mirror of
https://github.com/langchain-ai/langgraph.git
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Add memory how-to (#2629)
This commit is contained in:
William FH
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{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# How to add semantic search to your agent's memory\n",
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"\n",
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"This guide shows how to enable semantic search in your agent's memory store. This lets search for items in the store by semantic similarity.\n",
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"\n",
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"First, install this guide's prerequisites."
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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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"metadata": {},
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"outputs": [],
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"source": [
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"%%capture --no-stderr\n",
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"%pip install -U langgraph langchain-openai langchain"
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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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"metadata": {},
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"outputs": [],
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"source": [
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"import getpass\n",
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"import os\n",
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"\n",
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"\n",
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"def _set_env(var: str):\n",
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" if not os.environ.get(var):\n",
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" os.environ[var] = getpass.getpass(f\"{var}: \")\n",
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"\n",
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"\n",
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"_set_env(\"OPENAI_API_KEY\")"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Next, create the store."
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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": 25,
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"metadata": {},
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"outputs": [],
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"source": [
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"from langchain.embeddings import init_embeddings\n",
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"from langgraph.store.memory import InMemoryStore\n",
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"\n",
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"# Create store with semantic search enabled\n",
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"embeddings = init_embeddings(\"openai:text-embedding-3-small\")\n",
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"store = InMemoryStore(\n",
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" index={\n",
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" \"embed\": embeddings,\n",
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" \"dims\": 1536,\n",
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" }\n",
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")"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Now let's store some memories:"
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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": 26,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Store some memories\n",
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"store.put((\"user_123\", \"memories\"), \"1\", {\"text\": \"I love pizza\"})\n",
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"store.put((\"user_123\", \"memories\"), \"2\", {\"text\": \"I prefer Italian food\"})\n",
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"store.put((\"user_123\", \"memories\"), \"3\", {\"text\": \"I don't like spicy food\"})\n",
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"store.put((\"user_123\", \"memories\"), \"3\", {\"text\": \"I am studying econometrics\"})\n",
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"store.put((\"user_123\", \"memories\"), \"3\", {\"text\": \"I am a plumber\"})"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Search memories using natural language:"
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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": 27,
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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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"Memory: I prefer Italian food (similarity: 0.46482669521168163)\n",
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"Memory: I love pizza (similarity: 0.35514845174380766)\n",
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"Memory: I am a plumber (similarity: 0.155698702336571)\n"
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]
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}
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],
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"source": [
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"# Find memories about food preferences\n",
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"memories = store.search((\"user_123\", \"memories\"), query=\"I like food?\", limit=5)\n",
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"\n",
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"for memory in memories:\n",
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" print(f'Memory: {memory.value[\"text\"]} (similarity: {memory.score})')"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Using in your agent\n",
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"\n",
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"Add semantic search to any node by injecting the store:"
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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": 40,
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"metadata": {},
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"outputs": [],
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"source": [
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"import uuid\n",
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"from typing import Optional\n",
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"\n",
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"from langchain.chat_models import init_chat_model\n",
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"from langchain_core.tools import InjectedToolArg\n",
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"from langgraph.store.base import BaseStore\n",
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"from typing_extensions import Annotated\n",
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"\n",
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"from langgraph.prebuilt import create_react_agent\n",
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"\n",
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"\n",
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"def add_memories(state, *, store: BaseStore):\n",
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" # Search based on user's last message\n",
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" items = store.search(\n",
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" (\"user_123\", \"memories\"), query=state[\"messages\"][-1].content, limit=2\n",
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" )\n",
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" memories = \"\\n\".join(item.value[\"text\"] for item in items)\n",
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" memories = f\"## Memories of user\\n{memories}\" if memories else \"\"\n",
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" return [\n",
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" {\"role\": \"system\", \"content\": f\"You are a helpful assistant.\\n{memories}\"}\n",
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" ] + state[\"messages\"]\n",
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"\n",
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"\n",
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"def upsert_memory(\n",
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" content: str,\n",
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" *,\n",
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" memory_id: Optional[uuid.UUID] = None,\n",
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" store: Annotated[BaseStore, InjectedToolArg],\n",
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"):\n",
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" \"\"\"Upsert a memory in the database.\"\"\"\n",
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" mem_id = memory_id or uuid.uuid4()\n",
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" store.put(\n",
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" (\"user_123\", \"memories\"),\n",
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" key=str(mem_id),\n",
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" value={\"text\": content},\n",
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" )\n",
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" return f\"Stored memory {mem_id}\"\n",
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"\n",
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"\n",
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"agent = create_react_agent(\n",
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" init_chat_model(\"openai:gpt-4o-mini\"),\n",
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" tools=[upsert_memory],\n",
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" state_modifier=add_memories,\n",
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" store=store,\n",
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")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 44,
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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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"What are you in the mood for? Since you love Italian food and pizza, would you like some recommendations for a delicious pizza or a different Italian dish?"
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]
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}
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],
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"source": [
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"async for message, metadata in agent.astream(\n",
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" input={\"messages\": [{\"role\": \"user\", \"content\": \"I'm hungry\"}]},\n",
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" stream_mode=\"messages\",\n",
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"):\n",
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" print(message.content, end=\"\")"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Advanced Usage\n",
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"\n",
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"#### Multi-vector indexing\n",
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"\n",
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"Store and search different aspects of memories separately to improve recall or omit certain fields from being indexed."
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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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"metadata": {},
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"outputs": [],
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"source": [
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"# Configure store to embed both memory content and emotional context\n",
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"store = InMemoryStore(\n",
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" index={\"embed\": embeddings, \"dims\": 1536, \"fields\": [\"memory\", \"emotional_context\"]}\n",
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")\n",
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"# Store memories with different content/emotion pairs\n",
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"store.put(\n",
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" (\"user_123\", \"memories\"),\n",
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" \"mem1\",\n",
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" {\n",
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" \"memory\": \"Had pizza with friends at Mario's\",\n",
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" \"emotional_context\": \"felt happy and connected\",\n",
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" \"this_isnt_indexed\": \"I prefer ravioli though\",\n",
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" },\n",
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")\n",
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"store.put(\n",
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" (\"user_123\", \"memories\"),\n",
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" \"mem2\",\n",
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" {\n",
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" \"memory\": \"Ate alone at home\",\n",
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" \"emotional_context\": \"felt a bit lonely\",\n",
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" \"this_isnt_indexed\": \"I like pie\",\n",
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" },\n",
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")\n",
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"\n",
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"# Search focusing on emotional state - matches mem2\n",
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"results = store.search(\n",
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" (\"user_123\", \"memories\"), query=\"times they felt isolated\", limit=1\n",
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")\n",
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"print(\"Expect mem 2\")\n",
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"for r in results:\n",
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" print(f\"Item: {r.key}; Score ({r.score})\")\n",
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" print(f\"Memory: {r.value['memory']}\")\n",
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" print(f\"Emotion: {r.value['emotional_context']}\\n\")\n",
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"\n",
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"# Search focusing on social eating - matches mem1\n",
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"print(\"Expect mem1\")\n",
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"results = store.search((\"user_123\", \"memories\"), query=\"fun pizza\", limit=1)\n",
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"for r in results:\n",
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" print(f\"Item: {r.key}; Score ({r.score})\")\n",
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" print(f\"Memory: {r.value['memory']}\")\n",
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" print(f\"Emotion: {r.value['emotional_context']}\\n\")\n",
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"\n",
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"print(\"Expect random lower score (ravioli not indexed)\")\n",
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"results = store.search((\"user_123\", \"memories\"), query=\"ravioli\", limit=1)\n",
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"for r in results:\n",
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" print(f\"Item: {r.key}; Score ({r.score})\")\n",
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" print(f\"Memory: {r.value['memory']}\")\n",
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" print(f\"Emotion: {r.value['emotional_context']}\\n\")"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"#### Override fields at storage time\n",
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"You can override which fields to embed when storing a specific memory using `put(..., index=[...fields])`, regardless of the store's default configuration."
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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": 57,
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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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"Expect mem1\n",
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"Item: mem1; Score (0.3374698138722726)\n",
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"Memory: I love spicy food\n",
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"Context: At a Thai restaurant\n",
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"\n",
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"Expect mem2\n",
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"Item: mem2; Score (0.3679447999059255)\n",
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"Memory: The restaurant was too loud\n",
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"Context: Dinner at an Italian place\n",
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"\n"
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]
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}
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],
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"source": [
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"embeddings = init_embeddings(\"openai:text-embedding-3-small\")\n",
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"store = InMemoryStore(\n",
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" index={\n",
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" \"embed\": embeddings,\n",
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" \"dims\": 1536,\n",
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" \"fields\": [\"memory\"],\n",
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" } # Default to embed memory field\n",
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")\n",
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"\n",
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"# Store one memory with default indexing\n",
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"store.put(\n",
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" (\"user_123\", \"memories\"),\n",
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" \"mem1\",\n",
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" {\"memory\": \"I love spicy food\", \"context\": \"At a Thai restaurant\"},\n",
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")\n",
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"\n",
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"# Store another overriding which fields to embed\n",
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"store.put(\n",
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" (\"user_123\", \"memories\"),\n",
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" \"mem2\",\n",
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" {\"memory\": \"The restaurant was too loud\", \"context\": \"Dinner at an Italian place\"},\n",
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" index=[\"context\"], # Override: only embed the context\n",
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")\n",
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"\n",
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"# Search about food - matches mem1 (using default field)\n",
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"print(\"Expect mem1\")\n",
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"results = store.search(\n",
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" (\"user_123\", \"memories\"), query=\"what food do they like\", limit=1\n",
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")\n",
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"for r in results:\n",
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" print(f\"Item: {r.key}; Score ({r.score})\")\n",
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" print(f\"Memory: {r.value['memory']}\")\n",
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" print(f\"Context: {r.value['context']}\\n\")\n",
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"\n",
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"# Search about restaurant atmosphere - matches mem2 (using overridden field)\n",
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"print(\"Expect mem2\")\n",
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"results = store.search(\n",
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" (\"user_123\", \"memories\"), query=\"restaurant environment\", limit=1\n",
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")\n",
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"for r in results:\n",
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" print(f\"Item: {r.key}; Score ({r.score})\")\n",
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" print(f\"Memory: {r.value['memory']}\")\n",
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" print(f\"Context: {r.value['context']}\\n\")"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"#### Disable Indexing for Specific Memories\n",
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"\n",
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"Some memories shouldn't be searchable by content. You can disable indexing for these while still storing them using \n",
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"`put(..., index=False)`. Example:"
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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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"metadata": {},
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"outputs": [],
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"source": [
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"store = InMemoryStore(index={\"embed\": embeddings, \"dims\": 1536, \"fields\": [\"memory\"]})\n",
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"\n",
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"# Store a normal indexed memory\n",
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"store.put(\n",
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" (\"user_123\", \"memories\"),\n",
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" \"mem1\",\n",
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" {\"memory\": \"I love chocolate ice cream\", \"type\": \"preference\"},\n",
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")\n",
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"\n",
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"# Store a system memory without indexing\n",
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"store.put(\n",
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" (\"user_123\", \"memories\"),\n",
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" \"mem2\",\n",
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" {\"memory\": \"User completed onboarding\", \"type\": \"system\"},\n",
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" index=False, # Disable indexing entirely\n",
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")\n",
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"\n",
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"# Search about food preferences - finds mem1\n",
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"print(\"Expect mem1\")\n",
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"results = store.search((\"user_123\", \"memories\"), query=\"what food preferences\", limit=1)\n",
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"for r in results:\n",
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" print(f\"Item: {r.key}; Score ({r.score})\")\n",
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" print(f\"Memory: {r.value['memory']}\")\n",
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" print(f\"Type: {r.value['type']}\\n\")\n",
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"\n",
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"# Search about onboarding - won't find mem2 (not indexed)\n",
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"print(\"Expect low score (mem2 not indexed)\")\n",
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"results = store.search((\"user_123\", \"memories\"), query=\"onboarding status\", limit=1)\n",
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"for r in results:\n",
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" print(f\"Item: {r.key}; Score ({r.score})\")\n",
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" print(f\"Memory: {r.value['memory']}\")\n",
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" print(f\"Type: {r.value['type']}\\n\")"
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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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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3 (ipykernel)",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.11.2"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 4
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}
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