90 lines
3.5 KiB
Markdown
90 lines
3.5 KiB
Markdown
# New LangGraph Project
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[](https://github.com/langchain-ai/new-langgraph-project/actions/workflows/unit-tests.yml)
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[](https://github.com/langchain-ai/new-langgraph-project/actions/workflows/integration-tests.yml)
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This template demonstrates a simple application implemented using [LangGraph](https://github.com/langchain-ai/langgraph), designed for showing how to get started with [LangGraph Studio](https://langchain-ai.github.io/langgraph/concepts/langgraph_studio/).
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<div align="center">
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<img src="./static/studio_ui.png" alt="Graph view in LangGraph studio UI" width="75%" />
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</div>
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The core logic defined in `src/agent/graph.py`, showcases a straightforward application that responds with a fixed string and the configuration provided.
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This template provides a basic foundation for a LangGraph application that can be extended to create more complex agents.
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## Getting Started
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1. Install the [LangGraph CLI](https://langchain-ai.github.io/langgraph/concepts/langgraph_cli/).
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```
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pip install --upgrade "langgraph-cli[inmem]"
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```
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2. Create a `.env` file.
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```bash
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cp .env.example .env
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```
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3. If desired, add your LangSmith API key in your `.env` file.
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```
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LANGSMITH_API_KEY=lsv2...
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```
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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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4. Install dependencies
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```bash
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cd path/to/your/app
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pip install -e .
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```
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5. Customize the code as needed.
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6. Start the LangGraph Server.
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```
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langgraph dev
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```
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## How to customize
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1. **Define a configuration**: Create a `configuration.py` file and define a configuration schema. 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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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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You can also quickly extend this template by:
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- Adding custom tools or functions to enhance the chatbot's capabilities.
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- Implementing additional logic for handling specific types of user queries or tasks.
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- Integrating external APIs or databases to provide more dynamic responses.
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## Development
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While iterating on your graph, you can edit past state and rerun your app from previous states to debug specific nodes. Local changes will be automatically applied via hot reload.
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Follow-up requests will be appended to the same thread. You can create an entirely new thread, clearing previous history, using the `+` button in the top right.
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For more advanced features and examples, refer to the [LangGraph documentation](https://langchain-ai.github.io/langgraph/). These resources can help you adapt this template for your specific use case and build more sophisticated conversational agents.
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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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