mirror of
https://github.com/hwchase17/langchain.git
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480626dc99
…tch]: import models from community ran ```bash git grep -l 'from langchain\.chat_models' | xargs -L 1 sed -i '' "s/from\ langchain\.chat_models/from\ langchain_community.chat_models/g" git grep -l 'from langchain\.llms' | xargs -L 1 sed -i '' "s/from\ langchain\.llms/from\ langchain_community.llms/g" git grep -l 'from langchain\.embeddings' | xargs -L 1 sed -i '' "s/from\ langchain\.embeddings/from\ langchain_community.embeddings/g" git checkout master libs/langchain/tests/unit_tests/llms git checkout master libs/langchain/tests/unit_tests/chat_models git checkout master libs/langchain/tests/unit_tests/embeddings/test_imports.py make format cd libs/langchain; make format cd ../experimental; make format cd ../core; make format ```
200 lines
4.5 KiB
Plaintext
200 lines
4.5 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "raw",
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"metadata": {},
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"source": [
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"---\n",
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"sidebar_label: Tencent Hunyuan\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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"metadata": {},
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"source": [
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"# Tencent Hunyuan\n",
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"\n",
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">[Tencent's hybrid model API](https://cloud.tencent.com/document/product/1729) (`Hunyuan API`) \n",
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"> implements dialogue communication, content generation, \n",
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"> analysis and understanding, and can be widely used in various scenarios such as intelligent \n",
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"> customer service, intelligent marketing, role playing, advertising copywriting, product description,\n",
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"> script creation, resume generation, article writing, code generation, data analysis, and content\n",
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"> analysis.\n",
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"\n",
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"See for [more information](https://cloud.tencent.com/document/product/1729)."
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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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"metadata": {
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"ExecuteTime": {
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"end_time": "2023-10-19T10:20:38.718834Z",
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"start_time": "2023-10-19T10:20:38.264050Z"
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}
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},
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"outputs": [],
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"source": [
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"from langchain.schema import HumanMessage\n",
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"from langchain_community.chat_models import ChatHunyuan"
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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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"metadata": {
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"ExecuteTime": {
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"end_time": "2023-10-19T10:19:53.529876Z",
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"start_time": "2023-10-19T10:19:53.526210Z"
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}
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},
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"outputs": [],
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"source": [
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"chat = ChatHunyuan(\n",
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" hunyuan_app_id=111111111,\n",
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" hunyuan_secret_id=\"YOUR_SECRET_ID\",\n",
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" hunyuan_secret_key=\"YOUR_SECRET_KEY\",\n",
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")"
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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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"metadata": {
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"ExecuteTime": {
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"end_time": "2023-10-19T10:19:56.054289Z",
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"start_time": "2023-10-19T10:19:53.531078Z"
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}
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},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"AIMessage(content=\"J'aime programmer.\")"
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]
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},
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"execution_count": 3,
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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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"chat(\n",
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" [\n",
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" HumanMessage(\n",
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" content=\"You are a helpful assistant that translates English to French.Translate this sentence from English to French. I love programming.\"\n",
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" )\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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"metadata": {
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"collapsed": false,
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"jupyter": {
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"outputs_hidden": false
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}
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},
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"source": [
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"## For ChatHunyuan with Streaming"
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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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"metadata": {
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"ExecuteTime": {
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"end_time": "2023-10-19T10:20:41.507720Z",
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"start_time": "2023-10-19T10:20:41.496456Z"
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},
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"collapsed": false,
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"jupyter": {
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"outputs_hidden": false
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}
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},
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"outputs": [],
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"source": [
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"chat = ChatHunyuan(\n",
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" hunyuan_app_id=\"YOUR_APP_ID\",\n",
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" hunyuan_secret_id=\"YOUR_SECRET_ID\",\n",
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" hunyuan_secret_key=\"YOUR_SECRET_KEY\",\n",
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" streaming=True,\n",
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")"
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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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"metadata": {
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"ExecuteTime": {
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"end_time": "2023-10-19T10:20:46.275673Z",
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"start_time": "2023-10-19T10:20:44.241097Z"
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},
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"collapsed": false,
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"jupyter": {
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"outputs_hidden": false
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}
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},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"AIMessageChunk(content=\"J'aime programmer.\")"
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]
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},
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"execution_count": 3,
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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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"chat(\n",
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" [\n",
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" HumanMessage(\n",
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" content=\"You are a helpful assistant that translates English to French.Translate this sentence from English to French. I love programming.\"\n",
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" )\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": "code",
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"execution_count": null,
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"metadata": {
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"ExecuteTime": {
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"start_time": "2023-10-19T10:19:56.233477Z"
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},
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"collapsed": false,
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"jupyter": {
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"outputs_hidden": false
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}
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},
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"outputs": [],
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"source": []
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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 (ipykernel)",
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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": 3
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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": "ipython3",
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"version": "3.10.12"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 4
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}
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