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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"#! pip install langchain"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"from langchain.chains import ConversationChain\n",
"from dotenv import load_dotenv\n",
"from langchain import OpenAI\n",
"import os"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"load_dotenv()\n",
"API_KEY = os.getenv('OPENAI_KEY')\n",
"client = OpenAI()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Conversación con Conversation Buffer Window Memory"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"from langchain.chains.conversation.memory import ConversationBufferWindowMemory"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [],
"source": [
"carse = OpenAI(model_name = \"ft:gpt-3.5-turbo-1106:personal:carse:8U71tg31\",\n",
" temperature = 0,\n",
" model_kwargs={\"stop\": \"\\nHuman:\"})"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [],
"source": [
"window_memory = ConversationBufferWindowMemory(k=3)"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [],
"source": [
"conversation = ConversationChain(\n",
" llm = carse,\n",
" verbose = True,\n",
" memory = window_memory\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"\n",
"\u001b[1m> Entering new ConversationChain chain...\u001b[0m\n",
"Prompt after formatting:\n",
"\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",
"\n",
"Current conversation:\n",
"\n",
"Human: Te amo. Sueña con nosotros\n",
"AI:\u001b[0m\n",
"\n",
"\u001b[1m> Finished chain.\u001b[0m\n"
]
},
{
"data": {
"text/plain": [
"' Te amo más. Sueña conmigo'"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"respuesta = input(\"Contesta aquí:\")\n",
"conversation.predict(input=respuesta)"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Human: Te amo. Sueña con nosotros\n",
"AI: Te amo más. Sueña conmigo\n"
]
}
],
"source": [
"print(conversation.memory.buffer)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "base",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.9.7"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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