> ## Documentation Index
> Fetch the complete documentation index at: https://eigendb.mintlify.site/llms.txt
> Use this file to discover all available pages before exploring further.

# Quickstart

> Getting started

## Getting it up and running

1. Clone the [repo](https://github.com/Eigen-DB/eigen-db) from GitHub:

```sh theme={null}
git clone https://github.com/Eigen-DB/eigen-db
```

2. To start EigenDB, run:

```sh theme={null}
docker compose up -d
```

Omit the `-d` flag if you don't want to run the it in the background.

**Your EigenDB instance should now be running on port `:8080`**

You can test the connection to your instance by running:

```sh theme={null}
curl http://localhost:8080/api/v1/health
```

3. Get your API key either from `apps/eigendb/eigen/api_key.txt` or from the container logs:

```sh theme={null}
docker logs CONTAINER_NAME
```

## Creating your first index

EigenDB can be interfaced with through our Python SDK or its robust REST API.

<AccordionGroup>
  <Accordion title="Python SDK">
    <Info>
      To install the Python SDK, check out this [page](/sdks/python).
    </Info>

    ```python python theme={null}
    import os
    from eigen_client.client import Client
    from eigen_client.data_types import Document

    client = Client(
        url="http://localhost:8080",
        api_key="eigendb-***",
    )

    index = client.create_index_from_model(
        index_name="food-facts",
        model_name="text-embedding-3-small",
        model_provider="openai",
        model_provider_api_key="your openai api key..."
    )

    documents = [
        Document(id=1, data="Fresh herbs boost flavor.", metadata={"recipe_id": "123"}),
        Document(id=2, data="Slow simmer blends soup.", metadata={"recipe_id": "456"}),
        Document(id=3, data="Homemade bread smells great.", metadata={"recipe_id": "789"}),
        Document(id=4, data="Grilled veggies taste sweeter.", metadata={"recipe_id": "987"}),
        Document(id=5, data="Cast iron sears steak well.", metadata={"recipe_id": "654"})
    ]

    index.upsert_docs(documents)

    results = index.search_docs(
        string="Baking",
        k=3
    )

    print(results)
    ```
  </Accordion>

  <Accordion title="REST API">
    To first create an index:

    ```bash curl theme={null}
    curl -X PUT http://localhost:8080/api/v1/indexes/[INDEX-NAME]/create \
      -H "Content-Type: application/json" \
      -H "X-Eigen-API-Key: eigendb-***" \
      -d '{
        "dimensions": 1536,
        "metric": "cosine"
      }'
    ```

    To upsert your embeddings:

    ```bash curl theme={null}
    curl -X PUT http://localhost:8080/api/v1/embeddings/[INDEX-NAME]/upsert \ 
      -H "Content-Type: application/json" \
      -H "X-Eigen-API-Key: eigendb-***" \
      -d '{
        "embeddings": [
          {
            "id": 1,
            "data": [0.1, 0.2, 0.3, ..., 0.384],
            "metadata": {"recipe_id": "123"}
          },
          {
            "id": 2,
            "data": [0.2, 0.1, 0.4, ..., 0.385],
            "metadata": {"recipe_id": "456"}
          },
          ...
        ]
      }'
    ```

    To search for similar embeddings:

    ```bash curl theme={null}
    curl -X POST http://localhost:8080/api/v1/embeddings/[INDEX-NAME]/search \
      -H "Content-Type: application/json" \
      -H "X-Eigen-API-Key: eigendb-***" \
      -d '{
        "queryVector": [0.1, 0.2, 0.3, ..., 0.384],
        "k": 3
      }'
    ```
  </Accordion>
</AccordionGroup>