Langchain's new member-A SQL+Vector database built on ClickHouse
MyScale is designed for the storage and analysis of massive vector data with structured metadata. If you are eager to find a high-performance vector search using SQL queries, MyScale could be your preferred option. Thanks to the advantages of native structural database support, it provides you with a flexible filter with a WHERE clause, even JOIN when you want to jointly search vectors with filters on relevant metadata from other tables. MyScale is now open for registration and offers millions of vectors‘ free tier plan for you! (https://myscale.com/) Now you can also use…
What it does
In the maker’s words, at launch
MyScale is designed for the storage and analysis of massive vector data with structured metadata. If you are eager to find a high-performance vector search using SQL queries, MyScale could be your preferred option. Thanks to the advantages of native structural database support, it provides you with a flexible filter with a WHERE clause, even JOIN when you want to jointly search vectors with filters on relevant metadata from other tables. MyScale is now open for registration and offers millions of vectors‘ free tier plan for you! (https://myscale.com/) Now you can also use MyScale as LangChain VectorStore 1. Go to MyScale (https://myscale.com/) and click on sign up 2. Go to your console to create a cluster: https://docs.myscale.com/en/cluster-management/ 3. Get your Connection Details under the Action column under the cluster table. 4. Set the parameters in MyScaleSettings Use the code snippets below to boost your chain with MyScale: ```python from langchain.vectorstores import MyScale, MyScaleSettings config = MyScaleSetting(host="<your-backend-url>", port=8443, ...) index = MyScale(embedding_function, config) index.add_documents(...) ``` P.S.: If you are interested in the super secret recipe for building high-performance AI+DB applications with MyScale, please visit: https://docs.myscale.com/en/advanced-applications/
Does the same job
all alternatives →
- OAOtterTune – Automated Database Tuning Service for RDS MySQL/Postgres2021 · ▲164
Yo. OtterTune is a database optimization service. It uses machine learning to automatically tune your MySQL and Postgres configuration (i.e., RDS parameter groups) to improve performance and reduce costs. It does this by only looking at your database's runtime metrics (e.g., INNODB_METRICS, pg_stat_database, CloudWatch). We don't need to examine sensitive queries or user tables. We spun this project out of my research group at Carnegie Mellon University in 2020. This week we've announced that OtterTune is now available to the public. We are offering everyone a starter account to try it out…
- SVSuperlinked – Vector Embeddings for Structured and Unstructured Data2024 · github.com · ▲7
Hi HN, I'm Daniel from Superlinked! We have built an open-source framework that improves vector search relevance and usefulness by combining structured metadata with unstructured data in your embeddings. We included self-hostable API server that sits between your data sources and vector database. Docs: https://docs.superlinked.com/ We're launching our cloud offering soon where you can use Superlinked to orchestrate high-performance retrieval for RAG, Search & Recommendation apps in your own cloud. Looking for feedback and happy to answer questions!
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Launched alongside, April 2023
the whole month →
- G4
Hi HN, Today we’re launching GPT-4 answers on Phind.com, a developer-focused search engine that uses generative AI to browse the web and answer technical questions, complete with code examples and detailed explanations. Unlike vanilla GPT-4, Phind feeds in relevant websites and technical documentation, reducing the model’s hallucination and keeping it up-to-date. To use it, simply enable the “Expert” toggle before doing a search. GPT-4 is making a night-and-day difference in terms of answer quality. For a question like “How can I RLHF a LLaMa model”, Phind in Expert mode delivers a…
AI · 2023 · phind.com
