# SPDX-License-Identifier: Apache-2.0 import asyncio from typing import Any, AsyncGenerator, Dict, List, Optional, Union, cast from fastapi import Request from vllm.config import ModelConfig from vllm.engine.protocol import EngineClient from vllm.entrypoints.logger import RequestLogger from vllm.entrypoints.openai.protocol import (ErrorResponse, RerankDocument, RerankRequest, RerankResponse, RerankResult, RerankUsage) from vllm.entrypoints.openai.serving_engine import OpenAIServing from vllm.entrypoints.openai.serving_models import OpenAIServingModels from vllm.inputs.data import TokensPrompt from vllm.logger import init_logger from vllm.outputs import PoolingRequestOutput, ScoringRequestOutput from vllm.transformers_utils.tokenizers.mistral import MistralTokenizer from vllm.utils import make_async, merge_async_iterators logger = init_logger(__name__) class JinaAIServingRerank(OpenAIServing): def __init__( self, engine_client: EngineClient, model_config: ModelConfig, models: OpenAIServingModels, *, request_logger: Optional[RequestLogger], ) -> None: super().__init__(engine_client=engine_client, model_config=model_config, models=models, request_logger=request_logger) async def do_rerank( self, request: RerankRequest, raw_request: Optional[Request] = None ) -> Union[RerankResponse, ErrorResponse]: """ Rerank API based on JinaAI's rerank API; implements the same API interface. Designed for compatibility with off-the-shelf tooling, since this is a common standard for reranking APIs See example client implementations at https://github.com/infiniflow/ragflow/blob/main/rag/llm/rerank_model.py numerous clients use this standard. """ error_check_ret = await self._check_model(request) if error_check_ret is not None: return error_check_ret model_name = request.model request_id = f"rerank-{self._base_request_id(raw_request)}" truncate_prompt_tokens = request.truncate_prompt_tokens query = request.query documents = request.documents request_prompts = [] engine_prompts = [] top_n = request.top_n if request.top_n > 0 else len(documents) try: ( lora_request, prompt_adapter_request, ) = self._maybe_get_adapters(request) tokenizer = await self.engine_client.get_tokenizer(lora_request) if prompt_adapter_request is not None: raise NotImplementedError("Prompt adapter is not supported " "for scoring models") if isinstance(tokenizer, MistralTokenizer): raise ValueError( "MistralTokenizer not supported for cross-encoding") if not self.model_config.is_cross_encoder: raise ValueError("Model is not cross encoder.") if truncate_prompt_tokens is not None and \ truncate_prompt_tokens > self.max_model_len: raise ValueError( f"truncate_prompt_tokens value ({truncate_prompt_tokens}) " f"is greater than max_model_len ({self.max_model_len})." f" Please, select a smaller truncation size.") for doc in documents: request_prompt = f"{query}{tokenizer.sep_token}{doc}" tokenization_kwargs: Dict[str, Any] = {} if truncate_prompt_tokens is not None: tokenization_kwargs["truncation"] = True tokenization_kwargs["max_length"] = truncate_prompt_tokens tokenize_async = make_async(tokenizer.__call__, executor=self._tokenizer_executor) prompt_inputs = await tokenize_async(text=query, text_pair=doc, **tokenization_kwargs) input_ids = prompt_inputs["input_ids"] text_token_prompt = \ self._validate_input(request, input_ids, request_prompt) engine_prompt = TokensPrompt( prompt_token_ids=text_token_prompt["prompt_token_ids"], token_type_ids=prompt_inputs.get("token_type_ids")) request_prompts.append(request_prompt) engine_prompts.append(engine_prompt) except ValueError as e: logger.exception("Error in preprocessing prompt inputs") return self.create_error_response(str(e)) # Schedule the request and get the result generator. generators: List[AsyncGenerator[PoolingRequestOutput, None]] = [] try: pooling_params = request.to_pooling_params() for i, engine_prompt in enumerate(engine_prompts): request_id_item = f"{request_id}-{i}" self._log_inputs(request_id_item, request_prompts[i], params=pooling_params, lora_request=lora_request, prompt_adapter_request=prompt_adapter_request) trace_headers = (None if raw_request is None else await self._get_trace_headers(raw_request.headers)) generator = self.engine_client.encode( engine_prompt, pooling_params, request_id_item, lora_request=lora_request, trace_headers=trace_headers, priority=request.priority, ) generators.append(generator) except ValueError as e: # TODO: Use a vllm-specific Validation Error return self.create_error_response(str(e)) result_generator = merge_async_iterators(*generators) num_prompts = len(engine_prompts) # Non-streaming response final_res_batch: List[Optional[PoolingRequestOutput]] final_res_batch = [None] * num_prompts try: async for i, res in result_generator: final_res_batch[i] = res assert all(final_res is not None for final_res in final_res_batch) final_res_batch_checked = cast(List[PoolingRequestOutput], final_res_batch) response = self.request_output_to_rerank_response( final_res_batch_checked, request_id, model_name, documents, top_n) except asyncio.CancelledError: return self.create_error_response("Client disconnected") except ValueError as e: # TODO: Use a vllm-specific Validation Error return self.create_error_response(str(e)) return response def request_output_to_rerank_response( self, final_res_batch: List[PoolingRequestOutput], request_id: str, model_name: str, documents: List[str], top_n: int) -> RerankResponse: """ Convert the output of do_rank to a RerankResponse """ results: List[RerankResult] = [] num_prompt_tokens = 0 for idx, final_res in enumerate(final_res_batch): classify_res = ScoringRequestOutput.from_base(final_res) result = RerankResult( index=idx, document=RerankDocument(text=documents[idx]), relevance_score=classify_res.outputs.score, ) results.append(result) prompt_token_ids = final_res.prompt_token_ids num_prompt_tokens += len(prompt_token_ids) # sort by relevance, then return the top n if set results.sort(key=lambda x: x.relevance_score, reverse=True) if top_n < len(documents): results = results[:top_n] return RerankResponse( id=request_id, model=model_name, results=results, usage=RerankUsage(total_tokens=num_prompt_tokens))