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
75 lines
2.6 KiB
Python
75 lines
2.6 KiB
Python
"""What the company already wrote about this.
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Before refining a section, the server looks for related published material the
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author may read and hands it to the prompt as a reference — so a suggestion
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stays consistent with the rest of the knowledge base instead of inventing a
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parallel version of it. The same chunks are surfaced to the editor's "?"
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inspector, so the author can see where a suggestion drew from.
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"""
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import uuid
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from sqlalchemy.ext.asyncio import AsyncSession
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from app.llm.errors import LLMError
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from app.models import User
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from app.rag.chunking import HEADING_RE
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from app.rag.similarity import (
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CAPTURE_CONTEXT_MAX_DISTANCE,
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SimilarChunk,
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similar_chunks,
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)
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# A section needs at least this much of its own text (beyond the heading)
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# before it is worth searching the knowledge base to ground the refinement:
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# a bare heading matches nothing useful and only adds noise.
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MIN_CHARS = 40
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TOP_K = 3
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# Cap each grounding excerpt so a few long ones cannot crowd out the section.
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EXCERPT_CHARS = 600
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def leading_heading(section_text: str) -> str | None:
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"""The heading text a section starts with, to match a template hint."""
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first = section_text.lstrip().splitlines()[0] if section_text.strip() else ""
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match = HEADING_RE.match(first)
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return match.group(2).strip() if match else None
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def reference(title: str, heading_path: str, content: str) -> str:
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"""One retrieved chunk, rendered for the prompt: where it comes from, then
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a bounded excerpt."""
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excerpt = content.strip()
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if len(excerpt) > EXCERPT_CHARS:
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excerpt = excerpt[:EXCERPT_CHARS].rstrip() + " ..."
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where = f'"{title}" ({heading_path})' if heading_path else f'"{title}"'
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return f"From {where}:\n{excerpt}"
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async def for_section(
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db: AsyncSession, section_text: str, user: User, document_id: uuid.UUID
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) -> list[SimilarChunk]:
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"""Related knowledge for one section, permission-filtered by construction.
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Returns nothing when the section is still too thin to match on, when the
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current document is the only match, or when the embedding endpoint is
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unavailable — the refinement then simply proceeds without grounding.
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"""
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query = section_text.strip()
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_, _, after_heading = query.partition("\n")
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body = after_heading.strip() if leading_heading(query) is not None else query
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if len(body) < MIN_CHARS:
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return []
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try:
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return await similar_chunks(
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db,
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query,
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user=user,
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top_k=TOP_K,
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max_distance=CAPTURE_CONTEXT_MAX_DISTANCE,
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exclude_builtin=True,
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exclude_document_id=document_id,
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)
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except LLMError:
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return []
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