Source code for langchain_community.retrievers.svm

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 SVMRetriever(BaseRetriever): """`SVM` retriever. Largely based on """ 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, ) -> SVMRetriever: 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, ) -> 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 ), 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