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AI code archaeology
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lib/ai/agent/conversation_summary.ex
defmodule AI.Agent.ConversationSummary do
@moduledoc """
Summarizes a conversation transcript for embedding generation.
Produces a concise natural-language summary that captures the topics
discussed, decisions made, and key outcomes. The summary is used as
input to the embedding model for semantic search over conversations.
"""
@model AI.Model.fast()
@prompt """
You are summarizing a conversation between a user and an AI assistant for semantic search indexing.
Produce a concise summary covering:
- The primary topics and questions discussed
- Key decisions, conclusions, or outcomes reached
- Notable code, files, or systems referenced
- Any unresolved questions or next steps mentioned
Write in plain, descriptive prose. Optimize for semantic search: someone searching
for a conversation about topic X should find this summary if that topic was discussed.
Keep your response brief - aim for a few paragraphs at most.
Do not include conversational filler or meta-commentary about the summarization process.
"""
@behaviour AI.Agent
@impl AI.Agent
def get_response(opts) do
case Map.fetch(opts, :transcript) do
{:ok, transcript} ->
AI.Accumulator.get_response(
model: @model,
prompt: @prompt,
input: transcript,
question: "Summarize this conversation for search indexing."
)
|> case do
{:ok, %{response: response}} -> {:ok, response}
{:error, reason} -> {:error, reason}
end
:error ->
{:error, :transcript_required}
end
end
end