diff --git a/docs/docs/integrations/retrievers/self_query/mongodb_atlas.ipynb b/docs/docs/integrations/retrievers/self_query/mongodb_atlas.ipynb
new file mode 100644
index 0000000000..3ee3b28c1c
--- /dev/null
+++ b/docs/docs/integrations/retrievers/self_query/mongodb_atlas.ipynb
@@ -0,0 +1,321 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# MongoDB Atlas\n",
+    "\n",
+    "[MongoDB Atlas](https://www.mongodb.com/) is a document database that can be \n",
+    "used as a vector databse.\n",
+    "\n",
+    "In the walkthrough, we'll demo the `SelfQueryRetriever` with a `MongoDB Atlas` vector store."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Creating a MongoDB Atlas vectorstore\n",
+    "First we'll want to create a MongoDB Atlas 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 `pymongo` package."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#!pip install lark pymongo"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We want to use `OpenAIEmbeddings` so we have to get the OpenAI API Key."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "\n",
+    "OPENAI_API_KEY = \"Use your OpenAI key\"\n",
+    "\n",
+    "os.environ[\"OPENAI_API_KEY\"] = OPENAI_API_KEY"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.embeddings.openai import OpenAIEmbeddings\n",
+    "from langchain.schema import Document\n",
+    "from langchain.vectorstores import MongoDBAtlasVectorSearch\n",
+    "from pymongo import MongoClient\n",
+    "\n",
+    "CONNECTION_STRING = \"Use your MongoDB Atlas connection string\"\n",
+    "DB_NAME = \"Name of your MongoDB Atlas database\"\n",
+    "COLLECTION_NAME = \"Name of your collection in the database\"\n",
+    "INDEX_NAME = \"Name of a search index defined on the collection\"\n",
+    "\n",
+    "MongoClient = MongoClient(CONNECTION_STRING)\n",
+    "collection = MongoClient[DB_NAME][COLLECTION_NAME]\n",
+    "\n",
+    "embeddings = OpenAIEmbeddings()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "docs = [\n",
+    "    Document(\n",
+    "        page_content=\"A bunch of scientists bring back dinosaurs and mayhem breaks loose\",\n",
+    "        metadata={\"year\": 1993, \"rating\": 7.7, \"genre\": \"action\"},\n",
+    "    ),\n",
+    "    Document(\n",
+    "        page_content=\"Leo DiCaprio gets lost in a dream within a dream within a dream within a ...\",\n",
+    "        metadata={\"year\": 2010, \"genre\": \"thriller\", \"rating\": 8.2},\n",
+    "    ),\n",
+    "    Document(\n",
+    "        page_content=\"A bunch of normal-sized women are supremely wholesome and some men pine after them\",\n",
+    "        metadata={\"year\": 2019, \"rating\": 8.3, \"genre\": \"drama\"},\n",
+    "    ),\n",
+    "    Document(\n",
+    "        page_content=\"Three men walk into the Zone, three men walk out of the Zone\",\n",
+    "        metadata={\"year\": 1979, \"rating\": 9.9, \"genre\": \"science fiction\"},\n",
+    "    ),\n",
+    "    Document(\n",
+    "        page_content=\"A psychologist / detective gets lost in a series of dreams within dreams within dreams and Inception reused the idea\",\n",
+    "        metadata={\"year\": 2006, \"genre\": \"thriller\", \"rating\": 9.0},\n",
+    "    ),\n",
+    "    Document(\n",
+    "        page_content=\"Toys come alive and have a blast doing so\",\n",
+    "        metadata={\"year\": 1995, \"genre\": \"animated\", \"rating\": 9.3},\n",
+    "    ),\n",
+    "]\n",
+    "\n",
+    "vectorstore = MongoDBAtlasVectorSearch.from_documents(\n",
+    "    docs,\n",
+    "    embeddings,\n",
+    "    collection=collection,\n",
+    "    index_name=INDEX_NAME,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now, let's create a vector search index on your cluster. In the below example, `embedding` is the name of the field that contains the embedding vector. Please refer to the [documentation](https://www.mongodb.com/docs/atlas/atlas-search/field-types/knn-vector) to get more details on how to define an Atlas Vector Search index.\n",
+    "You can name the index `{COLLECTION_NAME}` and create the index on the namespace `{DB_NAME}.{COLLECTION_NAME}`. Finally, write the following definition in the JSON editor on MongoDB Atlas:\n",
+    "\n",
+    "```json\n",
+    "{\n",
+    "  \"mappings\": {\n",
+    "    \"dynamic\": true,\n",
+    "    \"fields\": {\n",
+    "      \"embedding\": {\n",
+    "        \"dimensions\": 1536,\n",
+    "        \"similarity\": \"cosine\",\n",
+    "        \"type\": \"knnVector\"\n",
+    "      },\n",
+    "      \"genre\": {\n",
+    "        \"type\": \"token\"\n",
+    "      },\n",
+    "      \"ratings\": {\n",
+    "        \"type\": \"number\"\n",
+    "      },\n",
+    "      \"year\": {\n",
+    "        \"type\": \"number\"\n",
+    "      }\n",
+    "    }\n",
+    "  }\n",
+    "}\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Creating our self-querying retriever\n",
+    "Now we can instantiate our retriever. To do this we'll need to provide some information upfront about the metadata fields that our documents support and a short description of the document contents."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.chains.query_constructor.base import AttributeInfo\n",
+    "from langchain.llms import OpenAI\n",
+    "from langchain.retrievers.self_query.base import SelfQueryRetriever\n",
+    "\n",
+    "metadata_field_info = [\n",
+    "    AttributeInfo(\n",
+    "        name=\"genre\",\n",
+    "        description=\"The genre of the movie\",\n",
+    "        type=\"string\",\n",
+    "    ),\n",
+    "    AttributeInfo(\n",
+    "        name=\"year\",\n",
+    "        description=\"The year the movie was released\",\n",
+    "        type=\"integer\",\n",
+    "    ),\n",
+    "    AttributeInfo(\n",
+    "        name=\"rating\", description=\"A 1-10 rating for the movie\", type=\"float\"\n",
+    "    ),\n",
+    "]\n",
+    "document_content_description = \"Brief summary of a movie\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "llm = OpenAI(temperature=0)\n",
+    "retriever = SelfQueryRetriever.from_llm(\n",
+    "    llm, vectorstore, document_content_description, metadata_field_info, verbose=True\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Testing it out\n",
+    "And now we can try actually using our retriever!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "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,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# This example specifies a filter\n",
+    "retriever.get_relevant_documents(\"What are some highly rated movies (above 9)?\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# This example only specifies a query and a filter\n",
+    "retriever.get_relevant_documents(\n",
+    "    \"I want to watch a movie about toys rated higher than 9\"\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# This example specifies a composite filter\n",
+    "retriever.get_relevant_documents(\n",
+    "    \"What's a highly rated (above or equal 9) thriller film?\"\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# This example specifies a query and composite filter\n",
+    "retriever.get_relevant_documents(\n",
+    "    \"What's a movie after 1990 but before 2005 that's all about dinosaurs, \\\n",
+    "    and preferably has a lot of action\"\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "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,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "retriever = SelfQueryRetriever.from_llm(\n",
+    "    llm,\n",
+    "    vectorstore,\n",
+    "    document_content_description,\n",
+    "    metadata_field_info,\n",
+    "    verbose=True,\n",
+    "    enable_limit=True,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# This example only specifies a relevant query\n",
+    "retriever.get_relevant_documents(\"What are two movies about dinosaurs?\")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": ".venv",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/libs/langchain/langchain/retrievers/self_query/base.py b/libs/langchain/langchain/retrievers/self_query/base.py
index cb51639622..dc1bf174b8 100644
--- a/libs/langchain/langchain/retrievers/self_query/base.py
+++ b/libs/langchain/langchain/retrievers/self_query/base.py
@@ -21,6 +21,7 @@ from langchain.retrievers.self_query.dashvector import DashvectorTranslator
 from langchain.retrievers.self_query.deeplake import DeepLakeTranslator
 from langchain.retrievers.self_query.elasticsearch import ElasticsearchTranslator
 from langchain.retrievers.self_query.milvus import MilvusTranslator
+from langchain.retrievers.self_query.mongodb_atlas import MongoDBAtlasTranslator
 from langchain.retrievers.self_query.myscale import MyScaleTranslator
 from langchain.retrievers.self_query.opensearch import OpenSearchTranslator
 from langchain.retrievers.self_query.pinecone import PineconeTranslator
@@ -36,6 +37,7 @@ from langchain.vectorstores import (
     DeepLake,
     ElasticsearchStore,
     Milvus,
+    MongoDBAtlasVectorSearch,
     MyScale,
     OpenSearchVectorSearch,
     Pinecone,
@@ -66,6 +68,7 @@ def _get_builtin_translator(vectorstore: VectorStore) -> Visitor:
         SupabaseVectorStore: SupabaseVectorTranslator,
         TimescaleVector: TimescaleVectorTranslator,
         OpenSearchVectorSearch: OpenSearchTranslator,
+        MongoDBAtlasVectorSearch: MongoDBAtlasTranslator,
     }
     if isinstance(vectorstore, Qdrant):
         return QdrantTranslator(metadata_key=vectorstore.metadata_payload_key)
diff --git a/libs/langchain/langchain/retrievers/self_query/mongodb_atlas.py b/libs/langchain/langchain/retrievers/self_query/mongodb_atlas.py
new file mode 100644
index 0000000000..a10e7b58fa
--- /dev/null
+++ b/libs/langchain/langchain/retrievers/self_query/mongodb_atlas.py
@@ -0,0 +1,74 @@
+"""Logic for converting internal query language to a valid MongoDB Atlas query."""
+from typing import Dict, Tuple, Union
+
+from langchain.chains.query_constructor.ir import (
+    Comparator,
+    Comparison,
+    Operation,
+    Operator,
+    StructuredQuery,
+    Visitor,
+)
+
+MULTIPLE_ARITY_COMPARATORS = [Comparator.IN, Comparator.NIN]
+
+
+class MongoDBAtlasTranslator(Visitor):
+    """Translate Mongo internal query language elements to valid filters."""
+
+    """Subset of allowed logical comparators."""
+    allowed_comparators = [
+        Comparator.EQ,
+        Comparator.NE,
+        Comparator.GT,
+        Comparator.GTE,
+        Comparator.LT,
+        Comparator.LTE,
+        Comparator.IN,
+        Comparator.NIN,
+    ]
+
+    """Subset of allowed logical operators."""
+    allowed_operators = [Operator.AND, Operator.OR]
+
+    ## Convert a operator or a comparator to Mongo Query Format
+    def _format_func(self, func: Union[Operator, Comparator]) -> str:
+        self._validate_func(func)
+        map_dict = {
+            Operator.AND: "$and",
+            Operator.OR: "$or",
+            Comparator.EQ: "$eq",
+            Comparator.NE: "$ne",
+            Comparator.GTE: "$gte",
+            Comparator.LTE: "$lte",
+            Comparator.LT: "$lt",
+            Comparator.GT: "$gt",
+            Comparator.IN: "$in",
+            Comparator.NIN: "$nin",
+        }
+        return map_dict[func]
+
+    def visit_operation(self, operation: Operation) -> Dict:
+        args = [arg.accept(self) for arg in operation.arguments]
+        return {self._format_func(operation.operator): args}
+
+    def visit_comparison(self, comparison: Comparison) -> Dict:
+        if comparison.comparator in MULTIPLE_ARITY_COMPARATORS and not isinstance(
+            comparison.value, list
+        ):
+            comparison.value = [comparison.value]
+
+        comparator = self._format_func(comparison.comparator)
+
+        attribute = comparison.attribute
+
+        return {attribute: {comparator: comparison.value}}
+
+    def visit_structured_query(
+        self, structured_query: StructuredQuery
+    ) -> Tuple[str, dict]:
+        if structured_query.filter is None:
+            kwargs = {}
+        else:
+            kwargs = {"pre_filter": structured_query.filter.accept(self)}
+        return structured_query.query, kwargs
diff --git a/libs/langchain/tests/unit_tests/retrievers/self_query/test_mongodb_atlas.py b/libs/langchain/tests/unit_tests/retrievers/self_query/test_mongodb_atlas.py
new file mode 100644
index 0000000000..9eceec6b70
--- /dev/null
+++ b/libs/langchain/tests/unit_tests/retrievers/self_query/test_mongodb_atlas.py
@@ -0,0 +1,131 @@
+from typing import Dict, Tuple
+
+from langchain.chains.query_constructor.ir import (
+    Comparator,
+    Comparison,
+    Operation,
+    Operator,
+    StructuredQuery,
+)
+from langchain.retrievers.self_query.mongodb_atlas import MongoDBAtlasTranslator
+
+DEFAULT_TRANSLATOR = MongoDBAtlasTranslator()
+
+
+def test_visit_comparison_lt() -> None:
+    comp = Comparison(comparator=Comparator.LT, attribute="qty", value=20)
+    expected = {"qty": {"$lt": 20}}
+    actual = DEFAULT_TRANSLATOR.visit_comparison(comp)
+    assert expected == actual
+
+
+def test_visit_comparison_eq() -> None:
+    comp = Comparison(comparator=Comparator.EQ, attribute="qty", value=10)
+    expected = {"qty": {"$eq": 10}}
+    actual = DEFAULT_TRANSLATOR.visit_comparison(comp)
+    assert expected == actual
+
+
+def test_visit_comparison_ne() -> None:
+    comp = Comparison(comparator=Comparator.NE, attribute="name", value="foo")
+    expected = {"name": {"$ne": "foo"}}
+    actual = DEFAULT_TRANSLATOR.visit_comparison(comp)
+    assert expected == actual
+
+
+def test_visit_comparison_in() -> None:
+    comp = Comparison(comparator=Comparator.IN, attribute="name", value="foo")
+    expected = {"name": {"$in": ["foo"]}}
+    actual = DEFAULT_TRANSLATOR.visit_comparison(comp)
+    assert expected == actual
+
+
+def test_visit_comparison_nin() -> None:
+    comp = Comparison(comparator=Comparator.NIN, attribute="name", value="foo")
+    expected = {"name": {"$nin": ["foo"]}}
+    actual = DEFAULT_TRANSLATOR.visit_comparison(comp)
+    assert expected == actual
+
+
+def test_visit_operation() -> None:
+    op = Operation(
+        operator=Operator.AND,
+        arguments=[
+            Comparison(comparator=Comparator.GTE, attribute="qty", value=10),
+            Comparison(comparator=Comparator.LTE, attribute="qty", value=20),
+            Comparison(comparator=Comparator.EQ, attribute="name", value="foo"),
+        ],
+    )
+    expected = {
+        "$and": [
+            {"qty": {"$gte": 10}},
+            {"qty": {"$lte": 20}},
+            {"name": {"$eq": "foo"}},
+        ]
+    }
+    actual = DEFAULT_TRANSLATOR.visit_operation(op)
+    assert expected == actual
+
+
+def test_visit_structured_query_no_filter() -> None:
+    query = "What is the capital of France?"
+    structured_query = StructuredQuery(
+        query=query,
+        filter=None,
+    )
+    expected: Tuple[str, Dict] = (query, {})
+    actual = DEFAULT_TRANSLATOR.visit_structured_query(structured_query)
+    assert expected == actual
+
+
+def test_visit_structured_query_one_attr() -> None:
+    query = "What is the capital of France?"
+    comp = Comparison(comparator=Comparator.IN, attribute="qty", value=[5, 15, 20])
+    structured_query = StructuredQuery(
+        query=query,
+        filter=comp,
+    )
+    expected = (
+        query,
+        {"pre_filter": {"qty": {"$in": [5, 15, 20]}}},
+    )
+    actual = DEFAULT_TRANSLATOR.visit_structured_query(structured_query)
+    assert expected == actual
+
+
+def test_visit_structured_query_deep_nesting() -> None:
+    query = "What is the capital of France?"
+    op = Operation(
+        operator=Operator.AND,
+        arguments=[
+            Comparison(comparator=Comparator.EQ, attribute="name", value="foo"),
+            Operation(
+                operator=Operator.OR,
+                arguments=[
+                    Comparison(comparator=Comparator.GT, attribute="qty", value=6),
+                    Comparison(
+                        comparator=Comparator.NIN,
+                        attribute="tags",
+                        value=["bar", "foo"],
+                    ),
+                ],
+            ),
+        ],
+    )
+    structured_query = StructuredQuery(
+        query=query,
+        filter=op,
+    )
+    expected = (
+        query,
+        {
+            "pre_filter": {
+                "$and": [
+                    {"name": {"$eq": "foo"}},
+                    {"$or": [{"qty": {"$gt": 6}}, {"tags": {"$nin": ["bar", "foo"]}}]},
+                ]
+            }
+        },
+    )
+    actual = DEFAULT_TRANSLATOR.visit_structured_query(structured_query)
+    assert expected == actual