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Byron Salty
Byron Salty

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Adding Embeddings to a Phoenix App

#phoenix #llm #embedding #elixir

Let's add OpenAI based Embeddings to a Phoenix app and store the vector in the database so we can use it for similarity searches.

This resulting code for this article is captured in this sample project:
https://github.com/byronsalty/questify

Specifically this commit:
https://github.com/byronsalty/questify/commit/cba277ce8104c6f3b29dc2a6f602158ba4d1f62a

Steps:

  1. Add PG Vector to deps
  2. Add a migration to install Vector db extension
  3. Add Postgrex.Types
  4. Configure OpenAI embedding endpoints
  5. Hit the API endpoint
  6. Setup your schema for vector types
  7. Incorporate vectors into your queries

Add PG Vector to deps

In mix.exs:

 {:pgvector, "~> 0.2.0"},
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Add Migration to install the vector extension

Create a migration to install the PGVector extension:

defmodule Questify.Repo.Migrations.AddPgVector do use Ecto.Migration def up do execute "CREATE EXTENSION IF NOT EXISTS vector" end def down do execute "DROP EXTENSION vector" end end
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Add Postgrex.Types

This is so you can use the term vector in your type definitions.

Add a file postgrex_types.ex in your /lib folder with the following contents:

Postgrex.Types.define( Questify.PostgrexTypes, [Pgvector.Extensions.Vector] ++ Ecto.Adapters.Postgres.extensions(), [] )
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Add this to your config.exs:

config :questify, Questify.Repo, types: Questify.PostgrexTypes
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Configure your app with the OpenAI endpoints

Here are the relevant lines to add to your configuration. I put this into the runtime.exs file:

openai_api_key = System.get_env("OPENAI_API_KEY") || raise """ environment variable OPENAI_API_KEY is missing """ config :questify, :openai, openai_api_key: openai_api_key, embedding_url: "https://api.openai.com/v1/embeddings", embedding_model: "text-embedding-ada-002"
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Hit the API

The embedding API will simply take in a text string and will return a JSON response with the embedding information.

You can hit the API with HTTPoison like so:

 response = HTTPoison.post( embedding_url, Jason.encode!(%{ input: text, model: Keyword.get(opts, :model, embedding_model) }), [ {"Content-Type", "application/json"}, {"Authorization", "Bearer #{openai_api_key}"} ] )
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See the whole file here: https://github.com/byronsalty/questify/blob/cba277ce8104c6f3b29dc2a6f602158ba4d1f62a/lib/questify/embeddings.ex

Add a vector to one of your schemas

Add a migration like this:

defmodule Questify.Repo.Migrations.AddCommandEmbeddings do use Ecto.Migration def change do alter table(:actions) do add :embedding, :vector, size: 1536 end end end
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Then update your schema with a new vector type field like this:

 field :embedding, Pgvector.Ecto.Vector
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Incorporate Vectors into Queries

You'll need to add this line at the top of an model where you plan to query with PGVector specific functions - importantly including the "distance" ones that we're looking to use.

 import Pgvector.Ecto.Query
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Now you can query like the following:

 def get_action_by_text(location, text) do embedding = Questify.Embeddings.embed!(text) min_distance = 1.0 Repo.all( from a in Action, order_by: cosine_distance(a.embedding, ^embedding), limit: 1, where: cosine_distance(a.embedding, ^embedding) < ^min_distance, where: a.from_id == ^location.id ) end
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Follow along for more ways to add LLM related capabilities into your Phoenix apps.

See more related content here.

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