diff --git a/docs/docs/integrations/retrievers/self_query/databricks_vector_search.ipynb b/docs/docs/integrations/retrievers/self_query/databricks_vector_search.ipynb new file mode 100644 index 0000000000..f32359602e --- /dev/null +++ b/docs/docs/integrations/retrievers/self_query/databricks_vector_search.ipynb @@ -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\",\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 +} diff --git a/libs/langchain/langchain/retrievers/self_query/base.py b/libs/langchain/langchain/retrievers/self_query/base.py index 1fe6bad807..47a34815f1 100644 --- a/libs/langchain/langchain/retrievers/self_query/base.py +++ b/libs/langchain/langchain/retrievers/self_query/base.py @@ -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): diff --git a/libs/langchain/langchain/retrievers/self_query/databricks_vector_search.py b/libs/langchain/langchain/retrievers/self_query/databricks_vector_search.py new file mode 100644 index 0000000000..f8ef053f5a --- /dev/null +++ b/libs/langchain/langchain/retrievers/self_query/databricks_vector_search.py @@ -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 diff --git a/libs/langchain/tests/unit_tests/retrievers/self_query/test_databricks_vector_search.py b/libs/langchain/tests/unit_tests/retrievers/self_query/test_databricks_vector_search.py new file mode 100644 index 0000000000..8d5937ab9d --- /dev/null +++ b/libs/langchain/tests/unit_tests/retrievers/self_query/test_databricks_vector_search.py @@ -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