Weaviate • vector database @weaviate_io Twitter profile

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Weaviate • vector database

14 days

We’re growing our team at Weaviate! 🚀 Join us in shaping the future of AI. Open roles include: • Technical Trainer: • Social Media & Content Intern: • Site Reliability Engineer: • Research Engineer:

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Weaviate • vector database

@weaviate_io

2 years

🔥 We are excited to announce our new generative search module 🔎 The generative @OpenAI module pipes your Weaviate results directly through the GPT-3 model! 🤩 After vector search and hybrid search, we now have generative search! 💥 Check out the 🧵!

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Weaviate • vector database

@weaviate_io

1 year

⚡️ #AutoGPT harnesses the power of GPT-4 and GPT-3.5 🔗 It chains together "thoughts" and completes various tasks autonomously 🧠 Learn how you can use it with #Weaviate to enable a short-term and long-term memory ✍️ By @ecardenas300 and @_jphwang :

Giving Auto-GPT Long-Term Memory with Weaviate | Weaviate

Learn about Auto-GPT and how to give it long-term memory with Weaviate!

weaviate.io

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Weaviate • vector database

@weaviate_io

4 months

Verba 1.0 is finally here! 🐕 With our latest release, you can now run a state-of-the-art Retrieval Augmented Generation (RAG) application locally on your computer, thanks to the new integration with @ollama . This allows you to use fantastic open source models like Llama 3,

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Weaviate • vector database

@weaviate_io

1 year

🥳 Super proud and happy to announce that we've closed our Series-B led by @IndexVentures with participation from @BatteryVentures and our existing investors ( @espricewright , @Dthakker02 , and @DanelDayan . Welcome to Weaviate)! 📰 Coverage in The Information:

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Weaviate • vector database

@weaviate_io

5 months

Thank you to everyone who attended the DSPy meetup and those of you who registered! We are so excited to share the recording 🎉 TL;DR of the talks: 1. @simigd from @cohere gave an overview of Cohere’s latest developments in RAG from Command R+ to embeddings and more 2.

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Weaviate • vector database

@weaviate_io

5 months

We've aggregated all of the DSPy resources from the Weaviate team on one page! It is broken into two categories: 1. Hands on Learning and 2. Read and Listen 📚 DSPy round-up: We're also very excited to host our in-person meetup with DSPy, @arizeai , and

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Weaviate • vector database

@weaviate_io

11 months

Verba, our open-source RAG framework 🐕 Keep your data private and 100% free using open-source models like SentenceTransformers and run Weaviate locally. Repo: Learn more about the modular architecture that powers you to build in the thread!

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Weaviate • vector database

@weaviate_io

1 year

You can build a chatbot using the new open source Llama 2 model with these tools 🦙 • @replicatehq for the endpoint to the llama13b-v2-chat model • @llama_index as the framework • @weaviate_io for the vector store Notebook:

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Weaviate • vector database

@weaviate_io

1 year

🧠 🔌 With #ChatGPT 's Retrieval Plugin powered by #Weaviate 's Vector Database, you can now conduct generative searches using your own data and overcome ChatGPT's lack of long-term memory! 🚀 ✍️ Read the blog post by @ZainHasan6 :

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Weaviate • vector database

@weaviate_io

11 months

Using LLMs to evaluate RAG systems lets us build better applications faster! 🧫 We have a new blog post from @ecardenas300 and @CShorten30 presenting new evaluation tools such as Ragas and ARES, and the current state of the art in RAG:

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Weaviate • vector database

@weaviate_io

6 months

Ready to build a “Chat with your code” application with @ollama , Weaviate, and @llama_index on @LightningAI Studio? We’ve just published a Lightning AI Studio template that you can simply copy. You’ll be up and running with your Retrieval-Augmented Generation (RAG) pipeline in

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Weaviate • vector database

@weaviate_io

27 days

Struggling with accurate retrieval for long context RAG? If you've ever built a RAG application that ingests long documents, the difficulty of high-quality search for over them will be a very familiar one. Late chunking may just be the solution you’ve been looking for! This new

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Weaviate • vector database

@weaviate_io

7 months

Generative Feedback Loops will transform how the world makes use of its data. Introducing Hurricane 🌀, an open-source web application that converts questions or topics into blog posts! We then feed these blog posts back into Weaviate to create an ever-growing knowledge base of

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Weaviate • vector database

@weaviate_io

2 years

😲 Building a custom chatbot using @LangChainAI and #Weaviate can be done in 20 lines of code 🖇 Simply connect to your Weaviate instance and specify the class and properties that you want LangChain to use (🥁) Last but not least, specify the @OpenAI model

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Weaviate • vector database

@weaviate_io

6 months

⭐ Updated @LangChainAI integration ⭐ We completely rewrote the Weaviate LangChain integration using the Python v4 client! It now supports all Weaviate features, including multitenancy and autocut. Also, hybrid search is even easier to implement! Docs:

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Weaviate • vector database

@weaviate_io

2 years

🤩 Learn how to combine LangChain and Weaviate to build a custom LLM chatbot powered with semantic search by @ecardenas300 ⚙️ LangChain offers various implementations to solve hallucination and limited token lengths! Read this blog post 👇

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Weaviate • vector database

@weaviate_io

8 months

Reranking is one of quickest ways to get more accurate search results! You can take this one step further by fine-tuning the reranker 🤝 In this blog post, @ecardenas300 goes over how you can fine-tune @cohere 's reranker model and generate synthetic data with DSPy:

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Weaviate • vector database

@weaviate_io

2 months

Build an End-to-End Multimodal RAG with Weaviate for Finance 🧠 Explore @zoumana_keita_ ’s Medium article on Multimodal Retrieval Augmented Generation (RAG) and its application in ESG investment analysis. Key Highlights: • Multimodal RAG: Integrate text, graphs, tables, and

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Weaviate • vector database

@weaviate_io

22 days

Late chunking, a new chunking method introduced by @JinaAI_ , allows you to retain more context and improve retrieval performance - and is available to use within Weaviate with only a few changes to existing code! It’s like ColBERT, but with the same storage costs as regular

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Weaviate • vector database

@weaviate_io

7 months

DSPy is a framework for programming LLM applications. You can now build next-level applications using Weaviate and DSPy! Watch the hands-on video by @ecardenas300 where she takes questions and converts them into entire blog posts. Video: Demo:

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Weaviate • vector database

@weaviate_io

4 months

With @ollama and Weaviate, you can easily build an on-premise Retrieval-Augmented Generation pipeline for privacy preservation. In our latest blog, our developer advocate, @helloiamleonie , shows you how to use local language models and a local vector database to build a simple

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Weaviate • vector database

@weaviate_io

1 year

Exciting news! Weaviate now supports the brand-new Google Pathways Language Model (PaLM) through two new modules. Enhance your #VectorSearch and generative tasks using PaLM's 540-billion 🤯 parameters!

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Weaviate • vector database

@weaviate_io

6 months

Are you ready to take your naive Retrieval Augmented Generation (RAG) pipelines to the next level? Try applying a few advanced RAG techniques with @llama_index and Weaviate. In her latest article, @helloiamleonie discusses current advanced RAG techniques, which can be

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Weaviate • vector database

@weaviate_io

1 year

Thanks to RAG, Customer Relationship Management (CRM) systems are getting a boost 🤝 @dlthub built a demo using Verba to chat with Zendesk tickets in just a few steps. Blog post: Demo:

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Weaviate • vector database

@weaviate_io

2 years

🥳 New release - Weaviate 1.18 is out now! 🚀 With a set of new great features, performance and UX improvements, and fixes Read the blog post: Release notes: Release podcast: 🧵 All features of v.1.18

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Weaviate • vector database

@weaviate_io

6 months

DSPy simplifies the process of building LLM applications by prioritizing programming over prompting. With DSPy and Weaviate, you can create next-generation LLM applications more efficiently! To learn more, check out the resources shared by our colleagues: Gentle Introduction to

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Weaviate • vector database

@weaviate_io

5 months

Remember our “Chat with your code” app? We’ve now added state-of-the-art RAG techniques such as reranking, code chunking, and few-shot prompting - using @ollama , Weaviate, and @llama_index . Simply copy our new @LightningAI Studio template to get started with your advanced

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Weaviate • vector database

@weaviate_io

6 months

Did you know about the Binoculars technique to reliably tell apart fake, LLM-generated, text from human-written text? Or have you heard about Modular RAG? What about Matryoshka Embeddings? These are just a few posts in our new paper reviews page, where we create 1-2 minute

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Weaviate • vector database

@weaviate_io

4 months

The model landscape is changing! text-embedding-ada-002 - a classic, but have you explored the latest embedding model options? Here are some strong alternatives with unique features: • Multilingual support: @cohere 's Embed 3 family • Domain-specific models: @Voyage_AI_ offers

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Weaviate • vector database

@weaviate_io

6 months

It's kind of crazy to think that you can throw out 97% of the information in a vector embedding and still retrieve accurately. But it's true! In this blog, we provide a super simple explanation of the buzz around binary quantization and help you get started with implementing and

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Weaviate • vector database

@weaviate_io

5 months

Cognee allows you to introduce more predictability and management into AI workflows through graph architectures, vector stores, and automation. With their new release, they’ve added more awesome features and integrations, like DSPy graph generation and @neo4j support! GitHub:

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Weaviate • vector database

@weaviate_io

1 year

💡 As a vector DB company, we rely on the powerful ML models our partners create. 👉 @CohereAI is an example of such a partner; their multilingual model is just amazing! 💪 The power of Weaviate + Cohere is shown in this awesome article.

The Embedding Archives: Millions of Wikipedia Article Embeddings in Many Languages

There’s no denying that we’re in the midst of a revolutionary time for Language AI. Developers are waking up to the vast emerging capabilities of language understanding and generation models. One of...

cohere.com

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Weaviate • vector database

@weaviate_io

3 months

Struggling with parsing documents for your RAG applications? We got you! Here are some recently developed solutions that can help: • LlamaParse from @llama_index : all languages, natural language instructions • @UnstructuredIO : Works with other frameworks like @LangChainAI or

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Weaviate • vector database

@weaviate_io

29 days

DSPy and Weaviate are seamlessly integrated together. Just import a Weaviate retrieval model and let DSPy do all the rest! The setup takes just a few lines of code, and no extra configuration is needed. It's that simple! Imagine a Retrieval Augmented Generation (RAG) pipeline

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Weaviate • vector database

@weaviate_io

1 year

One distance metric does not fit all embedding models 📏 For example, the @cohere multilingual model uses dot product, whereas the English model uses cosine. Read this blog post by @ecardenas300 where she covers the various distance metrics in Weaviate:

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Weaviate • vector database

@weaviate_io

6 months

Prompt engineering is a key part of getting better responses from Large Language Models. The particular wording of the instructions and examples can determine the fate of the performance 😵‍💫 DSPy Compilers simplify the process of writing the perfect prompt! In this blog post,

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Weaviate • vector database

@weaviate_io

2 months

Ready to build a “Chat with your code” application with @ollama , @weaviate_io , @llama_index , and @streamlit ? We’ve published a @LightningAI Studio template that you can copy. In minutes, you’ll be up and running with your Retrieval-Augmented Generation (RAG) pipeline. Start

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Weaviate • vector database

@weaviate_io

1 year

Weaviate 1.20 is here! 🚢 Covering: • Multi-tenancy • PQ + re-scoring • Autocut • Search re-ranking • New hybrid search ranking algorithm Learn more about each feature! Blog post: Release podcast:

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Weaviate • vector database

@weaviate_io

1 year

🥱 Say goodbye to googling for Weaviate docs 🔎 now has SITE SEARCH! 🚀 Quickly search through our docs without leaving the website Happy searching! #SiteSearch #WeaviateDocs

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Weaviate • vector database

@weaviate_io

4 months

How do you build an AI application that runs entirely (LLM and Vector Search) on your development environment? Use @ollama with Weaviate! In Weaviate 1.25, we added text2vec-ollama and generative-ollama, which allows you to run RAG applications entirely on your computer. Read

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Weaviate • vector database

@weaviate_io

4 months

Multimodal search and RAG systems are gaining more and more popularity - when you can combine images, videos, text, and audio into any system, the possibilities are endless! @sebawita just published a course on @DeepLearningAI on how multimodal systems work and how to build a

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Weaviate • vector database

@weaviate_io

4 months

The right LLM can unlock the full potential of AI for your project. But with so many options, where do you begin? Here are the 4 key factors to consider: • Quality: Look for an LLM that consistently performs well across various tasks. This could involve metrics like chatbot

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Weaviate • vector database

@weaviate_io

3 years

🎉 Weaviate v1.10.0 is here! Besides a lot of new features, we've released the OpenAI Weaviate module that directly integrates with @OpenAI 's embeddings API! You can now vectorize, search through, and even mix different ML models with the power of GPT-3 all out-of-the-box!

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Weaviate • vector database

@weaviate_io

1 year

🎉 Weaviate 1.19 is now available! 🚀 Packed with new features, improved performance, and UX enhancements. 📚 Learn more about this release by checking out our: 📝 Blog post: 📑 Release notes:

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Weaviate • vector database

@weaviate_io

8 months

HNSW is a fast, memory-efficient algorithm for finding similar data points in large datasets. It uses a multi-layered graph to 'jump' over unrelated data, reducing search time and memory usage. Here are five key terms and concepts to help you better understand HNSW 🧵 Read more

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Weaviate • vector database

@weaviate_io

20 days

Is your Retrieval Augmented Generation (RAG) model not quite hitting the mark? Boost your outputs with these exciting new features! 👤 Claude 3.5 Sonnet from Anthropic excels in generating creative answers, and could provide higher quality answers to your prompts. 📈 Use DSPy to

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Weaviate • vector database

@weaviate_io

4 months

With @modal_labs and Weaviate, you can embed and ingest 40+ million objects in 2 hours 🚀 @charles_irl and @ecardenas300 share all the technical details in this blog post: They cover: • Async Indexing • Product Quantization • Vector Index

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Weaviate • vector database

@weaviate_io

1 year

LLMs and search go hand in hand 🤝 In this blog post, @CShorten30 and @ecardenas300 share five components on the intersection between LLMs and search! Read the blog post:

Large Language Models and Search | Weaviate

Learn about the intersection between LLMs and Search

weaviate.io

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Weaviate • vector database

@weaviate_io

6 months

Want to get some hands-on experience with building Retrieval-Augmented Generation (RAG) pipelines? Why not join the current @kaggle competition “Google – AI Assistants for Data Tasks with Gemma”? To get started, you can check out @helloiamleonie 's Kaggle Notebooks: 📌 RAG with

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Weaviate • vector database

@weaviate_io

1 year

How can a workation in Croatia enhance team collaboration and infuse positive energy into your team's dynamics? Our recent gathering in Croatia worked wonders, forging strong bonds, boosting teamwork, and creating memorable memories. 🏝️

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Weaviate • vector database

@weaviate_io

1 month

💫 Check out Cerebras Inference! @cerebrassystems Inference is launched and with incredibly fast inference. How fast are we talking? 1850t/s for Llama3.1-8b and 450 t/s for Llama3.1-70b, with Cerebras you can make your RAG experiences incredibly fast! Try out this Chat with your

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Weaviate • vector database

@weaviate_io

2 years

🙋 Hello, Weaviate friends! 🚀 Join our (first!) introduction to Weaviate workshop with @_jphwang on April 4th, 1-2 PM CET 📚 Covering vector databases + language models, and hands-on demos using Python ✍️ Limited spaces available - sign up now at

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Weaviate • vector database

@weaviate_io

27 days

We’re excited to announce the free RAG in Production course in collaboration with @weights_biases and @cohere 🧠 Learn about data ingestion and preprocessing, query enhancement, retrieval and re-ranking, and more! You can register for the pre-order here:

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Weaviate • vector database

@weaviate_io

11 months

We're teaming up with @DeepLearningAI for a short course: Vector Databases - From Embeddings to Applications 🚀 Ready to get started with vector databases? Enroll now:

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Weaviate • vector database

@weaviate_io

4 months

How can we take RAG applications to the next level? Generative Feedback Loops allow you to store the outputs generated from LLMs in RAG systems back into a vector database! We can then search through the generated data in near real-time for future applications. This video

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Weaviate • vector database

@weaviate_io

4 months

Long-context language models enable you to include task examples in the prompt to improve output performance. This is an alternative to fine-tuning! While fine-tuning is great, it can be expensive, time-consuming, and inflexible. Here's why prompt-tuning might be a better fit:

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Weaviate • vector database

@weaviate_io

9 months

Verba, our Retrieval Augmented Generation demo, uses hybrid search 👀 Hybrid search can help a RAG app retrieve the most relevant results for answer generation because it has the best of both worlds - the combination of keyword and vector search. This allows Verba to retrieve

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Weaviate • vector database

@weaviate_io

2 years

🎉 We’re happy to announce a new #Weaviate module – text2vec-cohere – which allows you to directly integrate with @CohereAI 's Large Language Models (LLMs) 👉

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Weaviate • vector database

@weaviate_io

2 months

Level up your Retrieval Augmented Generation (RAG) skills with @ZainHasan6 ! In this week's live, hands-on workshop, Zain discussed how to improve indexing, retrieval, and generation quality. The main takeaway is to think of RAG as a framework, and each portion of the R, A, and G

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Weaviate • vector database

@weaviate_io

1 year

Retrieval-Augmented Generation enables you to combine large language models with your data 🚢 @llama_index and Weaviate are a powerful RAG stack for your LLM application @jerryjliu0 shares an overview on LlamaIndex and goes through a demo notebook

LlamaIndex and Weaviate | Weaviate

An introductory overview of LlamaIndex, the LLM framework

weaviate.io

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Weaviate • vector database

@weaviate_io

1 year

At Weaviate, we 💚 open source, so today, we’re releasing a full-stack open source RAG application. Meet Verba, The Golden RAGtriever! 🐕 Verba is designed to offer a streamlined, user-friendly interface for RAG apps! Video:

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Weaviate • vector database

@weaviate_io

1 year

Weaviate Turns Four! 🎂🚀 Co-founders @etiennedi & @bobvanluijt reflect on an incredible journey of growth, paradigm shifts, and an unwavering belief in AI. Read their story and learn about the changes that shaped Weaviate's path:

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Weaviate • vector database

@weaviate_io

1 year

🤩 Are you attending #HaystackUS 2023 this week in Charlottesville, VA? 🗣️ Join @ecardenas300 from Team #Weaviate as she talks about Building Recommendation Systems with Vector Search on April 26. 🎟️ Don't miss this great talk! Get your tickets here:

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Weaviate • vector database

@weaviate_io

5 months

Weaviate is hiring! 🌟 Join our team and help shape the future of AI technology. Check out our open roles: • Senior Software Engineer Database: • Event Marketing Manager: • Revenue Operations Manager: •

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Weaviate • vector database

@weaviate_io

6 months

What if you could effortlessly access all your scattered information over different apps with ease? From important links in Slack to comments in notion to emails in Gmail, information is scattered across multiple places. This leads to time wasted on searching rather than

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Weaviate • vector database

@weaviate_io

11 months

Weaviate 1.22 is here! 💫 Discover the latest upgrades: • Nested JSON object storage • Async vector indexing • Additional gRPC support • v4 Python client beta Read the release blog post here: Explore the release now on WCS:

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Weaviate • vector database

@weaviate_io

2 years

📣 welcome @bernhardsson to @SeMI_tech 's advisory board 🎆 As one of the early pioneers bringing ANN-based search end-to-end to users, we are honored to work with him on encapsulating his knowledge into @weaviate_io

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Weaviate • vector database

@weaviate_io

1 year

Don't know where to start learning about vector databases? Vector databases can help enhance your AI applications by vectorizing data into embedding spaces that allow for quick retrieval and compariso 🤖 Check out these 4 key concepts 🧵

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Weaviate • vector database

@weaviate_io

12 days

How do you get the most out of your Retrieval-Augmented Generation (RAG) apps? First crucial step is chunking! The process of breaking down large documents or texts into smaller, manageable pieces called ‘chunks’. This simple yet powerful pre-processing step is key to boosting

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Weaviate • vector database

@weaviate_io

1 year

🚀 BIG NEWS: We're excited to partner with @streamlit for the LLM Hackathon, starting today! Build an innovative LLM-based Streamlit app to win amazing prizes! 📅 September 5-19, 2023 Dive in now:

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Weaviate • vector database

@weaviate_io

9 months

Multimodal search is as easy as 1, 2, 3 in Weaviate 🤩 1) Create a collection that can store audio, images, and videos. 2) Upload your data into the Weaviate collection you just defined. This creates a unified embedding space of all the modalities. 3) Now you can search! Follow

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Weaviate • vector database

@weaviate_io

1 year

Weaviate was always about scale. But lately, many of you required a different kind of scale: Running a single Weaviate set up for millions of tenants. Read @etiennedi 's preview of how millions of tenants will soon become a reality with Weaviate v1.20.

Multi-Tenancy Vector Search with millions of tenants | Weaviate

Learn about the new native multi-tenancy feature

weaviate.io

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Weaviate • vector database

@weaviate_io

1 year

🚢 It's shipping time! 🔥 Weaviate Cloud Service has a newly redesigned management console! It offers managed Weaviate instances on the ☁️, and you can now create fully managed Weaviate clusters with minimal input! 🚀 Get started here: More in the 🧵 !

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Weaviate • vector database

@weaviate_io

6 months

Large Language Models have significantly revolutionized the way we handle, store, and search through data. Their ability to quickly and affordably generate and modify information has improved user experiences through personalized content. We’ve gathered 5 terms/concepts to help

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Weaviate • vector database

@weaviate_io

3 months

Binary and Product Quantization are two techniques that significantly reduce memory usage and indexing times - while keeping accuracy surprisingly high. See how to use them with just a few lines of code in our Kaggle Notebook using @cohere embeddings!

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Weaviate • vector database

@weaviate_io

6 months

How can you reduce your Weaviate memory usage by 32x? Learn how binary quantization(BQ) works and how it can be used to enable brute force vector search, reduced memory usage, and faster indexing times. Read @ZainHasan6 and @AbdelRo68071159 's blog post, where they also share

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Weaviate • vector database

@weaviate_io

2 months

How can you optimize the performance of your AI application while minimizing costs? High-end models like GPT-4 are powerful but expensive. Here's a trick: use a smaller model to tweak responses from a generative model based on past Q&A, saving resources! Watch the full video on

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Weaviate • vector database

@weaviate_io

8 months

🐍 The #Python client for @weaviate_io just got a major upgrade! v4 is out of beta, and brings you faster performance with gRPC, enhanced IDE support, and next-level type safety. It also comes with many quality-of-life improvements like import rate limits, iterators and more!

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Weaviate • vector database

@weaviate_io

3 years

🔥🔥🔥 📹 Another amazing interview on the Weaviate Podcast. This time @CShorten30 interviews @OpenAI 's @arvind_io ! Since OpenAI released their embeddings API, the integration is available as an OOTB module in the Weaviate vector search engine!

Weaviate Podcast #7 • Arvind Neelakantan of OpenAI • Embeddings API...

Arvind Neelakantan, Research Lead at Open AI, talks with Connor Shorten about their newly released embeddings API, his work at Open AI, the integration into ...

www.youtube.com

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Weaviate • vector database

@weaviate_io

3 months

Weaviate is 5 years old today! Some highlights we’re proud of ✨ surpassing 1M downloads per month of our open source products, 10K GitHub stars, thousands of monthly users on our cloud service, and making Forbes AI 50 list. We couldn't have made it this far without our

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Weaviate • vector database

@weaviate_io

8 months

The multi2vec-bind module, powered by @Meta 's ImageBind, enables Weaviate to generate vector data from various sources, including images, videos, audio, IMU (Inertial Measurement Unit) data, and single-channel depth images. What happens when we cross that with Next.js? Find

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Weaviate • vector database

@weaviate_io

2 months

In this blog, @ZainHasan6 breaks down RAG into indexing, retrieval, and generation components and proposes 2 to 3 practical steps to improve each part of your RAG pipeline. Covering everything from chunking techniques, filtered search, and hybrid search, to reranking,

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Weaviate • vector database

@weaviate_io

5 months

💚 Built with Weaviate Community showcase 💚 @CyrillHug from @HPE has built a fully on-premise LLM-powered chatbot to chat with your car manual. This application is developed and deployed entirely ON-PREMISE using Weaviate via Kubernetes. Thus, this example can be used even for

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Weaviate • vector database

@weaviate_io

10 months

Are you at NeurIPS in New Orleans? Look for @cshorten30 and @ecardenas300 for Weaviate stickers and an on-the-go short podcast! 😎

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Weaviate • vector database

@weaviate_io

8 months

Reranking is a quick way to boost search relevance in your RAG application ☝️ We have a @cohere reranker module that supports the following models: • rerank-english-v2.0 • rerank-multilingual-v2.0 Test it out in this recipe demo:

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Weaviate • vector database

@weaviate_io

1 year

🔥 Spotted Weaviate in @fireship_dev 's video on building an image search engine 🤩 Check it out and see how you can build your own using Weaviate! #ImageSearch #AI #MachineLearning

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Weaviate • vector database

@weaviate_io

2 months

Embedding models convert data into a machine-understandable format that captures its underlying meaning and relationships and is then used powering search, recommendations, and more! Check out this 60-second explainer video by @femke_plantinga ✨ Find out all about Weaviate’s

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Weaviate • vector database

@weaviate_io

1 year

We are beyond excited about the AutoGPT Arena Hacks starting today! 🚀 🤖 Create your AI agent powered by AutoGPT with Weaviate. 🔗 Join now and check out the **Hackathon Kick-Off Stream**: Read more 🧵↓ @Auto_GPT @lablabai

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Weaviate • vector database

@weaviate_io

10 months

Weaviate 1.23, our last release of 2023, is live now! 🚀 Here’s what’s included: 1️⃣ AutoPQ for effortless vector indexing 2️⃣ Flat vector index and Binary Quantization, a new index for small collections 3️⃣ Open source LLM integration with generative-anyscale 4️⃣ Python client

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Weaviate • vector database

@weaviate_io

3 months

Retrieval Augmented Generation (RAG) is one of the most promising AI use cases, as it enables teams across all industries to leverage the power of LLMs with their own data. But it's one thing to develop a prototype - another to use RAG in production. In our upcoming online

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Weaviate • vector database

@weaviate_io

4 months

Weaviate 1.25 is here! ⭐ Here’s what’s new: • Dynamic Vector Index: Switch to HNSW dynamically for efficient scaling • Raft: Enhances schema management and multi-node cluster reliability • New Modules: Host open-source embedding and language models locally • Batch

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Weaviate • vector database

@weaviate_io

2 years

💡 New article by @laura_hamham : "Using Cross-Encoders as reranker in multistage vector search"

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Weaviate • vector database

@weaviate_io

7 months

Weaviate 1.24 is now available! This new version is packed with amazing features and enhancements, including: 1️⃣ Named vectors 2️⃣ HNSW and binary quantization (BQ) 3️⃣ Simplified Docker configuration 4️⃣ Backend improvements 5️⃣ Python client update For more details, check out our

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Weaviate • vector database

@weaviate_io

2 years

🔥 Vectors on disk (DiskANN) is coming to Weaviate! 📉 The graph (by @AbdelRo68071159 & @trengrj ) displays the memory drop after Weaviate writes the vectors from memory to disk 👀 Note how we use SSD instead of RAM! RAM dropped from 1.2 GB to 0.2 GB 🧵More below

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Weaviate • vector database

@weaviate_io

1 year

👋 We'd like you to meet our new Developer Growth Team, with Philip Vollet ( @philipvollet ) and Edward Schmuhl ( @aestheticedwar1 ) 🥳 We can't wait to see their contributions. Welcome to Team Weaviate! 🎉 #Weaviate #DeveloperGrowth #TeamWeaviate

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Weaviate • vector database

@weaviate_io

4 months

What are Vector Databases? @femke_plantinga explains this in under 60 seconds ✨ Read more in @helloiamleonie 's blog post:

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Weaviate • vector database

@weaviate_io

4 months

🤩 The Weaviate repository just hit a new milestone: 10K GitHub stars and counting! 🤩 A massive shoutout to our open source community! Thank you to everyone who has forked, starred, and contributed. If you haven’t done so yet, check out our repository and don’t forget to star

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Weaviate • vector database

@weaviate_io

7 months

Weaviate is an AI native open source vector database! This means you can deploy and use Weaviate however you’d like, but this comes with challenges. In Weaviate 1.24, we are releasing anonymized minimal telemetry to better understand how we can build the best version of Weaviate

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Weaviate • vector database

@weaviate_io

3 years

Now also available in the Weaviate vector search engine. Awesome work @Nils_Reimers and co!

Nils Reimers

@Nils_Reimers

3 years

🚨Model Alert🚨 🏋️‍♂️ State-of-the-art sentence & paragraph embedding models 🍻State-of-the-art semantic search models 🔢State-of-the-art on MS MARCO for dense retrieval 📂1.2B training pairs corpus 👩‍🎓215M Q&A-training pairs 🌐Everything Available: 🧵

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Weaviate • vector database

@weaviate_io

1 year

Join us for the @DataCamp webinar with @_jphwang • Learn how vectors represent meaning • Discover the incredible capabilities of vector databases • Master Weaviate to perform semantic searches 🗓️ August 15, at 11 AM ET, 17 PM CET, 8 AM PT Register:

Vector Databases for Data Science with Weaviate in Python

In this live training, JP shows you how to use Weaviate, a leading open source vector database, to build apps that can understand and manipulate them based on meaning.

www.datacamp.com

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