langchain/libs/community/langchain_community/vectorstores/vald.py

"""Wrapper around Vald vector database."""
from __future__ import annotations

from typing import Any, Iterable, List, Optional, Tuple, Type

import numpy as np
from langchain_core.documents import Document
from langchain_core.embeddings import Embeddings
from langchain_core.vectorstores import VectorStore

from langchain_community.vectorstores.utils import maximal_marginal_relevance


class Vald(VectorStore):
    """Vald vector database.

    To use, you should have the ``vald-client-python`` python package installed.

    Example:
        .. code-block:: python

            from langchain_community.embeddings import HuggingFaceEmbeddings
            from langchain_community.vectorstores import Vald

            texts = ['foo', 'bar', 'baz']
            vald = Vald.from_texts(
                texts=texts,
                embedding=HuggingFaceEmbeddings(),
                host="localhost",
                port=8080,
                skip_strict_exist_check=False,
             )
    """

    def __init__(
        self,
        embedding: Embeddings,
        host: str = "localhost",
        port: int = 8080,
        grpc_options: Tuple = (
            ("grpc.keepalive_time_ms", 1000 * 10),
            ("grpc.keepalive_timeout_ms", 1000 * 10),
        ),
        grpc_use_secure: bool = False,
        grpc_credentials: Optional[Any] = None,
    ):
        self._embedding = embedding
        self.target = host + ":" + str(port)
        self.grpc_options = grpc_options
        self.grpc_use_secure = grpc_use_secure
        self.grpc_credentials = grpc_credentials

    @property
    def embeddings(self) -> Optional[Embeddings]:
        return self._embedding

    def _get_channel(self) -> Any:
        try:
            import grpc
        except ImportError:
            raise ValueError(
                "Could not import grpcio python package. "
                "Please install it with `pip install grpcio`."
            )
        return (
            grpc.secure_channel(
                self.target, self.grpc_credentials, options=self.grpc_options
            )
            if self.grpc_use_secure
            else grpc.insecure_channel(self.target, options=self.grpc_options)
        )

    def add_texts(
        self,
        texts: Iterable[str],
        metadatas: Optional[List[dict]] = None,
        grpc_metadata: Optional[Any] = None,
        skip_strict_exist_check: bool = False,
        **kwargs: Any,
    ) -> List[str]:
        """
        Args:
            skip_strict_exist_check: Deprecated. This is not used basically.
        """
        try:
            from vald.v1.payload import payload_pb2
            from vald.v1.vald import upsert_pb2_grpc
        except ImportError:
            raise ValueError(
                "Could not import vald-client-python python package. "
                "Please install it with `pip install vald-client-python`."
            )

        channel = self._get_channel()
        # Depending on the network quality,
        # it is necessary to wait for ChannelConnectivity.READY.
        # _ = grpc.channel_ready_future(channel).result(timeout=10)
        stub = upsert_pb2_grpc.UpsertStub(channel)
        cfg = payload_pb2.Upsert.Config(skip_strict_exist_check=skip_strict_exist_check)

        ids = []
        embs = self._embedding.embed_documents(list(texts))
        for text, emb in zip(texts, embs):
            vec = payload_pb2.Object.Vector(id=text, vector=emb)
            res = stub.Upsert(
                payload_pb2.Upsert.Request(vector=vec, config=cfg),
                metadata=grpc_metadata,
            )
            ids.append(res.uuid)

        channel.close()
        return ids

    def delete(
        self,
        ids: Optional[List[str]] = None,
        skip_strict_exist_check: bool = False,
        grpc_metadata: Optional[Any] = None,
        **kwargs: Any,
    ) -> Optional[bool]:
        """
        Args:
            skip_strict_exist_check: Deprecated. This is not used basically.
        """
        try:
            from vald.v1.payload import payload_pb2
            from vald.v1.vald import remove_pb2_grpc
        except ImportError:
            raise ValueError(
                "Could not import vald-client-python python package. "
                "Please install it with `pip install vald-client-python`."
            )

        if ids is None:
            raise ValueError("No ids provided to delete")

        channel = self._get_channel()
        # Depending on the network quality,
        # it is necessary to wait for ChannelConnectivity.READY.
        # _ = grpc.channel_ready_future(channel).result(timeout=10)
        stub = remove_pb2_grpc.RemoveStub(channel)
        cfg = payload_pb2.Remove.Config(skip_strict_exist_check=skip_strict_exist_check)

        for _id in ids:
            oid = payload_pb2.Object.ID(id=_id)
            _ = stub.Remove(
                payload_pb2.Remove.Request(id=oid, config=cfg), metadata=grpc_metadata
            )

        channel.close()
        return True

    def similarity_search(
        self,
        query: str,
        k: int = 4,
        radius: float = -1.0,
        epsilon: float = 0.01,
        timeout: int = 3000000000,
        grpc_metadata: Optional[Any] = None,
        **kwargs: Any,
    ) -> List[Document]:
        docs_and_scores = self.similarity_search_with_score(
            query, k, radius, epsilon, timeout, grpc_metadata
        )

        docs = []
        for doc, _ in docs_and_scores:
            docs.append(doc)

        return docs

    def similarity_search_with_score(
        self,
        query: str,
        k: int = 4,
        radius: float = -1.0,
        epsilon: float = 0.01,
        timeout: int = 3000000000,
        grpc_metadata: Optional[Any] = None,
        **kwargs: Any,
    ) -> List[Tuple[Document, float]]:
        emb = self._embedding.embed_query(query)
        docs_and_scores = self.similarity_search_with_score_by_vector(
            emb, k, radius, epsilon, timeout, grpc_metadata
        )

        return docs_and_scores

    def similarity_search_by_vector(
        self,
        embedding: List[float],
        k: int = 4,
        radius: float = -1.0,
        epsilon: float = 0.01,
        timeout: int = 3000000000,
        grpc_metadata: Optional[Any] = None,
        **kwargs: Any,
    ) -> List[Document]:
        docs_and_scores = self.similarity_search_with_score_by_vector(
            embedding, k, radius, epsilon, timeout, grpc_metadata
        )

        docs = []
        for doc, _ in docs_and_scores:
            docs.append(doc)

        return docs

    def similarity_search_with_score_by_vector(
        self,
        embedding: List[float],
        k: int = 4,
        radius: float = -1.0,
        epsilon: float = 0.01,
        timeout: int = 3000000000,
        grpc_metadata: Optional[Any] = None,
        **kwargs: Any,
    ) -> List[Tuple[Document, float]]:
        try:
            from vald.v1.payload import payload_pb2
            from vald.v1.vald import search_pb2_grpc
        except ImportError:
            raise ValueError(
                "Could not import vald-client-python python package. "
                "Please install it with `pip install vald-client-python`."
            )

        channel = self._get_channel()
        # Depending on the network quality,
        # it is necessary to wait for ChannelConnectivity.READY.
        # _ = grpc.channel_ready_future(channel).result(timeout=10)
        stub = search_pb2_grpc.SearchStub(channel)
        cfg = payload_pb2.Search.Config(
            num=k, radius=radius, epsilon=epsilon, timeout=timeout
        )

        res = stub.Search(
            payload_pb2.Search.Request(vector=embedding, config=cfg),
            metadata=grpc_metadata,
        )

        docs_and_scores = []
        for result in res.results:
            docs_and_scores.append((Document(page_content=result.id), result.distance))

        channel.close()
        return docs_and_scores

    def max_marginal_relevance_search(
        self,
        query: str,
        k: int = 4,
        fetch_k: int = 20,
        lambda_mult: float = 0.5,
        radius: float = -1.0,
        epsilon: float = 0.01,
        timeout: int = 3000000000,
        grpc_metadata: Optional[Any] = None,
        **kwargs: Any,
    ) -> List[Document]:
        emb = self._embedding.embed_query(query)
        docs = self.max_marginal_relevance_search_by_vector(
            emb,
            k=k,
            fetch_k=fetch_k,
            radius=radius,
            epsilon=epsilon,
            timeout=timeout,
            lambda_mult=lambda_mult,
            grpc_metadata=grpc_metadata,
        )

        return docs

    def max_marginal_relevance_search_by_vector(
        self,
        embedding: List[float],
        k: int = 4,
        fetch_k: int = 20,
        lambda_mult: float = 0.5,
        radius: float = -1.0,
        epsilon: float = 0.01,
        timeout: int = 3000000000,
        grpc_metadata: Optional[Any] = None,
        **kwargs: Any,
    ) -> List[Document]:
        try:
            from vald.v1.payload import payload_pb2
            from vald.v1.vald import object_pb2_grpc
        except ImportError:
            raise ValueError(
                "Could not import vald-client-python python package. "
                "Please install it with `pip install vald-client-python`."
            )
        channel = self._get_channel()
        # Depending on the network quality,
        # it is necessary to wait for ChannelConnectivity.READY.
        # _ = grpc.channel_ready_future(channel).result(timeout=10)
        stub = object_pb2_grpc.ObjectStub(channel)

        docs_and_scores = self.similarity_search_with_score_by_vector(
            embedding,
            fetch_k=fetch_k,
            radius=radius,
            epsilon=epsilon,
            timeout=timeout,
            grpc_metadata=grpc_metadata,
        )

        docs = []
        embs = []
        for doc, _ in docs_and_scores:
            vec = stub.GetObject(
                payload_pb2.Object.VectorRequest(
                    id=payload_pb2.Object.ID(id=doc.page_content)
                ),
                metadata=grpc_metadata,
            )
            embs.append(vec.vector)
            docs.append(doc)

        mmr = maximal_marginal_relevance(
            np.array(embedding),
            embs,
            lambda_mult=lambda_mult,
            k=k,
        )

        channel.close()
        return [docs[i] for i in mmr]

    @classmethod
    def from_texts(
        cls: Type[Vald],
        texts: List[str],
        embedding: Embeddings,
        metadatas: Optional[List[dict]] = None,
        host: str = "localhost",
        port: int = 8080,
        grpc_options: Tuple = (
            ("grpc.keepalive_time_ms", 1000 * 10),
            ("grpc.keepalive_timeout_ms", 1000 * 10),
        ),
        grpc_use_secure: bool = False,
        grpc_credentials: Optional[Any] = None,
        grpc_metadata: Optional[Any] = None,
        skip_strict_exist_check: bool = False,
        **kwargs: Any,
    ) -> Vald:
        """
        Args:
            skip_strict_exist_check: Deprecated. This is not used basically.
        """
        vald = cls(
            embedding=embedding,
            host=host,
            port=port,
            grpc_options=grpc_options,
            grpc_use_secure=grpc_use_secure,
            grpc_credentials=grpc_credentials,
            **kwargs,
        )
        vald.add_texts(
            texts=texts,
            metadatas=metadatas,
            grpc_metadata=grpc_metadata,
            skip_strict_exist_check=skip_strict_exist_check,
        )
        return vald


"""We will support if there are any requests."""
#    async def aadd_texts(
#        self,
#        texts: Iterable[str],
#        metadatas: Optional[List[dict]] = None,
#        **kwargs: Any,
#    ) -> List[str]:
#        pass
#
#    def _select_relevance_score_fn(self) -> Callable[[float], float]:
#        pass
#
#    def _similarity_search_with_relevance_scores(
#        self,
#        query: str,
#        k: int = 4,
#        **kwargs: Any,
#    ) -> List[Tuple[Document, float]]:
#        pass
#
#    def similarity_search_with_relevance_scores(
#        self,
#        query: str,
#        k: int = 4,
#        **kwargs: Any,
#    ) -> List[Tuple[Document, float]]:
#        pass
#
#    async def amax_marginal_relevance_search_by_vector(
#        self,
#        embedding: List[float],
#        k: int = 4,
#        fetch_k: int = 20,
#        lambda_mult: float = 0.5,
#        **kwargs: Any,
#    ) -> List[Document]:
#        pass
#
#    @classmethod
#    async def afrom_texts(
#        cls: Type[VST],
#        texts: List[str],
#        embedding: Embeddings,
#        metadatas: Optional[List[dict]] = None,
#        **kwargs: Any,
#    ) -> VST:
#        pass