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
32 lines
1.5 KiB
Python
32 lines
1.5 KiB
Python
"""Prompt rendering for modes — always natural language, never raw YAML or
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JSON dumps.
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The base texts (the assistant's system prompt, the no-sources note) are
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admin-editable via `app/prompts/overrides.py::get_prompt`; the query mode reads
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`query_system` directly and `render_context_turn` reads `query_no_sources`.
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"""
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from app.prompts.overrides import get_prompt
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from app.rag.retrieval import SearchResult
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def render_context_turn(results: list[SearchResult], question: str) -> str:
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"""The final user turn: the retrieval for THIS question, then the question.
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Deliberately NOT part of the system prompt: keeping the excerpts here lets
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the system prompt AND the conversation history stay byte-identical across a
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conversation's turns, so the endpoint's prompt cache reuses them and only
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this turn's excerpts are fresh work (docs/architecture.md, prompt caching).
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"""
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if not results:
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# Refusing to answer a greeting because retrieval found nothing makes the
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# assistant feel broken. It answers from general knowledge, just never as
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# if that were company policy (the UI labels these source-free).
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return f"{get_prompt('query_no_sources')}\n\n{question}"
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blocks = [
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f"[{index}] {result.heading_path or result.title}\n{result.content}"
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for index, result in enumerate(results, start=1)
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]
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excerpts = "\n\n---\n\n".join(blocks)
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return f"Knowledge base excerpts:\n\n{excerpts}\n\nQuestion:\n{question}"
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