--- title: Semantic search (search by meaning) - Rails, OpenAI, langchain.rb, pg_vector slug: semantic-search-search-by-meaning-rails-openai-langchain-rb-pg_vector published_at: 2023-10-13 12:00:06 +0000 updated_at: 2026-03-04 20:15:15 +0000 summary: description: In this video, you'll learn how to use tools like OpenAI, LangChain.rb, and Postgres with pgVector to build semantic search for a Rails app, breaking content into chunks, generating embeddings for each chunk with OpenAI, storing those in Postgres, and then using nearest neighbor search to find relevant chunks for a search query. This allows searching by meaning rather than just matching text exactly. https://medium.com/@rubyroidlabs/how-to-build-an-ai-chatbot-with-ruby-on-rails-and-chatgpt-9a48f292c37c https://github.com/ankane/neighbor https://github.com/andreibondarev/langchainrb https://github.com/andreibondarev/langchainrb/blob/main/lib/langchain/chunker/recursive_text.rb https://github.com/IAPark/tiktoken_ruby https://stdgems.org/matrix/ https://www.youtube.com/@RabbitHoleSyndrome https://www.youtube.com/@jamesbriggs https://www.youtube.com/watch?v=NTk8L2de_2Y #rubyonrails #openai #rails tags: [cjav_dev, web development tutorials, web development for beginners, vim, ruby, rails, javascript, ruby on rails, active record, ruby on rails tutorial, open ai, langchain.rb, langchainrb, semantic search, neighbors, pg_vector] views: 3723 author: CJ Avilla url: https://www.cjav.dev/videos/semantic-search-search-by-meaning-rails-openai-langchain-rb-pg_vector youtube_url: https://www.youtube.com/watch?v=799wrxVZXWA youtube_id: 799wrxVZXWA embed_url: https://www.youtube.com/embed/799wrxVZXWA thumbnail_url: https://i.ytimg.com/vi/799wrxVZXWA/hqdefault.jpg type: video --- # Semantic search (search by meaning) - Rails, OpenAI, langchain.rb, pg_vector *Published: October 13, 2023* *Views: 3723* ## Watch [Watch on YouTube](https://www.youtube.com/watch?v=799wrxVZXWA) [![Semantic search (search by meaning) - Rails, OpenAI, langchain.rb, pg_vector](https://i.ytimg.com/vi/799wrxVZXWA/hqdefault.jpg)](https://www.youtube.com/watch?v=799wrxVZXWA) ## Description In this video, you'll learn how to use tools like OpenAI, LangChain.rb, and Postgres with pgVector to build semantic search for a Rails app, breaking content into chunks, generating embeddings for each chunk with OpenAI, storing those in Postgres, and then using nearest neighbor search to find relevant chunks for a search query. This allows searching by meaning rather than just matching text exactly. https://medium.com/@rubyroidlabs/how-to-build-an-ai-chatbot-with-ruby-on-rails-and-chatgpt-9a48f292c37c https://github.com/ankane/neighbor https://github.com/andreibondarev/langchainrb https://github.com/andreibondarev/langchainrb/blob/main/lib/langchain/chunker/recursive_text.rb https://github.com/IAPark/tiktoken_ruby https://stdgems.org/matrix/ https://www.youtube.com/@RabbitHoleSyndrome https://www.youtube.com/@jamesbriggs https://www.youtube.com/watch?v=NTk8L2de_2Y #rubyonrails #openai #rails ## Transcript what's up welcome back in this episode we're going to build semantic search so that when you search for something like I don't know python it'll give you stuff in the results that are similar to or related to the word python right now it is searching on exact matches for text so if I say like error table JavaScript implementation right now it's searching through the content the title The body and the transcripts of my videos and not returning anything so we want to make it so that it's it matches meaning now I did make a video called using pine cone VOR DB from rails for open AI this one I didn't actually make it work it didn't get there uh there was a lot of missing pieces and a lot of trying to figure out how pine cone works and there was some gaps in my knowledge thanks to Rabbit Hole syndrome and James Briggs two channels that I highly recommend go check them out subscribe uh Rabbit Hole goes deep on lots of different things it's awesome James Briggs has some amazing overview videos to give you some basic highle understandings of how a lot of this ml stuff works so go check those out we're going to also use a tool today called Lang chain RB Lang chain I was like for a long time I was like what the heck is Lang chain you could think of Lang chain as a tool set of a bunch of different collection of tools where they have really clear sort of use cases for each of these different tools and there's lots of third parties that are providing things like large language models for doing embeddings or large language models for doing completions and then there's also tools for like how do you split up based on tokens or based on readme docs and L chain is just a collection of those different things so instead of having to go out and get different gems for all your different use cases L chain RB should be packaging those up it is pretty new for example if you go to the wiki and preparing data one of the things we're going to use today is chunkers which the docs are work in progress so to figure out how the chunkers worked I was just digging around in the source here and if we look at the chunkers in the lib directory this is where we're going to find some of those details for instance if you look at text this is a gem that does some text splitting if you look at the semantic one this is actually going to go out to use a large language model in this case they recommend using anthropic whatever we're going to get into that we're also going to use a tool called Neighbor From Andrew Kane Andrew Kane has been putting out amazing stuff we've been using tons of his tools lately group date Blazer aoy go check out Andrew's stuff and thank you Andrew FR your hard work neighbor is a tool for finding like nearest neighbors so we're going to use that now there are here's the different steps we're going to do today we want to first break our content into chunks then we're going to generate and store those in beddings for each of those chunks now when we're storing the chunks I'm not only going to store the embedding and reference back to the original object but I also want to store the text that was used to generate that chunk for a future use case we're not going to use that today but we want to use that later for retrieval augmented generation so we can build a little chatbot but for search and for semantic search you really just need the embedding and then some metadata or reference back to the original object or to like the URL where you can find the result and we're going to do the generate and store those embeddings and then we're also going to add some tools for looking up those chunks we're going to use this nearest neighbor to figure that out okay let's jump into it so we're g to say bundle bundle add Matrix Lang chain RB and neighbor and this should add those three gems for us the Lang chain RB underneath the chunker the one we're going to use today is called recursive text so Lang chain chunker recursive text and our first step today is actually going to be to break up the content into those different things so if we open up the rails console we can explore with this oh okay so I am on like the bleeding edge of rails so I've got 7.2 Alpha installed and I'm having issues with this polyamorous blah blah blah this is actually because I have ransac in here and ransack is not played nicely with rails 7.2 yet I'm sure it will eventually but conveniently we're working on changing search we don't actually need that anymore all right let's open up rails console and we want to do Lang chain chunker recursive text. new and we're going to pass in this is some text and then maybe we break on new line this is another paragraph just to show you how this works we might say chunk size is I know 25 and then we can have a chunk overlap this example is not going to have any good detail for that and then separators is going to be an array of what you want to split on so if we do double new line here that gives us back this chunker object and now we can say do chunks and that will split up our original text into two different chunks I guess 25 isn't long enough because it ended up splitting this is another paragraph into raft and this might or this must be a token right so this is some text this is another paragraph Cool also notice that the chunks that we get back is an array of these dictionaries where we have the the text and we have a cursor I don't actually know what we use the cursor for we're just going to use the text today and the idea is that we're going to create a model in the database and we're going to store the each of these different chunks from the original source so if we look at like article. firstbody we have in this case a markdown a markdown article about air table air table is a popular tool for building no code applications blah blah blah this is something that I wrote and this is usually too long to build embeddings for most articles that I have are going to be bigger than the number of the number of tokens that are accepted as input into the current models today and so what I'm going to do is break these up into chunks that make sense and that are of a decent size so that we can take those Snippets of text and get embeddings for it the embedding there's lots of videos that'll explain like what embeddings are much better than I can but it's just a point in a an n-dimensional space the dimension is defined by the model that we're using in this case 1,536 because we're going to use Ada 002 and that will give us back this like giant array of numbers so let's do this sort of chunking part first so in the neighbor thing we want to say rails G migrate or rails G neighbor colon vector and this is going to create a migration for us called install neighbor vector and this just says enable the extension Vector that's to enable the PG Vector thing I think so now we can say rails DB migrate there's a bunch here that's for like rails 7.2 this is the last one this is what actually ran was enabling that Vector now what we can do is we can say rails G model chunk and each chunk is going to have a reference back to the article and in the case of this application we're going to have articles podcast episodes and videos are all going to have some sort of content that I want to Chunk Up and then make searchable and so in this case I'm going to call it chunk as a uh polymorphic uh Association back to the content type that I want to like actually Chunk Up and then we'll have the content this is the chunk content so just that small snippet and then we're going to have some embeddings uh and this is going to be a vector okay so if we open up this create chunks migration we want the limit of our our embedding Vector to match the size of our model so if we look at the text embedding Ada 002 model the output Dimension here is 1,536 you might use a different model but that's the one that we're using today there's uh a great video by in in the rabbit hole on the rabbit hole page about picking Which models to use and why but we're just going to use this 80 2 one today okay rails DB migrate okay an article is going to have many chunks has many chunks as uh trunkable and okay so now let's make a concern called trunkable and our trunkable concern is going to help us like organize our Logic for chunking things up in a way that we can reuse it across a couple of these different models so we'll make a chunk method that will go through some text content and then break it up into chunks okay so the the the text content is going to be different if we have like an article then the content is going to be we want to use the body but if we have a podcast episode then the content we want to use is going to be like the transcripts or something and I think I have yeah formatted transcripts there's a couple different things we're going to want to run over or split over in each of these chunk methods I'm going to say two maybe chunk s that'll be like the chunk string and here we'll just say body in the case of article and here we can make this chunk S as the first argument and then we have chunk size 1536 and we have chunk overlap and we'll make it 200 and I think that's going to get us pretty close right ah separators so the separators are actually going to be a little bit different depending on the type oh gosh how do we want to do this yeah I guess this is another thing we could put in here trunkable separators and for articles I want to split on like a new line followed by P sign or a new line followed by 2 PB signs or three pound signs because it's marked down this is going to be like an H1 H2 H3 and that should give us a pretty good pretty good breakdown and so we'll have to we'll have to implement chunk s and chunk separators in all of our methods that are chunk so we're going to say include chunk and for now we'll just P chunk okay we'll just print it out so if I say rails console and we say article. first. Chunk we should get back a bunch of chunks and we do these are all the different chunks that we're going to use we've got air table is a popular tool for building blah blah blah okay and the the content of each of these is in chunk text okay this is I guess we've got the first step done right we break it we've broken it into chunks the next step is we want to generate and store those embeddings for each chunk so now what we want to do is we want to get the embeddings and this is going to be available through by in this case we'll hit open AI so we'll say open AI is going to be a lang chain LM ai. new and we already have this set up in rails. application. credentials. openen AI key okay so if you go to open AI you can get your API key from the dashboard we're moving pretty quickly the whole idea here is just to show like how to set up some of these pieces in Ruby there's lots of documentation and videos out there about how to get started and get set up so the method on Lang chain is called embed and you pass it some text and embedding is going to give us back this giant array of array or this giant array of 1,536 numbers that represent this specific body of text and as a result we want to create a chunk and the chunk is going to have the content and it's also going to have the embedding okay now we also when we run this we also want to like delete all the existing chunks before we go and create a bunch of new chunks and then maybe at the bottom we'll say puts I don't know chunks. count chunks created sure self. class. name I don't know that's fine ID okay so if we reload this and say article. first. chunk now okay so now we're going out to open Ai and hitting the API a bunch of times to generate embeddings we generated 12 chunks for one or for article one and now if we say chunk chunk. last now we have this giant um thing embedding and right now it looks like a string right and it's actually like this giant array and then we also have the content and this was the content that was stuffed in there we also have our polymorphic Fields here for relating back to article with id1 and if we call chunk. first. embedding then right now we still get it back as a string so if we go to if we go back to the chunk model one thing that's available from this neighbors method is has neighbors and then you tell it which column you use for for neighbors and now we can grab that embedding and we get back an array what's cool about this is that it's a little easier to work with the array I don't know it's a little easier to work with the array than the embedding thing but there's also some really cool methods inside of here that let you find the nearest Neighbors given some other embedding so if we were to use let's see so if we were to use Lang chain llm open ai. new with our API key and now we say open ai. embed and we pass in some text and this is going to be our query I don't know web hook like web hooks are fun or something and we're going to get back some embedding query or or like query embedding is going to be some other giant list of numbers what we can do now is we can compare our query embedding and our chunk embedding to figure out how similar they are what we want to do is use the Matrix gem to convert this into a vector so we can say Vector is going to take in the query embedding and then we want to say inner product of a vector of the chunk. first. edding and this tells us 0.74 it's I don't know it's close somewhat close if we change our query to something else hot dogs taste yummy or something right and then compare these it should be further from 74 I would guess 66 okay so hot dogss are hot dogs taste yummy is less relevant to an article about air table than web hooks are fun web hooks are fun gives us 74 and T dog hot dogs are yummy or taste yummy gives us 66 now if we say if we make another query here that's how to set up air table and we now compare now we're getting 08 so the closer we get to one the more more similar the vectors are and we can use this similarity in this case it's just like dot product similarity but we can use the similarity to find other chunks that are similar okay what's cool about this is that we can use the built-in nearest neighbors tooling to automatically find those results for us all right so now we've got our generate and store embeddings for each chunk the next step is we want to look up chunks by a query and ultimately find that Source content so what we want to do now is go back to our yeah so we want to figure out how to search all of these different things so maybe we make a method called search and this should be like a class method which I can't I think for for a concern uh I don't know we'll see search is going to take in a query and it will first generate embeddings for that query and the content is going to be the query and then what we want to do is find the nearest neighbors for that query and over here if we look at this nearest neighbors method it takes in I guess this is probably the column name first I'm not actually sure and then it's going to take in the embedding and then it takes in the distance and I've found that using cosine Sim similarity for distance is good and then what else do we want and then we're going to just grab the first 10 and then we'll map chunk. unique okay and that should give us something decent I believe so what we're doing now is we're finding the chunks that are most similar semantically to the query that we're passing in based on the cosine similarity between the open Ai and in of the query and the open AI embedding of each of those chunks to find the first 10 chunks that are most like the query we passed in and then we're mapping over those chunks to pull out the chunk so this is going to be the article or video or whatever and then we're saying make that unique we could probably make this more maybe like the first 20 or something and then once we get unique grab fewer I don't know like by saying the first 10 chunks as soon soon as we map it to chunk and then make it unique like it's possible that 10 chunks are all about the same article or 10 chunks are all about the same video or whatever this isn't going to do anything with an API so we can make this bigger and then say dot first 10 or something and now if we reload and we say article. search for air table okay so this oh we need to no okay this method needs to be moved into something else how does that work ah okay so I think this is supposed to extend and then class methods do and then we got to move this thing up [Music] there okay and this probably itself reload okay now we also need okay there's a way to make this work in both places but we're just going to rush and it's going to be fine going to be fine okay find me the article about air table boom all right and then we got back the article look at that so now we are semantically searching based on some string the next step is to just wire that up to the UI which is relatively straightforward so we can go to the videos index and right now there is this giant like search thing on the video page the article index doesn't actually have one yet so let's add that and our search name is just going to be Q now so if we look at Local Host 3000 nope our server isn't started okay start up the server okay now we go to articles if we say error table JavaScript thing and search oh okay we've got another issue here this should just be pams Q air table oh right we also need to wire it up through the controller too articles controller we'll just say if Rams q. presentes articles is going to be article. search and then we'll just return early okay aor table JavaScript stuff but okay payy is going to be nil so article index if page. present we're moving quick we're hacking stuff together okay air table web hooks what does that give us oh my gosh okay so air table web hooks like the this content air table web hooks is not mentioned anywhere in here if we search for air table web hooks that content does not exist but it was able to figure it out semantically that's oh that's so cool I love this this is amazing now I just got to do it on all the other ones but yes we have it working it's using open AI every time someone searches we should cash the results and if we see the same search term again we should just return those same results so that we don't get charged too much money but semantic search up and running with open Ai and PG Vector we're using postgress we're using our own postgress database we don't have to use uh another thirdparty Vector DB so that is sick thank you so much for watching I don't ask often but if you're down I would really appreciate a subscribe and if you think someone else would like this video let them know uh also if you yeah if you want to follow along I plan on trying to build a little chat bot where you can talk to the videos and articles and podcasts so we'll do that in a future episode with uh retrieval augmented generation so we'll reuse those same chunks to figure out how to embed some context in a prompt it's going to be fun thanks for watching --- [Back to all videos](https://www.cjav.dev/videos) --- ## Hey Agent, thanks for visiting! 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