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>
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James O'Dwyer
GitHub
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docs/modules/memory/examples/motorhead_memory_managed.ipynb
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docs/modules/memory/examples/motorhead_memory_managed.ipynb
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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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@ -5,19 +5,47 @@ import requests
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from langchain.memory.chat_memory import BaseChatMemory
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from langchain.schema import get_buffer_string
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MANAGED_URL = "https://api.getmetal.io/v1/motorhead"
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# LOCAL_URL = "http://localhost:8080"
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class MotorheadMemory(BaseChatMemory):
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url: str = "http://localhost:8080"
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url: str = MANAGED_URL
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timeout = 3000
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memory_key = "history"
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session_id: str
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context: Optional[str] = None
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# Managed Params
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api_key: Optional[str] = None
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client_id: Optional[str] = None
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def __get_headers(self) -> Dict[str, str]:
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is_managed = self.url == MANAGED_URL
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headers = {
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"Content-Type": "application/json",
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}
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if is_managed and not (self.api_key and self.client_id):
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raise ValueError(
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"""
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You must provide an API key or a client ID to use the managed
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version of Motorhead. Visit https://getmetal.io for more information.
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"""
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)
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if is_managed and self.api_key and self.client_id:
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headers["x-metal-api-key"] = self.api_key
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headers["x-metal-client-id"] = self.client_id
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return headers
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async def init(self) -> None:
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res = requests.get(
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f"{self.url}/sessions/{self.session_id}/memory",
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timeout=self.timeout,
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headers={"Content-Type": "application/json"},
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headers=self.__get_headers(),
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)
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res_data = res.json()
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messages = res_data.get("messages", [])
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@ -53,6 +81,6 @@ class MotorheadMemory(BaseChatMemory):
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{"role": "AI", "content": f"{output_str}"},
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]
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},
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headers={"Content-Type": "application/json"},
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headers=self.__get_headers(),
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)
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super().save_context(inputs, outputs)
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