feat(api): folder_indexer.summarize_text via gpt-4o-mini

app/core/folder_indexer.py

@@ -0,0 +1,66 @@
+"""Per-file summarisation for project folder integration."""
+from __future__ import annotations
+
+from dataclasses import dataclass
+
+from langchain_core.messages import HumanMessage, SystemMessage
+
+from app.core.langfuse_client import (
+    compile_prompt,
+    extract_usage,
+    get_prompt_or_fallback,
+)
+from app.core.llm import get_llm
+
+_TEXT_FALLBACK = (
+    "You are summarising a file for an AI assistant that helps the user manage a project.\n"
+    "Produce a single sentence (<=30 words, <=200 chars) that captures the file's purpose "
+    "and most important detail.\nFile extension: {ext}\nFile name: {name}\nContent (truncated if long):\n{content}"
+)
+_IMAGE_FALLBACK = (
+    "You are summarising an image attached to a project folder.\n"
+    "Produce a single sentence (<=30 words, <=200 chars) describing what the image shows "
+    "and any obvious purpose (logo, screenshot, diagram, photo of a whiteboard, etc.)."
+)
+_MAX_INPUT_CHARS = 6000
+
+
+@dataclass
+class IndexResult:
+    summary: str
+    tokens_used: int
+
+
+async def _llm_text(messages: list) -> object:
+    """Make the LLM call for text summarisation.
+
+    Defined as a standalone async function so tests can patch it cleanly
+    without needing to mock the LLM object itself.
+    """
+    llm = get_llm(model="gpt-4o-mini", temperature=0.2)
+    return await llm.ainvoke(messages)
+
+
+async def summarize_text(*, content: str, ext: str, name: str) -> IndexResult:
+    """Return a compact summary of a text file.
+
+    Parameters
+    ----------
+    content:
+        Raw text content of the file (will be truncated to _MAX_INPUT_CHARS).
+    ext:
+        File extension including the leading dot, e.g. ``".md"``.
+    name:
+        File name, e.g. ``"kickoff.md"``.
+    """
+    template, prompt_obj = get_prompt_or_fallback("folder_file_summary_text", _TEXT_FALLBACK)
+    truncated = content[:_MAX_INPUT_CHARS]
+    compiled = compile_prompt(template, prompt_obj, ext=ext, name=name, content=truncated)
+    messages = [
+        SystemMessage(content=compiled),
+        HumanMessage(content="Summarise this file."),
+    ]
+    response = await _llm_text(messages)
+    usage = extract_usage(response)
+    summary = (response.content or "").strip()[:500]
+    return IndexResult(summary=summary, tokens_used=usage.get("total", 0))

tests/test_folder_indexer.py

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+"""Folder indexer LLM helpers."""
+from __future__ import annotations
+
+from unittest.mock import AsyncMock, patch
+
+import pytest
+
+from app.core.folder_indexer import summarize_text, IndexResult
+
+pytestmark = pytest.mark.asyncio
+
+
+async def test_summarize_text_returns_summary_and_tokens():
+    mock_resp = AsyncMock()
+    mock_resp.content = "Kickoff notes covering scope and deadlines."
+    mock_resp.usage_metadata = {"input_tokens": 320, "output_tokens": 18, "total_tokens": 338}
+    with patch("app.core.folder_indexer._llm_text", new=AsyncMock(return_value=mock_resp)):
+        result = await summarize_text(content="hello world", ext=".md", name="kickoff.md")
+    assert isinstance(result, IndexResult)
+    assert result.summary == "Kickoff notes covering scope and deadlines."
+    assert result.tokens_used == 338
+
+
+async def test_summarize_text_truncates_summary_at_500_chars():
+    mock_resp = AsyncMock()
+    mock_resp.content = "x" * 1000
+    mock_resp.usage_metadata = {"total_tokens": 100}
+    with patch("app.core.folder_indexer._llm_text", new=AsyncMock(return_value=mock_resp)):
+        result = await summarize_text(content="x", ext=".md", name="x.md")
+    assert len(result.summary) <= 500