feat(api): introduce /rerank

# What does this PR do?


## Test Plan
This commit is contained in:
Eric Huang 2025-08-14 16:14:38 -07:00
parent e69acbafbf
commit 306625025a
8 changed files with 336 additions and 1 deletions

View file

@ -473,6 +473,28 @@ class EmbeddingsResponse(BaseModel):
embeddings: list[list[float]]
@json_schema_type
class RerankData(BaseModel):
"""A single rerank result from a reranking response.
:param index: The original index of the document in the input list
:param relevance_score: The relevance score from the model output. Values are inverted when applicable so that higher scores indicate greater relevance.
"""
index: int
relevance_score: float
@json_schema_type
class RerankResponse(BaseModel):
"""Response from a reranking request.
:param data: List of rerank result objects, sorted by relevance score (descending)
"""
data: list[RerankData]
@json_schema_type
class OpenAIChatCompletionContentPartTextParam(BaseModel):
"""Text content part for OpenAI-compatible chat completion messages.
@ -1131,6 +1153,24 @@ class InferenceProvider(Protocol):
"""
...
@webmethod(route="/inference/rerank", method="POST", experimental=True)
async def rerank(
self,
model: str,
query: str | OpenAIChatCompletionContentPartTextParam | OpenAIChatCompletionContentPartImageParam,
items: list[str | OpenAIChatCompletionContentPartTextParam | OpenAIChatCompletionContentPartImageParam],
max_num_results: int | None = None,
) -> RerankResponse:
"""Rerank a list of documents based on their relevance to a query.
:param model: The identifier of the reranking model to use.
:param query: The search query to rank items against. Can be a string, text content part, or image content part. The input must not exceed the model's max input token length.
:param items: List of items to rerank. Each item can be a string, text content part, or image content part. Each input must not exceed the model's max input token length.
:param max_num_results: (Optional) Maximum number of results to return. Default: returns all.
:returns: RerankResponse with indices sorted by relevance score (descending).
"""
raise NotImplementedError("Reranking is not implemented")
@webmethod(route="/openai/v1/completions", method="POST")
async def openai_completion(
self,