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Pinecone raises $100M Series B

pinecone.io

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Re: Pinecone raises $100M Series B

#71

Post by dmix »

hospitalJail wrote: post #70
dmix wrote: post #61

Earlier quoted context omitted.

A good sales team, tons of devs, and some custom integrations might answer this question in the future

>A good sales team It quite angers me that people (on HN) will consider the following to be benefits worth mentioning as pros to the consumer: >Sleek/shiny finish >Marketing/Branding >Ability to Monetize We arent shareholders, all 3 of these are bad for the customer.

I wasn't making a personal recommendation to you? I was answering more broadly why someone would use Pinecone in the future.

Every new software company like this has "why wouldn't everyone just use x existing open source project, why even try to make it a real business with a hundred devs, actual support/marketing, and big ambitions to be more than a plugin to Postgres?"

Based on the videos and interviews with their lead dev I've seen Pinecone has some quite large plans by integrating with a wider stack and integrating with company databases, well beyond what they have done so far releasing an early version of the DB.

Regardless, getting wider adoption via actual businesses investing in marketing/sales to seed ideas in the market can spur development and potentially progress/innovate the tooling across the wider market, that feeds back into open source.

Re: Pinecone raises $100M Series B

#72

Post by danielmarkbruce »

Is it possible Andreessen are misunderstanding how pinecone/vector dbs are used? It seems like they are pitching it as "memory for large language models" or something. Are people using vector db's in some way I'm not aware of? To me it's a database to help you do a semantic search. A multi-token string is converted into a single embedding. Like maybe 1000 words into one embedding. This is helpful because you can quickly find the relevant parts of a document to answer a question and there are token limits into an LLM, but the idea that it's helping the LLM keep state or something seems off?

Is it possible they are confusing the use of embeddings across whole swaths of text to do a semantic search with the embeddings that happen on a per token basis as data runs through an LLM? Same word, same basic idea, but used so differently that they may as well be different words?

Re: Pinecone raises $100M Series B

#73

Post by skim_milk »

I'm still surprised by their generous free tier, I have a database of 300k embeddings on Pinecone and it's only 10% full by their metrics. Now, I'm only averaging a request every other minute with 90ms per query, but it would take crazy amounts of traffic or a ton more data for me to convert from their free tier.

Who is even paying?

Re: Pinecone raises $100M Series B

#74

Post by opisthenar84 »

swyx wrote: post #31

there's at least $168m being poured into vector db's this year. recent vector database fundraises: - Chroma - $18M seed https://www.trychroma.com/blog/seed - Weaviate - $50m A https://www.theinformation.com/articles/index-ventures-leads... - Pinecone - $100M B

Zilliz also raised $60M, although it was last year before the ChatGPT hype. https://www.businesswire.com/news/home/20220824005057/en/Vec...

Re: Pinecone raises $100M Series B

#75

Post by gk1 »

skim_milk wrote: post #73

I'm still surprised by their generous free tier, I have a database of 300k embeddings on Pinecone and it's only 10% full by their metrics. Now, I'm only averaging a request every other minute with 90ms per query, but it would take crazy amounts of traffic or a ton more data for me to convert from their free tier. Who is even paying?

> 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 with tens of millions, hundreds of millions, and even billions of embeddings. If they care about performance and reliability, and don't want to tie up an entire team of engineers to manage a self-hosted solution, then Pinecone makes a lot of sense for them.

In a way this also answers the many questions about "Pinecone vs [whatever]" ... If you're dealing with <1M embeddings the differences between your options will hardly matter — just pick whatever's easiest for you. If you're already using a managed DB that introduced something that's good enough for you... just use that. Though we still work hard to make Pinecone the easiest choice and have features that many basic solutions don't have, such as hybrid search (sparse + dense vector embeddings) for better search results.

Re: Pinecone raises $100M Series B

#76

Post by darkteflon »

danielmarkbruce wrote: post #72

Is it possible Andreessen are misunderstanding how pinecone/vector dbs are used? It seems like they are pitching it as "memory for large language models" or something. Are people using vector db's in some way I'm not aware of? To me it's a database to help you do a semantic search. A multi-token string is converted into a single embedding. Like maybe 1000 words into one embedding. This is helpful because you can quic…

I might be mistaken, but my understanding from having played around with LangChain for a couple months is that because you’ve got to keep all your state in the context window, giving the model access to a vectorstore containing the entire chat history allows it to retrieve relevant messages against your query that can then be stuffed or mapreduced into the context window for the next response.

The alternative - and I believe the way the ChatGPT web app currently works - is just to stuff/mapreduce user input and machine response into the context window on each step, which quickly gets quite lossy.

Re: Pinecone raises $100M Series B

#77

Post by satvikpendem »

gamegoblin wrote: post #47
satvikpendem wrote: post #44

Can someone explain how vector databases can be used as long term memory for AI (and LLMs specifically)? Isn't there still a token limit as to how much ChatGPT can hold in working memory? Is the goal that ChatGPT can query the vector database directly to get information out, and if so, how is that different than using a regular database?

Vector databases make it so semantically similar sentences get mapped to be closer together in the vector space. So the sentence "I started working as a programmer" will be very close to "I began my job as a software developer". This makes it very powerful for natural language search. So when the user asks a bot "Find the text message John sent me 3 years ago about wanting to found a company, I think it was like a ha…

So you have to feed in the items to ChatGPT manually (or via some script) it looks like? In the future I guess ChatGPT with plugins could query the database on its own?

Does it work for text data or can it work for other types of data as well?

Re: Pinecone raises $100M Series B

#78

Post by kordlessagain »

darkteflon wrote: post #76
danielmarkbruce wrote: post #72

Is it possible Andreessen are misunderstanding how pinecone/vector dbs are used? It seems like they are pitching it as "memory for large language models" or something. Are people using vector db's in some way I'm not aware of? To me it's a database to help you do a semantic search. A multi-token string is converted into a single embedding. Like maybe 1000 words into one embedding. This is helpful because you can quic…

I might be mistaken, but my understanding from having played around with LangChain for a couple months is that because you’ve got to keep all your state in the context window, giving the model access to a vectorstore containing the entire chat history allows it to retrieve relevant messages against your query that can then be stuffed or mapreduced into the context window for the next response. The alternative - and I…

You aren't mistaken. Keeping state, or storing memories, is where it's at with prompts. The trick is knowing what to remember and what to forget.

I consider vector engines to be "hot" models, given they are storing the vector representations of text already run through the "frozen" model.

Having written something a while back that indexes documents and enters into discussion with them, I'm pretty sure ChatGPT is using some type of embedding lookup/match/distance on the history in the window. That means not all text is submitted at the next entry, but whatever mostly matches what is entered by the user (in vector space) is likely pulled in and sent over in the final prompt.

Re: Pinecone raises $100M Series B

#79

Post by danielmarkbruce »

kordlessagain wrote: post #78
darkteflon wrote: post #76

Earlier quoted context omitted.

I might be mistaken, but my understanding from having played around with LangChain for a couple months is that because you’ve got to keep all your state in the context window, giving the model access to a vectorstore containing the entire chat history allows it to retrieve relevant messages against your query that can then be stuffed or mapreduced into the context window for the next response. The alternative - and I…

You aren't mistaken. Keeping state, or storing memories, is where it's at with prompts. The trick is knowing what to remember and what to forget. I consider vector engines to be "hot" models, given they are storing the vector representations of text already run through the "frozen" model. Having written something a while back that indexes documents and enters into discussion with them, I'm pretty sure ChatGPT is usin…

Sure - but a vector db is helping you keep your prompts to under size X. It isn't adding state and there are various mechanisms to keep your prompt to under size X - like summarization, providing a table of contents etc. It seems to me that vector db and semantic search are one trick in a pile of tricks to keep prompt sizes down until we can get the input sizes up (although gpt4 already takes 32,000 tokens).

Using semantic search to find relevant chunks seems misguided but practical in the short term. One of the key benefits of LLMs is they can take into account a lot of context.

Re: Pinecone raises $100M Series B

#80

Post by gamegoblin »

satvikpendem wrote: post #77
gamegoblin wrote: post #47

Earlier quoted context omitted.

Vector databases make it so semantically similar sentences get mapped to be closer together in the vector space. So the sentence "I started working as a programmer" will be very close to "I began my job as a software developer". This makes it very powerful for natural language search. So when the user asks a bot "Find the text message John sent me 3 years ago about wanting to found a company, I think it was like a ha…

So you have to feed in the items to ChatGPT manually (or via some script) it looks like? In the future I guess ChatGPT with plugins could query the database on its own? Does it work for text data or can it work for other types of data as well?

You call the ChatGPT API programmatically, entirely automated. You call the API, parse the response, make a decision in code, call the API again, etc. The GPT API just becomes a natural language reasoning module in an otherwise normal codebase.
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