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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 SVMRetriever(BaseRetriever):
"""`SVM` retriever.
Largely based on
https://github.com/karpathy/randomfun/blob/master/knn_vs_svm.ipynb
"""
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,
) -> SVMRetriever:
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,
) -> SVMRetriever:
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]:
try:
from sklearn import svm
except ImportError:
raise ImportError(
"Could not import scikit-learn, please install with `pip install "
"scikit-learn`."
)
query_embeds = np.array(self.embeddings.embed_query(query))
x = np.concatenate([query_embeds[None, ...], self.index])
y = np.zeros(x.shape[0])
y[0] = 1
clf = svm.LinearSVC(
class_weight="balanced", verbose=False, max_iter=10000, tol=1e-6, C=0.1
)
clf.fit(x, y)
similarities = clf.decision_function(x)
sorted_ix = np.argsort(-similarities)
# svm.LinearSVC in scikit-learn is non-deterministic.
# if a text is the same as a query, there is no guarantee
# the query will be in the first index.
# this performs a simple swap, this works because anything
# left of the 0 should be equivalent.
zero_index = np.where(sorted_ix == 0)[0][0]
if zero_index != 0:
sorted_ix[0], sorted_ix[zero_index] = sorted_ix[zero_index], sorted_ix[0]
denominator = np.max(similarities) - np.min(similarities) + 1e-6
normalized_similarities = (similarities - np.min(similarities)) / denominator
top_k_results = []
for row in sorted_ix[1 : self.k + 1]:
if (
self.relevancy_threshold is None
or normalized_similarities[row] >= self.relevancy_threshold
):
metadata = self.metadatas[row - 1] if self.metadatas else {}
doc = Document(page_content=self.texts[row - 1], metadata=metadata)
top_k_results.append(doc)
return top_k_results
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