from __future__ import annotations from copy import deepcopy from enum import Enum from typing import TYPE_CHECKING, Any, Dict, Optional, Sequence from langchain.retrievers.document_compressors.base import BaseDocumentCompressor from langchain_core.callbacks.manager import Callbacks from langchain_core.documents import Document from langchain_core.pydantic_v1 import Extra, Field, PrivateAttr, root_validator from langchain_core.utils import get_from_dict_or_env if TYPE_CHECKING: from rank_llm.data import Candidate, Query, Request else: # Avoid pydantic annotation issues when actually instantiating # while keeping this import optional try: from rank_llm.data import Candidate, Query, Request except ImportError: pass class RankLLMRerank(BaseDocumentCompressor): """Document compressor using Flashrank interface.""" client: Any = None """RankLLM client to use for compressing documents""" top_n: int = Field(default=3) """Top N documents to return.""" model: str = Field(default="zephyr") """Name of model to use for reranking.""" step_size: int = Field(default=10) """Step size for moving sliding window.""" gpt_model: str = Field(default="gpt-3.5-turbo") """OpenAI model name.""" _retriever: Any = PrivateAttr() class Config: """Configuration for this pydantic object.""" extra = Extra.forbid arbitrary_types_allowed = True @root_validator(pre=True) def validate_environment(cls, values: Dict) -> Dict: """Validate python package exists in environment.""" if not values.get("client"): client_name = values.get("model", "zephyr") try: model_enum = ModelType(client_name.lower()) except ValueError: raise ValueError( "Unsupported model type. Please use 'vicuna', 'zephyr', or 'gpt'." ) try: if model_enum == ModelType.VICUNA: from rank_llm.rerank.vicuna_reranker import VicunaReranker values["client"] = VicunaReranker() elif model_enum == ModelType.ZEPHYR: from rank_llm.rerank.zephyr_reranker import ZephyrReranker values["client"] = ZephyrReranker() elif model_enum == ModelType.GPT: from rank_llm.rerank.rank_gpt import SafeOpenai from rank_llm.rerank.reranker import Reranker openai_api_key = get_from_dict_or_env( values, "open_api_key", "OPENAI_API_KEY" ) agent = SafeOpenai( model=values["gpt_model"], context_size=4096, keys=openai_api_key, ) values["client"] = Reranker(agent) except ImportError: raise ImportError( "Could not import rank_llm python package. " "Please install it with `pip install rank_llm`." ) return values def compress_documents( self, documents: Sequence[Document], query: str, callbacks: Optional[Callbacks] = None, ) -> Sequence[Document]: request = Request( query=Query(text=query, qid=1), candidates=[ Candidate(doc={"text": doc.page_content}, docid=index, score=1) for index, doc in enumerate(documents) ], ) rerank_results = self.client.rerank( request, rank_end=len(documents), window_size=min(20, len(documents)), step=10, ) final_results = [] for res in rerank_results.candidates: doc = documents[int(res.docid)] doc_copy = Document(doc.page_content, metadata=deepcopy(doc.metadata)) final_results.append(doc_copy) return final_results[: self.top_n] class ModelType(Enum): VICUNA = "vicuna" ZEPHYR = "zephyr" GPT = "gpt"