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langchain/docs/modules/memory/examples/cassandra_chat_message_hist...

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{
"cells": [
{
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"cell_type": "markdown",
"id": "91c6a7ef",
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"source": [
"# Cassandra Chat Message History\n",
"\n",
"This notebook goes over how to use Cassandra to store chat message history.\n",
"\n",
"Cassandra is a distributed database that is well suited for storing large amounts of data. \n",
"\n",
"It is a good choice for storing chat message history because it is easy to scale and can handle a large number of writes.\n"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "47a601d2",
"metadata": {},
"outputs": [],
"source": [
"# List of contact points to try connecting to Cassandra cluster.\n",
"contact_points = [\"cassandra\"]"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "d15e3302",
"metadata": {},
"outputs": [],
"source": [
"from langchain.memory import CassandraChatMessageHistory\n",
"\n",
"message_history = CassandraChatMessageHistory(\n",
" contact_points=contact_points, session_id=\"test-session\"\n",
")\n",
"\n",
"message_history.add_user_message(\"hi!\")\n",
"\n",
"message_history.add_ai_message(\"whats up?\")"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "64fc465e",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[HumanMessage(content='hi!', additional_kwargs={}, example=False),\n",
" AIMessage(content='whats up?', additional_kwargs={}, example=False)]"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"message_history.messages"
]
}
],
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"kernelspec": {
"display_name": "Python 3 (ipykernel)",
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},
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