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286 lines
7.4 KiB
Plaintext
286 lines
7.4 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "46196aa3",
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"metadata": {},
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"source": [
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"## ConversationBufferMemory\n",
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"\n",
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"This notebook shows how to use `ConversationBufferMemory`. This memory allows for storing of messages and then extracts the messages in a variable.\n",
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"\n",
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"We can first extract it as a string."
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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": "3bac84f3",
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"metadata": {},
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"outputs": [],
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"source": [
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"from langchain.memory import ConversationBufferMemory"
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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": "cef35e7f",
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"metadata": {},
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"outputs": [],
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"source": [
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"memory = ConversationBufferMemory()\n",
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"memory.save_context({\"input\": \"hi\"}, {\"ouput\": \"whats up\"})"
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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": "2c9b39af",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"{'history': 'Human: hi\\nAI: whats up'}"
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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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"memory.load_memory_variables({})"
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]
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},
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{
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"cell_type": "markdown",
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"id": "567f7c16",
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"metadata": {},
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"source": [
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"We can also get the history as a list of messages (this is useful if you are using this with a chat model)."
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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": "a481a415",
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"metadata": {},
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"outputs": [],
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"source": [
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"memory = ConversationBufferMemory(return_messages=True)\n",
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"memory.save_context({\"input\": \"hi\"}, {\"ouput\": \"whats up\"})"
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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": 6,
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"id": "86a56348",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"{'history': [HumanMessage(content='hi', additional_kwargs={}),\n",
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" AIMessage(content='whats up', additional_kwargs={})]}"
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]
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},
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"execution_count": 6,
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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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"memory.load_memory_variables({})"
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]
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},
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{
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"cell_type": "markdown",
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"id": "d051c1da",
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"metadata": {},
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"source": [
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"## Using in a chain\n",
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"Finally, let's take a look at using this in a chain (setting `verbose=True` so we can see the prompt)."
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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": 15,
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"id": "54301321",
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"metadata": {},
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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.chains import ConversationChain\n",
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"\n",
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"\n",
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"llm = OpenAI(temperature=0)\n",
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"conversation = ConversationChain(\n",
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" llm=llm, \n",
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" verbose=True, \n",
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" memory=ConversationBufferMemory()\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": 16,
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"id": "ae046bff",
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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 ConversationChain chain...\u001b[0m\n",
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"Prompt after formatting:\n",
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"\u001b[32;1m\u001b[1;3mThe following is a friendly conversation between a human and an AI. The AI is talkative and provides lots of specific details from its context. If the AI does not know the answer to a question, it truthfully says it does not know.\n",
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"\n",
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"Current conversation:\n",
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"\n",
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"Human: Hi there!\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 there! It's nice to meet you. How can I help you today?\""
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]
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},
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"execution_count": 16,
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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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"conversation.predict(input=\"Hi there!\")"
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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": 17,
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"id": "d8e2a6ff",
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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 ConversationChain chain...\u001b[0m\n",
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"Prompt after formatting:\n",
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"\u001b[32;1m\u001b[1;3mThe following is a friendly conversation between a human and an AI. The AI is talkative and provides lots of specific details from its context. If the AI does not know the answer to a question, it truthfully says it does not know.\n",
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"\n",
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"Current conversation:\n",
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"Human: Hi there!\n",
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"AI: Hi there! It's nice to meet you. How can I help you today?\n",
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"Human: I'm doing well! Just having a conversation with an AI.\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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"\" That's great! It's always nice to have a conversation with someone new. What would you like to talk about?\""
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]
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},
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"execution_count": 17,
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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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"conversation.predict(input=\"I'm doing well! Just having a conversation with an AI.\")"
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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": 18,
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"id": "15eda316",
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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 ConversationChain chain...\u001b[0m\n",
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"Prompt after formatting:\n",
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"\u001b[32;1m\u001b[1;3mThe following is a friendly conversation between a human and an AI. The AI is talkative and provides lots of specific details from its context. If the AI does not know the answer to a question, it truthfully says it does not know.\n",
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"\n",
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"Current conversation:\n",
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"Human: Hi there!\n",
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"AI: Hi there! It's nice to meet you. How can I help you today?\n",
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"Human: I'm doing well! Just having a conversation with an AI.\n",
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"AI: That's great! It's always nice to have a conversation with someone new. What would you like to talk about?\n",
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"Human: Tell me about yourself.\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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"\" Sure! I'm an AI created to help people with their everyday tasks. I'm programmed to understand natural language and provide helpful information. I'm also constantly learning and updating my knowledge base so I can provide more accurate and helpful answers.\""
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]
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},
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"execution_count": 18,
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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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"conversation.predict(input=\"Tell me about yourself.\")"
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]
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},
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{
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"cell_type": "markdown",
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"id": "bd0146c2",
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"metadata": {},
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"source": [
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"And that's it for the getting started! There are plenty of different types of memory, check out our examples to see them all"
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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": "447c138d",
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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": 5
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}
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