mirror of
https://github.com/hwchase17/langchain
synced 2024-11-10 01:10:59 +00:00
109 lines
3.9 KiB
Python
109 lines
3.9 KiB
Python
from __future__ import annotations
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from copy import deepcopy
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from typing import Any, Dict, List, Optional, Sequence, Union
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import cohere
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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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from langchain_core.utils import get_from_dict_or_env
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class CohereRerank(BaseDocumentCompressor):
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"""Document compressor that uses `Cohere Rerank API`."""
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client: Any = None
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"""Cohere client to use for compressing documents."""
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top_n: Optional[int] = 3
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"""Number of documents to return."""
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model: str = "rerank-english-v2.0"
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"""Model to use for reranking."""
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cohere_api_key: Optional[str] = None
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"""Cohere API key. Must be specified directly or via environment variable
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COHERE_API_KEY."""
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user_agent: str = "langchain"
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"""Identifier for the application making the request."""
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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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if not values.get("client"):
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cohere_api_key = get_from_dict_or_env(
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values, "cohere_api_key", "COHERE_API_KEY"
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)
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client_name = values.get("user_agent", "langchain")
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values["client"] = cohere.Client(cohere_api_key, client_name=client_name)
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return values
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def rerank(
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self,
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documents: Sequence[Union[str, Document, dict]],
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query: str,
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*,
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model: Optional[str] = None,
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top_n: Optional[int] = -1,
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max_chunks_per_doc: Optional[int] = None,
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) -> List[Dict[str, Any]]:
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"""Returns an ordered list of documents ordered by their relevance to the provided query.
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Args:
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query: The query to use for reranking.
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documents: A sequence of documents to rerank.
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model: The model to use for re-ranking. Default to self.model.
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top_n : The number of results to return. If None returns all results.
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Defaults to self.top_n.
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max_chunks_per_doc : The maximum number of chunks derived from a document.
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""" # noqa: E501
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if len(documents) == 0: # to avoid empty api call
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return []
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docs = [
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doc.page_content if isinstance(doc, Document) else doc for doc in documents
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]
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model = model or self.model
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top_n = top_n if (top_n is None or top_n > 0) else self.top_n
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results = self.client.rerank(
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query=query,
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documents=docs,
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model=model,
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top_n=top_n,
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max_chunks_per_doc=max_chunks_per_doc,
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)
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result_dicts = []
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for res in results.results:
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result_dicts.append(
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{"index": res.index, "relevance_score": res.relevance_score}
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)
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return result_dicts
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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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"""
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Compress documents using Cohere's rerank API.
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Args:
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documents: A sequence of documents to compress.
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query: The query to use for compressing the documents.
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callbacks: Callbacks to run during the compression process.
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Returns:
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A sequence of compressed documents.
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"""
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compressed = []
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for res in self.rerank(documents, query):
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doc = documents[res["index"]]
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doc_copy = Document(doc.page_content, metadata=deepcopy(doc.metadata))
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doc_copy.metadata["relevance_score"] = res["relevance_score"]
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compressed.append(doc_copy)
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return compressed
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