forked from Archives/langchain
Add Managed Motorhead (#5507)
# Add Managed Motorhead This change enabled MotorheadMemory to utilize Metal's managed version of Motorhead. We can easily enable this by passing in a `api_key` and `client_id` in order to hit the managed url and access the memory api on Metal. Twitter: [@softboyjimbo](https://twitter.com/softboyjimbo) ## Who can review? Community members can review the PR once tests pass. Tag maintainers/contributors who might be interested: @dev2049 @hwchase17 --------- Co-authored-by: Dev 2049 <dev.dev2049@gmail.com>searx_updates
James O'Dwyer
12 months ago
committed by
GitHub
parent
5ffa924488
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{
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"cells": [
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{
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"attachments": {},
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Motörhead Memory (Managed)\n",
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"[Motörhead](https://github.com/getmetal/motorhead) is a memory server implemented in Rust. It automatically handles incremental summarization in the background and allows for stateless applications.\n",
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"\n",
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"## Setup\n",
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"\n",
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"See instructions at [Motörhead](https://docs.getmetal.io/motorhead/introduction) for running the managed version of Motorhead. You can retrieve your `api_key` and `client_id` by creating an account on [Metal](https://getmetal.io).\n",
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"\n"
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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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"outputs": [],
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"source": [
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"from langchain.memory.motorhead_memory import MotorheadMemory\n",
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"from langchain import OpenAI, LLMChain, PromptTemplate\n",
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"\n",
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"template = \"\"\"You are a chatbot having a conversation with a human.\n",
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"\n",
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"{chat_history}\n",
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"Human: {human_input}\n",
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"AI:\"\"\"\n",
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"\n",
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"prompt = PromptTemplate(\n",
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" input_variables=[\"chat_history\", \"human_input\"], \n",
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" template=template\n",
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")\n",
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"memory = MotorheadMemory(\n",
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" api_key=\"YOUR_API_KEY\",\n",
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" client_id=\"YOUR_CLIENT_ID\"\n",
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" session_id=\"testing-1\",\n",
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" memory_key=\"chat_history\"\n",
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")\n",
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"\n",
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"await memory.init(); # loads previous state from Motörhead 🤘\n",
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"\n",
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"llm_chain = LLMChain(\n",
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" llm=OpenAI(), \n",
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" prompt=prompt, \n",
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" verbose=True, \n",
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" memory=memory,\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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"cell_type": "code",
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"execution_count": 2,
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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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"\n",
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"\n",
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"\u001b[1m> Entering new LLMChain chain...\u001b[0m\n",
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"Prompt after formatting:\n",
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"\u001b[32;1m\u001b[1;3mYou are a chatbot having a conversation with a human.\n",
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"\n",
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"\n",
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"Human: hi im bob\n",
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"AI:\u001b[0m\n",
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"\n",
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"\u001b[1m> Finished chain.\u001b[0m\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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"' Hi Bob, nice to meet you! How are you doing today?'"
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]
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},
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"execution_count": 2,
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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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"llm_chain.run(\"hi im bob\")"
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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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"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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"\n",
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"\n",
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"\u001b[1m> Entering new LLMChain chain...\u001b[0m\n",
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"Prompt after formatting:\n",
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"\u001b[32;1m\u001b[1;3mYou are a chatbot having a conversation with a human.\n",
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"\n",
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"Human: hi im bob\n",
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"AI: Hi Bob, nice to meet you! How are you doing today?\n",
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"Human: whats my name?\n",
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"AI:\u001b[0m\n",
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"\n",
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"\u001b[1m> Finished chain.\u001b[0m\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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"' You said your name is Bob. Is that correct?'"
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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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"llm_chain.run(\"whats my name?\")"
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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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"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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"\n",
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"\n",
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"\u001b[1m> Entering new LLMChain chain...\u001b[0m\n",
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"Prompt after formatting:\n",
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"\u001b[32;1m\u001b[1;3mYou are a chatbot having a conversation with a human.\n",
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"\n",
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"Human: hi im bob\n",
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"AI: Hi Bob, nice to meet you! How are you doing today?\n",
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"Human: whats my name?\n",
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"AI: You said your name is Bob. Is that correct?\n",
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"Human: whats for dinner?\n",
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"AI:\u001b[0m\n",
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"\n",
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"\u001b[1m> Finished chain.\u001b[0m\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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"\" I'm sorry, I'm not sure what you're asking. Could you please rephrase your question?\""
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]
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},
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"execution_count": 4,
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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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"llm_chain.run(\"whats for dinner?\")"
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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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"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": "3.9.1"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}
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