Source code for langchain_community.retrievers.knn

"""KNN Retriever.
Largely based on"""

from __future__ import annotations

import concurrent.futures
from typing import Any, Iterable, List, Optional

import numpy as np
from langchain_core.callbacks import CallbackManagerForRetrieverRun
from langchain_core.documents import Document
from langchain_core.embeddings import Embeddings
from langchain_core.retrievers import BaseRetriever

[docs]def create_index(contexts: List[str], embeddings: Embeddings) -> np.ndarray: """ Create an index of embeddings for a list of contexts. Args: contexts: List of contexts to embed. embeddings: Embeddings model to use. Returns: Index of embeddings. """ with concurrent.futures.ThreadPoolExecutor() as executor: return np.array(list(, contexts)))
[docs]class KNNRetriever(BaseRetriever): """`KNN` retriever.""" embeddings: Embeddings """Embeddings model to use.""" index: Any """Index of embeddings.""" texts: List[str] """List of texts to index.""" metadatas: Optional[List[dict]] = None """List of metadatas corresponding with each text.""" k: int = 4 """Number of results to return.""" relevancy_threshold: Optional[float] = None """Threshold for relevancy.""" class Config: """Configuration for this pydantic object.""" arbitrary_types_allowed = True
[docs] @classmethod def from_texts( cls, texts: List[str], embeddings: Embeddings, metadatas: Optional[List[dict]] = None, **kwargs: Any, ) -> KNNRetriever: index = create_index(texts, embeddings) return cls( embeddings=embeddings, index=index, texts=texts, metadatas=metadatas, **kwargs, )
[docs] @classmethod def from_documents( cls, documents: Iterable[Document], embeddings: Embeddings, **kwargs: Any, ) -> KNNRetriever: texts, metadatas = zip(*((d.page_content, d.metadata) for d in documents)) return cls.from_texts( texts=texts, embeddings=embeddings, metadatas=metadatas, **kwargs )
def _get_relevant_documents( self, query: str, *, run_manager: CallbackManagerForRetrieverRun ) -> List[Document]: query_embeds = np.array(self.embeddings.embed_query(query)) # calc L2 norm index_embeds = self.index / np.sqrt((self.index**2).sum(1, keepdims=True)) query_embeds = query_embeds / np.sqrt((query_embeds**2).sum()) similarities = sorted_ix = np.argsort(-similarities) denominator = np.max(similarities) - np.min(similarities) + 1e-6 normalized_similarities = (similarities - np.min(similarities)) / denominator top_k_results = [ Document( page_content=self.texts[row], metadata=self.metadatas[row] if self.metadatas else {}, ) for row in sorted_ix[0 : self.k] if ( self.relevancy_threshold is None or normalized_similarities[row] >= self.relevancy_threshold ) ] return top_k_results