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"""KNN Retriever.
Largely based on
https://github.com/karpathy/randomfun/blob/master/knn_vs_svm.ipynb"""
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
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(executor.map(embeddings.embed_query, contexts)))
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
@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,
)
@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 = index_embeds.dot(query_embeds)
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
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