From 9bcfd58580c52dd49eecc7503934d13c3dc89ec5 Mon Sep 17 00:00:00 2001 From: Xiaoyu Xee Date: Mon, 4 Sep 2023 11:51:04 +0800 Subject: [PATCH] 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 Co-authored-by: Harrison Chase --- .../retrievers/self_query/dashvector.ipynb | 434 ++++++++++++++++++ .../langchain/retrievers/self_query/base.py | 3 + .../retrievers/self_query/dashvector.py | 64 +++ libs/langchain/poetry.lock | 24 +- libs/langchain/pyproject.toml | 2 + .../retrievers/self_query/test_dashvector.py | 52 +++ 6 files changed, 577 insertions(+), 2 deletions(-) create mode 100644 docs/extras/modules/data_connection/retrievers/self_query/dashvector.ipynb create mode 100644 libs/langchain/langchain/retrievers/self_query/dashvector.py create mode 100644 libs/langchain/tests/unit_tests/retrievers/self_query/test_dashvector.py diff --git a/docs/extras/modules/data_connection/retrievers/self_query/dashvector.ipynb b/docs/extras/modules/data_connection/retrievers/self_query/dashvector.ipynb new file mode 100644 index 0000000000..d1048ee5fa --- /dev/null +++ b/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=, 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=, 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=, arguments=[Comparison(comparator=, attribute='genre', value='science fiction'), Comparison(comparator=, 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 +} diff --git a/libs/langchain/langchain/retrievers/self_query/base.py b/libs/langchain/langchain/retrievers/self_query/base.py index 806d4e1502..9c64b3c44d 100644 --- a/libs/langchain/langchain/retrievers/self_query/base.py +++ b/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, diff --git a/libs/langchain/langchain/retrievers/self_query/dashvector.py b/libs/langchain/langchain/retrievers/self_query/dashvector.py new file mode 100644 index 0000000000..24ae50239a --- /dev/null +++ b/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 diff --git a/libs/langchain/poetry.lock b/libs/langchain/poetry.lock index 6a13a8433b..d742e5a896 100644 --- a/libs/langchain/poetry.lock +++ b/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" diff --git a/libs/langchain/pyproject.toml b/libs/langchain/pyproject.toml index 0856f190d8..9fda48eb81 100644 --- a/libs/langchain/pyproject.toml +++ b/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", ] diff --git a/libs/langchain/tests/unit_tests/retrievers/self_query/test_dashvector.py b/libs/langchain/tests/unit_tests/retrievers/self_query/test_dashvector.py new file mode 100644 index 0000000000..137de85fdc --- /dev/null +++ b/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