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# 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

    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