---
title: Using pinecone vector db from Rails with OpenAI for semantic search
slug: using-pinecone-vector-db-from-rails-with-openai-for-semantic-search
published_at: 2023-01-31 15:00:16 +0000
updated_at: 2026-03-04 20:12:57 +0000
summary: 
description: Using pinecone vector db from Rails with OpenAI for semantic search  #rubyonrails #ruby #rails #gpt3 #openai #pinecone
tags: [cjav_dev, web development tutorials, web development for beginners, vim, ruby, rails, javascript, ruby on rails, active record, ruby on rails tutorial, openai, pinecone, semantic search, tutorial, open ai tutorial, open ai]
views: 2276
author: CJ Avilla
url: https://www.cjav.dev/videos/using-pinecone-vector-db-from-rails-with-openai-for-semantic-search
youtube_url: https://www.youtube.com/watch?v=NTk8L2de_2Y
youtube_id: NTk8L2de_2Y
embed_url: https://www.youtube.com/embed/NTk8L2de_2Y
thumbnail_url: https://i.ytimg.com/vi/NTk8L2de_2Y/hqdefault.jpg
type: video
---

# Using pinecone vector db from Rails with OpenAI for semantic search

*Published: January 31, 2023*
*Views: 2276*

## Watch

[Watch on YouTube](https://www.youtube.com/watch?v=NTk8L2de_2Y)

[![Using pinecone vector db from Rails with OpenAI for semantic search](https://i.ytimg.com/vi/NTk8L2de_2Y/hqdefault.jpg)](https://www.youtube.com/watch?v=NTk8L2de_2Y)

## Description

Using pinecone vector db from Rails with OpenAI for semantic search

#rubyonrails #ruby #rails #gpt3 #openai #pinecone

## Transcript

all right so I am going to go through the process of signing up for Pinecone and my intention is to build a semantic search engine for my blog posts podcasts and videos that are on my personal site here at cjav.dev I've got a really basic video search that works with ransack right now so I can look at show me all the stuff show me the few videos I did on Pearl and okay so that's just gives us 17 results back not all of these are about Pearl but what I want something like much better and so my plan is to use openai and the embeddings stuff to generate vectors based on transcripts from videos transcripts for podcasts and the content of the blog posts and I'm going to store those somewhere I initially I was trying to do it on postgres using PG Vector but I'm on Heroku and then I'll let you do that so we're gonna go through the process now of signing up for Pinecone just so we can see what the developer experience is like so this bubble like the outline here looks off a little bit so the pixels on the header with this x is a little bit bigger than the modal that popped out so let's see welcome would you like to take a guided tour of the console it's like a yes or no question and then it says get started or skip whatever so get started click here to create a sample index okay Squad if an API open AI trick I don't know what squad or Trek means but okay pod type this is helpful seeing like best storage capacity faster queries lowest latency Etc I think I saw something about the prices being maybe different too so I'm on this like free plan that I want to try new or from collection what is that doesn't actually seem to do anything but maybe it's because I'm not actually entering anything all right create the index open API or open AI track check out the following okay so this is an open collab where I have the Pinecone package and the openai package I'm actually not going to use I don't want to use Python I don't know we'll see if I have to use Python but I want to use Ruby okay what caused the Great Depression text similarity that's going to give us back our embeddings response from openai and then we're going to use that here to query the index so the index here is given to you by name and then you get back okay so this is going to give you this takes the embedding for this input query and then it's going to include the values and give those back to you so then you can take this result which is going to have a bunch of matches and then you iterate over those matches and print them out okay cool click on the query tab to view the query sure show the query results okay and fetch delete I don't know what those do or if it actually matters if I use those or not so is this Trek thing is this like an existing does this already come with data or something I don't know what this index is about okay so I've got my collections I think I'm done with the tour probably and then I'm gonna have some API Keys here all right and then maybe I can collaborate with some people maybe I can use these public okay so this must be some all right so this is a data set on hugging face which has maybe some questions and some other stuff trick the text retrieval conference question classification data set all right so that's like a pre-built data set so I want to create a new index and this is going to be called like cjav Dev docs or something or content I don't know what's called CJ Dev and the dimensions I think I saw 2048 I don't know metric I don't know like why I would pick one or another but cosine similarity seems fine create index let's see okay so you can't even once you go through the tutorial you can't actually do anything until you delete the existing index all right so let's say create index C drive Dev and 2048 cosine similarity P2 create the index so then this gives me like this URL and I need to figure out how to connect to that URL so what I want to do now is go to docs so I'll open that a new tab docs with a question mark next to it is interesting yeah yeah I'd expect like a document yeah like this this Emoji here or icon okay so it has python examples and curl examples but I'm going to use Ruby so let's look up Ruby pine cone Vector DB is there not a ruby SDK and if there's not then we'll have to rebuild it from curl okay so I'm going to open up my thing here and drop in this curl command now I have to change my environment and I have to change my API key so I'm gonna go to API keys and I'm assuming I can create a new one so I'll just show this for now and then I'll roll it later okay so there's my API key my environment it's like region and environment or a little bit loaded I don't know so let's just see what happens here okay so I got my project name back and some stuff all right so what else here the fetching vectors updating vectors partial update I want to like insert some vectors I think insert data Okay so to connect to the index this says create a variable that references the index and then here we're calling upsert I see so I'm gonna have to build my own Json thingy which is fine and I'm assuming I can just copy this and paste it and then do the same exact thing as we did before we are going to all right so it's going to have your index Dash your project so your index whoops wait is that the thing environment pine cone IO okay so this I think this is probably the you are or like the domain base and then uh gosh API keys okay take this back over here all right and then execute that Vector Dimension eight does not match the dimension of the index oh that makes sense Okay so what I need to do is hit all right so let's try this I'm going to create a class in Ruby called Pinecone and this is going to have a few different methods uh the first thing we need is we're just going to have his space URL which I'm just going to hard code for now I'm also just going to hard code my API key and my base URL is in here I'm gonna grab this what okay this should be easy to copy right now having that where it's at in the UI it ends up having a new line at the end and it's funky so it'd be nice if there was a copy button next to this since I assume people are going to grab that a lot also I've been navigating around and I fixed this issue but this thing didn't go away so that should have gone that should have gone away okay so what let's see so let's call this upsert and it is going to be this curl request so what we need to do is use a ruby HTTP client so let's say bundle let's see I don't know if I already have oh yes I already have rest client okay so I should be able to say rest client dot post something like that and get back response yeah it's gonna want a body and some other stuff all right whatever so we gotta redirect that's fine so now what I can do is I can go into our pine cone class and I can say rest client dot post and here I'm going to have that be base URL slash vectors upsert and then vectors and then I'm just going to pass in vectors as an array and we'll have to figure out what that is somewhere else and then I think yeah that's our headers okay and then we also need our API key is passed the same way all right so the vectors are of the form ID points at string values and the values is like the actual vector array of floats okay so let's say that I wanted to hit the open AI API so I can go to my so I have I've got this summarized job oops actually Vector oh this should just take in vectors right and then I think I can just open up rails console say index is pine cone dot new and then I can say index upsert and pass in some vectors right now it's empty and so I get back 400 bad request so what I want to do is use the open AI stuff so this client so open AI client dot new with my excess token rails the application. credentials dot open AI key okay so now I should be able to say open AI Dot embeddings for something so let's see okay so perimeters okay the model is going to be Babbage similarity I think and then the input is going to be something like Pearl payments how to take payment with Perl okay so that hits open Ai and gives me back a bunch of stuff now the question I have is so result is going to be underscore so result dot Keys what is in here so we have the data so result.data or yeah okay result.data dot length or something dot Keys what's in here it looks like a hash of something result dot keys so that is an array so if I get the first thing okay okay so the first result in the data array is object index and something else so let's say index what does that give us zero object embedding and then embedding is the actual array and that's what we want to see if the length is 2048 and it is great so what we want to do is we want to call like index.upsert with result of data zero embedding and what is that pine cone okay index equals pine cone dot new and then index to upsert passing in that result okay so we got a 400 response and we don't know what's happening so let's go to Pine Cone again and we'll wrap this thing in a try catch and we'll print out e and puts e Dot message and puts e Dot spawns I don't know we'll see what we get back reload and we have to create a new instance of pinecon since we changed it let's upsert unexpected token vectors okay oh because we can't just pass in pass in the thing okay so this is all right so vectors should have actually been it's not just result data that's like the that's the values so we want to call this with something like yeah values is this and we also need the ID which is going to be like the thing that we searched for which I can't remember what that actually is it was something like how to take payment with pearl I don't actually remember but we're gonna have to figure out okay Vector ID was wrong so what's going on here this is the ID vectors okay so vectors is what we're passing and that should be an array which it is an array and then inside there we have a hash with ID points at a all right let's try it shouldn't matter but let's see if we have to use string Keys here unexpected token how to in vectors maybe this it's possible this needs to be an array and also it's possible that this rest client needs a different format so let's see press client.post yeah maybe we need to call Dot two Json on it let's see hey look at that we got a 200 that's fantastic let's store the response dot body oh okay all right so upserted count is one so it just tells us how many are upserted so if I go over to my dashboard is am I gonna see anything about what's in here let's see index okay clicking on this does nothing it's a link and it has the affordance that I should be able to see something but I don't and metrics in here index information total vectors okay so there is that is that's the information is that I see one total Vector this is weird that these boxes are not like lined up this one has a namespace and a number of vectors and then yeah it's this UA you like the UI here could be tightened up a bit so what I want to do then is I want to take the result here symbolize the names and we should get some upserted count thing going on so what I want to do now is yeah let's okay so I was actually upserting a question or like the vector for a question but I'm curious if I can take like part of a blog post let's see article dot last dot body so can I take this blog post and use that as input so the length of the blog post is 16739 I don't know if I can actually pass that to embeddings and get back something reasonable let's just try it out so we're going to say open AI let's see okay so open AI dot embeddings okay and then the input in this case let's pass in article Dot last body and just see okay it's 2046 tokens I passed in way too much so what I have heard is that if you have more than the maximum number of tokens you can build chunks pass those in and then take the average Vector of those chunks and that is supposed to be like pretty close to whatever document that you're trying to do in terms of semantic whatever semantic search so what I'm wondering is how do we do that actually what I think might make more more sense is just saying give me the body from zero up to I don't know 2000 or I want 2 000 tokens so that's actually like up to I don't know four thousand or something 10 000. six thousand let's see what that does okay that gave us back a result and then if we again stick this inside of our thing yeah what is the ID is the ID article dot last dot body 6000 is that the actual ID for this thing oh what okay so you also can't have ASCII characters in it so we're gonna have to like G sub anything that's not anything that is not in A to Z or a to z or zero to nine or something I don't know let's gosh all right okay this was somewhat Illuminating because I know that I need to go a couple of things a couple takeaways is like I need to be able to chunk up documents better so that I can work with them as tokens in openai number two I need to figure out how to take two vectors inside of Pinecone and get the average of them and let's see how would I figure this out so if I search for like average speed latency I thought okay [Music] Vector operations maybe that's got some Vector operations oh look it has Ruby nice yeah that's like a little bit of an incongruence seeing that you have Ruby here but not on the other side and then also I think this is probably like Swagger code gen so what we want to do is we want to understand the average of two right or we want to yeah like we want to add them together I can't remember average two vectors yeah there's no mean or average is it called mean in here let's see oops mean means meanings mean mean okay so maybe there's like some math thing like get in fact dermine with Ruby like I don't know if I have to do this outside or if I can do it as part of as part of Pinecone I expected I could pass in chunks and then maybe it doesn't matter like maybe as long as they find that chunk also is the ID here supposed to be the ID of the article should I just be passing in I don't know like article I like the Articles sgid or something like should this just be some globally unique identifier for the article to sgid even that doesn't count as okay to I don't know let's see just ID invalid value 30 okay 2s I don't know all right so we got back something and so now we have uh we should have two vectors now we'd have two vectors and now if I wanted to query this if I said like index.query that's like the next thing we want to add to our Pinecone class right and that's going to take in a vector and it's probably going to be basically this same stuff obviously we'll split this out into like proper classes and things later okay so what does the query API look like so let's go to query Pinecone it's just slash quick slash query all right top K let's just give okay actually let me see this back in curl five okay and then I don't want to filter but that seems like it will be valuable later include metadata true I don't know what that gives us include the values no I don't need the values do I need the values back I don't know and then the vector right this is going to be I don't know one two three this is going to be like the actual query Vector right Okay so to get this working here we're going to say include what is it called values true include metadata true this can I think now that we're using two Json we should be able to just take this and make it normal okay and then we have a vector and finally top K top k is five all right let's see what that does so now there's only two vectors in there so no matter what I say here index is time cone.new index.query oh I need to get okay biddings okay let's see model is this model and the input is going to be like I don't know bananas and it should still give me back the same exact like the two vectors that we have I think because we're asking for top K but so now we should do like index or so this is going to be like our query vector is this thing so now we want to say index.3 vector data zero values something like that okay we gotta do this again data zero embedding there we go okay that gives us the value we want so now we can say index.query that thing I don't know if we need to pass anymore oh look at that we got some results result all right cool so we got back some data so result.matches dot length result that matches result matches what result are keys result s.length zero okay so let's search again but this time let's make the embedding Pearl and see if it gives us back our first question okay so results here is showing the empty array result but there's some other stuff here I hate scrolling around through all this garbage but this is I don't know what is this giving us back hold it matches okay so matches okay so 36 all right so it's going to give us okay so how many matches do we get result result matches link so there's two matches Dot first dot keys so the first ones score is 74. second one score is 74. they're exactly the same and okay all right let's do this article Dot yeah so I do think result matches I first keys yeah like I think this number is important and this is how I'm guessing this is how we tie back to the correct document right I've got to figure out a way to include some globally unique ID for the article video or whatever that will work so what I want to do here is say something like yeah rails Global ID so there's this Global ID Ruby package which lets me convert something to a global ID so if I just say to Global ID does that work okay so what I want to do now is create a new create a new embedding and the input this time is going to be article no yeah article Dot first dot body 6000 to 10 000 characters so this is going to give me a new critical bidding and then what I can do is take that and put it into Pinecone actually let's do this let's go to yeah I'm just going to make a helper method inside of this Pinecone class called like index for particle and it's going to take in an article or index article and we're going to take the article dot body Dot we're going to split on new line with a pound sign in front of it and that's going to give us a breakdown of all the different paragraphs or like sections because they're written in markdown and what we're going to do with each of those paragraphs or like chunks is we're going to call upsert yep where the ID is the Articles oh this is what I wanted to figure out was like should I do like article Dot last two global ID so this gives us back like this long thing article.last.to Global id.url okay so this is what I think I want to send as the ID I don't know if it'll let me I also to because when it's okay so when it's yeah this I don't know why this isn't taking as an argument it's like maybe it just can't take the equal signs or something I don't know this is this should be totally Json serializable so yeah this is what I was hoping to do was like okay I don't want to put in paragraph the values that I want here is from openai so I'm going to say open AI is equal to this thing okay so that's gonna give me now I can say the values are open AI Dot embeddings parameters model and input is the paragraph the model here is Babbage similarity I keep copying it because I don't know how to spell Babbage okay so let's actually just pull this out values is this thing because it's we have to dig into it remember it's something like values data zero embeddings okay so that's what we want to upsert so here we're going to split our article into paragraphs then we're going to hit the open AI API and get back a bunch of embedding data for each of those paragraphs and then we're going to upsert and the ID is going to be article that lasts at 2sgid.2s and for now I think let's just start with one and we'll see how it goes yeah this isn't necessarily guaranteed to be small enough to work but I think it will be okay and then this embeddings thing we tested that we tested this so all of this should go in just fine so let's reload rails console and we're going to say indexes Pinecone dot new say index dot index article cool.last and what else let's see wrong number of arguments given one given zero expected on 18. maybe it's like this let's see okay that finds the article it seems like it's doing something 400 bad request Vector Dimension zero does not meet the dimension of the thingy all right so let's print out values let's also import kind of require five bug and we'll rate here so we can see what's going on oops we need a new instance of index since we change the code for it or of Pinecone okay so what is values oh it's it's singular embedding I said embeddings here this is where the bug is bye bug okay quit yes rails console index is playing we can actually just do pinecone.new dot index article article last values data zero betting dot link 2048 okay so cool upserted Count is one now if we look over here in Pinecone what I'm curious about these vectors is can I is there a way from the UI for me to see the IDS because what the other thing that would be cool is to pass in like in the ID to show a specific paragraph or something I don't know whatever so let's refactor this again to go back to each do paragraph and all right so that should iterate over each of the paragraphs it's going to make an API call to get the embeddings it's going to make an API call to upsert it and we should be good to go all right let's add some debugging here puts upserting yeah okay now we should have I don't know several new vectors index info wait total Vector zero what the heck absurd okay now it says 15. all right all right whatever that means okay okay so now let's go over several articles so let's just say like article Dot all or article.order published at descending dot take I don't know five dot each do article Pinecone dot new DOT index article a and then this is what I want to run but before we do I want to make sure that I reloaded the terminal I can't remember if I did because I want the new code that's going to tell me that it's actually in upserting okay absurding cool cool so here we go look at that all right okay 30. 32. 34. 37 okay so it's putting a bunch of articles into the database I don't know if I'm going to go over my billing limit let's see pricing am I where am I at personal management account I have been using the API a lot today and it looks like I'm still at just a dollar eighty three all right look at that it looks like it finished okay so how many vectors 64 vectors we're at okay so now I should be able to do some more serious querying where I say something like index is pinecone.new now what I want to let's make it let's make it better let's make a method in here called search and it's just going to take in the query and this will do the two steps for us so the first step is going to be get the embeddings for my search query and then it's also going to do the open AI part or the Pinecone part okay here's my query and then I want to use that use that query to query my stuff so this is going to be data zero embedding singular right that's how query works query takes in a vector queries the stuff gives me the top K yeah okay so then yeah I want I want to be able to get this yeah okay so we're gonna we're gonna store that off in a result so reload index is Pinecone Dot new index.search for like payments with Perl or I don't know I don't know which article Okay so the articles that I put in the database should be the first five here so let's ask about let's just say like charge yeah charge types or something or payment intents index.searched our payment links tell me about payment links okay oh we want to save that into some answer is the result so answer matches dot length should be five right because we're doing top k and okay first or let's just do like this so it gives us okay and then score okay so 80 80 74 74 65 okay this should fail okay so the one that or like the two that have 80 something yeah it seems like they are in order of score I wasn't sure if that was going to happen or if that's like truly the case but let's look at the I don't know one ID how to take payment with pearl all right what was my question also in warp do you know how to disable the up Arrow going to the previous block that's super annoying okay tell me more about payment links this one has literally the word payment in it so I don't know maybe that's where it landed okay let me let's just go through all of the articles so then we're gonna say dot yeah we'll just go through all of them and we will index yeah I'm still not sure if the ID matters absurding why are these all say upserting 36 that doesn't make me feel like that's right upserting the article ID so they're all upsetting 36 I don't know this to sgid.2s 2 okay that's all part of the first or like the most recent article absurding 36 36 36. 36 and then I wonder if my ID is not unique am I overriding the other stuff because it seems like I'm getting new vectors in here but I don't know if it matters that the IDS are coming back different or something I don't know what that means metrics Vector count yeah all right 135 vectors oh and then we crashed because something happened Pinecone line 48 I'll just like check if values that data embedding any then do that thing I am like re-indexing the same paragraphs I don't know how that's messing things up but I'm also like double using my openai tokens every time so that's annoying but foreign oh because there's no data that came back from that one that's weird all right so fix the wrong problem but let's just see if we can search now so we'll map over the scores and just to get a sense of um matches mapped okay so the first one the first two were close but not really I don't know okay so now I'm wondering if we should be using a different model is Babbage similarity the best model for this I don't know yeah so does this have a guide about building indexes troubleshooting open AI Integrations okay so this one says use this model maybe I should have been using that the whole time let's see yeah okay I'm gonna do a couple things the first is I'm going to change the model the second is I'm going to add to my Vector IDs here I'm going to add just a paragraph number each with index I can never remember if the index comes first or last second okay so we're going to use this yeah sure whatever okay and I wonder if there's a is there like a way to just drop all of these can I just yeah so I'm just going to say delete give me a new give me a new index because I'm because I'm making all these changes okay so it shows initializing ready okay so what we're going to do is we're going to iterate over all of the articles and for each one we're going to index it and it should have its own unique ID right yeah all right let's see how this goes all right upserting 13 okay 36 3 36 9. so I could probably get smarter about this too and remove the code bits like the code Snippets and images and HTML and whatever the HTML is there because of like an export error when I'm converting from Google Docs into markdown but the rest of this like the actual content of the blog posts should be in terms of like stuff that's being upserted oh we're again going to get to a point where python doesn't have it's gonna crash because yeah so values all right so if that's the case I think I can do offset five or something to skip the first five because what did we get to we got to I bet this one was too long because there's not enough headings in it okay we got this upserting six all right undefined method okay so if there's no if it has the data key and the values all right yeah I might want to go back and add a bunch of headings just so that it's easier to break this stuff up see if that works okay it looks like we are continuing forward yeah all right cool so let's see so we should be getting more than 100 vectors being inserted into our Pinecone database now 115 118 120 cool all right 197 vectors all right so now if I say index is Pinecone dot new index Dot search for payment links let's see result matches dot map give me the scores okay so the first one is the closest still and then yeah first ID okay so in the second paragraph of whatever this is so I can say lowball ID locator dot locate signed of this so that gives me back an object rails with payments auth and Es build so it's in like the second paragraph of this thing so if I go to the website and I go to articles slash that thing in the second ish paragraph it should talk about payment links Maybe maybe not underwhelming how to accept payment Pro oh you know what Django all right Global ID locator dot locate signed hey Django quick start that's pretty sick okay nice all right so that's actually finding the right thing oh that's cool all right so then that would have found this Django article how I start Django apps which I think might be the only one I talk about Django one so for that match or result uh match it first score how is this okay 0.75 that's not actually that high I thought it was gonna be way higher than that but that is the right one that is the one I was looking for so let's see if we can find another one so let's search for SVG to PNG in next search how to convert image from SVG to PNG let's actually yeah okay so I'm going to fix search that it actually just spits out the article okay so this is going to give us query so I actually just care about the top one so I'm going to change top K to top one and I'm thinking actually top K should be passed in by default but in the case of search I'm just going to set my top k equal to 1. top K is 1. and then what we're going to do is we're going to take this result and we're going to say results not just a first dot ID so we want to find the article with that ID or I guess we gotta shoot all right because I yeah okay the ID has the paragraph number in it which should have put in a different way Dot split so I want to split it on that thing and I want to take everything up to but not including that and then we want to say Global ID locator Dot locate assigned I don't know I'm just taking a guess at this we'll see what happens all right so reload index.search for this thing okay bad request invalid starting object oh we didn't path okay should be like I don't know what the heck let's see okay that didn't work uh all right what are we trying to do here okay so there are results matches not length is one okay so something about this is broken oh I shouldn't use the ID here this should be a symbol ID okay that didn't work as expected either so this is probably like up two but not including the last one there we go and this should give me back the article about PNG it totally does not rails payments with okay all right what we can do is let's map all of the results so let's actually change our top K to three and what we're going to do is we're going to iterate over all of the results matches dot each do result and we're actually going to map over that and get back all of the objects like several objects and yeah let's see how that works okay so this should just have its own ID now error driven development rails payments auth yes build solving presence rails sitemaps so that didn't do ex like at all what I expected Pinecone with JavaScript no it's just giving me the same answers back I must be doing something wrong so I I don't know what's up but yeah it was fun to play around with Pinecone and open AI so yeah we'll have to keep messing around with this to see what we can do to uh to improve it so I did see that there is this metadata concept when you're inserting things so I'm thinking about adding adding the paragraph As metadata I don't know how that would work but here you can see like the output looks pretty cool so yeah all right thanks so much for watching and we'll see in the next one

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