"""**Vector store** stores embedded data and performs vector search.

One of the most common ways to store and search over unstructured data is to
embed it and store the resulting embedding vectors, and then query the store
and retrieve the data that are 'most similar' to the embedded query.

**Class hierarchy:**

.. code-block::

    VectorStore --> <name>  # Examples: Annoy, FAISS, Milvus

    BaseRetriever --> VectorStoreRetriever --> <name>Retriever  # Example: VespaRetriever

**Main helpers:**

.. code-block::

    Embeddings, Document
"""  # noqa: E501
import warnings
from typing import Any

from langchain_core._api import LangChainDeprecationWarning
from langchain_core.vectorstores import VectorStore

from langchain.utils.interactive_env import is_interactive_env


def __getattr__(name: str) -> Any:
    from langchain_community import vectorstores

    # If not in interactive env, raise warning.
    if not is_interactive_env():
        warnings.warn(
            "Importing vector stores from langchain is deprecated. Importing from "
            "langchain will no longer be supported as of langchain==0.2.0. "
            "Please import from langchain-community instead:\n\n"
            f"`from langchain_community.vectorstores import {name}`.\n\n"
            "To install langchain-community run `pip install -U langchain-community`.",
            category=LangChainDeprecationWarning,
        )

    return getattr(vectorstores, name)


__all__ = [
    "AlibabaCloudOpenSearch",
    "AlibabaCloudOpenSearchSettings",
    "AnalyticDB",
    "Annoy",
    "AtlasDB",
    "AwaDB",
    "AzureSearch",
    "Bagel",
    "Cassandra",
    "AstraDB",
    "Chroma",
    "Clarifai",
    "Clickhouse",
    "ClickhouseSettings",
    "DashVector",
    "DatabricksVectorSearch",
    "DeepLake",
    "Dingo",
    "DocArrayHnswSearch",
    "DocArrayInMemorySearch",
    "ElasticKnnSearch",
    "ElasticVectorSearch",
    "ElasticsearchStore",
    "Epsilla",
    "FAISS",
    "Hologres",
    "LanceDB",
    "LLMRails",
    "Marqo",
    "MatchingEngine",
    "Meilisearch",
    "Milvus",
    "MomentoVectorIndex",
    "MongoDBAtlasVectorSearch",
    "MyScale",
    "MyScaleSettings",
    "Neo4jVector",
    "OpenSearchVectorSearch",
    "PGEmbedding",
    "PGVector",
    "Pinecone",
    "Qdrant",
    "Redis",
    "Rockset",
    "SKLearnVectorStore",
    "ScaNN",
    "SemaDB",
    "SingleStoreDB",
    "SQLiteVSS",
    "StarRocks",
    "SupabaseVectorStore",
    "Tair",
    "TileDB",
    "Tigris",
    "TimescaleVector",
    "Typesense",
    "USearch",
    "Vald",
    "Vearch",
    "Vectara",
    "VespaStore",
    "Weaviate",
    "Yellowbrick",
    "ZepVectorStore",
    "Zilliz",
    "TencentVectorDB",
    "AzureCosmosDBVectorSearch",
    "VectorStore",
]