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AI code archaeology
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lib/ai/util.ex
defmodule AI.Util do
@role_system "developer"
@role_user "user"
@role_assistant "assistant"
@role_tool "tool"
def note_format_prompt do
"""
Your audience is another AI LLM agent.
Optimize token usage and efficiency using the following guidelines:
- Avoid human-specific language conventions like articles, connecting phrases, or redundant words.
- Use a structured, non-linear format with concise key-value pairs, hierarchical lists, or markup-like tags.
- Prioritize key information first, followed by secondary details as needed.
- Use shorthand or domain-specific terms wherever possible.
- Ensure the output is unambiguous but not necessarily human-readable.
Respond STRICTLY in the `topic` format below. **Do not deviate.**
**Required format:**
- Use this structure: `{topic <topic> {fact <fact>} {fact <fact>} ...}`
- `<topic>` and `<fact>` are either:
- Bare string: a short string that does NOT contain `{` or `}`
- Quoted string: a string bounded by `"`s which may contain escaped `"`
- Place exactly ONE topic per line.
- Failure to adhere to the exact format will result in an invalid output.
Example output:
{topic dog {fact is mammal} {fact 4 legs} {fact strong sense smell}}
{topic cat {fact is mammal} {fact 4 legs} {fact assholes}}
{topic bird {fact is avian} {fact 2 wings} {fact some fly}}
{topic "sea creature" {fact is aquatic} {fact "can be delicious"} {fact "not always a \"fish\""}}
"""
end
@spec validate_notes_string(String.t()) ::
{:ok, [String.t()]}
| {:error, :invalid_format}
def validate_notes_string(notes_string) do
notes_string
|> parse_topic_list()
|> Enum.reduce_while([], fn text, acc ->
if Store.Project.Note.is_valid_format?(text) do
{:cont, [text | acc]}
else
{:halt, :invalid_format}
end
end)
|> case do
:invalid_format -> {:error, :invalid_format}
notes -> {:ok, notes}
end
end
@spec parse_topic_list(String.t()) :: [String.t()]
def parse_topic_list(input_str) do
input_str
|> String.trim("```")
|> String.trim("'''")
|> String.trim("\"\"\"")
|> String.trim()
|> String.split("\n")
|> Enum.map(&String.trim/1)
end
# Computes the cosine similarity between two vectors
def cosine_similarity(vec1, vec2) do
if length(vec1) != length(vec2) do
raise ArgumentError, """
Vectors must have the same length to compute cosine similarity.
- Left: #{length(vec1)}
- Right: #{length(vec2)}
"""
end
dot_product = Enum.zip(vec1, vec2) |> Enum.reduce(0.0, fn {a, b}, acc -> acc + a * b end)
magnitude1 = :math.sqrt(Enum.reduce(vec1, 0.0, fn x, acc -> acc + x * x end))
magnitude2 = :math.sqrt(Enum.reduce(vec2, 0.0, fn x, acc -> acc + x * x end))
if magnitude1 == 0.0 or magnitude2 == 0.0 do
0.0
else
dot_product / (magnitude1 * magnitude2)
end
end
# -----------------------------------------------------------------------------
# Building transcripts
# -----------------------------------------------------------------------------
@doc """
Builds a "transcript" of the research process by converting the messages into
text. This is most commonly used to generate a transcript of the research
performed in a conversation for various agents and tool calls.
"""
def research_transcript(msgs) do
# Make a lookup for tool call args by id
tool_call_args = build_tool_call_args(msgs)
msgs
# Remove the first message, which is the orchestrating agent's system prompt
|> Enum.drop(1)
# Convert messages into text
|> Enum.reduce([], fn
%{role: @role_user, content: content}, acc ->
["User Query: #{content}" | acc]
%{role: @role_assistant, content: content}, acc when is_binary(content) ->
[content | acc]
# Not supported in reasoning models, but still may be present in older
# conversations.
%{role: "system", content: _}, acc ->
acc
%{role: @role_system, content: _content}, acc ->
acc
%{role: @role_tool, tool_call_id: id, name: name, content: content}, acc ->
args = tool_call_args[id] |> Jason.encode!()
text = """
Performed research using the tool, `#{name}`, with the following arguments:
`#{args}`
Result:
#{content}
"""
[text | acc]
_msg, acc ->
acc
end)
|> Enum.reverse()
|> Enum.join("\n-----\n")
end
defp build_tool_call_args(msgs) do
msgs
|> Enum.reduce(%{}, fn msg, acc ->
case msg do
%{role: @role_assistant, content: nil, tool_calls: tool_calls} ->
tool_calls
|> Enum.map(fn %{id: id, function: %{arguments: args}} -> {id, args} end)
|> Enum.into(acc)
_ ->
acc
end
end)
end
@doc """
Extracts the user's *most recent* query from the conversation messages.
"""
def user_query(messages) do
messages
|> Enum.filter(&(&1.role == @role_user))
|> List.first()
|> then(& &1.content)
end
# -----------------------------------------------------------------------------
# Messages
# -----------------------------------------------------------------------------
@doc """
Creates a system message object, used to define the assistant's behavior for
the conversation.
"""
def system_msg(msg) do
%{
role: @role_system,
content: msg
}
end
@doc """
Creates a user message object, representing the user's input prompt.
"""
def user_msg(msg) do
%{
role: @role_user,
content: msg
}
end
@doc """
Creates an assistant message object, representing the assistant's response.
"""
def assistant_msg(msg) do
%{
role: @role_assistant,
content: msg
}
end
@doc """
This is the tool outputs message, which must come immediately after the
`assistant_tool_msg/3` message with the same `tool_call_id` (`id`).
"""
def tool_msg(id, func, output) do
%{
role: @role_tool,
name: func,
tool_call_id: id,
content: output
}
end
@doc """
This is the tool call message, which must come immediately before the
`tool_msg/3` message with the same `tool_call_id` (`id`).
"""
def assistant_tool_msg(id, func, args) do
%{
role: @role_assistant,
content: nil,
tool_calls: [
%{
id: id,
type: "function",
function: %{
name: func,
arguments: args
}
}
]
}
end
end