Update 2026-05-13 16:43:53

dataclaw-api/app/schemas/knowledge.py

@@ -0,0 +1,162 @@
+from datetime import datetime
+from typing import Any, Dict, List, Optional
+
+from pydantic import BaseModel, Field, field_validator
+
+
+class KnowledgeDocumentBase(BaseModel):
+    title: str = Field(..., min_length=1, max_length=200)
+    content: str = Field(..., min_length=1)
+    metadata: Dict[str, Any] = Field(default_factory=dict)
+
+
+class KnowledgeDocumentCreate(KnowledgeDocumentBase):
+    pass
+
+
+class KnowledgeDocumentUpdate(BaseModel):
+    title: Optional[str] = Field(None, min_length=1, max_length=200)
+    content: Optional[str] = Field(None, min_length=1)
+    metadata: Optional[Dict[str, Any]] = None
+
+
+class KnowledgeDocument(KnowledgeDocumentBase):
+    id: str
+    created_at: datetime
+    updated_at: datetime
+
+
+class KnowledgeBaseConfigBase(BaseModel):
+    name: str = Field(..., min_length=1, max_length=120)
+    description: Optional[str] = None
+    project_id: Optional[int] = None
+    embedding_model: Optional[str] = None
+    chunk_size: int = Field(default=512, ge=64, le=4096)
+    chunk_overlap: int = Field(default=50, ge=0, le=512)
+    top_k: int = Field(default=3, ge=1, le=20)
+    is_active: bool = True
+
+
+class KnowledgeBaseCreate(KnowledgeBaseConfigBase):
+    pass
+
+
+class KnowledgeBaseUpdate(BaseModel):
+    name: Optional[str] = Field(None, min_length=1, max_length=120)
+    description: Optional[str] = None
+    project_id: Optional[int] = None
+    embedding_model: Optional[str] = None
+    chunk_size: Optional[int] = Field(None, ge=64, le=4096)
+    chunk_overlap: Optional[int] = Field(None, ge=0, le=512)
+    top_k: Optional[int] = Field(None, ge=1, le=20)
+    is_active: Optional[bool] = None
+
+
+class KnowledgeBase(KnowledgeBaseConfigBase):
+    id: str
+    created_at: datetime
+    updated_at: datetime
+    documents: List[KnowledgeDocument] = Field(default_factory=list)
+
+
+class KnowledgeSearchRequest(BaseModel):
+    query: str = Field(..., min_length=1)
+    top_k: Optional[int] = Field(default=None, ge=1, le=20)
+
+
+class KnowledgeSearchHit(BaseModel):
+    doc_id: str
+    title: str
+    chunk: str
+    score: float
+    metadata: Dict[str, Any] = Field(default_factory=dict)
+
+
+class KnowledgeSearchResponse(BaseModel):
+    answer: str
+    hits: List[KnowledgeSearchHit] = Field(default_factory=list)
+
+
+class KnowledgeGlobalConfigUpdate(BaseModel):
+    api_base: Optional[str] = None
+    api_key: Optional[str] = None
+    default_embedding_model: Optional[str] = None
+
+    @field_validator("api_base")
+    @classmethod
+    def validate_api_base(cls, value: Optional[str]) -> Optional[str]:
+        if value is None:
+            return None
+        normalized = value.strip()
+        if not normalized:
+            return None
+        if not (normalized.startswith("http://") or normalized.startswith("https://")):
+            raise ValueError("api_base must start with http:// or https://")
+        return normalized.rstrip("/")
+
+    @field_validator("api_key")
+    @classmethod
+    def validate_api_key(cls, value: Optional[str]) -> Optional[str]:
+        if value is None:
+            return None
+        normalized = value.strip()
+        if not normalized:
+            return None
+        if len(normalized) > 512:
+            raise ValueError("api_key is too long")
+        return normalized
+
+    @field_validator("default_embedding_model")
+    @classmethod
+    def validate_default_embedding_model(cls, value: Optional[str]) -> Optional[str]:
+        if value is None:
+            return None
+        normalized = value.strip()
+        if not normalized:
+            return None
+        if len(normalized) > 200:
+            raise ValueError("default_embedding_model is too long")
+        return normalized
+
+
+class KnowledgeGlobalConfig(BaseModel):
+    api_base: Optional[str] = None
+    api_key: Optional[str] = None
+    api_key_masked: Optional[str] = None
+    has_api_key: bool = False
+    default_embedding_model: Optional[str] = None
+
+
+class KnowledgeConnectionTestRequest(BaseModel):
+    api_base: Optional[str] = None
+    api_key: Optional[str] = None
+    model_name: Optional[str] = None
+
+    @field_validator("api_base")
+    @classmethod
+    def validate_test_api_base(cls, value: Optional[str]) -> Optional[str]:
+        if value is None:
+            return None
+        normalized = value.strip()
+        if not normalized:
+            return None
+        if not (normalized.startswith("http://") or normalized.startswith("https://")):
+            raise ValueError("api_base must start with http:// or https://")
+        return normalized.rstrip("/")
+
+    @field_validator("api_key", "model_name")
+    @classmethod
+    def normalize_test_value(cls, value: Optional[str]) -> Optional[str]:
+        if value is None:
+            return None
+        normalized = value.strip()
+        return normalized or None
+
+
+class KnowledgeConnectionTestResponse(BaseModel):
+    success: bool
+    message: str
+    model_name: Optional[str] = None
+    embedding_dimension: Optional[int] = None
+    resolved_api_base: Optional[str] = None
+    available_models: List[str] = Field(default_factory=list)