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Add ntbk for testing
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‎examples/research/src/agent/agent.ipynb‎

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{
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"cells": [
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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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"vscode": {
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"languageId": "shellscript"
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}
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},
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"outputs": [],
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"source": [
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"! pip install langchain-mcp-adapters langgraph \"langchain[anthropic]\" langgraph-swarm"
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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": 1,
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"metadata": {},
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"outputs": [
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{
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"ename": "ModuleNotFoundError",
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"evalue": "No module named 'langchain_anthropic'",
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"output_type": "error",
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"traceback": [
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"\u001b[31m---------------------------------------------------------------------------\u001b[39m",
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"\u001b[31mModuleNotFoundError\u001b[39m Traceback (most recent call last)",
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"\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[1]\u001b[39m\u001b[32m, line 2\u001b[39m\n\u001b[32m 1\u001b[39m \u001b[38;5;66;03m# Imports\u001b[39;00m\n\u001b[32m----> \u001b[39m\u001b[32m2\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mlangchain_anthropic\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m ChatAnthropic\n\u001b[32m 3\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mlangchain_mcp_adapters\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mclient\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m MultiServerMCPClient\n\u001b[32m 5\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mlanggraph\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mprebuilt\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m create_react_agent\n",
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"\u001b[31mModuleNotFoundError\u001b[39m: No module named 'langchain_anthropic'"
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]
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}
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],
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"source": [
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"# Imports\n",
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"from langchain_anthropic import ChatAnthropic\n",
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"from langchain_mcp_adapters.client import MultiServerMCPClient\n",
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"\n",
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"from langgraph.prebuilt import create_react_agent\n",
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"from langgraph_swarm import create_handoff_tool, create_swarm\n",
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"\n",
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"planner_prompt = \"\"\"\n",
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"<Task>\n",
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"You will help plan the steps to implement a LangGraph application. \n",
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"</Task>\n",
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"\n",
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"<Instructions>\n",
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"1. Reflect on the user's request. \n",
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"2. Use the list_doc_sources tool to fetch and the fetch_docs tool to read the llms.txt file.\n",
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"3. Identify documents that are relevant to the user's request.\n",
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"4. Ask follow-up questions to help refine the project scope and narrow the set of documents to be used for the project.\n",
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"5. When the project scope is clear produce a short description of the project with relevant URLs.\n",
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"6. Finally, transfer to transfer_to_researcher_agent.\n",
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"</Instructions>\n",
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"\"\"\"\n",
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"\n",
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"researcher_prompt = \"\"\"\n",
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"<Task>\n",
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"You will perform research on the project scope. \n",
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"</Task>\n",
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"\n",
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"<Instructions>\n",
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"1. Reflect on the project scope and provided URLs from the planner.\n",
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"2. Use the fetch_docs tool to fetch and read each URL.\n",
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"3. Use the information in these URLs to implement the solution to the user's request.\n",
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"4. If you need further clarification or additional sources to implement the solution, transfer to transfer_to_planner_agent.\n",
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"</Instructions>\n",
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"\"\"\"\n",
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"\n",
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"# LLM\n",
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"model = ChatAnthropic(model=\"claude-3-7-sonnet-latest\")\n",
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"\n",
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"# Handoff tools\n",
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"transfer_to_planner_agent = create_handoff_tool(\n",
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" agent_name=\"planner_agent\",\n",
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" description=\"Transfer user to the planner_agent to address clarifying questions or help them plan the steps to complete the user's request.\"\n",
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")\n",
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"transfer_to_researcher_agent = create_handoff_tool(\n",
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" agent_name=\"researcher_agent\",\n",
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" description=\"Transfer user to researcher_agent to perform research on the user's request.\"\n",
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")\n",
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"\n",
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"# TODO: Move to configuration\n",
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"#configurable = Configuration.from_runnable_config(config)\n",
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"#llms_txt_urls = configurable.llms_txt\n",
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"llms_txt_urls = \"LangGraph:https://langchain-ai.github.io/langgraph/llms.txt\"\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": 7,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Input \n",
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"messages = {\"role\": \"user\", \"content\": \"Create a prompt chain that makes and improves a joke based on the user's input.\"}"
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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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"# Research and planning tools \n",
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"async with MultiServerMCPClient(\n",
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" {\n",
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" \"research-server\": {\n",
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" \"command\": \"npx\",\n",
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" \"args\": [\"@playwright/mcp\"],\n",
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" \"transport\": \"stdio\",\n",
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" \"env\": {\n",
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" \"PATH\": \"/Users/rlm/.cursor/extensions/ms-python.python-2024.12.3-darwin-arm64/python_files/deactivate/zsh:/Users/rlm/Desktop/Code/langgraph-swarm/.venv/bin:/Users/rlm/.bun/bin:/Users/rlm/.poetry/bin:/Users/rlm/Library/Python/3.13/bin:/opt/homebrew/bin:/opt/homebrew/sbin:/usr/local/bin:/System/Cryptexes/App/usr/bin:/usr/bin:/bin:/usr/sbin:/sbin:/Library/TeX/texbin:/Users/rlm/.cargo/bin:/Users/rlm/miniforge3/condabin:/Users/rlm/.local/bin\"\n",
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" }\n",
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" },\n",
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" \"planning-server\": {\n",
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" \"command\": \"uvx\",\n",
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" \"args\": [\n",
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" \"--from\",\n",
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" \"mcpdoc\",\n",
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" \"mcpdoc\",\n",
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" \"--urls\",\n",
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" llms_txt_urls,\n",
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" \"--transport\",\n",
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" \"stdio\",\n",
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" \"--port\",\n",
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" \"8081\",\n",
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" \"--host\",\n",
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" \"localhost\"\n",
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" ],\n",
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" \"transport\": \"stdio\",\n",
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" }\n",
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" }\n",
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"\n",
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") as client:\n",
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" # Planner agent\n",
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" planner_agent = create_react_agent(model,\n",
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" prompt=planner_prompt, \n",
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" tools=client.server_name_to_tools[\"planning-server\"].append(transfer_to_researcher_agent),\n",
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" name=\"planner_agent\") \n",
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"\n",
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" # Researcher agent\n",
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" researcher_agent = create_react_agent(model, \n",
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" prompt=researcher_prompt, \n",
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" tools=client.server_name_to_tools[\"research-server\"].append(transfer_to_planner_agent),\n",
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" name=\"researcher_agent\") \n",
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"\n",
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" # Swarm\n",
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" agent_swarm = create_swarm([planner_agent, researcher_agent], default_active_agent=\"planner_agent\")\n",
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"\n",
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" # app = agent_swarm.compile(config_schema=Configuration)\n",
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" agent = agent_swarm.compile()\n",
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" response = await agent.ainvoke({\"messages\": messages})\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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"vscode": {
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"languageId": "shellscript"
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}
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},
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"outputs": [],
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"source": [
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"for m in agent_response['messages']:\n",
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" m.pretty_print()"
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]
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},
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{
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"cell_type": "code",
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}
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],
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"kernelspec": {
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"display_name": ".venv",
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"language": "python",
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"name": "python3"
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},
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},
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"nbconvert_exporter": "python",
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"nbformat": 4,
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"nbformat_minor": 2
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}

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