mirror of
https://github.com/langchain-ai/langgraph.git
synced 2026-09-09 11:17:53 +02:00
Merge branch 'main' into wfh/docs/memstoreconcept
This commit is contained in:
William Fu-Hinthorn
@@ -13,7 +13,7 @@ serve-clean-docs: clean-docs
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poetry run python -m mkdocs serve -c -f docs/mkdocs.yml --strict -w ./libs/langgraph
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serve-docs: build-typedoc
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poetry run python -m mkdocs serve -f docs/mkdocs.yml -w ./libs/langgraph --dirty
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poetry run python -m mkdocs serve -f docs/mkdocs.yml -w ./libs/langgraph -w ./libs/checkpoint --dirty
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||||
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clean-docs:
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find ./docs/docs -name "*.ipynb" -type f -delete
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|
||||
File diff suppressed because one or more lines are too long
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@@ -0,0 +1,424 @@
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{
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||||
"cells": [
|
||||
{
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||||
"cell_type": "markdown",
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||||
"metadata": {},
|
||||
"source": [
|
||||
"# How to add semantic search to your agent's memory\n",
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||||
"\n",
|
||||
"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",
|
||||
"First, install this guide's prerequisites."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
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||||
"execution_count": 4,
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||||
"metadata": {},
|
||||
"outputs": [],
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||||
"source": [
|
||||
"%%capture --no-stderr\n",
|
||||
"%pip install -U langgraph langchain-openai langchain"
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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": [
|
||||
"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",
|
||||
"store.put((\"user_123\", \"memories\"), \"3\", {\"text\": \"I am a plumber\"})"
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||||
]
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||||
},
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||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Search memories using natural language:"
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||||
]
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||||
},
|
||||
{
|
||||
"cell_type": "code",
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||||
"execution_count": 27,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"Memory: I prefer Italian food (similarity: 0.46482669521168163)\n",
|
||||
"Memory: I love pizza (similarity: 0.35514845174380766)\n",
|
||||
"Memory: I am a plumber (similarity: 0.155698702336571)\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"# 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",
|
||||
" print(f'Memory: {memory.value[\"text\"]} (similarity: {memory.score})')"
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||||
]
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||||
},
|
||||
{
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||||
"cell_type": "markdown",
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||||
"metadata": {},
|
||||
"source": [
|
||||
"## 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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||||
{
|
||||
"cell_type": "code",
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||||
"execution_count": 40,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import uuid\n",
|
||||
"from typing import Optional\n",
|
||||
"\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",
|
||||
" memories = f\"## Memories of user\\n{memories}\" if memories else \"\"\n",
|
||||
" return [\n",
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||||
" {\"role\": \"system\", \"content\": f\"You are a helpful assistant.\\n{memories}\"}\n",
|
||||
" ] + state[\"messages\"]\n",
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"\n",
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||||
"\n",
|
||||
"def upsert_memory(\n",
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||||
" content: str,\n",
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||||
" *,\n",
|
||||
" memory_id: Optional[uuid.UUID] = None,\n",
|
||||
" 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",
|
||||
" value={\"text\": content},\n",
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||||
" )\n",
|
||||
" return f\"Stored memory {mem_id}\"\n",
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||||
"\n",
|
||||
"\n",
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||||
"agent = create_react_agent(\n",
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||||
" init_chat_model(\"openai:gpt-4o-mini\"),\n",
|
||||
" tools=[upsert_memory],\n",
|
||||
" state_modifier=add_memories,\n",
|
||||
" 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": [
|
||||
{
|
||||
"name": "stdout",
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||||
"output_type": "stream",
|
||||
"text": [
|
||||
"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?"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"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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||||
{
|
||||
"cell_type": "code",
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||||
"execution_count": null,
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||||
"metadata": {},
|
||||
"outputs": [],
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||||
"source": [
|
||||
"# 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",
|
||||
" \"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",
|
||||
" \"memory\": \"Ate alone at home\",\n",
|
||||
" \"emotional_context\": \"felt a bit lonely\",\n",
|
||||
" \"this_isnt_indexed\": \"I like pie\",\n",
|
||||
" },\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",
|
||||
"\n",
|
||||
"# Search focusing on social eating - matches mem1\n",
|
||||
"print(\"Expect mem1\")\n",
|
||||
"results = store.search((\"user_123\", \"memories\"), query=\"fun pizza\", limit=1)\n",
|
||||
"for r in results:\n",
|
||||
" print(f\"Item: {r.key}; Score ({r.score})\")\n",
|
||||
" print(f\"Memory: {r.value['memory']}\")\n",
|
||||
" print(f\"Emotion: {r.value['emotional_context']}\\n\")\n",
|
||||
"\n",
|
||||
"print(\"Expect random lower score (ravioli not indexed)\")\n",
|
||||
"results = store.search((\"user_123\", \"memories\"), query=\"ravioli\", limit=1)\n",
|
||||
"for r in results:\n",
|
||||
" print(f\"Item: {r.key}; Score ({r.score})\")\n",
|
||||
" print(f\"Memory: {r.value['memory']}\")\n",
|
||||
" print(f\"Emotion: {r.value['emotional_context']}\\n\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"#### Override fields at storage time\n",
|
||||
"You can override which fields to embed when storing a specific memory using `put(..., index=[...fields])`, regardless of the store's default configuration."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 57,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"Expect mem1\n",
|
||||
"Item: mem1; Score (0.3374698138722726)\n",
|
||||
"Memory: I love spicy food\n",
|
||||
"Context: At a Thai restaurant\n",
|
||||
"\n",
|
||||
"Expect mem2\n",
|
||||
"Item: mem2; Score (0.3679447999059255)\n",
|
||||
"Memory: The restaurant was too loud\n",
|
||||
"Context: Dinner at an Italian place\n",
|
||||
"\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"embeddings = init_embeddings(\"openai:text-embedding-3-small\")\n",
|
||||
"store = InMemoryStore(\n",
|
||||
" index={\n",
|
||||
" \"embed\": embeddings,\n",
|
||||
" \"dims\": 1536,\n",
|
||||
" \"fields\": [\"memory\"],\n",
|
||||
" } # Default to embed memory field\n",
|
||||
")\n",
|
||||
"\n",
|
||||
"# Store one memory with default indexing\n",
|
||||
"store.put(\n",
|
||||
" (\"user_123\", \"memories\"),\n",
|
||||
" \"mem1\",\n",
|
||||
" {\"memory\": \"I love spicy food\", \"context\": \"At a Thai restaurant\"},\n",
|
||||
")\n",
|
||||
"\n",
|
||||
"# Store another overriding which fields to embed\n",
|
||||
"store.put(\n",
|
||||
" (\"user_123\", \"memories\"),\n",
|
||||
" \"mem2\",\n",
|
||||
" {\"memory\": \"The restaurant was too loud\", \"context\": \"Dinner at an Italian place\"},\n",
|
||||
" index=[\"context\"], # Override: only embed the context\n",
|
||||
")\n",
|
||||
"\n",
|
||||
"# Search about food - matches mem1 (using default field)\n",
|
||||
"print(\"Expect mem1\")\n",
|
||||
"results = store.search(\n",
|
||||
" (\"user_123\", \"memories\"), query=\"what food do they like\", limit=1\n",
|
||||
")\n",
|
||||
"for r in results:\n",
|
||||
" print(f\"Item: {r.key}; Score ({r.score})\")\n",
|
||||
" print(f\"Memory: {r.value['memory']}\")\n",
|
||||
" print(f\"Context: {r.value['context']}\\n\")\n",
|
||||
"\n",
|
||||
"# Search about restaurant atmosphere - matches mem2 (using overridden field)\n",
|
||||
"print(\"Expect mem2\")\n",
|
||||
"results = store.search(\n",
|
||||
" (\"user_123\", \"memories\"), query=\"restaurant environment\", limit=1\n",
|
||||
")\n",
|
||||
"for r in results:\n",
|
||||
" print(f\"Item: {r.key}; Score ({r.score})\")\n",
|
||||
" print(f\"Memory: {r.value['memory']}\")\n",
|
||||
" print(f\"Context: {r.value['context']}\\n\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"#### Disable Indexing for Specific Memories\n",
|
||||
"\n",
|
||||
"Some memories shouldn't be searchable by content. You can disable indexing for these while still storing them using \n",
|
||||
"`put(..., index=False)`. Example:"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"store = InMemoryStore(index={\"embed\": embeddings, \"dims\": 1536, \"fields\": [\"memory\"]})\n",
|
||||
"\n",
|
||||
"# Store a normal indexed memory\n",
|
||||
"store.put(\n",
|
||||
" (\"user_123\", \"memories\"),\n",
|
||||
" \"mem1\",\n",
|
||||
" {\"memory\": \"I love chocolate ice cream\", \"type\": \"preference\"},\n",
|
||||
")\n",
|
||||
"\n",
|
||||
"# Store a system memory without indexing\n",
|
||||
"store.put(\n",
|
||||
" (\"user_123\", \"memories\"),\n",
|
||||
" \"mem2\",\n",
|
||||
" {\"memory\": \"User completed onboarding\", \"type\": \"system\"},\n",
|
||||
" index=False, # Disable indexing entirely\n",
|
||||
")\n",
|
||||
"\n",
|
||||
"# Search about food preferences - finds mem1\n",
|
||||
"print(\"Expect mem1\")\n",
|
||||
"results = store.search((\"user_123\", \"memories\"), query=\"what food preferences\", limit=1)\n",
|
||||
"for r in results:\n",
|
||||
" print(f\"Item: {r.key}; Score ({r.score})\")\n",
|
||||
" print(f\"Memory: {r.value['memory']}\")\n",
|
||||
" print(f\"Type: {r.value['type']}\\n\")\n",
|
||||
"\n",
|
||||
"# Search about onboarding - won't find mem2 (not indexed)\n",
|
||||
"print(\"Expect low score (mem2 not indexed)\")\n",
|
||||
"results = store.search((\"user_123\", \"memories\"), query=\"onboarding status\", limit=1)\n",
|
||||
"for r in results:\n",
|
||||
" print(f\"Item: {r.key}; Score ({r.score})\")\n",
|
||||
" print(f\"Memory: {r.value['memory']}\")\n",
|
||||
" print(f\"Type: {r.value['type']}\\n\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"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": "3.11.2"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 4
|
||||
}
|
||||
@@ -164,6 +164,7 @@ nav:
|
||||
- how-tos/memory/manage-conversation-history.ipynb
|
||||
- how-tos/memory/delete-messages.ipynb
|
||||
- how-tos/memory/add-summary-conversation-history.ipynb
|
||||
- how-tos/memory/semantic-search.ipynb
|
||||
- Human-in-the-loop:
|
||||
- Human-in-the-loop: how-tos#human-in-the-loop
|
||||
- how-tos/human_in_the_loop/breakpoints.ipynb
|
||||
@@ -225,6 +226,7 @@ nav:
|
||||
- cloud/deployment/setup.md
|
||||
- cloud/deployment/setup_pyproject.md
|
||||
- cloud/deployment/setup_javascript.md
|
||||
- cloud/deployment/semantic_search.md
|
||||
- cloud/deployment/custom_docker.md
|
||||
- cloud/deployment/test_locally.md
|
||||
- cloud/deployment/graph_rebuild.md
|
||||
|
||||
@@ -72,6 +72,8 @@ class AsyncPostgresStore(AsyncBatchedBaseStore, BasePostgresStore[_ainternal.Con
|
||||
# Store documents
|
||||
await store.aput(("docs",), "doc1", {"text": "Python tutorial"})
|
||||
await store.aput(("docs",), "doc2", {"text": "TypeScript guide"})
|
||||
# Don't index the following
|
||||
await store.aput(("docs",), "doc3", {"text": "Other guide"}, index=False)
|
||||
|
||||
# Search by similarity
|
||||
results = await store.asearch(("docs",), query="python programming")
|
||||
|
||||
@@ -569,6 +569,7 @@ class PostgresStore(BaseStore, BasePostgresStore[_pg_internal.Conn]):
|
||||
# Store documents
|
||||
store.put(("docs",), "doc1", {"text": "Python tutorial"})
|
||||
store.put(("docs",), "doc2", {"text": "TypeScript guide"})
|
||||
store.put(("docs",), "doc2", {"text": "Other guide"}, index=False) # don't index
|
||||
|
||||
# Search by similarity
|
||||
results = store.search(("docs",), query="python programming")
|
||||
|
||||
@@ -133,7 +133,7 @@ class GetOp(NamedTuple):
|
||||
This operation allows precise retrieval of stored items using their full path
|
||||
(namespace) and unique identifier (key) combination.
|
||||
|
||||
???+example "Examples"
|
||||
???+ example "Examples"
|
||||
|
||||
Basic item retrieval:
|
||||
```python
|
||||
@@ -145,7 +145,7 @@ class GetOp(NamedTuple):
|
||||
namespace: tuple[str, ...]
|
||||
"""Hierarchical path that uniquely identifies the item's location.
|
||||
|
||||
???+example "Examples"
|
||||
???+ example "Examples"
|
||||
|
||||
```python
|
||||
("users",) # Root level users namespace
|
||||
@@ -156,7 +156,7 @@ class GetOp(NamedTuple):
|
||||
key: str
|
||||
"""Unique identifier for the item within its specific namespace.
|
||||
|
||||
???+example "Examples"
|
||||
???+ example "Examples"
|
||||
|
||||
```python
|
||||
"user123" # For a user profile
|
||||
@@ -175,7 +175,7 @@ class SearchOp(NamedTuple):
|
||||
Note:
|
||||
Natural language search support depends on your store implementation.
|
||||
|
||||
???+example "Examples"
|
||||
???+ example "Examples"
|
||||
Search with filters and pagination:
|
||||
```python
|
||||
SearchOp(
|
||||
@@ -199,7 +199,7 @@ class SearchOp(NamedTuple):
|
||||
namespace_prefix: tuple[str, ...]
|
||||
"""Hierarchical path prefix defining the search scope.
|
||||
|
||||
???+example "Examples"
|
||||
???+ example "Examples"
|
||||
|
||||
```python
|
||||
() # Search entire store
|
||||
@@ -221,7 +221,7 @@ class SearchOp(NamedTuple):
|
||||
- $lt: Less than
|
||||
- $lte: Less than or equal to
|
||||
|
||||
???+example "Examples"
|
||||
???+ example "Examples"
|
||||
Simple exact match:
|
||||
|
||||
```python
|
||||
@@ -253,7 +253,7 @@ class SearchOp(NamedTuple):
|
||||
query: Optional[str] = None
|
||||
"""Natural language search query for semantic search capabilities.
|
||||
|
||||
???+example "Examples"
|
||||
???+ example "Examples"
|
||||
- "technical documentation about REST APIs"
|
||||
- "machine learning papers from 2023"
|
||||
"""
|
||||
@@ -263,7 +263,7 @@ class SearchOp(NamedTuple):
|
||||
NamespacePath = tuple[Union[str, Literal["*"]], ...]
|
||||
"""A tuple representing a namespace path that can include wildcards.
|
||||
|
||||
???+example "Examples"
|
||||
???+ example "Examples"
|
||||
```python
|
||||
("users",) # Exact users namespace
|
||||
("documents", "*") # Any sub-namespace under documents
|
||||
@@ -288,7 +288,7 @@ class MatchCondition(NamedTuple):
|
||||
pattern that can include wildcards to flexibly match different namespace
|
||||
hierarchies.
|
||||
|
||||
???+example "Examples"
|
||||
???+ example "Examples"
|
||||
Prefix matching:
|
||||
```python
|
||||
MatchCondition(match_type="prefix", path=("users", "profiles"))
|
||||
@@ -318,7 +318,7 @@ class ListNamespacesOp(NamedTuple):
|
||||
This operation allows exploring the organization of data, finding specific
|
||||
collections, and navigating the namespace hierarchy.
|
||||
|
||||
???+example "Examples"
|
||||
???+ example "Examples"
|
||||
|
||||
List all namespaces under the "documents" path:
|
||||
```python
|
||||
@@ -341,7 +341,7 @@ class ListNamespacesOp(NamedTuple):
|
||||
match_conditions: Optional[tuple[MatchCondition, ...]] = None
|
||||
"""Optional conditions for filtering namespaces.
|
||||
|
||||
???+example "Examples"
|
||||
???+ example "Examples"
|
||||
All user namespaces:
|
||||
```python
|
||||
(MatchCondition(match_type="prefix", path=("users",)),)
|
||||
@@ -383,7 +383,7 @@ class PutOp(NamedTuple):
|
||||
The namespace acts as a folder-like structure to organize items.
|
||||
Each element in the tuple represents one level in the hierarchy.
|
||||
|
||||
???+example "Examples"
|
||||
???+ example "Examples"
|
||||
Root level documents
|
||||
```python
|
||||
("documents",)
|
||||
@@ -429,9 +429,9 @@ class PutOp(NamedTuple):
|
||||
"""Controls how the item's fields are indexed for search operations.
|
||||
|
||||
Indexing configuration determines how the item can be found through search:
|
||||
- None (default): Uses the store's default indexing configuration (if provided)
|
||||
- False: Disables indexing for this item
|
||||
- list[str]: Specifies which json path fields to index for search
|
||||
- None (default): Uses the store's default indexing configuration (if provided)
|
||||
- False: Disables indexing for this item
|
||||
- list[str]: Specifies which json path fields to index for search
|
||||
|
||||
The item remains accessible through direct get() operations regardless of indexing.
|
||||
When indexed, fields can be searched using natural language queries through
|
||||
@@ -445,15 +445,14 @@ class PutOp(NamedTuple):
|
||||
- Last element: "array[-1]"
|
||||
- All elements (each individually): "array[*]"
|
||||
|
||||
???+example "Examples"
|
||||
- None - Use store defaults
|
||||
- False - Don't index this item
|
||||
???+ example "Examples"
|
||||
- None - Use store defaults (whole item)
|
||||
- list[str] - List of fields to index
|
||||
|
||||
```python
|
||||
[
|
||||
"metadata.title", # Nested field access
|
||||
"chapters[*].content", # Index content from all chapters as separate vectors
|
||||
"context[*].content", # Index content from all context as separate vectors
|
||||
"authors[0].name", # First author's name
|
||||
"revisions[-1].changes", # Most recent revision's changes
|
||||
"sections[*].paragraphs[*].text", # All text from all paragraphs in all sections
|
||||
@@ -495,7 +494,7 @@ class IndexConfig(TypedDict, total=False):
|
||||
2. A synchronous embedding function (EmbeddingsFunc)
|
||||
3. An asynchronous embedding function (AEmbeddingsFunc)
|
||||
|
||||
???+example "Examples"
|
||||
???+ example "Examples"
|
||||
Using LangChain's initialization with InMemoryStore:
|
||||
```python
|
||||
from langchain.embeddings import init_embeddings
|
||||
@@ -557,7 +556,37 @@ class IndexConfig(TypedDict, total=False):
|
||||
fields: Optional[list[str]]
|
||||
"""Fields to extract text from for embedding generation.
|
||||
|
||||
Defaults to the root ["$"], which embeds the json object as a whole.
|
||||
Controls which parts of stored items are embedded for semantic search. Follows JSON path syntax:
|
||||
|
||||
- ["$"]: Embeds the entire JSON object as one vector (default)
|
||||
- ["field1", "field2"]: Embeds specific top-level fields
|
||||
- ["parent.child"]: Embeds nested fields using dot notation
|
||||
- ["array[*].field"]: Embeds field from each array element separately
|
||||
|
||||
Note:
|
||||
You can always override this behavior when storing an item using the
|
||||
`index` parameter in the `put` or `aput` operations.
|
||||
|
||||
???+ example "Examples"
|
||||
```python
|
||||
# Embed entire document (default)
|
||||
fields=["$"]
|
||||
|
||||
# Embed specific fields
|
||||
fields=["text", "summary"]
|
||||
|
||||
# Embed nested fields
|
||||
fields=["metadata.title", "content.body"]
|
||||
|
||||
# Embed from arrays
|
||||
fields=["messages[*].content"] # Each message content separately
|
||||
fields=["context[0].text"] # First context item's text
|
||||
```
|
||||
|
||||
Note:
|
||||
- Fields missing from a document are skipped
|
||||
- Array notation creates separate embeddings for each element
|
||||
- Complex nested paths are supported (e.g., "a.b[*].c.d")
|
||||
"""
|
||||
|
||||
|
||||
@@ -645,7 +674,7 @@ class BaseStore(ABC):
|
||||
index={
|
||||
"dims": 1536, # embedding dimensions
|
||||
"embed": your_embedding_function, # function to create embeddings
|
||||
"fields": ["text"] # fields to embed
|
||||
"fields": ["text"] # fields to embed. Defaults to ["$"]
|
||||
}
|
||||
)
|
||||
|
||||
@@ -680,7 +709,10 @@ class BaseStore(ABC):
|
||||
value: Dictionary containing the item's data. Must contain string keys
|
||||
and JSON-serializable values.
|
||||
index: Controls how the item's fields are indexed for search:
|
||||
- None (default): Use store's default indexing configuration
|
||||
|
||||
- None (default): Use `fields` you configured when creating the store (if any)
|
||||
If you do not initialize the store with indexing capabilities,
|
||||
the `index` parameter will be ignored
|
||||
- False: Disable indexing for this item
|
||||
- list[str]: List of field paths to index, supporting:
|
||||
- Nested fields: "metadata.title"
|
||||
@@ -688,23 +720,25 @@ class BaseStore(ABC):
|
||||
- Specific indices: "authors[0].name"
|
||||
|
||||
Note:
|
||||
Indexing capabilities depend on your store implementation.
|
||||
Some implementations may support only a subset of indexing features.
|
||||
Indexing support depends on your store implementation.
|
||||
If you do not initialize the store with indexing capabilities,
|
||||
the `index` parameter will be ignored.
|
||||
|
||||
???+example "Examples"
|
||||
Simple storage without special indexing (respects store defaults)
|
||||
???+ example "Examples"
|
||||
Store item. Indexing depends on how you configure the store.
|
||||
```python
|
||||
store.put(("docs",), "report", {"title": "Annual Report"})
|
||||
store.put(("docs",), "report", {"memory": "Will likes ai"})
|
||||
```
|
||||
|
||||
Index specific fields for search (if store configured to index items)
|
||||
Do not index item for semantic search. Still accessible through get()
|
||||
and search() operations but won't have a vector representation.
|
||||
```python
|
||||
store.put(("docs",), "report", {"title": "Annual Report"}, index=["title"])
|
||||
store.put(("docs",), "report", {"memory": "Will likes ai"}, index=False)
|
||||
```
|
||||
|
||||
Do not index for semantic search
|
||||
Index specific fields for search.
|
||||
```python
|
||||
store.put(("docs",), "report", {"title": "Annual Report"}, index=False)
|
||||
store.put(("docs",), "report", {"memory": "Will likes ai"}, index=["memory"])
|
||||
```
|
||||
"""
|
||||
_validate_namespace(namespace)
|
||||
@@ -745,7 +779,7 @@ class BaseStore(ABC):
|
||||
List[Tuple[str, ...]]: A list of namespace tuples that match the criteria.
|
||||
Each tuple represents a full namespace path up to `max_depth`.
|
||||
|
||||
???+example "Examples":
|
||||
???+ example "Examples":
|
||||
Setting max_depth=3. Given the namespaces:
|
||||
```python
|
||||
# Example if you have the following namespaces:
|
||||
@@ -862,7 +896,10 @@ class BaseStore(ABC):
|
||||
value: Dictionary containing the item's data. Must contain string keys
|
||||
and JSON-serializable values.
|
||||
index: Controls how the item's fields are indexed for search:
|
||||
- None (default): Use store's default indexing configuration
|
||||
|
||||
- None (default): Use `fields` you configured when creating the store (if any)
|
||||
If you do not initialize the store with indexing capabilities,
|
||||
the `index` parameter will be ignored
|
||||
- False: Disable indexing for this item
|
||||
- list[str]: List of field paths to index, supporting:
|
||||
- Nested fields: "metadata.title"
|
||||
@@ -870,13 +907,20 @@ class BaseStore(ABC):
|
||||
- Specific indices: "authors[0].name"
|
||||
|
||||
Note:
|
||||
Indexing capabilities depend on your store implementation.
|
||||
Some implementations may support only a subset of indexing features.
|
||||
Indexing support depends on your store implementation.
|
||||
If you do not initialize the store with indexing capabilities,
|
||||
the `index` parameter will be ignored.
|
||||
|
||||
???+example "Examples"
|
||||
Simple storage without special indexing:
|
||||
???+ example "Examples"
|
||||
Store item. Indexing depends on how you configure the store.
|
||||
```python
|
||||
await store.aput(("docs",), "report", {"title": "Annual Report"})
|
||||
await store.aput(("docs",), "report", {"memory": "Will likes ai"})
|
||||
```
|
||||
|
||||
Do not index item for semantic search. Still accessible through get()
|
||||
and search() operations but won't have a vector representation.
|
||||
```python
|
||||
await store.aput(("docs",), "report", {"memory": "Will likes ai"}, index=False)
|
||||
```
|
||||
|
||||
Index specific fields for search (if store configured to index items):
|
||||
@@ -885,10 +929,10 @@ class BaseStore(ABC):
|
||||
("docs",),
|
||||
"report",
|
||||
{
|
||||
"title": "Q4 Report",
|
||||
"chapters": [{"content": "..."}, {"content": "..."}]
|
||||
"memory": "Will likes ai",
|
||||
"context": [{"content": "..."}, {"content": "..."}]
|
||||
},
|
||||
index=["title", "chapters[*].content"]
|
||||
index=["memory", "context[*].content"]
|
||||
)
|
||||
```
|
||||
"""
|
||||
@@ -930,7 +974,7 @@ class BaseStore(ABC):
|
||||
List[Tuple[str, ...]]: A list of namespace tuples that match the criteria.
|
||||
Each tuple represents a full namespace path up to `max_depth`.
|
||||
|
||||
???+example "Examples"
|
||||
???+ example "Examples"
|
||||
Setting max_depth=3 with existing namespaces:
|
||||
```python
|
||||
# Given the following namespaces:
|
||||
|
||||
@@ -673,7 +673,7 @@ class Pregel(PregelProtocol):
|
||||
self, config: RunnableConfig, *, subgraphs: bool = False
|
||||
) -> StateSnapshot:
|
||||
"""Get the current state of the graph."""
|
||||
checkpointer: Optional[BaseCheckpointSaver] = config[CONF].get(
|
||||
checkpointer: Optional[BaseCheckpointSaver] = ensure_config(config)[CONF].get(
|
||||
CONFIG_KEY_CHECKPOINTER, self.checkpointer
|
||||
)
|
||||
if not checkpointer:
|
||||
@@ -710,7 +710,7 @@ class Pregel(PregelProtocol):
|
||||
self, config: RunnableConfig, *, subgraphs: bool = False
|
||||
) -> StateSnapshot:
|
||||
"""Get the current state of the graph."""
|
||||
checkpointer: Optional[BaseCheckpointSaver] = config[CONF].get(
|
||||
checkpointer: Optional[BaseCheckpointSaver] = ensure_config(config)[CONF].get(
|
||||
CONFIG_KEY_CHECKPOINTER, self.checkpointer
|
||||
)
|
||||
if not checkpointer:
|
||||
@@ -751,8 +751,9 @@ class Pregel(PregelProtocol):
|
||||
before: Optional[RunnableConfig] = None,
|
||||
limit: Optional[int] = None,
|
||||
) -> Iterator[StateSnapshot]:
|
||||
config = ensure_config(config)
|
||||
"""Get the history of the state of the graph."""
|
||||
checkpointer: Optional[BaseCheckpointSaver] = config[CONF].get(
|
||||
checkpointer: Optional[BaseCheckpointSaver] = ensure_config(config)[CONF].get(
|
||||
CONFIG_KEY_CHECKPOINTER, self.checkpointer
|
||||
)
|
||||
if not checkpointer:
|
||||
@@ -800,8 +801,9 @@ class Pregel(PregelProtocol):
|
||||
before: Optional[RunnableConfig] = None,
|
||||
limit: Optional[int] = None,
|
||||
) -> AsyncIterator[StateSnapshot]:
|
||||
config = ensure_config(config)
|
||||
"""Get the history of the state of the graph."""
|
||||
checkpointer: Optional[BaseCheckpointSaver] = config[CONF].get(
|
||||
checkpointer: Optional[BaseCheckpointSaver] = ensure_config(config)[CONF].get(
|
||||
CONFIG_KEY_CHECKPOINTER, self.checkpointer
|
||||
)
|
||||
if not checkpointer:
|
||||
@@ -855,7 +857,7 @@ class Pregel(PregelProtocol):
|
||||
node `as_node`. If `as_node` is not provided, it will be set to the last node
|
||||
that updated the state, if not ambiguous.
|
||||
"""
|
||||
checkpointer: Optional[BaseCheckpointSaver] = config[CONF].get(
|
||||
checkpointer: Optional[BaseCheckpointSaver] = ensure_config(config)[CONF].get(
|
||||
CONFIG_KEY_CHECKPOINTER, self.checkpointer
|
||||
)
|
||||
if not checkpointer:
|
||||
@@ -1130,7 +1132,7 @@ class Pregel(PregelProtocol):
|
||||
values: dict[str, Any] | Any,
|
||||
as_node: Optional[str] = None,
|
||||
) -> RunnableConfig:
|
||||
checkpointer: Optional[BaseCheckpointSaver] = config[CONF].get(
|
||||
checkpointer: Optional[BaseCheckpointSaver] = ensure_config(config)[CONF].get(
|
||||
CONFIG_KEY_CHECKPOINTER, self.checkpointer
|
||||
)
|
||||
if not checkpointer:
|
||||
|
||||
Generated
+20
-23
@@ -1,4 +1,4 @@
|
||||
# This file is automatically @generated by Poetry 1.8.4 and should not be changed by hand.
|
||||
# This file is automatically @generated by Poetry 1.8.3 and should not be changed by hand.
|
||||
|
||||
[[package]]
|
||||
name = "aiohappyeyeballs"
|
||||
@@ -2862,30 +2862,30 @@ adal = ["adal (>=1.0.2)"]
|
||||
|
||||
[[package]]
|
||||
name = "langchain"
|
||||
version = "0.3.1"
|
||||
version = "0.3.9"
|
||||
description = "Building applications with LLMs through composability"
|
||||
optional = false
|
||||
python-versions = "<4.0,>=3.9"
|
||||
files = [
|
||||
{file = "langchain-0.3.1-py3-none-any.whl", hash = "sha256:94e5ee7464d4366e4b158aa5704953c39701ea237b9ed4b200096d49e83bb3ae"},
|
||||
{file = "langchain-0.3.1.tar.gz", hash = "sha256:54d6e3abda2ec056875a231a418a4130ba7576e629e899067e499bfc847b7586"},
|
||||
{file = "langchain-0.3.9-py3-none-any.whl", hash = "sha256:ade5a1fee2f94f2e976a6c387f97d62cc7f0b9f26cfe0132a41d2bda761e1045"},
|
||||
{file = "langchain-0.3.9.tar.gz", hash = "sha256:4950c4ad627d0aa95ce6bda7de453e22059b7e7836b562a8f781fb0b05d7294c"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
aiohttp = ">=3.8.3,<4.0.0"
|
||||
async-timeout = {version = ">=4.0.0,<5.0.0", markers = "python_version < \"3.11\""}
|
||||
langchain-core = ">=0.3.6,<0.4.0"
|
||||
langchain-core = ">=0.3.21,<0.4.0"
|
||||
langchain-text-splitters = ">=0.3.0,<0.4.0"
|
||||
langsmith = ">=0.1.17,<0.2.0"
|
||||
numpy = [
|
||||
{version = ">=1,<2", markers = "python_version < \"3.12\""},
|
||||
{version = ">=1.26.0,<2.0.0", markers = "python_version >= \"3.12\""},
|
||||
{version = ">=1.22.4,<2", markers = "python_version < \"3.12\""},
|
||||
{version = ">=1.26.2,<3", markers = "python_version >= \"3.12\""},
|
||||
]
|
||||
pydantic = ">=2.7.4,<3.0.0"
|
||||
PyYAML = ">=5.3"
|
||||
requests = ">=2,<3"
|
||||
SQLAlchemy = ">=1.4,<3"
|
||||
tenacity = ">=8.1.0,<8.4.0 || >8.4.0,<9.0.0"
|
||||
tenacity = ">=8.1.0,<8.4.0 || >8.4.0,<10"
|
||||
|
||||
[[package]]
|
||||
name = "langchain-anthropic"
|
||||
@@ -2933,13 +2933,13 @@ tenacity = ">=8.1.0,<8.4.0 || >8.4.0,<9.0.0"
|
||||
|
||||
[[package]]
|
||||
name = "langchain-core"
|
||||
version = "0.3.15"
|
||||
version = "0.3.21"
|
||||
description = "Building applications with LLMs through composability"
|
||||
optional = false
|
||||
python-versions = "<4.0,>=3.9"
|
||||
files = [
|
||||
{file = "langchain_core-0.3.15-py3-none-any.whl", hash = "sha256:3d4ca6dbb8ed396a6ee061063832a2451b0ce8c345570f7b086ffa7288e4fa29"},
|
||||
{file = "langchain_core-0.3.15.tar.gz", hash = "sha256:b1a29787a4ffb7ec2103b4e97d435287201da7809b369740dd1e32f176325aba"},
|
||||
{file = "langchain_core-0.3.21-py3-none-any.whl", hash = "sha256:7e723dff80946a1198976c6876fea8326dc82566ef9bcb5f8d9188f738733665"},
|
||||
{file = "langchain_core-0.3.21.tar.gz", hash = "sha256:561b52b258ffa50a9fb11d7a1940ebfd915654d1ec95b35e81dfd5ee84143411"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
@@ -3035,7 +3035,7 @@ langchain-core = ">=0.3.0,<0.4.0"
|
||||
|
||||
[[package]]
|
||||
name = "langgraph"
|
||||
version = "0.2.52"
|
||||
version = "0.2.54"
|
||||
description = "Building stateful, multi-actor applications with LLMs"
|
||||
optional = false
|
||||
python-versions = ">=3.9.0,<4.0"
|
||||
@@ -3045,7 +3045,7 @@ develop = true
|
||||
[package.dependencies]
|
||||
langchain-core = ">=0.2.43,<0.4.0,!=0.3.0,!=0.3.1,!=0.3.2,!=0.3.3,!=0.3.4,!=0.3.5,!=0.3.6,!=0.3.7,!=0.3.8,!=0.3.9,!=0.3.10,!=0.3.11,!=0.3.12,!=0.3.13,!=0.3.14"
|
||||
langgraph-checkpoint = "^2.0.4"
|
||||
langgraph-sdk = "^0.1.32"
|
||||
langgraph-sdk = "^0.1.42"
|
||||
|
||||
[package.source]
|
||||
type = "directory"
|
||||
@@ -3053,7 +3053,7 @@ url = "libs/langgraph"
|
||||
|
||||
[[package]]
|
||||
name = "langgraph-checkpoint"
|
||||
version = "2.0.5"
|
||||
version = "2.0.8"
|
||||
description = "Library with base interfaces for LangGraph checkpoint savers."
|
||||
optional = false
|
||||
python-versions = "^3.9.0,<4.0"
|
||||
@@ -3070,7 +3070,7 @@ url = "libs/checkpoint"
|
||||
|
||||
[[package]]
|
||||
name = "langgraph-checkpoint-postgres"
|
||||
version = "2.0.3"
|
||||
version = "2.0.7"
|
||||
description = "Library with a Postgres implementation of LangGraph checkpoint saver."
|
||||
optional = false
|
||||
python-versions = "^3.9.0,<4.0"
|
||||
@@ -3078,10 +3078,10 @@ files = []
|
||||
develop = true
|
||||
|
||||
[package.dependencies]
|
||||
langgraph-checkpoint = "^2.0.2"
|
||||
langgraph-checkpoint = "^2.0.7"
|
||||
orjson = ">=3.10.1"
|
||||
psycopg = "^3.0.0"
|
||||
psycopg-pool = "^3.0.0"
|
||||
psycopg = "^3.2.0"
|
||||
psycopg-pool = "^3.2.0"
|
||||
|
||||
[package.source]
|
||||
type = "directory"
|
||||
@@ -3106,7 +3106,7 @@ url = "libs/checkpoint-sqlite"
|
||||
|
||||
[[package]]
|
||||
name = "langgraph-sdk"
|
||||
version = "0.1.36"
|
||||
version = "0.1.42"
|
||||
description = "SDK for interacting with LangGraph API"
|
||||
optional = false
|
||||
python-versions = "^3.9.0,<4.0"
|
||||
@@ -3115,7 +3115,6 @@ develop = true
|
||||
|
||||
[package.dependencies]
|
||||
httpx = ">=0.25.2"
|
||||
httpx-sse = ">=0.4.0"
|
||||
orjson = ">=3.10.1"
|
||||
|
||||
[package.source]
|
||||
@@ -3586,7 +3585,6 @@ optional = false
|
||||
python-versions = ">=3.6"
|
||||
files = [
|
||||
{file = "mkdocs-redirects-1.2.1.tar.gz", hash = "sha256:9420066d70e2a6bb357adf86e67023dcdca1857f97f07c7fe450f8f1fb42f861"},
|
||||
{file = "mkdocs_redirects-1.2.1-py3-none-any.whl", hash = "sha256:497089f9e0219e7389304cffefccdfa1cac5ff9509f2cb706f4c9b221726dffb"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
@@ -6964,7 +6962,6 @@ description = "Automatically mock your HTTP interactions to simplify and speed u
|
||||
optional = false
|
||||
python-versions = ">=3.8"
|
||||
files = [
|
||||
{file = "vcrpy-6.0.1-py2.py3-none-any.whl", hash = "sha256:621c3fb2d6bd8aa9f87532c688e4575bcbbde0c0afeb5ebdb7e14cac409edfdd"},
|
||||
{file = "vcrpy-6.0.1.tar.gz", hash = "sha256:9e023fee7f892baa0bbda2f7da7c8ac51165c1c6e38ff8688683a12a4bde9278"},
|
||||
]
|
||||
|
||||
@@ -7476,4 +7473,4 @@ type = ["pytest-mypy"]
|
||||
[metadata]
|
||||
lock-version = "2.0"
|
||||
python-versions = "^3.10"
|
||||
content-hash = "776ee42630769f08e3896338f18ec81830166695d32d2208dc31dedb22d3b22d"
|
||||
content-hash = "cf18eed5e183fc4f7786d095540b6c9261e130750f2d1fcc427e08b78d522c61"
|
||||
|
||||
+1
-1
@@ -34,7 +34,7 @@ ruff = "^0.6.8"
|
||||
jupyter = "^1.1.1"
|
||||
|
||||
[tool.poetry.group.test.dependencies]
|
||||
langchain = "^0.3.1"
|
||||
langchain = "^0.3.8"
|
||||
langchain-openai = "^0.2.0"
|
||||
langchain-anthropic = "^0.2.1"
|
||||
langchain-nomic = "^0.1.3"
|
||||
|
||||
Reference in New Issue
Block a user