Session()
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session.auth = signer
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url = (
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f"https://agent-runtime.generativeai.{region}.oci.oraclecloud.com/20240531/"
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f"agentEndpoints/{agent_endpoint_id}/sessions"
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)
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payload = {
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"description": f"Session for {session_key}",
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"displayName": session_key,
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"idleTimeoutInSeconds": str(int(SESSION_TTL.total_seconds()))
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}
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resp = session.post(url, json=payload)
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resp.raise_for_status()
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data = resp.json()
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SESSION_STORE[session_key] = {
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"sessionId": data.get("id"), "createdAt": now, "lastUsedAt": now, "sessionKey": session_key
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}
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data["sessionKey"] = session_key
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data["reused"] = False
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return data
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except Exception as e:
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return {"error": str(e), "sessionKey": session_key}
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# ==========================
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# Funções de interação (Agente + Inference)
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# ==========================
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def ask_agent(region, agent_endpoint_id, session_id, user_message):
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if TEST_MODE:
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return {
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"message": f"Resposta simulada para: {user_message}",
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"sessionId": session_id,
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"timestamp": datetime.utcnow().isoformat() + "Z"
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}
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session = requests.Session()
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session.auth = signer
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base_url = f"https://agent-runtime.generativeai.{region}.oci.oraclecloud.com/20240531"
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chat_url = f"{base_url}/agentEndpoints/{agent_endpoint_id}/actions/chat"
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payload = {"userMessage": user_message, "shouldStream": False, "sessionId": session_id}
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response = session.post(chat_url, json=payload)
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response.raise_for_status()
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return response.json()
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def call_inference_model(region, compartment_id, model_id, prompt):
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print(">>> /inference payload recebido:")
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data = {"prompt": prompt, "region": region, "compartment_id": compartment_id, "model_id": model_id}
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if TEST_MODE:
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return {"response": f"Resposta simulada para o prompt: {prompt}"}
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try:
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endpoint = f"https://inference.generativeai.{region}.oci.oraclecloud.com"
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generative_ai_inference_client = oci.generative_ai_inference.GenerativeAiInferenceClient(
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config=config, service_endpoint=endpoint, retry_strategy=oci.retry.NoneRetryStrategy(), timeout=(10, 240)
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)
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chat_detail = oci.generative_ai_inference.models.ChatDetails()
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content = oci.generative_ai_inference.models.TextContent(); content.text = f"{prompt}"
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message = oci.generative_ai_inference.models.Message(); message.role = "USER"; message.content = [content]
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chat_request = oci.generative_ai_inference.models.GenericChatRequest()
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chat_request.api_format = oci.generative_ai_inference.models.BaseChatRequest.API_FORMAT_GENERIC
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chat_request.messages = [message]
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chat_request.max_tokens = 50000
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chat_request.temperature = 1
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chat_request.top_p = 1
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chat_request.top_k = 0
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chat_detail.serving_mode = oci.generative_ai_inference.models.OnDemandServingMode(model_id=model_id)
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chat_detail.chat_request = chat_request
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chat_detail.compartment_id = compartment_id
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chat_response = generative_ai_inference_client.chat(chat_detail)
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chat_choices = chat_response.data.chat_response.choices
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chat_data = {
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"text": chat_choices[0].message.content[0].text,
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"finish_reason": chat_choices[0].finish_reason
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}
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return {"response": chat_data}
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except Exception as e:
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return {"error": str(e)}
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# ==========================
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# Utilitários (OpenAI v1)
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# ==========================
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ROLE_MAP = {"system": "SYSTEM", "user": "USER", "assistant": "ASSISTANT"}
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def ensure_data_url(image_url: str) -> str:
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if not image_url:
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return image_url
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if image_url.startswith("data:"):
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return image_url
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try:
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resp = requests.get(image_url, timeout=30); resp.raise_for_status()
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content = resp.content
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mime = resp.headers.get("Content-Type") or guess_mime(image_url, "image/jpeg")
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b64 = base64.b64encode(content).decode("utf-8")
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return f"data:{mime};base64,{b64}"
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except Exception as e:
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print(f"[warn] Falha ao baixar imagem '{image_url}': {e}")
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return image_url
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def resolve_model_and_params(body: Dict[str, Any], path_model_id: str) -> Dict[str, Any]:
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"""
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Resolve o OCID do modelo a partir de:
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1) body['model'] se for chave suportada;
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2) body['model'] se for OCID;
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3) path_model_id se for chave suportada ou OCID.
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Mescla defaults + overrides do corpo (OpenAI-like).
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"""
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supported = get_supported_models() # HOT-RELOAD ⟵ lê JSON a cada chamada
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user_model = body.get("model")
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model_key = None
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model_ocid = None
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if isinstance(user_model, str) and user_model in supported:
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model_key = user_model
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model_ocid = supported[user_model]["id"]
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defaults = supported[user_model].get("params", {}).copy()
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elif isinstance(user_model, str) and user_model.startswith("ocid1.generativeaimodel"):
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model_ocid = user_model
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defaults = {}
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else:
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if path_model_id and path_model_id.startswith("ocid1.generativeaimodel"):
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model_ocid = path_model_id
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defaults = {}
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elif path_model_id in supported:
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model_key = path_model_id
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model_ocid = supported[path_model_id]["id"]
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defaults = supported[path_model_id].get("params", {}).copy()
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else:
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raise ValueError("Modelo ausente ou não suportado: use um dos "
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f"{list(supported.keys())} ou forneça um OCID válido.")
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overrides = {}
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for k in [
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"temperature", "top_p", "top_k", "max_tokens", "frequency_penalty", "presence_penalty",
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"reasoning_effort", "verbosity", "max_completion_tokens"
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]:
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if k in body and body[k] is not None:
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overrides[k] = body[k]
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merged = {**defaults, **overrides}
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return {"model_key": model_key, "model_ocid": model_ocid, "params": merged}
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|
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def to_oci_messages(openai_messages: List[Dict[str, Any]]) -> List[Dict[str, Any]]:
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oci_msgs: List[Dict[str, Any]] = []
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for m in openai_messages:
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role = ROLE_MAP.get(str(m.get("role", "")).lower(), "USER")
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content = m.get("content", "")
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parts: List[Dict[str, Any]] = []
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if isinstance(content, list):
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for p in content:
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if isinstance(p, dict) and p.get("type") == "text":
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txt = p.get("text", "")
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if txt: parts.append({"type": "TEXT", "text": txt})
|
|
elif isinstance(p, dict) and p.get("type") == "image_url":
|
|
url = p.get("image_url", {})
|
|
if isinstance(url, dict): url = url.get("url", "")
|
|
if isinstance(url, str) and url:
|
|
data_url = ensure_data_url(url)
|
|
parts.append({"type": "IMAGE_URL", "url": data_url})
|
|
elif isinstance(p, str):
|
|
parts.append({"type": "TEXT", "text": p})
|
|
elif isinstance(content, str):
|
|
parts.append({"type": "TEXT", "text": content})
|
|
else:
|
|
parts.append({"type": "TEXT", "text": json.dumps(content, ensure_ascii=False)})
|
|
|
|
oci_msgs.append({"role": role, "content": parts})
|
|
return oci_msgs
|
|
|
|
def build_oci_chat_payload(messages: List[Dict[str, Any]], params: Dict[str, Any]) -> Dict[str, Any]:
|
|
payload = {"messages": messages}
|
|
if "temperature" in params: payload["temperature"] = params["temperature"]
|
|
if "top_p" in params: payload["top_p"] = params["top_p"]
|
|
if "top_k" in params: payload["top_k"] = params["top_k"]
|
|
if "frequency_penalty" in params: payload["frequency_penalty"] = params["frequency_penalty"]
|
|
if "presence_penalty" in params: payload["presence_penalty"] = params["presence_penalty"]
|
|
if "max_completion_tokens" in params:
|
|
payload["max_completion_tokens"] = params["max_completion_tokens"]
|
|
elif "max_tokens" in params:
|
|
payload["max_completion_tokens"] = params["max_tokens"]
|
|
if "reasoning_effort" in params: payload["reasoning_effort"] = params["reasoning_effort"]
|
|
if "verbosity" in params: payload["verbosity"] = params["verbosity"]
|
|
return payload
|
|
|
|
def oci_chat_invoke(region: str, compartment_id: str, model_ocid: str, oci_payload: Dict[str, Any]) -> Dict[str, Any]:
|
|
print(">>> OCI CHAT REQUEST (payload que será enviado):")
|
|
print(json.dumps(oci_payload, ensure_ascii=False, indent=2))
|
|
|
|
if TEST_MODE:
|
|
return {
|
|
"dry_run": True,
|
|
"note": "TEST_MODE=True — retorno simulado.",
|
|
"payload": oci_payload,
|
|
"output_text": "[dry-run] ambiente de teste — valide o payload impresso no console."
|
|
}
|
|
|
|
try:
|
|
endpoint = f"https://inference.generativeai.{region}.oci.oraclecloud.com"
|
|
client = oci.generative_ai_inference.GenerativeAiInferenceClient(
|
|
config=config, service_endpoint=endpoint, retry_strategy=oci.retry.NoneRetryStrategy(), timeout=(10, 240)
|
|
)
|
|
|
|
chat_detail = oci.generative_ai_inference.models.ChatDetails()
|
|
generic = oci.generative_ai_inference.models.GenericChatRequest()
|
|
generic.api_format = oci.generative_ai_inference.models.BaseChatRequest.API_FORMAT_GENERIC
|
|
|
|
# Converte nosso payload em objetos do SDK
|
|
sdk_messages = []
|
|
for m in oci_payload["messages"]:
|
|
sdk_msg = oci.generative_ai_inference.models.Message()
|
|
sdk_msg.role = m["role"]
|
|
parts = []
|
|
for c in m["content"]:
|
|
ctype = c.get("type")
|
|
if ctype == "TEXT":
|
|
tc = oci.generative_ai_inference.models.TextContent(); tc.text = c.get("text", ""); parts.append(tc)
|
|
elif ctype == "IMAGE_URL":
|
|
ic = oci.generative_ai_inference.models.ImageContent()
|
|
iu = oci.generative_ai_inference.models.ImageUrl(); iu.url = c.get("url", "")
|
|
ic.image_url = iu; parts.append(ic)
|
|
sdk_msg.content = parts
|
|
sdk_messages.append(sdk_msg)
|
|
|
|
generic.messages = sdk_messages
|
|
|
|
# Parâmetros
|
|
if "temperature" in oci_payload: generic.temperature = oci_payload["temperature"]
|
|
if "top_p" in oci_payload: generic.top_p = oci_payload["top_p"]
|
|
if "top_k" in oci_payload: generic.top_k = oci_payload["top_k"]
|
|
if "frequency_penalty" in oci_payload: generic.frequency_penalty = oci_payload["frequency_penalty"]
|
|
if "presence_penalty" in oci_payload: generic.presence_penalty = oci_payload["presence_penalty"]
|
|
if "max_completion_tokens" in oci_payload: generic.max_tokens = oci_payload["max_completion_tokens"]
|
|
|
|
chat_detail.serving_mode = oci.generative_ai_inference.models.OnDemandServingMode(model_id=model_ocid)
|
|
chat_detail.chat_request = generic
|
|
chat_detail.compartment_id = compartment_id
|
|
|
|
chat_response = client.chat(chat_detail)
|
|
data = chat_response.data
|
|
|
|
if hasattr(data, "chat_response") and data.chat_response and data.chat_response.choices:
|
|
choice = data.chat_response.choices[0]
|
|
text = None
|
|
if choice.message and choice.message.content:
|
|
for block in choice.message.content:
|
|
if hasattr(block, "text") and block.text:
|
|
text = block.text; break
|
|
return {"output_text": text, "raw": "sdk"}
|
|
return {"output_text": None, "raw": "unknown"}
|
|
except Exception as e:
|
|
return {"error": f"Falha ao chamar OCI: {e}"}
|
|
|
|
def to_openai_chat_response(model_label: str, content_text: str, finish_reason: str = "stop") -> Dict[str, Any]:
|
|
now = int(time.time()); rid = f"chatcmpl-{uuid.uuid4().hex[:24]}"
|
|
return {
|
|
"id": rid, "object": "chat.completion", "created": now, "model": model_label,
|
|
"choices": [{"index": 0, "message": {"role": "assistant", "content": content_text}, "finish_reason": finish_reason}],
|
|
"usage": {"prompt_tokens": None, "completion_tokens": None, "total_tokens": None}
|
|
}
|
|
|
|
def to_openai_text_response(model_label: str, content_text: str, finish_reason: str = "stop") -> Dict[str, Any]:
|
|
now = int(time.time()); rid = f"cmpl-{uuid.uuid4().hex[:24]}"
|
|
return {
|
|
"id": rid, "object": "text_completion", "created": now, "model": model_label,
|
|
"choices": [{"index": 0, "text": content_text, "finish_reason": finish_reason, "logprobs": None}],
|
|
"usage": {"prompt_tokens": None, "completion_tokens": None, "total_tokens": None}
|
|
}
|
|
|
|
def sse_chat_stream(model_label: str, full_text: str) -> Generator[str, None, None]:
|
|
rid = f"chatcmpl-{uuid.uuid4().hex[:24]}"; now = int(time.time())
|
|
first = {"id": rid, "object": "chat.completion.chunk", "created": now, "model": model_label,
|
|
"choices": [{"index": 0, "delta": {"role": "assistant"}, "finish_reason": None}]}
|
|
yield f"data: {json.dumps(first)}\n\n"
|
|
for ch in full_text or "":
|
|
chunk = {"id": rid, "object": "chat.completion.chunk", "created": now, "model": model_label,
|
|
"choices": [{"index": 0, "delta": {"content": ch}, "finish_reason": None}]}
|
|
yield f"data: {json.dumps(chunk, ensure_ascii=False)}\n\n"
|
|
endchunk = {"id": rid, "object": "chat.completion.chunk", "created": now, "model": model_label,
|
|
"choices": [{"index": 0, "delta": {}, "finish_reason": "stop"}]}
|
|
yield f"data: {json.dumps(endchunk)}\n\n"
|
|
yield "data: [DONE]\n\n"
|
|
|
|
# ==========================
|
|
# Endpoints nativos
|
|
# ==========================
|
|
|
|
@app.route("/", methods=["GET"])
|
|
def test():
|
|
return jsonify({"test": "ok"})
|
|
|
|
@app.route("/test/<myvar>/copy", methods=["GET"])
|
|
def var_copy(myvar):
|
|
return jsonify({"myvar": myvar})
|
|
|
|
@app.route("/genai-agent/<region>/<agent_endpoint_id>/session", methods=["POST"])
|
|
def manage_session(region, agent_endpoint_id):
|
|
data = request.get_json() or {}
|
|
print(">>> /genai-agent/.../session payload recebido:")
|
|
channel = data.get("channel"); cuid = data.get("cuid")
|
|
if not all([channel, cuid]):
|
|
return jsonify({"error": "Parâmetros 'channel' e 'cuid' são obrigatórios"}), 400
|
|
response_data = session_controller(region, agent_endpoint_id, channel, cuid)
|
|
return jsonify(response_data)
|
|
|
|
@app.route("/genai-agent/<region>/<agent_endpoint_id>/<session_id>/chat", methods=["POST"])
|
|
def agent_chat(region, agent_endpoint_id, session_id):
|
|
data = request.get_json() or {}
|
|
print(">>> /genai-agent/.../chat payload recebido:")
|
|
user_message = data.get("userMessage")
|
|
if not user_message:
|
|
return jsonify({"error": "userMessage é obrigatório"}), 400
|
|
response_data = ask_agent(region, agent_endpoint_id, session_id, user_message)
|
|
return jsonify({"agentResponse": response_data})
|
|
|
|
@app.route("/genai/<region>/<compartment_id>/<model_id>/inference", methods=["POST"])
|
|
def inference(region, compartment_id, model_id):
|
|
data = request.get_json() or {}
|
|
print(">>> /inference request body:")
|
|
prompt = data.get("prompt")
|
|
if not prompt:
|
|
return jsonify({"error": "Campo 'prompt' é obrigatório."}), 400
|
|
response_data = call_inference_model(region, compartment_id, model_id, prompt)
|
|
return jsonify(response_data)
|
|
|
|
# ==========================
|
|
# Endpoints OpenAI v1 compat — CHAT
|
|
# ==========================
|
|
|
|
@app.route("/genai/<region>/<compartment_id>/<path_model_id>/v1/chat/completions", methods=["POST"])
|
|
def v1_chat_completions(region, compartment_id, path_model_id):
|
|
try:
|
|
body = request.get_json(force=True, silent=False) or {}
|
|
except Exception as e:
|
|
return jsonify({"error": f"JSON inválido: {e}"}), 400
|
|
|
|
print(">>> /v1/chat/completions body recebido:")
|
|
print(body)
|
|
|
|
try:
|
|
resolved = resolve_model_and_params(body, path_model_id)
|
|
except Exception as e:
|
|
return jsonify({"error": str(e)}), 400
|
|
|
|
model_label = body.get("model") or resolved["model_key"] or resolved["model_ocid"]
|
|
msgs = body.get("messages") or []
|
|
if not isinstance(msgs, list) or not msgs:
|
|
return jsonify({"error": "Campo 'messages' é obrigatório e deve ser uma lista."}), 400
|
|
|
|
oci_msgs = to_oci_messages(msgs)
|
|
oci_payload = build_oci_chat_payload(oci_msgs, resolved["params"])
|
|
oci_result = oci_chat_invoke(region, compartment_id, resolved["model_ocid"], oci_payload)
|
|
|
|
if isinstance(oci_result, dict):
|
|
output_text = (
|
|
oci_result.get("output_text")
|
|
or oci_result.get("generated_text")
|
|
or oci_result.get("inference_response", {}).get("output_text")
|
|
or oci_result.get("payload", {}).get("output_text") # dry-run
|
|
)
|
|
else:
|
|
output_text = None
|
|
|
|
if output_text is None:
|
|
output_text = json.dumps(oci_result, ensure_ascii=False)
|
|
|
|
if body.get("stream") is True:
|
|
return Response(stream_with_context(sse_chat_stream(model_label, output_text)),
|
|
mimetype="text/event-stream")
|
|
|
|
return jsonify(to_openai_chat_response(model_label, output_text))
|
|
|
|
@app.route("/genai/<region>/<compartment_id>/<path_model_id>/v1/completions", methods=["POST"])
|
|
def v1_text_completions(region, compartment_id, path_model_id):
|
|
try:
|
|
body = request.get_json(force=True, silent=False) or {}
|
|
except Exception as e:
|
|
return jsonify({"error": f"JSON inválido: {e}"}), 400
|
|
|
|
print(">>> /v1/completions body recebido:")
|
|
print(body)
|
|
|
|
try:
|
|
resolved = resolve_model_and_params(body, path_model_id)
|
|
except Exception as e:
|
|
return jsonify({"error": str(e)}), 400
|
|
|
|
model_label = body.get("model") or resolved["model_key"] or resolved["model_ocid"]
|
|
prompt = body.get("prompt")
|
|
if prompt is None:
|
|
return jsonify({"error": "Campo 'prompt' é obrigatório."}), 400
|
|
|
|
prompt_text = "\n".join([str(p) for p in prompt]) if isinstance(prompt, list) else str(prompt)
|
|
msgs = [{"role": "user", "content": prompt_text}]
|
|
oci_msgs = to_oci_messages(msgs)
|
|
oci_payload = build_oci_chat_payload(oci_msgs, resolved["params"])
|
|
oci_result = oci_chat_invoke(region, compartment_id, resolved["model_ocid"], oci_payload)
|
|
|
|
if isinstance(oci_result, dict):
|
|
output_text = (
|
|
oci_result.get("output_text")
|
|
or oci_result.get("generated_text")
|
|
or oci_result.get("inference_response", {}).get("output_text")
|
|
or oci_result.get("payload", {}).get("output_text")
|
|
)
|
|
else:
|
|
output_text = None
|
|
|
|
if output_text is None:
|
|
output_text = json.dumps(oci_result, ensure_ascii=False)
|
|
|
|
if body.get("stream") is True:
|
|
return Response(stream_with_context(sse_chat_stream(model_label, output_text)),
|
|
mimetype="text/event-stream")
|
|
|
|
return jsonify(to_openai_text_response(model_label, output_text))
|
|
|
|
# ==========================
|
|
# Endpoints OpenAI v1 — FILES
|
|
# ==========================
|
|
|
|
def _files_upload_handler():
|
|
if "file" not in request.files:
|
|
return jsonify({"error": "Campo 'file' é obrigatório"}), 400
|
|
f = request.files["file"]
|
|
result = upload_file_to_bucket(f, f.filename)
|
|
return jsonify(result)
|
|
|
|
def _files_list_handler():
|
|
if TEST_MODE:
|
|
return jsonify({"data": [
|
|
{"id": fid, "object": "file", "filename": os.path.basename(obj), "bytes": 0}
|
|
for fid, obj in FILE_INDEX.items()
|
|
]})
|
|
resp = object_client.list_objects(namespace, BUCKET_NAME, prefix=UPLOAD_PREFIX)
|
|
files = []
|
|
for obj in resp.data.objects:
|
|
files.append({
|
|
"id": f"file-{uuid.uuid4().hex[:12]}",
|
|
"object": "file",
|
|
"filename": obj.name.replace(UPLOAD_PREFIX, ""),
|
|
"bytes": obj.size
|
|
})
|
|
return jsonify({"data": files})
|
|
|
|
@app.route("/genai/<region>/<compartment_id>/<path_model_id>/v1/files", methods=["POST"])
|
|
def v1_files_upload(region=None, compartment_id=None, path_model_id=None):
|
|
return _files_upload_handler()
|
|
|
|
@app.route("/genai/<region>/<compartment_id>/<path_model_id>/v1/files", methods=["GET"])
|
|
def v1_files_list(region=None, compartment_id=None, path_model_id=None):
|
|
return _files_list_handler()
|
|
|
|
@app.route("/genai/<region>/<compartment_id>/<path_model_id>/v1/files/<file_id>/content", methods=["GET"])
|
|
def v1_files_content(file_id, region=None, compartment_id=None, path_model_id=None):
|
|
if TEST_MODE:
|
|
return jsonify({"note": "TEST_MODE — conteúdo não disponível"}), 200
|
|
obj = FILE_INDEX.get(file_id)
|
|
if not obj:
|
|
return jsonify({"error": "file_id não encontrado neste servidor"}), 404
|
|
obj_resp = object_client.get_object(namespace, BUCKET_NAME, obj)
|
|
data = obj_resp.data.content
|
|
filename = os.path.basename(obj)
|
|
return send_file(
|
|
io.BytesIO(data.read()),
|
|
mimetype=guess_mime(filename, "application/octet-stream"),
|
|
as_attachment=False,
|
|
download_name=filename
|
|
)
|
|
|
|
# ==========================
|
|
# Endpoints OpenAI v1 — IMAGES (gera/edita/varia) com retorno via PAR
|
|
# ==========================
|
|
|
|
def _store_image_bytes_and_return_url(image_bytes: bytes, filename: str) -> str:
|
|
if TEST_MODE:
|
|
return f"https://objectstorage.{region}.oraclecloud.com/test/{UPLOAD_PREFIX}{uuid.uuid4().hex}_{filename}"
|
|
object_name = f"{UPLOAD_PREFIX}{uuid.uuid4().hex}_{filename}"
|
|
object_client.put_object(
|
|
namespace, BUCKET_NAME, object_name, image_bytes, content_type=guess_mime(filename, "image/png")
|
|
)
|
|
return create_par_for_object(object_name, hours_valid=24)
|
|
|
|
@app.route("/genai/<region>/<compartment_id>/<path_model_id>/v1/images/generations", methods=["POST"])
|
|
def v1_images_generations(region=None, compartment_id=None, path_model_id=None):
|
|
body = request.form or request.get_json(force=True, silent=True) or {}
|
|
prompt = body.get("prompt")
|
|
if not prompt:
|
|
return jsonify({"error": "Campo 'prompt' é obrigatório"}), 400
|
|
png_bytes = base64.b64decode(
|
|
"iVBORw0KGgoAAAANSUhEUgAAAAEAAAABCAYAAAAfFcSJAAAADUlEQVR42mP8/5+hHgAHuwKp9w8H2AAAAABJRU5ErkJggg=="
|
|
)
|
|
url = _store_image_bytes_and_return_url(png_bytes, "generation.png")
|
|
return jsonify({"created": int(time.time()), "data": [{"url": url}]})
|
|
|
|
@app.route("/genai/<region>/<compartment_id>/<path_model_id>/v1/images/edits", methods=["POST"])
|
|
def v1_images_edits(region=None, compartment_id=None, path_model_id=None):
|
|
if "image" not in request.files:
|
|
return jsonify({"error": "Campo 'image' (multipart) é obrigatório"}), 400
|
|
_ = request.form.get("prompt", "")
|
|
base_img = request.files["image"].read()
|
|
url = _store_image_bytes_and_return_url(base_img, "edit.png")
|
|
return jsonify({"created": int(time.time()), "data": [{"url": url, "note": "mock edit"}]})
|
|
|
|
@app.route("/genai/<region>/<compartment_id>/<path_model_id>/v1/images/variations", methods=["POST"])
|
|
def v1_images_variations(region=None, compartment_id=None, path_model_id=None):
|
|
if "image" not in request.files:
|
|
return jsonify({"error": "Campo 'image' (multipart) é obrigatório"}), 400
|
|
base_img = request.files["image"].read()
|
|
url = _store_image_bytes_and_return_url(base_img, "variation.png")
|
|
return jsonify({"created": int(time.time()), "data": [{"url": url, "note": "mock variation"}]})
|
|
|
|
# ==========================
|
|
# Endpoint OpenAI v1 /models
|
|
# ==========================
|
|
|
|
@app.route("/genai/<region>/<compartment_id>/<path_model_id>/v1/models", methods=["GET"])
|
|
def v1_models(region, compartment_id, path_model_id):
|
|
"""
|
|
Lista os modelos disponíveis (do JSON hot-reload), em formato OpenAI-like.
|
|
Ignora path_model_id (mantido apenas para compat. com o padrão de URL existente).
|
|
"""
|
|
supported = get_supported_models()
|
|
data = []
|
|
for k, v in supported.items():
|
|
data.append({
|
|
"id": k, # expõe o apelido p/ uso direto em { model: "<apelido>" }
|
|
"object": "model",
|
|
"owned_by": "oci.genai",
|
|
"ocid": v.get("id"),
|
|
"params": v.get("params", {})
|
|
})
|
|
return jsonify({"object": "list", "data": data})
|
|
|
|
# ==========================
|
|
# Main
|
|
# ==========================
|
|
|
|
if __name__ == '__main__':
|
|
# Observação: para produção, use um servidor WSGI (gunicorn/uwsgi) atrás de um proxy.
|
|
app.run(host='0.0.0.0', port=8000)
|