mirror of
https://github.com/hwchase17/langchain
synced 2024-11-10 01:10:59 +00:00
77 lines
2.4 KiB
Python
77 lines
2.4 KiB
Python
from __future__ import annotations
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from typing import TYPE_CHECKING, Dict, Optional, Sequence
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from langchain_core.callbacks.manager import Callbacks
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from langchain_core.documents import BaseDocumentCompressor, Document
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from langchain_core.pydantic_v1 import Extra, root_validator
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if TYPE_CHECKING:
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from flashrank import Ranker, RerankRequest
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else:
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# Avoid pydantic annotation issues when actually instantiating
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# while keeping this import optional
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try:
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from flashrank import Ranker, RerankRequest
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except ImportError:
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pass
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DEFAULT_MODEL_NAME = "ms-marco-MultiBERT-L-12"
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class FlashrankRerank(BaseDocumentCompressor):
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"""Document compressor using Flashrank interface."""
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client: Ranker
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"""Flashrank client to use for compressing documents"""
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top_n: int = 3
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"""Number of documents to return."""
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model: Optional[str] = None
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"""Model to use for reranking."""
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class Config:
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"""Configuration for this pydantic object."""
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extra = Extra.forbid
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arbitrary_types_allowed = True
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@root_validator(pre=True)
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def validate_environment(cls, values: Dict) -> Dict:
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"""Validate that api key and python package exists in environment."""
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try:
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from flashrank import Ranker
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except ImportError:
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raise ImportError(
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"Could not import flashrank python package. "
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"Please install it with `pip install flashrank`."
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)
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values["model"] = values.get("model", DEFAULT_MODEL_NAME)
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values["client"] = Ranker(model_name=values["model"])
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return values
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def compress_documents(
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self,
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documents: Sequence[Document],
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query: str,
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callbacks: Optional[Callbacks] = None,
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) -> Sequence[Document]:
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passages = [
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{"id": i, "text": doc.page_content, "meta": doc.metadata}
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for i, doc in enumerate(documents)
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]
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rerank_request = RerankRequest(query=query, passages=passages)
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rerank_response = self.client.rerank(rerank_request)[: self.top_n]
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final_results = []
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for r in rerank_response:
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metadata = r["meta"]
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metadata["relevance_score"] = r["score"]
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doc = Document(
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page_content=r["text"],
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metadata=metadata,
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)
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final_results.append(doc)
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return final_results
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