feat(models): 组织、用户、角色、客户、会话与消息模型
按组织边界定义数据关系，补齐仓储层与通用工具，迁移脚本可重复执行。
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"""
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Copyright 2024-2026 ReplyPier
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Licensed under the Apache License, Version 2.0 (the "License");
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you may not use this file except in compliance with the License.
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You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software
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distributed under the License is distributed on an "AS IS" BASIS,
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WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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See the License for the specific language governing permissions and
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limitations under the License.
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"""
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import re
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from agno.agent import Agent
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from agno.models.openai import OpenAIChat
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from openai import AsyncOpenAI, OpenAI
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from app.core.logger import get_logger
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from app.core.config import settings
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from app.core.outbound_http import guarded_async_http_client, guarded_http_client
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from app.core.outbound_policy import check_outbound_target, public_only
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from app.utils.agno_patches import apply_agno_patches
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from typing import Dict, Any, Optional, List
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from fastapi import HTTPException
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logger = get_logger(__name__)
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# Every code path that builds an agno model goes through this module, so this
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# is the single choke point where the 1.7.6 runtime patches get applied.
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apply_agno_patches()
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class PinnedOpenAIChat(OpenAIChat):
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"""OpenAIChat for a user-supplied base URL under OUTBOUND_URL_POLICY=
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public_only: every connection, redirect included, goes to a vetted public
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address. agno 1.7.6 takes a single http_client for both its sync and
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async OpenAI clients, so each getter supplies its own guarded one, fresh
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per call like agno's default."""
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def get_client(self):
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# follow_redirects matches the OpenAI SDK's own default client.
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return OpenAI(**{**self._get_client_params(),
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"http_client": guarded_http_client(follow_redirects=True)})
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def get_async_client(self):
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return AsyncOpenAI(**{**self._get_client_params(),
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"http_client": guarded_async_http_client(follow_redirects=True)})
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def create_model(model_type: str, api_key: str, model_name: str, max_tokens: int = 1000, response_format: Optional[Dict[str, Any]] = None, base_url: Optional[str] = None) -> Any:
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"""
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Create and return the specified model based on model_type.
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Args:
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model_type: The type of model (OPENAI, GROQ, etc.)
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api_key: The API key
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model_name: The name/ID of the model
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max_tokens: Maximum tokens for model output
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response_format: Optional response format specification
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base_url: Custom API endpoint, used by OPENAI_COMPATIBLE
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Returns:
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The initialized model object
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Raises:
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HTTPException: If the model type is not supported
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"""
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model_type = model_type.upper()
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try:
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if model_type == 'OPENAI' or model_type == 'REPLYPIER': # own model for enterprise customers
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if response_format:
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return OpenAIChat(api_key=api_key, id=model_name, max_tokens=max_tokens, response_format=response_format)
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else:
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return OpenAIChat(api_key=api_key, id=model_name, max_tokens=max_tokens)
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elif model_type == 'OPENAI_COMPATIBLE':
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if not base_url:
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raise ValueError("base_url is required for OPENAI_COMPATIBLE")
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# Re-checked on every build, not only on save: DNS can change.
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check_outbound_target(base_url, "Base URL")
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base_url = re.sub(r'/chat/completions/?$', '', base_url.strip(), flags=re.IGNORECASE).rstrip('/')
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model_cls = PinnedOpenAIChat if public_only() else OpenAIChat
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if response_format:
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return model_cls(api_key=api_key, id=model_name, base_url=base_url, max_tokens=max_tokens, response_format=response_format)
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else:
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return model_cls(api_key=api_key, id=model_name, base_url=base_url, max_tokens=max_tokens)
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elif model_type == 'ANTHROPIC':
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from agno.models.anthropic import Claude
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return Claude(api_key=api_key, id=model_name, max_tokens=max_tokens)
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elif model_type == 'DEEPSEEK':
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from agno.models.deepseek import DeepSeek
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return DeepSeek(api_key=api_key, id=model_name, max_tokens=max_tokens)
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elif model_type == 'GOOGLE':
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from agno.models.google import Gemini
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# Gemini's agno model exposes max_output_tokens, not max_tokens.
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return Gemini(api_key=api_key, id=model_name, max_output_tokens=max_tokens)
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elif model_type == 'GOOGLEVERTEX':
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from agno.models.vertexai import Gemini
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return Gemini(api_key=api_key, id=model_name, max_tokens=max_tokens)
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elif model_type == 'GROQ':
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from agno.models.groq import Groq
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# Create a patched Groq model that handles the response_format + tools conflict
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class PatchedGroq(Groq):
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def __post_init__(self):
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"""Set supports_native_structured_outputs after initialization"""
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super().__post_init__() if hasattr(super(), '__post_init__') else None
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# Override to indicate Groq supports structured outputs via response_format
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object.__setattr__(self, 'supports_native_structured_outputs', True)
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def get_request_params(self, response_format=None, tools=None, tool_choice=None):
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"""
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Override get_request_params to handle the conflict between response_format and tools.
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Groq API doesn't allow JSON response_format when tools are present.
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"""
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# Get the base parameters from parent class
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base_params = {
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"frequency_penalty": self.frequency_penalty,
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"logit_bias": self.logit_bias,
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"logprobs": self.logprobs,
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"max_tokens": self.max_tokens,
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"presence_penalty": self.presence_penalty,
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"seed": self.seed,
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"stop": self.stop,
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"temperature": self.temperature,
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"top_logprobs": self.top_logprobs,
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"top_p": self.top_p,
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"user": self.user,
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"extra_headers": self.extra_headers,
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"extra_query": self.extra_query,
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}
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# Filter out None values
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request_params = {k: v for k, v in base_params.items() if v is not None}
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# Add tools first
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if tools is not None:
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request_params["tools"] = tools
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if tool_choice is not None:
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request_params["tool_choice"] = tool_choice
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# Don't add response_format when tools are present to avoid conflict
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logger.debug("Groq: Skipping response_format due to tools being present")
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else:
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# Only add response_format if no tools are present
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if response_format is not None:
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request_params["response_format"] = response_format
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# Add additional request params if provided
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if self.request_params:
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request_params.update(self.request_params)
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return request_params
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# Groq model doesn't support response_format parameter in constructor
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return PatchedGroq(api_key=api_key, id=model_name, max_tokens=max_tokens)
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elif model_type == 'MISTRAL':
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from agno.models.mistral import MistralChat
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return MistralChat(api_key=api_key, id=model_name, max_tokens=max_tokens)
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elif model_type == 'HUGGINGFACE':
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from agno.models.huggingface import HuggingFace
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return HuggingFace(api_key=api_key, id=model_name, max_tokens=max_tokens)
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elif model_type == 'OLLAMA':
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from agno.models.ollama import Ollama
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return Ollama(id=model_name)
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elif model_type == 'XAI':
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from agno.models.xai import xAI
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return xAI(api_key=api_key, id=model_name, max_tokens=max_tokens)
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else:
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raise ValueError(f"Unsupported model type: {model_type}")
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except ImportError as e:
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logger.error(f"Import error when creating model type {model_type}: {str(e)}")
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raise HTTPException(status_code=400, detail=f"Model type {model_type} is not available in this installation")
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except Exception as e:
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logger.error(f"Error creating model type {model_type}: {str(e)}")
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raise HTTPException(status_code=500, detail=f"Failed to initialize model: {str(e)}")
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async def test_model_api_key(api_key: str, model_type: str, model_name: str, base_url: Optional[str] = None) -> bool:
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"""
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Test if the API key is valid for the given model type.
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Args:
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api_key: The API key to test
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model_type: The type of model
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model_name: The name of the model
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base_url: Custom API endpoint, used by OPENAI_COMPATIBLE
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Returns:
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bool: True if the API key is valid, False otherwise
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"""
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try:
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# Create a simple model and agent for testing
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model = create_model(model_type, api_key, model_name, base_url=base_url)
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test_agent = Agent(
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name="Test Agent",
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model=model,
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instructions="You are a test agent. Just respond with 'Test successful.'",
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debug_mode=False
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)
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# Run a simple test query
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await test_agent.arun(message="This is a test message.")
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return True
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except Exception as e:
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logger.error(f"API key test failed: {str(e)}")
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return False
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