41–50 of 91 posts
There needs to be a standard for benchmarking performance of these solutions. Milvus qps seems to be in a completely different tier of performance than the rest.
It lets you run you run the benchmarks using your own API keys. Although it is made by Zilliz (maintainers of Milvus), you can take a look and see what is going on and judge if its fair.
Coming at this from a diffeeent angle, does anyone have any links to tutorials for use-cases? I’d love to see what vectorDB hype is about but as a regular engineer I’m unable to even grasp how to use a vectorDB
Coming at this from a diffeeent angle, does anyone have any links to tutorials for use-cases? I’d love to see what vectorDB hype is about but as a regular engineer I’m unable to even grasp how to use a vectorDB
I was playing around with making my own UI for interfacing with chatgpt. I saved the chat transcripts in a normal postgres DB, along with the open AI embeddings for each message in a vector db, with a pointer to the message id in postgres in the vector DB metadata.
Then as you chatted, i had chatgpt continuously creating a summary of the current conversation you were having in the background and doing a search in the vector db for previous messages about whatever we're talking about, and it would inject that into the chat context invisibly. So you can do something like say: "Hey do you remember when we talked about baseball" and it would find a previous conversation where you talked about so and so hitting a home run into the context and the bot would have access to that, even though you never mentioned the word "baseball" in the previous conversation -- home run is semantically similar enough that it finds it.
If you're using openai embeddings as your vectors, it's _extremely_ impressive how well it finds similar topics, even when the actual words used are completely different.
As much as I like pg_vector, I think right now what we need the most is a pre-packaged version of sqlite-vss and a Pythonic wrapper for bootstrapping projects. This would lower barriers to entry even more for those using LLMs solely via APIs, and save people the trouble of setting up a database server or risking getting locked in to yet another prickly SaaS while iterating on a concept. Scaling can come later, after…
Pinecone makes it super easy to get up and running with RAG asap. Those prices are ridiculous though and any project with legitimate scale will move on to a more affordable solution.
Even so, driving the cost down for large workloads like that is a priority for us. We recognize the GenAI / RAG stack is a completely new line item in most companies' budgets so anything to keep that low can help these projects move forward.
* Shameless plug and a free ticket: Etienne Dilocker, The Co-founder/CTO of Weaviate and Ram Sriharsha, the VP of R&D at Pinecone are both presenting at The AI Conference. Lots of other smart people are presenting including Nazneen from Hugging Face, Harrison from Langchain, Jerry from Llamaindex, Ben the co-founder of Anthropic and many more. A hackathon is happening in the evening at the event as well. If you can't…
* Shameless plug and a free ticket: Etienne Dilocker, The Co-founder/CTO of Weaviate and Ram Sriharsha, the VP of R&D at Pinecone are both presenting at The AI Conference. Lots of other smart people are presenting including Nazneen from Hugging Face, Harrison from Langchain, Jerry from Llamaindex, Ben the co-founder of Anthropic and many more. A hackathon is happening in the evening at the event as well. If you can't…
Coming at this from a diffeeent angle, does anyone have any links to tutorials for use-cases? I’d love to see what vectorDB hype is about but as a regular engineer I’m unable to even grasp how to use a vectorDB
I recommend starting at https://www.pinecone.io/learn/vector-database/
As much as I like pg_vector, I think right now what we need the most is a pre-packaged version of sqlite-vss and a Pythonic wrapper for bootstrapping projects. This would lower barriers to entry even more for those using LLMs solely via APIs, and save people the trouble of setting up a database server or risking getting locked in to yet another prickly SaaS while iterating on a concept. Scaling can come later, after…