mirror of
https://github.com/GNS3/gns3-server.git
synced 2026-08-28 13:00:15 +03:00
bbe57f34b9
Security Enhancement: - API keys are now always filtered to null in all LLM config API responses - Created LLMModelConfigDataWithoutSecret schema for response validation - Added _filter_api_key_from_config() helper in API routes - Modified repository to always hide api_key in get_user_effective_configs() - Update/create operations still accept and store api_key securely Documentation Updates: - Updated API key visibility section to reflect new security policy - Updated all response examples to show api_key as null - Marked max_tokens field as reserved for future use - Added "Reserved Fields" section explaining unused fields This defense-in-depth approach prevents API keys from being leaked through logs, browser devtools, or network monitoring.
208 lines
8.2 KiB
Python
208 lines
8.2 KiB
Python
#
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# Copyright (C) 2026 GNS3 Technologies Inc.
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#
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# This program is free software: you can redistribute it and/or modify
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# it under the terms of the GNU General Public License as published by
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# the Free Software Foundation, either version 3 of the License, or
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# (at your option) any later version.
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#
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# This program is distributed in the hope that it will be useful,
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# but WITHOUT ANY WARRANTY; without even the implied warranty of
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# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
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# GNU General Public License for more details.
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#
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# You should have received a copy of the GNU General Public License
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# along with this program. If not, see <http://www.gnu.org/licenses/>.
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from typing import Optional, Literal, Union
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from pydantic import BaseModel, Field, ConfigDict, field_validator
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from uuid import UUID
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from .base import DateTimeModelMixin
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# Valid model types
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ModelType = Literal['text', 'vision', 'stt', 'tts', 'multimodal', 'embedding', 'reranking', 'other']
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# Core model config schema (stored in config JSONB field)
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class LLMModelConfigData(BaseModel):
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"""
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LLM model configuration data.
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Stored in the config JSONB column (provider, base_url, model, etc.).
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IMPORTANT: context_limit is REQUIRED to ensure proper context window management.
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Model providers frequently update context limits, so users must configure this value.
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NOTE: context_limit unit is K tokens (1 K = 1000 tokens).
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Example: 128 means 128K tokens (128,000 tokens).
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"""
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provider: str = Field(..., description="LLM provider (e.g., 'openai', 'anthropic', 'ollama')")
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base_url: str = Field(..., description="API base URL")
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model: str = Field(..., description="Model name")
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temperature: float = Field(default=0.7, ge=0.0, le=2.0, description="Temperature parameter")
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api_key: Optional[str] = Field(None, description="API key (will be encrypted)")
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max_tokens: Optional[int] = Field(None, gt=0, description="Max tokens for generation")
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context_limit: int = Field(
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..., gt=0, description="Model context window limit in K tokens (e.g., 128 = 128K tokens)"
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)
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context_strategy: Literal["conservative", "balanced", "aggressive"] = Field(
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"balanced", description="Context trimming strategy: conservative (60%), balanced (75%), aggressive (85%)"
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)
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copilot_mode: Optional[str] = Field(
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None,
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description="GNS3-Copilot mode: 'teaching_assistant' or 'lab_automation_assistant'"
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)
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# Allow extra fields for extensibility
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# Ensure all fields are included in serialization, even if None
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model_config = ConfigDict(extra="allow", populate_by_name=True)
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# Request schemas
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class LLMModelConfigCreate(BaseModel):
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"""Request to create a new LLM model configuration."""
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name: str = Field(..., min_length=1, max_length=100, description="Configuration name")
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model_type: ModelType = Field(..., description="Model type")
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is_default: Optional[bool] = Field(False, description="Set as default configuration")
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# Config fields
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provider: str = Field(..., description="LLM provider")
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base_url: str = Field(..., description="API base URL")
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model: str = Field(..., description="Model name")
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temperature: float = Field(default=0.7, ge=0.0, le=2.0)
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api_key: Optional[str] = None
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max_tokens: Optional[int] = Field(None, gt=0)
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context_limit: int = Field(
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..., gt=0, description="Model context window limit in K tokens (e.g., 128 = 128K tokens)"
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)
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context_strategy: Literal["conservative", "balanced", "aggressive"] = Field(
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"balanced", description="Context trimming strategy"
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)
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copilot_mode: Optional[str] = Field(
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None,
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description="GNS3-Copilot mode: 'teaching_assistant' or 'lab_automation_assistant'"
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)
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# Allow extra config fields
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model_config = ConfigDict(extra="allow")
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class LLMModelConfigUpdate(BaseModel):
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"""Request to update an existing LLM model configuration."""
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# Table-level fields
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name: Optional[str] = Field(None, min_length=1, max_length=100)
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model_type: Optional[ModelType] = None
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is_default: Optional[bool] = None
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expected_version: Optional[int] = Field(None, description="Expected version for optimistic locking")
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# Config fields
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provider: Optional[str] = None
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base_url: Optional[str] = None
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model: Optional[str] = None
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temperature: Optional[float] = Field(None, ge=0.0, le=2.0)
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api_key: Optional[str] = None
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max_tokens: Optional[Union[int, str]] = Field(None, description="Max tokens for generation (can be null)")
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context_limit: Optional[int] = Field(
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None, gt=0, description="Model context window limit in K tokens (e.g., 128 = 128K tokens)"
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)
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context_strategy: Optional[Literal["conservative", "balanced", "aggressive"]] = Field(
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None, description="Context trimming strategy"
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)
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copilot_mode: Optional[str] = Field(
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None,
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description="GNS3-Copilot mode: 'teaching_assistant' or 'lab_automation_assistant'"
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)
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# Allow extra config fields
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model_config = ConfigDict(extra="allow")
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@field_validator('max_tokens', mode='before')
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@classmethod
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def validate_max_tokens(cls, v):
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"""Handle string 'null' values for max_tokens."""
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if v == "null" or v == "":
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return None
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if v is None:
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return None
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# Convert to int if it's a valid integer string
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if isinstance(v, str) and v.isdigit():
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return int(v)
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return v
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# Response schema without API key (for security)
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class LLMModelConfigDataWithoutSecret(BaseModel):
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"""LLM model configuration data WITHOUT sensitive information."""
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provider: str = Field(..., description="LLM provider (e.g., 'openai', 'anthropic', 'ollama')")
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base_url: str = Field(..., description="API base URL")
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model: str = Field(..., description="Model name")
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temperature: float = Field(default=0.7, ge=0.0, le=2.0, description="Temperature parameter")
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api_key: Optional[str] = Field(None, description="API key (always hidden in API responses)")
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max_tokens: Optional[int] = Field(None, gt=0, description="Max tokens for generation")
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context_limit: int = Field(
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..., gt=0, description="Model context window limit in K tokens (e.g., 128 = 128K tokens)"
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)
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context_strategy: Literal["conservative", "balanced", "aggressive"] = Field(
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"balanced", description="Context trimming strategy: conservative (60%), balanced (75%), aggressive (85%)"
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)
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copilot_mode: Optional[str] = Field(
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None,
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description="GNS3-Copilot mode: 'teaching_assistant' or 'lab_automation_assistant'"
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)
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# Allow extra fields for extensibility
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model_config = ConfigDict(extra="allow", populate_by_name=True)
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# Response schemas
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class LLMModelConfigResponse(DateTimeModelMixin):
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"""LLM model configuration response (without API key for security)."""
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config_id: UUID
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name: str
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model_type: ModelType
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config: LLMModelConfigDataWithoutSecret
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user_id: Optional[UUID] = None
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group_id: Optional[UUID] = None
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is_default: bool
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version: int = Field(..., description="Optimistic locking version")
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model_config = ConfigDict(from_attributes=True)
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class LLMModelConfigWithSource(DateTimeModelMixin):
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"""Model configuration with source information (for inheritance, without API key for security)."""
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config_id: UUID
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name: str
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model_type: ModelType
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config: LLMModelConfigDataWithoutSecret
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user_id: Optional[UUID] = None
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group_id: Optional[UUID] = None
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is_default: bool
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version: int
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source: str = Field(..., description="Source: 'user' or 'group'")
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group_name: Optional[str] = Field(None, description="Group name if source is 'group'")
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model_config = ConfigDict(from_attributes=True, extra="allow")
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class LLMModelConfigInheritedResponse(BaseModel):
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"""Response containing user's effective configs (own + inherited from groups)."""
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configs: list[LLMModelConfigWithSource]
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default_config: Optional[LLMModelConfigWithSource] = None
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total: int
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class LLMModelConfigListResponse(BaseModel):
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"""Response containing a list of model configurations with default."""
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configs: list[LLMModelConfigResponse]
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default_config: Optional[LLMModelConfigResponse] = None
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total: int
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