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.
We use the Python SDK in this quickstart, but since ColiVara is an API, you can use any language by making standard API calls.
API Keys
Install the Python SDK
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.
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.
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
Do I need a vector database?
No - ColiVara uses Postgres and pgVector to store vectors for you. You DO NOT need to generate, save, or manage embeddings in anyway.
Do you convert the documents to markdown/text?
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.
How does non-pdf documents or web pages work?
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.
Can I use my vector database?
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.
