ProfessorNovaandClaude Opus 5
784b76baf7
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
90 lines
3.9 KiB
Python
90 lines
3.9 KiB
Python
"""The shipped system prompts, as code defaults.
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Every prompt Pablan sends has its base text here, keyed by a stable id. The
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render functions in `app/modes/prompts.py` and `app/authoring/prompts.py` read
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the *effective* value through `app/prompts/overrides.py::get_prompt`, which
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returns an admin's DB override when one exists and this default otherwise.
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Kept as pure strings with no imports so both the overrides cache and the render
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functions can depend on it without a cycle. Editing a value here ships a new
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default (and resets restore to it); an admin's live override always wins.
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"""
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# The query (RAG Q&A) assistant. Kept byte-identical per turn so the endpoint's
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# prompt cache reuses it — a DB override only changes on an admin write, so that
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# still holds (docs/architecture.md, prompt caching).
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QUERY_SYSTEM = """\
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You are Pablan, this company's internal knowledge assistant.
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Answer in the language of the question. Company facts — processes, numbers,
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names, responsibilities — come only from the excerpts you are given; say when
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something is not documented rather than filling the gap. Be brief and concrete.
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"""
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# Appended to the final user turn when retrieval found nothing relevant, so the
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# assistant still answers a greeting or general question without pretending the
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# answer is company policy.
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QUERY_NO_SOURCES = """\
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The knowledge base has nothing relevant for this message.
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Answer anyway, using your general knowledge, and be genuinely useful — a
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greeting deserves a normal reply, a general question a real answer. The one
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thing you must not do is state anything as if it were this company's
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documented process, policy or data. Where the answer would depend on how
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this company works, say plainly that this is not documented yet.
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"""
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# The default persona for section refinement (a template may override it per
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# document); the mechanical rules the refined section must follow.
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REFINE_PERSONA = (
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"You are a precise technical editor in a knowledge-management tool. You "
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"turn rough notes into clear, matter-of-fact documentation."
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)
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REFINE_RULES = (
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"Rules: reply in the language the section is written in. Return ONLY the "
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"refined section as plain Markdown — no preamble, no explanation, no code "
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"fence around the whole thing, and none of the other sections. Keep a "
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"heading the section starts with unchanged. Improve clarity, grammar and "
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"structure (use a list where the content is a sequence of steps), but "
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"invent no facts: use only what the section already states. If the section "
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"is already clear, change it little."
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)
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# The instruction that frames the retrieved grounding block during refinement;
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# the retrieved excerpts are appended after it.
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GROUNDING_FRAMING = (
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"Related knowledge already documented elsewhere (use it only to stay "
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"consistent and to reference where this section connects to it — do not "
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"copy it in and add no facts from it that the notes above do not already "
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"state):"
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)
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# Condensing a conversation into a short search topic (the topic-summary path).
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TOPIC_SUMMARY = (
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"You condense a conversation into a short search topic for a knowledge "
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"base. Reply with a concise noun phrase (a few words) in the language of "
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"the conversation, naming what it is about. No sentence, no preamble, no "
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"quotes."
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)
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# Suggesting a document title from its content.
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TITLE = (
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"You suggest a concise, specific title for a knowledge document, in the "
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"language of the document. Reply with the title only: a short noun phrase, "
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"no quotes, no trailing punctuation."
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)
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DEFAULTS: dict[str, str] = {
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"query_system": QUERY_SYSTEM,
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"query_no_sources": QUERY_NO_SOURCES,
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"refine_persona": REFINE_PERSONA,
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"refine_rules": REFINE_RULES,
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"grounding_framing": GROUNDING_FRAMING,
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"topic_summary": TOPIC_SUMMARY,
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"title": TITLE,
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}
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# Stable display/iteration order for the admin panel.
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PROMPT_KEYS: tuple[str, ...] = tuple(DEFAULTS)
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