# Faiss

Faiss is a library for efficient similarity search and clustering of dense vectors. Unlike cloud vector stores, Faiss runs locally on your machine and saves data to your file system.

<figure><img src="/files/gLOJlCir4u9XatsyPmEj" alt="" width="307"><figcaption><p>Faiss Node</p></figcaption></figure>

## Prerequisites

### Method 1: NPM/Source Installation

For local installations, ensure your machine can compile C++ modules.

* **Linux:** Install `build-essential`.
* **MacOS:** Run `xcode-select --install` to get Clang/C++ tools.
* **Windows:** Install Visual Studio Build Tools and Python.

### Method 2: Docker

If you are running Flowise using Docker, Faiss is ready to use.

## Setup

### Inputs

| Input          | Description                                         |
| -------------- | --------------------------------------------------- |
| **Document**   | Connect any node from the Document Loader category. |
| **Embeddings** | Connect any node from the Embeddings category.      |

### Parameters

* **Base Path:** The folder path where the index files (`faiss.index` and `docstore.json`) will be saved.
  * If left blank, data is stored in RAM and will be lost when Flowise restarts.
  * **Docker Path Example:** `/root/.flowise/faiss`
  * > **Note:** Ensure the path is mapped to a volume in your `docker-compose.yml` file to prevent data loss upon container restart.
  * **Local Path Example:** `C:\Users\YourName\flowise_data`

## Configuration and Ingestion

1. Add a new **Faiss** node on the canvas.
2. Enter a folder path in the **Faiss** node’s **Base Path** field.
3. Connect your Document Loader and Embeddings nodes to the **Faiss** node.
4. Click **Upsert Vector Database** to process your documents.

## Verify

To verify your data has been upserted, navigate to the folder you specified in the **Base Path** field. You should see files `faiss.index` and `docstore.json`.

## Resources

* [LangChain JS Faiss Documentation](https://docs.langchain.com/oss/javascript/integrations/vectorstores/faiss)


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