# Langflow with Upstash Vector

Langflow provides an intuitive, visual interface to design LLM workflows. You can seamlessly integrate Upstash Vector into your Langflow projects to enable vector-based semantic search and context retrieval.

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    <img src="/img/vector/integrations/langflow/final-workflow.png" />
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## Install

To get started, install Langflow and Upstash Vector locally or use the Langflow dashboard from [DataStax](https://www.datastax.com/products/langflow). For local installation, run:

```bash
pip install langflow upstash-vector
```

## Usage

### Creating an Upstash Vector Index

Visit the [Upstash Console](https://console.upstash.com/vector) to create a vector index. To learn more about index creation, you can check out [this page](https://docs.upstash.com/vector/overall/getstarted).

### Adding Upstash Vector to Langflow

In Langflow, you can integrate Upstash Vector for document indexing and semantic search. Use the following steps:

1. Create a workflow with the **File**, **Split**, and **Upstash** components to process and store documents in the Upstash Vector index.
2. Perform a vector search by connecting the **Upstash** component to your query input.

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    <img src="/img/vector/integrations/langflow/insert-workflow.png" />
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### Example Workflow

Enhance your chatbot by combining Langflow’s OpenAI integration with Upstash Vector. Create a RAG workflow to retrieve relevant context from your index and use it to answer user queries.

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    <img src="/img/vector/integrations/langflow/final-workflow.png" />
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## Learn More

For a detailed guide on building a RAG chatbot with Langflow and Upstash Vector, check out this [blog post](https://upstash.com/blog/langflow-upstash-vector).