diff --git a/docs/modules/indexes/retrievers/examples/myscale_self_query.ipynb b/docs/modules/indexes/retrievers/examples/myscale_self_query.ipynb
new file mode 100644
index 0000000000000..c9a5753472286
--- /dev/null
+++ b/docs/modules/indexes/retrievers/examples/myscale_self_query.ipynb
@@ -0,0 +1,370 @@
+{
+ "cells": [
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "id": "13afcae7",
+   "metadata": {},
+   "source": [
+    "# Self-querying with MyScale\n",
+    "\n",
+    ">[MyScale](https://docs.myscale.com/en/) is an integrated vector database. You can access your database in SQL and also from here, LangChain. MyScale can make a use of [various data types and functions for filters](https://blog.myscale.com/2023/06/06/why-integrated-database-solution-can-boost-your-llm-apps/#filter-on-anything-without-constraints). It will boost up your LLM app no matter if you are scaling up your data or expand your system to broader application.\n",
+    "\n",
+    "In the notebook we'll demo the `SelfQueryRetriever` wrapped around a MyScale vector store with some extra piece we contributed to LangChain. In short, it can be concluded into 4 points:\n",
+    "1. Add `contain` comparator to match list of any if there is more than one element matched\n",
+    "2. Add `timestamp` data type for datetime match (ISO-format, or YYYY-MM-DD)\n",
+    "3. Add `like` comparator for string pattern search\n",
+    "4. Add arbitrary function capability"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "id": "68e75fb9",
+   "metadata": {},
+   "source": [
+    "## Creating a MyScale vectorstore\n",
+    "MyScale has already been integrated to LangChain for a while. So you can follow [this notebook](../../vectorstores/examples/myscale.ipynb) to create your own vectorstore for a self-query retriever.\n",
+    "\n",
+    "NOTE: All self-query retrievers requires you to have `lark` installed (`pip install lark`). We use `lark` for grammar definition. Before you proceed to the next step, we also want to remind you that `clickhouse-connect` is also needed to interact with your MyScale backend."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "63a8af5b",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "! pip install lark clickhouse-connect"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "id": "83811610-7df3-4ede-b268-68a6a83ba9e2",
+   "metadata": {},
+   "source": [
+    "In this tutorial we follow other example's setting and use `OpenAIEmbeddings`. Remember to get a OpenAI API Key for valid accesss to LLMs."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "dd01b61b-7d32-4a55-85d6-b2d2d4f18840",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "import getpass\n",
+    "\n",
+    "os.environ['OPENAI_API_KEY'] = getpass.getpass('OpenAI API Key:')\n",
+    "os.environ['MYSCALE_HOST'] = getpass.getpass('MyScale URL:')\n",
+    "os.environ['MYSCALE_PORT'] = getpass.getpass('MyScale Port:')\n",
+    "os.environ['MYSCALE_USERNAME'] = getpass.getpass('MyScale Username:')\n",
+    "os.environ['MYSCALE_PASSWORD'] = getpass.getpass('MyScale Password:')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "cb4a5787",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "from langchain.schema import Document\n",
+    "from langchain.embeddings.openai import OpenAIEmbeddings\n",
+    "from langchain.vectorstores import MyScale\n",
+    "\n",
+    "embeddings = OpenAIEmbeddings()"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "id": "bf7f6fc4",
+   "metadata": {},
+   "source": [
+    "## Create some sample data\n",
+    "As you can see, the data we created has some difference to other self-query retrievers. We replaced keyword `year` to `date` which gives you a finer control on timestamps. We also altered the type of keyword `gerne` to list of strings, where LLM can use a new `contain` comparator to construct filters. We also provides comparator `like` and arbitrary function support to filters, which will be introduced in next few cells.\n",
+    "\n",
+    "Now let's look at the data first."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "bcbe04d9",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "docs = [\n",
+    "    Document(page_content=\"A bunch of scientists bring back dinosaurs and mayhem breaks loose\", metadata={\"date\": \"1993-07-02\", \"rating\": 7.7, \"genre\": [\"science fiction\"]}),\n",
+    "    Document(page_content=\"Leo DiCaprio gets lost in a dream within a dream within a dream within a ...\", metadata={\"date\": \"2010-12-30\", \"director\": \"Christopher Nolan\", \"rating\": 8.2}),\n",
+    "    Document(page_content=\"A psychologist / detective gets lost in a series of dreams within dreams within dreams and Inception reused the idea\", metadata={\"date\": \"2006-04-23\", \"director\": \"Satoshi Kon\", \"rating\": 8.6}),\n",
+    "    Document(page_content=\"A bunch of normal-sized women are supremely wholesome and some men pine after them\", metadata={\"date\": \"2019-08-22\", \"director\": \"Greta Gerwig\", \"rating\": 8.3}),\n",
+    "    Document(page_content=\"Toys come alive and have a blast doing so\", metadata={\"date\": \"1995-02-11\", \"genre\": [\"animated\"]}),\n",
+    "    Document(page_content=\"Three men walk into the Zone, three men walk out of the Zone\", metadata={\"date\": \"1979-09-10\", \"rating\": 9.9, \"director\": \"Andrei Tarkovsky\", \"genre\": [\"science fiction\", \"adventure\"], \"rating\": 9.9})\n",
+    "]\n",
+    "vectorstore = MyScale.from_documents(\n",
+    "    docs, \n",
+    "    embeddings, \n",
+    ")"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "id": "5ecaab6d",
+   "metadata": {},
+   "source": [
+    "## Creating our self-querying retriever\n",
+    "Just like other retrievers... Simple and nice."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "86e34dbf",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "from langchain.llms import OpenAI\n",
+    "from langchain.retrievers.self_query.base import SelfQueryRetriever\n",
+    "from langchain.chains.query_constructor.base import AttributeInfo\n",
+    "\n",
+    "metadata_field_info=[\n",
+    "    AttributeInfo(\n",
+    "        name=\"genre\",\n",
+    "        description=\"The genres of the movie\", \n",
+    "        type=\"list[string]\", \n",
+    "    ),\n",
+    "    # If you want to include length of a list, just define it as a new column\n",
+    "    # This will teach the LLM to use it as a column when constructing filter.\n",
+    "    AttributeInfo(\n",
+    "        name=\"length(genre)\",\n",
+    "        description=\"The lenth of genres of the movie\", \n",
+    "        type=\"integer\", \n",
+    "    ),\n",
+    "    # Now you can define a column as timestamp. By simply set the type to timestamp.\n",
+    "    AttributeInfo(\n",
+    "        name=\"date\",\n",
+    "        description=\"The date the movie was released\", \n",
+    "        type=\"timestamp\", \n",
+    "    ),\n",
+    "    AttributeInfo(\n",
+    "        name=\"director\",\n",
+    "        description=\"The name of the movie director\", \n",
+    "        type=\"string\", \n",
+    "    ),\n",
+    "    AttributeInfo(\n",
+    "        name=\"rating\",\n",
+    "        description=\"A 1-10 rating for the movie\",\n",
+    "        type=\"float\"\n",
+    "    ),\n",
+    "]\n",
+    "document_content_description = \"Brief summary of a movie\"\n",
+    "llm = OpenAI(temperature=0)\n",
+    "retriever = SelfQueryRetriever.from_llm(llm, vectorstore, document_content_description, metadata_field_info, verbose=True)"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "id": "ea9df8d4",
+   "metadata": {},
+   "source": [
+    "## Testing it out with self-query retriever's existing functionalities\n",
+    "And now we can try actually using our retriever!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "38a126e9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# This example only specifies a relevant query\n",
+    "retriever.get_relevant_documents(\"What are some movies about dinosaurs\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "fc3f1e6e",
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [],
+   "source": [
+    "# This example only specifies a filter\n",
+    "retriever.get_relevant_documents(\"I want to watch a movie rated higher than 8.5\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b19d4da0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# This example specifies a query and a filter\n",
+    "retriever.get_relevant_documents(\"Has Greta Gerwig directed any movies about women\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f900e40e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# This example specifies a composite filter\n",
+    "retriever.get_relevant_documents(\"What's a highly rated (above 8.5) science fiction film?\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "12a51522",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# This example specifies a query and composite filter\n",
+    "retriever.get_relevant_documents(\"What's a movie after 1990 but before 2005 that's all about toys, and preferably is animated\")"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "id": "86371ac8",
+   "metadata": {},
+   "source": [
+    "# Wait a second... What else?\n",
+    "\n",
+    "Self-query retriever with MyScale can do more! Let's find out."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "1d043096",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# You can use length(genres) to do anything you want\n",
+    "retriever.get_relevant_documents(\"What's a movie that have more than 1 genres?\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d570d33c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Fine-grained datetime? You got it already.\n",
+    "retriever.get_relevant_documents(\"What's a movie that release after feb 1995?\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "fbe0b21b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Don't know what your exact filter should be? Use string pattern match!\n",
+    "retriever.get_relevant_documents(\"What's a movie whose name is like Andrei?\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6a514104",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Contain works for lists: so you can match a list with contain comparator!\n",
+    "retriever.get_relevant_documents(\"What's a movie who has genres science fiction and adventure?\")"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "id": "39bd1de1-b9fe-4a98-89da-58d8a7a6ae51",
+   "metadata": {},
+   "source": [
+    "## Filter k\n",
+    "\n",
+    "We can also use the self query retriever to specify `k`: the number of documents to fetch.\n",
+    "\n",
+    "We can do this by passing `enable_limit=True` to the constructor."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "bff36b88-b506-4877-9c63-e5a1a8d78e64",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "retriever = SelfQueryRetriever.from_llm(\n",
+    "    llm, \n",
+    "    vectorstore, \n",
+    "    document_content_description, \n",
+    "    metadata_field_info, \n",
+    "    enable_limit=True,\n",
+    "    verbose=True\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2758d229-4f97-499c-819f-888acaf8ee10",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# This example only specifies a relevant query\n",
+    "retriever.get_relevant_documents(\"what are two movies about dinosaurs\")"
+   ]
+  }
+ ],
+ "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": "3.8.8"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/docs/modules/indexes/retrievers/examples/qdrant_self_query.ipynb b/docs/modules/indexes/retrievers/examples/qdrant_self_query.ipynb
index 231e267148053..33cc87e0047d6 100644
--- a/docs/modules/indexes/retrievers/examples/qdrant_self_query.ipynb
+++ b/docs/modules/indexes/retrievers/examples/qdrant_self_query.ipynb
@@ -1,6 +1,7 @@
 {
  "cells": [
   {
+   "attachments": {},
    "cell_type": "markdown",
    "id": "13afcae7",
    "metadata": {},
@@ -13,12 +14,13 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "id": "68e75fb9",
    "metadata": {},
    "source": [
     "## Creating a Qdrant vectorstore\n",
-    "First we'll want to create a Chroma VectorStore and seed it with some data. We've created a small demo set of documents that contain summaries of movies.\n",
+    "First we'll want to create a Qdrant VectorStore and seed it with some data. We've created a small demo set of documents that contain summaries of movies.\n",
     "\n",
     "NOTE: The self-query retriever requires you to have `lark` installed (`pip install lark`). We also need the `qdrant-client` package."
    ]
@@ -36,6 +38,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "id": "83811610-7df3-4ede-b268-68a6a83ba9e2",
    "metadata": {},
@@ -100,6 +103,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "id": "5ecaab6d",
    "metadata": {},
@@ -149,6 +153,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "id": "ea9df8d4",
    "metadata": {},
@@ -309,6 +314,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "id": "39bd1de1-b9fe-4a98-89da-58d8a7a6ae51",
    "metadata": {},
diff --git a/docs/modules/indexes/vectorstores/examples/myscale.ipynb b/docs/modules/indexes/vectorstores/examples/myscale.ipynb
index 15ae20621b2bf..b3da965bac819 100644
--- a/docs/modules/indexes/vectorstores/examples/myscale.ipynb
+++ b/docs/modules/indexes/vectorstores/examples/myscale.ipynb
@@ -67,7 +67,7 @@
     "1. Environment Variables\n",
     "\n",
     "    Before you run the app, please set the environment variable with `export`:\n",
-    "    `export MYSCALE_URL='<your-endpoints-url>' MYSCALE_PORT=<your-endpoints-port> MYSCALE_USERNAME=<your-username> MYSCALE_PASSWORD=<your-password> ...`\n",
+    "    `export MYSCALE_HOST='<your-endpoints-url>' MYSCALE_PORT=<your-endpoints-port> MYSCALE_USERNAME=<your-username> MYSCALE_PASSWORD=<your-password> ...`\n",
     "\n",
     "    You can easily find your account, password and other info on our SaaS. For details please refer to [this document](https://docs.myscale.com/en/cluster-management/)\n",
     "\n",
@@ -119,18 +119,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": null,
    "id": "6e104aee",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Inserting data...: 100%|██████████| 42/42 [00:18<00:00,  2.21it/s]\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "for d in docs:\n",
     "    d.metadata = {'some': 'metadata'}\n",
@@ -142,32 +134,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": null,
    "id": "9c608226",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "As Frances Haugen, who is here with us tonight, has shown, we must hold social media platforms accountable for the national experiment they’re conducting on our children for profit. \n",
-      "\n",
-      "It’s time to strengthen privacy protections, ban targeted advertising to children, demand tech companies stop collecting personal data on our children. \n",
-      "\n",
-      "And let’s get all Americans the mental health services they need. More people they can turn to for help, and full parity between physical and mental health care. \n",
-      "\n",
-      "Third, support our veterans. \n",
-      "\n",
-      "Veterans are the best of us. \n",
-      "\n",
-      "I’ve always believed that we have a sacred obligation to equip all those we send to war and care for them and their families when they come home. \n",
-      "\n",
-      "My administration is providing assistance with job training and housing, and now helping lower-income veterans get VA care debt-free.  \n",
-      "\n",
-      "Our troops in Iraq and Afghanistan faced many dangers.\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "print(docs[0].page_content)"
    ]
@@ -208,18 +178,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": null,
    "id": "232055f6",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Inserting data...: 100%|██████████| 42/42 [00:15<00:00,  2.69it/s]\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "from langchain.vectorstores import MyScale, MyScaleSettings\n",
     "from langchain.document_loaders import TextLoader\n",
@@ -257,21 +219,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": null,
    "id": "ddbcee77",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "0.252379834651947 {'doc_id': 6, 'some': ''} And I’m taking robus...\n",
-      "0.25022566318511963 {'doc_id': 1, 'some': ''} Groups of citizens b...\n",
-      "0.2469480037689209 {'doc_id': 8, 'some': ''} And so many families...\n",
-      "0.2428302764892578 {'doc_id': 0, 'some': 'metadata'} As Frances Haugen, w...\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "meta = docsearch.metadata_column\n",
     "output = docsearch.similarity_search_with_relevance_scores('What did the president say about Ketanji Brown Jackson?', \n",
@@ -324,7 +275,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.6"
+   "version": "3.8.8"
   }
  },
  "nbformat": 4,
diff --git a/langchain/chains/query_constructor/ir.py b/langchain/chains/query_constructor/ir.py
index f131de3a1495f..6d7264a9052e2 100644
--- a/langchain/chains/query_constructor/ir.py
+++ b/langchain/chains/query_constructor/ir.py
@@ -71,6 +71,8 @@ class Comparator(str, Enum):
     GTE = "gte"
     LT = "lt"
     LTE = "lte"
+    CONTAIN = "contain"
+    LIKE = "like"
 
 
 class FilterDirective(Expr, ABC):
diff --git a/langchain/chains/query_constructor/parser.py b/langchain/chains/query_constructor/parser.py
index 4c560bfe27a0d..e26206c3e4888 100644
--- a/langchain/chains/query_constructor/parser.py
+++ b/langchain/chains/query_constructor/parser.py
@@ -1,3 +1,4 @@
+import datetime
 from typing import Any, Optional, Sequence, Union
 
 try:
@@ -34,12 +35,14 @@ def v_args(*args: Any, **kwargs: Any) -> Any:  # type: ignore
 
     ?value: SIGNED_INT -> int
         | SIGNED_FLOAT -> float
+        | TIMESTAMP -> timestamp
         | list
         | string
         | ("false" | "False" | "FALSE") -> false
         | ("true" | "True" | "TRUE") -> true
 
     args: expr ("," expr)*
+    TIMESTAMP.2: /["'](\d{4}-[01]\d-[0-3]\d)["']/
     string: /'[^']*'/ | ESCAPED_STRING
     list: "[" [args] "]"
 
@@ -120,6 +123,10 @@ def int(self, item: Any) -> int:
     def float(self, item: Any) -> float:
         return float(item)
 
+    def timestamp(self, item: Any) -> datetime.date:
+        item = item.replace("'", '"')
+        return datetime.datetime.strptime(item, '"%Y-%m-%d"').date()
+
     def string(self, item: Any) -> str:
         # Remove escaped quotes
         return str(item).strip("\"'")
diff --git a/langchain/chains/query_constructor/prompt.py b/langchain/chains/query_constructor/prompt.py
index ae7530b70f0c3..22cf248af819c 100644
--- a/langchain/chains/query_constructor/prompt.py
+++ b/langchain/chains/query_constructor/prompt.py
@@ -141,6 +141,8 @@
 Make sure that you only use the comparators and logical operators listed above and \
 no others.
 Make sure that filters only refer to attributes that exist in the data source.
+Make sure that filters only use the attributed names with its function names if there are functions applied on them.
+Make sure that filters only use format `YYYY-MM-DD` when handling timestamp data typed values.
 Make sure that filters take into account the descriptions of attributes and only make \
 comparisons that are feasible given the type of data being stored.
 Make sure that filters are only used as needed. If there are no filters that should be \
@@ -179,6 +181,8 @@
 Make sure that you only use the comparators and logical operators listed above and \
 no others.
 Make sure that filters only refer to attributes that exist in the data source.
+Make sure that filters only use the attributed names with its function names if there are functions applied on them.
+Make sure that filters only use format `YYYY-MM-DD` when handling timestamp data typed values.
 Make sure that filters take into account the descriptions of attributes and only make \
 comparisons that are feasible given the type of data being stored.
 Make sure that filters are only used as needed. If there are no filters that should be \
diff --git a/langchain/chat_models/anthropic.py b/langchain/chat_models/anthropic.py
index 5f21cdeb3c908..a9f3d0e02dba8 100644
--- a/langchain/chat_models/anthropic.py
+++ b/langchain/chat_models/anthropic.py
@@ -44,6 +44,10 @@ def _llm_type(self) -> str:
         """Return type of chat model."""
         return "anthropic-chat"
 
+    @property
+    def lc_serializable(self) -> bool:
+        return True
+
     def _convert_one_message_to_text(self, message: BaseMessage) -> str:
         if isinstance(message, ChatMessage):
             message_text = f"\n\n{message.role.capitalize()}: {message.content}"
diff --git a/langchain/chat_models/base.py b/langchain/chat_models/base.py
index f3521df6fb17d..440336f275e29 100644
--- a/langchain/chat_models/base.py
+++ b/langchain/chat_models/base.py
@@ -190,9 +190,10 @@ def __call__(
         messages: List[BaseMessage],
         stop: Optional[List[str]] = None,
         callbacks: Callbacks = None,
+        **kwargs: Any,
     ) -> BaseMessage:
         generation = self.generate(
-            [messages], stop=stop, callbacks=callbacks
+            [messages], stop=stop, callbacks=callbacks, **kwargs
         ).generations[0][0]
         if isinstance(generation, ChatGeneration):
             return generation.message
@@ -227,7 +228,7 @@ def predict(
             _stop = None
         else:
             _stop = list(stop)
-        result = self([HumanMessage(content=text)], stop=_stop)
+        result = self([HumanMessage(content=text)], stop=_stop, **kwargs)
         return result.content
 
     def predict_messages(
diff --git a/langchain/load/serializable.py b/langchain/load/serializable.py
index 9c8c60bfe9313..56c97dfae3b32 100644
--- a/langchain/load/serializable.py
+++ b/langchain/load/serializable.py
@@ -55,7 +55,7 @@ def lc_attributes(self) -> Dict:
         """
         return {}
 
-    lc_kwargs: Dict[str, Any] = Field(default_factory=dict, exclude=True)
+    lc_kwargs: Dict[str, Any] = Field(default_factory=dict, exclude=True, repr=False)
 
     def __init__(self, **kwargs: Any) -> None:
         super().__init__(**kwargs)
diff --git a/langchain/retrievers/self_query/base.py b/langchain/retrievers/self_query/base.py
index 6fed65b45310b..98283696d85b0 100644
--- a/langchain/retrievers/self_query/base.py
+++ b/langchain/retrievers/self_query/base.py
@@ -5,15 +5,24 @@
 
 from langchain import LLMChain
 from langchain.base_language import BaseLanguageModel
+from langchain.callbacks.manager import Callbacks
 from langchain.chains.query_constructor.base import load_query_constructor_chain
 from langchain.chains.query_constructor.ir import StructuredQuery, Visitor
 from langchain.chains.query_constructor.schema import AttributeInfo
 from langchain.retrievers.self_query.chroma import ChromaTranslator
+from langchain.retrievers.self_query.myscale import MyScaleTranslator
 from langchain.retrievers.self_query.pinecone import PineconeTranslator
 from langchain.retrievers.self_query.qdrant import QdrantTranslator
 from langchain.retrievers.self_query.weaviate import WeaviateTranslator
 from langchain.schema import BaseRetriever, Document
-from langchain.vectorstores import Chroma, Pinecone, Qdrant, VectorStore, Weaviate
+from langchain.vectorstores import (
+    Chroma,
+    MyScale,
+    Pinecone,
+    Qdrant,
+    VectorStore,
+    Weaviate,
+)
 
 
 def _get_builtin_translator(vectorstore: VectorStore) -> Visitor:
@@ -24,6 +33,7 @@ def _get_builtin_translator(vectorstore: VectorStore) -> Visitor:
         Chroma: ChromaTranslator,
         Weaviate: WeaviateTranslator,
         Qdrant: QdrantTranslator,
+        MyScale: MyScaleTranslator,
     }
     if vectorstore_cls not in BUILTIN_TRANSLATORS:
         raise ValueError(
@@ -32,6 +42,8 @@ def _get_builtin_translator(vectorstore: VectorStore) -> Visitor:
         )
     if isinstance(vectorstore, Qdrant):
         return QdrantTranslator(metadata_key=vectorstore.metadata_payload_key)
+    elif isinstance(vectorstore, MyScale):
+        return MyScaleTranslator(metadata_key=vectorstore.metadata_column)
     return BUILTIN_TRANSLATORS[vectorstore_cls]()
 
 
@@ -50,6 +62,8 @@ class SelfQueryRetriever(BaseRetriever, BaseModel):
     structured_query_translator: Visitor
     """Translator for turning internal query language into vectorstore search params."""
     verbose: bool = False
+    """Use original query instead of the revised new query from LLM"""
+    use_original_query: bool = False
 
     class Config:
         """Configuration for this pydantic object."""
@@ -65,7 +79,9 @@ def validate_translator(cls, values: Dict) -> Dict:
             )
         return values
 
-    def get_relevant_documents(self, query: str) -> List[Document]:
+    def get_relevant_documents(
+        self, query: str, callbacks: Callbacks = None
+    ) -> List[Document]:
         """Get documents relevant for a query.
 
         Args:
@@ -76,7 +92,8 @@ def get_relevant_documents(self, query: str) -> List[Document]:
         """
         inputs = self.llm_chain.prep_inputs({"query": query})
         structured_query = cast(
-            StructuredQuery, self.llm_chain.predict_and_parse(callbacks=None, **inputs)
+            StructuredQuery,
+            self.llm_chain.predict_and_parse(callbacks=callbacks, **inputs),
         )
         if self.verbose:
             print(structured_query)
@@ -85,6 +102,9 @@ def get_relevant_documents(self, query: str) -> List[Document]:
         )
         if structured_query.limit is not None:
             new_kwargs["k"] = structured_query.limit
+                        
+        if self.use_original_query:
+            new_query = query
 
         search_kwargs = {**self.search_kwargs, **new_kwargs}
         docs = self.vectorstore.search(new_query, self.search_type, **search_kwargs)
@@ -103,6 +123,7 @@ def from_llm(
         structured_query_translator: Optional[Visitor] = None,
         chain_kwargs: Optional[Dict] = None,
         enable_limit: bool = False,
+        use_original_query: bool = False,
         **kwargs: Any,
     ) -> "SelfQueryRetriever":
         if structured_query_translator is None:
@@ -127,6 +148,7 @@ def from_llm(
         return cls(
             llm_chain=llm_chain,
             vectorstore=vectorstore,
+            use_original_query=use_original_query,
             structured_query_translator=structured_query_translator,
             **kwargs,
         )
diff --git a/langchain/retrievers/self_query/myscale.py b/langchain/retrievers/self_query/myscale.py
new file mode 100644
index 0000000000000..7b5129e328046
--- /dev/null
+++ b/langchain/retrievers/self_query/myscale.py
@@ -0,0 +1,104 @@
+import datetime
+import re
+from typing import Dict, Tuple, Union, Callable, Any
+
+from langchain.chains.query_constructor.ir import (
+    Comparator,
+    Comparison,
+    Operation,
+    Operator,
+    StructuredQuery,
+    Visitor,
+)
+
+
+def DEFAULT_COMPOSER(op_name: str) -> Callable:
+    def f(*args: Any) -> str:
+        args: Tuple[str] = map(str, args)
+        return f" {op_name} ".join(args)
+    return f
+
+
+def FUNCTION_COMPOSER(op_name: str) -> Callable:
+    def f(*args: Any) -> str:
+        args: Tuple[str] = map(str, args)
+        return f"{op_name}({','.join(args)})"
+    return f
+
+
+class MyScaleTranslator(Visitor):
+    """Logic for converting internal query language elements to valid filters."""
+
+    allowed_operators = [Operator.AND, Operator.OR, Operator.NOT]
+    """Subset of allowed logical operators."""
+
+    allowed_comparators = [
+        Comparator.EQ,
+        Comparator.GT,
+        Comparator.GTE,
+        Comparator.LT,
+        Comparator.LTE,
+        Comparator.CONTAIN,
+        Comparator.LIKE,
+    ]
+
+    map_dict = {
+        Operator.AND: DEFAULT_COMPOSER("AND"),
+        Operator.OR: DEFAULT_COMPOSER("OR"),
+        Operator.NOT: DEFAULT_COMPOSER("NOT"),
+        Comparator.EQ: DEFAULT_COMPOSER("="),
+        Comparator.GT: DEFAULT_COMPOSER(">"),
+        Comparator.GTE: DEFAULT_COMPOSER(">="),
+        Comparator.LT: DEFAULT_COMPOSER("<"),
+        Comparator.LTE: DEFAULT_COMPOSER("<="),
+        Comparator.CONTAIN: FUNCTION_COMPOSER("has"),
+        Comparator.LIKE: DEFAULT_COMPOSER("ILIKE"),
+    }
+
+    def __init__(self, metadata_key: str = "metadata") -> None:
+        super().__init__()
+        self.metadata_key = metadata_key
+
+    def visit_operation(self, operation: Operation) -> Dict:
+        args = [arg.accept(self) for arg in operation.arguments]
+        func = operation.operator
+        self._validate_func(func)
+        return self.map_dict[func](*args)
+
+    def visit_comparison(self, comparison: Comparison) -> Dict:
+        regex = "\((.*?)\)"
+        matched = re.search("\(\w+\)", comparison.attribute)
+
+        # If arbitrary function is applied to an attribute
+        if matched:
+            attr = re.sub(
+                regex,
+                f"({self.metadata_key}.{matched.group(0)[1:-1]})",
+                comparison.attribute,
+            )
+        else:
+            attr = f"{self.metadata_key}.{comparison.attribute}"
+        value = comparison.value
+        comp = comparison.comparator
+
+        value = f"'{value}'" if type(value) is str else value
+
+        # convert timestamp for datetime objects
+        if type(value) is datetime.date:
+            attr = f"parseDateTime32BestEffort({attr})"
+            value = f"parseDateTime32BestEffort('{value.strftime('%Y-%m-%d')}')"
+
+        # string pattern match
+        if comp is Comparator.LIKE:
+            value = f"'%{value[1:-1]}%'"
+        return self.map_dict[comp](attr, value)
+
+    def visit_structured_query(
+        self, structured_query: StructuredQuery
+    ) -> Tuple[str, dict]:
+        print(structured_query)
+        if structured_query.filter is None:
+            kwargs = {}
+        else:
+            kwargs = {"where_str": structured_query.filter.accept(self)}
+        return structured_query.query, kwargs
diff --git a/langchain/tools/base.py b/langchain/tools/base.py
index ea69731b67d0f..ca3bebb041460 100644
--- a/langchain/tools/base.py
+++ b/langchain/tools/base.py
@@ -68,18 +68,14 @@ def _create_subset_model(
     name: str, model: BaseModel, field_names: list
 ) -> Type[BaseModel]:
     """Create a pydantic model with only a subset of model's fields."""
-    fields = {
-        field_name: (
-            model.__fields__[field_name].type_,
-            model.__fields__[field_name].default,
-        )
-        for field_name in field_names
-        if field_name in model.__fields__
-    }
+    fields = {}
+    for field_name in field_names:
+        field = model.__fields__[field_name]
+        fields[field_name] = (field.type_, field.field_info)
     return create_model(name, **fields)  # type: ignore
 
 
-def get_filtered_args(
+def _get_filtered_args(
     inferred_model: Type[BaseModel],
     func: Callable,
 ) -> dict:
@@ -100,15 +96,22 @@ def create_schema_from_function(
     model_name: str,
     func: Callable,
 ) -> Type[BaseModel]:
-    """Create a pydantic schema from a function's signature."""
+    """Create a pydantic schema from a function's signature.
+    Args:
+        model_name: Name to assign to the generated pydandic schema
+        func: Function to generate the schema from
+    Returns:
+        A pydantic model with the same arguments as the function
+    """
+    # https://docs.pydantic.dev/latest/usage/validation_decorator/
     validated = validate_arguments(func, config=_SchemaConfig)  # type: ignore
     inferred_model = validated.model  # type: ignore
     if "run_manager" in inferred_model.__fields__:
         del inferred_model.__fields__["run_manager"]
     # Pydantic adds placeholder virtual fields we need to strip
-    filtered_args = get_filtered_args(inferred_model, func)
+    valid_properties = _get_filtered_args(inferred_model, func)
     return _create_subset_model(
-        f"{model_name}Schema", inferred_model, list(filtered_args)
+        f"{model_name}Schema", inferred_model, list(valid_properties)
     )
 
 
@@ -534,6 +537,30 @@ def from_function(
         infer_schema: bool = True,
         **kwargs: Any,
     ) -> StructuredTool:
+        """Create tool from a given function.
+
+        A classmethod that helps to create a tool from a function.
+
+        Args:
+            func: The function from which to create a tool
+            name: The name of the tool. Defaults to the function name
+            description: The description of the tool. Defaults to the function docstring
+            return_direct: Whether to return the result directly or as a callback
+            args_schema: The schema of the tool's input arguments
+            infer_schema: Whether to infer the schema from the function's signature
+            **kwargs: Additional arguments to pass to the tool
+
+        Returns:
+            The tool
+
+        Examples:
+            ... code-block:: python
+                def add(a: int, b: int) -> int:
+                    \"\"\"Add two numbers\"\"\"
+                    return a + b
+                tool = StructuredTool.from_function(add)
+                tool.run(1, 2) # 3
+        """
         name = name or func.__name__
         description = description or func.__doc__
         assert (
diff --git a/langchain/vectorstores/mongodb_atlas.py b/langchain/vectorstores/mongodb_atlas.py
index 7a16a6e06979e..f2b2d99393195 100644
--- a/langchain/vectorstores/mongodb_atlas.py
+++ b/langchain/vectorstores/mongodb_atlas.py
@@ -9,6 +9,8 @@
     Iterable,
     List,
     Optional,
+    Sequence,
+    Mapping,
     Tuple,
     TypeVar,
     Union,
@@ -129,7 +131,7 @@ def _insert_texts(self, texts: List[str], metadatas: List[Dict[str, Any]]) -> Li
             return []
         # Embed and create the documents
         embeddings = self._embedding.embed_documents(texts)
-        to_insert = [
+        to_insert: List[MongoDBDocumentType] = [
             {self._text_key: t, self._embedding_key: embedding, **m}
             for t, m, embedding in zip(texts, metadatas, embeddings)
         ]
@@ -172,7 +174,7 @@ def similarity_search_with_score(
         }
         if pre_filter:
             knn_beta["filter"] = pre_filter
-        pipeline = [
+        pipeline: Sequence[Mapping[str, Any]] = [
             {
                 "$search": {
                     "index": self._index_name,
diff --git a/poetry.lock b/poetry.lock
index 6d5bb1e0c2ba2..84c2a6d86389d 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -1,4 +1,4 @@
-# This file is automatically @generated by Poetry and should not be changed by hand.
+# This file is automatically @generated by Poetry 1.4.0 and should not be changed by hand.
 
 [[package]]
 name = "absl-py"
@@ -670,14 +670,14 @@ aio = ["aiohttp (>=3.0)"]
 
 [[package]]
 name = "azure-cosmos"
-version = "4.4.0b2"
+version = "4.4.0"
 description = "Microsoft Azure Cosmos Client Library for Python"
 category = "main"
 optional = true
 python-versions = ">=3.6"
 files = [
-    {file = "azure-cosmos-4.4.0b2.zip", hash = "sha256:fa443bcbca47c03fecf0870f20dec68a94f53b783e6217ca75694d10f4b7b6e9"},
-    {file = "azure_cosmos-4.4.0b2-py3-none-any.whl", hash = "sha256:26a34a70e43dc2c88873e7a0e125752c89d13539c9ef1c6dacd3fea7733235e6"},
+    {file = "azure-cosmos-4.4.0.zip", hash = "sha256:4c6785489704f037aa67f725c41eef850f693d99eb6ea3ec49f785a1e35ab021"},
+    {file = "azure_cosmos-4.4.0-py3-none-any.whl", hash = "sha256:5ba74668a77f69e60ac7d11e755be7d5d4852ec03482c0a625d221eb10be5de2"},
 ]
 
 [package.dependencies]
@@ -1662,14 +1662,14 @@ files = [
 
 [[package]]
 name = "dataclasses-json"
-version = "0.5.7"
+version = "0.5.8"
 description = "Easily serialize dataclasses to and from JSON"
 category = "main"
 optional = false
 python-versions = ">=3.6"
 files = [
-    {file = "dataclasses-json-0.5.7.tar.gz", hash = "sha256:c2c11bc8214fbf709ffc369d11446ff6945254a7f09128154a7620613d8fda90"},
-    {file = "dataclasses_json-0.5.7-py3-none-any.whl", hash = "sha256:bc285b5f892094c3a53d558858a88553dd6a61a11ab1a8128a0e554385dcc5dd"},
+    {file = "dataclasses-json-0.5.8.tar.gz", hash = "sha256:6572ac08ad9340abcb74fd8c4c8e9752db2a182a402c8e871d0a8aa119e3804e"},
+    {file = "dataclasses_json-0.5.8-py3-none-any.whl", hash = "sha256:65b167c15fdf9bde27569c09ac18dd39bf1cc5b7998525024cb4678d2653946c"},
 ]
 
 [package.dependencies]
@@ -1678,47 +1678,7 @@ marshmallow-enum = ">=1.5.1,<2.0.0"
 typing-inspect = ">=0.4.0"
 
 [package.extras]
-dev = ["flake8", "hypothesis", "ipython", "mypy (>=0.710)", "portray", "pytest (>=6.2.3)", "simplejson", "types-dataclasses"]
-
-[[package]]
-name = "datasets"
-version = "1.18.3"
-description = "HuggingFace community-driven open-source library of datasets"
-category = "main"
-optional = true
-python-versions = "*"
-files = [
-    {file = "datasets-1.18.3-py3-none-any.whl", hash = "sha256:5862670a3e213af1aa68995a32ff0ce761b9d71d2677c3fa59e7088eb5e2a841"},
-    {file = "datasets-1.18.3.tar.gz", hash = "sha256:dfdf75c255069f4ed25ccdd0d3f0730c1ff1e2b27f8d4bd1af395b10fe8ebc63"},
-]
-
-[package.dependencies]
-aiohttp = "*"
-dill = "*"
-fsspec = {version = ">=2021.05.0", extras = ["http"]}
-huggingface-hub = ">=0.1.0,<1.0.0"
-multiprocess = "*"
-numpy = ">=1.17"
-packaging = "*"
-pandas = "*"
-pyarrow = ">=3.0.0,<4.0.0 || >4.0.0"
-requests = ">=2.19.0"
-tqdm = ">=4.62.1"
-xxhash = "*"
-
-[package.extras]
-apache-beam = ["apache-beam (>=2.26.0)"]
-audio = ["librosa"]
-benchmarks = ["numpy (==1.18.5)", "tensorflow (==2.3.0)", "torch (==1.6.0)", "transformers (==3.0.2)"]
-dev = ["Pillow (>=6.2.1)", "Werkzeug (>=1.0.1)", "absl-py", "aiobotocore", "apache-beam (>=2.26.0)", "bert-score (>=0.3.6)", "black (==21.4b0)", "boto3", "botocore", "bs4", "conllu", "elasticsearch", "fairseq", "faiss-cpu (>=1.6.4)", "fastBPE (==0.1.0)", "flake8 (>=3.8.3)", "fsspec[s3]", "h5py", "importlib-resources", "isort (>=5.0.0)", "jiwer", "langdetect", "librosa", "lxml", "mauve-text", "moto[s3,server] (==2.0.4)", "mwparserfromhell", "nltk", "openpyxl", "py7zr", "pytest", "pytest-datadir", "pytest-xdist", "pytorch-lightning", "pytorch-nlp (==0.5.0)", "pyyaml (>=5.3.1)", "rarfile (>=4.0)", "requests-file (>=1.5.1)", "rouge-score", "s3fs (==2021.08.1)", "sacrebleu", "scikit-learn", "scipy", "sentencepiece", "seqeval", "six (>=1.15.0,<1.16.0)", "soundfile", "tensorflow (>=2.3,!=2.6.0,!=2.6.1)", "texttable (>=1.6.3)", "tldextract", "tldextract (>=3.1.0)", "toml (>=0.10.1)", "torch", "torchaudio", "torchmetrics (==0.6.0)", "transformers", "wget (>=3.2)", "zstandard"]
-docs = ["Markdown (!=3.3.5)", "docutils (==0.16.0)", "fsspec (<2021.9.0)", "myst-parser", "recommonmark", "s3fs", "sphinx (==3.1.2)", "sphinx-copybutton", "sphinx-inline-tabs", "sphinx-markdown-tables", "sphinx-panels", "sphinx-rtd-theme (==0.4.3)", "sphinxext-opengraph (==0.4.1)"]
-quality = ["black (==21.4b0)", "flake8 (>=3.8.3)", "isort (>=5.0.0)", "pyyaml (>=5.3.1)"]
-s3 = ["boto3", "botocore", "fsspec", "s3fs"]
-tensorflow = ["tensorflow (>=2.2.0,!=2.6.0,!=2.6.1)"]
-tensorflow-gpu = ["tensorflow-gpu (>=2.2.0,!=2.6.0,!=2.6.1)"]
-tests = ["Pillow (>=6.2.1)", "Werkzeug (>=1.0.1)", "absl-py", "aiobotocore", "apache-beam (>=2.26.0)", "bert-score (>=0.3.6)", "boto3", "botocore", "bs4", "conllu", "elasticsearch", "fairseq", "faiss-cpu (>=1.6.4)", "fastBPE (==0.1.0)", "fsspec[s3]", "h5py", "importlib-resources", "jiwer", "langdetect", "librosa", "lxml", "mauve-text", "moto[s3,server] (==2.0.4)", "mwparserfromhell", "nltk", "openpyxl", "py7zr", "pytest", "pytest-datadir", "pytest-xdist", "pytorch-lightning", "pytorch-nlp (==0.5.0)", "rarfile (>=4.0)", "requests-file (>=1.5.1)", "rouge-score", "s3fs (==2021.08.1)", "sacrebleu", "scikit-learn", "scipy", "sentencepiece", "seqeval", "six (>=1.15.0,<1.16.0)", "soundfile", "tensorflow (>=2.3,!=2.6.0,!=2.6.1)", "texttable (>=1.6.3)", "tldextract", "tldextract (>=3.1.0)", "toml (>=0.10.1)", "torch", "torchaudio", "torchmetrics (==0.6.0)", "transformers", "wget (>=3.2)", "zstandard"]
-torch = ["torch"]
-vision = ["Pillow (>=6.2.1)"]
+dev = ["flake8", "hypothesis", "ipython", "mypy (>=0.710)", "portray", "pytest (>=7.2.0)", "simplejson", "types-dataclasses"]
 
 [[package]]
 name = "debugpy"
@@ -1762,13 +1722,13 @@ files = [
 
 [[package]]
 name = "deeplake"
-version = "3.6.1"
+version = "3.6.2"
 description = "Activeloop Deep Lake"
 category = "main"
 optional = false
 python-versions = "*"
 files = [
-    {file = "deeplake-3.6.1.tar.gz", hash = "sha256:78b0280e3e21c6731a96a9a2519a24e767df708c309e934ab473dfbc17b13581"},
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 [package.dependencies]
@@ -1785,12 +1745,12 @@ pyjwt = "*"
 tqdm = "*"
 
 [package.extras]
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 audio = ["av (>=8.1.0)"]
 av = ["av (>=8.1.0)"]
 azure = ["azure-cli", "azure-identity", "azure-storage-blob"]
 dicom = ["nibabel", "pydicom"]
-enterprise = ["libdeeplake (==0.0.56)", "pyjwt"]
+enterprise = ["libdeeplake (==0.0.58)", "pyjwt"]
 gcp = ["google-auth (>=2.0.1,<2.1.0)", "google-auth-oauthlib (>=0.4.5,<0.5.0)", "google-cloud-storage (>=1.42.0,<1.43.0)"]
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 medical = ["nibabel", "pydicom"]
@@ -1939,59 +1899,13 @@ files = [
 
 [[package]]
 name = "duckdb"
-version = "0.8.0"
+version = "0.8.1"
 description = "DuckDB embedded database"
 category = "dev"
 optional = false
 python-versions = "*"
 files = [
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-    {file = "duckdb-0.8.0.tar.gz", hash = "sha256:c68da35bab5072a64ada2646a5b343da620ddc75a7a6e84aa4a1e0628a7ec18f"},
+    {file = "duckdb-0.8.1-cp37-cp37m-macosx_10_9_x86_64.whl", hash = "sha256:60e07a62782f88420046e30cc0e3de842d0901c4fd5b8e4d28b73826ec0c3f5e"},
 ]
 
 [[package]]
@@ -2164,25 +2078,22 @@ files = [
 
 [[package]]
 name = "fastapi"
-version = "0.96.0"
+version = "0.97.0"
 description = "FastAPI framework, high performance, easy to learn, fast to code, ready for production"
 category = "main"
 optional = false
 python-versions = ">=3.7"
 files = [
-    {file = "fastapi-0.96.0-py3-none-any.whl", hash = "sha256:b8e11fe81e81eab4e1504209917338e0b80f783878a42c2b99467e5e1019a1e9"},
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+    {file = "fastapi-0.97.0-py3-none-any.whl", hash = "sha256:95d757511c596409930bd20673358d4a4d709004edb85c5d24d6ffc48fabcbf2"},
+    {file = "fastapi-0.97.0.tar.gz", hash = "sha256:b53248ee45f64f19bb7600953696e3edf94b0f7de94df1e5433fc5c6136fa986"},
 ]
 
 [package.dependencies]
-pydantic = ">=1.6.2,<1.7 || >1.7,<1.7.1 || >1.7.1,<1.7.2 || >1.7.2,<1.7.3 || >1.7.3,<1.8 || >1.8,<1.8.1 || >1.8.1,<2.0.0"
+pydantic = ">=1.7.4,<1.8 || >1.8,<1.8.1 || >1.8.1,<2.0.0"
 starlette = ">=0.27.0,<0.28.0"
 
 [package.extras]
 all = ["email-validator (>=1.1.1)", "httpx (>=0.23.0)", "itsdangerous (>=1.1.0)", "jinja2 (>=2.11.2)", "orjson (>=3.2.1)", "python-multipart (>=0.0.5)", "pyyaml (>=5.3.1)", "ujson (>=4.0.1,!=4.0.2,!=4.1.0,!=4.2.0,!=4.3.0,!=5.0.0,!=5.1.0)", "uvicorn[standard] (>=0.12.0)"]
-dev = ["pre-commit (>=2.17.0,<3.0.0)", "ruff (==0.0.138)", "uvicorn[standard] (>=0.12.0,<0.21.0)"]
-doc = ["mdx-include (>=1.4.1,<2.0.0)", "mkdocs (>=1.1.2,<2.0.0)", "mkdocs-markdownextradata-plugin (>=0.1.7,<0.3.0)", "mkdocs-material (>=8.1.4,<9.0.0)", "pyyaml (>=5.3.1,<7.0.0)", "typer-cli (>=0.0.13,<0.0.14)", "typer[all] (>=0.6.1,<0.8.0)"]
-test = ["anyio[trio] (>=3.2.1,<4.0.0)", "black (==23.1.0)", "coverage[toml] (>=6.5.0,<8.0)", "databases[sqlite] (>=0.3.2,<0.7.0)", "email-validator (>=1.1.1,<2.0.0)", "flask (>=1.1.2,<3.0.0)", "httpx (>=0.23.0,<0.24.0)", "isort (>=5.0.6,<6.0.0)", "mypy (==0.982)", "orjson (>=3.2.1,<4.0.0)", "passlib[bcrypt] (>=1.7.2,<2.0.0)", "peewee (>=3.13.3,<4.0.0)", "pytest (>=7.1.3,<8.0.0)", "python-jose[cryptography] (>=3.3.0,<4.0.0)", "python-multipart (>=0.0.5,<0.0.7)", "pyyaml (>=5.3.1,<7.0.0)", "ruff (==0.0.138)", "sqlalchemy (>=1.3.18,<1.4.43)", "types-orjson (==3.6.2)", "types-ujson (==5.7.0.1)", "ujson (>=4.0.1,!=4.0.2,!=4.1.0,!=4.2.0,!=4.3.0,!=5.0.0,!=5.1.0,<6.0.0)"]
 
 [[package]]
 name = "fastjsonschema"
@@ -2216,19 +2127,19 @@ sgmllib3k = "*"
 
 [[package]]
 name = "filelock"
-version = "3.12.0"
+version = "3.12.2"
 description = "A platform independent file lock."
 category = "main"
 optional = false
 python-versions = ">=3.7"
 files = [
-    {file = "filelock-3.12.0-py3-none-any.whl", hash = "sha256:ad98852315c2ab702aeb628412cbf7e95b7ce8c3bf9565670b4eaecf1db370a9"},
-    {file = "filelock-3.12.0.tar.gz", hash = "sha256:fc03ae43288c013d2ea83c8597001b1129db351aad9c57fe2409327916b8e718"},
+    {file = "filelock-3.12.2-py3-none-any.whl", hash = "sha256:cbb791cdea2a72f23da6ac5b5269ab0a0d161e9ef0100e653b69049a7706d1ec"},
+    {file = "filelock-3.12.2.tar.gz", hash = "sha256:002740518d8aa59a26b0c76e10fb8c6e15eae825d34b6fdf670333fd7b938d81"},
 ]
 
 [package.extras]
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-testing = ["covdefaults (>=2.3)", "coverage (>=7.2.3)", "diff-cover (>=7.5)", "pytest (>=7.3.1)", "pytest-cov (>=4)", "pytest-mock (>=3.10)", "pytest-timeout (>=2.1)"]
+docs = ["furo (>=2023.5.20)", "sphinx (>=7.0.1)", "sphinx-autodoc-typehints (>=1.23,!=1.23.4)"]
+testing = ["covdefaults (>=2.3)", "coverage (>=7.2.7)", "diff-cover (>=7.5)", "pytest (>=7.3.1)", "pytest-cov (>=4.1)", "pytest-mock (>=3.10)", "pytest-timeout (>=2.1)"]
 
 [[package]]
 name = "flatbuffers"
@@ -2370,20 +2281,16 @@ files = [
 
 [[package]]
 name = "fsspec"
-version = "2023.5.0"
+version = "2023.6.0"
 description = "File-system specification"
 category = "main"
 optional = false
 python-versions = ">=3.8"
 files = [
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-    {file = "fsspec-2023.5.0.tar.gz", hash = "sha256:b3b56e00fb93ea321bc9e5d9cf6f8522a0198b20eb24e02774d329e9c6fb84ce"},
+    {file = "fsspec-2023.6.0-py3-none-any.whl", hash = "sha256:1cbad1faef3e391fba6dc005ae9b5bdcbf43005c9167ce78c915549c352c869a"},
+    {file = "fsspec-2023.6.0.tar.gz", hash = "sha256:d0b2f935446169753e7a5c5c55681c54ea91996cc67be93c39a154fb3a2742af"},
 ]
 
-[package.dependencies]
-aiohttp = {version = "<4.0.0a0 || >4.0.0a0,<4.0.0a1 || >4.0.0a1", optional = true, markers = "extra == \"http\""}
-requests = {version = "*", optional = true, markers = "extra == \"http\""}
-
 [package.extras]
 abfs = ["adlfs"]
 adl = ["adlfs"]
@@ -2595,21 +2502,21 @@ requests = "*"
 
 [[package]]
 name = "googleapis-common-protos"
-version = "1.59.0"
+version = "1.59.1"
 description = "Common protobufs used in Google APIs"
 category = "main"
 optional = true
 python-versions = ">=3.7"
 files = [
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+    {file = "googleapis-common-protos-1.59.1.tar.gz", hash = "sha256:b35d530fe825fb4227857bc47ad84c33c809ac96f312e13182bdeaa2abe1178a"},
+    {file = "googleapis_common_protos-1.59.1-py2.py3-none-any.whl", hash = "sha256:0cbedb6fb68f1c07e18eb4c48256320777707e7d0c55063ae56c15db3224a61e"},
 ]
 
 [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 = ">=3.19.5,<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,<5.0.0.dev0"
 
 [package.extras]
-grpc = ["grpcio (>=1.44.0,<2.0.0dev)"]
+grpc = ["grpcio (>=1.44.0,<2.0.0.dev0)"]
 
 [[package]]
 name = "gptcache"
@@ -3296,14 +3203,14 @@ files = [
 
 [[package]]
 name = "ipykernel"
-version = "6.23.1"
+version = "6.23.2"
 description = "IPython Kernel for Jupyter"
 category = "dev"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "ipykernel-6.23.1-py3-none-any.whl", hash = "sha256:77aeffab056c21d16f1edccdc9e5ccbf7d96eb401bd6703610a21be8b068aadc"},
-    {file = "ipykernel-6.23.1.tar.gz", hash = "sha256:1aba0ae8453e15e9bc6b24e497ef6840114afcdb832ae597f32137fa19d42a6f"},
+    {file = "ipykernel-6.23.2-py3-none-any.whl", hash = "sha256:7ccb6e2d32fd958c21453db494c914f3474908a2fdefd99ab548a5375b548d1f"},
+    {file = "ipykernel-6.23.2.tar.gz", hash = "sha256:fcfb67c5b504aa1bfcda1c5b3716636239e0f7b9290958f1c558c79b4c0e7ed5"},
 ]
 
 [package.dependencies]
@@ -4035,18 +3942,18 @@ files = [
 
 [[package]]
 name = "lancedb"
-version = "0.1.6"
+version = "0.1.8"
 description = "lancedb"
 category = "main"
 optional = true
 python-versions = ">=3.8"
 files = [
-    {file = "lancedb-0.1.6-py3-none-any.whl", hash = "sha256:0ffba97ea8a3c905ef2753555593cd9cf4708ae113cccfa24f9f812def144bf9"},
-    {file = "lancedb-0.1.6.tar.gz", hash = "sha256:54c061506eb529976dcd9ea806670605b0ea1d651f41ae2a1921633e2bcbdda1"},
+    {file = "lancedb-0.1.8-py3-none-any.whl", hash = "sha256:09e0ae7d989b4ababeaefa2d50b784dd036e231e90fceee543d01ed24bd601de"},
+    {file = "lancedb-0.1.8.tar.gz", hash = "sha256:65a3e93829f39db57ad66ab474d380c9e36e696e68b60bed37434ac1ed1fe215"},
 ]
 
 [package.dependencies]
-pylance = ">=0.4.17"
+pylance = ">=0.4.20"
 ratelimiter = "*"
 retry = "*"
 tqdm = "*"
@@ -4054,18 +3961,18 @@ tqdm = "*"
 [package.extras]
 dev = ["black", "pre-commit", "ruff"]
 docs = ["mkdocs", "mkdocs-jupyter", "mkdocs-material", "mkdocstrings[python]"]
-tests = ["pytest", "pytest-mock"]
+tests = ["doctest", "pytest", "pytest-mock"]
 
 [[package]]
 name = "langchainplus-sdk"
-version = "0.0.7"
+version = "0.0.9"
 description = "Client library to connect to the LangChainPlus LLM Tracing and Evaluation Platform."
 category = "main"
 optional = false
 python-versions = ">=3.8.1,<4.0"
 files = [
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-    {file = "langchainplus_sdk-0.0.7.tar.gz", hash = "sha256:b58565bdcaf301d2e6e7dd8898f0b8ccf549a35476258e0c14d871d6de02d210"},
+    {file = "langchainplus_sdk-0.0.9-py3-none-any.whl", hash = "sha256:4fe1a60f28c93ae0e145dcd53e4dc5293374ed0a8518abcc51e201081809bf0b"},
+    {file = "langchainplus_sdk-0.0.9.tar.gz", hash = "sha256:bbfdc54c64df5ca4334068ab2d7b89d3a894f313b1285939b4c4532fea62eeb7"},
 ]
 
 [package.dependencies]
@@ -4090,26 +3997,23 @@ data = ["language-data (>=1.1,<2.0)"]
 
 [[package]]
 name = "langkit"
-version = "0.0.1b4"
+version = "0.0.1b7"
 description = "A collection of text metric udfs for whylogs profiling and monitoring in WhyLabs"
 category = "main"
 optional = true
 python-versions = ">=3.8,<4.0"
 files = [
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+    {file = "langkit-0.0.1b7-py3-none-any.whl", hash = "sha256:a5b3e70dbb4ad32c0431b3f1460461443bdbe84c248ae4b4b83b9f5fb3cf4767"},
+    {file = "langkit-0.0.1b7.tar.gz", hash = "sha256:df19e131d9395a2ad37840ea6b93cb7e4aab745c72f78b227a3ef256f9801970"},
 ]
 
 [package.dependencies]
-datasets = "*"
-nltk = ">=3.8.1,<4.0.0"
 pandas = "*"
-sentence-transformers = ">=2.2.2,<3.0.0"
 textstat = ">=0.7.3,<0.8.0"
-whylogs = "1.1.43.dev0"
+whylogs = "1.1.45.dev4"
 
 [package.extras]
-all = ["openai", "torch"]
+all = ["datasets (>=2.12.0,<3.0.0)", "nltk (>=3.8.1,<4.0.0)", "openai (>=0.27.6,<0.28.0)", "sentence-transformers (>=2.2.2,<3.0.0)", "torch"]
 
 [[package]]
 name = "lark"
@@ -4633,14 +4537,14 @@ files = [
 
 [[package]]
 name = "momento"
-version = "1.5.2"
+version = "1.6.0"
 description = "SDK for Momento"
 category = "main"
 optional = false
 python-versions = ">=3.7,<4.0"
 files = [
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+    {file = "momento-1.6.0.tar.gz", hash = "sha256:55a2f628d750b1da1c5013b7413102b1d9e5545dcf53ce20a0259f35be32a961"},
 ]
 
 [package.dependencies]
@@ -6505,35 +6409,35 @@ files = [
 
 [[package]]
 name = "platformdirs"
-version = "3.5.1"
+version = "3.5.3"
 description = "A small Python package for determining appropriate platform-specific dirs, e.g. a \"user data dir\"."
-category = "dev"
+category = "main"
 optional = false
 python-versions = ">=3.7"
 files = [
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 ]
 
 [package.extras]
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-test = ["appdirs (==1.4.4)", "covdefaults (>=2.3)", "pytest (>=7.3.1)", "pytest-cov (>=4)", "pytest-mock (>=3.10)"]
+docs = ["furo (>=2023.5.20)", "proselint (>=0.13)", "sphinx (>=7.0.1)", "sphinx-autodoc-typehints (>=1.23,!=1.23.4)"]
+test = ["appdirs (==1.4.4)", "covdefaults (>=2.3)", "pytest (>=7.3.1)", "pytest-cov (>=4.1)", "pytest-mock (>=3.10)"]
 
 [[package]]
 name = "playwright"
-version = "1.34.0"
+version = "1.35.0"
 description = "A high-level API to automate web browsers"
 category = "dev"
 optional = false
 python-versions = ">=3.7"
 files = [
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 ]
 
 [package.dependencies]
@@ -7480,24 +7384,24 @@ image = ["Pillow"]
 
 [[package]]
 name = "pypdfium2"
-version = "4.14.0"
+version = "4.15.0"
 description = "Python bindings to PDFium"
 category = "main"
 optional = true
 python-versions = ">=3.6"
 files = [
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+    {file = "pypdfium2-4.15.0.tar.gz", hash = "sha256:1d7ea82690c5b195c065e714380f9bce11b3fdaac5acfac0508c841ef862ab60"},
 ]
 
 [[package]]
@@ -7632,14 +7536,14 @@ Pillow = ">=8.0.0"
 
 [[package]]
 name = "pytest"
-version = "7.3.1"
+version = "7.3.2"
 description = "pytest: simple powerful testing with Python"
 category = "dev"
 optional = false
 python-versions = ">=3.7"
 files = [
-    {file = "pytest-7.3.1-py3-none-any.whl", hash = "sha256:3799fa815351fea3a5e96ac7e503a96fa51cc9942c3753cda7651b93c1cfa362"},
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+    {file = "pytest-7.3.2-py3-none-any.whl", hash = "sha256:cdcbd012c9312258922f8cd3f1b62a6580fdced17db6014896053d47cddf9295"},
+    {file = "pytest-7.3.2.tar.gz", hash = "sha256:ee990a3cc55ba808b80795a79944756f315c67c12b56abd3ac993a7b8c17030b"},
 ]
 
 [package.dependencies]
@@ -7651,7 +7555,7 @@ pluggy = ">=0.12,<2.0"
 tomli = {version = ">=1.0.0", markers = "python_version < \"3.11\""}
 
 [package.extras]
-testing = ["argcomplete", "attrs (>=19.2.0)", "hypothesis (>=3.56)", "mock", "nose", "pygments (>=2.7.2)", "requests", "xmlschema"]
+testing = ["argcomplete", "attrs (>=19.2.0)", "hypothesis (>=3.56)", "mock", "nose", "pygments (>=2.7.2)", "requests", "setuptools", "xmlschema"]
 
 [[package]]
 name = "pytest-asyncio"
@@ -8430,18 +8334,18 @@ files = [
 
 [[package]]
 name = "rich"
-version = "13.4.1"
+version = "13.4.2"
 description = "Render rich text, tables, progress bars, syntax highlighting, markdown and more to the terminal"
 category = "main"
 optional = true
 python-versions = ">=3.7.0"
 files = [
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+    {file = "rich-13.4.2-py3-none-any.whl", hash = "sha256:8f87bc7ee54675732fa66a05ebfe489e27264caeeff3728c945d25971b6485ec"},
+    {file = "rich-13.4.2.tar.gz", hash = "sha256:d653d6bccede5844304c605d5aac802c7cf9621efd700b46c7ec2b51ea914898"},
 ]
 
 [package.dependencies]
-markdown-it-py = ">=2.2.0,<3.0.0"
+markdown-it-py = ">=2.2.0"
 pygments = ">=2.13.0,<3.0.0"
 typing-extensions = {version = ">=4.0.0,<5.0", markers = "python_version < \"3.9\""}
 
@@ -9266,53 +9170,53 @@ test = ["pytest"]
 
 [[package]]
 name = "sqlalchemy"
-version = "2.0.15"
+version = "2.0.16"
 description = "Database Abstraction Library"
 category = "main"
 optional = false
 python-versions = ">=3.7"
 files = [
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 [package.dependencies]
@@ -9339,6 +9243,7 @@ postgresql-pg8000 = ["pg8000 (>=1.29.1)"]
 postgresql-psycopg = ["psycopg (>=3.0.7)"]
 postgresql-psycopg2binary = ["psycopg2-binary"]
 postgresql-psycopg2cffi = ["psycopg2cffi"]
+postgresql-psycopgbinary = ["psycopg[binary] (>=3.0.7)"]
 pymysql = ["pymysql"]
 sqlcipher = ["sqlcipher3-binary"]
 
@@ -10234,14 +10139,14 @@ test = ["argcomplete (>=2.0)", "pre-commit", "pytest", "pytest-mock"]
 
 [[package]]
 name = "transformers"
-version = "4.30.0"
+version = "4.30.1"
 description = "State-of-the-art Machine Learning for JAX, PyTorch and TensorFlow"
 category = "main"
 optional = false
 python-versions = ">=3.7.0"
 files = [
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 ]
 
 [package.dependencies]
@@ -10732,19 +10637,19 @@ files = [
 
 [[package]]
 name = "weaviate-client"
-version = "3.19.2"
-description = "A python native weaviate client"
+version = "3.20.0"
+description = "A python native Weaviate client"
 category = "main"
 optional = false
 python-versions = ">=3.8"
 files = [
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 [package.dependencies]
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-requests = ">=2.28.0,<2.29.0"
+requests = ">=2.28.0,<=2.31.0"
 tqdm = ">=4.59.0,<5.0.0"
 validators = ">=0.18.2,<=0.21.0"
 
@@ -10938,24 +10843,27 @@ urllib3 = ">=1.25.3"
 
 [[package]]
 name = "whylogs"
-version = "1.1.43.dev0"
+version = "1.1.45.dev4"
 description = "Profile and monitor your ML data pipeline end-to-end"
 category = "main"
 optional = true
 python-versions = ">=3.7.1,<4"
 files = [
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 ]
 
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 [[package]]
 name = "whylogs-sketching"
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-
 [[package]]
 name = "yarl"
 version = "1.9.2"
@@ -11472,13 +11271,13 @@ cffi = {version = ">=1.11", markers = "platform_python_implementation == \"PyPy\
 cffi = ["cffi (>=1.11)"]
 
 [extras]
-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", "pymongo", "weaviate-client", "redis", "google-api-python-client", "google-auth", "wolframalpha", "qdrant-client", "tensorflow-text", "pypdf", "networkx", "nomic", "aleph-alpha-client", "deeplake", "pgvector", "psycopg2-binary", "pyowm", "pytesseract", "html2text", "atlassian-python-api", "gptcache", "duckduckgo-search", "arxiv", "azure-identity", "clickhouse-connect", "azure-cosmos", "lancedb", "langkit", "lark", "pexpect", "pyvespa", "O365", "jq", "docarray", "steamship", "pdfminer-six", "lxml", "requests-toolbelt", "neo4j", "openlm", "azure-ai-formrecognizer", "azure-ai-vision", "azure-cognitiveservices-speech", "momento", "singlestoredb", "tigrisdb", "nebula3-python", "awadb"]
-azure = ["azure-identity", "azure-cosmos", "openai", "azure-core", "azure-ai-formrecognizer", "azure-ai-vision", "azure-cognitiveservices-speech", "azure-search-documents"]
+all = ["O365", "aleph-alpha-client", "anthropic", "arxiv", "atlassian-python-api", "awadb", "azure-ai-formrecognizer", "azure-ai-vision", "azure-cognitiveservices-speech", "azure-cosmos", "azure-identity", "beautifulsoup4", "clickhouse-connect", "cohere", "deeplake", "docarray", "duckduckgo-search", "elasticsearch", "faiss-cpu", "google-api-python-client", "google-auth", "google-search-results", "gptcache", "html2text", "huggingface_hub", "jina", "jinja2", "jq", "lancedb", "langkit", "lark", "lxml", "manifest-ml", "momento", "nebula3-python", "neo4j", "networkx", "nlpcloud", "nltk", "nomic", "openai", "openlm", "opensearch-py", "pdfminer-six", "pexpect", "pgvector", "pinecone-client", "pinecone-text", "psycopg2-binary", "pymongo", "pyowm", "pypdf", "pytesseract", "pyvespa", "qdrant-client", "redis", "requests-toolbelt", "sentence-transformers", "singlestoredb", "spacy", "steamship", "tensorflow-text", "tigrisdb", "tiktoken", "torch", "transformers", "weaviate-client", "wikipedia", "wolframalpha"]
+azure = ["azure-ai-formrecognizer", "azure-ai-vision", "azure-cognitiveservices-speech", "azure-core", "azure-cosmos", "azure-identity", "azure-search-documents", "openai"]
 cohere = ["cohere"]
 docarray = ["docarray"]
 embeddings = ["sentence-transformers"]
-extended-testing = ["beautifulsoup4", "bibtexparser", "chardet", "jq", "pdfminer-six", "pypdf", "pymupdf", "pypdfium2", "tqdm", "lxml", "atlassian-python-api", "beautifulsoup4", "pandas", "telethon", "psychicapi", "zep-python", "gql", "requests-toolbelt", "html2text", "py-trello", "scikit-learn", "pyspark", "openai"]
-llms = ["anthropic", "cohere", "openai", "openlm", "nlpcloud", "huggingface_hub", "manifest-ml", "torch", "transformers"]
+extended-testing = ["atlassian-python-api", "beautifulsoup4", "beautifulsoup4", "bibtexparser", "chardet", "gql", "html2text", "jq", "lxml", "openai", "pandas", "pdfminer-six", "psychicapi", "py-trello", "pymupdf", "pypdf", "pypdfium2", "pyspark", "requests-toolbelt", "scikit-learn", "telethon", "tqdm", "zep-python"]
+llms = ["anthropic", "cohere", "huggingface_hub", "manifest-ml", "nlpcloud", "openai", "openlm", "torch", "transformers"]
 openai = ["openai", "tiktoken"]
 qdrant = ["qdrant-client"]
 text-helpers = ["chardet"]
diff --git a/tests/integration_tests/vectorstores/test_mongodb_atlas.py b/tests/integration_tests/vectorstores/test_mongodb_atlas.py
index d36bb0e0b7f95..366bacb640a3a 100644
--- a/tests/integration_tests/vectorstores/test_mongodb_atlas.py
+++ b/tests/integration_tests/vectorstores/test_mongodb_atlas.py
@@ -21,7 +21,7 @@
 
 # Instantiate as constant instead of pytest fixture to prevent needing to make multiple
 # connections.
-TEST_CLIENT = MongoClient(CONNECTION_STRING)
+TEST_CLIENT: MongoClient = MongoClient(CONNECTION_STRING)
 collection = TEST_CLIENT[DB_NAME][COLLECTION_NAME]
 
 
diff --git a/tests/unit_tests/retrievers/self_query/test_myscale.py b/tests/unit_tests/retrievers/self_query/test_myscale.py
new file mode 100644
index 0000000000000..c05ecfa8504e7
--- /dev/null
+++ b/tests/unit_tests/retrievers/self_query/test_myscale.py
@@ -0,0 +1,43 @@
+import pytest
+
+from typing import Tuple, Any
+from langchain.chains.query_constructor.ir import (
+    Comparator,
+    Comparison,
+    Operation,
+    Operator,
+)
+from langchain.retrievers.self_query.myscale import MyScaleTranslator
+
+DEFAULT_TRANSLATOR = MyScaleTranslator()
+
+
+@pytest.mark.parametrize(
+    "triplet",
+    [
+        (Comparator.LT, 2, "metadata.foo < 2"),
+        (Comparator.LTE, 2, "metadata.foo <= 2"),
+        (Comparator.GT, 2, "metadata.foo > 2"),
+        (Comparator.GTE, 2, "metadata.foo >= 2"),
+        (Comparator.CONTAIN, 2, "has(metadata.foo,2)"),
+        (Comparator.LIKE, "bar", "metadata.foo ILIKE '%bar%'"),
+    ],
+)
+def test_visit_comparison(triplet: Tuple[Comparator, Any, str]) -> None:
+    comparator, value, expected = triplet
+    comp = Comparison(comparator=comparator, attribute="foo", value=value)
+    actual = DEFAULT_TRANSLATOR.visit_comparison(comp)
+    assert expected == actual
+
+
+def test_visit_operation() -> None:
+    op = Operation(
+        operator=Operator.AND,
+        arguments=[
+            Comparison(comparator=Comparator.LT, attribute="foo", value=2),
+            Comparison(comparator=Comparator.EQ, attribute="bar", value="baz"),
+        ],
+    )
+    expected = "metadata.foo < 2 AND metadata.bar = 'baz'"
+    actual = DEFAULT_TRANSLATOR.visit_operation(op)
+    assert expected == actual
diff --git a/tests/unit_tests/tools/test_base.py b/tests/unit_tests/tools/test_base.py
index cea017d79d271..9f05faa0c3613 100644
--- a/tests/unit_tests/tools/test_base.py
+++ b/tests/unit_tests/tools/test_base.py
@@ -315,6 +315,39 @@ def test_tool_lambda_args_schema() -> None:
     assert tool.args == expected_args
 
 
+def test_structured_tool_from_function_docstring() -> None:
+    """Test that structured tools can be created from functions."""
+
+    def foo(bar: int, baz: str) -> str:
+        """Docstring
+        Args:
+            bar: int
+            baz: str
+        """
+        raise NotImplementedError()
+
+    structured_tool = StructuredTool.from_function(foo)
+    assert structured_tool.name == "foo"
+    assert structured_tool.args == {
+        "bar": {"title": "Bar", "type": "integer"},
+        "baz": {"title": "Baz", "type": "string"},
+    }
+
+    assert structured_tool.args_schema.schema() == {
+        "properties": {
+            "bar": {"title": "Bar", "type": "integer"},
+            "baz": {"title": "Baz", "type": "string"},
+        },
+        "title": "fooSchemaSchema",
+        "type": "object",
+        "required": ["bar", "baz"],
+    }
+
+    prefix = "foo(bar: int, baz: str) -> str - "
+    assert foo.__doc__ is not None
+    assert structured_tool.description == prefix + foo.__doc__.strip()
+
+
 def test_structured_tool_lambda_multi_args_schema() -> None:
     """Test args schema inference when the tool argument is a lambda function."""
     tool = StructuredTool.from_function(
@@ -577,12 +610,13 @@ def foo(bar: int, baz: str) -> str:
     }
 
     assert structured_tool.args_schema.schema() == {
+        "title": "fooSchemaSchema",
+        "type": "object",
         "properties": {
             "bar": {"title": "Bar", "type": "integer"},
             "baz": {"title": "Baz", "type": "string"},
         },
-        "title": "fooSchemaSchema",
-        "type": "object",
+        "required": ["bar", "baz"],
     }
 
     prefix = "foo(bar: int, baz: str) -> str - "