mirror of https://github.com/hwchase17/langchain
Supabase vector self querying retriever (#10304)
## Description Adds Supabase Vector as a self-querying retriever. - Designed to be backwards compatible with existing `filter` logic on `SupabaseVectorStore`. - Adds new filter `postgrest_filter` to `SupabaseVectorStore` `similarity_search()` methods - Supports entire PostgREST [filter query language](https://postgrest.org/en/stable/references/api/tables_views.html#read) (used by self-querying retriever, but also works as an escape hatch for more query control) - `SupabaseVectorTranslator` converts Langchain filter into the above PostgREST query - Adds Jupyter Notebook for the self-querying retriever - Adds tests ## Tag maintainer @hwchase17 ## Twitter handle [@ggrdson](https://twitter.com/ggrdson)bagatur/konko
Greg Richardson
1 year ago
committed by
GitHub
parent
20c742d8a2
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300559695b
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{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "13afcae7",
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"metadata": {},
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"source": [
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"# Supabase Vector self-querying \n",
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"\n",
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">[Supabase](https://supabase.com/docs) is an open source `Firebase` alternative. \n",
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"> `Supabase` is built on top of `PostgreSQL`, which offers strong `SQL` \n",
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"> querying capabilities and enables a simple interface with already-existing tools and frameworks.\n",
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"\n",
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">[PostgreSQL](https://en.wikipedia.org/wiki/PostgreSQL) also known as `Postgres`,\n",
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"> is a free and open-source relational database management system (RDBMS) \n",
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"> emphasizing extensibility and `SQL` compliance.\n",
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"\n",
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"In the notebook we'll demo the `SelfQueryRetriever` wrapped around a Supabase vector store.\n",
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"\n",
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"Specifically we will:\n",
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"1. Create a Supabase database\n",
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"2. Enable the `pgvector` extension\n",
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"3. Create a `documents` table and `match_documents` function that will be used by `SupabaseVectorStore`\n",
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"4. Load sample documents into the vector store (database table)\n",
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"5. Build and test a self-querying retriever"
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]
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},
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{
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"cell_type": "markdown",
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"id": "347935ad",
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"metadata": {},
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"source": [
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"## Setup Supabase Database\n",
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"\n",
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"1. Head over to https://database.new to provision your Supabase database.\n",
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"2. In the studio, jump to the [SQL editor](https://supabase.com/dashboard/project/_/sql/new) and run the following script to enable `pgvector` and setup your database as a vector store:\n",
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" ```sql\n",
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" -- Enable the pgvector extension to work with embedding vectors\n",
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" create extension if not exists vector;\n",
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"\n",
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" -- Create a table to store your documents\n",
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" create table\n",
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" documents (\n",
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" id uuid primary key,\n",
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" content text, -- corresponds to Document.pageContent\n",
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" metadata jsonb, -- corresponds to Document.metadata\n",
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" embedding vector (1536) -- 1536 works for OpenAI embeddings, change if needed\n",
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" );\n",
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"\n",
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" -- Create a function to search for documents\n",
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" create function match_documents (\n",
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" query_embedding vector (1536),\n",
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" filter jsonb default '{}'\n",
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" ) returns table (\n",
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" id uuid,\n",
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" content text,\n",
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" metadata jsonb,\n",
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" similarity float\n",
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" ) language plpgsql as $$\n",
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" #variable_conflict use_column\n",
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" begin\n",
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" return query\n",
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" select\n",
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" id,\n",
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" content,\n",
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" metadata,\n",
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" 1 - (documents.embedding <=> query_embedding) as similarity\n",
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" from documents\n",
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" where metadata @> filter\n",
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" order by documents.embedding <=> query_embedding;\n",
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" end;\n",
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" $$;\n",
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" ```"
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]
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},
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{
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"cell_type": "markdown",
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"id": "68e75fb9",
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"metadata": {},
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"source": [
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"## Creating a Supabase vector store\n",
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"Next we'll want to create a Supabase vector store and seed it with some data. We've created a small demo set of documents that contain summaries of movies.\n",
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"\n",
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"Be sure to install the latest version of `langchain`:"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "78546fd7",
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"metadata": {},
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"outputs": [],
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"source": [
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"%pip install langchain"
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]
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},
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{
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"cell_type": "markdown",
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"id": "e06df198",
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"metadata": {},
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"source": [
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"The self-query retriever requires you to have `lark` installed:"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "63a8af5b",
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"%pip install lark"
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]
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},
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{
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"cell_type": "markdown",
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"id": "114f768f",
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"metadata": {},
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"source": [
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"We also need the `openai` and `supabase` packages:"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "434ae558",
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"metadata": {},
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"outputs": [],
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"source": [
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"%pip install openai"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "22431060-52c4-48a7-a97b-9f542b8b0928",
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"%pip install supabase==1.0.0"
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]
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},
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{
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"cell_type": "markdown",
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"id": "83811610-7df3-4ede-b268-68a6a83ba9e2",
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"metadata": {},
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"source": [
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"Since we are using `SupabaseVectorStore` and `OpenAIEmbeddings`, we have to load their API keys.\n",
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"\n",
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"- To find your `SUPABASE_URL` and `SUPABASE_SERVICE_KEY`, head to your Supabase project's [API settings](https://supabase.com/dashboard/project/_/settings/api).\n",
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" - `SUPABASE_URL` corresponds to the Project URL\n",
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" - `SUPABASE_SERVICE_KEY` corresponds to the `service_role` API key\n",
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"\n",
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"- To get your `OPENAI_API_KEY`, navigate to [API keys](https://platform.openai.com/account/api-keys) on your OpenAI account and create a new secret key."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"id": "dd01b61b-7d32-4a55-85d6-b2d2d4f18840",
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"import os\n",
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"import getpass\n",
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"\n",
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"os.environ[\"SUPABASE_URL\"] = getpass.getpass(\"Supabase URL:\")\n",
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"os.environ[\"SUPABASE_SERVICE_KEY\"] = getpass.getpass(\"Supabase Service Key:\")\n",
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"os.environ[\"OPENAI_API_KEY\"] = getpass.getpass(\"OpenAI API Key:\")"
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]
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},
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{
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"cell_type": "markdown",
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"id": "3aaf5075",
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"metadata": {},
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"source": [
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"_Optional:_ If you're storing your Supabase and OpenAI API keys in a `.env` file, you can load them with [`dotenv`](https://github.com/theskumar/python-dotenv)."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "e0089221",
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"metadata": {},
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"outputs": [],
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"source": [
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"%pip install python-dotenv"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "3d56c5ef",
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"metadata": {},
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"outputs": [],
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"source": [
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"from dotenv import load_dotenv\n",
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"\n",
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"load_dotenv()"
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]
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},
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{
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"cell_type": "markdown",
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"id": "f6dd9aef",
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"metadata": {},
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"source": [
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"First we'll create a Supabase client and instantiate a OpenAI embeddings class."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"id": "cb4a5787",
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"import os\n",
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"from supabase.client import Client, create_client\n",
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"from langchain.schema import Document\n",
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"from langchain.embeddings.openai import OpenAIEmbeddings\n",
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"from langchain.vectorstores import SupabaseVectorStore\n",
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"\n",
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"supabase_url = os.environ.get(\"SUPABASE_URL\")\n",
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"supabase_key = os.environ.get(\"SUPABASE_SERVICE_KEY\")\n",
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"supabase: Client = create_client(supabase_url, supabase_key)\n",
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"\n",
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"embeddings = OpenAIEmbeddings()"
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]
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},
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{
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"cell_type": "markdown",
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"id": "0fca9b0b",
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"metadata": {},
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"source": [
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"Next let's create our documents."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"id": "bcbe04d9",
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"docs = [\n",
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" Document(\n",
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" page_content=\"A bunch of scientists bring back dinosaurs and mayhem breaks loose\",\n",
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" metadata={\"year\": 1993, \"rating\": 7.7, \"genre\": \"science fiction\"},\n",
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" ),\n",
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" Document(\n",
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" page_content=\"Leo DiCaprio gets lost in a dream within a dream within a dream within a ...\",\n",
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" metadata={\"year\": 2010, \"director\": \"Christopher Nolan\", \"rating\": 8.2},\n",
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" ),\n",
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" Document(\n",
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" page_content=\"A psychologist / detective gets lost in a series of dreams within dreams within dreams and Inception reused the idea\",\n",
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" metadata={\"year\": 2006, \"director\": \"Satoshi Kon\", \"rating\": 8.6},\n",
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" ),\n",
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" Document(\n",
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" page_content=\"A bunch of normal-sized women are supremely wholesome and some men pine after them\",\n",
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" metadata={\"year\": 2019, \"director\": \"Greta Gerwig\", \"rating\": 8.3},\n",
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" ),\n",
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" Document(\n",
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" page_content=\"Toys come alive and have a blast doing so\",\n",
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" metadata={\"year\": 1995, \"genre\": \"animated\"},\n",
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" ),\n",
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" Document(\n",
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" page_content=\"Three men walk into the Zone, three men walk out of the Zone\",\n",
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" metadata={\n",
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" \"year\": 1979,\n",
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" \"rating\": 9.9,\n",
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" \"director\": \"Andrei Tarkovsky\",\n",
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" \"genre\": \"science fiction\",\n",
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" \"rating\": 9.9,\n",
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" },\n",
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" ),\n",
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"]\n",
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"\n",
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"vectorstore = SupabaseVectorStore.from_documents(docs, embeddings, client=supabase, table_name=\"documents\", query_name=\"match_documents\")"
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]
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},
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{
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"cell_type": "markdown",
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"id": "5ecaab6d",
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"metadata": {},
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"source": [
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"## Creating our self-querying retriever\n",
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"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."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"id": "86e34dbf",
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"from langchain.llms import OpenAI\n",
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"from langchain.retrievers.self_query.base import SelfQueryRetriever\n",
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"from langchain.chains.query_constructor.base import AttributeInfo\n",
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"\n",
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"metadata_field_info = [\n",
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" AttributeInfo(\n",
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" name=\"genre\",\n",
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" description=\"The genre of the movie\",\n",
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" type=\"string or list[string]\",\n",
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" ),\n",
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" AttributeInfo(\n",
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" name=\"year\",\n",
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" description=\"The year the movie was released\",\n",
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" type=\"integer\",\n",
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" ),\n",
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" AttributeInfo(\n",
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" name=\"director\",\n",
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" description=\"The name of the movie director\",\n",
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" type=\"string\",\n",
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" ),\n",
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" AttributeInfo(\n",
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" name=\"rating\", description=\"A 1-10 rating for the movie\", type=\"float\"\n",
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" ),\n",
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"]\n",
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"document_content_description = \"Brief summary of a movie\"\n",
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"llm = OpenAI(temperature=0)\n",
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"retriever = SelfQueryRetriever.from_llm(\n",
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" llm, vectorstore, document_content_description, metadata_field_info, verbose=True\n",
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")"
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]
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},
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{
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"cell_type": "markdown",
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"id": "ea9df8d4",
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"metadata": {},
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"source": [
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"## Testing it out\n",
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"And now we can try actually using our retriever!"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"id": "38a126e9",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"query='dinosaur' filter=None limit=None\n"
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]
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},
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{
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"data": {
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"text/plain": [
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"[Document(page_content='A bunch of scientists bring back dinosaurs and mayhem breaks loose', metadata={'year': 1993, 'genre': 'science fiction', 'rating': 7.7}),\n",
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" Document(page_content='Toys come alive and have a blast doing so', metadata={'year': 1995, 'genre': 'animated'}),\n",
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" Document(page_content='Three men walk into the Zone, three men walk out of the Zone', metadata={'year': 1979, 'genre': 'science fiction', 'rating': 9.9, 'director': 'Andrei Tarkovsky'}),\n",
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" 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, 'rating': 8.6, 'director': 'Satoshi Kon'})]"
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]
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},
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"execution_count": 5,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"# This example only specifies a relevant query\n",
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"retriever.get_relevant_documents(\"What are some movies about dinosaurs\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 7,
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"id": "fc3f1e6e",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"query=' ' filter=Comparison(comparator=<Comparator.GT: 'gt'>, attribute='rating', value=8.5) limit=None\n"
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]
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},
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{
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"data": {
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"text/plain": [
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"[Document(page_content='Three men walk into the Zone, three men walk out of the Zone', metadata={'year': 1979, 'genre': 'science fiction', 'rating': 9.9, 'director': 'Andrei Tarkovsky'}),\n",
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" 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, 'rating': 8.6, 'director': 'Satoshi Kon'})]"
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]
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},
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"execution_count": 7,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"# This example only specifies a filter\n",
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"retriever.get_relevant_documents(\"I want to watch a movie rated higher than 8.5\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 9,
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"id": "b19d4da0",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"query='women' filter=Comparison(comparator=<Comparator.EQ: 'eq'>, attribute='director', value='Greta Gerwig') limit=None\n"
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]
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},
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{
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"data": {
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"text/plain": [
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"[Document(page_content='A bunch of normal-sized women are supremely wholesome and some men pine after them', metadata={'year': 2019, 'rating': 8.3, 'director': 'Greta Gerwig'})]"
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]
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},
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"execution_count": 9,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"# This example specifies a query and a filter\n",
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"retriever.get_relevant_documents(\"Has Greta Gerwig directed any movies about women?\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 8,
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"id": "f900e40e",
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"metadata": {},
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"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"query=' ' filter=Operation(operator=<Operator.AND: 'and'>, arguments=[Comparison(comparator=<Comparator.GTE: 'gte'>, attribute='rating', value=8.5), Comparison(comparator=<Comparator.EQ: 'eq'>, attribute='genre', value='science fiction')]) 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, 'genre': 'science fiction', 'rating': 9.9, 'director': 'Andrei Tarkovsky'})]"
|
||||
]
|
||||
},
|
||||
"execution_count": 8,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"# This example specifies a composite filter\n",
|
||||
"retriever.get_relevant_documents(\n",
|
||||
" \"What's a highly rated (above 8.5) science fiction film?\"\n",
|
||||
")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 9,
|
||||
"id": "12a51522",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"query='toys' filter=Operation(operator=<Operator.AND: 'and'>, arguments=[Comparison(comparator=<Comparator.GT: 'gt'>, attribute='year', value=1990), Comparison(comparator=<Comparator.LTE: 'lte'>, attribute='year', value=2005), Comparison(comparator=<Comparator.LIKE: 'like'>, attribute='genre', value='animated')]) limit=None\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"[Document(page_content='Toys come alive and have a blast doing so', metadata={'year': 1995, 'genre': 'animated'})]"
|
||||
]
|
||||
},
|
||||
"execution_count": 9,
|
||||
"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 (or on) 2005 that's all about toys, and preferably is animated\"\n",
|
||||
")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "39bd1de1-b9fe-4a98-89da-58d8a7a6ae51",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Filter k\n",
|
||||
"\n",
|
||||
"We can also use the self query retriever to specify `k`: the number of documents to fetch.\n",
|
||||
"\n",
|
||||
"We can do this by passing `enable_limit=True` to the constructor."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 10,
|
||||
"id": "bff36b88-b506-4877-9c63-e5a1a8d78e64",
|
||||
"metadata": {
|
||||
"tags": []
|
||||
},
|
||||
"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",
|
||||
")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 11,
|
||||
"id": "2758d229-4f97-499c-819f-888acaf8ee10",
|
||||
"metadata": {
|
||||
"tags": []
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"query='dinosaur' 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, 'genre': 'science fiction', 'rating': 7.7}),\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": {
|
||||
"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": "3.10.12"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 5
|
||||
}
|
||||
@ -0,0 +1,97 @@
|
||||
from typing import Any, Dict, Tuple
|
||||
|
||||
from langchain.chains.query_constructor.ir import (
|
||||
Comparator,
|
||||
Comparison,
|
||||
Operation,
|
||||
Operator,
|
||||
StructuredQuery,
|
||||
Visitor,
|
||||
)
|
||||
|
||||
|
||||
class SupabaseVectorTranslator(Visitor):
|
||||
"""Translate Langchain filters to Supabase PostgREST filters."""
|
||||
|
||||
allowed_operators = [Operator.AND, Operator.OR]
|
||||
"""Subset of allowed logical operators."""
|
||||
|
||||
allowed_comparators = [
|
||||
Comparator.EQ,
|
||||
Comparator.NE,
|
||||
Comparator.GT,
|
||||
Comparator.GTE,
|
||||
Comparator.LT,
|
||||
Comparator.LTE,
|
||||
Comparator.LIKE,
|
||||
]
|
||||
"""Subset of allowed logical comparators."""
|
||||
|
||||
metadata_column = "metadata"
|
||||
|
||||
def _map_comparator(self, comparator: Comparator) -> str:
|
||||
"""
|
||||
Maps Langchain comparator to PostgREST comparator:
|
||||
|
||||
https://postgrest.org/en/stable/references/api/tables_views.html#operators
|
||||
"""
|
||||
postgrest_comparator = {
|
||||
Comparator.EQ: "eq",
|
||||
Comparator.NE: "neq",
|
||||
Comparator.GT: "gt",
|
||||
Comparator.GTE: "gte",
|
||||
Comparator.LT: "lt",
|
||||
Comparator.LTE: "lte",
|
||||
Comparator.LIKE: "like",
|
||||
}.get(comparator)
|
||||
|
||||
if postgrest_comparator is None:
|
||||
raise Exception(
|
||||
f"Comparator '{comparator}' is not currently "
|
||||
"supported in Supabase Vector"
|
||||
)
|
||||
|
||||
return postgrest_comparator
|
||||
|
||||
def _get_json_operator(self, value: Any) -> str:
|
||||
if isinstance(value, str):
|
||||
return "->>"
|
||||
else:
|
||||
return "->"
|
||||
|
||||
def visit_operation(self, operation: Operation) -> str:
|
||||
args = [arg.accept(self) for arg in operation.arguments]
|
||||
return f"{operation.operator.value}({','.join(args)})"
|
||||
|
||||
def visit_comparison(self, comparison: Comparison) -> str:
|
||||
if isinstance(comparison.value, list):
|
||||
return self.visit_operation(
|
||||
Operation(
|
||||
operator=Operator.AND,
|
||||
arguments=(
|
||||
Comparison(
|
||||
comparator=comparison.comparator,
|
||||
attribute=comparison.attribute,
|
||||
value=value,
|
||||
)
|
||||
for value in comparison.value
|
||||
),
|
||||
)
|
||||
)
|
||||
|
||||
return ".".join(
|
||||
[
|
||||
f"{self.metadata_column}{self._get_json_operator(comparison.value)}{comparison.attribute}",
|
||||
f"{self._map_comparator(comparison.comparator)}",
|
||||
f"{comparison.value}",
|
||||
]
|
||||
)
|
||||
|
||||
def visit_structured_query(
|
||||
self, structured_query: StructuredQuery
|
||||
) -> Tuple[str, Dict[str, str]]:
|
||||
if structured_query.filter is None:
|
||||
kwargs = {}
|
||||
else:
|
||||
kwargs = {"postgrest_filter": structured_query.filter.accept(self)}
|
||||
return structured_query.query, kwargs
|
||||
@ -0,0 +1,85 @@
|
||||
from typing import Dict, Tuple
|
||||
|
||||
from langchain.chains.query_constructor.ir import (
|
||||
Comparator,
|
||||
Comparison,
|
||||
Operation,
|
||||
Operator,
|
||||
StructuredQuery,
|
||||
)
|
||||
from langchain.retrievers.self_query.supabase import SupabaseVectorTranslator
|
||||
|
||||
DEFAULT_TRANSLATOR = SupabaseVectorTranslator()
|
||||
|
||||
|
||||
def test_visit_comparison() -> None:
|
||||
comp = Comparison(comparator=Comparator.LT, attribute="foo", value=["1", "2"])
|
||||
expected = "and(metadata->>foo.lt.1,metadata->>foo.lt.2)"
|
||||
actual = DEFAULT_TRANSLATOR.visit_comparison(comp)
|
||||
assert expected == actual
|
||||
|
||||
|
||||
def test_visit_operation() -> None:
|
||||
op = Operation(
|
||||
operator=Operator.AND,
|
||||
arguments=[
|
||||
Comparison(comparator=Comparator.LT, attribute="foo", value=2),
|
||||
Comparison(comparator=Comparator.EQ, attribute="bar", value="baz"),
|
||||
Comparison(comparator=Comparator.LT, attribute="abc", value=["1", "2"]),
|
||||
],
|
||||
)
|
||||
expected = (
|
||||
"and("
|
||||
"metadata->foo.lt.2,"
|
||||
"metadata->>bar.eq.baz,"
|
||||
"and(metadata->>abc.lt.1,metadata->>abc.lt.2)"
|
||||
")"
|
||||
)
|
||||
actual = DEFAULT_TRANSLATOR.visit_operation(op)
|
||||
assert expected == actual
|
||||
|
||||
|
||||
def test_visit_structured_query() -> 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
|
||||
|
||||
comp = Comparison(comparator=Comparator.LT, attribute="foo", value=["1", "2"])
|
||||
expected = (
|
||||
query,
|
||||
{"postgrest_filter": "and(metadata->>foo.lt.1,metadata->>foo.lt.2)"},
|
||||
)
|
||||
structured_query = StructuredQuery(
|
||||
query=query,
|
||||
filter=comp,
|
||||
)
|
||||
actual = DEFAULT_TRANSLATOR.visit_structured_query(structured_query)
|
||||
assert expected == actual
|
||||
|
||||
op = Operation(
|
||||
operator=Operator.AND,
|
||||
arguments=[
|
||||
Comparison(comparator=Comparator.LT, attribute="foo", value=2),
|
||||
Comparison(comparator=Comparator.EQ, attribute="bar", value="baz"),
|
||||
Comparison(comparator=Comparator.LT, attribute="abc", value=["1", "2"]),
|
||||
],
|
||||
)
|
||||
structured_query = StructuredQuery(
|
||||
query=query,
|
||||
filter=op,
|
||||
)
|
||||
expected = (
|
||||
query,
|
||||
{
|
||||
"postgrest_filter": (
|
||||
"and(metadata->foo.lt.2,metadata->>bar.eq.baz,and(metadata->>abc.lt.1,metadata->>abc.lt.2))"
|
||||
)
|
||||
},
|
||||
)
|
||||
actual = DEFAULT_TRANSLATOR.visit_structured_query(structured_query)
|
||||
assert expected == actual
|
||||
Loading…
Reference in New Issue