haystack/haystack/document_stores/faiss.py

from typing import TYPE_CHECKING, Any, Union, List, Optional, Dict, Generator

if TYPE_CHECKING:
    from haystack.nodes.retriever import BaseRetriever

import json
import logging
import warnings
import numpy as np
from copy import deepcopy
from pathlib import Path
from tqdm.auto import tqdm
from inspect import Signature, signature

try:
    import faiss
    from haystack.document_stores.sql import (
        SQLDocumentStore,
    )  # its deps are optional, but get installed with the `faiss` extra
except (ImportError, ModuleNotFoundError) as ie:
    from haystack.utils.import_utils import _optional_component_not_installed

    _optional_component_not_installed(__name__, "faiss", ie)

from haystack.schema import Document
from haystack.document_stores.base import get_batches_from_generator


logger = logging.getLogger(__name__)


class FAISSDocumentStore(SQLDocumentStore):
    """
    Document store for very large scale embedding based dense retrievers like the DPR.

    It implements the FAISS library(https://github.com/facebookresearch/faiss)
    to perform similarity search on vectors.

    The document text and meta-data (for filtering) are stored using the SQLDocumentStore, while
    the vector embeddings are indexed in a FAISS Index.
    """

    def __init__(
        self,
        sql_url: str = "sqlite:///faiss_document_store.db",
        vector_dim: int = None,
        embedding_dim: int = 768,
        faiss_index_factory_str: str = "Flat",
        faiss_index: Optional[faiss.swigfaiss.Index] = None,
        return_embedding: bool = False,
        index: str = "document",
        similarity: str = "dot_product",
        embedding_field: str = "embedding",
        progress_bar: bool = True,
        duplicate_documents: str = "overwrite",
        faiss_index_path: Union[str, Path] = None,
        faiss_config_path: Union[str, Path] = None,
        isolation_level: str = None,
        **kwargs,
    ):
        """
        :param sql_url: SQL connection URL for database. It defaults to local file based SQLite DB. For large scale
                        deployment, Postgres is recommended.
        :param vector_dim: Deprecated. Use embedding_dim instead.
        :param embedding_dim: The embedding vector size. Default: 768.
        :param faiss_index_factory_str: Create a new FAISS index of the specified type.
                                        The type is determined from the given string following the conventions
                                        of the original FAISS index factory.
                                        Recommended options:
                                        - "Flat" (default): Best accuracy (= exact). Becomes slow and RAM intense for > 1 Mio docs.
                                        - "HNSW": Graph-based heuristic. If not further specified,
                                                  we use the following config:
                                                  HNSW64, efConstruction=80 and efSearch=20
                                        - "IVFx,Flat": Inverted Index. Replace x with the number of centroids aka nlist.
                                                          Rule of thumb: nlist = 10 * sqrt (num_docs) is a good starting point.
                                        For more details see:
                                        - Overview of indices https://github.com/facebookresearch/faiss/wiki/Faiss-indexes
                                        - Guideline for choosing an index https://github.com/facebookresearch/faiss/wiki/Guidelines-to-choose-an-index
                                        - FAISS Index factory https://github.com/facebookresearch/faiss/wiki/The-index-factory
                                        Benchmarks: XXX
        :param faiss_index: Pass an existing FAISS Index, i.e. an empty one that you configured manually
                            or one with docs that you used in Haystack before and want to load again.
        :param return_embedding: To return document embedding. Unlike other document stores, FAISS will return normalized embeddings
        :param index: Name of index in document store to use.
        :param similarity: The similarity function used to compare document vectors. 'dot_product' is the default since it is
                   more performant with DPR embeddings. 'cosine' is recommended if you are using a Sentence-Transformer model.
                   In both cases, the returned values in Document.score are normalized to be in range [0,1]:
                   For `dot_product`: expit(np.asarray(raw_score / 100))
                   FOr `cosine`: (raw_score + 1) / 2
        :param embedding_field: Name of field containing an embedding vector.
        :param progress_bar: Whether to show a tqdm progress bar or not.
                             Can be helpful to disable in production deployments to keep the logs clean.
        :param duplicate_documents: Handle duplicates document based on parameter options.
                                    Parameter options : ( 'skip','overwrite','fail')
                                    skip: Ignore the duplicates documents
                                    overwrite: Update any existing documents with the same ID when adding documents.
                                    fail: an error is raised if the document ID of the document being added already
                                    exists.
        :param faiss_index_path: Stored FAISS index file. Can be created via calling `save()`.
            If specified no other params besides faiss_config_path must be specified.
        :param faiss_config_path: Stored FAISS initial configuration parameters.
            Can be created via calling `save()`
        :param isolation_level: see SQLAlchemy's `isolation_level` parameter for `create_engine()` (https://docs.sqlalchemy.org/en/14/core/engines.html#sqlalchemy.create_engine.params.isolation_level)
        """
        # special case if we want to load an existing index from disk
        # load init params from disk and run init again
        if faiss_index_path is not None:
            sig = signature(self.__class__.__init__)
            self._validate_params_load_from_disk(sig, locals(), kwargs)
            init_params = self._load_init_params_from_config(faiss_index_path, faiss_config_path)
            self.__class__.__init__(self, **init_params)  # pylint: disable=non-parent-init-called
            return

        if similarity in ("dot_product", "cosine"):
            self.similarity = similarity
            self.metric_type = faiss.METRIC_INNER_PRODUCT
        elif similarity == "l2":
            self.similarity = similarity
            self.metric_type = faiss.METRIC_L2
        else:
            raise ValueError(
                "The FAISS document store can currently only support dot_product, cosine and l2 similarity. "
                "Please set similarity to one of the above."
            )

        if vector_dim is not None:
            warnings.warn(
                "The 'vector_dim' parameter is deprecated, " "use 'embedding_dim' instead.", DeprecationWarning, 2
            )
            self.embedding_dim = vector_dim
        else:
            self.embedding_dim = embedding_dim

        self.faiss_index_factory_str = faiss_index_factory_str
        self.faiss_indexes: Dict[str, faiss.swigfaiss.Index] = {}
        if faiss_index:
            self.faiss_indexes[index] = faiss_index
        else:
            self.faiss_indexes[index] = self._create_new_index(
                embedding_dim=self.embedding_dim,
                index_factory=faiss_index_factory_str,
                metric_type=self.metric_type,
                **kwargs,
            )

        self.return_embedding = return_embedding
        self.embedding_field = embedding_field

        self.progress_bar = progress_bar

        super().__init__(
            url=sql_url, index=index, duplicate_documents=duplicate_documents, isolation_level=isolation_level
        )

        self._validate_index_sync()

    def _validate_params_load_from_disk(self, sig: Signature, locals: dict, kwargs: dict):
        allowed_params = ["faiss_index_path", "faiss_config_path", "self", "kwargs"]
        invalid_param_set = False

        for param in sig.parameters.values():
            if param.name not in allowed_params and param.default != locals[param.name]:
                invalid_param_set = True
                break

        if invalid_param_set or len(kwargs) > 0:
            raise ValueError("if faiss_index_path is passed no other params besides faiss_config_path are allowed.")

    def _validate_index_sync(self):
        # This check ensures the correct document database was loaded.
        # If it fails, make sure you provided the path to the database
        # used when creating the original FAISS index
        if not self.get_document_count() == self.get_embedding_count():
            raise ValueError(
                "The number of documents present in the SQL database does not "
                "match the number of embeddings in FAISS. Make sure your FAISS "
                "configuration file correctly points to the same database that "
                "was used when creating the original index."
            )

    def _create_new_index(self, embedding_dim: int, metric_type, index_factory: str = "Flat", **kwargs):
        if index_factory == "HNSW":
            # faiss index factory doesn't give the same results for HNSW IP, therefore direct init.
            # defaults here are similar to DPR codebase (good accuracy, but very high RAM consumption)
            n_links = kwargs.get("n_links", 64)
            index = faiss.IndexHNSWFlat(embedding_dim, n_links, metric_type)
            index.hnsw.efSearch = kwargs.get("efSearch", 20)  # 20
            index.hnsw.efConstruction = kwargs.get("efConstruction", 80)  # 80
            if "ivf" in index_factory.lower():  # enable reconstruction of vectors for inverted index
                self.faiss_indexes[index].set_direct_map_type(faiss.DirectMap.Hashtable)

            logger.info(
                f"HNSW params: n_links: {n_links}, efSearch: {index.hnsw.efSearch}, efConstruction: {index.hnsw.efConstruction}"
            )
        else:
            index = faiss.index_factory(embedding_dim, index_factory, metric_type)
        return index

    def write_documents(
        self,
        documents: Union[List[dict], List[Document]],
        index: Optional[str] = None,
        batch_size: int = 10_000,
        duplicate_documents: Optional[str] = None,
        headers: Optional[Dict[str, str]] = None,
    ) -> None:
        """
        Add new documents to the DocumentStore.

        :param documents: List of `Dicts` or List of `Documents`. If they already contain the embeddings, we'll index
                          them right away in FAISS. If not, you can later call update_embeddings() to create & index them.
        :param index: (SQL) index name for storing the docs and metadata
        :param batch_size: When working with large number of documents, batching can help reduce memory footprint.
        :param duplicate_documents: Handle duplicates document based on parameter options.
                                    Parameter options : ( 'skip','overwrite','fail')
                                    skip: Ignore the duplicates documents
                                    overwrite: Update any existing documents with the same ID when adding documents.
                                    fail: an error is raised if the document ID of the document being added already
                                    exists.
        :raises DuplicateDocumentError: Exception trigger on duplicate document
        :return: None
        """
        if headers:
            raise NotImplementedError("FAISSDocumentStore does not support headers.")

        index = index or self.index
        duplicate_documents = duplicate_documents or self.duplicate_documents
        assert (
            duplicate_documents in self.duplicate_documents_options
        ), f"duplicate_documents parameter must be {', '.join(self.duplicate_documents_options)}"

        if not self.faiss_indexes.get(index):
            self.faiss_indexes[index] = self._create_new_index(
                embedding_dim=self.embedding_dim,
                index_factory=self.faiss_index_factory_str,
                metric_type=faiss.METRIC_INNER_PRODUCT,
            )

        field_map = self._create_document_field_map()
        document_objects = [Document.from_dict(d, field_map=field_map) if isinstance(d, dict) else d for d in documents]
        document_objects = self._handle_duplicate_documents(
            documents=document_objects, index=index, duplicate_documents=duplicate_documents
        )
        if len(document_objects) > 0:
            add_vectors = False if document_objects[0].embedding is None else True

            if self.duplicate_documents == "overwrite" and add_vectors:
                logger.warning(
                    "You have to provide `duplicate_documents = 'overwrite'` arg and "
                    "`FAISSDocumentStore` does not support update in existing `faiss_index`.\n"
                    "Please call `update_embeddings` method to repopulate `faiss_index`"
                )

            vector_id = self.faiss_indexes[index].ntotal
            with tqdm(
                total=len(document_objects), disable=not self.progress_bar, position=0, desc="Writing Documents"
            ) as progress_bar:
                for i in range(0, len(document_objects), batch_size):
                    if add_vectors:
                        embeddings = [doc.embedding for doc in document_objects[i : i + batch_size]]
                        embeddings_to_index = np.array(embeddings, dtype="float32")

                        if self.similarity == "cosine":
                            self.normalize_embedding(embeddings_to_index)

                        self.faiss_indexes[index].add(embeddings_to_index)

                    docs_to_write_in_sql = []
                    for doc in document_objects[i : i + batch_size]:
                        meta = doc.meta
                        if add_vectors:
                            meta["vector_id"] = vector_id
                            vector_id += 1
                        docs_to_write_in_sql.append(doc)

                    super(FAISSDocumentStore, self).write_documents(
                        docs_to_write_in_sql,
                        index=index,
                        duplicate_documents=duplicate_documents,
                        batch_size=batch_size,
                    )
                    progress_bar.update(batch_size)
            progress_bar.close()

    def _create_document_field_map(self) -> Dict:
        return {self.index: self.embedding_field}

    def update_embeddings(
        self,
        retriever: "BaseRetriever",
        index: Optional[str] = None,
        update_existing_embeddings: bool = True,
        filters: Optional[Dict[str, Any]] = None,  # TODO: Adapt type once we allow extended filters in FAISSDocStore
        batch_size: int = 10_000,
    ):
        """
        Updates the embeddings in the the document store using the encoding model specified in the retriever.
        This can be useful if want to add or change the embeddings for your documents (e.g. after changing the retriever config).

        :param retriever: Retriever to use to get embeddings for text
        :param index: Index name for which embeddings are to be updated. If set to None, the default self.index is used.
        :param update_existing_embeddings: Whether to update existing embeddings of the documents. If set to False,
                                           only documents without embeddings are processed. This mode can be used for
                                           incremental updating of embeddings, wherein, only newly indexed documents
                                           get processed.
        :param filters: Optional filters to narrow down the documents for which embeddings are to be updated.
                        Example: {"name": ["some", "more"], "category": ["only_one"]}
        :param batch_size: When working with large number of documents, batching can help reduce memory footprint.
        :return: None
        """
        index = index or self.index

        if update_existing_embeddings is True:
            if filters is None:
                self.faiss_indexes[index].reset()
                self.reset_vector_ids(index)
            else:
                raise Exception("update_existing_embeddings=True is not supported with filters.")

        if not self.faiss_indexes.get(index):
            raise ValueError("Couldn't find a FAISS index. Try to init the FAISSDocumentStore() again ...")

        document_count = self.get_document_count(index=index)
        if document_count == 0:
            logger.warning("Calling DocumentStore.update_embeddings() on an empty index")
            return

        logger.info(f"Updating embeddings for {document_count} docs...")
        vector_id = sum([index.ntotal for index in self.faiss_indexes.values()])

        result = self._query(
            index=index,
            vector_ids=None,
            batch_size=batch_size,
            filters=filters,
            only_documents_without_embedding=not update_existing_embeddings,
        )
        batched_documents = get_batches_from_generator(result, batch_size)
        with tqdm(
            total=document_count, disable=not self.progress_bar, position=0, unit=" docs", desc="Updating Embedding"
        ) as progress_bar:
            for document_batch in batched_documents:
                embeddings = retriever.embed_documents(document_batch)  # type: ignore
                assert len(document_batch) == len(embeddings)

                embeddings_to_index = np.array(embeddings, dtype="float32")

                if self.similarity == "cosine":
                    self.normalize_embedding(embeddings_to_index)

                self.faiss_indexes[index].add(embeddings_to_index)

                vector_id_map = {}
                for doc in document_batch:
                    vector_id_map[str(doc.id)] = str(vector_id)
                    vector_id += 1
                self.update_vector_ids(vector_id_map, index=index)
                progress_bar.set_description_str("Documents Processed")
                progress_bar.update(batch_size)

    def get_all_documents(
        self,
        index: Optional[str] = None,
        filters: Optional[Dict[str, Any]] = None,  # TODO: Adapt type once we allow extended filters in FAISSDocStore
        return_embedding: Optional[bool] = None,
        batch_size: int = 10_000,
        headers: Optional[Dict[str, str]] = None,
    ) -> List[Document]:
        if headers:
            raise NotImplementedError("FAISSDocumentStore does not support headers.")

        result = self.get_all_documents_generator(
            index=index, filters=filters, return_embedding=return_embedding, batch_size=batch_size
        )
        documents = list(result)
        return documents

    def get_all_documents_generator(
        self,
        index: Optional[str] = None,
        filters: Optional[Dict[str, Any]] = None,  # TODO: Adapt type once we allow extended filters in FAISSDocStore
        return_embedding: Optional[bool] = None,
        batch_size: int = 10_000,
        headers: Optional[Dict[str, str]] = None,
    ) -> Generator[Document, None, None]:
        """
        Get all documents from the document store. Under-the-hood, documents are fetched in batches from the
        document store and yielded as individual documents. This method can be used to iteratively process
        a large number of documents without having to load all documents in memory.

        :param index: Name of the index to get the documents from. If None, the
                      DocumentStore's default index (self.index) will be used.
        :param filters: Optional filters to narrow down the documents to return.
                        Example: {"name": ["some", "more"], "category": ["only_one"]}
        :param return_embedding: Whether to return the document embeddings. Unlike other document stores, FAISS will return normalized embeddings
        :param batch_size: When working with large number of documents, batching can help reduce memory footprint.
        """
        if headers:
            raise NotImplementedError("FAISSDocumentStore does not support headers.")

        index = index or self.index
        documents = super(FAISSDocumentStore, self).get_all_documents_generator(
            index=index, filters=filters, batch_size=batch_size, return_embedding=False
        )
        if return_embedding is None:
            return_embedding = self.return_embedding

        for doc in documents:
            if return_embedding:
                if doc.meta and doc.meta.get("vector_id") is not None:
                    doc.embedding = self.faiss_indexes[index].reconstruct(int(doc.meta["vector_id"]))
            yield doc

    def get_documents_by_id(
        self,
        ids: List[str],
        index: Optional[str] = None,
        batch_size: int = 10_000,
        headers: Optional[Dict[str, str]] = None,
    ) -> List[Document]:
        if headers:
            raise NotImplementedError("FAISSDocumentStore does not support headers.")

        index = index or self.index
        documents = super(FAISSDocumentStore, self).get_documents_by_id(ids=ids, index=index, batch_size=batch_size)
        if self.return_embedding:
            for doc in documents:
                if doc.meta and doc.meta.get("vector_id") is not None:
                    doc.embedding = self.faiss_indexes[index].reconstruct(int(doc.meta["vector_id"]))
        return documents

    def get_embedding_count(self, index: Optional[str] = None, filters: Optional[Dict[str, Any]] = None) -> int:
        """
        Return the count of embeddings in the document store.
        """
        if filters:
            raise Exception("filters are not supported for get_embedding_count in FAISSDocumentStore")
        index = index or self.index
        return self.faiss_indexes[index].ntotal

    def train_index(
        self,
        documents: Optional[Union[List[dict], List[Document]]],
        embeddings: Optional[np.ndarray] = None,
        index: Optional[str] = None,
    ):
        """
        Some FAISS indices (e.g. IVF) require initial "training" on a sample of vectors before you can add your final vectors.
        The train vectors should come from the same distribution as your final ones.
        You can pass either documents (incl. embeddings) or just the plain embeddings that the index shall be trained on.

        :param documents: Documents (incl. the embeddings)
        :param embeddings: Plain embeddings
        :param index: Name of the index to train. If None, the DocumentStore's default index (self.index) will be used.
        :return: None
        """
        index = index or self.index
        if embeddings and documents:
            raise ValueError("Either pass `documents` or `embeddings`. You passed both.")
        if documents:
            document_objects = [Document.from_dict(d) if isinstance(d, dict) else d for d in documents]
            doc_embeddings = [doc.embedding for doc in document_objects]
            embeddings_for_train = np.array(doc_embeddings, dtype="float32")
            self.faiss_indexes[index].train(embeddings_for_train)
        if embeddings:
            self.faiss_indexes[index].train(embeddings)

    def delete_all_documents(
        self,
        index: Optional[str] = None,
        filters: Optional[Dict[str, Any]] = None,  # TODO: Adapt type once we allow extended filters in FAISSDocStore
        headers: Optional[Dict[str, str]] = None,
    ):
        """
        Delete all documents from the document store.
        """
        if headers:
            raise NotImplementedError("FAISSDocumentStore does not support headers.")

        logger.warning(
            """DEPRECATION WARNINGS: 
                1. delete_all_documents() method is deprecated, please use delete_documents method
                For more details, please refer to the issue: https://github.com/deepset-ai/haystack/issues/1045
                """
        )
        self.delete_documents(index, None, filters)

    def delete_documents(
        self,
        index: Optional[str] = None,
        ids: Optional[List[str]] = None,
        filters: Optional[Dict[str, Any]] = None,  # TODO: Adapt type once we allow extended filters in FAISSDocStore
        headers: Optional[Dict[str, str]] = None,
    ):
        """
        Delete documents from the document store. All documents are deleted if no filters are passed.

        :param index: Index name to delete the documents from. If None, the
                      DocumentStore's default index (self.index) will be used.
        :param ids: Optional list of IDs to narrow down the documents to be deleted.
        :param filters: Optional filters to narrow down the documents to be deleted.
            Example filters: {"name": ["some", "more"], "category": ["only_one"]}.
            If filters are provided along with a list of IDs, this method deletes the
            intersection of the two query results (documents that match the filters and
            have their ID in the list).
        :return: None
        """
        if headers:
            raise NotImplementedError("FAISSDocumentStore does not support headers.")

        index = index or self.index
        if index in self.faiss_indexes.keys():
            if not filters and not ids:
                self.faiss_indexes[index].reset()
            else:
                affected_docs = self.get_all_documents(filters=filters)
                if ids:
                    affected_docs = [doc for doc in affected_docs if doc.id in ids]
                doc_ids = [
                    doc.meta.get("vector_id")
                    for doc in affected_docs
                    if doc.meta and doc.meta.get("vector_id") is not None
                ]
                self.faiss_indexes[index].remove_ids(np.array(doc_ids, dtype="int64"))

        super().delete_documents(index=index, ids=ids, filters=filters)

    def query_by_embedding(
        self,
        query_emb: np.ndarray,
        filters: Optional[Dict[str, Any]] = None,  # TODO: Adapt type once we allow extended filters in FAISSDocStore
        top_k: int = 10,
        index: Optional[str] = None,
        return_embedding: Optional[bool] = None,
        headers: Optional[Dict[str, str]] = None,
    ) -> List[Document]:
        """
        Find the document that is most similar to the provided `query_emb` by using a vector similarity metric.

        :param query_emb: Embedding of the query (e.g. gathered from DPR)
        :param filters: Optional filters to narrow down the search space.
                        Example: {"name": ["some", "more"], "category": ["only_one"]}
        :param top_k: How many documents to return
        :param index: Index name to query the document from.
        :param return_embedding: To return document embedding. Unlike other document stores, FAISS will return normalized embeddings
        :return:
        """
        if headers:
            raise NotImplementedError("FAISSDocumentStore does not support headers.")

        if filters:
            logger.warning("Query filters are not implemented for the FAISSDocumentStore.")

        index = index or self.index
        if not self.faiss_indexes.get(index):
            raise Exception(f"Index named '{index}' does not exists. Use 'update_embeddings()' to create an index.")

        if return_embedding is None:
            return_embedding = self.return_embedding

        query_emb = query_emb.reshape(1, -1).astype(np.float32)

        if self.similarity == "cosine":
            self.normalize_embedding(query_emb)

        score_matrix, vector_id_matrix = self.faiss_indexes[index].search(query_emb, top_k)
        vector_ids_for_query = [str(vector_id) for vector_id in vector_id_matrix[0] if vector_id != -1]

        documents = self.get_documents_by_vector_ids(vector_ids_for_query, index=index)

        # assign query score to each document
        scores_for_vector_ids: Dict[str, float] = {
            str(v_id): s for v_id, s in zip(vector_id_matrix[0], score_matrix[0])
        }
        for doc in documents:
            raw_score = scores_for_vector_ids[doc.meta["vector_id"]]
            doc.score = self.finalize_raw_score(raw_score, self.similarity)

            if return_embedding is True:
                doc.embedding = self.faiss_indexes[index].reconstruct(int(doc.meta["vector_id"]))

        return documents

    def save(self, index_path: Union[str, Path], config_path: Optional[Union[str, Path]] = None):
        """
        Save FAISS Index to the specified file.

        :param index_path: Path to save the FAISS index to.
        :param config_path: Path to save the initial configuration parameters to.
            Defaults to the same as the file path, save the extension (.json).
            This file contains all the parameters passed to FAISSDocumentStore()
            at creation time (for example the SQL path, embedding_dim, etc), and will be
            used by the `load` method to restore the index with the appropriate configuration.
        :return: None
        """
        if not config_path:
            index_path = Path(index_path)
            config_path = index_path.with_suffix(".json")

        faiss.write_index(self.faiss_indexes[self.index], str(index_path))

        config_to_save = deepcopy(self._component_config["params"])
        keys_to_remove = ["faiss_index", "faiss_index_path"]
        for key in keys_to_remove:
            if key in config_to_save.keys():
                del config_to_save[key]

        with open(config_path, "w") as ipp:
            json.dump(config_to_save, ipp, default=str)

    def _load_init_params_from_config(
        self, index_path: Union[str, Path], config_path: Optional[Union[str, Path]] = None
    ):
        if not config_path:
            index_path = Path(index_path)
            config_path = index_path.with_suffix(".json")

        init_params: dict = {}
        try:
            with open(config_path, "r") as ipp:
                init_params = json.load(ipp)
        except OSError as e:
            raise ValueError(
                f"Can't open FAISS configuration file `{config_path}`. "
                "Make sure the file exists and the you have the correct permissions "
                "to access it."
            ) from e

        faiss_index = faiss.read_index(str(index_path))

        # Add other init params to override the ones defined in the init params file
        init_params["faiss_index"] = faiss_index
        init_params["embedding_dim"] = faiss_index.d

        return init_params

    @classmethod
    def load(cls, index_path: Union[str, Path], config_path: Optional[Union[str, Path]] = None):
        """
        Load a saved FAISS index from a file and connect to the SQL database.
        Note: In order to have a correct mapping from FAISS to SQL,
              make sure to use the same SQL DB that you used when calling `save()`.

        :param index_path: Stored FAISS index file. Can be created via calling `save()`
        :param config_path: Stored FAISS initial configuration parameters.
            Can be created via calling `save()`
        """
        return cls(faiss_index_path=index_path, faiss_config_path=config_path)