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Merge branch 'master' into pprados/02-pymupdf
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‎docs/docs/integrations/graphs/amazon_neptune_open_cypher.ipynb‎

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@@ -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",
@@ -83,21 +91,169 @@
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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(\"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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"result = chain.invoke(\n",
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" {\"query\": \"How many outgoing routes does the Austin airport have?\"}\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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}
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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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"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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