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AI code archaeology
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lib/search/conversations.ex
defmodule Search.Conversations do
@moduledoc """
Semantic search over indexed conversations.
This module uses conversation embeddings stored via
`Store.Project.ConversationIndex` to find relevant conversations for a
natural language query.
"""
alias Store.Project
alias Store.Project.Conversation
alias Store.Project.ConversationIndex
@default_limit 5
@spec search(Project.t(), String.t(), keyword()) :: {:ok, [map()]} | {:error, term()}
def search(%Project{} = project, query, opts \\ []) when is_binary(query) do
limit = Keyword.get(opts, :limit, @default_limit)
with {:ok, query_vec} <- Indexer.impl().get_embeddings(query) do
project
|> ConversationIndex.all_embeddings()
|> Util.async_stream(fn {id, emb_vec, _meta} ->
score = AI.Util.cosine_similarity(query_vec, emb_vec)
build_result(project, id, score)
end)
|> Enum.reduce([], fn
{:ok, nil}, acc -> acc
{:ok, result}, acc -> [result | acc]
end)
|> Enum.sort_by(fn %{score: sc} -> sc end, :desc)
|> Enum.take(limit)
|> Enum.sort_by(
fn %{timestamp: ts} ->
case ts do
%DateTime{} = dt -> DateTime.to_unix(dt)
ts when is_integer(ts) -> ts
_ -> 0
end
end,
:desc
)
|> then(&{:ok, &1})
end
end
defp build_result(project, id, score) do
convo = Conversation.new(id, project)
if Conversation.exists?(convo) do
ts = Conversation.timestamp(convo)
title = unwrap_question(Conversation.question(convo))
length = Conversation.num_messages(convo)
%{
conversation_id: id,
title: title,
timestamp: ts,
length: length,
score: score
}
else
nil
end
end
defp unwrap_question({:ok, q}), do: q
defp unwrap_question(_), do: "(no user question found)"
end