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langchain[minor]: Databricks vector search self query integration (#20627)

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- Enable self querying feature for databricks vector search

---------

Co-authored-by: Bagatur <baskaryan@gmail.com>
Co-authored-by: Bagatur <22008038+baskaryan@users.noreply.github.com>
pull/20254/head
Sivaudha 3 months ago committed by GitHub
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548
docs/docs/integrations/retrievers/self_query/databricks_vector_search.ipynb
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@ -0,0 +1,548 @@
{
"cells": [
{
"cell_type": "markdown",
"id": "1ad7250ddd99fba9",
"metadata": {
"collapsed": false,
"jupyter": {
"outputs_hidden": false
}
},
"source": [
"# Databricks Vector Search\n",
"\n",
">[Databricks Vector Search](https://docs.databricks.com/en/generative-ai/vector-search.html) is a serverless similarity search engine that allows you to store a vector representation of your data, including metadata, in a vector database. With Vector Search, you can create auto-updating vector search indexes from Delta tables managed by Unity Catalog and query them with a simple API to return the most similar vectors.\n",
"\n",
"\n",
"In the walkthrough, we'll demo the `SelfQueryRetriever` with a Databricks Vector Search."
]
},
{
"cell_type": "markdown",
"id": "209652d4ab38ba7f",
"metadata": {
"collapsed": false,
"jupyter": {
"outputs_hidden": false
}
},
"source": [
"## create Databricks vector store index\n",
"First we'll want to create a databricks vector store index 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`) along with integration-specific requirements."
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "b68da3303b0625f2",
"metadata": {
"ExecuteTime": {
"end_time": "2024-03-29T02:39:28.887634Z",
"start_time": "2024-03-29T02:39:27.277978Z"
},
"collapsed": false,
"jupyter": {
"outputs_hidden": false
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Note: you may need to restart the kernel to use updated packages.\n"
]
}
],
"source": [
"%pip install --upgrade --quiet langchain-core databricks-vectorsearch langchain-openai tiktoken"
]
},
{
"cell_type": "markdown",
"id": "a1113af6008f3f3d",
"metadata": {
"collapsed": false,
"jupyter": {
"outputs_hidden": false
}
},
"source": [
"We want to use `OpenAIEmbeddings` so we have to get the OpenAI API Key."
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "c243e15bcf72d539",
"metadata": {
"ExecuteTime": {
"end_time": "2024-03-29T02:40:59.788206Z",
"start_time": "2024-03-29T02:40:59.783798Z"
},
"collapsed": false,
"jupyter": {
"outputs_hidden": false
}
},
"outputs": [
{
"name": "stdin",
"output_type": "stream",
"text": [
"OpenAI API Key: ········\n",
"Databricks host: ········\n",
"Databricks token: ········\n"
]
}
],
"source": [
"import getpass\n",
"import os\n",
"\n",
"os.environ[\"OPENAI_API_KEY\"] = getpass.getpass(\"OpenAI API Key:\")\n",
"databricks_host = getpass.getpass(\"Databricks host:\")\n",
"databricks_token = getpass.getpass(\"Databricks token:\")"
]
},
{
"cell_type": "code",
"execution_count": 13,
"id": "fd0c70c0be7d7130",
"metadata": {
"ExecuteTime": {
"end_time": "2024-03-29T02:42:28.467682Z",
"start_time": "2024-03-29T02:42:21.255335Z"
},
"collapsed": false,
"jupyter": {
"outputs_hidden": false
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[NOTICE] Using a Personal Authentication Token (PAT). Recommended for development only. For improved performance, please use Service Principal based authentication. To disable this message, pass disable_notice=True to VectorSearchClient().\n"
]
}
],
"source": [
"from databricks.vector_search.client import VectorSearchClient\n",
"from langchain_openai import OpenAIEmbeddings\n",
"\n",
"embeddings = OpenAIEmbeddings()\n",
"emb_dim = len(embeddings.embed_query(\"hello\"))\n",
"\n",
"vector_search_endpoint_name = \"vector_search_demo_endpoint\"\n",
"\n",
"\n",
"vsc = VectorSearchClient(\n",
" workspace_url=databricks_host, personal_access_token=databricks_token\n",
")\n",
"vsc.create_endpoint(name=vector_search_endpoint_name, endpoint_type=\"STANDARD\")"
]
},
{
"cell_type": "code",
"execution_count": 14,
"id": "3ead3943-7dd6-448c-bead-01157a000221",
"metadata": {},
"outputs": [],
"source": [
"index_name = \"udhay_demo.10x.demo_index\"\n",
"\n",
"index = vsc.create_direct_access_index(\n",
" endpoint_name=vector_search_endpoint_name,\n",
" index_name=index_name,\n",
" primary_key=\"id\",\n",
" embedding_dimension=emb_dim,\n",
" embedding_vector_column=\"text_vector\",\n",
" schema={\n",
" \"id\": \"string\",\n",
" \"page_content\": \"string\",\n",
" \"year\": \"int\",\n",
" \"rating\": \"float\",\n",
" \"genre\": \"string\",\n",
" \"text_vector\": \"array<float>\",\n",
" },\n",
")\n",
"\n",
"index.describe()"
]
},
{
"cell_type": "code",
"execution_count": 15,
"id": "3e62fc39-51d9-4757-a449-f543638b3cd1",
"metadata": {},
"outputs": [],
"source": [
"index = vsc.get_index(endpoint_name=vector_search_endpoint_name, index_name=index_name)\n",
"\n",
"index.describe()"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "13863677-8123-4b36-82bc-2c28ee2a90fb",
"metadata": {},
"outputs": [],
"source": [
"from langchain_core.documents import Document\n",
"\n",
"docs = [\n",
" Document(\n",
" page_content=\"A bunch of scientists bring back dinosaurs and mayhem breaks loose\",\n",
" metadata={\"id\": 1, \"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={\"id\": 2, \"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={\"id\": 3, \"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={\"id\": 4, \"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={\"id\": 5, \"year\": 2006, \"genre\": \"thriller\", \"rating\": 9.0},\n",
" ),\n",
" Document(\n",
" page_content=\"Toys come alive and have a blast doing so\",\n",
" metadata={\"id\": 6, \"year\": 1995, \"genre\": \"animated\", \"rating\": 9.3},\n",
" ),\n",
"]"
]
},
{
"cell_type": "code",
"execution_count": 16,
"id": "6fdc8f55-5b4c-4506-97ac-59d9b9ef8ffc",
"metadata": {},
"outputs": [],
"source": [
"from langchain_community.vectorstores import DatabricksVectorSearch\n",
"\n",
"vector_store = DatabricksVectorSearch(\n",
" index,\n",
" text_column=\"page_content\",\n",
" embedding=embeddings,\n",
" columns=[\"year\", \"rating\", \"genre\"],\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "826375af-3fd7-4d41-9c7b-c273653c46b6",
"metadata": {},
"outputs": [],
"source": [
"vector_store.add_documents(docs)"
]
},
{
"cell_type": "markdown",
"id": "3810b731a981a957",
"metadata": {
"collapsed": false,
"jupyter": {
"outputs_hidden": false
}
},
"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": 17,
"id": "7095b68ea997468c",
"metadata": {
"ExecuteTime": {
"end_time": "2024-03-29T02:42:37.901230Z",
"start_time": "2024-03-29T02:42:36.836827Z"
},
"collapsed": false,
"jupyter": {
"outputs_hidden": false
}
},
"outputs": [],
"source": [
"from langchain.chains.query_constructor.base import AttributeInfo\n",
"from langchain.retrievers.self_query.base import SelfQueryRetriever\n",
"from langchain_openai import OpenAI\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\"\n",
"llm = OpenAI(temperature=0)\n",
"retriever = SelfQueryRetriever.from_llm(\n",
" llm, vector_store, document_content_description, metadata_field_info, verbose=True\n",
")"
]
},
{
"cell_type": "markdown",
"id": "65ff2054be9d5236",
"metadata": {
"collapsed": false,
"jupyter": {
"outputs_hidden": false
}
},
"source": [
"## Test it out\n",
"And now we can try actually using our retriever!\n"
]
},
{
"cell_type": "code",
"execution_count": 18,
"id": "267e2a68f26505b1",
"metadata": {
"ExecuteTime": {
"end_time": "2024-03-29T02:42:51.526470Z",
"start_time": "2024-03-29T02:42:48.328191Z"
},
"collapsed": false,
"jupyter": {
"outputs_hidden": false
}
},
"outputs": [
{
"data": {
"text/plain": [
"[Document(page_content='A bunch of scientists bring back dinosaurs and mayhem breaks loose', metadata={'year': 1993.0, 'rating': 7.7, 'genre': 'action', 'id': 1.0}),\n",
" Document(page_content='Toys come alive and have a blast doing so', metadata={'year': 1995.0, 'rating': 9.3, 'genre': 'animated', 'id': 6.0}),\n",
" Document(page_content='Three men walk into the Zone, three men walk out of the Zone', metadata={'year': 1979.0, 'rating': 9.9, 'genre': 'science fiction', 'id': 4.0}),\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': 2006.0, 'rating': 9.0, 'genre': 'thriller', 'id': 5.0})]"
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# This example only specifies a relevant query\n",
"retriever.get_relevant_documents(\"What are some movies about dinosaurs\")"
]
},
{
"cell_type": "code",
"execution_count": 19,
"id": "3afd98ca20782dda",
"metadata": {
"ExecuteTime": {
"end_time": "2024-03-29T02:42:55.179002Z",
"start_time": "2024-03-29T02:42:53.057022Z"
},
"collapsed": false,
"jupyter": {
"outputs_hidden": false
}
},
"outputs": [
{
"data": {
"text/plain": [
"[Document(page_content='Toys come alive and have a blast doing so', metadata={'year': 1995.0, 'rating': 9.3, 'genre': 'animated', 'id': 6.0}),\n",
" Document(page_content='Three men walk into the Zone, three men walk out of the Zone', metadata={'year': 1979.0, 'rating': 9.9, 'genre': 'science fiction', 'id': 4.0})]"
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# This example specifies a filter\n",
"retriever.get_relevant_documents(\"What are some highly rated movies (above 9)?\")"
]
},
{
"cell_type": "code",
"execution_count": 20,
"id": "9974f641e11abfe8",
"metadata": {
"ExecuteTime": {
"end_time": "2024-03-29T02:42:58.472620Z",
"start_time": "2024-03-29T02:42:56.131594Z"
},
"collapsed": false,
"jupyter": {
"outputs_hidden": false
}
},
"outputs": [
{
"data": {
"text/plain": [
"[Document(page_content='A psychologist / detective gets lost in a series of dreams within dreams within dreams and Inception reused the idea', metadata={'year': 2006.0, 'rating': 9.0, 'genre': 'thriller', 'id': 5.0}),\n",
" Document(page_content='Leo DiCaprio gets lost in a dream within a dream within a dream within a ...', metadata={'year': 2010.0, 'rating': 8.2, 'genre': 'thriller', 'id': 2.0})]"
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# This example specifies both a relevant query and a filter\n",
"retriever.get_relevant_documents(\"What are the thriller movies that are highly rated?\")"
]
},
{
"cell_type": "code",
"execution_count": 21,
"id": "edd31040-ede0-40bb-bfcd-962118df4ffb",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[Document(page_content='A bunch of scientists bring back dinosaurs and mayhem breaks loose', metadata={'year': 1993.0, 'rating': 7.7, 'genre': 'action', 'id': 1.0})]"
]
},
"execution_count": 21,
"metadata": {},
"output_type": "execute_result"
}
],
"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",
"id": "be593d3a6c508517",
"metadata": {
"collapsed": false,
"jupyter": {
"outputs_hidden": false
}
},
"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": "markdown",
"id": "7e17a10f-4187-4164-ab8f-b427c6b86cc0",
"metadata": {
"collapsed": false,
"jupyter": {
"outputs_hidden": false
}
},
"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": 22,
"id": "e255b69c937fa424",
"metadata": {
"ExecuteTime": {
"end_time": "2024-03-29T02:43:02.779337Z",
"start_time": "2024-03-29T02:43:02.759900Z"
},
"collapsed": false,
"jupyter": {
"outputs_hidden": false
}
},
"outputs": [],
"source": [
"retriever = SelfQueryRetriever.from_llm(\n",
" llm,\n",
" vector_store,\n",
" document_content_description,\n",
" metadata_field_info,\n",
" verbose=True,\n",
" enable_limit=True,\n",
")"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "45674137c7f8a9d",
"metadata": {
"ExecuteTime": {
"end_time": "2024-03-29T02:43:07.357830Z",
"start_time": "2024-03-29T02:43:04.854323Z"
},
"collapsed": false,
"jupyter": {
"outputs_hidden": false
}
},
"outputs": [],
"source": [
"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": "3.9.16"
}
},
"nbformat": 4,
"nbformat_minor": 5
}

7
libs/langchain/langchain/retrievers/self_query/base.py
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@ -7,6 +7,7 @@ from langchain_community.vectorstores import (
AstraDB,
Chroma,
DashVector,
DatabricksVectorSearch,
DeepLake,
Dingo,
Milvus,
@ -43,6 +44,9 @@ from langchain.chains.query_constructor.schema import AttributeInfo
from langchain.retrievers.self_query.astradb import AstraDBTranslator
from langchain.retrievers.self_query.chroma import ChromaTranslator
from langchain.retrievers.self_query.dashvector import DashvectorTranslator
from langchain.retrievers.self_query.databricks_vector_search import (
DatabricksVectorSearchTranslator,
)
from langchain.retrievers.self_query.deeplake import DeepLakeTranslator
from langchain.retrievers.self_query.dingo import DingoDBTranslator
from langchain.retrievers.self_query.elasticsearch import ElasticsearchTranslator
@ -85,7 +89,8 @@ def _get_builtin_translator(vectorstore: VectorStore) -> Visitor:
OpenSearchVectorSearch: OpenSearchTranslator,
MongoDBAtlasVectorSearch: MongoDBAtlasTranslator,
}
if isinstance(vectorstore, DatabricksVectorSearch):
return DatabricksVectorSearchTranslator()
if isinstance(vectorstore, Qdrant):
return QdrantTranslator(metadata_key=vectorstore.metadata_payload_key)
elif isinstance(vectorstore, MyScale):

90
libs/langchain/langchain/retrievers/self_query/databricks_vector_search.py
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@ -0,0 +1,90 @@
from collections import ChainMap
from itertools import chain
from typing import Dict, Tuple
from langchain.chains.query_constructor.ir import (
Comparator,
Comparison,
Operation,
Operator,
StructuredQuery,
Visitor,
)
_COMPARATOR_TO_SYMBOL = {
Comparator.EQ: "",
Comparator.GT: " >",
Comparator.GTE: " >=",
Comparator.LT: " <",
Comparator.LTE: " <=",
Comparator.IN: "",
Comparator.LIKE: " LIKE",
}
class DatabricksVectorSearchTranslator(Visitor):
"""Translate `Databricks vector search` internal query language elements to
valid filters."""
"""Subset of allowed logical operators."""
allowed_operators = [Operator.AND, Operator.NOT, Operator.OR]
"""Subset of allowed logical comparators."""
allowed_comparators = [
Comparator.EQ,
Comparator.GT,
Comparator.GTE,
Comparator.LT,
Comparator.LTE,
Comparator.IN,
Comparator.LIKE,
]
def _visit_and_operation(self, operation: Operation) -> Dict:
return dict(ChainMap(*[arg.accept(self) for arg in operation.arguments]))
def _visit_or_operation(self, operation: Operation) -> Dict:
filter_args = [arg.accept(self) for arg in operation.arguments]
flattened_args = list(
chain.from_iterable(filter_arg.items() for filter_arg in filter_args)
)
return {
" OR ".join(key for key, _ in flattened_args): [
value for _, value in flattened_args
]
}
def _visit_not_operation(self, operation: Operation) -> Dict:
if len(operation.arguments) > 1:
raise ValueError(
f'"{operation.operator.value}" can have only one argument '
f"in Databricks vector search"
)
filter_arg = operation.arguments[0].accept(self)
return {
f"{colum_with_bool_expression} NOT": value
for colum_with_bool_expression, value in filter_arg.items()
}
def visit_operation(self, operation: Operation) -> Dict:
self._validate_func(operation.operator)
if operation.operator == Operator.AND:
return self._visit_and_operation(operation)
elif operation.operator == Operator.OR:
return self._visit_or_operation(operation)
elif operation.operator == Operator.NOT:
return self._visit_not_operation(operation)
def visit_comparison(self, comparison: Comparison) -> Dict:
self._validate_func(comparison.comparator)
comparator_symbol = _COMPARATOR_TO_SYMBOL[comparison.comparator]
return {f"{comparison.attribute}{comparator_symbol}": comparison.value}
def visit_structured_query(
self, structured_query: StructuredQuery
) -> Tuple[str, dict]:
if structured_query.filter is None:
kwargs = {}
else:
kwargs = {"filters": structured_query.filter.accept(self)}
return structured_query.query, kwargs

141
libs/langchain/tests/unit_tests/retrievers/self_query/test_databricks_vector_search.py
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@ -0,0 +1,141 @@
from typing import Any, Dict, Tuple
import pytest
from langchain.chains.query_constructor.ir import (
Comparator,
Comparison,
Operation,
Operator,
StructuredQuery,
)
from langchain.retrievers.self_query.databricks_vector_search import (
DatabricksVectorSearchTranslator,
)
DEFAULT_TRANSLATOR = DatabricksVectorSearchTranslator()
@pytest.mark.parametrize(
"triplet",
[
(Comparator.EQ, 2, {"foo": 2}),
(Comparator.GT, 2, {"foo >": 2}),
(Comparator.GTE, 2, {"foo >=": 2}),
(Comparator.LT, 2, {"foo <": 2}),
(Comparator.LTE, 2, {"foo <=": 2}),
(Comparator.IN, ["bar", "abc"], {"foo": ["bar", "abc"]}),
(Comparator.LIKE, "bar", {"foo LIKE": "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_and() -> None:
op = Operation(
operator=Operator.AND,
arguments=[
Comparison(comparator=Comparator.LT, attribute="foo", value=2),
Comparison(comparator=Comparator.EQ, attribute="bar", value="baz"),
],
)
expected = {"foo <": 2, "bar": "baz"}
actual = DEFAULT_TRANSLATOR.visit_operation(op)
assert expected == actual
def test_visit_operation_or() -> None:
op = Operation(
operator=Operator.OR,
arguments=[
Comparison(comparator=Comparator.EQ, attribute="foo", value=2),
Comparison(comparator=Comparator.EQ, attribute="bar", value="baz"),
],
)
expected = {"foo OR bar": [2, "baz"]}
actual = DEFAULT_TRANSLATOR.visit_operation(op)
assert expected == actual
def test_visit_operation_not() -> None:
op = Operation(
operator=Operator.NOT,
arguments=[
Comparison(comparator=Comparator.EQ, attribute="foo", value=2),
],
)
expected = {"foo NOT": 2}
actual = DEFAULT_TRANSLATOR.visit_operation(op)
assert expected == actual
def test_visit_operation_not_that_raises_for_more_than_one_filter_condition() -> None:
with pytest.raises(Exception) as exc_info:
op = Operation(
operator=Operator.NOT,
arguments=[
Comparison(comparator=Comparator.EQ, attribute="foo", value=2),
Comparison(comparator=Comparator.EQ, attribute="bar", value="baz"),
],
)
DEFAULT_TRANSLATOR.visit_operation(op)
assert (
str(exc_info.value) == '"not" can have only one argument in '
"Databricks vector search"
)
def test_visit_structured_query_with_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_with_one_arg_filter() -> None:
query = "What is the capital of France?"
comp = Comparison(comparator=Comparator.EQ, attribute="country", value="France")
structured_query = StructuredQuery(
query=query,
filter=comp,
)
expected = (query, {"filters": {"country": "France"}})
actual = DEFAULT_TRANSLATOR.visit_structured_query(structured_query)
assert expected == actual
def test_visit_structured_query_with_multiple_arg_filter_and_operator() -> None:
query = "What is the capital of France in the years between 1888 and 1900?"
op = Operation(
operator=Operator.AND,
arguments=[
Comparison(comparator=Comparator.EQ, attribute="country", value="France"),
Comparison(comparator=Comparator.GTE, attribute="year", value=1888),
Comparison(comparator=Comparator.LTE, attribute="year", value=1900),
],
)
structured_query = StructuredQuery(
query=query,
filter=op,
)
expected = (
query,
{"filters": {"country": "France", "year >=": 1888, "year <=": 1900}},
)
actual = DEFAULT_TRANSLATOR.visit_structured_query(structured_query)
assert expected == actual
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