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AI code archaeology
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Files
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 System.get_env("OPENAI_API_KEY")
@api_timeout 45_000
@embedding_model "text-embedding-3-large"
@summary_model "gpt-4o-mini"
@summary_prompt """
You are a command line program that summarizes the content of a file, whether
it is code or documentation, like an intelligent `ctags`.
Based on the type of file you receive, produce the following data:
### For Code Files:
- **Synopsis**
- **Languages present in the file**
- **Business logic and behaviors**
- **List of symbols**
- **Map of calls to other modules**
### For Documentation Files (e.g., README, Wiki Pages, General Documentation):
- **Synopsis**: A brief overview of what the document covers.
- **Topics and Sections**: A list of main topics or sections in the document.
- **Definitions and Key Terms**: Any specialized terms or jargon defined in the document.
- **Links and References**: Important links or references included in the document.
- **Key Points and Highlights**: Main points or takeaways from the document.
Restrict your analysis to only what appears in the file. This is used to
generate a search index, so we want to avoid false positives from external
sources.
Respond ONLY with your markdown-formatted summary.
"""
@callback new() :: struct()
@callback get_embeddings(struct(), String.t()) :: {:ok, [String.t()]} | {:error, term()}
@callback get_summary(struct(), String.t(), String.t()) :: {:ok, String.t()} | {:error, term()}
@behaviour AI
@impl AI
@doc """
Create a new AI instance. Instances share the same client connection.
"""
def new() do
openai = OpenaiEx.new(@api_key) |> OpenaiEx.with_receive_timeout(@api_timeout)
%AI{client: openai}
end
# -----------------------------------------------------------------------------
# Embeddings
# -----------------------------------------------------------------------------
@impl AI
@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
embeddings =
split_text(text, 8192)
|> Enum.map(fn chunk ->
OpenaiEx.Embeddings.create(
ai.client,
OpenaiEx.Embeddings.new(
model: @embedding_model,
input: chunk
)
)
|> case do
{:ok, %{"data" => [%{"embedding" => embedding}]}} -> embedding
_ -> nil
end
end)
|> Enum.filter(fn x -> not is_nil(x) end)
{:ok, embeddings}
end
# -----------------------------------------------------------------------------
# Summaries
# -----------------------------------------------------------------------------
@impl AI
@doc """
Get a summary of the given text. The text is truncated to 128k tokens to
avoid exceeding the model's input limit. Returns a summary of the text.
"""
def get_summary(ai, file, text) do
input = "# File name: #{file}\n```\n#{text}\n```"
# The model is limited to 128k tokens input, so, for now, we'll just
# truncate the input if it's too long.
input = truncate_text(input, 128_000)
OpenaiEx.Chat.Completions.create(
ai.client,
OpenaiEx.Chat.Completions.new(
model: @summary_model,
messages: [
OpenaiEx.ChatMessage.system(@summary_prompt),
OpenaiEx.ChatMessage.user(input)
]
)
)
|> case do
{:ok, %{"choices" => [%{"message" => %{"content" => summary}}]}} -> {:ok, summary}
{:error, reason} -> {:error, reason}
response -> {:error, "unexpected response: #{inspect(response)}"}
end
end
# -----------------------------------------------------------------------------
# Assistants
# -----------------------------------------------------------------------------
def create_assistant(ai, request) do
OpenaiEx.Beta.Assistants.create(ai.client, request)
end
def get_assistant(ai, assistant_id) do
OpenaiEx.Beta.Assistants.retrieve(ai.client, assistant_id)
end
def update_assistant(ai, assistant_id, request) do
OpenaiEx.Beta.Assistants.update(ai.client, assistant_id, request)
end
defp truncate_text(text, max_tokens) do
if String.length(text) > max_tokens do
String.slice(text, 0, max_tokens)
else
text
end
end
# -----------------------------------------------------------------------------
# Threads
# -----------------------------------------------------------------------------
def start_thread(ai) do
OpenaiEx.Beta.Threads.create(ai.client)
end
def add_user_message(ai, thread_id, message) do
request =
OpenaiEx.Beta.Threads.Messages.new(%{
role: "user",
content: message
})
OpenaiEx.Beta.Threads.Messages.create(ai.client, thread_id, request)
end
def get_messages(ai, thread_id) do
OpenaiEx.Beta.Threads.Messages.list(ai.client, thread_id)
end
# TODO streaming
def run_thread(ai, assistant_id, thread_id) do
request =
OpenaiEx.Beta.Threads.Runs.new(%{
thread_id: thread_id,
assistant_id: assistant_id
})
OpenaiEx.Beta.Threads.Runs.create(ai.client, request)
end
def get_run_status(ai, thread_id, run_id) do
OpenaiEx.Beta.Threads.Runs.retrieve(ai.client, %{
thread_id: thread_id,
run_id: run_id
})
end
def submit_tool_outputs(ai, thread_id, run_id, outputs) do
request = %{
thread_id: thread_id,
run_id: run_id,
tool_outputs: outputs
}
OpenaiEx.Beta.Threads.Runs.submit_tool_outputs(ai.client, request)
end
# -----------------------------------------------------------------------------
# Utilities
# -----------------------------------------------------------------------------
def split_text(input, max_tokens) do
Gpt3Tokenizer.encode(input)
|> Enum.chunk_every(max_tokens)
|> Enum.map(&Gpt3Tokenizer.decode(&1))
end
end