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Pinecone raises $100M Series B
- generativeai 3y agohttps://vectara.com/compare-pinecone-io-vs-vectara/ https://vectara.com/compare-pinecone-io-vs-vectara/
- indeed30 3y agoYou have to say that Pinecone got the timing absolutely right.
- alooPotato 3y agowhen were they founded?
- chicagobuss 3y ago2021
- tinyhouse 3y agoI don't know their financials to know if it's a good investment or not, but I have a feeling that they are over hyped. Vector DBs are becoming a commodity with many hosting options too. Again, I don't know what they have in plan for growth. Long term memory is too vague. We'll see how it goes.
- m1117 3y agoNow, be careful with what you say to Alexa, it might remember.
- bugglebeetle 3y agoPerfect example of AI gold rush nonsense. Pinecone has zero moat and quite a few free alternatives (Faiss, Weviate, pg-vector). Their biggest selling point is that AI hype train people don’t Google “alternatives to pinecone” when cloning the newest trending repo (or I guess, ask ChatGPT).
- m1117 3y agoAndreessen horowitz should've just googled
- moab 3y agoSpot on. There is zero moat and the self-hosted alternatives are rapidly improving (if not better) than Pinecone. There are good open-source contributions coming from bigcorp beyond Meta too, e.g., DiskANN (https://github.com/microsoft/DiskANN https://github.com/microsoft/DiskANN).
- zzzzzzzza 3y agowhy use this instead of something like pgvector?
- findjashua 3y agoi mean it’s Andreesen. They’re like the Softbank of Silicon Valley
- chadash 3y agoOracle DB has quite a few free alternatives. Yet Larry Ellison is the world's 4th wealthiest person.
- mmq 3y agoMost demos shared by people can use numpy arrays similar to this https://twitter.com/karpathy/status/1647374645316968449 https://twitter.com/karpathy/status/1647374645316968449
- bugglebeetle 3y agoIt’s true, but I also wouldn’t want any developer beside Karpathy to implement a production service using numpy arrays (I use Faiss).
- teaearlgraycold 3y agoIt has a moat for those that start using it. Vendor lock in.
- Laaas 3y agoThought this was about https://github.com/matrix-org/pinecone https://github.com/matrix-org/pinecone
- rgrieselhuber 3y agoI think the real money in this space is going to be in custom applications of this tech, and it won’t need to be on Pinecone.
- esafak 3y agoWhat do you mean by "this space"? Pinecone is ML infrastructure, search.
- yding 3y agoCongrats to the Pinecone team!
- ArcaneMoose 3y agoHappy for them, has been a very smooth developer experience using Pinecone and I think there is more than meets the eye with the combined keyword+vector search and handling of filtering. It's not going to solve all problems, but it's a well scoped solution at a good time.
- mikeryan 3y agoI honestly hope they use it to improve their documentation. I consider myself and pretty adept developer but without much background in AI and was looking for a solution for building out a recommendation engine and ended up at Pinecone. Maybe I'm not the target audience but after spending some time poking around I couldn't honestly couldn't even figure out how to use it. Even a simple Goole Search for "What is a Vector Database" ends up with this page. https://www.pinecone.io/learn/vector-database/#what-is-a-vector-database https://www.pinecone.io/learn/vector-database/#what-is-a-vec... Pinecone is a vector database that makes it easy for developers to add vector-search features to their applications Um okay... what's "vector-search"? For that sake what the eff is a "Vector" to begin with? Finally after getting about a third down the page we start defining what a vector is.... Maybe I'm not their target audience but I ended up poking around for about an hour or two before just throwing up my hands and thinking it wasn't for me. Ended up just sticking with Algolia, since we had them in place for Search anyway...
- jurassic 3y agoI don't have an ML background and have found the examples pretty easy to follow. https://docs.pinecone.io/docs/examples https://docs.pinecone.io/docs/examples
- linuxdude314 3y agoRespectfully if you don’t know what a vector is, you probably don’t need a vector DB. When they say “vector-search” they mean semantic search. I.e. “which document is the most semantically similar to the query text”. So how do we establish semantic similarity? In a database like Elasticsearch, you store text and the DB indexes the text so you can search. In a vector DB you don’t just store the raw text, you store a vectorized version of the text. A vector can be thought of as an array of numbers. To get a vector representation we need some way to take a string and map it to an array while also capturing the notion of semantics. This is hard, but machine learning models save the day! The first popular model used for this was called “word2vec” while a more modern model is BERT. These take an input like “fish” and output a vector like [ 3.12 … 4.092 ] (with over a thousand more elements). So let’s say we have a sentence that we vectorized and we want to compare some user input to see how similar it is to that sentence. How? If we call our sentence A and the input vector B, we can compute a number between zero and one that tells us how similar they are. This is called cosine similarity and is computed by taking the dot product of the two vectors and dividing by both of their magnitudes. When you load a bunch of vectors in a vector DB, the principal operation you will perform is “give me the top K documents that are similar to the input”. The databases indexing process computes k nearest neighbors algorithm on all vectors in the DB and stores this for use at query time. Without the indexing process there is no real difference between a vector db and key value store.
- faizshah 3y agoI’ve wanted to ask this question but I don’t know who to ask. Can someone explain what the use case is for vector DBs like pinecone, milvus etc. vs a fully featured search engine like Vespa, ElasticSearch etc. which also support vector search features? Is there something about running this type of index operationally that is particularly difficult?
- fizx 3y agoEase-of-use for python devs
- nostrebored 3y agoI’ve been playing around here a bit. One feature is that documents have embeddings stored at index time which means that query performance is quite good. Another is that the model you’re using to embed and query can be changed and configured based on specific use cases. A pretty cool feature of Marqo is multi-statement queries that let you search for multiple positive queries and even include negative queries to filter out results. Overall I’m really enjoying the tech.
- esafak 3y agoThey are for user-facing queries, where performance counts.
- asadm 3y agoI guess there is value in providing better dev experience here. This can be firebase/supabase equivalent for this niche. You can always use an open-source alternative. But you probably shouldn't self-host at early stage atleast.
- JumpCrisscross 3y agoMy alarm bells are going off given the Andreessen participation—what am I missing?
- bastardoperator 3y agoVector databases, so hot right now. I haven't really looked into them, I'm just wondering when or if postgres will do it better?
- arecurrence 3y agoPostgres has pgvector. It’s awesome.
- bugglebeetle 3y agoIt already does and it’s free - https://github.com/pgvector/pgvector https://github.com/pgvector/pgvector Only a sucker being forced to by their investors would use pinecone.
- esafak 3y agoHow big is your pgvector installation, and what are your p50/p99 latencies like? pinecone and the like are not necessary for toy projects.
- arecurrence 3y agoI was using pinecone before installing pgvector in Postgres. Pinecone works and all but having the vectors in Postgres resulted in an explosion of use for us. Full relational queries with where clauses and order by etc AND vector embeddings is wicked.
- javier2 3y agoHow is disk usage in pgvector?
- arecurrence 3y agoThere's a section in the readme describing vector space https://github.com/pgvector https://github.com/pgvector. . Also, the maintainer is actively working on product quantization https://github.com/pgvector/pgvector/issues/93 https://github.com/pgvector/pgvector/issues/93 Supabase was also asking for sparse vectors https://github.com/pgvector/pgvector/issues/81 https://github.com/pgvector/pgvector/issues/81 Speaking of the repo, they have a number of features they want to add if anyone is interested in contributing, there's lots of room for advancement. Many of these features already have active branches https://github.com/pgvector/pgvector/issues/27 https://github.com/pgvector/pgvector/issues/27
- itake 3y agoWhy do you use pgvector instead of pgANN? My understanding is pgANN is built with FAISS. When I compared pgvector with FAISS, pgvector was 3-5x slower. https://github.com/netrasys/pgANN https://github.com/netrasys/pgANN
- extesy 3y agoIf I understand correctly, pgAnn is using a standard "cube" extension: https://github.com/netrasys/pgANN#setup https://github.com/netrasys/pgANN#setup
- arecurrence 3y agoThere is certainly a wide variety of problems today for which pgvector is unsuitable due to performance limitations... but fear not! This is an area that is getting significant focus right now. A marqo.ai dev is currently working on adding HNSW-IVF and HNSW support https://news.ycombinator.com/item?id=35551684 https://news.ycombinator.com/item?id=35551684 and the maintainer has recently noted that they are actively working on an IVFPQ/ScaNN implementation https://github.com/pgvector/pgvector/issues/93 https://github.com/pgvector/pgvector/issues/93 The pgAnn creator actually asked about performance a month ago here https://github.com/pgvector/pgvector/issues/58 https://github.com/pgvector/pgvector/issues/58 Expect to see performance improve dramatically later this year.
- swyx 3y agothere'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 https://www.trychroma.com/blog/seed - Weaviate - $50m A https://www.theinformation.com/articles/index-ventures-leads-50-million-investment-in-ai-startup-weaviate https://www.theinformation.com/articles/index-ventures-leads... - Pinecone - $100M B
- opisthenar84 3y agoImo the most advanced vector DB out there is Vespa https://vespa.ai/ https://vespa.ai/. Harder to set up than wrappers like Chroma, but very powerful.
- swyx 3y agowhat makes it most advanced/when do you need something like Vespa vs the newer options
- 1xdevloper 3y ago- Qdrant - $7.5m seed https://qdrant.tech/articles/seed-round/ https://qdrant.tech/articles/seed-round/
- swyx 3y agoi had a feeling i missed something.. thanks!
- opisthenar84 3y agoZilliz also raised $60M, although it was last year before the ChatGPT hype. https://www.businesswire.com/news/home/20220824005057/en/Vector-Database-Company-Zilliz-Raises-60-Million-Series-B-Extension-to-Expand-Its-Operations-in-Silicon-Valley https://www.businesswire.com/news/home/20220824005057/en/Vec...
- mmq 3y agoNot recent, but the company that runs on top of milvus: https://www.businesswire.com/news/home/20220824005057/en/Vector-Database-Company-Zilliz-Raises-60-Million-Series-B-Extension-to-Expand-Its-Operations-in-Silicon-Valley https://www.businesswire.com/news/home/20220824005057/en/Vec...
- monological 3y agoWhy not just use pgvector for Postgres. It’s free and works great. You can even do cosine distance between embeddings.
- breckenedge 3y agoHow fast is it compared to Pinecone?
- dmix 3y agoA good sales team, tons of devs, and some custom integrations might answer this question in the future
- hospitalJail 3y ago>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.
- dmix 3y agoI 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.
- jillesvangurp 3y agoSame with opensearch and elasticsearch, both of which have added vector search as well (slight differences between their implementations). And since vector search is computationally expensive, there is a lot of value in narrowing down your result set with a regular query before calculating the best matches from the narrowed down result set. From what I've seen, the big limitation currently is dimensionality. Most of the more advanced models have a high dimensionality and especially Elasticsearch and Lucene limit the dimensionality to 1024. E.g. several of the openai models have a much higher dimensionality. Opensearch works around this by supporting alternate implentations to lucene for vectors. Of course it's a sane limitation from a cost and computation point of view, having these huge embeddings doesn't scale that well. But it does limit the quality of the results unless you can train your own models and tailor them to your use case. If you are curious on how to use this stuff, I invested some time a few weeks ago getting my kt-search kotlin library to support this and wrote some documentation for this: https://jillesvangurp.github.io/kt-search/manual/KnnSearch.html https://jillesvangurp.github.io/kt-search/manual/KnnSearch.h.... The quality was underwhelming IMHO but that might be my complete lack of experience with this stuff. I have no experience with pinecone and I'm sure it's great. But I do share the sentiment that they might not come out on top for this. There are too many players here and it's a fast moving field. OpenAI just majorly moved the whole field forward enormously in terms of what is possible and feasible.
- ttymck 3y agoIs fundraising back?
- UncleOxidant 3y agoOh... not that Pine. Here I thought we were going to get some really rock'n SBCs and PinePhones.
- satvikpendem 3y agoCan 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?
- gamegoblin 3y agoVector 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 hang gliding company? or parasailing? idk" Behind the scenes you can ask GPT "Output a list of 10 candidate sentences that are plausible text messages that John may have sent", and it spits out "I'm thinking of starting a hang gliding company" and "I might found a parasailing company" etc. Then you query the vector DB for those imaginary sentences, and as a result you get sentences that are semantically similar. You take the top N nearest neighbors and plug them in to the GPT context window and say: "Here are 100 sentences that might match the original query. If any of them match, select the number that matches. Otherwise output null" If you engineer this system well, you can get pretty decent results. This is great for key:value type querying, but IMO there is a lot of ground to be explored by extending it with more graph-like links. I.e. use vector search to get some initial nodes that are better than random, but then start doing a little beam-search algorithm from those nodes to find nodes they are "linked" to in some way that may answer the query better.
- satvikpendem 3y agoSo 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?
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- tasubotadas 3y agoWhat's the business model for companies like these? 750M valuation implies that they should be able to generate at least 75M revenue per year.
- alooPotato 3y agoPublic companies are trading far lower than 10x, they'll need to generate more revenue eventually.
- sandstrom 3y agoThis is a fairly good review of the many vector databases that have cropped up recently: https://towardsdatascience.com/milvus-pinecone-vespa-weaviate-vald-gsi-what-unites-these-buzz-words-and-what-makes-each-9c65a3bd0696 https://towardsdatascience.com/milvus-pinecone-vespa-weaviat...
- nemo44x 3y agoThey're so hot right now that you can't even signup for a starter account. I'm guessing this money will help them fix that so as to not slow down their potential customer base. It's a really easy DB to use for people with no idea about vector DBs, etc. It "just works".
- jabo 3y agoPlug: If you're ever looking for an open source alternative to Pinecone, we recently added vector search to Typesense: https://typesense.org/docs/0.24.1/api/vector-search.html https://typesense.org/docs/0.24.1/api/vector-search.html The key thing is that it's in-memory and allows you to combine attribute-based filtering, together with nearest-neighbor search. We're also working on a way to automatically generate embeddings from within Typesense using any ML models of your choice. So Algolia + Pinecone + Open Source + Self-Hostable with a cloud hosted option = Typesense
- Dachande663 3y agoThis is what we’re using. We already sync database content to a typesense DB for regular search so it wasn’t much more work to add in embeddings and now we can do semantic search.
- gangster_dave 3y agoCould you do boosting with Typesense, like favoring more recent results?
- jabo 3y agoYup. If you store timestamps as Unix timestamps you can gave Typesense sort on text match score and the timestamp field: https://typesense.org/docs/guide/ranking-and-relevance.html#tie-breaking-based-ranking https://typesense.org/docs/guide/ranking-and-relevance.html#...
- chx 3y ago> We raised $100 million in Series B funding, led by Andreessen Horowitz, I closed the browser tab here. It's a usual techbro hustle, nothing to see here, move along. Hype, whatnot, couldn't care less. I am sure they will make money but it'll be detrimental to society. As always.
- danielmarkbruce 3y agoIs 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?
- darkteflon 3y agoI 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.
- kordlessagain 3y agoYou 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.
- skim_milk 3y agoI'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?
- gk1 3y 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 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.
- sireat 3y agoA little bit less serious take on vector databases: https://www.youtube.com/watch?v=klTvEwg3oJ4 https://www.youtube.com/watch?v=klTvEwg3oJ4
- archibaldJ 3y agodoes anyone know if it can do search of any tensor across latent space? or is it mostly on text tensor?
- ryanlime 3y agoWhat are Pinecone’s advantages compared to other vector databases? From what I understand one of the founders has experience building similar features at AWS previously.