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Showing 12 open source projects for "you"
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Full-stack observability with actually useful AI | Grafana CloudBuilt on open standards like Prometheus and OpenTelemetry, Grafana Cloud includes Kubernetes Monitoring, Application Observability, Incident Response, plus the AI-powered Grafana Assistant. Get started with our generous free tier today.
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Gemini 3 and 200+ AI Models on One PlatformBuild generative AI apps with Vertex AI. Switch between models without switching platforms.
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MTEB
MTEB: Massive Text Embedding Benchmark
Text embeddings are commonly evaluated on a small set of datasets from a single task not covering their possible applications to other tasks. It is unclear whether state-of-the-art embeddings on semantic textual similarity (STS) can be equally well applied to other tasks like clustering or reranking. This makes progress in the field difficult to track, as various models are constantly being proposed without proper evaluation. To solve this problem, we introduce the Massive Text Embedding... -
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refinery
Open-source choice to scale, assess and maintain natural language data
...Also, the makers of refinery currently work on integrations to other labeling tools, such that you can easily switch between different choices. refinery is a multi-repository project, you can find all integrated services in the architecture below. The app builds on top of Hugging Face and spaCy to leverage pre-built language models for your NLP tasks, as well as qdrant for neural search. -
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Cherche
Neural Search
...These modules are compatible with Hugging Face's pre-trained models and fully integrated into neural search pipelines. Search is fully compatible with the collaborative filtering library Implicit. It is advantageous if you have a history associated with users and you want to retrieve / re-rank documents based on user preferences. -
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Jina
Build cross-modal and multimodal applications on the cloud
...Seamless Docker container integration: sharing, exploring, sandboxing, versioning and dependency control via Jina Hub. Fast deployment to Kubernetes, Docker Compose and Jina Cloud. Improved engineering efficiency thanks to the Jina AI ecosystem, so you can focus on innovating with the data applications you build. -
Go from Code to Production URL in SecondsSkip the Kubernetes configs. Cloud Run handles HTTPS, scaling, and infrastructure automatically. Two million requests free per month.
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VectorDB
A Python vector database you just need, no more, no less
...This synergy culminates in a robust, yet user-friendly vector database experience, that's vectordb for you. -
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CLIP-as-service
Embed images and sentences into fixed-length vectors
CLIP-as-service is a low-latency high-scalability service for embedding images and text. It can be easily integrated as a microservice into neural search solutions. Serve CLIP models with TensorRT, ONNX runtime and PyTorch w/o JIT with 800QPS[*]. Non-blocking duplex streaming on requests and responses, designed for large data and long-running tasks. Horizontally scale up and down multiple CLIP models on single GPU, with automatic load balancing. Easy-to-use. No learning curve, minimalist... -
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finetuner
Task-oriented finetuning for better embeddings on neural search
...Jina AI’s Finetuner makes fine-tuning easier and faster by streamlining the workflow and handling all the complexity and infrastructure in the cloud. With Finetuner, you can easily enhance the performance of pre-trained models, making them production-ready without extensive labeling or expensive hardware. Create high-quality embeddings for semantic search, visual similarity search, cross-modal text image search, recommendation systems, clustering, duplication detection, anomaly detection, or other uses. ... -
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Prime QA
State-of-the-art Multilingual Question Answering research
PrimeQA is a public open source repository that enables researchers and developers to train state-of-the-art models for question answering (QA). By using PrimeQA, a researcher can replicate the experiments outlined in a paper published in the latest NLP conference while also enjoying the capability to download pre-trained models (from an online repository) and run them on their own custom data. PrimeQA is built on top of the Transformers toolkit and uses datasets and models that are directly... -
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AnnLite
A fast embedded library for approximate nearest neighbor search
...It is easy to use and intuitive to set up to production. The library uses a highly optimized approximate nearest neighbor search algorithm (HNSW) to search for nearest neighbors. The library allows you to search for nearest neighbors within a subset of the dataset. Smooth integration with neural search ecosystem including Jina and DocArray, so that users can easily expose search API with gRPC and/or HTTP. The library is easy to install and use. It is designed to be used with Python. To support search with filters, the annlite must be created with colums parameter, which is a series of fields you want to filter by. -
Fully Managed MySQL, PostgreSQL, and SQL ServerCloud SQL handles your database ops end to end, so you can focus on your app.
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NOW
No-code tool for creating a neural search solution in minutes
...In this case, NOW asks for the URI to the S3 bucket, as well as the credentials and region thereof. A final step in loading your data is to choose the fields of your data that you would like to use for search and filter respectively. -
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Aquila DB
An easy to use Neural Search Engine
Aquila DB is a Neural search engine. In other words, it is a database to index Latent Vectors generated by ML models along with JSON Metadata to perform k-NN retrieval. It is dead simple to set up, language-agnostic, and drop in addition to your Machine Learning Applications. Aquila DB, as of current features is a ready solution for Machine Learning engineers and Data scientists to build Neural Information Retrieval applications out of the box with minimal dependencies. -
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Vector AI
A platform for building vector based applications
...The backbone of many popular A.I use cases like reverse image search, recommendations, personalization, etc. There are scenarios where vector search is not as effective as traditional search, e.g. searching for skus. Vector AI lets you combine vector search with all the features of traditional search such as filtering, fuzzy search, and keyword matching to create an even more powerful search.
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