mirror of
https://github.com/hwchase17/langchain
synced 2024-11-06 03:20:49 +00:00
Harrison/chat memory (#1495)
This commit is contained in:
Harrison Chase
GitHub
parent
7bec461782
commit
f276bfad8e
@ -26,7 +26,7 @@
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{
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"cell_type": "code",
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"execution_count": 1,
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"id": "356e7c73",
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"id": "87235cf1",
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"metadata": {},
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"outputs": [],
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"source": [
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@ -36,7 +36,7 @@
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{
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"cell_type": "code",
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"execution_count": 2,
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"id": "54f90bd7",
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"id": "4404d509",
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"metadata": {},
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"outputs": [],
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"source": [
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@ -46,7 +46,7 @@
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{
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"cell_type": "code",
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"execution_count": 3,
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"id": "3884ff81",
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"id": "78c1a67b",
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"metadata": {},
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"outputs": [],
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"source": [
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@ -56,7 +56,7 @@
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{
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"cell_type": "code",
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"execution_count": 4,
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"id": "b48d5844",
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"id": "525ce606",
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"metadata": {},
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"outputs": [],
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"source": [
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@ -66,7 +66,7 @@
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{
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"cell_type": "code",
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"execution_count": 5,
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"id": "495dcff0",
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"id": "be030822",
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"metadata": {},
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"outputs": [
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{
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@ -87,7 +87,7 @@
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},
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{
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"cell_type": "markdown",
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"id": "46196aa3",
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"id": "2c0328fb",
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"metadata": {},
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"source": [
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"## ConversationBufferMemory\n",
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@ -100,7 +100,7 @@
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{
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"cell_type": "code",
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"execution_count": 7,
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"id": "3bac84f3",
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"id": "a382b160",
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"metadata": {},
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"outputs": [],
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"source": [
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@ -110,7 +110,7 @@
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{
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"cell_type": "code",
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"execution_count": 10,
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"id": "cef35e7f",
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"id": "a280d337",
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"metadata": {},
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"outputs": [],
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"source": [
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@ -122,7 +122,7 @@
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{
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"cell_type": "code",
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"execution_count": 12,
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"id": "2c9b39af",
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"id": "1b739c0a",
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"metadata": {},
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"outputs": [
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{
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@ -142,7 +142,7 @@
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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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"id": "989e9425",
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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"
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@ -151,7 +151,7 @@
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{
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"cell_type": "code",
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"execution_count": 13,
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"id": "a481a415",
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"id": "798ceb1c",
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"metadata": {},
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"outputs": [],
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"source": [
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@ -163,7 +163,7 @@
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{
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"cell_type": "code",
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"execution_count": 14,
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"id": "86a56348",
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"id": "698688fd",
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"metadata": {},
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"outputs": [
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{
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@ -187,6 +187,7 @@
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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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@ -332,7 +333,7 @@
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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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"id": "7826c210",
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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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@ -341,7 +342,7 @@
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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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"id": "3dd37d93",
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"metadata": {},
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"outputs": [],
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"source": []
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@ -100,6 +100,7 @@
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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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@ -2,7 +2,7 @@
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"cells": [
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{
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"cell_type": "markdown",
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"id": "716c8cab",
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"id": "a20c4e38",
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"metadata": {},
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"source": [
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"## ConversationBufferWindowMemory\n",
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@ -15,7 +15,7 @@
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{
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"cell_type": "code",
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"execution_count": 3,
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"id": "dc9d10b0",
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"id": "1196da3f",
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"metadata": {},
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"outputs": [],
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"source": [
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@ -37,7 +37,7 @@
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{
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"cell_type": "code",
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"execution_count": 6,
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"id": "40664f10",
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"id": "0c034a90",
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"metadata": {},
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"outputs": [
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{
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@ -57,7 +57,7 @@
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},
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{
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"cell_type": "markdown",
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"id": "b1932b49",
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"id": "8c5cce1d",
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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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@ -66,7 +66,7 @@
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{
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"cell_type": "code",
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"execution_count": 8,
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"id": "5fd077d5",
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"id": "9b15b427",
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"metadata": {},
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"outputs": [],
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"source": [
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@ -78,7 +78,7 @@
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{
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"cell_type": "code",
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"execution_count": 9,
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"id": "b94b750f",
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"id": "3bb47191",
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"metadata": {},
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"outputs": [
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{
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@ -99,9 +99,10 @@
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},
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{
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"cell_type": "markdown",
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"id": "ac59a682",
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"id": "a95af04c",
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"metadata": {},
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"source": [
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"## Using in a chain\n",
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"Let's walk through an example, again setting `verbose=True` so we can see the prompt."
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]
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},
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@ -14,7 +14,7 @@
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{
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"cell_type": "code",
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"execution_count": 1,
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"id": "7b165e71",
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"id": "1bea1181",
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"metadata": {},
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"outputs": [],
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"source": [
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@ -26,7 +26,7 @@
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{
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"cell_type": "code",
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"execution_count": 2,
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"id": "25354d39",
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"id": "34425079",
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"metadata": {},
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"outputs": [],
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"source": [
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@ -42,7 +42,7 @@
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{
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"cell_type": "code",
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"execution_count": 3,
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"id": "71c75295",
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"id": "b425642c",
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"metadata": {},
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"outputs": [
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{
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@ -64,7 +64,7 @@
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{
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"cell_type": "code",
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"execution_count": 4,
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"id": "bb8a7943",
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"id": "3bf89b46",
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"metadata": {},
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"outputs": [],
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"source": [
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@ -80,7 +80,7 @@
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{
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"cell_type": "code",
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"execution_count": 5,
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"id": "a37ac236",
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"id": "3e37d126",
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"metadata": {},
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"outputs": [
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{
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@ -102,9 +102,10 @@
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},
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{
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"cell_type": "markdown",
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"id": "655ab2c4",
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"id": "ee5ad043",
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"metadata": {},
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"source": [
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"## Using in a chain\n",
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"Let's now use it in a chain!"
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]
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},
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@ -189,7 +190,7 @@
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{
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"cell_type": "code",
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"execution_count": 9,
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"id": "dc1c0d5e",
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"id": "0269f513",
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"metadata": {},
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"outputs": [
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{
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@ -26,7 +26,7 @@
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{
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"cell_type": "code",
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"execution_count": 2,
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"id": "1d9a5b18",
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"id": "2f4a3c85",
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"metadata": {},
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"outputs": [],
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"source": [
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@ -39,7 +39,7 @@
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{
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"cell_type": "code",
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"execution_count": 3,
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"id": "3f5da288",
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"id": "72283b4f",
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"metadata": {},
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"outputs": [
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{
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@ -59,7 +59,7 @@
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},
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{
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"cell_type": "markdown",
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"id": "2ab23035",
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"id": "0c8ff11e",
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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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@ -68,7 +68,7 @@
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{
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"cell_type": "code",
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"execution_count": 4,
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"id": "43dbbc43",
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"id": "44df43af",
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"metadata": {},
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"outputs": [],
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"source": [
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@ -80,7 +80,7 @@
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{
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"cell_type": "code",
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"execution_count": 5,
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"id": "ef6a4f60",
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"id": "4726b1c8",
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"metadata": {},
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"outputs": [
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{
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@ -100,9 +100,68 @@
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},
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{
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"cell_type": "markdown",
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"id": "5ba0dde9",
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"id": "dc956b0e",
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"metadata": {},
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"source": [
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"We can also more modularly get current entities from a new message (will use previous messages as context.)"
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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": "36331ca5",
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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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"['Sam']"
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]
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},
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"execution_count": 9,
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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.get_current_entities(\"what's Sams favorite color?\")"
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]
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},
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{
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"cell_type": "markdown",
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"id": "e8749134",
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"metadata": {},
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"source": [
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"We can also more modularly get knowledge triplets from a new message (will use previous messages as context.)"
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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": "b02d44db",
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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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"[KnowledgeTriple(subject='Sam', predicate='favorite color', object_='red')]"
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]
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},
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"execution_count": 10,
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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.get_knowledge_triplets(\"her favorite color is red\")"
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]
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},
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{
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"cell_type": "markdown",
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"id": "f7a02ef3",
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"metadata": {},
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"source": [
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"## Using in a chain\n",
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"Let's now use this in a chain!"
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]
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},
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"cells": [
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{
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"cell_type": "markdown",
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"id": "5d3f2f0f",
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"id": "1674bfd6",
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"metadata": {},
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"source": [
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"## ConversationSummaryMemory\n",
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{
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"cell_type": "code",
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"execution_count": 1,
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"id": "a36c34d6",
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"id": "c5565e5c",
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"metadata": {},
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"outputs": [],
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"source": [
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{
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"cell_type": "code",
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"execution_count": 2,
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"id": "89646c34",
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"id": "61621239",
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"metadata": {},
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"outputs": [],
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"source": [
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{
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"cell_type": "code",
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"execution_count": 3,
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"id": "dea210aa",
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"id": "3bcb8b02",
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"metadata": {},
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"outputs": [
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{
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},
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{
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"cell_type": "markdown",
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"id": "3838fe93",
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"id": "dedf0698",
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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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"cell_type": "code",
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"execution_count": 4,
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"id": "114dbef6",
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"id": "6cb06b22",
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"metadata": {},
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"outputs": [],
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"source": [
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{
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"cell_type": "code",
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"execution_count": 5,
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"id": "39c8c106",
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"id": "47b03ed7",
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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': [SystemMessage(content='\\nThe human greets the AI and the AI responds with a casual greeting.', additional_kwargs={})]}"
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"{'history': [SystemMessage(content='\\nThe human greets the AI, to which the AI responds.', additional_kwargs={})]}"
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]
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},
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"execution_count": 5,
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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": "9ec0a0ee",
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"metadata": {},
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"source": [
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"We can also utilize the `predict_new_summary` method directly."
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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": "9c4dafb9",
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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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"'\\nThe human greets the AI, to which the AI responds.'"
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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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"messages = memory.chat_memory.messages\n",
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"previous_summary = \"\"\n",
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"memory.predict_new_summary(messages, previous_summary)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "4fad9448",
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"metadata": {},
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"source": [
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"## Using in a chain\n",
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"Let's walk through an example of using this in a chain, again setting `verbose=True` so we can see the prompt."
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]
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},
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"cells": [
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{
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"cell_type": "markdown",
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"id": "2e90817b",
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"id": "ff4be5f3",
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"metadata": {},
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"source": [
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"## ConversationSummaryBufferMemory\n",
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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": "b8aec547",
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"execution_count": 1,
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"id": "da3384db",
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"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
@ -26,8 +26,8 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 8,
|
||||
"id": "2594c8f1",
|
||||
"execution_count": 2,
|
||||
"id": "e00d4938",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
@ -38,17 +38,17 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 9,
|
||||
"id": "a25087e0",
|
||||
"execution_count": 3,
|
||||
"id": "2fe28a28",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"{'history': 'System: \\nThe human greets the AI, to which the AI responds inquiring what is up.\\nHuman: not much you\\nAI: not much'}"
|
||||
"{'history': 'System: \\nThe human says \"hi\", and the AI responds with \"whats up\".\\nHuman: not much you\\nAI: not much'}"
|
||||
]
|
||||
},
|
||||
"execution_count": 9,
|
||||
"execution_count": 3,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
@ -59,7 +59,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "3a6e7905",
|
||||
"id": "cf57b97a",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"We can also get the history as a list of messages (this is useful if you are using this with a chat model)."
|
||||
@ -67,8 +67,8 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 10,
|
||||
"id": "e439451f",
|
||||
"execution_count": 4,
|
||||
"id": "3422a3a8",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
@ -77,17 +77,49 @@
|
||||
"memory.save_context({\"input\": \"not much you\"}, {\"ouput\": \"not much\"})"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "a1dcaaee",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"We can also utilize the `predict_new_summary` method directly."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 5,
|
||||
"id": "fd7d7d6b",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"'\\nThe human and AI state that they are not doing much.'"
|
||||
]
|
||||
},
|
||||
"execution_count": 5,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"messages = memory.chat_memory.messages\n",
|
||||
"previous_summary = \"\"\n",
|
||||
"memory.predict_new_summary(messages, previous_summary)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "a6d2569f",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Using in a chain\n",
|
||||
"Let's walk through an example, again setting `verbose=True` so we can see the prompt."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 11,
|
||||
"execution_count": 6,
|
||||
"id": "ebd68c10",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
@ -112,10 +144,10 @@
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"\" Hi there! I'm doing great. I'm spending some time learning about the latest developments in AI technology. How about you?\""
|
||||
"\" Hi there! I'm doing great. I'm learning about the latest advances in artificial intelligence. What about you?\""
|
||||
]
|
||||
},
|
||||
"execution_count": 11,
|
||||
"execution_count": 6,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
|
||||
@ -4,7 +4,7 @@ from pydantic import BaseModel, Field
|
||||
|
||||
from langchain.chains.llm import LLMChain
|
||||
from langchain.graphs import NetworkxEntityGraph
|
||||
from langchain.graphs.networkx_graph import get_entities, parse_triples
|
||||
from langchain.graphs.networkx_graph import KnowledgeTriple, get_entities, parse_triples
|
||||
from langchain.llms.base import BaseLLM
|
||||
from langchain.memory.chat_memory import BaseChatMemory
|
||||
from langchain.memory.prompt import (
|
||||
@ -78,9 +78,7 @@ class ConversationKGMemory(BaseChatMemory, BaseModel):
|
||||
return list(outputs.keys())[0]
|
||||
return self.output_key
|
||||
|
||||
def _get_current_entities(self, inputs: Dict[str, Any]) -> List[str]:
|
||||
"""Get the current entities in the conversation."""
|
||||
prompt_input_key = self._get_prompt_input_key(inputs)
|
||||
def get_current_entities(self, input_string: str) -> List[str]:
|
||||
chain = LLMChain(llm=self.llm, prompt=self.entity_extraction_prompt)
|
||||
buffer_string = get_buffer_string(
|
||||
self.chat_memory.messages[-self.k * 2 :],
|
||||
@ -89,14 +87,17 @@ class ConversationKGMemory(BaseChatMemory, BaseModel):
|
||||
)
|
||||
output = chain.predict(
|
||||
history=buffer_string,
|
||||
input=inputs[prompt_input_key],
|
||||
input=input_string,
|
||||
)
|
||||
return get_entities(output)
|
||||
|
||||
def _get_and_update_kg(self, inputs: Dict[str, Any]) -> None:
|
||||
"""Get and update knowledge graph from the conversation history."""
|
||||
chain = LLMChain(llm=self.llm, prompt=self.knowledge_extraction_prompt)
|
||||
def _get_current_entities(self, inputs: Dict[str, Any]) -> List[str]:
|
||||
"""Get the current entities in the conversation."""
|
||||
prompt_input_key = self._get_prompt_input_key(inputs)
|
||||
return self.get_current_entities(inputs[prompt_input_key])
|
||||
|
||||
def get_knowledge_triplets(self, input_string: str) -> List[KnowledgeTriple]:
|
||||
chain = LLMChain(llm=self.llm, prompt=self.knowledge_extraction_prompt)
|
||||
buffer_string = get_buffer_string(
|
||||
self.chat_memory.messages[-self.k * 2 :],
|
||||
human_prefix=self.human_prefix,
|
||||
@ -104,10 +105,16 @@ class ConversationKGMemory(BaseChatMemory, BaseModel):
|
||||
)
|
||||
output = chain.predict(
|
||||
history=buffer_string,
|
||||
input=inputs[prompt_input_key],
|
||||
input=input_string,
|
||||
verbose=True,
|
||||
)
|
||||
knowledge = parse_triples(output)
|
||||
return knowledge
|
||||
|
||||
def _get_and_update_kg(self, inputs: Dict[str, Any]) -> None:
|
||||
"""Get and update knowledge graph from the conversation history."""
|
||||
prompt_input_key = self._get_prompt_input_key(inputs)
|
||||
knowledge = self.get_knowledge_triplets(inputs[prompt_input_key])
|
||||
for triple in knowledge:
|
||||
self.kg.add_triple(triple)
|
||||
|
||||
|
||||
@ -8,17 +8,32 @@ from langchain.memory.chat_memory import BaseChatMemory
|
||||
from langchain.memory.prompt import SUMMARY_PROMPT
|
||||
from langchain.memory.utils import get_buffer_string
|
||||
from langchain.prompts.base import BasePromptTemplate
|
||||
from langchain.schema import SystemMessage
|
||||
from langchain.schema import BaseMessage, SystemMessage
|
||||
|
||||
|
||||
class ConversationSummaryMemory(BaseChatMemory, BaseModel):
|
||||
"""Conversation summarizer to memory."""
|
||||
|
||||
buffer: str = ""
|
||||
class SummarizerMixin(BaseModel):
|
||||
human_prefix: str = "Human"
|
||||
ai_prefix: str = "AI"
|
||||
llm: BaseLLM
|
||||
prompt: BasePromptTemplate = SUMMARY_PROMPT
|
||||
|
||||
def predict_new_summary(
|
||||
self, messages: List[BaseMessage], existing_summary: str
|
||||
) -> str:
|
||||
new_lines = get_buffer_string(
|
||||
messages,
|
||||
human_prefix=self.human_prefix,
|
||||
ai_prefix=self.ai_prefix,
|
||||
)
|
||||
|
||||
chain = LLMChain(llm=self.llm, prompt=self.prompt)
|
||||
return chain.predict(summary=existing_summary, new_lines=new_lines)
|
||||
|
||||
|
||||
class ConversationSummaryMemory(BaseChatMemory, SummarizerMixin, BaseModel):
|
||||
"""Conversation summarizer to memory."""
|
||||
|
||||
buffer: str = ""
|
||||
memory_key: str = "history" #: :meta private:
|
||||
|
||||
@property
|
||||
@ -52,15 +67,10 @@ class ConversationSummaryMemory(BaseChatMemory, BaseModel):
|
||||
def save_context(self, inputs: Dict[str, Any], outputs: Dict[str, str]) -> None:
|
||||
"""Save context from this conversation to buffer."""
|
||||
super().save_context(inputs, outputs)
|
||||
new_lines = get_buffer_string(
|
||||
self.chat_memory.messages[-2:],
|
||||
human_prefix=self.human_prefix,
|
||||
ai_prefix=self.ai_prefix,
|
||||
self.buffer = self.predict_new_summary(
|
||||
self.chat_memory.messages[-2:], self.buffer
|
||||
)
|
||||
|
||||
chain = LLMChain(llm=self.llm, prompt=self.prompt)
|
||||
self.buffer = chain.predict(summary=self.buffer, new_lines=new_lines)
|
||||
|
||||
def clear(self) -> None:
|
||||
"""Clear memory contents."""
|
||||
super().clear()
|
||||
|
||||
@ -2,24 +2,17 @@ from typing import Any, Dict, List
|
||||
|
||||
from pydantic import BaseModel, root_validator
|
||||
|
||||
from langchain.chains.llm import LLMChain
|
||||
from langchain.llms.base import BaseLLM
|
||||
from langchain.memory.chat_memory import BaseChatMemory
|
||||
from langchain.memory.prompt import SUMMARY_PROMPT
|
||||
from langchain.memory.summary import SummarizerMixin
|
||||
from langchain.memory.utils import get_buffer_string
|
||||
from langchain.prompts.base import BasePromptTemplate
|
||||
from langchain.schema import BaseMessage, SystemMessage
|
||||
|
||||
|
||||
class ConversationSummaryBufferMemory(BaseChatMemory, BaseModel):
|
||||
class ConversationSummaryBufferMemory(BaseChatMemory, SummarizerMixin, BaseModel):
|
||||
"""Buffer with summarizer for storing conversation memory."""
|
||||
|
||||
max_token_limit: int = 2000
|
||||
human_prefix: str = "Human"
|
||||
ai_prefix: str = "AI"
|
||||
moving_summary_buffer: str = ""
|
||||
llm: BaseLLM
|
||||
prompt: BasePromptTemplate = SUMMARY_PROMPT
|
||||
memory_key: str = "history"
|
||||
|
||||
@property
|
||||
@ -77,16 +70,8 @@ class ConversationSummaryBufferMemory(BaseChatMemory, BaseModel):
|
||||
while curr_buffer_length > self.max_token_limit:
|
||||
pruned_memory.append(buffer.pop(0))
|
||||
curr_buffer_length = sum(self.get_num_tokens_list(buffer))
|
||||
chain = LLMChain(llm=self.llm, prompt=self.prompt)
|
||||
self.moving_summary_buffer = chain.predict(
|
||||
summary=self.moving_summary_buffer,
|
||||
new_lines=(
|
||||
get_buffer_string(
|
||||
pruned_memory,
|
||||
human_prefix=self.human_prefix,
|
||||
ai_prefix=self.ai_prefix,
|
||||
)
|
||||
),
|
||||
self.moving_summary_buffer = self.predict_new_summary(
|
||||
pruned_memory, self.moving_summary_buffer
|
||||
)
|
||||
|
||||
def clear(self) -> None:
|
||||
|
||||
Loading…
Reference in New Issue
Block a user