awesome-ai-safety
qdrant
awesome-ai-safety | qdrant | |
---|---|---|
5 | 142 | |
140 | 18,219 | |
9.3% | 4.8% | |
5.6 | 9.9 | |
7 months ago | 2 days ago | |
Rust | ||
Apache License 2.0 | Apache License 2.0 |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
awesome-ai-safety
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Ask HN: Who is hiring? (October 2023)
Giskard - Testing framework for ML models| Multiple roles | Full-time | France | https://giskard.ai/
We are building the first collaborative & open-source Quality Assurance platform for all ML models - including Large Language Models.
Founded in 2021 in Paris by ex-Dataiku engineers, we are an emerging player in the fast-growing market of AI Quality & Safety.
Giskard helps Data Scientists & ML Engineering teams collaborate to evaluate, test & monitor AI models. We help organizations increase the efficiency of their AI development workflow, eliminate risks of AI biases and ensure robust, reliable & ethical AI models. Our open-source platform is used by dozens of ML teams across industries, both at enterprise companies & startups.
In 2022, we raised our first round of 1.5 million euros, led by Elaia, with participation from Bessemer and notable angel investors including the CTO of Hugging Face. To read more about this fundraising and how it will accelerate our growth, you can read this announcement: https://www.giskard.ai/knowledge/news-fundraising-2022
In 2023, we received a strategic investment from the European Commission to build a SaaS platform to automate compliance with the upcoming EU AI regulation. You can read more here: https://www.giskard.ai/knowledge/1-000-github-stars-3meu-and...
We are assembling a team of champions: Software Engineers, Machine Learning researchers, and Data Scientists ; to build our AI Quality platform and expand it to new types of AI models and industries. We have a culture of continuous learning & quality, and we help each other achieve high standards & goals!
We aim to grow from 15 to 25 people in the next 12 months. We're hiring the following roles:
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Ask HN: Who is hiring? (August 2023)
Giskard - Testing framework for ML models| Multiple roles | Full-time | France | https://giskard.ai/
We are building the first collaborative & open-source Quality Assurance platform for all ML models - including Large Language Models.
Founded in 2021 in Paris by ex-Dataiku engineers, we are an emerging player in the fast-growing market of AI Safety & Security.
Giskard helps Data Scientists & ML Engineering teams collaborate to evaluate, test & monitor AI models. We help organizations increase the efficiency of their AI development workflow, eliminate risks of AI biases and ensure robust, reliable & ethical AI models. Our open-source platform is used by dozens of ML teams across industries, both at enterprise companies & startups.
In 2022, we raised our first round of 1.5 million euros, led by Elaia, with participation from Bessemer and notable angel investors including the CTO of Hugging Face. To read more about this fundraising and how it will accelerate our growth, you can read this announcement: https://www.giskard.ai/knowledge/news-fundraising-2022
In 2023, we received a strategic investment from the European Commission to build a SaaS platform to automate compliance with the upcoming EU AI regulation. You can read more here: https://www.giskard.ai/knowledge/1-000-github-stars-3meu-and...
We are assembling a team of champions: Software Engineers, Machine Learning researchers, and Data Scientists ; to build our AI Quality platform and expand it to new types of AI models and industries. We have a culture of continuous learning & quality, and we help each other achieve high standards & goals!
We aim to grow from 15 to 25 people in the next 12 months. We're hiring the following roles:
* Software Engineer - https://apply.workable.com/giskard/j/AD2C90B581/ (Python, Java, Typescript, Vue.js, Cloud skills)
* Machine Learning Researcher - https://apply.workable.com/giskard/j/E89FE8E310/ (post-PhD)
* Data Science lead - https://apply.workable.com/giskard/j/E89FE8E310/ (ML + consulting experience required)
* Growth marketing intern - https://apply.workable.com/giskard/j/C8635E9B0C/
* Data Science intern - https://apply.workable.com/giskard/j/7F0B341852/
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Show HN: Python library to scan ML models for vulnerabilities
Hi! Iāve been working on this automatic scanner for ML models to detect issues like underperforming data slices, overconfidence in predictions, robustness problems, and others. It supports all main Python ML frameworks (sklearn, torch, xgboost, ā¦) and integrates with the quality assurance solution we are building at Giskard AI (https://giskard.ai) to systematically test models before putting them in production.
It is still a beta and I would love to hear your feedback if you have the time to try it out.
We have quite a few tutorials in the docs with ready-made colab notebooks to make it easy to get started.
If you are interested in the code:
https://github.com/Giskard-AI/giskard/tree/main/python-clien...
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[R] Awesome AI Safety ā A curated list of papers & technical articles on AI Quality & Safety
Repository: https://github.com/Giskard-AI/awesome-ai-safety
- AI Safety ā curated papers for safer, ethical, and reliable AI
qdrant
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Hindi-Language AI Chatbot for Enterprises Using Qdrant, MLFlow, and LangChain
Great. Now that we have the embeddings, we need to store them in a vector database. We will be using Qdrant for this purpose. Qdrant is an open-source vector database that allows you to store and query high-dimensional vectors. The easiest way to get started with the Qdrant database is using the docker.
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Boost Your Code's Efficiency: Introducing Semantic Cache with Qdrant
I took Qdrant for this project. The reason was that Qdrant stands for high-performance vector search, the best choice against use cases like finding similar function calls based on semantic similarity. Qdrant is not only powerful but also scalable to support a variety of advanced search features that are greatly useful to nuanced caching mechanisms like ours.
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Ask HN: Has Anyone Trained a personal LLM using their personal notes?
I'm currently looking to implement locally, using QDrant [1] for instance.
I'm just playing around, but it makes sense to have a runnable example for our users at work too :) [2].
[1]. https://qdrant.tech/
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Show HN: A fast HNSW implementation in Rust
Also compare with qdrant's Rust implementation; they tout their performance. https://github.com/qdrant/qdrant/tree/master/lib/segment/src...
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pgvecto.rs alternatives - qdrant and Weaviate
3 projects | 13 Mar 2024
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Open-source Rust-based RAG
There are much better known examples, such as https://qdrant.tech/ and https://github.com/lancedb/lancedb
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Qdrant 1.8.0 - Major Performance Enhancements
For more information, see our release notes. Qdrant is an open source project. We welcome your contributions; raise issues, or contribute via pull requests!
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Perform Image-Driven Reverse Image Search on E-Commerce Sites with ImageBind and Qdrant
Initialize the Qdrant Client with in-memory storage. The collection name will be āimagebind_dataā and we will be using cosine distance.
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7 Vector Databases Every Developer ShouldĀ Know!
Qdrant is an open-source vector search engine optimized for performance and flexibility. It supports both exact and approximate nearest neighbor search, providing a balance between accuracy and speed for various AI and ML applications.
- Ask HN: Who is hiring? (February 2024)
What are some alternatives?
opentofu - OpenTofu lets you declaratively manage your cloud infrastructure.
Milvus - A cloud-native vector database, storage for next generation AI applications
tabby - Self-hosted AI coding assistant
Weaviate - Weaviate is an open-source vector database that stores both objects and vectors, allowing for the combination of vector search with structured filtering with the fault tolerance and scalability of a cloud-native databaseā.
awesome-langchain - š Awesome list of tools and projects with the awesome LangChain framework
faiss - A library for efficient similarity search and clustering of dense vectors.
giskard - š¢ Open-Source Evaluation & Testing for LLMs and ML models
pgvector - Open-source vector similarity search for Postgres
refact - WebUI for Fine-Tuning and Self-hosting of Open-Source Large Language Models for Coding
Elasticsearch - Free and Open, Distributed, RESTful Search Engine
nl-wallet - NL Public Reference Wallet
towhee - Towhee is a framework that is dedicated to making neural data processing pipelines simple and fast.