mirror of
https://github.com/hwchase17/langchain.git
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docs: Message history for Neptune chains (#29260)
Expanded the Amazon Neptune documentation with new sections detailing usage of chat message history with the `create_neptune_opencypher_qa_chain` and `create_neptune_sparql_qa_chain` functions.
This commit is contained in:
Michael Chin
GitHub
parent
d5360b9bd6
commit
36ff83a0b5
@ -70,9 +70,17 @@
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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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"execution_count": 12,
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"metadata": {},
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"outputs": [],
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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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"Austin airport has 98 outgoing routes.\n"
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]
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}
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],
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"source": [
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"from langchain_aws import ChatBedrockConverse\n",
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"from langchain_aws.chains import create_neptune_opencypher_qa_chain\n",
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@ -83,13 +91,161 @@
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" temperature=0,\n",
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")\n",
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"\n",
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"chain = create_neptune_opencypher_qa_chain(\n",
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" llm=llm,\n",
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" graph=graph,\n",
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")\n",
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"chain = create_neptune_opencypher_qa_chain(llm=llm, graph=graph)\n",
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"\n",
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"result = chain.invoke(\n",
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" {\"query\": \"How many outgoing routes does the Austin airport have?\"}\n",
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"result = chain.invoke(\"How many outgoing routes does the Austin airport have?\")\n",
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"print(result[\"result\"].content)"
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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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"### Adding Message History\n",
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"\n",
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"The Neptune openCypher QA chain has the ability to be wrapped by [`RunnableWithMessageHistory`](https://python.langchain.com/v0.2/api_reference/core/runnables/langchain_core.runnables.history.RunnableWithMessageHistory.html#langchain_core.runnables.history.RunnableWithMessageHistory). This adds message history to the chain, allowing us to create a chatbot that retains conversation state across multiple invocations.\n",
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"\n",
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"To start, we need a way to store and load the message history. For this purpose, each thread will be created as an instance of [`InMemoryChatMessageHistory`](https://python.langchain.com/api_reference/core/chat_history/langchain_core.chat_history.InMemoryChatMessageHistory.html), and stored into a dictionary for repeated access.\n",
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"\n",
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"(Also see: https://python.langchain.com/docs/versions/migrating_memory/chat_history/#chatmessagehistory)"
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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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"from langchain_core.chat_history import InMemoryChatMessageHistory\n",
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"\n",
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"chats_by_session_id = {}\n",
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"\n",
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"\n",
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"def get_chat_history(session_id: str) -> InMemoryChatMessageHistory:\n",
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" chat_history = chats_by_session_id.get(session_id)\n",
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" if chat_history is None:\n",
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" chat_history = InMemoryChatMessageHistory()\n",
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" chats_by_session_id[session_id] = chat_history\n",
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" return chat_history"
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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, the QA chain and message history storage can be used to create the new `RunnableWithMessageHistory`. Note that we must set `query` as the input key to match the format expected by the base chain."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"from langchain_core.runnables.history import RunnableWithMessageHistory\n",
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"\n",
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"runnable_with_history = RunnableWithMessageHistory(\n",
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" chain,\n",
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" get_chat_history,\n",
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" input_messages_key=\"query\",\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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"Before invoking the chain, a unique `session_id` needs to be generated for the conversation that the new `InMemoryChatMessageHistory` will remember."
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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 uuid\n",
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"\n",
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"session_id = uuid.uuid4()"
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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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"Finally, invoke the message history enabled chain with the `session_id`."
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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": 8,
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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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"You can fly directly to 98 destinations from Austin airport.\n"
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]
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}
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],
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"source": [
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"result = runnable_with_history.invoke(\n",
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" {\"query\": \"How many destinations can I fly to directly from Austin airport?\"},\n",
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" config={\"configurable\": {\"session_id\": session_id}},\n",
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")\n",
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"print(result[\"result\"].content)"
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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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"As the chain continues to be invoked with the same `session_id`, responses will be returned in the context of previous queries in the conversation.\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": 9,
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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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"You can fly directly to 4 destinations in Europe from Austin airport.\n"
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]
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}
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],
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"source": [
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"result = runnable_with_history.invoke(\n",
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" {\"query\": \"Out of those destinations, how many are in Europe?\"},\n",
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" config={\"configurable\": {\"session_id\": session_id}},\n",
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")\n",
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"print(result[\"result\"].content)"
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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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"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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"The four European destinations you can fly to directly from Austin airport are:\n",
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"- AMS (Amsterdam Airport Schiphol)\n",
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"- FRA (Frankfurt am Main)\n",
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"- LGW (London Gatwick)\n",
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"- LHR (London Heathrow)\n"
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]
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}
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],
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"source": [
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"result = runnable_with_history.invoke(\n",
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" {\"query\": \"Give me the codes and names of those airports.\"},\n",
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" config={\"configurable\": {\"session_id\": session_id}},\n",
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")\n",
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"print(result[\"result\"].content)"
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]
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@ -97,7 +253,7 @@
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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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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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},
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@ -111,7 +267,7 @@
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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": "3.10.12"
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"version": "3.10.13"
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}
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},
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"nbformat": 4,
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@ -48,7 +48,7 @@
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"\n",
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"Seed the W3C organizational data, W3C org ontology plus some instances. \n",
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" \n",
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"You will need an S3 bucket in the same region and account. Set `STAGE_BUCKET`as the name of that bucket."
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"You will need an S3 bucket in the same region and account as the Neptune cluster. Set `STAGE_BUCKET`as the name of that bucket."
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]
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},
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{
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@ -60,11 +60,6 @@
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"STAGE_BUCKET = \"<bucket-name>\""
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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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},
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{
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"cell_type": "code",
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"execution_count": null,
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@ -89,7 +84,50 @@
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Bulk-load the org ttl - both ontology and instances"
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"We will use the `%load` magic command from the `graph-notebook` package to insert the W3C data into the Neptune graph. Before running `%load`, use `%%graph_notebook_config` to set the graph connection parameters."
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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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"!pip install --upgrade --quiet graph-notebook"
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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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"%load_ext graph_notebook.magics"
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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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"%%graph_notebook_config\n",
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"{\n",
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" \"host\": \"<neptune-endpoint>\",\n",
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" \"neptune_service\": \"neptune-db\",\n",
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" \"port\": 8182,\n",
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" \"auth_mode\": \"<[DEFAULT|IAM]>\",\n",
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" \"load_from_s3_arn\": \"<neptune-cluster-load-role-arn>\",\n",
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" \"ssl\": true,\n",
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" \"aws_region\": \"<region>\"\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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"Bulk-load the org ttl - both ontology and instances."
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]
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},
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{
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@ -246,7 +284,9 @@
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": "### Create the Neptune Database RDF Graph"
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"source": [
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"### Create the Neptune Database RDF Graph"
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]
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},
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{
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"cell_type": "code",
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@ -297,7 +337,7 @@
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" examples=EXAMPLES,\n",
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")\n",
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"\n",
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"result = chain.invoke({\"query\": \"How many organizations are in the graph?\"})\n",
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"result = chain.invoke(\"How many organizations are in the graph?\")\n",
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"print(result[\"result\"].content)"
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]
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},
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@ -305,7 +345,6 @@
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Extra questions\n",
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"Here are a few more prompts to try on the graph data that was ingested.\n"
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]
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},
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@ -315,7 +354,8 @@
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"metadata": {},
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"outputs": [],
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"source": [
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"chain.invoke({\"query\": \"Are there any mergers or acquisitions?\"})"
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"result = chain.invoke(\"Are there any mergers or acquisitions?\")\n",
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"print(result[\"result\"].content)"
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]
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},
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{
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@ -324,7 +364,8 @@
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"metadata": {},
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"outputs": [],
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"source": [
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"chain.invoke({\"query\": \"Find organizations.\"})"
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"result = chain.invoke(\"Find organizations.\")\n",
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"print(result[\"result\"].content)"
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]
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},
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{
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@ -333,7 +374,8 @@
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"metadata": {},
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"outputs": [],
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"source": [
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"chain.invoke({\"query\": \"Find sites of MegaSystems or MegaFinancial.\"})"
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"result = chain.invoke(\"Find sites of MegaSystems or MegaFinancial.\")\n",
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"print(result[\"result\"].content)"
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]
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},
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{
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@ -342,7 +384,8 @@
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"metadata": {},
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"outputs": [],
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"source": [
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"chain.invoke({\"query\": \"Find a member who is a manager of one or more members.\"})"
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"result = chain.invoke(\"Find a member who is a manager of one or more members.\")\n",
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"print(result[\"result\"].content)"
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]
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},
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{
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@ -351,7 +394,8 @@
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"metadata": {},
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"outputs": [],
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"source": [
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"chain.invoke({\"query\": \"Find five members and their managers.\"})"
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"result = chain.invoke(\"Find five members and their managers.\")\n",
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"print(result[\"result\"].content)"
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]
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},
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{
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@ -360,17 +404,128 @@
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"metadata": {},
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"outputs": [],
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"source": [
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"chain.invoke(\n",
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" {\n",
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" \"query\": \"Find org units or suborganizations of The Mega Group. What are the sites of those units?\"\n",
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" }\n",
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"result = chain.invoke(\n",
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" \"Find org units or suborganizations of The Mega Group. What are the sites of those units?\"\n",
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")\n",
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"print(result[\"result\"].content)"
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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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"### Adding Message History\n",
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"\n",
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"The Neptune SPARQL QA chain has the ability to be wrapped by [`RunnableWithMessageHistory`](https://python.langchain.com/v0.2/api_reference/core/runnables/langchain_core.runnables.history.RunnableWithMessageHistory.html#langchain_core.runnables.history.RunnableWithMessageHistory). This adds message history to the chain, allowing us to create a chatbot that retains conversation state across multiple invocations.\n",
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"\n",
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"To start, we need a way to store and load the message history. For this purpose, each thread will be created as an instance of [`InMemoryChatMessageHistory`](https://python.langchain.com/api_reference/core/chat_history/langchain_core.chat_history.InMemoryChatMessageHistory.html), and stored into a dictionary for repeated access.\n",
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"\n",
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"(Also see: https://python.langchain.com/docs/versions/migrating_memory/chat_history/#chatmessagehistory)"
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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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"from langchain_core.chat_history import InMemoryChatMessageHistory\n",
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"\n",
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"chats_by_session_id = {}\n",
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"\n",
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"\n",
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"def get_chat_history(session_id: str) -> InMemoryChatMessageHistory:\n",
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" chat_history = chats_by_session_id.get(session_id)\n",
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" if chat_history is None:\n",
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" chat_history = InMemoryChatMessageHistory()\n",
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" chats_by_session_id[session_id] = chat_history\n",
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" return chat_history"
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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, the QA chain and message history storage can be used to create the new `RunnableWithMessageHistory`. Note that we must set `query` as the input key to match the format expected by the base chain."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"from langchain_core.runnables.history import RunnableWithMessageHistory\n",
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"\n",
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"runnable_with_history = RunnableWithMessageHistory(\n",
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" chain,\n",
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" get_chat_history,\n",
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" input_messages_key=\"query\",\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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"Before invoking the chain, a unique `session_id` needs to be generated for the conversation that the new `InMemoryChatMessageHistory` will remember.\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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"import uuid\n",
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"\n",
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"session_id = uuid.uuid4()"
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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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"Finally, invoke the message history enabled chain with the `session_id`.\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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"result = runnable_with_history.invoke(\n",
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" {\"query\": \"How many org units or suborganizations does the The Mega Group have?\"},\n",
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" config={\"configurable\": {\"session_id\": session_id}},\n",
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")\n",
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"print(result[\"result\"].content)"
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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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"As the chain continues to be invoked with the same `session_id`, responses will be returned in the context of previous queries in the conversation.\n"
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]
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},
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{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"result = runnable_with_history.invoke(\n",
|
||||
" {\"query\": \"List the sites for each of the units.\"},\n",
|
||||
" config={\"configurable\": {\"session_id\": session_id}},\n",
|
||||
")\n",
|
||||
"print(result[\"result\"].content)"
|
||||
]
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3 (ipykernel)",
|
||||
"display_name": "Python 3",
|
||||
"language": "python",
|
||||
"name": "python3"
|
||||
},
|
||||
@ -384,7 +539,7 @@
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.10.12"
|
||||
"version": "3.10.13"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
|
||||
Loading…
Reference in New Issue
Block a user