mirror of
https://github.com/langchain-ai/langgraph.git
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add how to guides for autogen integration (#2466)
This commit is contained in:
Harrison Chase
File diff suppressed because one or more lines are too long
@@ -0,0 +1,173 @@
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{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "8381b6e0-29a6-48c5-b451-5d2549351249",
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"metadata": {},
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"source": [
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"# How to use LangGraph Platform to deploy CrewAI, AutoGen, and other frameworks\n",
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"\n",
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"[LangGraph Platform](https://langchain-ai.github.io/langgraph/concepts/langgraph_platform/) provides infrastructure for deploying agents. This integrates seamlessly with LangGraph, but can also work with other frameworks. The way to make this work is to wrap the agent in a single LangGraph node, and have that be the entire graph.\n",
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"\n",
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"Doing so will allow you to deploy to LangGraph Platform, and allows you to get a lot of the [benefits](https://langchain-ai.github.io/langgraph/concepts/langgraph_platform/). You get horizontally scalable infrastructure, a task queue to handle bursty operations, a persistence layer to power short term memory, and long term memory support.\n",
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"\n",
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"In this guide we show how to do this with an AutoGen agent, but this method should work for agents defined in other frameworks like CrewAI, LlamaIndex, and others as well."
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]
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},
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{
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"cell_type": "markdown",
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"id": "1113cb16-b538-448c-924c-85731ce96ebd",
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"metadata": {},
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"source": [
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"## Setup"
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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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"id": "f05993fa-9d03-4f45-bc13-0a8d87260d86",
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"metadata": {
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"scrolled": true
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},
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"outputs": [],
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"source": [
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"# %pip install autogen langgraph"
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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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"id": "f4e0ca12-1714-4776-a30a-9527e519799b",
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"metadata": {},
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"outputs": [],
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"source": [
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"# import os\n",
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"# import getpass\n",
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"\n",
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"# os.environ[\"OPENAI_API_KEY\"] = getpass.getpass()"
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]
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},
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{
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"cell_type": "markdown",
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"id": "1926bbc3-6b06-41e0-9604-860a2bbf8fa3",
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"metadata": {},
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"source": [
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"## Define autogen agent\n",
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"\n",
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"Here we define our AutoGen agent. From https://github.com/microsoft/autogen/blob/0.2/notebook/agentchat_web_info.ipynb"
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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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"id": "d4a14dc7-d565-4207-8788-525f85b9fb27",
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"metadata": {},
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"outputs": [],
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"source": [
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"import autogen\n",
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"import os\n",
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"\n",
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"config_list = [{\"model\": \"gpt-4o\", \"api_key\": os.environ[\"OPENAI_API_KEY\"]}]\n",
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"\n",
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"llm_config = {\n",
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" \"timeout\": 600,\n",
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" \"cache_seed\": 42,\n",
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" \"config_list\": config_list,\n",
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" \"temperature\": 0,\n",
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"}\n",
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"\n",
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"autogen_agent = autogen.AssistantAgent(\n",
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" name=\"assistant\",\n",
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" llm_config=llm_config,\n",
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")\n",
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"\n",
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"user_proxy = autogen.UserProxyAgent(\n",
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" name=\"user_proxy\",\n",
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" human_input_mode=\"NEVER\",\n",
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" max_consecutive_auto_reply=10,\n",
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" is_termination_msg=lambda x: x.get(\"content\", \"\").rstrip().endswith(\"TERMINATE\"),\n",
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" code_execution_config={\n",
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" \"work_dir\": \"web\",\n",
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" \"use_docker\": False,\n",
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" }, # Please set use_docker=True if docker is available to run the generated code. Using docker is safer than running the generated code directly.\n",
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" llm_config=llm_config,\n",
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" system_message=\"Reply TERMINATE if the task has been solved at full satisfaction. Otherwise, reply CONTINUE, or the reason why the task is not solved yet.\",\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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"id": "b1170836-f23e-4e4c-ab83-ce791cd7fbd2",
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"metadata": {},
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"source": [
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"## Wrap in LangGraph\n",
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"\n",
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"We now wrap the AutoGen agent in a single LangGraph node, and make that the entire graph.\n",
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"The main thing this involves is defining an Input and Output schema for the node, which you would need to do if deploying this manually, so it's no extra work"
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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": 11,
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"id": "7b417c16-ff4e-4d5c-a9a9-0aaeeef6ede5",
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"metadata": {},
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"outputs": [],
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"source": [
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"from langgraph.graph import StateGraph, MessagesState\n",
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"\n",
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"\n",
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"def call_autogen_agent(state: MessagesState):\n",
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" last_message = state[\"messages\"][-1]\n",
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" response = user_proxy.initiate_chat(autogen_agent, message=last_message.content)\n",
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" # get the final response from the agent\n",
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" content = response.chat_history[-1][\"content\"]\n",
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" return {\"messages\": {\"role\": \"assistant\", \"content\": content}}\n",
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"\n",
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"\n",
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"graph = StateGraph(MessagesState)\n",
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"graph.add_node(call_autogen_agent)\n",
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"graph.set_entry_point(\"call_autogen_agent\")\n",
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"graph = graph.compile()"
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]
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},
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{
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"cell_type": "markdown",
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"id": "f6a18377-ac29-478f-a76a-b213f1a3c85d",
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"metadata": {},
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"source": [
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"## Deploy with LangGraph Platform\n",
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"\n",
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"You can now deploy this as you normally would with LangGraph Platform. See [these instructions](https://langchain-ai.github.io/langgraph/concepts/deployment_options/) for more details."
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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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"id": "2b9c3ecb-0f36-4cfb-a10f-8a2e0ef6c730",
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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": "Python 3 (ipykernel)",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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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": "3.11.1"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}
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@@ -345,7 +345,7 @@
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"name": "python",
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"nbconvert_exporter": "python",
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||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.11.4"
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"version": "3.11.1"
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}
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},
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"nbformat": 4,
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@@ -103,6 +103,7 @@ These how-to guides show common patterns for tool calling with LangGraph:
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- [How to force function calling agent to structure output](react-agent-structured-output.ipynb)
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- [How to pass custom LangSmith run ID for graph runs](run-id-langsmith.ipynb)
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- [How to return state before hitting recursion limit](return-when-recursion-limit-hits.ipynb)
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- [How to integrate LangGraph with AutoGen, CrewAI, and other frameworks](autogen-integration.ipynb)
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### Prebuilt ReAct Agent
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@@ -141,6 +142,7 @@ Learn how to set up your app for deployment to LangGraph Platform:
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- [How to customize Dockerfile](../cloud/deployment/custom_docker.md)
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- [How to test locally](../cloud/deployment/test_locally.md)
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- [How to rebuild graph at runtime](../cloud/deployment/graph_rebuild.md)
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- [How to use LangGraph Platform to deploy CrewAI, AutoGen, and other frameworks](autogen-langgraph-platform.ipynb)
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### Deployment
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Generated
+156
-11
@@ -1,4 +1,4 @@
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# This file is automatically @generated by Poetry 1.8.3 and should not be changed by hand.
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# This file is automatically @generated by Poetry 1.8.4 and should not be changed by hand.
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[[package]]
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name = "aiohappyeyeballs"
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@@ -390,6 +390,59 @@ docs = ["cogapp", "furo", "myst-parser", "sphinx", "sphinx-notfound-page", "sphi
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tests = ["cloudpickle", "hypothesis", "mypy (>=1.11.1)", "pympler", "pytest (>=4.3.0)", "pytest-mypy-plugins", "pytest-xdist[psutil]"]
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tests-mypy = ["mypy (>=1.11.1)", "pytest-mypy-plugins"]
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||||
[[package]]
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||||
name = "autogen"
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version = "0.3.2"
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description = "A programming framework for agentic AI"
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optional = false
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python-versions = "<3.13,>=3.8"
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files = [
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{file = "autogen-0.3.2-py3-none-any.whl", hash = "sha256:e37a9df0ad84cde3429ec63298b8e9eb4e6306a28eec2627171e14b9a61ea64d"},
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{file = "autogen-0.3.2.tar.gz", hash = "sha256:9f8a1170ac2e5a1fc9efc3cfa6e23261dd014db97b17c8c416f97ee14951bc7b"},
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]
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||||
|
||||
[package.dependencies]
|
||||
diskcache = "*"
|
||||
docker = "*"
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flaml = "*"
|
||||
numpy = ">=1.17.0,<2"
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||||
openai = ">=1.3"
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packaging = "*"
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pydantic = ">=1.10,<2.6.0 || >2.6.0,<3"
|
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python-dotenv = "*"
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||||
termcolor = "*"
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||||
tiktoken = "*"
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||||
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[package.extras]
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||||
anthropic = ["anthropic (>=0.23.1)"]
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||||
autobuild = ["chromadb", "huggingface-hub", "pysqlite3", "sentence-transformers"]
|
||||
bedrock = ["boto3 (>=1.34.149)"]
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blendsearch = ["flaml[blendsearch]"]
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cerebras = ["cerebras-cloud-sdk (>=1.0.0)"]
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cohere = ["cohere (>=5.5.8)"]
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cosmosdb = ["azure-cosmos (>=4.2.0)"]
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||||
gemini = ["google-auth", "google-cloud-aiplatform", "google-generativeai (>=0.5,<1)", "pillow", "pydantic"]
|
||||
graph = ["matplotlib", "networkx"]
|
||||
graph-rag-falkor-db = ["graphrag-sdk"]
|
||||
groq = ["groq (>=0.9.0)"]
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||||
jupyter-executor = ["ipykernel (>=6.29.0)", "jupyter-client (>=8.6.0)", "jupyter-kernel-gateway", "requests", "websocket-client"]
|
||||
lmm = ["pillow", "replicate"]
|
||||
long-context = ["llmlingua (<0.3)"]
|
||||
mathchat = ["pydantic (==1.10.9)", "sympy", "wolframalpha"]
|
||||
mistral = ["mistralai (>=1.0.1)"]
|
||||
ollama = ["fix-busted-json (>=0.0.18)", "ollama (>=0.3.3)"]
|
||||
redis = ["redis"]
|
||||
retrievechat = ["beautifulsoup4", "chromadb (==0.5.3)", "ipython", "markdownify", "protobuf (==4.25.3)", "pypdf", "sentence-transformers"]
|
||||
retrievechat-mongodb = ["beautifulsoup4", "chromadb (==0.5.3)", "ipython", "markdownify", "protobuf (==4.25.3)", "pymongo (>=4.0.0)", "pypdf", "sentence-transformers"]
|
||||
retrievechat-pgvector = ["beautifulsoup4", "chromadb (==0.5.3)", "ipython", "markdownify", "pgvector (>=0.2.5)", "protobuf (==4.25.3)", "psycopg (>=3.1.18)", "pypdf", "sentence-transformers"]
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||||
retrievechat-qdrant = ["beautifulsoup4", "chromadb (==0.5.3)", "fastembed (>=0.3.1)", "ipython", "markdownify", "protobuf (==4.25.3)", "pypdf", "qdrant-client", "sentence-transformers"]
|
||||
teachable = ["chromadb"]
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||||
test = ["ipykernel", "nbconvert", "nbformat", "pandas", "pre-commit", "pytest (>=6.1.1,<8)", "pytest-asyncio", "pytest-cov (>=5)"]
|
||||
together = ["together (>=1.2)"]
|
||||
types = ["ipykernel (>=6.29.0)", "jupyter-client (>=8.6.0)", "jupyter-kernel-gateway", "mypy (==1.9.0)", "pytest (>=6.1.1,<8)", "requests", "websocket-client"]
|
||||
websockets = ["websockets (>=12.0,<13)"]
|
||||
websurfer = ["beautifulsoup4", "markdownify", "pathvalidate", "pdfminer.six"]
|
||||
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[[package]]
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||||
name = "babel"
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||||
version = "2.16.0"
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||||
@@ -1135,6 +1188,17 @@ wrapt = ">=1.10,<2"
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[package.extras]
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dev = ["PyTest", "PyTest-Cov", "bump2version (<1)", "sphinx (<2)", "tox"]
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[[package]]
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||||
name = "diskcache"
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||||
version = "5.6.3"
|
||||
description = "Disk Cache -- Disk and file backed persistent cache."
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optional = false
|
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python-versions = ">=3"
|
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files = [
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{file = "diskcache-5.6.3-py3-none-any.whl", hash = "sha256:5e31b2d5fbad117cc363ebaf6b689474db18a1f6438bc82358b024abd4c2ca19"},
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{file = "diskcache-5.6.3.tar.gz", hash = "sha256:2c3a3fa2743d8535d832ec61c2054a1641f41775aa7c556758a109941e33e4fc"},
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]
|
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|
||||
[[package]]
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name = "distro"
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version = "1.9.0"
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@@ -1166,6 +1230,28 @@ idna = ["idna (>=3.6)"]
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||||
trio = ["trio (>=0.23)"]
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||||
wmi = ["wmi (>=1.5.1)"]
|
||||
|
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[[package]]
|
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name = "docker"
|
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version = "7.1.0"
|
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description = "A Python library for the Docker Engine API."
|
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optional = false
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python-versions = ">=3.8"
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files = [
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{file = "docker-7.1.0-py3-none-any.whl", hash = "sha256:c96b93b7f0a746f9e77d325bcfb87422a3d8bd4f03136ae8a85b37f1898d5fc0"},
|
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{file = "docker-7.1.0.tar.gz", hash = "sha256:ad8c70e6e3f8926cb8a92619b832b4ea5299e2831c14284663184e200546fa6c"},
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]
|
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|
||||
[package.dependencies]
|
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pywin32 = {version = ">=304", markers = "sys_platform == \"win32\""}
|
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requests = ">=2.26.0"
|
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urllib3 = ">=1.26.0"
|
||||
|
||||
[package.extras]
|
||||
dev = ["coverage (==7.2.7)", "pytest (==7.4.2)", "pytest-cov (==4.1.0)", "pytest-timeout (==2.1.0)", "ruff (==0.1.8)"]
|
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docs = ["myst-parser (==0.18.0)", "sphinx (==5.1.1)"]
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ssh = ["paramiko (>=2.4.3)"]
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websockets = ["websocket-client (>=1.3.0)"]
|
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|
||||
[[package]]
|
||||
name = "durationpy"
|
||||
version = "0.7"
|
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@@ -1271,6 +1357,43 @@ httpx-sse = "*"
|
||||
Pillow = "*"
|
||||
pydantic = "*"
|
||||
|
||||
[[package]]
|
||||
name = "flaml"
|
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version = "2.3.2"
|
||||
description = "A fast library for automated machine learning and tuning"
|
||||
optional = false
|
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python-versions = ">=3.8"
|
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files = [
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{file = "FLAML-2.3.2-py3-none-any.whl", hash = "sha256:1ee6e8e76bf1d741b4da41e2a2a8c0638b36d90b0f60aac323b5568f54dcb9e7"},
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{file = "flaml-2.3.2.tar.gz", hash = "sha256:4a1ec289ddaec36850cfc66f6fb335b8521df49ea31f6adb54ea63a5cebb6865"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
NumPy = ">=1.17"
|
||||
|
||||
[package.extras]
|
||||
autogen = ["diskcache", "openai (==0.27.8)", "termcolor"]
|
||||
automl = ["lightgbm (>=2.3.1)", "pandas (>=1.1.4)", "scikit-learn (>=1.0.0)", "scipy (>=1.4.1)", "xgboost (>=0.90,<3.0.0)"]
|
||||
autozero = ["packaging", "pandas", "scikit-learn"]
|
||||
azureml = ["azureml-mlflow"]
|
||||
benchmark = ["catboost (>=0.26)", "pandas (==1.1.4)", "psutil (==5.8.0)", "xgboost (==1.3.3)"]
|
||||
blendsearch = ["optuna (>=2.8.0,<=3.6.1)", "packaging"]
|
||||
catboost = ["catboost (>=0.26,<1.2)", "catboost (>=0.26,<=1.2.5)"]
|
||||
forecast = ["hcrystalball (==0.1.10)", "holidays (<0.14)", "prophet (>=1.0.1)", "pytorch-forecasting (>=0.9.0)", "pytorch-lightning (==1.9.0)", "statsmodels (>=0.12.2)", "tensorboardX (==2.6)"]
|
||||
hf = ["datasets", "nltk (<=3.8.1)", "rouge-score", "seqeval", "transformers[torch] (==4.26)"]
|
||||
mathchat = ["diskcache", "openai (==0.27.8)", "pydantic (==1.10.9)", "sympy", "termcolor", "wolframalpha"]
|
||||
nlp = ["datasets", "nltk (<=3.8.1)", "rouge-score", "seqeval", "transformers[torch] (==4.26)"]
|
||||
nni = ["nni"]
|
||||
notebook = ["jupyter"]
|
||||
openai = ["diskcache", "openai (==0.27.8)"]
|
||||
ray = ["ray[tune] (>=1.13,<2.0)"]
|
||||
retrievechat = ["chromadb", "diskcache", "openai (==0.27.8)", "sentence-transformers", "termcolor", "tiktoken"]
|
||||
spark = ["joblib (<=1.3.2)", "joblibspark (>=0.5.0)", "pyspark (>=3.2.0)"]
|
||||
synapse = ["joblibspark (>=0.5.0)", "optuna (>=2.8.0,<=3.6.1)", "pyspark (>=3.2.0)"]
|
||||
test = ["catboost (>=0.26)", "catboost (>=0.26,<1.2)", "coverage (>=5.3)", "dataclasses", "datasets", "dill", "hcrystalball (==0.1.10)", "ipykernel", "joblib (<=1.3.2)", "joblibspark (>=0.5.0)", "jupyter", "lightgbm (>=2.3.1)", "mlflow (==2.15.1)", "nbconvert", "nbformat", "nltk (<=3.8.1)", "openml", "optuna (>=2.8.0,<=3.6.1)", "packaging", "pandas (>=1.1.4)", "pandas (>=1.1.4,<2.0.0)", "pre-commit", "psutil (==5.8.0)", "pydantic (==1.10.9)", "pytest (>=6.1.1)", "pytorch-forecasting (>=0.9.0,<=0.10.1)", "pytorch-lightning (<1.9.1)", "requests (<2.29.0)", "rgf-python", "rouge-score", "scikit-learn (>=1.0.0)", "scipy (>=1.4.1)", "seqeval", "statsmodels (>=0.12.2)", "sympy", "tensorboardX (==2.6)", "thop", "torch", "torchvision", "transformers[torch] (==4.26)", "wolframalpha", "xgboost (>=0.90,<2.0.0)"]
|
||||
ts-forecast = ["hcrystalball (==0.1.10)", "holidays (<0.14)", "prophet (>=1.0.1)", "statsmodels (>=0.12.2)"]
|
||||
vw = ["scikit-learn", "vowpalwabbit (>=8.10.0,<9.0.0)"]
|
||||
|
||||
[[package]]
|
||||
name = "flatbuffers"
|
||||
version = "24.3.25"
|
||||
@@ -2912,7 +3035,7 @@ langchain-core = ">=0.3.0,<0.4.0"
|
||||
|
||||
[[package]]
|
||||
name = "langgraph"
|
||||
version = "0.2.34"
|
||||
version = "0.2.52"
|
||||
description = "Building stateful, multi-actor applications with LLMs"
|
||||
optional = false
|
||||
python-versions = ">=3.9.0,<4.0"
|
||||
@@ -2920,8 +3043,9 @@ files = []
|
||||
develop = true
|
||||
|
||||
[package.dependencies]
|
||||
langchain-core = ">=0.2.39,<0.4"
|
||||
langgraph-checkpoint = "^2.0.0"
|
||||
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"
|
||||
|
||||
[package.source]
|
||||
type = "directory"
|
||||
@@ -2929,7 +3053,7 @@ url = "libs/langgraph"
|
||||
|
||||
[[package]]
|
||||
name = "langgraph-checkpoint"
|
||||
version = "2.0.1"
|
||||
version = "2.0.5"
|
||||
description = "Library with base interfaces for LangGraph checkpoint savers."
|
||||
optional = false
|
||||
python-versions = "^3.9.0,<4.0"
|
||||
@@ -2946,7 +3070,7 @@ url = "libs/checkpoint"
|
||||
|
||||
[[package]]
|
||||
name = "langgraph-checkpoint-postgres"
|
||||
version = "2.0.1"
|
||||
version = "2.0.3"
|
||||
description = "Library with a Postgres implementation of LangGraph checkpoint saver."
|
||||
optional = false
|
||||
python-versions = "^3.9.0,<4.0"
|
||||
@@ -2954,7 +3078,7 @@ files = []
|
||||
develop = true
|
||||
|
||||
[package.dependencies]
|
||||
langgraph-checkpoint = "^2.0.0"
|
||||
langgraph-checkpoint = "^2.0.2"
|
||||
orjson = ">=3.10.1"
|
||||
psycopg = "^3.0.0"
|
||||
psycopg-pool = "^3.0.0"
|
||||
@@ -2965,7 +3089,7 @@ url = "libs/checkpoint-postgres"
|
||||
|
||||
[[package]]
|
||||
name = "langgraph-checkpoint-sqlite"
|
||||
version = "2.0.0"
|
||||
version = "2.0.1"
|
||||
description = "Library with a SQLite implementation of LangGraph checkpoint saver."
|
||||
optional = false
|
||||
python-versions = "^3.9.0"
|
||||
@@ -2974,7 +3098,7 @@ develop = true
|
||||
|
||||
[package.dependencies]
|
||||
aiosqlite = "^0.20.0"
|
||||
langgraph-checkpoint = "^2.0.0"
|
||||
langgraph-checkpoint = "^2.0.2"
|
||||
|
||||
[package.source]
|
||||
type = "directory"
|
||||
@@ -2982,7 +3106,7 @@ url = "libs/checkpoint-sqlite"
|
||||
|
||||
[[package]]
|
||||
name = "langgraph-sdk"
|
||||
version = "0.1.32"
|
||||
version = "0.1.36"
|
||||
description = "SDK for interacting with LangGraph API"
|
||||
optional = false
|
||||
python-versions = "^3.9.0,<4.0"
|
||||
@@ -4974,6 +5098,7 @@ description = "Pure-Python implementation of ASN.1 types and DER/BER/CER codecs
|
||||
optional = false
|
||||
python-versions = ">=3.8"
|
||||
files = [
|
||||
{file = "pyasn1-0.6.1-py3-none-any.whl", hash = "sha256:0d632f46f2ba09143da3a8afe9e33fb6f92fa2320ab7e886e2d0f7672af84629"},
|
||||
{file = "pyasn1-0.6.1.tar.gz", hash = "sha256:6f580d2bdd84365380830acf45550f2511469f673cb4a5ae3857a3170128b034"},
|
||||
]
|
||||
|
||||
@@ -4984,6 +5109,7 @@ description = "A collection of ASN.1-based protocols modules"
|
||||
optional = false
|
||||
python-versions = ">=3.8"
|
||||
files = [
|
||||
{file = "pyasn1_modules-0.4.1-py3-none-any.whl", hash = "sha256:49bfa96b45a292b711e986f222502c1c9a5e1f4e568fc30e2574a6c7d07838fd"},
|
||||
{file = "pyasn1_modules-0.4.1.tar.gz", hash = "sha256:c28e2dbf9c06ad61c71a075c7e0f9fd0f1b0bb2d2ad4377f240d33ac2ab60a7c"},
|
||||
]
|
||||
|
||||
@@ -6043,6 +6169,11 @@ files = [
|
||||
{file = "scikit_learn-1.5.2-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:f60021ec1574e56632be2a36b946f8143bf4e5e6af4a06d85281adc22938e0dd"},
|
||||
{file = "scikit_learn-1.5.2-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:394397841449853c2290a32050382edaec3da89e35b3e03d6cc966aebc6a8ae6"},
|
||||
{file = "scikit_learn-1.5.2-cp312-cp312-win_amd64.whl", hash = "sha256:57cc1786cfd6bd118220a92ede80270132aa353647684efa385a74244a41e3b1"},
|
||||
{file = "scikit_learn-1.5.2-cp313-cp313-macosx_10_13_x86_64.whl", hash = "sha256:e9a702e2de732bbb20d3bad29ebd77fc05a6b427dc49964300340e4c9328b3f5"},
|
||||
{file = "scikit_learn-1.5.2-cp313-cp313-macosx_12_0_arm64.whl", hash = "sha256:b0768ad641981f5d3a198430a1d31c3e044ed2e8a6f22166b4d546a5116d7908"},
|
||||
{file = "scikit_learn-1.5.2-cp313-cp313-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:178ddd0a5cb0044464fc1bfc4cca5b1833bfc7bb022d70b05db8530da4bb3dd3"},
|
||||
{file = "scikit_learn-1.5.2-cp313-cp313-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:f7284ade780084d94505632241bf78c44ab3b6f1e8ccab3d2af58e0e950f9c12"},
|
||||
{file = "scikit_learn-1.5.2-cp313-cp313-win_amd64.whl", hash = "sha256:b7b0f9a0b1040830d38c39b91b3a44e1b643f4b36e36567b80b7c6bd2202a27f"},
|
||||
{file = "scikit_learn-1.5.2-cp39-cp39-macosx_10_9_x86_64.whl", hash = "sha256:757c7d514ddb00ae249832fe87100d9c73c6ea91423802872d9e74970a0e40b9"},
|
||||
{file = "scikit_learn-1.5.2-cp39-cp39-macosx_12_0_arm64.whl", hash = "sha256:52788f48b5d8bca5c0736c175fa6bdaab2ef00a8f536cda698db61bd89c551c1"},
|
||||
{file = "scikit_learn-1.5.2-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:643964678f4b5fbdc95cbf8aec638acc7aa70f5f79ee2cdad1eec3df4ba6ead8"},
|
||||
@@ -6362,6 +6493,20 @@ files = [
|
||||
doc = ["reno", "sphinx"]
|
||||
test = ["pytest", "tornado (>=4.5)", "typeguard"]
|
||||
|
||||
[[package]]
|
||||
name = "termcolor"
|
||||
version = "2.5.0"
|
||||
description = "ANSI color formatting for output in terminal"
|
||||
optional = false
|
||||
python-versions = ">=3.9"
|
||||
files = [
|
||||
{file = "termcolor-2.5.0-py3-none-any.whl", hash = "sha256:37b17b5fc1e604945c2642c872a3764b5d547a48009871aea3edd3afa180afb8"},
|
||||
{file = "termcolor-2.5.0.tar.gz", hash = "sha256:998d8d27da6d48442e8e1f016119076b690d962507531df4890fcd2db2ef8a6f"},
|
||||
]
|
||||
|
||||
[package.extras]
|
||||
tests = ["pytest", "pytest-cov"]
|
||||
|
||||
[[package]]
|
||||
name = "terminado"
|
||||
version = "0.18.1"
|
||||
@@ -7331,4 +7476,4 @@ type = ["pytest-mypy"]
|
||||
[metadata]
|
||||
lock-version = "2.0"
|
||||
python-versions = "^3.10"
|
||||
content-hash = "738e69cf406b140217cc8c3c0f2ccb5c2027d4701bcf2a347ba4b107f700ab2a"
|
||||
content-hash = "776ee42630769f08e3896338f18ec81830166695d32d2208dc31dedb22d3b22d"
|
||||
|
||||
@@ -54,6 +54,7 @@ motor = "^3.5.1"
|
||||
grandalf = "^0.8"
|
||||
pyppeteer = "^2.0.0"
|
||||
networkx = "^3.3"
|
||||
autogen = { version = "^0.3.0", python = "<3.13,>=3.8" }
|
||||
|
||||
[tool.poetry.group.test]
|
||||
optional = true
|
||||
|
||||
Reference in New Issue
Block a user