Pablan, as it stands

Self-hosted knowledge management for SMEs: a split-screen Markdown editor
whose sections an LLM refines while you write, and RAG question answering
over the documents that result. FastAPI + Postgres/pgvector on the back,
SvelteKit on the front, everything OpenAI-compatible and self-hostable.

Squashed into a single commit; the development history stays local.

Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01CA43ZJda8Rbp2hKXNy8f6b

backend/app/authoring/context.py

@@ -0,0 +1,67 @@
+"""Conversation context for a capture.
+
+When a document is written out of a chat, that chat's subject is useful twice:
+to find existing documents the user might extend, and as background for section
+refinement. Both use a short LLM topic summary of the conversation. All LLM
+traffic goes through `llm/client.py`; nothing here logs content — a failure
+degrades quietly rather than breaking the capture.
+"""
+
+import logging
+
+from pydantic import BaseModel
+
+from app.authoring.prompts import render_topic_summary_prompt
+from app.llm.client import chat_json
+from app.llm.errors import LLMError
+from app.models import Conversation, MessageRole
+
+logger = logging.getLogger("pablan.authoring")
+
+# How many recent turns feed the summary — enough for the subject, bounded so
+# a long thread cannot blow up the utility prompt.
+MAX_CONTEXT_MESSAGES = 12
+
+# Skip the reasoning model's hidden thinking: this is a short, latency-
+# sensitive utility call.
+_NO_THINKING = {"chat_template_kwargs": {"enable_thinking": False}}
+
+
+class _TopicSummary(BaseModel):
+    topic: str
+
+
+def conversation_transcript(conversation: Conversation) -> str:
+    """The recent user/assistant turns as a plain transcript."""
+    turns = [
+        message
+        for message in conversation.messages
+        if message.role in (MessageRole.user, MessageRole.assistant)
+    ][-MAX_CONTEXT_MESSAGES:]
+    return "\n".join(
+        f"{'User' if message.role == MessageRole.user else 'Assistant'}: "
+        f"{message.content}"
+        for message in turns
+    )
+
+
+async def summarize_transcript(transcript: str) -> str:
+    """A short topic summary of a conversation transcript, or '' if none can
+    be made. Failures degrade quietly."""
+    if not transcript.strip():
+        return ""
+    try:
+        result = await chat_json(
+            render_topic_summary_prompt(transcript),
+            _TopicSummary,
+            extra_body=_NO_THINKING,
+        )
+    except LLMError:
+        logger.info("topic summary failed", extra={"event": "topic_summary_failed"})
+        return ""
+    return result.topic.strip()
+
+
+async def summarize_conversation(conversation: Conversation) -> str:
+    """A short topic summary of the conversation, or '' if none can be made."""
+    return await summarize_transcript(conversation_transcript(conversation))