diff --git a/docs/modules/indexes/vectorstores/examples/docarray_hnsw.ipynb b/docs/modules/indexes/vectorstores/examples/docarray_hnsw.ipynb
new file mode 100644
index 0000000000..01686c6ab7
--- /dev/null
+++ b/docs/modules/indexes/vectorstores/examples/docarray_hnsw.ipynb
@@ -0,0 +1,227 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "2ce41f46-5711-4311-b04d-2fe233ac5b1b",
+   "metadata": {},
+   "source": [
+    "# DocArrayHnswSearch\n",
+    "\n",
+    ">[DocArrayHnswSearch](https://docs.docarray.org/user_guide/storing/index_hnswlib/) is a lightweight Document Index implementation provided by [Docarray](https://docs.docarray.org/) that runs fully locally and is best suited for small- to medium-sized datasets. It stores vectors on disk in [hnswlib](https://github.com/nmslib/hnswlib), and stores all other data in [SQLite](https://www.sqlite.org/index.html).\n",
+    "\n",
+    "This notebook shows how to use functionality related to the `DocArrayHnswSearch`."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7ee37d28",
+   "metadata": {},
+   "source": [
+    "# Setup\n",
+    "\n",
+    "Uncomment the below cells to install docarray and get/set your OpenAI api key if you haven't already done so."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8ce1b8cb-dbf0-40c3-99ee-04f28143331b",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# !pip install \"docarray[hnswlib]\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "878f17df-100f-4854-9e87-472cf36d51f3",
+   "metadata": {
+    "scrolled": true,
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# Get an OpenAI token: https://platform.openai.com/account/api-keys\n",
+    "\n",
+    "# import os\n",
+    "# from getpass import getpass\n",
+    "\n",
+    "# OPENAI_API_KEY = getpass()\n",
+    "\n",
+    "# os.environ[\"OPENAI_API_KEY\"] = OPENAI_API_KEY"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8dbb6de2",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "# Using DocArrayHnswSearch"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b757afef-ef0a-465d-8e8a-9aadb9c32b88",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "from langchain.embeddings.openai import OpenAIEmbeddings\n",
+    "from langchain.text_splitter import CharacterTextSplitter\n",
+    "from langchain.vectorstores import DocArrayHnswSearch\n",
+    "from langchain.document_loaders import TextLoader"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "605e200e-e711-486b-b36e-cbe5dd2512d7",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "documents = TextLoader('../../../state_of_the_union.txt').load()\n",
+    "text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)\n",
+    "docs = text_splitter.split_documents(documents)\n",
+    "\n",
+    "embeddings = OpenAIEmbeddings()\n",
+    "\n",
+    "db = DocArrayHnswSearch.from_documents(docs, embeddings, work_dir='hnswlib_store/', n_dim=1536)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ed6f905b-4853-4a44-9730-614aa8e22b78",
+   "metadata": {},
+   "source": [
+    "## Similarity search"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "4d7e742f-2002-449d-a10e-16046890906c",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "query = \"What did the president say about Ketanji Brown Jackson\"\n",
+    "docs = db.similarity_search(query)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "0da9e26f-1fc2-48e6-95a7-f692c853bbd3",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Tonight. I call on the Senate to: Pass the Freedom to Vote Act. Pass the John Lewis Voting Rights Act. And while you’re at it, pass the Disclose Act so Americans can know who is funding our elections. \n",
+      "\n",
+      "Tonight, I’d like to honor someone who has dedicated his life to serve this country: Justice Stephen Breyer—an Army veteran, Constitutional scholar, and retiring Justice of the United States Supreme Court. Justice Breyer, thank you for your service. \n",
+      "\n",
+      "One of the most serious constitutional responsibilities a President has is nominating someone to serve on the United States Supreme Court. \n",
+      "\n",
+      "And I did that 4 days ago, when I nominated Circuit Court of Appeals Judge Ketanji Brown Jackson. One of our nation’s top legal minds, who will continue Justice Breyer’s legacy of excellence.\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(docs[0].page_content)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3febb987-e903-416f-af26-6897d84c8d61",
+   "metadata": {},
+   "source": [
+    "## Similarity search with score"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "40764fdd-357d-475a-8152-5f1979d61a45",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "docs = db.similarity_search_with_score(query)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "a479fc46-b299-4330-89b9-e9b5a218ea03",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(Document(page_content='Tonight. I call on the Senate to: Pass the Freedom to Vote Act. Pass the John Lewis Voting Rights Act. And while you’re at it, pass the Disclose Act so Americans can know who is funding our elections. \\n\\nTonight, I’d like to honor someone who has dedicated his life to serve this country: Justice Stephen Breyer—an Army veteran, Constitutional scholar, and retiring Justice of the United States Supreme Court. Justice Breyer, thank you for your service. \\n\\nOne of the most serious constitutional responsibilities a President has is nominating someone to serve on the United States Supreme Court. \\n\\nAnd I did that 4 days ago, when I nominated Circuit Court of Appeals Judge Ketanji Brown Jackson. One of our nation’s top legal minds, who will continue Justice Breyer’s legacy of excellence.', metadata={}),\n",
+       " 0.36962226)"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "docs[0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "4d3d4e97-5d2b-4571-8ff9-e3f6b6778714",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "import shutil\n",
+    "# delete the dir\n",
+    "shutil.rmtree('hnswlib_store')"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/docs/modules/indexes/vectorstores/examples/docarray_in_memory.ipynb b/docs/modules/indexes/vectorstores/examples/docarray_in_memory.ipynb
new file mode 100644
index 0000000000..8bc6ffdf2c
--- /dev/null
+++ b/docs/modules/indexes/vectorstores/examples/docarray_in_memory.ipynb
@@ -0,0 +1,210 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "a3afefb0-7e99-4912-a222-c6b186da11af",
+   "metadata": {},
+   "source": [
+    "# DocArrayInMemorySearch\n",
+    "\n",
+    ">[DocArrayInMemorySearch](https://docs.docarray.org/user_guide/storing/index_in_memory/) is a document index provided by [Docarray](https://docs.docarray.org/) that stores documents in memory. It is a great starting point for small datasets, where you may not want to launch a database server.\n",
+    "\n",
+    "This notebook shows how to use functionality related to the `DocArrayInMemorySearch`."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5031a3ec",
+   "metadata": {},
+   "source": [
+    "# Setup\n",
+    "\n",
+    "Uncomment the below cells to install docarray and get/set your OpenAI api key if you haven't already done so."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7cd7391f-7759-4a21-952a-2ec972d818c6",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# !pip install \"docarray\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c6a40ad8-920e-4370-818d-3227e2f506ed",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# Get an OpenAI token: https://platform.openai.com/account/api-keys\n",
+    "\n",
+    "# import os\n",
+    "# from getpass import getpass\n",
+    "\n",
+    "# OPENAI_API_KEY = getpass()\n",
+    "\n",
+    "# os.environ[\"OPENAI_API_KEY\"] = OPENAI_API_KEY"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e49be085-ddf1-4028-8c0c-97836ce4a873",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "from langchain.embeddings.openai import OpenAIEmbeddings\n",
+    "from langchain.text_splitter import CharacterTextSplitter\n",
+    "from langchain.vectorstores import DocArrayInMemorySearch\n",
+    "from langchain.document_loaders import TextLoader"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "38222aee-adc5-44c2-913c-97977b394cf5",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "documents = TextLoader('../../../state_of_the_union.txt').load()\n",
+    "text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)\n",
+    "docs = text_splitter.split_documents(documents)\n",
+    "\n",
+    "embeddings = OpenAIEmbeddings()\n",
+    "\n",
+    "db = DocArrayInMemorySearch.from_documents(docs, embeddings)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "efbb6684-3846-4332-a624-ddd4d75844c1",
+   "metadata": {},
+   "source": [
+    "## Similarity search"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "aa28a7f8-41d0-4299-84eb-91d1576e8a63",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "query = \"What did the president say about Ketanji Brown Jackson\"\n",
+    "docs = db.similarity_search(query)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "1eb16d2a-b466-456a-b412-5e74bb8523dd",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Tonight. I call on the Senate to: Pass the Freedom to Vote Act. Pass the John Lewis Voting Rights Act. And while you’re at it, pass the Disclose Act so Americans can know who is funding our elections. \n",
+      "\n",
+      "Tonight, I’d like to honor someone who has dedicated his life to serve this country: Justice Stephen Breyer—an Army veteran, Constitutional scholar, and retiring Justice of the United States Supreme Court. Justice Breyer, thank you for your service. \n",
+      "\n",
+      "One of the most serious constitutional responsibilities a President has is nominating someone to serve on the United States Supreme Court. \n",
+      "\n",
+      "And I did that 4 days ago, when I nominated Circuit Court of Appeals Judge Ketanji Brown Jackson. One of our nation’s top legal minds, who will continue Justice Breyer’s legacy of excellence.\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(docs[0].page_content)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "43896697-f99e-47b6-9117-47a25e9afa9c",
+   "metadata": {},
+   "source": [
+    "## Similarity search with score"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "8e9eef05-1516-469a-ad36-880c69aef7a9",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "docs = db.similarity_search_with_score(query)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "bd5fb0e4-2a94-4bb4-af8a-27327ecb1a7f",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(Document(page_content='Tonight. I call on the Senate to: Pass the Freedom to Vote Act. Pass the John Lewis Voting Rights Act. And while you’re at it, pass the Disclose Act so Americans can know who is funding our elections. \\n\\nTonight, I’d like to honor someone who has dedicated his life to serve this country: Justice Stephen Breyer—an Army veteran, Constitutional scholar, and retiring Justice of the United States Supreme Court. Justice Breyer, thank you for your service. \\n\\nOne of the most serious constitutional responsibilities a President has is nominating someone to serve on the United States Supreme Court. \\n\\nAnd I did that 4 days ago, when I nominated Circuit Court of Appeals Judge Ketanji Brown Jackson. One of our nation’s top legal minds, who will continue Justice Breyer’s legacy of excellence.', metadata={}),\n",
+       " 0.8154190158347903)"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "docs[0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3e5da522-ef0e-4a59-91ea-89e563f7b825",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/langchain/vectorstores/__init__.py b/langchain/vectorstores/__init__.py
index ff7c9e7a18..ade924590c 100644
--- a/langchain/vectorstores/__init__.py
+++ b/langchain/vectorstores/__init__.py
@@ -5,6 +5,7 @@ from langchain.vectorstores.atlas import AtlasDB
 from langchain.vectorstores.base import VectorStore
 from langchain.vectorstores.chroma import Chroma
 from langchain.vectorstores.deeplake import DeepLake
+from langchain.vectorstores.docarray import DocArrayHnswSearch, DocArrayInMemorySearch
 from langchain.vectorstores.elastic_vector_search import ElasticVectorSearch
 from langchain.vectorstores.faiss import FAISS
 from langchain.vectorstores.lancedb import LanceDB
@@ -40,4 +41,6 @@ __all__ = [
     "AnalyticDB",
     "Tair",
     "LanceDB",
+    "DocArrayHnswSearch",
+    "DocArrayInMemorySearch",
 ]
diff --git a/langchain/vectorstores/docarray/__init__.py b/langchain/vectorstores/docarray/__init__.py
new file mode 100644
index 0000000000..be3d5bde65
--- /dev/null
+++ b/langchain/vectorstores/docarray/__init__.py
@@ -0,0 +1,7 @@
+from langchain.vectorstores.docarray.hnsw import DocArrayHnswSearch
+from langchain.vectorstores.docarray.in_memory import DocArrayInMemorySearch
+
+__all__ = [
+    "DocArrayHnswSearch",
+    "DocArrayInMemorySearch",
+]
diff --git a/langchain/vectorstores/docarray/base.py b/langchain/vectorstores/docarray/base.py
new file mode 100644
index 0000000000..d7b2f3c9ac
--- /dev/null
+++ b/langchain/vectorstores/docarray/base.py
@@ -0,0 +1,199 @@
+from abc import ABC
+from typing import TYPE_CHECKING, Any, Iterable, List, Optional, Tuple, Type
+
+import numpy as np
+from pydantic import Field
+
+from langchain.embeddings.base import Embeddings
+from langchain.schema import Document
+from langchain.vectorstores import VectorStore
+from langchain.vectorstores.utils import maximal_marginal_relevance
+
+if TYPE_CHECKING:
+    from docarray import BaseDoc
+    from docarray.index.abstract import BaseDocIndex
+
+
+def _check_docarray_import() -> None:
+    try:
+        import docarray
+
+        da_version = docarray.__version__.split(".")
+        if int(da_version[0]) == 0 and int(da_version[1]) <= 30:
+            raise ValueError(
+                f"To use the DocArrayHnswSearch VectorStore the docarray "
+                f"version >=0.31.0 is expected, received: {docarray.__version__}."
+                f"To upgrade, please run: `pip install -U docarray`."
+            )
+    except ImportError:
+        raise ImportError(
+            "Could not import docarray python package. "
+            'Please install it with `pip install "langchain[docarray]"`.'
+        )
+
+
+class DocArrayIndex(VectorStore, ABC):
+    def __init__(
+        self,
+        doc_index: "BaseDocIndex",
+        embedding: Embeddings,
+    ):
+        """Initialize a vector store from DocArray's DocIndex."""
+        self.doc_index = doc_index
+        self.embedding = embedding
+
+    @staticmethod
+    def _get_doc_cls(**embeddings_params: Any) -> Type["BaseDoc"]:
+        """Get docarray Document class describing the schema of DocIndex."""
+        from docarray import BaseDoc
+        from docarray.typing import NdArray
+
+        class DocArrayDoc(BaseDoc):
+            text: Optional[str]
+            embedding: Optional[NdArray] = Field(**embeddings_params)
+            metadata: Optional[dict]
+
+        return DocArrayDoc
+
+    @property
+    def doc_cls(self) -> Type["BaseDoc"]:
+        if self.doc_index._schema is None:
+            raise ValueError("doc_index expected to have non-null _schema attribute.")
+        return self.doc_index._schema
+
+    def add_texts(
+        self,
+        texts: Iterable[str],
+        metadatas: Optional[List[dict]] = None,
+        **kwargs: Any,
+    ) -> List[str]:
+        """Run more texts through the embeddings and add to the vectorstore.
+
+        Args:
+            texts: Iterable of strings to add to the vectorstore.
+            metadatas: Optional list of metadatas associated with the texts.
+
+        Returns:
+            List of ids from adding the texts into the vectorstore.
+        """
+        ids: List[str] = []
+        embeddings = self.embedding.embed_documents(list(texts))
+        for i, (t, e) in enumerate(zip(texts, embeddings)):
+            m = metadatas[i] if metadatas else {}
+            doc = self.doc_cls(text=t, embedding=e, metadata=m)
+            self.doc_index.index([doc])
+            ids.append(str(doc.id))
+
+        return ids
+
+    def similarity_search_with_score(
+        self, query: str, k: int = 4, **kwargs: Any
+    ) -> List[Tuple[Document, float]]:
+        """Return docs most similar to query.
+
+        Args:
+            query: Text to look up documents similar to.
+            k: Number of Documents to return. Defaults to 4.
+
+        Returns:
+            List of Documents most similar to the query and score for each.
+        """
+        query_embedding = self.embedding.embed_query(query)
+        query_doc = self.doc_cls(embedding=query_embedding)  # type: ignore
+        docs, scores = self.doc_index.find(query_doc, search_field="embedding", limit=k)
+
+        result = [
+            (Document(page_content=doc.text, metadata=doc.metadata), score)
+            for doc, score in zip(docs, scores)
+        ]
+        return result
+
+    def similarity_search(
+        self, query: str, k: int = 4, **kwargs: Any
+    ) -> List[Document]:
+        """Return docs most similar to query.
+
+        Args:
+            query: Text to look up documents similar to.
+            k: Number of Documents to return. Defaults to 4.
+
+        Returns:
+            List of Documents most similar to the query.
+        """
+        results = self.similarity_search_with_score(query, k=k, **kwargs)
+        return [doc for doc, _ in results]
+
+    def _similarity_search_with_relevance_scores(
+        self,
+        query: str,
+        k: int = 4,
+        **kwargs: Any,
+    ) -> List[Tuple[Document, float]]:
+        """Return docs and relevance scores, normalized on a scale from 0 to 1.
+
+        0 is dissimilar, 1 is most similar.
+        """
+        raise NotImplementedError
+
+    def similarity_search_by_vector(
+        self, embedding: List[float], k: int = 4, **kwargs: Any
+    ) -> List[Document]:
+        """Return docs most similar to embedding vector.
+
+        Args:
+            embedding: Embedding to look up documents similar to.
+            k: Number of Documents to return. Defaults to 4.
+
+        Returns:
+            List of Documents most similar to the query vector.
+        """
+
+        query_doc = self.doc_cls(embedding=embedding)  # type: ignore
+        docs = self.doc_index.find(
+            query_doc, search_field="embedding", limit=k
+        ).documents
+
+        result = [
+            Document(page_content=doc.text, metadata=doc.metadata) for doc in docs
+        ]
+        return result
+
+    def max_marginal_relevance_search(
+        self,
+        query: str,
+        k: int = 4,
+        fetch_k: int = 20,
+        lambda_mult: float = 0.5,
+        **kwargs: Any,
+    ) -> List[Document]:
+        """Return docs selected using the maximal marginal relevance.
+
+        Maximal marginal relevance optimizes for similarity to query AND diversity
+        among selected documents.
+
+        Args:
+            query: Text to look up documents similar to.
+            k: Number of Documents to return. Defaults to 4.
+            fetch_k: Number of Documents to fetch to pass to MMR algorithm.
+            lambda_mult: Number between 0 and 1 that determines the degree
+                of diversity among the results with 0 corresponding
+                to maximum diversity and 1 to minimum diversity.
+                Defaults to 0.5.
+        Returns:
+            List of Documents selected by maximal marginal relevance.
+        """
+        query_embedding = self.embedding.embed_query(query)
+        query_doc = self.doc_cls(embedding=query_embedding)  # type: ignore
+
+        docs = self.doc_index.find(
+            query_doc, search_field="embedding", limit=fetch_k
+        ).documents
+
+        mmr_selected = maximal_marginal_relevance(
+            np.array(query_embedding), docs.embedding, k=k
+        )
+        results = [
+            Document(page_content=docs[idx].text, metadata=docs[idx].metadata)
+            for idx in mmr_selected
+        ]
+        return results
diff --git a/langchain/vectorstores/docarray/hnsw.py b/langchain/vectorstores/docarray/hnsw.py
new file mode 100644
index 0000000000..9e334c3c47
--- /dev/null
+++ b/langchain/vectorstores/docarray/hnsw.py
@@ -0,0 +1,109 @@
+"""Wrapper around Hnswlib store."""
+from __future__ import annotations
+
+from typing import Any, List, Literal, Optional
+
+from langchain.embeddings.base import Embeddings
+from langchain.vectorstores.docarray.base import (
+    DocArrayIndex,
+    _check_docarray_import,
+)
+
+
+class DocArrayHnswSearch(DocArrayIndex):
+    """Wrapper around HnswLib storage.
+
+    To use it, you should have the ``docarray[hnswlib]`` package with version >=0.31.0
+    installed. You can install it with `pip install "langchain[hnswlib]"`.
+    """
+
+    @classmethod
+    def from_params(
+        cls,
+        embedding: Embeddings,
+        work_dir: str,
+        n_dim: int,
+        dist_metric: Literal["cosine", "ip", "l2"] = "cosine",
+        max_elements: int = 1024,
+        index: bool = True,
+        ef_construction: int = 200,
+        ef: int = 10,
+        M: int = 16,
+        allow_replace_deleted: bool = True,
+        num_threads: int = 1,
+        **kwargs: Any,
+    ) -> DocArrayHnswSearch:
+        """Initialize DocArrayHnswSearch store.
+
+        Args:
+            embedding (Embeddings): Embedding function.
+            work_dir (str): path to the location where all the data will be stored.
+            n_dim (int): dimension of an embedding.
+            dist_metric (str): Distance metric for DocArrayHnswSearch can be one of:
+                "cosine", "ip", and "l2". Defaults to "cosine".
+            max_elements (int): Maximum number of vectors that can be stored.
+                Defaults to 1024.
+            index (bool): Whether an index should be built for this field.
+                Defaults to True.
+            ef_construction (int): defines a construction time/accuracy trade-off.
+                Defaults to 200.
+            ef (int): parameter controlling query time/accuracy trade-off.
+                Defaults to 10.
+            M (int): parameter that defines the maximum number of outgoing
+                connections in the graph. Defaults to 16.
+            allow_replace_deleted (bool): Enables replacing of deleted elements
+                with new added ones. Defaults to True.
+            num_threads (int): Sets the number of cpu threads to use. Defaults to 1.
+            **kwargs: Other keyword arguments to be passed to the get_doc_cls method.
+        """
+        _check_docarray_import()
+        from docarray.index import HnswDocumentIndex
+
+        doc_cls = cls._get_doc_cls(
+            dim=n_dim,
+            space=dist_metric,
+            max_elements=max_elements,
+            index=index,
+            ef_construction=ef_construction,
+            ef=ef,
+            M=M,
+            allow_replace_deleted=allow_replace_deleted,
+            num_threads=num_threads,
+            **kwargs,
+        )
+        doc_index = HnswDocumentIndex[doc_cls](work_dir=work_dir)  # type: ignore
+        return cls(doc_index, embedding)
+
+    @classmethod
+    def from_texts(
+        cls,
+        texts: List[str],
+        embedding: Embeddings,
+        metadatas: Optional[List[dict]] = None,
+        work_dir: Optional[str] = None,
+        n_dim: Optional[int] = None,
+        **kwargs: Any,
+    ) -> DocArrayHnswSearch:
+        """Create an DocArrayHnswSearch store and insert data.
+
+
+        Args:
+            texts (List[str]): Text data.
+            embedding (Embeddings): Embedding function.
+            metadatas (Optional[List[dict]]): Metadata for each text if it exists.
+                Defaults to None.
+            work_dir (str): path to the location where all the data will be stored.
+            n_dim (int): dimension of an embedding.
+            **kwargs: Other keyword arguments to be passed to the __init__ method.
+
+        Returns:
+            DocArrayHnswSearch Vector Store
+        """
+        if work_dir is None:
+            raise ValueError("`work_dir` parameter has not been set.")
+        if n_dim is None:
+            raise ValueError("`n_dim` parameter has not been set.")
+
+        store = cls.from_params(embedding, work_dir, n_dim, **kwargs)
+        store.add_texts(texts=texts, metadatas=metadatas)
+        return store
diff --git a/langchain/vectorstores/docarray/in_memory.py b/langchain/vectorstores/docarray/in_memory.py
new file mode 100644
index 0000000000..8ab664859e
--- /dev/null
+++ b/langchain/vectorstores/docarray/in_memory.py
@@ -0,0 +1,69 @@
+"""Wrapper around in-memory storage."""
+from __future__ import annotations
+
+from typing import Any, Dict, List, Literal, Optional
+
+from langchain.embeddings.base import Embeddings
+from langchain.vectorstores.docarray.base import (
+    DocArrayIndex,
+    _check_docarray_import,
+)
+
+
+class DocArrayInMemorySearch(DocArrayIndex):
+    """Wrapper around in-memory storage for exact search.
+
+    To use it, you should have the ``docarray`` package with version >=0.31.0 installed.
+    You can install it with `pip install "langchain[in_memory_store]"`.
+    """
+
+    @classmethod
+    def from_params(
+        cls,
+        embedding: Embeddings,
+        metric: Literal[
+            "cosine_sim", "euclidian_dist", "sgeuclidean_dist"
+        ] = "cosine_sim",
+        **kwargs: Any,
+    ) -> DocArrayInMemorySearch:
+        """Initialize DocArrayInMemorySearch store.
+
+        Args:
+            embedding (Embeddings): Embedding function.
+            metric (str): metric for exact nearest-neighbor search.
+                Can be one of: "cosine_sim", "euclidean_dist" and "sqeuclidean_dist".
+                Defaults to "cosine_sim".
+            **kwargs: Other keyword arguments to be passed to the get_doc_cls method.
+        """
+        _check_docarray_import()
+        from docarray.index import InMemoryExactNNIndex
+
+        doc_cls = cls._get_doc_cls(space=metric, **kwargs)
+        doc_index = InMemoryExactNNIndex[doc_cls]()  # type: ignore
+        return cls(doc_index, embedding)
+
+    @classmethod
+    def from_texts(
+        cls,
+        texts: List[str],
+        embedding: Embeddings,
+        metadatas: Optional[List[Dict[Any, Any]]] = None,
+        **kwargs: Any,
+    ) -> DocArrayInMemorySearch:
+        """Create an DocArrayInMemorySearch store and insert data.
+
+        Args:
+            texts (List[str]): Text data.
+            embedding (Embeddings): Embedding function.
+            metadatas (Optional[List[Dict[Any, Any]]]): Metadata for each text
+                if it exists. Defaults to None.
+            metric (str): metric for exact nearest-neighbor search.
+                Can be one of: "cosine_sim", "euclidean_dist" and "sqeuclidean_dist".
+                Defaults to "cosine_sim".
+
+        Returns:
+            DocArrayInMemorySearch Vector Store
+        """
+        store = cls.from_params(embedding, **kwargs)
+        store.add_texts(texts=texts, metadatas=metadatas)
+        return store
diff --git a/poetry.lock b/poetry.lock
index d29d7498a8..00c4316201 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -883,14 +883,14 @@ files = [
 
 [[package]]
 name = "certifi"
-version = "2022.12.7"
+version = "2023.5.7"
 description = "Python package for providing Mozilla's CA Bundle."
 category = "main"
 optional = false
 python-versions = ">=3.6"
 files = [
-    {file = "certifi-2022.12.7-py3-none-any.whl", hash = "sha256:4ad3232f5e926d6718ec31cfc1fcadfde020920e278684144551c91769c7bc18"},
-    {file = "certifi-2022.12.7.tar.gz", hash = "sha256:35824b4c3a97115964b408844d64aa14db1cc518f6562e8d7261699d1350a9e3"},
+    {file = "certifi-2023.5.7-py3-none-any.whl", hash = "sha256:c6c2e98f5c7869efca1f8916fed228dd91539f9f1b444c314c06eef02980c716"},
+    {file = "certifi-2023.5.7.tar.gz", hash = "sha256:0f0d56dc5a6ad56fd4ba36484d6cc34451e1c6548c61daad8c320169f91eddc7"},
 ]
 
 [[package]]
@@ -1560,43 +1560,51 @@ wmi = ["wmi (>=1.5.1,<2.0.0)"]
 
 [[package]]
 name = "docarray"
-version = "0.21.0"
-description = "The data structure for unstructured data"
+version = "0.31.0"
+description = "The data structure for multimodal data"
 category = "main"
 optional = true
-python-versions = "*"
+python-versions = ">=3.7,<4.0"
 files = [
-    {file = "docarray-0.21.0.tar.gz", hash = "sha256:3c9f605123800c1b0cdf8c458be3fb19c05e9a81f723e51200ef531b02e689ee"},
+    {file = "docarray-0.31.0-py3-none-any.whl", hash = "sha256:3783e9bdcf0d59b17499660e54577f4e3d202545998afca9306ebcc09cf0e14e"},
+    {file = "docarray-0.31.0.tar.gz", hash = "sha256:a79d1ed70bd143b3e2a53ff90a62e4b3ce7231d5d237a2fab9b8311d7ae7d245"},
 ]
 
 [package.dependencies]
-jina-hubble-sdk = ">=0.24.0"
-numpy = "*"
-rich = ">=12.0.0"
+numpy = ">=1.17.3"
+orjson = ">=3.8.2"
+pydantic = ">=1.10.2"
+rich = ">=13.1.0"
+types-requests = ">=2.28.11.6"
+typing-inspect = ">=0.8.0"
 
 [package.extras]
-annlite = ["annlite"]
-benchmark = ["h5py", "matplotlib", "pandas", "seaborn"]
-common = ["Pillow", "fastapi", "lz4", "matplotlib", "protobuf (>=3.13.0)", "pydantic (>=1.9.0)", "requests", "uvicorn"]
-elasticsearch = ["elasticsearch (>=8.2.0)"]
-full = ["Pillow", "av", "fastapi", "grpcio (>=1.46.0,<1.48.1)", "grpcio-health-checking (>=1.46.0,<1.48.1)", "grpcio-reflection (>=1.46.0,<1.48.1)", "ipython", "lz4", "matplotlib", "protobuf (>=3.13.0)", "pydantic (>=1.9.0)", "requests", "scipy", "strawberry-graphql", "trimesh[easy]", "uvicorn"]
-milvus = ["pymilvus (>=2.1.0,<2.2.0)"]
-opensearch = ["opensearch-py (==2.0.1)"]
-qdrant = ["qdrant-client (>=0.10.3,<0.11.0)"]
-redis = ["redis (>=4.3.0)"]
-test = ["annlite", "black (==22.3.0)", "datasets", "elasticsearch (>=8.2.0)", "jina", "jupyterlab", "mock", "onnx", "onnxruntime", "opensearch-py (==2.0.1)", "paddlepaddle", "protobuf (>=3.13.0,<=3.20.0)", "pymilvus (==2.1.3)", "pytest", "pytest-cov (==3.0.0)", "pytest-custom_exit_code", "pytest-mock", "pytest-mock", "pytest-repeat", "pytest-reraise", "pytest-timeout", "redis (>=4.3.0)", "tensorflow (==2.7.0)", "torch (==1.9.0)", "torchvision (==0.10.0)", "transformers (>=4.16.2)", "weaviate-client (>=3.9.0,<3.10.0)"]
-weaviate = ["weaviate-client (>=3.9.0,<3.10.0)"]
+audio = ["pydub (>=0.25.1,<0.26.0)"]
+aws = ["smart-open[s3] (>=6.3.0)"]
+elasticsearch = ["elastic-transport (>=8.4.0,<9.0.0)", "elasticsearch (>=7.10.1)"]
+full = ["av (>=10.0.0)", "lz4 (>=1.0.0)", "pandas (>=1.1.0)", "pillow (>=9.3.0)", "protobuf (>=3.19.0)", "pydub (>=0.25.1,<0.26.0)", "trimesh[easy] (>=3.17.1)", "types-pillow (>=9.3.0.1)"]
+hnswlib = ["hnswlib (>=0.6.2)"]
+image = ["pillow (>=9.3.0)", "types-pillow (>=9.3.0.1)"]
+jac = ["jina-hubble-sdk (>=0.34.0)"]
+mesh = ["trimesh[easy] (>=3.17.1)"]
+pandas = ["pandas (>=1.1.0)"]
+proto = ["lz4 (>=1.0.0)", "protobuf (>=3.19.0)"]
+qdrant = ["qdrant-client (>=1.1.4)"]
+torch = ["torch (>=1.0.0)"]
+video = ["av (>=10.0.0)"]
+weaviate = ["weaviate-client (>=3.15)"]
+web = ["fastapi (>=0.87.0)"]
 
 [[package]]
 name = "docker"
-version = "6.0.1"
+version = "6.1.1"
 description = "A Python library for the Docker Engine API."
 category = "main"
 optional = true
 python-versions = ">=3.7"
 files = [
-    {file = "docker-6.0.1-py3-none-any.whl", hash = "sha256:dbcb3bd2fa80dca0788ed908218bf43972772009b881ed1e20dfc29a65e49782"},
-    {file = "docker-6.0.1.tar.gz", hash = "sha256:896c4282e5c7af5c45e8b683b0b0c33932974fe6e50fc6906a0a83616ab3da97"},
+    {file = "docker-6.1.1-py3-none-any.whl", hash = "sha256:8308b23d3d0982c74f7aa0a3abd774898c0c4fba006e9c3bde4f68354e470fe2"},
+    {file = "docker-6.1.1.tar.gz", hash = "sha256:5ec18b9c49d48ee145a5b5824bb126dc32fc77931e18444783fc07a7724badc0"},
 ]
 
 [package.dependencies]
@@ -2039,14 +2047,14 @@ files = [
 
 [[package]]
 name = "fsspec"
-version = "2023.4.0"
+version = "2023.5.0"
 description = "File-system specification"
 category = "main"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "fsspec-2023.4.0-py3-none-any.whl", hash = "sha256:f398de9b49b14e9d84d2c2d11b7b67121bc072fe97b930c4e5668ac3917d8307"},
-    {file = "fsspec-2023.4.0.tar.gz", hash = "sha256:bf064186cd8808f0b2f6517273339ba0a0c8fb1b7048991c28bc67f58b8b67cd"},
+    {file = "fsspec-2023.5.0-py3-none-any.whl", hash = "sha256:51a4ad01a5bb66fcc58036e288c0d53d3975a0df2a5dc59a93b59bade0391f2a"},
+    {file = "fsspec-2023.5.0.tar.gz", hash = "sha256:b3b56e00fb93ea321bc9e5d9cf6f8522a0198b20eb24e02774d329e9c6fb84ce"},
 ]
 
 [package.extras]
@@ -2099,26 +2107,24 @@ files = [
 
 [[package]]
 name = "google-api-core"
-version = "2.11.0"
+version = "2.8.2"
 description = "Google API client core library"
 category = "main"
 optional = true
-python-versions = ">=3.7"
+python-versions = ">=3.6"
 files = [
-    {file = "google-api-core-2.11.0.tar.gz", hash = "sha256:4b9bb5d5a380a0befa0573b302651b8a9a89262c1730e37bf423cec511804c22"},
-    {file = "google_api_core-2.11.0-py3-none-any.whl", hash = "sha256:ce222e27b0de0d7bc63eb043b956996d6dccab14cc3b690aaea91c9cc99dc16e"},
+    {file = "google-api-core-2.8.2.tar.gz", hash = "sha256:06f7244c640322b508b125903bb5701bebabce8832f85aba9335ec00b3d02edc"},
+    {file = "google_api_core-2.8.2-py3-none-any.whl", hash = "sha256:93c6a91ccac79079ac6bbf8b74ee75db970cc899278b97d53bc012f35908cf50"},
 ]
 
 [package.dependencies]
-google-auth = ">=2.14.1,<3.0dev"
+google-auth = ">=1.25.0,<3.0dev"
 googleapis-common-protos = ">=1.56.2,<2.0dev"
-protobuf = ">=3.19.5,<3.20.0 || >3.20.0,<3.20.1 || >3.20.1,<4.21.0 || >4.21.0,<4.21.1 || >4.21.1,<4.21.2 || >4.21.2,<4.21.3 || >4.21.3,<4.21.4 || >4.21.4,<4.21.5 || >4.21.5,<5.0.0dev"
+protobuf = ">=3.15.0,<5.0.0dev"
 requests = ">=2.18.0,<3.0.0dev"
 
 [package.extras]
-grpc = ["grpcio (>=1.33.2,<2.0dev)", "grpcio (>=1.49.1,<2.0dev)", "grpcio-status (>=1.33.2,<2.0dev)", "grpcio-status (>=1.49.1,<2.0dev)"]
-grpcgcp = ["grpcio-gcp (>=0.2.2,<1.0dev)"]
-grpcio-gcp = ["grpcio-gcp (>=0.2.2,<1.0dev)"]
+grpc = ["grpcio (>=1.33.2,<2.0dev)", "grpcio-status (>=1.33.2,<2.0dev)"]
 
 [[package]]
 name = "google-api-python-client"
@@ -2232,32 +2238,32 @@ requests = "*"
 
 [[package]]
 name = "googleapis-common-protos"
-version = "1.59.0"
+version = "1.56.4"
 description = "Common protobufs used in Google APIs"
 category = "main"
 optional = true
 python-versions = ">=3.7"
 files = [
-    {file = "googleapis-common-protos-1.59.0.tar.gz", hash = "sha256:4168fcb568a826a52f23510412da405abd93f4d23ba544bb68d943b14ba3cb44"},
-    {file = "googleapis_common_protos-1.59.0-py2.py3-none-any.whl", hash = "sha256:b287dc48449d1d41af0c69f4ea26242b5ae4c3d7249a38b0984c86a4caffff1f"},
+    {file = "googleapis-common-protos-1.56.4.tar.gz", hash = "sha256:c25873c47279387cfdcbdafa36149887901d36202cb645a0e4f29686bf6e4417"},
+    {file = "googleapis_common_protos-1.56.4-py2.py3-none-any.whl", hash = "sha256:8eb2cbc91b69feaf23e32452a7ae60e791e09967d81d4fcc7fc388182d1bd394"},
 ]
 
 [package.dependencies]
-protobuf = ">=3.19.5,<3.20.0 || >3.20.0,<3.20.1 || >3.20.1,<4.21.1 || >4.21.1,<4.21.2 || >4.21.2,<4.21.3 || >4.21.3,<4.21.4 || >4.21.4,<4.21.5 || >4.21.5,<5.0.0dev"
+protobuf = ">=3.15.0,<5.0.0dev"
 
 [package.extras]
-grpc = ["grpcio (>=1.44.0,<2.0.0dev)"]
+grpc = ["grpcio (>=1.0.0,<2.0.0dev)"]
 
 [[package]]
 name = "gptcache"
-version = "0.1.21"
+version = "0.1.22"
 description = "GPTCache, a powerful caching library that can be used to speed up and lower the cost of chat applications that rely on the LLM service. GPTCache works as a memcache for AIGC applications, similar to how Redis works for traditional applications."
 category = "main"
 optional = false
 python-versions = ">=3.8.1"
 files = [
-    {file = "gptcache-0.1.21-py3-none-any.whl", hash = "sha256:76322a19f96fe0576857072c62a7b29bc680dfde719d551d5bc1aedd0d8a5f34"},
-    {file = "gptcache-0.1.21.tar.gz", hash = "sha256:4c3eb750b972cc95681233358a7bbfd61233827e76ae81be47668552132a9002"},
+    {file = "gptcache-0.1.22-py3-none-any.whl", hash = "sha256:081fada6f4f2f57ef2955ee1ce1224eb4f3511546d7efd483648f1ff8d257fdb"},
+    {file = "gptcache-0.1.22.tar.gz", hash = "sha256:5fb7b7eb7ae774f2ec6c9b9dbcd7829da4a83925776d5edc98181811d8f71a0f"},
 ]
 
 [package.dependencies]
@@ -2565,7 +2571,7 @@ numpy = ">=1.14.5"
 name = "hnswlib"
 version = "0.7.0"
 description = "hnswlib"
-category = "dev"
+category = "main"
 optional = false
 python-versions = "*"
 files = [
@@ -2856,14 +2862,14 @@ files = [
 
 [[package]]
 name = "ipykernel"
-version = "6.22.0"
+version = "6.23.0"
 description = "IPython Kernel for Jupyter"
 category = "dev"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "ipykernel-6.22.0-py3-none-any.whl", hash = "sha256:1ae6047c1277508933078163721bbb479c3e7292778a04b4bacf0874550977d6"},
-    {file = "ipykernel-6.22.0.tar.gz", hash = "sha256:302558b81f1bc22dc259fb2a0c5c7cf2f4c0bdb21b50484348f7bafe7fb71421"},
+    {file = "ipykernel-6.23.0-py3-none-any.whl", hash = "sha256:fc886f1dcdc0ec17f277e4d21fd071c857d381adcb04f3f3735d25325ca323c6"},
+    {file = "ipykernel-6.23.0.tar.gz", hash = "sha256:bd6f487d9e2744c84f6e667d46462d7647a4c862e70e08282f05a52b9d4b705f"},
 ]
 
 [package.dependencies]
@@ -2890,14 +2896,14 @@ test = ["flaky", "ipyparallel", "pre-commit", "pytest (>=7.0)", "pytest-asyncio"
 
 [[package]]
 name = "ipython"
-version = "8.12.1"
+version = "8.12.2"
 description = "IPython: Productive Interactive Computing"
 category = "dev"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "ipython-8.12.1-py3-none-any.whl", hash = "sha256:e3015a1a4aa09b3984fb81b9cef4f0772af5a549878b81efb094cda8bb121993"},
-    {file = "ipython-8.12.1.tar.gz", hash = "sha256:2442915417763b62181009259782975fa50bb5eedb97ae97fb614204bf6ecc21"},
+    {file = "ipython-8.12.2-py3-none-any.whl", hash = "sha256:ea8801f15dfe4ffb76dea1b09b847430ffd70d827b41735c64a0638a04103bfc"},
+    {file = "ipython-8.12.2.tar.gz", hash = "sha256:c7b80eb7f5a855a88efc971fda506ff7a91c280b42cdae26643e0f601ea281ea"},
 ]
 
 [package.dependencies]
@@ -3038,20 +3044,20 @@ testing = ["Django (<3.1)", "attrs", "colorama", "docopt", "pytest (<7.0.0)"]
 
 [[package]]
 name = "jina"
-version = "3.15.0"
+version = "3.14.1"
 description = "Build multimodal AI services via cloud native technologies · Neural Search · Generative AI · MLOps"
 category = "main"
 optional = true
 python-versions = "*"
 files = [
-    {file = "jina-3.15.0.tar.gz", hash = "sha256:18a3be8ddca14ed66a554d8480a277bcb7620ebc6ae11352a9835c91865f9d1e"},
+    {file = "jina-3.14.1.tar.gz", hash = "sha256:00b1f5995b13c9a49a2287bd534bd32eb8c05706064752035d569e616a15b411"},
 ]
 
 [package.dependencies]
 aiofiles = "*"
 aiohttp = "*"
 aiostream = "*"
-docarray = ">=0.16.4,<0.30.0"
+docarray = ">=0.16.4"
 docker = "*"
 fastapi = ">=0.76.0"
 filelock = "*"
@@ -3085,14 +3091,14 @@ websockets = "*"
 aiofiles = ["aiofiles"]
 aiohttp = ["aiohttp"]
 aiostream = ["aiostream"]
-all = ["Pillow", "aiofiles", "aiohttp", "aiostream", "black (==22.3.0)", "bs4", "coverage (==6.2)", "docarray (>=0.16.4,<0.30.0)", "docker", "fastapi (>=0.76.0)", "filelock", "flaky", "grpcio (>=1.46.0,<1.48.1)", "grpcio-health-checking (>=1.46.0,<1.48.1)", "grpcio-reflection (>=1.46.0,<1.48.1)", "jcloud (>=0.0.35)", "jina-hubble-sdk (>=0.30.4)", "jsonschema", "kubernetes (>=18.20.0)", "mock", "numpy", "opentelemetry-api (>=1.12.0)", "opentelemetry-exporter-otlp (>=1.12.0)", "opentelemetry-exporter-otlp-proto-grpc (>=1.13.0)", "opentelemetry-exporter-prometheus (>=1.12.0rc1)", "opentelemetry-instrumentation-aiohttp-client (>=0.33b0)", "opentelemetry-instrumentation-fastapi (>=0.33b0)", "opentelemetry-instrumentation-grpc (>=0.35b0)", "opentelemetry-sdk (>=1.14.0)", "opentelemetry-test-utils (>=0.33b0)", "packaging (>=20.0)", "pathspec", "portforward (>=0.2.4,<0.4.3)", "prometheus-api-client (>=0.5.1)", "prometheus_client (>=0.12.0)", "protobuf (>=3.19.0)", "psutil", "pydantic", "pytest", "pytest-asyncio", "pytest-cov (==3.0.0)", "pytest-custom_exit_code", "pytest-kind (==22.11.1)", "pytest-lazy-fixture", "pytest-mock", "pytest-repeat", "pytest-reraise", "pytest-timeout", "python-multipart", "pyyaml (>=5.3.1)", "requests", "requests-mock", "scipy (>=1.6.1)", "sgqlc", "strawberry-graphql (>=0.96.0)", "tensorflow (>=2.0)", "torch", "uvicorn[standard]", "uvloop", "watchfiles (>=0.18.0)", "websockets"]
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 black = ["black (==22.3.0)"]
 bs4 = ["bs4"]
-cicd = ["bs4", "jsonschema", "portforward (>=0.2.4,<0.4.3)", "sgqlc", "strawberry-graphql (>=0.96.0)", "tensorflow (>=2.0)", "torch"]
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+cicd = ["bs4", "jsonschema", "portforward (>=0.2.4)", "sgqlc", "strawberry-graphql (>=0.96.0)", "tensorflow (>=2.0)", "torch"]
+core = ["docarray (>=0.16.4)", "grpcio (>=1.46.0,<1.48.1)", "grpcio-health-checking (>=1.46.0,<1.48.1)", "grpcio-reflection (>=1.46.0,<1.48.1)", "jcloud (>=0.0.35)", "jina-hubble-sdk (>=0.30.4)", "numpy", "opentelemetry-api (>=1.12.0)", "opentelemetry-instrumentation-grpc (>=0.35b0)", "packaging (>=20.0)", "protobuf (>=3.19.0)", "pyyaml (>=5.3.1)"]
 coverage = ["coverage (==6.2)"]
-devel = ["aiofiles", "aiohttp", "docker", "fastapi (>=0.76.0)", "filelock", "opentelemetry-exporter-otlp (>=1.12.0)", "opentelemetry-exporter-otlp-proto-grpc (>=1.13.0)", "opentelemetry-exporter-prometheus (>=1.12.0rc1)", "opentelemetry-instrumentation-aiohttp-client (>=0.33b0)", "opentelemetry-instrumentation-fastapi (>=0.33b0)", "opentelemetry-sdk (>=1.14.0)", "pathspec", "prometheus_client (>=0.12.0)", "pydantic", "python-multipart", "requests", "sgqlc", "strawberry-graphql (>=0.96.0)", "uvicorn[standard]", "uvloop", "watchfiles (>=0.18.0)", "websockets"]
-docarray = ["docarray (>=0.16.4,<0.30.0)"]
+devel = ["aiofiles", "aiohttp", "aiostream", "docker", "fastapi (>=0.76.0)", "filelock", "opentelemetry-exporter-otlp (>=1.12.0)", "opentelemetry-exporter-otlp-proto-grpc (>=1.13.0)", "opentelemetry-exporter-prometheus (>=1.12.0rc1)", "opentelemetry-instrumentation-aiohttp-client (>=0.33b0)", "opentelemetry-instrumentation-fastapi (>=0.33b0)", "opentelemetry-sdk (>=1.14.0)", "pathspec", "prometheus_client (>=0.12.0)", "pydantic", "python-multipart", "requests", "sgqlc", "strawberry-graphql (>=0.96.0)", "uvicorn[standard]", "uvloop", "watchfiles (>=0.18.0)", "websockets"]
+docarray = ["docarray (>=0.16.4)"]
 docker = ["docker"]
 fastapi = ["fastapi (>=0.76.0)"]
 filelock = ["filelock"]
@@ -3119,7 +3125,7 @@ packaging = ["packaging (>=20.0)"]
 pathspec = ["pathspec"]
 perf = ["opentelemetry-exporter-otlp (>=1.12.0)", "opentelemetry-exporter-otlp-proto-grpc (>=1.13.0)", "opentelemetry-exporter-prometheus (>=1.12.0rc1)", "opentelemetry-instrumentation-aiohttp-client (>=0.33b0)", "opentelemetry-instrumentation-fastapi (>=0.33b0)", "opentelemetry-sdk (>=1.14.0)", "prometheus_client (>=0.12.0)", "uvloop"]
 pillow = ["Pillow"]
-portforward = ["portforward (>=0.2.4,<0.4.3)"]
+portforward = ["portforward (>=0.2.4)"]
 prometheus-api-client = ["prometheus-api-client (>=0.5.1)"]
 prometheus-client = ["prometheus_client (>=0.12.0)"]
 protobuf = ["protobuf (>=3.19.0)"]
@@ -3141,7 +3147,7 @@ requests = ["requests"]
 requests-mock = ["requests-mock"]
 scipy = ["scipy (>=1.6.1)"]
 sgqlc = ["sgqlc"]
-standard = ["aiofiles", "aiohttp", "docker", "fastapi (>=0.76.0)", "filelock", "opentelemetry-exporter-otlp (>=1.12.0)", "opentelemetry-exporter-prometheus (>=1.12.0rc1)", "opentelemetry-instrumentation-aiohttp-client (>=0.33b0)", "opentelemetry-instrumentation-fastapi (>=0.33b0)", "opentelemetry-sdk (>=1.14.0)", "pathspec", "prometheus_client (>=0.12.0)", "pydantic", "python-multipart", "requests", "uvicorn[standard]", "uvloop", "websockets"]
+standard = ["aiofiles", "aiohttp", "aiostream", "docker", "fastapi (>=0.76.0)", "filelock", "opentelemetry-exporter-otlp (>=1.12.0)", "opentelemetry-exporter-prometheus (>=1.12.0rc1)", "opentelemetry-instrumentation-aiohttp-client (>=0.33b0)", "opentelemetry-instrumentation-fastapi (>=0.33b0)", "opentelemetry-sdk (>=1.14.0)", "pathspec", "prometheus_client (>=0.12.0)", "pydantic", "python-multipart", "requests", "uvicorn[standard]", "uvloop", "websockets"]
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 strawberry-graphql = ["strawberry-graphql (>=0.96.0)"]
 tensorflow = ["tensorflow (>=2.0)"]
@@ -3579,18 +3585,18 @@ files = [
 
 [[package]]
 name = "lancedb"
-version = "0.1.1"
+version = "0.1.2"
 description = "lancedb"
 category = "main"
 optional = true
 python-versions = ">=3.8"
 files = [
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 ]
 
 [package.dependencies]
-pylance = ">=0.4.4"
+pylance = ">=0.4.6"
 ratelimiter = "*"
 retry = "*"
 tqdm = "*"
@@ -4349,14 +4355,14 @@ testing = ["beautifulsoup4", "coverage[toml]", "pytest (>=6,<7)", "pytest-cov",
 
 [[package]]
 name = "nbclassic"
-version = "0.5.6"
+version = "1.0.0"
 description = "Jupyter Notebook as a Jupyter Server extension."
 category = "dev"
 optional = false
 python-versions = ">=3.7"
 files = [
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 ]
 
 [package.dependencies]
@@ -4408,14 +4414,14 @@ test = ["flaky", "ipykernel", "ipython", "ipywidgets", "nbconvert (>=7.0.0)", "p
 
 [[package]]
 name = "nbconvert"
-version = "7.3.1"
+version = "7.4.0"
 description = "Converting Jupyter Notebooks"
 category = "dev"
 optional = false
 python-versions = ">=3.7"
 files = [
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 ]
 
 [package.dependencies]
@@ -5201,6 +5207,62 @@ numpy = ">=1.7"
 docs = ["numpydoc", "sphinx (==1.2.3)", "sphinx-rtd-theme", "sphinxcontrib-napoleon"]
 tests = ["pytest", "pytest-cov", "pytest-pep8"]
 
+[[package]]
+name = "orjson"
+version = "3.8.12"
+description = "Fast, correct Python JSON library supporting dataclasses, datetimes, and numpy"
+category = "main"
+optional = true
+python-versions = ">=3.7"
+files = [
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 [[package]]
 name = "packaging"
 version = "23.1"
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 [[package]]
@@ -6223,16 +6284,16 @@ tests = ["coverage[toml] (==5.0.4)", "pytest (>=6.0.0,<7.0.0)"]
 
 [[package]]
 name = "pylance"
-version = "0.4.4"
+version = "0.4.6"
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 python-versions = ">=3.8"
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 [package.dependencies]
@@ -6923,14 +6984,14 @@ urllib3 = ">=1.26.14,<2.0.0"
 
 [[package]]
 name = "qtconsole"
-version = "5.4.2"
+version = "5.4.3"
 description = "Jupyter Qt console"
 category = "dev"
 optional = false
 python-versions = ">= 3.7"
 files = [
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 [package.dependencies]
@@ -7268,14 +7329,14 @@ files = [
 
 [[package]]
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+version = "0.6.1"
 description = "An Amazon S3 Transfer Manager"
 category = "main"
 optional = false
 python-versions = ">= 3.7"
 files = [
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 ]
 
 [package.dependencies]
@@ -8267,18 +8328,18 @@ files = [
 
 [[package]]
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+version = "0.12.0"
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 optional = true
 python-versions = "*"
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 [package.dependencies]
 numpy = ">=1.12.0"
-protobuf = ">=3.19.6"
+protobuf = ">=3.8.0"
 
 [package.extras]
 make-image-classifier = ["keras-preprocessing[image]"]
@@ -8904,14 +8965,14 @@ files = [
 
 [[package]]
 name = "types-redis"
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 description = "Typing stubs for redis"
 category = "dev"
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 python-versions = "*"
 files = [
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 ]
 
 [package.dependencies]
@@ -8920,18 +8981,18 @@ types-pyOpenSSL = "*"
 
 [[package]]
 name = "types-requests"
-version = "2.29.0.0"
+version = "2.30.0.0"
 description = "Typing stubs for requests"
-category = "dev"
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 python-versions = "*"
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 ]
 
 [package.dependencies]
-types-urllib3 = "<1.27"
+types-urllib3 = "*"
 
 [[package]]
 name = "types-toml"
@@ -8949,7 +9010,7 @@ files = [
 name = "types-urllib3"
 version = "1.26.25.12"
 description = "Typing stubs for urllib3"
-category = "dev"
+category = "main"
 optional = false
 python-versions = "*"
 files = [
@@ -9271,14 +9332,14 @@ files = [
 
 [[package]]
 name = "weaviate-client"
-version = "3.16.2"
+version = "3.17.1"
 description = "A python native weaviate client"
 category = "main"
 optional = false
 python-versions = ">=3.8"
 files = [
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 ]
 
 [package.dependencies]
@@ -9337,82 +9398,82 @@ test = ["websockets"]
 
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+version = "11.0.3"
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 ]
 
 [[package]]
@@ -9476,7 +9537,7 @@ name = "wikipedia"
 version = "1.4.0"
 description = "Wikipedia API for Python"
 category = "main"
-optional = true
+optional = false
 python-versions = "*"
 files = [
     {file = "wikipedia-1.4.0.tar.gz", hash = "sha256:db0fad1829fdd441b1852306e9856398204dc0786d2996dd2e0c8bb8e26133b2"},
@@ -9798,11 +9859,13 @@ cffi = {version = ">=1.11", markers = "platform_python_implementation == \"PyPy\
 cffi = ["cffi (>=1.11)"]
 
 [extras]
-all = ["O365", "aleph-alpha-client", "anthropic", "arxiv", "atlassian-python-api", "azure-cosmos", "azure-identity", "beautifulsoup4", "clickhouse-connect", "cohere", "deeplake", "duckduckgo-search", "elasticsearch", "faiss-cpu", "google-api-python-client", "google-search-results", "gptcache", "html2text", "huggingface_hub", "jina", "jinja2", "jq", "lancedb", "lark", "manifest-ml", "networkx", "nlpcloud", "nltk", "nomic", "openai", "opensearch-py", "pexpect", "pgvector", "pinecone-client", "pinecone-text", "psycopg2-binary", "pyowm", "pypdf", "pytesseract", "pyvespa", "qdrant-client", "redis", "sentence-transformers", "spacy", "tensorflow-text", "tiktoken", "torch", "transformers", "weaviate-client", "wikipedia", "wolframalpha"]
+all = ["O365", "aleph-alpha-client", "anthropic", "arxiv", "atlassian-python-api", "azure-cosmos", "azure-identity", "beautifulsoup4", "clickhouse-connect", "cohere", "deeplake", "docarray", "duckduckgo-search", "elasticsearch", "faiss-cpu", "google-api-python-client", "google-search-results", "gptcache", "hnswlib", "html2text", "huggingface_hub", "jina", "jinja2", "jq", "lancedb", "lark", "manifest-ml", "networkx", "nlpcloud", "nltk", "nomic", "openai", "opensearch-py", "pexpect", "pgvector", "pinecone-client", "pinecone-text", "protobuf", "psycopg2-binary", "pyowm", "pypdf", "pytesseract", "pyvespa", "qdrant-client", "redis", "sentence-transformers", "spacy", "tensorflow-text", "tiktoken", "torch", "transformers", "weaviate-client", "wikipedia", "wolframalpha"]
 azure = ["azure-core", "azure-cosmos", "azure-identity", "openai"]
 cohere = ["cohere"]
 embeddings = ["sentence-transformers"]
 extended-testing = ["pdfminer-six", "pypdf"]
+hnswlib = ["docarray", "hnswlib", "protobuf"]
+in-memory-store = ["docarray"]
 llms = ["anthropic", "cohere", "huggingface_hub", "manifest-ml", "nlpcloud", "openai", "torch", "transformers"]
 openai = ["openai"]
 qdrant = ["qdrant-client"]
@@ -9810,4 +9873,4 @@ qdrant = ["qdrant-client"]
 [metadata]
 lock-version = "2.0"
 python-versions = ">=3.8.1,<4.0"
-content-hash = "63324f95fc04b48b631353c5e1a1a2ecc1b9dc757441181d4920e9d77cd8951f"
+content-hash = "302163050738bb858c34c066dbf684aab5940a430756999940dc9ba66a4f24a6"
diff --git a/pyproject.toml b/pyproject.toml
index 439ea62e02..8b10724ac4 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -78,6 +78,9 @@ pyvespa = {version = "^0.33.0", optional = true}
 O365 = {version = "^2.0.26", optional = true}
 jq = {version = "^1.4.1", optional = true}
 pdfminer-six = {version = "^20221105", optional = true}
+docarray = {version="^0.31.0", optional=true}
+protobuf = {version="3.19", optional=true}
+hnswlib = {version="^0.7.0", optional=true}
 
 
 [tool.poetry.group.docs.dependencies]
@@ -159,9 +162,11 @@ llms = ["anthropic", "cohere", "openai", "nlpcloud", "huggingface_hub", "manifes
 qdrant = ["qdrant-client"]
 openai = ["openai"]
 cohere = ["cohere"]
+in_memory_store = ["docarray"]
+hnswlib = ["docarray", "protobuf", "hnswlib"]
 embeddings = ["sentence-transformers"]
 azure = ["azure-identity", "azure-cosmos", "openai", "azure-core"]
-all = ["anthropic", "cohere", "openai", "nlpcloud", "huggingface_hub", "jina", "manifest-ml", "elasticsearch", "opensearch-py", "google-search-results", "faiss-cpu", "sentence-transformers", "transformers", "spacy", "nltk", "wikipedia", "beautifulsoup4", "tiktoken", "torch", "jinja2", "pinecone-client", "pinecone-text", "weaviate-client", "redis", "google-api-python-client", "wolframalpha", "qdrant-client", "tensorflow-text", "pypdf", "networkx", "nomic", "aleph-alpha-client", "deeplake", "pgvector", "psycopg2-binary", "boto3", "pyowm", "pytesseract", "html2text", "atlassian-python-api", "gptcache", "duckduckgo-search", "arxiv", "azure-identity", "clickhouse-connect", "azure-cosmos", "lancedb", "lark", "pexpect", "pyvespa", "O365", "jq"]
+all = ["anthropic", "cohere", "openai", "nlpcloud", "huggingface_hub", "jina", "manifest-ml", "elasticsearch", "opensearch-py", "google-search-results", "faiss-cpu", "sentence-transformers", "transformers", "spacy", "nltk", "wikipedia", "beautifulsoup4", "tiktoken", "torch", "jinja2", "pinecone-client", "pinecone-text", "weaviate-client", "redis", "google-api-python-client", "wolframalpha", "qdrant-client", "tensorflow-text", "pypdf", "networkx", "nomic", "aleph-alpha-client", "deeplake", "pgvector", "psycopg2-binary", "boto3", "pyowm", "pytesseract", "html2text", "atlassian-python-api", "gptcache", "duckduckgo-search", "arxiv", "azure-identity", "clickhouse-connect", "azure-cosmos", "lancedb", "lark", "pexpect", "pyvespa", "O365", "jq", "docarray", "protobuf", "hnswlib"]
 # An extra used to be able to add extended testing.
 extended_testing = ["pypdf", "pdfminer.six"]
 
diff --git a/tests/integration_tests/vectorstores/docarray/__init__.py b/tests/integration_tests/vectorstores/docarray/__init__.py
new file mode 100644
index 0000000000..e69de29bb2
diff --git a/tests/integration_tests/vectorstores/docarray/test_hnsw.py b/tests/integration_tests/vectorstores/docarray/test_hnsw.py
new file mode 100644
index 0000000000..0143660f12
--- /dev/null
+++ b/tests/integration_tests/vectorstores/docarray/test_hnsw.py
@@ -0,0 +1,148 @@
+from pathlib import Path
+from typing import List
+
+import numpy as np
+import pytest
+
+from langchain.schema import Document
+from langchain.vectorstores.docarray import DocArrayHnswSearch
+from tests.integration_tests.vectorstores.fake_embeddings import FakeEmbeddings
+
+
+@pytest.fixture
+def texts() -> List[str]:
+    return ["foo", "bar", "baz"]
+
+
+def test_from_texts(texts: List[str], tmp_path: Path) -> None:
+    """Test end to end construction and simple similarity search."""
+    docsearch = DocArrayHnswSearch.from_texts(
+        texts,
+        FakeEmbeddings(),
+        work_dir=str(tmp_path),
+        n_dim=10,
+    )
+    assert docsearch.doc_index.num_docs() == 3
+
+
+def test_add_texts(texts: List[str], tmp_path: Path) -> None:
+    """Test end to end construction and simple similarity search."""
+    docsearch = DocArrayHnswSearch.from_params(
+        work_dir=str(tmp_path),
+        n_dim=10,
+        embedding=FakeEmbeddings(),
+    )
+    docsearch.add_texts(texts=texts)
+    assert docsearch.doc_index.num_docs() == 3
+
+
+@pytest.mark.parametrize("metric", ["cosine", "l2"])
+def test_sim_search(metric: str, texts: List[str], tmp_path: Path) -> None:
+    """Test end to end construction and simple similarity search."""
+    hnsw_vec_store = DocArrayHnswSearch.from_texts(
+        texts,
+        FakeEmbeddings(),
+        work_dir=str(tmp_path),
+        n_dim=10,
+        dist_metric=metric,
+        index=True,
+    )
+    output = hnsw_vec_store.similarity_search("foo", k=1)
+    assert output == [Document(page_content="foo")]
+
+
+@pytest.mark.parametrize("metric", ["cosine", "l2"])
+def test_sim_search_all_configurations(
+    metric: str, texts: List[str], tmp_path: Path
+) -> None:
+    """Test end to end construction and simple similarity search."""
+    hnsw_vec_store = DocArrayHnswSearch.from_texts(
+        texts,
+        FakeEmbeddings(),
+        work_dir=str(tmp_path),
+        dist_metric=metric,
+        n_dim=10,
+        max_elements=8,
+        ef_construction=300,
+        ef=20,
+        M=8,
+        allow_replace_deleted=False,
+        num_threads=2,
+    )
+    output = hnsw_vec_store.similarity_search("foo", k=1)
+    assert output == [Document(page_content="foo")]
+
+
+@pytest.mark.parametrize("metric", ["cosine", "l2"])
+def test_sim_search_by_vector(metric: str, texts: List[str], tmp_path: Path) -> None:
+    """Test end to end construction and similarity search by vector."""
+    hnsw_vec_store = DocArrayHnswSearch.from_texts(
+        texts,
+        FakeEmbeddings(),
+        work_dir=str(tmp_path),
+        n_dim=10,
+        dist_metric=metric,
+    )
+    embedding = [1.0] * 10
+    output = hnsw_vec_store.similarity_search_by_vector(embedding, k=1)
+
+    assert output == [Document(page_content="bar")]
+
+
+@pytest.mark.parametrize("metric", ["cosine", "l2"])
+def test_sim_search_with_score(metric: str, tmp_path: Path) -> None:
+    """Test end to end construction and similarity search with score."""
+    texts = ["foo", "bar", "baz"]
+    hnsw_vec_store = DocArrayHnswSearch.from_texts(
+        texts,
+        FakeEmbeddings(),
+        work_dir=str(tmp_path),
+        n_dim=10,
+        dist_metric=metric,
+    )
+    output = hnsw_vec_store.similarity_search_with_score("foo", k=1)
+    assert len(output) == 1
+
+    out_doc, out_score = output[0]
+    assert out_doc == Document(page_content="foo")
+    assert np.isclose(out_score, 0.0, atol=1.0e-6)
+
+
+def test_sim_search_with_score_for_ip_metric(texts: List[str], tmp_path: Path) -> None:
+    """
+    Test end to end construction and similarity search with score for ip
+    (inner-product) metric.
+    """
+    hnsw_vec_store = DocArrayHnswSearch.from_texts(
+        texts,
+        FakeEmbeddings(),
+        work_dir=str(tmp_path),
+        n_dim=10,
+        dist_metric="ip",
+    )
+    output = hnsw_vec_store.similarity_search_with_score("foo", k=3)
+    assert len(output) == 3
+
+    for result in output:
+        assert result[1] == -8.0
+
+
+@pytest.mark.parametrize("metric", ["cosine", "l2"])
+def test_max_marginal_relevance_search(
+    metric: str, texts: List[str], tmp_path: Path
+) -> None:
+    """Test MRR search."""
+    metadatas = [{"page": i} for i in range(len(texts))]
+    docsearch = DocArrayHnswSearch.from_texts(
+        texts,
+        FakeEmbeddings(),
+        metadatas=metadatas,
+        dist_metric=metric,
+        work_dir=str(tmp_path),
+        n_dim=10,
+    )
+    output = docsearch.max_marginal_relevance_search("foo", k=2, fetch_k=3)
+    assert output == [
+        Document(page_content="foo", metadata={"page": 0}),
+        Document(page_content="bar", metadata={"page": 1}),
+    ]
diff --git a/tests/integration_tests/vectorstores/docarray/test_in_memory.py b/tests/integration_tests/vectorstores/docarray/test_in_memory.py
new file mode 100644
index 0000000000..ca556b11cc
--- /dev/null
+++ b/tests/integration_tests/vectorstores/docarray/test_in_memory.py
@@ -0,0 +1,95 @@
+from pathlib import Path
+from typing import List
+
+import numpy as np
+import pytest
+
+from langchain.schema import Document
+from langchain.vectorstores.docarray import DocArrayInMemorySearch
+from tests.integration_tests.vectorstores.fake_embeddings import FakeEmbeddings
+
+
+@pytest.fixture
+def texts() -> List[str]:
+    return ["foo", "bar", "baz"]
+
+
+def test_from_texts(texts: List[str]) -> None:
+    """Test end to end construction and simple similarity search."""
+    docsearch = DocArrayInMemorySearch.from_texts(
+        texts,
+        FakeEmbeddings(),
+    )
+    assert isinstance(docsearch, DocArrayInMemorySearch)
+    assert docsearch.doc_index.num_docs() == 3
+
+
+def test_add_texts(texts: List[str], tmp_path: Path) -> None:
+    """Test end to end construction and simple similarity search."""
+    docsearch = DocArrayInMemorySearch.from_params(FakeEmbeddings())
+    assert isinstance(docsearch, DocArrayInMemorySearch)
+    assert docsearch.doc_index.num_docs() == 0
+
+    docsearch.add_texts(texts=texts)
+    assert docsearch.doc_index.num_docs() == 3
+
+
+@pytest.mark.parametrize("metric", ["cosine_sim", "euclidean_dist", "sqeuclidean_dist"])
+def test_sim_search(metric: str, texts: List[str]) -> None:
+    """Test end to end construction and simple similarity search."""
+    texts = ["foo", "bar", "baz"]
+    in_memory_vec_store = DocArrayInMemorySearch.from_texts(
+        texts=texts,
+        embedding=FakeEmbeddings(),
+        metric=metric,
+    )
+
+    output = in_memory_vec_store.similarity_search("foo", k=1)
+    assert output == [Document(page_content="foo")]
+
+
+@pytest.mark.parametrize("metric", ["cosine_sim", "euclidean_dist", "sqeuclidean_dist"])
+def test_sim_search_with_score(metric: str, texts: List[str]) -> None:
+    """Test end to end construction and similarity search with score."""
+    in_memory_vec_store = DocArrayInMemorySearch.from_texts(
+        texts=texts,
+        embedding=FakeEmbeddings(),
+        metric=metric,
+    )
+
+    output = in_memory_vec_store.similarity_search_with_score("foo", k=1)
+
+    out_doc, out_score = output[0]
+    assert out_doc == Document(page_content="foo")
+
+    expected_score = 0.0 if "dist" in metric else 1.0
+    assert np.isclose(out_score, expected_score, atol=1.0e-6)
+
+
+@pytest.mark.parametrize("metric", ["cosine_sim", "euclidean_dist", "sqeuclidean_dist"])
+def test_sim_search_by_vector(metric: str, texts: List[str]) -> None:
+    """Test end to end construction and similarity search by vector."""
+    in_memory_vec_store = DocArrayInMemorySearch.from_texts(
+        texts=texts,
+        embedding=FakeEmbeddings(),
+        metric=metric,
+    )
+
+    embedding = [1.0] * 10
+    output = in_memory_vec_store.similarity_search_by_vector(embedding, k=1)
+
+    assert output == [Document(page_content="bar")]
+
+
+@pytest.mark.parametrize("metric", ["cosine_sim", "euclidean_dist", "sqeuclidean_dist"])
+def test_max_marginal_relevance_search(metric: str, texts: List[str]) -> None:
+    """Test MRR search."""
+    metadatas = [{"page": i} for i in range(len(texts))]
+    docsearch = DocArrayInMemorySearch.from_texts(
+        texts, FakeEmbeddings(), metadatas=metadatas, metric=metric
+    )
+    output = docsearch.max_marginal_relevance_search("foo", k=2, fetch_k=3)
+    assert output == [
+        Document(page_content="foo", metadata={"page": 0}),
+        Document(page_content="bar", metadata={"page": 1}),
+    ]