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from pydantic import BaseModel, Field |
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from typing import Optional, List, Dict, Any |
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class ChatRequest(BaseModel): |
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"""Request model for chat endpoint""" |
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user_id: str = Field(..., description="User ID from Telegram") |
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question: str = Field(..., description="User's question") |
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include_history: bool = Field(True, description="Whether to include user history in prompt") |
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use_rag: bool = Field(True, description="Whether to use RAG") |
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similarity_top_k: int = Field(6, description="Number of top similar documents to return (after filtering)") |
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limit_k: int = Field(10, description="Maximum number of documents to retrieve from vector store") |
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similarity_metric: str = Field("cosine", description="Similarity metric to use (cosine, dotproduct, euclidean)") |
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similarity_threshold: float = Field(0.75, description="Threshold for vector similarity (0-1)") |
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session_id: Optional[str] = Field(None, description="Session ID for tracking conversations") |
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first_name: Optional[str] = Field(None, description="User's first name") |
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last_name: Optional[str] = Field(None, description="User's last name") |
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username: Optional[str] = Field(None, description="User's username") |
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class SourceDocument(BaseModel): |
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"""Model for source documents""" |
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text: str = Field(..., description="Text content of the document") |
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source: Optional[str] = Field(None, description="Source of the document") |
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score: Optional[float] = Field(None, description="Raw similarity score of the document") |
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normalized_score: Optional[float] = Field(None, description="Normalized similarity score (0-1)") |
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metadata: Optional[Dict[str, Any]] = Field(None, description="Metadata of the document") |
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class ChatResponse(BaseModel): |
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"""Response model for chat endpoint""" |
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answer: str = Field(..., description="Generated answer") |
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processing_time: float = Field(..., description="Processing time in seconds") |
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class ChatResponseInternal(BaseModel): |
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"""Internal model for chat response with sources - used only for logging""" |
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answer: str |
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sources: Optional[List[SourceDocument]] = Field(None, description="Source documents used for generating answer") |
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processing_time: Optional[float] = None |
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class EmbeddingRequest(BaseModel): |
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"""Request model for embedding endpoint""" |
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text: str = Field(..., description="Text to generate embedding for") |
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class EmbeddingResponse(BaseModel): |
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"""Response model for embedding endpoint""" |
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embedding: List[float] = Field(..., description="Generated embedding") |
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text: str = Field(..., description="Text that was embedded") |
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model: str = Field(..., description="Model used for embedding") |
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class HealthResponse(BaseModel): |
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"""Response model for health endpoint""" |
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status: str |
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services: Dict[str, bool] |
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timestamp: str |
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class UserMessageModel(BaseModel): |
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"""Model for user messages sent to the RAG API""" |
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user_id: str = Field(..., description="User ID from the client application") |
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session_id: str = Field(..., description="Session ID for tracking the conversation") |
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message: str = Field(..., description="User's message/question") |
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similarity_top_k: Optional[int] = Field(None, description="Number of top similar documents to return (after filtering)") |
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limit_k: Optional[int] = Field(None, description="Maximum number of documents to retrieve from vector store") |
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similarity_metric: Optional[str] = Field(None, description="Similarity metric to use (cosine, dotproduct, euclidean)") |
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similarity_threshold: Optional[float] = Field(None, description="Threshold for vector similarity (0-1)") |
