mirror of
https://github.com/hwchase17/langchain
synced 2024-11-10 01:10:59 +00:00
82 lines
2.5 KiB
Python
82 lines
2.5 KiB
Python
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"""KNN Retriever.
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Largely based on
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https://github.com/karpathy/randomfun/blob/master/knn_vs_svm.ipynb"""
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from __future__ import annotations
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import concurrent.futures
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from typing import Any, List, Optional
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import numpy as np
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from langchain_core.callbacks import CallbackManagerForRetrieverRun
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from langchain_core.documents import Document
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from langchain_core.embeddings import Embeddings
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from langchain_core.retrievers import BaseRetriever
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def create_index(contexts: List[str], embeddings: Embeddings) -> np.ndarray:
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"""
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Create an index of embeddings for a list of contexts.
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Args:
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contexts: List of contexts to embed.
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embeddings: Embeddings model to use.
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Returns:
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Index of embeddings.
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"""
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with concurrent.futures.ThreadPoolExecutor() as executor:
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return np.array(list(executor.map(embeddings.embed_query, contexts)))
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class KNNRetriever(BaseRetriever):
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"""`KNN` retriever."""
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embeddings: Embeddings
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"""Embeddings model to use."""
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index: Any
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"""Index of embeddings."""
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texts: List[str]
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"""List of texts to index."""
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k: int = 4
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"""Number of results to return."""
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relevancy_threshold: Optional[float] = None
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"""Threshold for relevancy."""
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class Config:
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"""Configuration for this pydantic object."""
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arbitrary_types_allowed = True
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@classmethod
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def from_texts(
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cls, texts: List[str], embeddings: Embeddings, **kwargs: Any
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) -> KNNRetriever:
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index = create_index(texts, embeddings)
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return cls(embeddings=embeddings, index=index, texts=texts, **kwargs)
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def _get_relevant_documents(
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self, query: str, *, run_manager: CallbackManagerForRetrieverRun
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) -> List[Document]:
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query_embeds = np.array(self.embeddings.embed_query(query))
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# calc L2 norm
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index_embeds = self.index / np.sqrt((self.index**2).sum(1, keepdims=True))
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query_embeds = query_embeds / np.sqrt((query_embeds**2).sum())
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similarities = index_embeds.dot(query_embeds)
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sorted_ix = np.argsort(-similarities)
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denominator = np.max(similarities) - np.min(similarities) + 1e-6
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normalized_similarities = (similarities - np.min(similarities)) / denominator
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top_k_results = [
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Document(page_content=self.texts[row])
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for row in sorted_ix[0 : self.k]
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if (
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self.relevancy_threshold is None
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or normalized_similarities[row] >= self.relevancy_threshold
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)
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]
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return top_k_results
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