Show HN: Epsilla – Open-source vector database with low query latency (github.com)
When do we need a vector database? For example, GPT-3.5 has a 16k context window limit. If we want to let it answer a question about a 300 page book, we cannot put the whole book content into the context. We have to choose the sections of the book that are most relevant to the question. Vector database is specialized at ranking and picking the most relevant content from a large pool of documents based on their semantic similarity.
Most vector databases utilize hierarchical navigational small world (HNSW) for indexing the vectors for high precision vector search, and its latency significantly degrades when the precision target is higher than 95%.
At a previous company, we worked on building the parallel graph traversal engine. We realized that the bottleneck of HNSW performance is because there are too many sequential traversal steps that don't fully leverage multi-core CPU computation resources. After some research, we found that there are algorithms such as SpeedANN that are targeting this problem, which is not leveraged by industry yet. So we built the Epsilla vector database to turn the research into a production system.
With Epsilla, we shoot for 10x lower vector search latency compared to HNSW based vector databases. We did an initial benchmark against the top open source vector databases: https://medium.com/@richard_50832/benchmarking-epsilla-with-...
We provide a Docker image for you to install Epsilla backend locally, and provide a Python client and a JavaScript client to connect and interact with it.
Quickstart:
docker pull epsilla/vectordb
docker run --pull=always -d -p 8888:8888 epsilla/vectordb
pip install pyepsilla
git clone https://github.com/epsilla-cloud/epsilla-python-client.git
cd examples
python hello_epsilla.py
25 comments
[ 3.3 ms ] story [ 24.4 ms ] threadThere are a bunch of possible areas to circle or ignore when making an ML-capable database of some sort. In rough order of data complexity:
1. Embeddings (context-free vectors, just an ID and the vector)
2. Metadata + Embedding (source data, JSON)
3. Binary Data + Metadata + Embedding (add documents)
Then there are tooling questions: in this matrix you'd want to decide if you're going to allow inference, and if so, will it be arbitrary, service-based, etc. against the documents, and if so, how will you store the results?
I'm curious how you're thinking about the design space. The embedding-only route is conceptually appealing because it's simple. In a larger engineering project, there's a tension between "where do I keep all this data," "how do I process and reprocess all this data", and "where do I keep the results of all the processing", and to me there aren't clear bright-line architectures that seem "best of".
Put another way, 15 years ago, we went memcached -> redis 1 -> redis (whatever it is now), and at the same time, we went mysql/postgres/oracle -> nosql json stores; today all of these have relatively well-defined use cases, (and for most of them sqlite is the best choice, obviously).
How are you seeing the ML db scene playing out, and where do you think the sqlite of this space will land on architecture?
For the database perspective, instead of dividing the table schema into 3 parts: id, metadata, embedding, we designed in a way closer to SQL, treat vector as another data type, and let user to define any number of fields in a table. ID is just an annotation of a field (composite key might be overkilling for now). There will be another debate on whether schemaful or schemaless is the right approach, we can leave it here for now
With this foundation, we already covers 1 and 2. And in our roadmap we also plan to cover 3, with multi-modal data type support. We think the real big advantage of embedding is on unstructured data (documents, images, video, audio, etc), and storing the embedding of multi-modal data and connect them through semantic relevance will open up big opportunities. And this fits with the table and fields-based design for introducing cross table embedding index on connecting different shape data.
And from the multi-modal data perspective comes the problem where do we store those data? One way is we provide a generic binary data type that let users put anything. Another way which most enterprise will do is integrate us with a larger data warehouse/data lake system. And this opens up the requirement for us on supporting data streaming in/out with kafka connector, spark connector, etc.
And totally agree that SQLite works so well in huge amount of scenarios, now there is DuckDB. We also see some other players like LanceDB taking this approach to be Vector DB space's SQLite. We are also pretty close to announce our Python in-process package support, so docker / a separate server is not a must have anymore.
For inference, this is a broader direction for us for now. We are open to explore this space and see if the serverless architecture on cloud can provide extra efficiency benefit to the market
Every few months I run up into a use case where I'm like "I want to get a whole bunch of data, analyze it, then search for it later with embeddings, and probably keep running different sorts of analysis on it, and store the embeddings of those analyses in a related way." This still feels fairly difficult to do, or at least there aren't canonical "right" architectures yet.
My instinct is if you nail the ml+dev+data ops needs with good architecture and api you could really have something -- good luck!
As far as I know they’re all relatively undifferentiated in performance and features.
Is there a viable long-term business here?
Because hnswlib does not use intra-threads it will scale much better in terms of full throughput, probably close to 7X-8X with 16 threads on 16 vCPUs (compared to Epsilla which saturates with 2.2X improvement from multiple threads). The main premise of Epsilla's solution is trading throughput for latency, which is probably legit but would not work for all.
Note that even though the hardware between the benchmarks is not controlled (Epsilla only says it is some AWS EC2 16C32G, ann benchmark uses AWS r6i.16xlarge), it does not matter that much since the single threaded cpu speeds are pretty stagnant over the years, so ann benchmark single-thread results can be transferred (unless Epsilla is using non-x64 hardware, which would be a weird choice). There is a constant overhead from communication between the nodes in Epsilla, but it is constant and should not affect the speed at high recalls (for which the hnswlib is also faster).
Maybe drop the disingenuous marketing and find something else to work on. The 50 other vector dbs will implement this trivial addition and you'll be left with nothing to show.
Sources: https://arxiv.org/abs/2201.13007 https://dl.acm.org/doi/pdf/10.1145/3572848.3577527