Source code for langchain.retrievers.document_compressors.cohere_rerank

from __future__ import annotations

from copy import deepcopy
from typing import Any, Dict, List, Optional, Sequence, Union

from langchain_core._api.deprecation import deprecated
from langchain_core.callbacks.manager import Callbacks
from langchain_core.documents import Document
from langchain_core.pydantic_v1 import Extra, root_validator
from langchain_core.utils import get_from_dict_or_env

from langchain.retrievers.document_compressors.base import BaseDocumentCompressor

[docs]@deprecated( since="0.0.30", removal="0.3.0", alternative_import="langchain_cohere.CohereRerank" ) class CohereRerank(BaseDocumentCompressor): """Document compressor that uses `Cohere Rerank API`.""" client: Any = None """Cohere client to use for compressing documents.""" top_n: Optional[int] = 3 """Number of documents to return.""" model: str = "rerank-english-v2.0" """Model to use for reranking.""" cohere_api_key: Optional[str] = None """Cohere API key. Must be specified directly or via environment variable COHERE_API_KEY.""" user_agent: str = "langchain" """Identifier for the application making the request.""" 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 that api key and python package exists in environment.""" if not values.get("client"): try: import cohere except ImportError: raise ImportError( "Could not import cohere python package. " "Please install it with `pip install cohere`." ) cohere_api_key = get_from_dict_or_env( values, "cohere_api_key", "COHERE_API_KEY" ) client_name = values.get("user_agent", "langchain") values["client"] = cohere.Client(cohere_api_key, client_name=client_name) return values
[docs] def rerank( self, documents: Sequence[Union[str, Document, dict]], query: str, *, model: Optional[str] = None, top_n: Optional[int] = -1, max_chunks_per_doc: Optional[int] = None, ) -> List[Dict[str, Any]]: """Returns an ordered list of documents ordered by their relevance to the provided query. Args: query: The query to use for reranking. documents: A sequence of documents to rerank. model: The model to use for re-ranking. Default to self.model. top_n : The number of results to return. If None returns all results. Defaults to self.top_n. max_chunks_per_doc : The maximum number of chunks derived from a document. """ # noqa: E501 if len(documents) == 0: # to avoid empty api call return [] docs = [ doc.page_content if isinstance(doc, Document) else doc for doc in documents ] model = model or self.model top_n = top_n if (top_n is None or top_n > 0) else self.top_n results = self.client.rerank( query=query, documents=docs, model=model, top_n=top_n, max_chunks_per_doc=max_chunks_per_doc, ) if hasattr(results, "results"): results = getattr(results, "results") result_dicts = [] for res in results: result_dicts.append( {"index": res.index, "relevance_score": res.relevance_score} ) return result_dicts
[docs] def compress_documents( self, documents: Sequence[Document], query: str, callbacks: Optional[Callbacks] = None, ) -> Sequence[Document]: """ Compress documents using Cohere's rerank API. Args: documents: A sequence of documents to compress. query: The query to use for compressing the documents. callbacks: Callbacks to run during the compression process. Returns: A sequence of compressed documents. """ compressed = [] for res in self.rerank(documents, query): doc = documents[res["index"]] doc_copy = Document(doc.page_content, metadata=deepcopy(doc.metadata)) doc_copy.metadata["relevance_score"] = res["relevance_score"] compressed.append(doc_copy) return compressed