From 0656fc96ffa5e3d2e18d5ce65aa4a9b81ec39258 Mon Sep 17 00:00:00 2001
From: Daniel Chalef <131175+danielchalef@users.noreply.github.com>
Date: Thu, 5 Oct 2023 13:26:26 -0700
Subject: [PATCH] remove experimental and deps

---
 examples/user_example.py                      |   37 +-
 poetry.lock                                   | 1956 ++++-------------
 pyproject.toml                                |   10 +-
 .../experimental/langchain/zep_vectorstore.py |  600 +----
 4 files changed, 466 insertions(+), 2137 deletions(-)

diff --git a/examples/user_example.py b/examples/user_example.py
index 08ae592..d25428e 100644
--- a/examples/user_example.py
+++ b/examples/user_example.py
@@ -1,17 +1,18 @@
 import uuid
 
-from zep_python import APIError, ZepClient
+import asyncio
+from zep_python import APIError, ZepClient, Session
 from zep_python.user import CreateUserRequest, UpdateUserRequest
 
 
-def main() -> None:
+async def main() -> None:
     base_url = "http://localhost:8000"  # TODO: Replace with Zep API URL
     api_key = "YOUR_API_KEY"  # TODO: Replace with your API key
 
     with ZepClient(base_url, api_key) as client:
         # Create multiple users
         for i in range(3):
-            user_id = uuid.uuid4().hex
+            user_id = f"user{i}"+uuid.uuid4().hex
             user_request = CreateUserRequest(
                 user_id=user_id,
                 email=f"user{i}@example.com",
@@ -20,13 +21,14 @@ def main() -> None:
                 metadata={"foo": "bar"},
             )
             try:
-                user = client.user.add(user_request)
+                user = await client.user.aadd(user_request)
                 print(f"Created user {i+1}: {user.user_id}")
             except APIError as e:
                 print(f"Failed to create user {i+1}: {e}")
 
         # Update the first user
-        user_id = client.user.list()[0].user_id
+        user_list = await client.user.alist(1)
+        user_id = user_list[0].user_id
         user_request = UpdateUserRequest(
             user_id=user_id,
             email="updated_user@example.com",
@@ -35,25 +37,36 @@ def main() -> None:
             metadata={"foo": "updated_bar"},
         )
         try:
-            updated_user = client.user.update(user_request)
+            updated_user = await client.user.aupdate(user_request)
             print(f"Updated user: {updated_user.user_id}")
         except APIError as e:
             print(f"Failed to update user: {e}")
 
+        # Create a Session for the first user
+        session_id = uuid.uuid4().hex
+        session = Session(
+            session_id=session_id, user_id=user_id, metadata={"session": i + 1}
+        )
+        try:
+            result = await client.memory.aadd_session(session)
+            print(f"Created session {i+1}: {result}")
+        except APIError as e:
+            print(f"Failed to create session {i+1}: {e}")
+
         # Delete the second user
-        user_id = client.user.list()[1].user_id
+        user_list = await client.user.alist(1, 1)
+        user_id = user_list[0].user_id
         try:
-            client.user.delete(user_id)
+            await client.user.adelete(user_id)
             print(f"Deleted user: {user_id}")
         except APIError as e:
             print(f"Failed to delete user: {e}")
 
         # List all users
         try:
-            users_generator = client.user.list_chunked()
+            users_generator = client.user.alist_chunked()
             print("All users:")
-            while True:
-                users = next(users_generator, None)
+            async for users in users_generator:
                 if users is None:
                     break
                 for user in users:
@@ -63,4 +76,4 @@ def main() -> None:
 
 
 if __name__ == "__main__":
-    main()
+    asyncio.run(main())
diff --git a/poetry.lock b/poetry.lock
index 4456ce5..e273d19 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -1,126 +1,18 @@
 # This file is automatically @generated by Poetry 1.6.1 and should not be changed by hand.
 
 [[package]]
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-version = "3.8.5"
-description = "Async http client/server framework (asyncio)"
-optional = false
-python-versions = ">=3.6"
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-charset-normalizer = ">=2.0,<4.0"
-frozenlist = ">=1.1.1"
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-yarl = ">=1.0,<2.0"
-
-[package.extras]
-speedups = ["Brotli", "aiodns", "cchardet"]
-
-[[package]]
-name = "aiosignal"
-version = "1.3.1"
-description = "aiosignal: a list of registered asynchronous callbacks"
+name = "annotated-types"
+version = "0.5.0"
+description = "Reusable constraint types to use with typing.Annotated"
 optional = false
 python-versions = ">=3.7"
 files = [
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 [package.dependencies]
-frozenlist = ">=1.1.0"
+typing-extensions = {version = ">=4.0.0", markers = "python_version < \"3.9\""}
 
 [[package]]
 name = "anyio"
@@ -143,96 +35,52 @@ doc = ["Sphinx (>=7)", "packaging", "sphinx-autodoc-typehints (>=1.2.0)"]
 test = ["anyio[trio]", "coverage[toml] (>=7)", "hypothesis (>=4.0)", "psutil (>=5.9)", "pytest (>=7.0)", "pytest-mock (>=3.6.1)", "trustme", "uvloop (>=0.17)"]
 trio = ["trio (>=0.22)"]
 
-[[package]]
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-dev = ["attrs[docs,tests]", "pre-commit"]
-docs = ["furo", "myst-parser", "sphinx", "sphinx-notfound-page", "sphinxcontrib-towncrier", "towncrier", "zope-interface"]
-tests = ["attrs[tests-no-zope]", "zope-interface"]
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-
 [[package]]
 name = "babel"
-version = "2.12.1"
+version = "2.13.0"
 description = "Internationalization utilities"
 optional = false
 python-versions = ">=3.7"
 files = [
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 ]
 
 [package.dependencies]
 pytz = {version = ">=2015.7", markers = "python_version < \"3.9\""}
 
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-
-[package.dependencies]
-soupsieve = ">1.2"
-
 [package.extras]
-html5lib = ["html5lib"]
-lxml = ["lxml"]
+dev = ["freezegun (>=1.0,<2.0)", "pytest (>=6.0)", "pytest-cov"]
 
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 name = "black"
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 description = "The uncompromising code formatter."
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 python-versions = ">=3.8"
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 [package.dependencies]
@@ -242,7 +90,7 @@ packaging = ">=22.0"
 pathspec = ">=0.9.0"
 platformdirs = ">=2"
 tomli = {version = ">=1.1.0", markers = "python_version < \"3.11\""}
-typing-extensions = {version = ">=3.10.0.0", markers = "python_version < \"3.10\""}
+typing-extensions = {version = ">=4.0.1", markers = "python_version < \"3.11\""}
 
 [package.extras]
 colorama = ["colorama (>=0.4.3)"]
@@ -274,86 +122,101 @@ files = [
 
 [[package]]
 name = "charset-normalizer"
-version = "3.2.0"
+version = "3.3.0"
 description = "The Real First Universal Charset Detector. Open, modern and actively maintained alternative to Chardet."
 optional = false
 python-versions = ">=3.7.0"
 files = [
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-
 [[package]]
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 version = "0.3.7"
@@ -438,13 +271,13 @@ test = ["pytest (>=6)"]
 
 [[package]]
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 python-versions = ">=3.8"
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-
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-cssselect = ["cssselect (>=0.7)"]
-html5 = ["html5lib"]
-htmlsoup = ["BeautifulSoup4"]
-source = ["Cython (>=0.29.35)"]
-
 [[package]]
 name = "markdown"
 version = "3.4.4"
@@ -1015,22 +476,6 @@ importlib-metadata = {version = ">=4.4", markers = "python_version < \"3.10\""}
 docs = ["mdx-gh-links (>=0.2)", "mkdocs (>=1.0)", "mkdocs-nature (>=0.4)"]
 testing = ["coverage", "pyyaml"]
 
-[[package]]
-name = "markdown2"
-version = "2.4.10"
-description = "A fast and complete Python implementation of Markdown"
-optional = false
-python-versions = ">=3.5, <4"
-files = [
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-]
-
-[package.extras]
-all = ["pygments (>=2.7.3)", "wavedrom"]
-code-syntax-highlighting = ["pygments (>=2.7.3)"]
-wavedrom = ["wavedrom"]
-
 [[package]]
 name = "markupsafe"
 version = "2.1.3"
@@ -1058,16 +503,6 @@ files = [
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@@ -1100,40 +535,6 @@ files = [
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-version = "3.20.1"
-description = "A lightweight library for converting complex datatypes to and from native Python datatypes."
-optional = false
-python-versions = ">=3.8"
-files = [
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-
-[package.dependencies]
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-
-[package.extras]
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-tests = ["pytest", "pytz", "simplejson"]
-
-[[package]]
-name = "marshmallow-enum"
-version = "1.5.1"
-description = "Enum field for Marshmallow"
-optional = false
-python-versions = "*"
-files = [
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-
-[package.dependencies]
-marshmallow = ">=2.0.0"
-
 [[package]]
 name = "mergedeep"
 version = "1.3.4"
@@ -1147,13 +548,13 @@ files = [
 
 [[package]]
 name = "mkdocs"
-version = "1.5.2"
+version = "1.5.3"
 description = "Project documentation with Markdown."
 optional = false
 python-versions = ">=3.7"
 files = [
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 ]
 
 [package.dependencies]
@@ -1193,39 +594,42 @@ mkdocs = ">=1.1"
 
 [[package]]
 name = "mkdocs-material"
-version = "9.2.6"
+version = "9.4.4"
 description = "Documentation that simply works"
 optional = false
-python-versions = ">=3.7"
+python-versions = ">=3.8"
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-jinja2 = ">=3.0"
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-markdown = ">=3.2"
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-pymdown-extensions = ">=9.9.1"
-readtime = ">=2.0"
-regex = ">=2022.4.24"
-requests = ">=2.26"
+babel = ">=2.10,<3.0"
+colorama = ">=0.4,<1.0"
+jinja2 = ">=3.0,<4.0"
+markdown = ">=3.2,<4.0"
+mkdocs = ">=1.5.3,<2.0"
+mkdocs-material-extensions = ">=1.2,<2.0"
+paginate = ">=0.5,<1.0"
+pygments = ">=2.16,<3.0"
+pymdown-extensions = ">=10.2,<11.0"
+regex = ">=2022.4"
+requests = ">=2.26,<3.0"
+
+[package.extras]
+git = ["mkdocs-git-committers-plugin-2 (>=1.1,<2.0)", "mkdocs-git-revision-date-localized-plugin (>=1.2,<2.0)"]
+imaging = ["cairosvg (>=2.6,<3.0)", "pillow (>=9.4,<10.0)"]
+recommended = ["mkdocs-minify-plugin (>=0.7,<1.0)", "mkdocs-redirects (>=1.2,<2.0)", "mkdocs-rss-plugin (>=1.6,<2.0)"]
 
 [[package]]
 name = "mkdocs-material-extensions"
-version = "1.1.1"
+version = "1.2"
 description = "Extension pack for Python Markdown and MkDocs Material."
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 python-versions = ">=3.7"
 files = [
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 ]
 
 [[package]]
@@ -1256,102 +660,19 @@ python-legacy = ["mkdocstrings-python-legacy (>=0.2.1)"]
 
 [[package]]
 name = "mkdocstrings-python"
-version = "1.6.0"
+version = "1.7.2"
 description = "A Python handler for mkdocstrings."
 optional = false
 python-versions = ">=3.8"
 files = [
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 [package.dependencies]
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 [[package]]
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-]
-
-[package.dependencies]
-idna = ">=2.0"
-multidict = ">=4.0"
-
 [[package]]
 name = "zipp"
-version = "3.16.2"
+version = "3.17.0"
 description = "Backport of pathlib-compatible object wrapper for zip files"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "zipp-3.16.2-py3-none-any.whl", hash = "sha256:679e51dd4403591b2d6838a48de3d283f3d188412a9782faadf845f298736ba0"},
-    {file = "zipp-3.16.2.tar.gz", hash = "sha256:ebc15946aa78bd63458992fc81ec3b6f7b1e92d51c35e6de1c3804e73b799147"},
+    {file = "zipp-3.17.0-py3-none-any.whl", hash = "sha256:0e923e726174922dce09c53c59ad483ff7bbb8e572e00c7f7c46b88556409f31"},
+    {file = "zipp-3.17.0.tar.gz", hash = "sha256:84e64a1c28cf7e91ed2078bb8cc8c259cb19b76942096c8d7b84947690cabaf0"},
 ]
 
 [package.extras]
-docs = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-lint"]
+docs = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (<7.2.5)", "sphinx (>=3.5)", "sphinx-lint"]
 testing = ["big-O", "jaraco.functools", "jaraco.itertools", "more-itertools", "pytest (>=6)", "pytest-black (>=0.3.7)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=2.2)", "pytest-ignore-flaky", "pytest-mypy (>=0.9.1)", "pytest-ruff"]
 
 [metadata]
 lock-version = "2.0"
 python-versions = ">=3.8.1,<4"
-content-hash = "54b4dcacd0effc2e414e0312be0af8e90ac2d948733a840d03113fe02ac7fc91"
+content-hash = "d2f4b7d3b8e195ccf4493b79cf2dfa9813f67ae37a724215861a956a925ef9b9"
diff --git a/pyproject.toml b/pyproject.toml
index 1566319..b0185bc 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -1,7 +1,7 @@
 [tool.poetry]
 name = "zep-python"
 version = "1.1.2"
-description = "Zep stores, manages, enriches, indexes, and searches long-term memory for conversational AI applications. This is the Python client for the Zep service."
+description = "Zep: Fast, scalable building blocks for LLM apps. This is the Python client for the Zep service."
 authors = ["Daniel Chalef <daniel.chalef@private.org>"]
 readme = "README.md"
 
@@ -31,14 +31,6 @@ pytest-httpx = "^0.22.0"
 faker = "^19.2.0"
 python-dotenv = "^1.0.0"
 
-
-[tool.poetry.group.experimental.dependencies]
-langchain = "^0.0.247"
-numpy = ">1.20.0"
-openai = "^0.27.8"
-tiktoken = "^0.4.0"
-llama-index = "^0.7.16"
-
 [build-system]
 requires = ["poetry-core"]
 build-backend = "poetry.core.masonry.api"
diff --git a/zep_python/experimental/langchain/zep_vectorstore.py b/zep_python/experimental/langchain/zep_vectorstore.py
index 6884ead..3ebe747 100644
--- a/zep_python/experimental/langchain/zep_vectorstore.py
+++ b/zep_python/experimental/langchain/zep_vectorstore.py
@@ -1,601 +1,25 @@
 from __future__ import annotations
 
-import warnings
 from typing import (
     Any,
     Dict,
-    Iterable,
     List,
     Optional,
-    Tuple,
-    Type,
 )
 
-import numpy as np
-from langchain.docstore.document import Document
-from langchain.embeddings.base import Embeddings
-from langchain.vectorstores.base import VectorStore
-from langchain.vectorstores.utils import maximal_marginal_relevance  # type: ignore
+class DeprecationError(Exception):
+    pass
 
-from zep_python.document import Document as ZepDocument
-from zep_python.document import DocumentCollection
-
-
-class ZepVectorStore(VectorStore):
+class ZepVectorStore:
     def __init__(
-        self,
-        collection: DocumentCollection,
-        texts: Optional[List[str]] = None,
-        metadata: Optional[List[Dict[str, Any]]] = None,
-        embedding: Optional[Embeddings] = None,
-        **kwargs: Any,
+            self,
+            collection: Any,
+            texts: Optional[List[str]] = None,
+            metadata: Optional[List[Dict[str, Any]]] = None,
+            embedding: Optional[Any] = None,
+            **kwargs: Any,
     ) -> None:
-        warnings.warn(
-            (
-                "This experimental class has been deprecated. Please use the "
-                "official ZepVectorStore class in the Langchain package."
-            ),
-            DeprecationWarning,
-            stacklevel=2,
-        )
-
-        if not isinstance(collection, DocumentCollection):  # type: ignore
-            raise ValueError(
-                "collection should be an instance of a Zep DocumentCollection"
-            )
-
-        self._collection: Optional[DocumentCollection] = collection
-        self._texts: Optional[List[str]] = texts
-        self._embedding: Optional[Embeddings] = embedding
-
-        if self._texts is not None:
-            self.add_texts(self._texts, metadatas=metadata, **kwargs)
-
-    def _generate_documents_to_add(
-        self,
-        texts: Iterable[str],
-        metadatas: Optional[List[Dict[Any, Any]]] = None,
-        document_ids: Optional[List[str]] = None,
-    ):
-        if (
-            self._collection
-            and self._collection.is_auto_embedded
-            and self._embedding is not None
-        ):
-            warnings.warn(
-                """The collection is set to auto-embed and an embedding 
-            function is present. Ignoring the embedding function.""",
-                stacklevel=2,
-            )
-            self._embedding = None
-
-        embeddings = None
-        if self._embedding is not None:
-            embeddings = self._embedding.embed_documents(list(texts))
-
-            if self._collection and self._collection.embedding_dimensions != len(
-                embeddings[0]
-            ):
-                raise ValueError(
-                    "The embedding dimensions of the collection and the embedding"
-                    " function do not match. Collection dimensions:"
-                    f" {self._collection.embedding_dimensions}, Embedding dimensions:"
-                    f" {len(embeddings[0])}"
-                )
-
-        documents: List[ZepDocument] = []
-        for i, d in enumerate(texts):
-            documents.append(
-                ZepDocument(
-                    content=d,
-                    metadata=metadatas[i] if metadatas else None,
-                    document_id=document_ids[i] if document_ids else None,
-                    embedding=embeddings[i] if embeddings else None,
-                )
-            )
-        return documents
-
-    def add_texts(
-        self,
-        texts: Iterable[str],
-        metadatas: Optional[List[Dict[str, Any]]] = None,
-        document_ids: Optional[List[str]] = 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.
-            document_ids: Optional list of document ids associated with the texts.
-            kwargs: vectorstore specific parameters
-
-        Returns:
-            List of ids from adding the texts into the vectorstore.
-        """
-
-        if not isinstance(self._collection, DocumentCollection):
-            raise ValueError(
-                "collection should be an instance of a Zep DocumentCollection"
-            )
-
-        documents = self._generate_documents_to_add(texts, metadatas, document_ids)
-        uuids = self._collection.add_documents(documents)
-
-        return uuids
-
-    async def aadd_texts(
-        self,
-        texts: Iterable[str],
-        metadatas: Optional[List[Dict[str, Any]]] = None,
-        document_ids: Optional[List[str]] = None,
-        **kwargs: Any,
-    ) -> List[str]:
-        """Run more texts through the embeddings and add to the vectorstore."""
-        if not isinstance(self._collection, DocumentCollection):
-            raise ValueError(
-                "collection should be an instance of a Zep DocumentCollection"
-            )
-
-        documents = self._generate_documents_to_add(texts, metadatas, document_ids)
-        uuids = await self._collection.aadd_documents(documents)
-
-        return uuids
-
-    def add_documents(self, documents: List[Document], **kwargs: Any) -> List[str]:
-        """Run more documents through the embeddings and add to the vectorstore.
-
-        Args:
-            documents List[Document]: Documents to add to the vectorstore.
-
-        Returns:
-            List[str]: List of UUIDs of the added texts.
-        """
-        texts = [doc.page_content for doc in documents]
-        metadatas = [doc.metadata for doc in documents]  # type: ignore
-        return self.add_texts(texts, metadatas, **kwargs)  # type: ignore
-
-    async def aadd_documents(
-        self, documents: List[Document], **kwargs: Any
-    ) -> List[str]:
-        """Run more documents through the embeddings and add to the vectorstore.
-
-        Args:
-            documents List[Document]: Documents to add to the vectorstore.
-
-        Returns:
-            List[str]: List of UUIDs of the added texts.
-        """
-        texts = [doc.page_content for doc in documents]
-        metadatas = [doc.metadata for doc in documents]  # type: ignore
-        return await self.aadd_texts(texts, metadatas, **kwargs)  # type: ignore
-
-    def search(
-        self,
-        query: str,
-        search_type: str,
-        metadata: Optional[Dict[str, Any]] = None,
-        k: int = 4,
-        **kwargs: Any,
-    ) -> List[Document]:
-        """Return docs most similar to query using specified search type."""
-        if search_type == "similarity":
-            return self.similarity_search(query, k, metadata, **kwargs)
-        elif search_type == "mmr":
-            return self.max_marginal_relevance_search(
-                query, k, metadata=metadata, **kwargs
-            )
-        else:
-            raise ValueError(
-                f"search_type of {search_type} not allowed. Expected "
-                "search_type to be 'similarity' or 'mmr'."
-            )
-
-    async def asearch(
-        self,
-        query: str,
-        search_type: str,
-        metadata: Optional[Dict[str, Any]] = None,
-        k: int = 5,
-        **kwargs: Any,
-    ) -> List[Document]:
-        """Return docs most similar to query using specified search type."""
-        if search_type == "similarity":
-            return await self.asimilarity_search(query, k, metadata, **kwargs)
-        elif search_type == "mmr":
-            return await self.amax_marginal_relevance_search(
-                query, k, metadata=metadata, **kwargs
-            )
-        else:
-            raise ValueError(
-                f"search_type of {search_type} not allowed. Expected "
-                "search_type to be 'similarity' or 'mmr'."
-            )
-
-    def similarity_search(
-        self,
-        query: str,
-        k: int = 4,
-        metadata: Optional[Dict[str, Any]] = None,
-        **kwargs: Any,
-    ) -> List[Document]:
-        """Return docs most similar to query."""
-
-        results = self._similarity_search_with_relevance_scores(
-            query, k, metadata=metadata, **kwargs
-        )
-        return [doc for doc, _ in results]
-
-    def similarity_search_with_score(  # type: ignore
-        self,
-        query: str,
-        k: int = 4,
-        metadata: Optional[Dict[str, Any]] = None,
-        *args: Any,
-        **kwargs: Any,
-    ) -> List[Tuple[Document, Optional[float]]]:
-        """Run similarity search with distance."""
-        return self._similarity_search_with_relevance_scores(
-            query, k, metadata=metadata, **kwargs
-        )
-
-    def _similarity_search_with_relevance_scores(  # type: ignore
-        self,
-        query: str,
-        k: int = 4,
-        metadata: Optional[Dict[str, Any]] = None,
-        **kwargs: Any,
-    ) -> List[Tuple[Document, Optional[float]]]:
-        """
-        Default similarity search with relevance scores. Modify if necessary
-        in subclass.
-        Return docs and relevance scores in the range [0, 1].
-
-        0 is dissimilar, 1 is most similar.
-
-        Args:
-            query: input text
-            k: Number of Documents to return. Defaults to 4.
-            metadata: Optional, metadata filter
-            **kwargs: kwargs to be passed to similarity search. Should include:
-                score_threshold: Optional, a floating point value between 0 to 1 and
-                    filter the resulting set of retrieved docs
-
-        Returns:
-            List of Tuples of (doc, similarity_score)
-        """
-
-        if not isinstance(self._collection, DocumentCollection):
-            raise ValueError(
-                "collection should be an instance of a Zep DocumentCollection"
-            )
-
-        if self._embedding:
-            query_vector = self._embedding.embed_query(query)
-            results = self._collection.search(
-                embedding=query_vector, limit=k, metadata=metadata, **kwargs
-            )
-        else:
-            results = self._collection.search(
-                query, limit=k, metadata=metadata, **kwargs
-            )
-
-        return [
-            (
-                Document(
-                    page_content=doc.content,
-                    metadata=doc.metadata,  # type: ignore
-                ),
-                doc.score,
-            )
-            for doc in results
-        ]
-
-    async def asimilarity_search_with_relevance_scores(  # type: ignore
-        self,
-        query: str,
-        k: int = 4,
-        metadata: Optional[Dict[str, Any]] = None,
-        **kwargs: Any,
-    ) -> List[Tuple[Document, Optional[float]]]:
-        """Return docs most similar to query."""
-
-        if not isinstance(self._collection, DocumentCollection):
-            raise ValueError(
-                "collection should be an instance of a Zep DocumentCollection"
-            )
-
-        if self._embedding:
-            query_vector = self._embedding.embed_query(query)
-            results = await self._collection.asearch(
-                embedding=query_vector, limit=k, metadata=metadata, **kwargs
-            )
-        else:
-            results = await self._collection.asearch(
-                query, limit=k, metadata=metadata, **kwargs
-            )
-
-        return [
-            (
-                Document(
-                    page_content=doc.content,
-                    metadata=doc.metadata,  # type: ignore
-                ),
-                doc.score,
-            )
-            for doc in results
-        ]
-
-    async def asimilarity_search(
-        self,
-        query: str,
-        k: int = 4,
-        metadata: Optional[Dict[str, Any]] = None,
-        **kwargs: Any,
-    ) -> List[Document]:
-        """Return docs most similar to query."""
-
-        results = await self.asimilarity_search_with_relevance_scores(
-            query, k, metadata=metadata, **kwargs
-        )
-
-        return [doc for doc, _ in results]
-
-    def similarity_search_by_vector(
-        self,
-        embedding: List[float],
-        k: int = 4,
-        metadata: Optional[Dict[str, Any]] = None,
-        **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.
-            metadata: Optional, metadata filter
-
-        Returns:
-            List of Documents most similar to the query vector.
-        """
-        if not isinstance(self._collection, DocumentCollection):
-            raise ValueError(
-                "collection should be an instance of a Zep DocumentCollection"
-            )
-
-        results = self._collection.search(
-            embedding=embedding, limit=k, metadata=metadata, **kwargs
-        )
-
-        return [
-            Document(
-                page_content=doc.content,
-                metadata=doc.metadata,  # type: ignore
-            )
-            for doc in results
-        ]
-
-    async def asimilarity_search_by_vector(
-        self,
-        embedding: List[float],
-        k: int = 4,
-        metadata: Optional[Dict[str, Any]] = None,
-        **kwargs: Any,
-    ) -> List[Document]:
-        """Return docs most similar to embedding vector."""
-        if not isinstance(self._collection, DocumentCollection):
-            raise ValueError(
-                "collection should be an instance of a Zep DocumentCollection"
-            )
-
-        results = self._collection.search(
-            embedding=embedding, limit=k, metadata=metadata, **kwargs
-        )
-
-        return [
-            Document(
-                page_content=doc.content,
-                metadata=doc.metadata,  # type: ignore
-            )
-            for doc in results
-        ]
-
-    def _max_marginal_relevance_selection(
-        self,
-        query_vector: List[float],
-        results: List[ZepDocument],
-        k: int = 4,
-        lambda_mult: float = 0.5,
-    ) -> List[Document]:
-        mmr_selected = maximal_marginal_relevance(
-            np.array([query_vector], dtype=np.float32),
-            [d.embedding for d in results],
-            k=k,
-            lambda_mult=lambda_mult,
-        )
-        selected = [results[i] for i in mmr_selected]
-        return [
-            Document(page_content=d.content, metadata=d.metadata)  # type: ignore
-            for d in selected
-        ]
-
-    def max_marginal_relevance_search(
-        self,
-        query: str,
-        k: int = 4,
-        fetch_k: int = 20,
-        lambda_mult: float = 0.5,
-        metadata: Optional[Dict[str, Any]] = None,
-        **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.
-            metadata: Optional, metadata to filter the resulting set of retrieved docs
-        Returns:
-            List of Documents selected by maximal marginal relevance.
-        """
-
-        if not isinstance(self._collection, DocumentCollection):
-            raise ValueError(
-                "collection should be an instance of a Zep DocumentCollection"
-            )
-
-        if self._embedding:
-            query_vector = self._embedding.embed_query(query)
-            results = self._collection.search(
-                embedding=query_vector, limit=k, metadata=metadata, **kwargs
-            )
-        else:
-            results, query_vector = self._collection.search_return_query_vector(
-                query, limit=k, metadata=metadata, **kwargs
-            )
-
-        return self._max_marginal_relevance_selection(
-            query_vector, results, k=k, lambda_mult=lambda_mult
-        )
-
-    async def amax_marginal_relevance_search(
-        self,
-        query: str,
-        k: int = 4,
-        fetch_k: int = 20,
-        lambda_mult: float = 0.5,
-        metadata: Optional[Dict[str, Any]] = None,
-        **kwargs: Any,
-    ) -> List[Document]:
-        """Return docs selected using the maximal marginal relevance."""
-
-        if not isinstance(self._collection, DocumentCollection):
-            raise ValueError(
-                "collection should be an instance of a Zep DocumentCollection"
-            )
-
-        if self._embedding:
-            query_vector = self._embedding.embed_query(query)
-            results = await self._collection.asearch(
-                embedding=query_vector, limit=k, metadata=metadata, **kwargs
-            )
-        else:
-            results, query_vector = await self._collection.asearch_return_query_vector(
-                query, limit=k, metadata=metadata, **kwargs
-            )
-
-        return self._max_marginal_relevance_selection(
-            query_vector, results, k=k, lambda_mult=lambda_mult
-        )
-
-    def max_marginal_relevance_search_by_vector(
-        self,
-        embedding: List[float],
-        k: int = 4,
-        fetch_k: int = 20,
-        lambda_mult: float = 0.5,
-        metadata: Optional[Dict[str, Any]] = None,
-        **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:
-            embedding: Embedding 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.
-            metadata: Optional, metadata to filter the resulting set of retrieved docs
-        Returns:
-            List of Documents selected by maximal marginal relevance.
-        """
-        if not isinstance(self._collection, DocumentCollection):
-            raise ValueError(
-                "collection should be an instance of a Zep DocumentCollection"
-            )
-
-        results = self._collection.search(
-            embedding=embedding, limit=k, metadata=metadata, **kwargs
+        raise DeprecationError(
+            "This experimental class has been deprecated. Please use the "
+            "official ZepVectorStore class in the Langchain package."
         )
-
-        return self._max_marginal_relevance_selection(
-            embedding, results, k=k, lambda_mult=lambda_mult
-        )
-
-    async def amax_marginal_relevance_search_by_vector(
-        self,
-        embedding: List[float],
-        k: int = 4,
-        fetch_k: int = 20,
-        lambda_mult: float = 0.5,
-        metadata: Optional[Dict[str, Any]] = None,
-        **kwargs: Any,
-    ) -> List[Document]:
-        """Return docs selected using the maximal marginal relevance."""
-        if not isinstance(self._collection, DocumentCollection):
-            raise ValueError(
-                "collection should be an instance of a Zep DocumentCollection"
-            )
-
-        results = await self._collection.asearch(
-            embedding=embedding, limit=k, metadata=metadata, **kwargs
-        )
-
-        return self._max_marginal_relevance_selection(
-            embedding, results, k=k, lambda_mult=lambda_mult
-        )
-
-    @classmethod
-    def from_texts(  # type: ignore
-        cls: Type[ZepVectorStore],
-        texts: List[str],
-        collection: DocumentCollection,
-        embedding: Optional[Embeddings] = None,
-        metadatas: Optional[List[Dict[str, Any]]] = None,
-        **kwargs: Any,
-    ) -> ZepVectorStore:
-        """Return VectorStore initialized from texts and embeddings."""
-        return cls(
-            collection=collection, texts=texts, embedding=embedding, metadata=metadatas
-        )
-
-    @property
-    def embeddings(self) -> Optional[Embeddings]:
-        return self._embedding
-
-    def delete(  # type: ignore
-        self, uuids: Optional[List[str]] = None, **kwargs: Any
-    ) -> None:
-        """Delete by Zep vector UUIDs.
-
-        Parameters
-        ----------
-        uuids : Optional[List[str]]
-            The UUIDs of the vectors to delete.
-
-        Raises
-        ------
-        ValueError
-            If no UUIDs are provided.
-        """
-
-        if uuids is None or len(uuids) == 0:
-            raise ValueError("No uuids provided to delete.")
-
-        if self._collection is None:
-            raise ValueError("No collection name provided.")
-
-        for u in uuids:
-            self._collection.delete_document(u)