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README.md
21
README.md
@@ -15,14 +15,6 @@ You can extend this graph to orchestrate more complex agentic workflows that can
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## Getting Started
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<!--
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Setup instruction auto-generated by `langgraph template lock`. DO NOT EDIT MANUALLY.
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-->
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<!--
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End setup instructions
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-->
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1. Install dependencies, along with the [LangGraph CLI](https://langchain-ai.github.io/langgraph/concepts/langgraph_cli/), which will be used to run the server.
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```bash
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@@ -53,7 +45,7 @@ For more information on getting started with LangGraph Server, [see here](https:
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## How to customize
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1. **Define configurable parameters**: Modify the `Configuration` class in the `graph.py` file to expose the arguments you want to configure. For example, in a chatbot application you may want to define a dynamic system prompt or LLM to use. For more information on configurations in LangGraph, [see here](https://langchain-ai.github.io/langgraph/concepts/low_level/?h=configuration#configuration).
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1. **Define runtime context**: Modify the `Context` class in the `graph.py` file to expose the arguments you want to configure per assistant. For example, in a chatbot application you may want to define a dynamic system prompt or LLM to use. For more information on runtime context in LangGraph, [see here](https://langchain-ai.github.io/langgraph/agents/context/?h=context#static-runtime-context).
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2. **Extend the graph**: The core logic of the application is defined in [graph.py](./src/agent/graph.py). You can modify this file to add new nodes, edges, or change the flow of information.
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@@ -67,14 +59,3 @@ For more advanced features and examples, refer to the [LangGraph documentation](
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LangGraph Studio also integrates with [LangSmith](https://smith.langchain.com/) for more in-depth tracing and collaboration with teammates, allowing you to analyze and optimize your chatbot's performance.
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<!--
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Configuration auto-generated by `langgraph template lock`. DO NOT EDIT MANUALLY.
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{
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"config_schemas": {
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"agent": {
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"type": "object",
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"properties": {}
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}
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}
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}
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-->
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