# Quickstart

<figure><img src="/files/jwk0adAvvSAUlWl9bHDp" alt="" width="188"><figcaption></figcaption></figure>

ColiVara is an web API that abstracts all the difficult parts about visual RAG. It embeds and saves documents, and then returns the highest matching pages when a user makes a query.

{% hint style="info" %}
We use the Python SDK in this quickstart, but since ColiVara is an API, you can use any language by making standard API calls.
{% endhint %}

### API Keys

Get an API Key from the [ColiVara Website](https://colivara.com) or via self-hosting.&#x20;

### Install the Python SDK

```bash
pip install colivara-py
```

### Index a document

Colivara accepts **a file url, or base64 encoded file, or a file path**. We support over 100 file formats including PDF, DOCX, PPTX, and more. We will also automatically take a screenshot of URLs (webpages) and index them.

```python
import os
from colivara_py import ColiVara

rag_client = ColiVara(
    # this is the default and can be omitted
    api_key=os.environ.get("COLIVARA_API_KEY"),
    # this is the default and can be omitted
    base_url="https://api.colivara.com"
)

# Upload a document to the default collection
document = rag_client.upsert_document(
    name="attention is all you need",
    url="https://arxiv.org/abs/1706.03762",
    metadata={"published_year": "2017"}
)
```

### Search

You can filter by collection name, collection metadata, and document metadata. You can also specify the number of results you want.

```python
results = rag_client.search(query="What is the role of self-attention in transformers?")
print(results) # top 3 pages with the most relevant information
```

### FAQ

<details>

<summary>Do I need a vector database?</summary>

No - ColiVara uses Postgres and pgVector to store vectors for you. You DO NOT need to generate, save, or manage embeddings in anyway.

</details>

<details>

<summary>Do you convert the documents to markdown/text?</summary>

No - ColiVara treats everything as an image, and uses vision models. There are no parsing, chunking, or OCR involved. This method outperforms chunking, and OCR for both text-based documents and visual documents.

</details>

<details>

<summary>How does non-pdf documents or web pages work?</summary>

We run a pipeline to convert them to images, and perform our normal image-based retrieval. This all happen for you under the hood, and you get the top-k pages when performing retrieval.

</details>

<details>

<summary>Can I use my vector database? </summary>

Yes - we have an embedding endpoint that only generates embeddings without saving or doing anything else. You can store these embeddings at your end. Keep in mind that we use late-interaction and multi-vectors, many vector databases do not support this yet.

</details>


---

# Agent Instructions: Querying This Documentation

If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question.

Perform an HTTP GET request on the current page URL with the `ask` query parameter:

```
GET https://docs.colivara.com/getting-started/quickstart.md?ask=<question>
```

The question should be specific, self-contained, and written in natural language.
The response will contain a direct answer to the question and relevant excerpts and sources from the documentation.

Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.