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ci: run notebooks in 'development' for PRs (#3188)

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Vadym Barda
2025-01-23 21:59:03 -05:00
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parent ad51bfdf71
commit 48040d8ea5
2 changed files with 2 additions and 2 deletions
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.github/workflows/run_notebooks.yml
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@@ -16,7 +16,7 @@ jobs:
runs-on: ubuntu-latest
strategy:
matrix:
lib-version: ${{ (github.event_name == 'pull_request') && fromJSON('["development"]') || fromJSON('["development", "latest"]') }}
lib-version: ${{ (github.event_name == 'workflow_dispatch' || github.event_name == 'schedule') && fromJSON('["development", "latest"]') || fromJSON('["development"]') }}
steps:
- uses: actions/checkout@v4
docs/docs/how-tos/react-agent-from-scratch.ipynb
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@@ -14,7 +14,7 @@
" - [Messages](https://python.langchain.com/docs/concepts/messages/)\n",
" - [LangGraph Glossary](../../concepts/low_level/)\n",
"\n",
"Using the prebuilt ReAct agent ([create_react_agent](https://langchain-ai.github.io/langgraph/reference/prebuilt/#langgraph.prebuilt.chat_agent_executor.create_react_agent)) is a great way to get started, but sometimes you might want more control and customization. In those cases, you can create a custom ReAct agent. This guide shows how to implement ReAct agent from scratch using LangGraph.\n",
"Using the prebuilt ReAct agent [create_react_agent][langgraph.prebuilt.chat_agent_executor.create_react_agent] is a great way to get started, but sometimes you might want more control and customization. In those cases, you can create a custom ReAct agent. This guide shows how to implement ReAct agent from scratch using LangGraph.\n",
"\n",
"## Setup\n",
"\n",