ProfessorNovaandClaude Opus 5
97dbff309c
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
82 lines
3.3 KiB
Python
82 lines
3.3 KiB
Python
"""Refinement prompt for the writing editor — natural language only.
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The model refines exactly ONE section of a document the user is writing. The
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rest of the document travels as prefix/suffix context so the section stays
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coherent with its surroundings, but the model regenerates ONLY the section —
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a large document is never re-emitted whole (FIM-style).
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The base texts (persona, rules, framings) are admin-editable: they come from
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`app/prompts/overrides.py::get_prompt`, which returns a DB override when one
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exists and the code default (`app/prompts/defaults.py`) otherwise.
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"""
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from app.llm.client import ChatMessage
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from app.prompts.overrides import get_prompt
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def render_refine_prompt(
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section: str,
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*,
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prefix: str,
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suffix: str,
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persona: str | None,
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hint: str | None,
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context: str | None = None,
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knowledge: list[str] | None = None,
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) -> list[ChatMessage]:
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# A template may carry its own persona; otherwise the admin-editable default.
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system_parts = [persona.strip() if persona else get_prompt("refine_persona")]
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if hint:
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system_parts.append(f"What this section should convey: {hint}")
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if context:
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# Background from the chat this capture came from, so the refinement
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# is on-topic — but only as orientation, never a source of new facts.
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system_parts.append(
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f"Background (the conversation this document came from, for "
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f"orientation only — do not invent facts from it): {context}"
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)
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system_parts.append(get_prompt("refine_rules"))
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# The document being edited leads the user turn (prefix/suffix/section);
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# the retrieved knowledge trails it, because it changes on every call and
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# keeping it last leaves the stable prompt prefix reusable between calls.
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user_parts: list[str] = []
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if prefix.strip():
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user_parts.append(
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f"Text before the section (context only, do not repeat it):\n{prefix}"
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)
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if suffix.strip():
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user_parts.append(
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f"Text after the section (context only, do not repeat it):\n{suffix}"
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)
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user_parts.append(f"Refine only this section:\n{section}")
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if knowledge:
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# What the company has already documented elsewhere. It is grounding,
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# not source material: it keeps terminology and facts consistent and
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# lets the section point at related documents, but it must not be
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# copied in or become a way to add facts the section's notes do not
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# support.
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joined = "\n\n".join(knowledge)
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user_parts.append(f"{get_prompt('grounding_framing')}\n{joined}")
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return [
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{"role": "system", "content": "\n\n".join(system_parts)},
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{"role": "user", "content": "\n\n".join(user_parts)},
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]
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def render_topic_summary_prompt(transcript: str) -> list[ChatMessage]:
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"""Condense a conversation into a short search topic (a few words)."""
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return [
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{"role": "system", "content": get_prompt("topic_summary")},
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{"role": "user", "content": f"Conversation:\n{transcript}"},
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]
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def render_title_prompt(content_md: str) -> list[ChatMessage]:
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"""Suggest a concise document title from its written content."""
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return [
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{"role": "system", "content": get_prompt("title")},
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{"role": "user", "content": f"Document:\n{content_md}"},
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]
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