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community: Update UC toolkit documentation to use LangGraph APIs (#26778)
- **Description:** Update UC toolkit documentation to show an example of using recommended LangGraph agent APIs before the existing LangChain AgentExecutor example. Tested by manually running the updated example notebook - **Dependencies:** No new dependencies --------- Signed-off-by: Sid Murching <sid.murching@databricks.com> Co-authored-by: Erick Friis <erick@langchain.dev>
This commit is contained in:
Siddharth Murching
GitHub
parent
c2072d909a
commit
cfff2a057e
@ -6,7 +6,7 @@
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"source": [
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"# Databricks Unity Catalog (UC)\n",
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"\n",
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"This notebook shows how to use UC functions as LangChain tools.\n",
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"This notebook shows how to use UC functions as LangChain tools, with both LangChain and LangGraph agent APIs.\n",
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"\n",
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"See Databricks documentation ([AWS](https://docs.databricks.com/en/sql/language-manual/sql-ref-syntax-ddl-create-sql-function.html)|[Azure](https://learn.microsoft.com/en-us/azure/databricks/sql/language-manual/sql-ref-syntax-ddl-create-sql-function)|[GCP](https://docs.gcp.databricks.com/en/sql/language-manual/sql-ref-syntax-ddl-create-sql-function.html)) to learn how to create SQL or Python functions in UC. Do not skip function and parameter comments, which are critical for LLMs to call functions properly.\n",
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"\n",
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@ -34,11 +34,19 @@
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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": 1,
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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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"Note: you may need to restart the kernel to use updated packages.\n"
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]
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}
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],
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"source": [
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"%pip install --upgrade --quiet databricks-sdk langchain-community mlflow"
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"%pip install --upgrade --quiet databricks-sdk langchain-community langchain-databricks langgraph mlflow"
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]
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},
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{
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@ -47,7 +55,7 @@
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"metadata": {},
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"outputs": [],
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"source": [
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"from langchain_community.chat_models.databricks import ChatDatabricks\n",
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"from langchain_databricks import ChatDatabricks\n",
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"\n",
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"llm = ChatDatabricks(endpoint=\"databricks-meta-llama-3-70b-instruct\")"
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]
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@ -58,6 +66,7 @@
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"metadata": {},
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"outputs": [],
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"source": [
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"from databricks.sdk import WorkspaceClient\n",
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"from langchain_community.tools.databricks import UCFunctionToolkit\n",
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"\n",
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"tools = (\n",
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@ -76,9 +85,16 @@
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"metadata": {
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"collapsed": false,
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"jupyter": {
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"outputs_hidden": false
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}
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},
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"source": [
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"(Optional) To increase the retry time for getting a function execution response, set environment variable UC_TOOL_CLIENT_EXECUTION_TIMEOUT. Default retry time value is 120s."
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"(Optional) To increase the retry time for getting a function execution response, set environment variable UC_TOOL_CLIENT_EXECUTION_TIMEOUT. Default retry time value is 120s.",
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"## LangGraph agent example"
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]
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},
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{
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@ -92,9 +108,68 @@
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"os.environ[\"UC_TOOL_CLIENT_EXECUTION_TIMEOUT\"] = \"200\""
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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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"collapsed": false,
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"jupyter": {
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"outputs_hidden": false
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}
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},
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"source": [
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"## LangGraph agent example"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"metadata": {
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"collapsed": false,
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"jupyter": {
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"outputs_hidden": false
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}
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},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"{'messages': [HumanMessage(content='36939 * 8922.4', additional_kwargs={}, response_metadata={}, id='1a10b10b-8e37-48c7-97a1-cac5006228d5'),\n",
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" AIMessage(content='', additional_kwargs={'tool_calls': [{'id': 'call_a8f3986f-4b91-40a3-8d6d-39f431dab69b', 'type': 'function', 'function': {'name': 'main__tools__python_exec', 'arguments': '{\"code\": \"print(36939 * 8922.4)\"}'}}]}, response_metadata={'prompt_tokens': 771, 'completion_tokens': 29, 'total_tokens': 800}, id='run-865c3613-20ba-4e80-afc8-fde1cfb26e5a-0', tool_calls=[{'name': 'main__tools__python_exec', 'args': {'code': 'print(36939 * 8922.4)'}, 'id': 'call_a8f3986f-4b91-40a3-8d6d-39f431dab69b', 'type': 'tool_call'}]),\n",
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" ToolMessage(content='{\"format\": \"SCALAR\", \"value\": \"329584533.59999996\\\\n\", \"truncated\": false}', name='main__tools__python_exec', id='8b63d4c8-1a3d-46a5-a719-393b2ef36770', tool_call_id='call_a8f3986f-4b91-40a3-8d6d-39f431dab69b'),\n",
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" AIMessage(content='The result of the multiplication is:\\n\\n329584533.59999996', additional_kwargs={}, response_metadata={'prompt_tokens': 846, 'completion_tokens': 22, 'total_tokens': 868}, id='run-22772404-611b-46e4-9956-b85e4a385f0f-0')]}"
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]
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},
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"execution_count": 4,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"from langgraph.prebuilt import create_react_agent\n",
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"\n",
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"agent = create_react_agent(\n",
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" llm,\n",
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" tools,\n",
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" state_modifier=\"You are a helpful assistant. Make sure to use tool for information.\",\n",
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")\n",
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"agent.invoke({\"messages\": [{\"role\": \"user\", \"content\": \"36939 * 8922.4\"}]})"
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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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"collapsed": false,
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"jupyter": {
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"outputs_hidden": false
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}
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},
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"source": [
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"## LangChain agent example"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"metadata": {},
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"outputs": [],
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"source": [
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@ -118,7 +193,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"execution_count": 6,
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"metadata": {},
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"outputs": [
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{
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@ -132,7 +207,9 @@
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"Invoking: `main__tools__python_exec` with `{'code': 'print(36939 * 8922.4)'}`\n",
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"\n",
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"\n",
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"\u001b[0m\u001b[36;1m\u001b[1;3m{\"format\": \"SCALAR\", \"value\": \"329584533.59999996\\n\", \"truncated\": false}\u001b[0m\u001b[32;1m\u001b[1;3mThe result of the multiplication 36939 * 8922.4 is 329,584,533.60.\u001b[0m\n",
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"\u001b[0m\u001b[36;1m\u001b[1;3m{\"format\": \"SCALAR\", \"value\": \"329584533.59999996\\n\", \"truncated\": false}\u001b[0m\u001b[32;1m\u001b[1;3mThe result of the multiplication is:\n",
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"\n",
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"329584533.59999996\u001b[0m\n",
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"\n",
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"\u001b[1m> Finished chain.\u001b[0m\n"
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]
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@ -141,10 +218,10 @@
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"data": {
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"text/plain": [
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"{'input': '36939 * 8922.4',\n",
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" 'output': 'The result of the multiplication 36939 * 8922.4 is 329,584,533.60.'}"
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" 'output': 'The result of the multiplication is:\\n\\n329584533.59999996'}"
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]
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},
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"execution_count": 5,
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"execution_count": 6,
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"metadata": {},
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"output_type": "execute_result"
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}
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@ -153,18 +230,11 @@
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"agent_executor = AgentExecutor(agent=agent, tools=tools, verbose=True)\n",
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"agent_executor.invoke({\"input\": \"36939 * 8922.4\"})"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "llm",
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"display_name": "Python 3 (ipykernel)",
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"language": "python",
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"name": "python3"
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},
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@ -178,9 +248,9 @@
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.11.9"
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"version": "3.11.10"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 2
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"nbformat_minor": 4
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}
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