gk1

42 ms·

781.
Practical Guide on Semantic Search over Your Private Data with LLMs(hackernoon.com)
1 points by gk1 3y ago|0 comments
782.
Fixing Hallucination with Knowledge Bases(pinecone.io)
134 points by gk1 3y ago|53 comments
783.
Venture Oracle: AI tools for venture capital(chatg.vc)
1 points by gk1 4y ago|0 comments
784.
by gk1 4y ago
It's not news when a 50-person company lays off 4 people (8%).
785.
Building Custom Tools for LLM Agents(pinecone.io)
1 points by gk1 4y ago|0 comments
786.
Chatbots with Pinecone(pinecone.io)
1 points by gk1 4y ago|0 comments
787.
by gk1 4y ago
> Who is even paying? The homepage offers a few clues: Shopify, Gong, Zapier, HubSpot, Expel, and several thousand others. That includes huge enterprises who tend not to want their names shown publicly. Basically there are many companies
788.
Opening up our free plan(pinecone.io)
2 points by gk1 4y ago|0 comments
789.
Chatbots with Pinecone(pinecone.io)
2 points by gk1 4y ago|0 comments
790.
An overview of Generative AI tools and infrastructure(base10.substack.com)
3 points by gk1 4y ago|0 comments
791.
AI Dream Home(realtor.com)
2 points by gk1 4y ago|0 comments
792.
AI Agents(pinecone.io)
4 points by gk1 4y ago|0 comments
793.
Supporting our growing number of free users(pinecone.io)
2 points by gk1 4y ago|0 comments
794.
by gk1 4y ago
Got it, thanks. Each pod has a limit to how much data it can store. We want to give an example of how many vector embeddings you can store in a single pod. Since the data size is affected by number of dimensions, we have to choose a sample
795.
by gk1 4y ago
Hi, I'm from Pinecone. We support up to 20,000-dimensional vectors.[1] A meaningful percentage of our customers use OpenAI models. If you got that 700 number from our site or docs I'd love to know where so we could correct it. [1]
796.
What GPT-4 Does Is Less Like “Figuring Out” and More Like “Already Knowing”(amistrongeryet.substack.com)
61 points by gk1 4y ago|85 comments
797.
Experimenting with LLMs to Research, Reflect, and Plan(eugeneyan.com)
141 points by gk1 4y ago|14 comments
798.
Chunking Strategies for LLM Applications(pinecone.io)
3 points by gk1 4y ago|0 comments
799.
Chunking Strategies(pinecone.io)
2 points by gk1 4y ago|0 comments
800.
Chunking Strategies for LLM Applications(pinecone.io)
2 points by gk1 4y ago|0 comments
801.
by gk1 4y ago
If anyone is interested in diving deeper into Faiss, we put together an unofficial manual after not finding much learning materials about it: https://www.pinecone.io/learn/faiss/
802.
by gk1 4y ago
We put together some stories here: https://www.pinecone.io/learn/wild/ Usually folks use a vector database alongside a doc store like Postgres, Snowflake, Elastic…
803.
by gk1 4y ago
Update: After a closer look we determined this article is not original and decided to take it down. We're also making some internal changes to prevent this from happening again. Thanks again for flagging this!
804.
by gk1 4y ago
Hi, I'm responsible for our marketing which includes educational content like this. I'm looking into this urgently. Thank you for flagging it.
805.
Introduction to Autoencoders(pinecone.io)
104 points by gk1 4y ago|11 comments
806.
by gk1 4y ago
> I get the benefit over Pinecone (which wasn't built with LLMs, etc in mind) What do you mean? Pinecone was specifically made to be used alongside LLMs and other embedding models. That’s how anyone uses Pinecone.
807.
Regularization in Neural Networks(pinecone.io)
2 points by gk1 4y ago|1 comments
808.
Multi-User Chatbots with LangChain(pinecone.io)
2 points by gk1 4y ago|0 comments
809.
by gk1 4y ago
The retrieval plugin does not use LangChain, afaik.
810.
by gk1 4y ago
Which is why Pinecone supports hybrid search, which has shown to provide better results for out-of-domain use cases than either semantic search or keyword search alone: https://www.pinecone.io/learn/hybrid-search-intro&

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