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AI code archaeology
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lib/ai.ex
defmodule AI do
@moduledoc """
AI is a behavior module that defines the interface for interacting with
OpenAI's API. It provides a common interface for the various OpenAI-powered
operations used by the application.
"""
defstruct [
:client,
:api_key
]
@type t :: %__MODULE__{
client: %AI.OpenAI{}
}
@api_timeout 5 * 60 * 1000
@default_max_attempts 3
@retry_interval 250
@doc """
Create a new AI instance. Instances share the same client connection.
"""
def new() do
client = AI.OpenAI.new(recv_timeout: @api_timeout)
%AI{client: client}
end
# -----------------------------------------------------------------------------
# Completions
# -----------------------------------------------------------------------------
def get_completion(ai, model, msgs, tools) do
request = [ai.client, model, msgs, tools]
do_get_completion(ai, request, @default_max_attempts, 1)
end
defp do_get_completion(_ai, _request, max, attempt) when attempt > max do
{:error, "Request timed out after #{attempt} attempts."}
end
defp do_get_completion(ai, request, max, attempt) do
if attempt > 1, do: Process.sleep(@retry_interval)
AI.OpenAI
|> apply(:get_completion, request)
|> case do
{:error, :timeout} -> do_get_completion(ai, request, max, attempt + 1)
etc -> etc
end
end
# -----------------------------------------------------------------------------
# Embeddings
# -----------------------------------------------------------------------------
@embeddings_model AI.Model.embeddings()
@doc """
Identical to `get_embeddings/2`, but raises an error if the request fails.
"""
def get_embeddings!(ai, text) do
with {:ok, embeddings} <- get_embeddings(ai, text) do
embeddings
else
{:error, reason} -> raise reason
end
end
@doc """
Get embeddings for the given text. The text is split into chunks of 8192
tokens to avoid exceeding the model's input limit. Returns a list of
embeddings for each chunk.
"""
def get_embeddings(ai, text) do
text
|> AI.Tokenizer.chunk(@embeddings_model)
|> Enum.map(&[ai.client, @embeddings_model, &1])
|> Enum.reduce_while([], fn request, acc ->
ai
|> get_embedding(request, @default_max_attempts, 1)
|> case do
{:ok, embedding} ->
if acc == [] do
{:cont, embedding}
else
# For each dimension, find the maximum value across all embeddings.
# This isn't necessarily the _most_ accurate, but it selects the
# highest rating for each dimension found in the file, which should
# be reasonable for semantic searching.
{:cont, Enum.zip_with(acc, embedding, fn a, b -> max(a, b) end)}
end
{:error, reason} ->
{:halt, {:error, reason}}
end
end)
|> case do
{:error, reason} -> {:error, inspect(reason)}
embeddings -> {:ok, embeddings}
end
end
defp get_embedding(_ai, _request, max, attempt) when attempt > max do
{:error, "Request timed out after #{attempt} attempts."}
end
defp get_embedding(ai, request, max, attempt) do
if attempt > 1 do
Process.sleep(@retry_interval)
end
AI.OpenAI
|> apply(:get_embedding, request)
|> case do
{:error, :timeout} -> get_embedding(ai, request, max, attempt + 1)
etc -> etc
end
end
end