Increase your LLM app perfomance with Memora instead of a simple vector database.
We've put all of Paul Graham's essays into Memora chunked by paragraph. Just type your questions below and see for yourself the difference.
results
results
Memora is designed as a tool that gets out of your way. You should focus on what differentiates your business — and not on what embedding model to use.
index.ts
main.py
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import memora from 'memora';
await memora.add('The Answer to the...');
An embedding crafted in-house.
Send us your data and watch as our cutting-edge Ultraviolet—1 embedding model seamlessly transforms it into precision-embedded vector and delivering it directly into our database. No more intermediary steps.
More than just an embedding model, it's crafted in-house for seamless integration with the rest of Memora. Designed to work extremely well in all type of data.
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Every single detail about our API & libraries were thought from the ground-up for RAG.
Just pass a query to Memora and we will find the most relevant results for you.
index.ts
main.py
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import memora from 'memora';
const docs = await memora.find('The meaning of life');
A four-stage pipeline that just works.
Outperforming simple semantic search, Memora's accuracy is born from our custom-built Ultraviolet—1 and the powerful Retrieval Engine.
Fueling the core of Memora's retrieval, the Retrieval Engine is powered by custom made in-house Transformer models to find the most relevant data for your query.
Incredible retrieval without the wait. Memora delivers superior results at lighthing speed.
Memora comes in different flavors. Choose the one that fits your needs.
For hobbyist & small projects.
Access to the same power as Pro
Up to a thousand documents
Three collections
A thousand searches per month
For startups & medium-sized projects.
Unlimited collections
Up to a 100k documents
1M searches per month
24/7/365 Slack support
Developers
Docs
API
Typescript library
Python library
Company