mirror of
https://github.com/hwchase17/langchain
synced 2024-10-29 17:07:25 +00:00
572 lines
14 KiB
Plaintext
572 lines
14 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "f36d938c",
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"metadata": {},
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"source": [
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"# LLM Caching\n",
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"This notebook covers how to cache results of individual LLM calls."
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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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"id": "10ad9224",
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"metadata": {},
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"outputs": [],
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"source": [
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"from langchain.llms import OpenAI"
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]
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},
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{
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"cell_type": "markdown",
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"id": "b50f0598",
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"metadata": {},
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"source": [
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"## In Memory Cache"
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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": "426ff912",
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"metadata": {},
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"outputs": [],
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"source": [
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"import langchain\n",
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"from langchain.cache import InMemoryCache\n",
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"langchain.llm_cache = InMemoryCache()"
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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": "f69f6283",
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"metadata": {},
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"outputs": [],
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"source": [
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"# To make the caching really obvious, lets use a slower model.\n",
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"llm = OpenAI(model_name=\"text-davinci-002\", n=2, best_of=2)"
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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": "64005d1f",
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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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"CPU times: user 30.7 ms, sys: 18.6 ms, total: 49.3 ms\n",
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"Wall time: 791 ms\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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"\"\\n\\nWhy couldn't the bicycle stand up by itself? Because it was...two tired!\""
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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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"%%time\n",
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"# The first time, it is not yet in cache, so it should take longer\n",
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"llm(\"Tell me a joke\")"
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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": "c8a1cb2b",
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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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"CPU times: user 80 µs, sys: 0 ns, total: 80 µs\n",
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"Wall time: 83.9 µs\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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"\"\\n\\nWhy couldn't the bicycle stand up by itself? Because it was...two tired!\""
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]
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},
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"execution_count": 5,
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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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"%%time\n",
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"# The second time it is, so it goes faster\n",
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"llm(\"Tell me a joke\")"
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]
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},
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{
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"cell_type": "markdown",
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"id": "4bf59c12",
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"metadata": {},
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"source": [
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"## SQLite Cache"
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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": 9,
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"id": "3ff65b00",
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"metadata": {},
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"outputs": [],
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"source": [
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"!rm .langchain.db"
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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": 10,
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"id": "5f036236",
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"metadata": {},
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"outputs": [],
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"source": [
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"# We can do the same thing with a SQLite cache\n",
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"from langchain.cache import SQLiteCache\n",
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"langchain.llm_cache = SQLiteCache(database_path=\".langchain.db\")"
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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": 11,
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"id": "fa18e3af",
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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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"CPU times: user 17 ms, sys: 9.76 ms, total: 26.7 ms\n",
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"Wall time: 825 ms\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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"'\\n\\nWhy did the chicken cross the road?\\n\\nTo get to the other side.'"
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]
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},
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"execution_count": 11,
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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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"%%time\n",
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"# The first time, it is not yet in cache, so it should take longer\n",
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"llm(\"Tell me a joke\")"
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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": 12,
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"id": "5bf2f6fd",
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"metadata": {
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"scrolled": true
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},
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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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"CPU times: user 2.46 ms, sys: 1.23 ms, total: 3.7 ms\n",
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"Wall time: 2.67 ms\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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"'\\n\\nWhy did the chicken cross the road?\\n\\nTo get to the other side.'"
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]
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},
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"execution_count": 12,
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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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"%%time\n",
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"# The second time it is, so it goes faster\n",
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"llm(\"Tell me a joke\")"
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]
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},
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{
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"cell_type": "markdown",
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"id": "278ad7ae",
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"metadata": {},
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"source": [
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"## Redis Cache"
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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": "39f6eb0b",
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"metadata": {},
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"outputs": [],
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"source": [
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"# We can do the same thing with a Redis cache\n",
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"# (make sure your local Redis instance is running first before running this example)\n",
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"from redis import Redis\n",
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"from langchain.cache import RedisCache\n",
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"langchain.llm_cache = RedisCache(redis_=Redis())"
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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": "28920749",
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"metadata": {},
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"outputs": [],
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"source": [
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"%%time\n",
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"# The first time, it is not yet in cache, so it should take longer\n",
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"llm(\"Tell me a joke\")"
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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": "94bf9415",
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"metadata": {},
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"outputs": [],
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"source": [
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"%%time\n",
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"# The second time it is, so it goes faster\n",
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"llm(\"Tell me a joke\")"
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]
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},
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{
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"cell_type": "markdown",
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"id": "934943dc",
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"metadata": {},
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"source": [
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"## SQLAlchemy Cache"
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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": "acccff40",
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"metadata": {},
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"outputs": [],
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"source": [
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"# You can use SQLAlchemyCache to cache with any SQL database supported by SQLAlchemy.\n",
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"\n",
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"# from langchain.cache import SQLAlchemyCache\n",
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"# from sqlalchemy import create_engine\n",
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"\n",
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"# engine = create_engine(\"postgresql://postgres:postgres@localhost:5432/postgres\")\n",
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"# langchain.llm_cache = SQLAlchemyCache(engine)"
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]
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},
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{
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"cell_type": "markdown",
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"source": [
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"### Custom SQLAlchemy Schemas"
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],
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"metadata": {
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"collapsed": false
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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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"outputs": [],
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"source": [
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"# You can define your own declarative SQLAlchemyCache child class to customize the schema used for caching. For example, to support high-speed fulltext prompt indexing with Postgres, use:\n",
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"\n",
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"from sqlalchemy import Column, Integer, String, Computed, Index, Sequence\n",
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"from sqlalchemy import create_engine\n",
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"from sqlalchemy.ext.declarative import declarative_base\n",
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"from sqlalchemy_utils import TSVectorType\n",
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"from langchain.cache import SQLAlchemyCache\n",
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"\n",
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"Base = declarative_base()\n",
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"\n",
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"\n",
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"class FulltextLLMCache(Base): # type: ignore\n",
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" \"\"\"Postgres table for fulltext-indexed LLM Cache\"\"\"\n",
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"\n",
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" __tablename__ = \"llm_cache_fulltext\"\n",
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" id = Column(Integer, Sequence('cache_id'), primary_key=True)\n",
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" prompt = Column(String, nullable=False)\n",
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" llm = Column(String, nullable=False)\n",
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" idx = Column(Integer)\n",
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" response = Column(String)\n",
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" prompt_tsv = Column(TSVectorType(), Computed(\"to_tsvector('english', llm || ' ' || prompt)\", persisted=True))\n",
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" __table_args__ = (\n",
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" Index(\"idx_fulltext_prompt_tsv\", prompt_tsv, postgresql_using=\"gin\"),\n",
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" )\n",
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"\n",
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"engine = create_engine(\"postgresql://postgres:postgres@localhost:5432/postgres\")\n",
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"langchain.llm_cache = SQLAlchemyCache(engine, FulltextLLMCache)"
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],
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"metadata": {
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"collapsed": false
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}
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},
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{
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"cell_type": "markdown",
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"id": "0c69d84d",
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"metadata": {},
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"source": [
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"## Optional Caching\n",
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"You can also turn off caching for specific LLMs should you choose. In the example below, even though global caching is enabled, we turn it off for a specific LLM"
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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": 13,
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"id": "6af46e2b",
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"metadata": {},
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"outputs": [],
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"source": [
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"llm = OpenAI(model_name=\"text-davinci-002\", n=2, best_of=2, cache=False)"
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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": 14,
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"id": "26c4fd8f",
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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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"CPU times: user 5.8 ms, sys: 2.71 ms, total: 8.51 ms\n",
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"Wall time: 745 ms\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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"'\\n\\nWhy did the chicken cross the road?\\n\\nTo get to the other side!'"
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]
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},
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"execution_count": 14,
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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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"%%time\n",
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"llm(\"Tell me a joke\")"
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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": "46846b20",
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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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"CPU times: user 4.91 ms, sys: 2.64 ms, total: 7.55 ms\n",
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"Wall time: 623 ms\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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"'\\n\\nTwo guys stole a calendar. They got six months each.'"
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]
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},
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"execution_count": 15,
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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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"%%time\n",
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"llm(\"Tell me a joke\")"
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]
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},
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{
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"cell_type": "markdown",
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"id": "5da41b77",
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"metadata": {},
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"source": [
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"## Optional Caching in Chains\n",
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"You can also turn off caching for particular nodes in chains. Note that because of certain interfaces, its often easier to construct the chain first, and then edit the LLM afterwards.\n",
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"\n",
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"As an example, we will load a summarizer map-reduce chain. We will cache results for the map-step, but then not freeze it for the combine step."
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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": "9afa3f7a",
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"metadata": {},
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"outputs": [],
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"source": [
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"llm = OpenAI(model_name=\"text-davinci-002\")\n",
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"no_cache_llm = OpenAI(model_name=\"text-davinci-002\", cache=False)"
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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": "98a78e8e",
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"metadata": {},
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"outputs": [],
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"source": [
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"from langchain.text_splitter import CharacterTextSplitter\n",
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"from langchain.chains.mapreduce import MapReduceChain\n",
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"\n",
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"text_splitter = CharacterTextSplitter()"
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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": "2bfb099b",
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"metadata": {},
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"outputs": [],
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"source": [
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"with open('../../state_of_the_union.txt') as f:\n",
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" state_of_the_union = f.read()\n",
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"texts = text_splitter.split_text(state_of_the_union)"
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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": 19,
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"id": "f78b7f51",
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"metadata": {},
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"outputs": [],
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"source": [
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"from langchain.docstore.document import Document\n",
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"docs = [Document(page_content=t) for t in texts[:3]]\n",
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"from langchain.chains.summarize import load_summarize_chain"
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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": 20,
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"id": "a2a30822",
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"metadata": {},
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"outputs": [],
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"source": [
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"chain = load_summarize_chain(llm, chain_type=\"map_reduce\", reduce_llm=no_cache_llm)"
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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": 21,
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"id": "a545b743",
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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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"CPU times: user 452 ms, sys: 60.3 ms, total: 512 ms\n",
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"Wall time: 5.09 s\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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"'\\n\\nPresident Biden is discussing the American Rescue Plan and the Bipartisan Infrastructure Law, which will create jobs and help Americans. He also talks about his vision for America, which includes investing in education and infrastructure. In response to Russian aggression in Ukraine, the United States is joining with European allies to impose sanctions and isolate Russia. American forces are being mobilized to protect NATO countries in the event that Putin decides to keep moving west. The Ukrainians are bravely fighting back, but the next few weeks will be hard for them. Putin will pay a high price for his actions in the long run. Americans should not be alarmed, as the United States is taking action to protect its interests and allies.'"
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]
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},
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"execution_count": 21,
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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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"%%time\n",
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"chain.run(docs)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "3ed85e9d",
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"metadata": {},
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"source": [
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"When we run it again, we see that it runs substantially faster but the final answer is different. This is due to caching at the map steps, but not at the reduce step."
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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": 22,
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"id": "39cbb282",
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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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"CPU times: user 11.5 ms, sys: 4.33 ms, total: 15.8 ms\n",
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"Wall time: 1.04 s\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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"'\\n\\nPresident Biden is discussing the American Rescue Plan and the Bipartisan Infrastructure Law, which will create jobs and help Americans. He also talks about his vision for America, which includes investing in education and infrastructure.'"
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]
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},
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"execution_count": 22,
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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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"%%time\n",
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"chain.run(docs)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"id": "9df0dab8",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": []
|
|
}
|
|
],
|
|
"metadata": {
|
|
"kernelspec": {
|
|
"display_name": "Python 3 (ipykernel)",
|
|
"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": "3.10.9"
|
|
}
|
|
},
|
|
"nbformat": 4,
|
|
"nbformat_minor": 5
|
|
}
|