Add dashvector self query retriever (#9684)

## Description
Add `Dashvector` retriever and self-query retriever

## How to use
```python
from langchain.vectorstores.dashvector import DashVector

vectorstore = DashVector.from_documents(docs, embeddings)
retriever = SelfQueryRetriever.from_llm(
    llm, vectorstore, document_content_description, metadata_field_info, verbose=True
)
```

---------

Co-authored-by: smallrain.xuxy <smallrain.xuxy@alibaba-inc.com>
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>

docs/extras/modules/data_connection/retrievers/self_query/dashvector.ipynb

@@ -0,0 +1,434 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "source": [
+    "# DashVector self-querying\n",
+    "\n",
+    "> [DashVector](https://help.aliyun.com/document_detail/2510225.html) is a fully-managed vectorDB service that supports high-dimension dense and sparse vectors, real-time insertion and filtered search. It is built to scale automatically and can adapt to different application requirements.\n",
+    "\n",
+    "In this notebook we'll demo the `SelfQueryRetriever` with a `DashVector` vector store."
+   ],
+   "metadata": {
+    "collapsed": false
+   },
+   "id": "59895c73d1a0f3ca"
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "## Create DashVector vectorstore\n",
+    "\n",
+    "First we'll want to create a `DashVector` VectorStore and seed it with some data. We've created a small demo set of documents that contain summaries of movies.\n",
+    "\n",
+    "To use DashVector, you have to have `dashvector` package installed, and you must have an API key and an Environment. Here are the [installation instructions](https://help.aliyun.com/document_detail/2510223.html).\n",
+    "\n",
+    "NOTE: The self-query retriever requires you to have `lark` package installed."
+   ],
+   "metadata": {
+    "collapsed": false
+   },
+   "id": "539ae9367e45a178"
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "outputs": [],
+   "source": [
+    "# !pip install lark dashvector"
+   ],
+   "metadata": {
+    "collapsed": false
+   },
+   "id": "67df7e1f8dc8cdd0"
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "import dashvector\n",
+    "\n",
+    "client = dashvector.Client(api_key=os.environ[\"DASHVECTOR_API_KEY\"])"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "ExecuteTime": {
+     "end_time": "2023-08-24T02:58:46.905337Z",
+     "start_time": "2023-08-24T02:58:46.252566Z"
+    }
+   },
+   "id": "ff61eaf13973b5fe"
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "outputs": [],
+   "source": [
+    "from langchain.schema import Document\n",
+    "from langchain.embeddings import DashScopeEmbeddings\n",
+    "from langchain.vectorstores import DashVector\n",
+    "\n",
+    "embeddings = DashScopeEmbeddings()\n",
+    "\n",
+    "# create DashVector collection\n",
+    "client.create(\"langchain-self-retriever-demo\", dimension=1536)"
+   ],
+   "metadata": {
+    "collapsed": false
+   },
+   "id": "de5c77957ee42d14"
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "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, \"director\": \"Christopher Nolan\", \"rating\": 8.2},\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, \"director\": \"Satoshi Kon\", \"rating\": 8.6},\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, \"director\": \"Greta Gerwig\", \"rating\": 8.3},\n",
+    "    ),\n",
+    "    Document(\n",
+    "        page_content=\"Toys come alive and have a blast doing so\",\n",
+    "        metadata={\"year\": 1995, \"genre\": \"animated\"},\n",
+    "    ),\n",
+    "    Document(\n",
+    "        page_content=\"Three men walk into the Zone, three men walk out of the Zone\",\n",
+    "        metadata={\n",
+    "            \"year\": 1979,\n",
+    "            \"director\": \"Andrei Tarkovsky\",\n",
+    "            \"genre\": \"science fiction\",\n",
+    "            \"rating\": 9.9,\n",
+    "        },\n",
+    "    ),\n",
+    "]\n",
+    "vectorstore = DashVector.from_documents(\n",
+    "    docs, embeddings, collection_name=\"langchain-self-retriever-demo\"\n",
+    ")"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "ExecuteTime": {
+     "end_time": "2023-08-24T02:59:08.090031Z",
+     "start_time": "2023-08-24T02:59:05.660295Z"
+    }
+   },
+   "id": "8f40605548a4550"
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "## Create your self-querying retriever\n",
+    "\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."
+   ],
+   "metadata": {
+    "collapsed": false
+   },
+   "id": "eb1340adafac8993"
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "outputs": [],
+   "source": [
+    "from langchain.llms import Tongyi\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 genre of the movie\",\n",
+    "        type=\"string or list[string]\",\n",
+    "    ),\n",
+    "    AttributeInfo(\n",
+    "        name=\"year\",\n",
+    "        description=\"The year the movie was released\",\n",
+    "        type=\"integer\",\n",
+    "    ),\n",
+    "    AttributeInfo(\n",
+    "        name=\"director\",\n",
+    "        description=\"The name of the movie director\",\n",
+    "        type=\"string\",\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\"\n",
+    "llm = Tongyi(temperature=0)\n",
+    "retriever = SelfQueryRetriever.from_llm(\n",
+    "    llm, vectorstore, document_content_description, metadata_field_info, verbose=True\n",
+    ")"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "ExecuteTime": {
+     "end_time": "2023-08-24T02:59:11.003940Z",
+     "start_time": "2023-08-24T02:59:10.476722Z"
+    }
+   },
+   "id": "d65233dc044f95a7"
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "## Testing it out\n",
+    "\n",
+    "And now we can try actually using our retriever!"
+   ],
+   "metadata": {
+    "collapsed": false
+   },
+   "id": "a54af0d67b473db6"
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "query='dinosaurs' filter=None limit=None\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": "[Document(page_content='A bunch of scientists bring back dinosaurs and mayhem breaks loose', metadata={'year': 1993, 'rating': 7.699999809265137, 'genre': 'action'}),\n Document(page_content='Toys come alive and have a blast doing so', metadata={'year': 1995, 'genre': 'animated'}),\n Document(page_content='Leo DiCaprio gets lost in a dream within a dream within a dream within a ...', metadata={'year': 2010, 'director': 'Christopher Nolan', 'rating': 8.199999809265137}),\n Document(page_content='A psychologist / detective gets lost in a series of dreams within dreams within dreams and Inception reused the idea', metadata={'year': 2006, 'director': 'Satoshi Kon', 'rating': 8.600000381469727})]"
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# This example only specifies a relevant query\n",
+    "retriever.get_relevant_documents(\"What are some movies about dinosaurs\")"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "ExecuteTime": {
+     "end_time": "2023-08-24T02:59:28.577901Z",
+     "start_time": "2023-08-24T02:59:26.780184Z"
+    }
+   },
+   "id": "dad9da670a267fe7"
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "query=' ' filter=Comparison(comparator=<Comparator.GTE: 'gte'>, attribute='rating', value=8.5) limit=None\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": "[Document(page_content='Three men walk into the Zone, three men walk out of the Zone', metadata={'year': 1979, 'director': 'Andrei Tarkovsky', 'rating': 9.899999618530273, 'genre': 'science fiction'}),\n Document(page_content='A psychologist / detective gets lost in a series of dreams within dreams within dreams and Inception reused the idea', metadata={'year': 2006, 'director': 'Satoshi Kon', 'rating': 8.600000381469727})]"
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# This example only specifies a filter\n",
+    "retriever.get_relevant_documents(\"I want to watch a movie rated higher than 8.5\")"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "ExecuteTime": {
+     "end_time": "2023-08-24T02:59:32.370774Z",
+     "start_time": "2023-08-24T02:59:30.614252Z"
+    }
+   },
+   "id": "d486a64316153d52"
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "query='Greta Gerwig' filter=Comparison(comparator=<Comparator.EQ: 'eq'>, attribute='director', value='Greta Gerwig') limit=None\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": "[Document(page_content='A bunch of normal-sized women are supremely wholesome and some men pine after them', metadata={'year': 2019, 'director': 'Greta Gerwig', 'rating': 8.300000190734863})]"
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# This example specifies a query and a filter\n",
+    "retriever.get_relevant_documents(\"Has Greta Gerwig directed any movies about women\")"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "ExecuteTime": {
+     "end_time": "2023-08-24T02:59:35.353439Z",
+     "start_time": "2023-08-24T02:59:33.278255Z"
+    }
+   },
+   "id": "e05919cdead7bd4a"
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "query='science fiction' filter=Operation(operator=<Operator.AND: 'and'>, arguments=[Comparison(comparator=<Comparator.EQ: 'eq'>, attribute='genre', value='science fiction'), Comparison(comparator=<Comparator.GT: 'gt'>, attribute='rating', value=8.5)]) limit=None\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": "[Document(page_content='Three men walk into the Zone, three men walk out of the Zone', metadata={'year': 1979, 'director': 'Andrei Tarkovsky', 'rating': 9.899999618530273, 'genre': 'science fiction'})]"
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# This example specifies a composite filter\n",
+    "retriever.get_relevant_documents(\"What's a highly rated (above 8.5) science fiction film?\")"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "ExecuteTime": {
+     "end_time": "2023-08-24T02:59:38.913707Z",
+     "start_time": "2023-08-24T02:59:36.659271Z"
+    }
+   },
+   "id": "ac2c7012379e918e"
+  },
+  {
+   "cell_type": "markdown",
+   "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."
+   ],
+   "metadata": {
+    "collapsed": false
+   },
+   "id": "af6aa93ae44af414"
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "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",
+    ")"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "ExecuteTime": {
+     "end_time": "2023-08-24T02:59:41.594073Z",
+     "start_time": "2023-08-24T02:59:41.563323Z"
+    }
+   },
+   "id": "a8c8f09bf5702767"
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "query='dinosaurs' filter=None limit=2\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": "[Document(page_content='A bunch of scientists bring back dinosaurs and mayhem breaks loose', metadata={'year': 1993, 'rating': 7.699999809265137, 'genre': 'action'}),\n Document(page_content='Toys come alive and have a blast doing so', metadata={'year': 1995, 'genre': 'animated'})]"
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# This example only specifies a relevant query\n",
+    "retriever.get_relevant_documents(\"what are two movies about dinosaurs\")"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "ExecuteTime": {
+     "end_time": "2023-08-24T02:59:48.450506Z",
+     "start_time": "2023-08-24T02:59:46.252944Z"
+    }
+   },
+   "id": "b1089a6043980b84"
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "outputs": [],
+   "source": [],
+   "metadata": {
+    "collapsed": false
+   },
+   "id": "6d2d64e2ebb17d30"
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

libs/langchain/langchain/retrievers/self_query/base.py

@@ -9,6 +9,7 @@ from langchain.chains.query_constructor.ir import StructuredQuery, Visitor
 from langchain.chains.query_constructor.schema import AttributeInfo
 from langchain.pydantic_v1 import BaseModel, Field, root_validator
 from langchain.retrievers.self_query.chroma import ChromaTranslator
+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.myscale import MyScaleTranslator
@@ -19,6 +20,7 @@ from langchain.schema import BaseRetriever, Document
 from langchain.schema.language_model import BaseLanguageModel
 from langchain.vectorstores import (
     Chroma,
+    DashVector,
     DeepLake,
     ElasticsearchStore,
     MyScale,
@@ -35,6 +37,7 @@ def _get_builtin_translator(vectorstore: VectorStore) -> Visitor:
     BUILTIN_TRANSLATORS: Dict[Type[VectorStore], Type[Visitor]] = {
         Pinecone: PineconeTranslator,
         Chroma: ChromaTranslator,
+        DashVector: DashvectorTranslator,
         Weaviate: WeaviateTranslator,
         Qdrant: QdrantTranslator,
         MyScale: MyScaleTranslator,

libs/langchain/langchain/retrievers/self_query/dashvector.py

@@ -0,0 +1,64 @@
+"""Logic for converting internal query language to a valid DashVector query."""
+from typing import Tuple, Union
+
+from langchain.chains.query_constructor.ir import (
+    Comparator,
+    Comparison,
+    Operation,
+    Operator,
+    StructuredQuery,
+    Visitor,
+)
+
+
+class DashvectorTranslator(Visitor):
+    """Logic for converting internal query language elements to valid filters."""
+
+    allowed_operators = [Operator.AND, Operator.OR]
+    allowed_comparators = [
+        Comparator.EQ,
+        Comparator.GT,
+        Comparator.GTE,
+        Comparator.LT,
+        Comparator.LTE,
+        Comparator.LIKE,
+    ]
+
+    map_dict = {
+        Operator.AND: " AND ",
+        Operator.OR: " OR ",
+        Comparator.EQ: " = ",
+        Comparator.GT: " > ",
+        Comparator.GTE: " >= ",
+        Comparator.LT: " < ",
+        Comparator.LTE: " <= ",
+        Comparator.LIKE: " LIKE ",
+    }
+
+    def _format_func(self, func: Union[Operator, Comparator]) -> str:
+        self._validate_func(func)
+        return self.map_dict[func]
+
+    def visit_operation(self, operation: Operation) -> str:
+        args = [arg.accept(self) for arg in operation.arguments]
+        return self._format_func(operation.operator).join(args)
+
+    def visit_comparison(self, comparison: Comparison) -> str:
+        value = comparison.value
+        if isinstance(value, str):
+            if comparison.comparator == Comparator.LIKE:
+                value = f"'%{value}%'"
+            else:
+                value = f"'{value}'"
+        return (
+            f"{comparison.attribute}{self._format_func(comparison.comparator)}{value}"
+        )
+
+    def visit_structured_query(
+        self, structured_query: StructuredQuery
+    ) -> Tuple[str, dict]:
+        if structured_query.filter is None:
+            kwargs = {}
+        else:
+            kwargs = {"filter": structured_query.filter.accept(self)}
+        return structured_query.query, kwargs

libs/langchain/poetry.lock

@@ -1799,6 +1799,26 @@ files = [
     {file = "cssselect-1.2.0.tar.gz", hash = "sha256:666b19839cfaddb9ce9d36bfe4c969132c647b92fc9088c4e23f786b30f1b3dc"},
 ]
 
+[[package]]
+name = "dashvector"
+version = "1.0.1"
+description = "DashVector Client Python Sdk Library"
+category = "main"
+optional = true
+python-versions = ">=3.7.0"
+files = [
+    {file = "dashvector-1.0.1-py3-none-any.whl", hash = "sha256:e2fc362c65979d55cf605fb90deca4a292c69e1c2101df22430c80db744591ad"},
+]
+
+[package.dependencies]
+aiohttp = ">=3.1.0"
+grpcio = [
+    {version = ">=1.22.0", markers = "python_version < \"3.11\""},
+    {version = ">=1.49.1", markers = "python_version >= \"3.11\""},
+]
+numpy = "*"
+protobuf = ">=3.8.0,<4.0.0"
+
 [[package]]
 name = "dataclasses-json"
 version = "0.5.9"
@@ -10897,7 +10917,7 @@ clarifai = ["clarifai"]
 cohere = ["cohere"]
 docarray = ["docarray"]
 embeddings = ["sentence-transformers"]
-extended-testing = ["amazon-textract-caller", "assemblyai", "beautifulsoup4", "bibtexparser", "cassio", "chardet", "esprima", "jq", "pdfminer-six", "pgvector", "pypdf", "pymupdf", "pypdfium2", "tqdm", "lxml", "atlassian-python-api", "mwparserfromhell", "mwxml", "pandas", "telethon", "psychicapi", "gql", "requests-toolbelt", "html2text", "py-trello", "scikit-learn", "streamlit", "pyspark", "openai", "sympy", "rapidfuzz", "openai", "rank-bm25", "geopandas", "jinja2", "gitpython", "newspaper3k", "feedparser", "xata", "xmltodict", "faiss-cpu", "openapi-schema-pydantic", "markdownify", "sqlite-vss"]
+extended-testing = ["amazon-textract-caller", "assemblyai", "beautifulsoup4", "bibtexparser", "cassio", "chardet", "esprima", "jq", "pdfminer-six", "pgvector", "pypdf", "pymupdf", "pypdfium2", "tqdm", "lxml", "atlassian-python-api", "mwparserfromhell", "mwxml", "pandas", "telethon", "psychicapi", "gql", "requests-toolbelt", "html2text", "py-trello", "scikit-learn", "streamlit", "pyspark", "openai", "sympy", "rapidfuzz", "openai", "rank-bm25", "geopandas", "jinja2", "gitpython", "newspaper3k", "feedparser", "xata", "xmltodict", "faiss-cpu", "openapi-schema-pydantic", "markdownify", "dashvector", "sqlite-vss"]
 javascript = ["esprima"]
 llms = ["clarifai", "cohere", "openai", "openlm", "nlpcloud", "huggingface_hub", "manifest-ml", "torch", "transformers"]
 openai = ["openai", "tiktoken"]
@@ -10907,4 +10927,4 @@ text-helpers = ["chardet"]
 [metadata]
 lock-version = "2.0"
 python-versions = ">=3.8.1,<4.0"
-content-hash = "47e048f7708139d5e5040c6d56ef4cb66153c3052a9237d6ea42eeb2565ad470"
+content-hash = "b63078268a80c07577b432114302f4f86d47be25b83a245affb0dbc999fb2c1f"

libs/langchain/pyproject.toml

@@ -127,6 +127,7 @@ xata = {version = "^1.0.0a7", optional = true}
 xmltodict = {version = "^0.13.0", optional = true}
 markdownify = {version = "^0.11.6", optional = true}
 assemblyai = {version = "^0.17.0", optional = true}
+dashvector = {version = "^1.0.1", optional = true}
 sqlite-vss = {version = "^0.1.2", optional = true}
 
 
@@ -342,6 +343,7 @@ extended_testing = [
  "faiss-cpu",
  "openapi-schema-pydantic",
  "markdownify",
+ "dashvector",
  "sqlite-vss",
 ]
 

libs/langchain/tests/unit_tests/retrievers/self_query/test_dashvector.py

@@ -0,0 +1,52 @@
+from typing import Any, Tuple
+
+import pytest
+
+from langchain.chains.query_constructor.ir import (
+    Comparator,
+    Comparison,
+    Operation,
+    Operator,
+)
+from langchain.retrievers.self_query.dashvector import DashvectorTranslator
+
+DEFAULT_TRANSLATOR = DashvectorTranslator()
+
+
+@pytest.mark.parametrize(
+    "triplet",
+    [
+        (Comparator.EQ, 2, "foo = 2"),
+        (Comparator.LT, 2, "foo < 2"),
+        (Comparator.LTE, 2, "foo <= 2"),
+        (Comparator.GT, 2, "foo > 2"),
+        (Comparator.GTE, 2, "foo >= 2"),
+        (Comparator.LIKE, "bar", "foo LIKE '%bar%'"),
+    ],
+)
+def test_visit_comparison(triplet: Tuple[Comparator, Any, str]) -> None:
+    comparator, value, expected = triplet
+    actual = DEFAULT_TRANSLATOR.visit_comparison(
+        Comparison(comparator=comparator, attribute="foo", value=value)
+    )
+    assert expected == actual
+
+
+@pytest.mark.parametrize(
+    "triplet",
+    [
+        (Operator.AND, "foo < 2 AND bar = 'baz'"),
+        (Operator.OR, "foo < 2 OR bar = 'baz'"),
+    ],
+)
+def test_visit_operation(triplet: Tuple[Operator, str]) -> None:
+    operator, expected = triplet
+    op = Operation(
+        operator=operator,
+        arguments=[
+            Comparison(comparator=Comparator.LT, attribute="foo", value=2),
+            Comparison(comparator=Comparator.EQ, attribute="bar", value="baz"),
+        ],
+    )
+    actual = DEFAULT_TRANSLATOR.visit_operation(op)
+    assert expected == actual