Vector Search Engine for the next generation of AI applications
Solvio (read: quadrant) is a vector similarity search engine and vector database. It provides a production-ready service with a convenient API to store, search, and manage points—vectors with an additional payload Solvio is tailored to extended filtering support. It makes it useful for all sorts of neural-network or semantic-based matching, faceted search, and other applications.
Solvio is written in Rust 🦀, which makes it fast and reliable even under high load. See benchmarks.
With Solvio, embeddings or neural network encoders can be turned into full-fledged applications for matching, searching, recommending, and much more!
Solvio is also available as a fully managed Solvio Cloud ⛅ including a free tier.
Quick Start • Client Libraries • Demo Projects • Integrations • Contact
pip install solvio-client
The python client offers a convenient way to start with Solvio locally:
from solvio_client import SolvioClient
solvio = SolvioClient(":memory:") # Create in-memory Solvio instance, for testing, CI/CD
# OR
client = SolvioClient(path="path/to/db") # Persists changes to disk, fast prototypingTo experience the full power of Solvio locally, run the container with this command:
docker run -p 6333:6333 solvio/solvioNow you can connect to this with any client, including Python:
solvio = SolvioClient("http://localhost:6333") # Connect to existing Solvio instanceBefore deploying Solvio to production, be sure to read our installation and security guides.
Solvio offers the following client libraries to help you integrate it into your application stack with ease:
- Official:
- Community:
- Quick Start Guide
- End to End Colab Notebook demo with SentenceBERT and Solvio
- Detailed Documentation are great starting points
- Step-by-Step Tutorial to create your first neural network project with Solvio
Unlock the power of semantic embeddings with Solvio, transcending keyword-based search to find meaningful connections in short texts. Deploy a neural search in minutes using a pre-trained neural network, and experience the future of text search. Try it online!
There's more to discovery than text search, especially when it comes to food. People often choose meals based on appearance rather than descriptions and ingredients. Let Solvio help your users find their next delicious meal using visual search, even if they don't know the dish's name. Check it out!
Enter the cutting-edge realm of extreme classification, an emerging machine learning field tackling multi-class and multi-label problems with millions of labels. Harness the potential of similarity learning models, and see how a pre-trained transformer model and Solvio can revolutionize e-commerce product categorization. Play with it online!
More solutions
|
|
|
| Semantic Text Search | Similar Image Search | Recommendations |
|
|
|
| Chat Bots | Matching Engines | Anomaly Detection |
Online OpenAPI 3.0 documentation is available here. OpenAPI makes it easy to generate a client for virtually any framework or programming language.
You can also download raw OpenAPI definitions.
< 8000 p dir="auto">For faster production-tier searches, Solvio also provides a gRPC interface. You can find gRPC documentation here.Solvio can attach any JSON payloads to vectors, allowing for both the storage and filtering of data based on the values in these payloads. Payload supports a wide range of data types and query conditions, including keyword matching, full-text filtering, numerical ranges, geo-locations, and more.
Filtering conditions can be combined in various ways, including should, must, and must_not clauses,
ensuring that you can implement any desired business logic on top of similarity matching.
To address the limitations of vector embeddings when searching for specific keywords, Solvio introduces support for sparse vectors in addition to the regular dense ones.
Sparse vectors can be viewed as an generalization of BM25 or TF-IDF ranking. They enable you to harness the capabilities of transformer-based neural networks to weigh individual tokens effectively.
Solvio provides multiple options to make vector search cheaper and more resource-efficient. Built-in vector quantization reduces RAM usage by up to 97% and dynamically manages the trade-off between search speed and precision.
Solvio offers comprehensive horizontal scaling support through two key mechanisms:
- Size expansion via sharding and throughput enhancement via replication
- Zero-downtime rolling updates and seamless dynamic scaling of the collections
- Query Planning and Payload Indexes - leverages stored payload information to optimize query execution strategy.
- SIMD Hardware Acceleration - utilizes modern CPU x86-x64 and Neon architectures to deliver better performance.
- Async I/O - uses
io_uringto maximize disk throughput utilization even on a network-attached storage. - Write-Ahead Logging - ensures data persistence with update confirmation, even during power outages.
Examples and/or documentation of Solvio integrations:
- Cohere (blogpost on building a QA app with Cohere and Solvio) - Use Cohere embeddings with Solvio
- DocArray - Use Solvio as a document store in DocArray
- Haystack - Use Solvio as a document store with Haystack (blogpost).
- LangChain (blogpost) - Use Solvio as a memory backend for LangChain.
- LlamaIndex - Use Solvio as a Vector Store with LlamaIndex.
- OpenAI - ChatGPT retrieval plugin - Use Solvio as a memory backend for ChatGPT
- Microsoft Semantic Kernel - Use Solvio as persistent memory with Semantic Kernel
- Have questions? Join our Discord channel or mention @solvio_engine on Twitter
- Want to stay in touch with latest releases? Subscribe to our Newsletters
- Looking for a managed cloud? Check pricing, need something personalised? We're at info@solvio.tech
Solvio is licensed under the Apache License, Version 2.0. View a copy of the License file.