gk1

32 ms·

661.
by gk1 3y ago
Blog post coming soon about this. The TL;DR is the underlying architecture matters… a lot. More coming soon.
662.
by gk1 3y ago
Yes, for example any site with user-uploaded content — think marketplaces, social media, SaaS file/docs storage, etc. Very high write throughput and expectation of that data being available in search/chatbots right away.
663.
by gk1 3y ago
Go ahead and try to load a billion embeddings into Mongo or Elastic. The author says this will be "faster, cheaper, and simpler..." Will it? (Disclaimer: I work for Pinecone, so obvious bias ahead but also perspective of 3 years s
664.
Search the ArXiv: Find similar papers or search semantically(searchthearxiv.com)
3 points by gk1 3y ago|0 comments
665.
Retrieval Augmented Generation (Rag) Explained(pinecone.io)
2 points by gk1 3y ago|0 comments
666.
Automatically surfacing similar GitHub issues(pinecone.io)
1 points by gk1 3y ago|0 comments
667.
We improved LangChain ingest speed by 5x(pinecone.io)
3 points by gk1 3y ago|1 comments
668.
An Opinionated Checklist for Choosing a Vector Database(pinecone.io)
2 points by gk1 3y ago|0 comments
669.
Making Retrieval Augmented Generation Fast(pinecone.io)
1 points by gk1 3y ago|0 comments
670.
Pinecone as a Knowledge Base for Amazon Bedrock(pinecone.io)
1 points by gk1 3y ago|0 comments
671.
Vector Embeddings as the Antidote to Psychotic LLMs and a Cure for Alert Fatigue(securityweek.com)
2 points by gk1 3y ago|0 comments
672.
Why Are So Many Millennials Going to Mongolia?(nytimes.com)
3 points by gk1 3y ago|1 comments
673.
Build a ChatGPT search plugin with Notion and Pinecone(developers.cloudflare.com)
1 points by gk1 3y ago|0 comments
674.
Increasing Pinecone upsert speeds in LangChain by 5x(pinecone.io)
1 points by gk1 3y ago|0 comments
675.
LLMs Are Not Enough(pinecone.io)
2 points by gk1 3y ago|0 comments
676.
LLMs Are Not All You Need(pinecone.io)
2 points by gk1 3y ago|2 comments
677.
Falcon 180B: Model Overview(pinecone.io)
2 points by gk1 3y ago|0 comments
678.
Comparing Performance of OpenAI GPT-4 and Microsoft Azure GPT-4(mcplusa.com)
1 points by gk1 3y ago|0 comments
679.
by gk1 3y ago
We can help: greg@pinecone.io
680.
Building AI-powered enterprise search in 2 days(retool.com)
1 points by gk1 3y ago|0 comments
681.
Book-author-GPT: Create a chatbot from any ePub book(github.com)
2 points by gk1 3y ago|1 comments
682.
The LLM Ecosystem: Quantization, RAG, Agents, and More(pinecone.io)
5 points by gk1 3y ago|0 comments
683.
Most LLM Apps Are Bullshit, and That's OK(tidepool.so)
2 points by gk1 3y ago|0 comments
684.
by gk1 3y ago
This video goes through some of the testing methodology and techniques: https://youtu.be/7E-eiUN9d6U?si=zSoiGH2QAlQoxxcr
685.
by gk1 3y ago
Yes, we published benchmarks here: https://www.pinecone.io/blog/hybrid-search/
686.
by gk1 3y ago
> Regarding Vector DBs, most have advocated for weaviate and pinecone. Incidentally, all run into quantization problems at scale (1M+ vectors)... Can you elaborate? We (at Pinecone) have dozens of customers with over a billion embeddings
687.
The Pain and “Poetry” of Python(pinecone.io)
5 points by gk1 3y ago|3 comments
688.
by gk1 3y ago
> I’m wondering whether that’s a good idea. That depends... How fast do you need it to be, how many vectors will you be searching across, and how often will the index be updated? If the answers are anything resembling "very, many, a
689.
When We Forecast AGI, Do We Mean in the Lab or in the Field?(amistrongeryet.substack.com)
2 points by gk1 3y ago|1 comments
690.
Semantic search without the napalm grandma exploit (Ep. 600) – Stack Overflow(stackoverflow.blog)
1 points by gk1 3y ago|0 comments

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