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feat: Support for Tigris Vector Database for vector search (#5703)
### Changes - New vector store integration - [Tigris](https://tigrisdata.com) - Adds [tigrisdb](https://pypi.org/project/tigrisdb/) optional dependency - Example notebook demonstrating usage Fixes #5535 Closes tigrisdata/tigris-client-python#40 #### Twitter handles We'd love a shoutout on our [@TigrisData](https://twitter.com/TigrisData) and [@adilansari](https://twitter.com/adilansari) twitter handles #### Who can review? @dev2049 --------- Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
This commit is contained in:
Adil Ansari
GitHub
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docs/modules/indexes/vectorstores/examples/tigris.ipynb
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docs/modules/indexes/vectorstores/examples/tigris.ipynb
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{
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"cells": [
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{
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"cell_type": "markdown",
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"source": [
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"# Tigris\n",
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"\n",
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"> [Tigris](htttps://tigrisdata.com) is an open source Serverless NoSQL Database and Search Platform designed to simplify building high-performance vector search applications.\n",
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"> Tigris eliminates the infrastructure complexity of managing, operating, and synchronizing multiple tools, allowing you to focus on building great applications instead."
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],
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"metadata": {
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"collapsed": false
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}
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},
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{
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"cell_type": "markdown",
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"source": [
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"This notebook guides you how to use Tigris as your VectorStore"
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],
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"metadata": {
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"collapsed": false
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}
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},
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{
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"cell_type": "markdown",
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"source": [
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"**Pre requisites**\n",
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"1. An OpenAI account. You can sign up for an account [here](https://platform.openai.com/)\n",
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"2. [Sign up for a free Tigris account](https://console.preview.tigrisdata.cloud). Once you have signed up for the Tigris account, create a new project called `vectordemo`. Next, make a note of the *Uri* for the region you've created your project in, the **clientId** and **clientSecret**. You can get all this information from the **Application Keys** section of the project."
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],
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"metadata": {
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"collapsed": false
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}
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},
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{
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"cell_type": "markdown",
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"source": [
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"Let's first install our dependencies:"
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],
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"metadata": {
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"collapsed": false
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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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"outputs": [],
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"source": [
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"!pip install tigrisdb openapi-schema-pydantic openai tiktoken"
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],
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"metadata": {
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"collapsed": false
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}
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},
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{
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"cell_type": "markdown",
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"source": [
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"We will load the `OpenAI` api key and `Tigris` credentials in our environment"
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],
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"metadata": {
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"collapsed": false
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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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"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['OPENAI_API_KEY'] = getpass.getpass('OpenAI API Key:')\n",
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"os.environ['TIGRIS_PROJECT'] = getpass.getpass('Tigris Project Name:')\n",
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"os.environ['TIGRIS_CLIENT_ID'] = getpass.getpass('Tigris Client Id:')\n",
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"os.environ['TIGRIS_CLIENT_SECRET'] = getpass.getpass('Tigris Client Secret:')"
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],
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"metadata": {
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"collapsed": false
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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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"outputs": [],
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"source": [
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"from langchain.embeddings.openai import OpenAIEmbeddings\n",
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"from langchain.text_splitter import CharacterTextSplitter\n",
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"from langchain.vectorstores import Tigris\n",
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"from langchain.document_loaders import TextLoader"
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],
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"metadata": {
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"collapsed": false
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}
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},
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{
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"cell_type": "markdown",
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"source": [
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"### Initialize Tigris vector store\n",
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"Let's import our test dataset:"
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],
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"metadata": {
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"collapsed": false
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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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"outputs": [],
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"source": [
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"loader = TextLoader('../../../state_of_the_union.txt')\n",
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"documents = loader.load()\n",
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"text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)\n",
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"docs = text_splitter.split_documents(documents)\n",
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"\n",
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"embeddings = OpenAIEmbeddings()"
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],
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"metadata": {
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"collapsed": false
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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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"outputs": [],
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"source": [
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"vector_store = Tigris.from_documents(docs, embeddings, index_name=\"my_embeddings\")"
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],
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"metadata": {
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"collapsed": false
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}
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},
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{
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"cell_type": "markdown",
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"source": [
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"### Similarity Search"
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],
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"metadata": {
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"collapsed": false
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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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"outputs": [],
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"source": [
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"query = \"What did the president say about Ketanji Brown Jackson\"\n",
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"found_docs = vector_store.similarity_search(query)\n",
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"print(found_docs)"
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],
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"metadata": {
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"collapsed": false
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}
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},
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{
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"cell_type": "markdown",
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"source": [
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"### Similarity Search with score (vector distance)"
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],
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"metadata": {
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"collapsed": false
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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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"outputs": [],
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"source": [
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"query = \"What did the president say about Ketanji Brown Jackson\"\n",
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"result = vector_store.similarity_search_with_score(query)\n",
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"for (doc, score) in result:\n",
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" print(f\"document={doc}, score={score}\")"
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],
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"metadata": {
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"collapsed": false
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}
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 2
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython2",
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"version": "2.7.6"
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}
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},
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"nbformat": 4,
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}
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