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"""
Response schemas for API endpoints.
This module defines all Pydantic models for API responses,
ensuring consistent output format across all endpoints.
"""
from typing import List, Literal
from pydantic import BaseModel, Field
from .common import ModelInfo
class BaseEmbedResponse(BaseModel):
"""
Base class for embedding responses.
Attributes:
model_id: Identifier of the model used
"""
model: str = Field(..., description="Model identifier used")
class EmbeddingObject(BaseModel):
"""Single embedding object."""
object: Literal["embedding"] = "embedding"
embedding: List[float] = Field(..., description="Embedding vector")
index: int = Field(..., description="Index of the embedding")
class TokenUsage(BaseModel):
"""Usage statistics."""
prompt_tokens: int
total_tokens: int
class DenseEmbedResponse(BaseEmbedResponse):
"""
Response model for single/batch dense embeddings.
Used for /embeddings endpoint dense models.
Attributes:
data: List of generated dense embeddings
model: Identifier of the model used
usage: Usage statistics
"""
object: Literal["list"] = "list"
data: List[EmbeddingObject]
model: str = Field(..., description="Model identifier used")
usage: TokenUsage = Field(..., description="Usage statistics")
class Config:
json_schema_extra = {
"example": {
"object": "list",
"data": [
{"object": "embedding", "embedding": [0.1, 0.2, 0.3], "index": 0},
{"object": "embedding", "embedding": [0.4, 0.5, 0.6], "index": 1},
],
"model": "qwen3-0.6b",
"usage": {"prompt_tokens": 10, "total_tokens": 10},
}
}
class RerankResult(BaseModel):
"""
Single reranking result.
Attributes:
text: The document text
score: Relevance score from the reranking model
index: Original index of the document in input list
"""
text: str = Field(..., description="Document text")
score: float = Field(..., description="Relevance score")
index: int = Field(..., description="Original index of the document")
class RerankResponse(BaseEmbedResponse):
"""
Response model for document reranking.
Attributes:
results: List of reranked documents with scores
query: The original search query
"""
query: str = Field(..., description="Original search query")
results: List[RerankResult] = Field(..., description="List of reranked documents")
class Config:
json_schema_extra = {
"example": {
"model_id": "jina-reranker-v3",
"query": "Rerank document",
"results": [
{"text": "document 1", "score": 0.6, "index": 0},
{"text": "document 2", "score": 0.5, "index": 1},
],
}
}
class ModelsListResponse(BaseModel):
"""
Response model for listing available models.
Attributes:
models: List of available models with their info
total: Total number of models
"""
models: List[ModelInfo] = Field(..., description="List of available models")
total: int = Field(..., description="Total number of models", ge=0)
class Config:
json_schema_extra = {
"example": {
"models": [
{
"id": "qwen3-0.6b",
"name": "Qwen/Qwen3-Embedding-0.6B",
"type": "embeddings",
"loaded": True,
}
],
"total": 1,
}
}
class RootResponse(BaseModel):
"""
Response model for root endpoint.
Attributes:
message: Welcome message
version: API version
docs_url: URL to API documentation
"""
message: str = Field(..., description="Welcome message")
version: str = Field(..., description="API version")
docs_url: str = Field(..., description="Documentation URL")
class Config:
json_schema_extra = {
"example": {
"message": "Unified Embedding API - Dense & Sparse Embeddings",
"version": "3.0.0",
"docs_url": "/docs",
}
}
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