ankn
a5f8fb49ab
Bahn: aisupport, Analyse-O2C-C2S, awesome-bahn-mcp-servers, beam-mcp,
Confluence_Bot, db-planet-mcp-server, O2C-Harness, project-audit,
Projekt-KIQ-HP, teamlandkarte-mcp
Dhive: Jury-Voting
Privat: CV, NoteGraph (NOTE: NoteGraph needs complete redo after consolidation)
Shared: AI-Orchestrator, OrgMyLife, power_skills_and_more
Shared/references: symphony (read-only)
Bahn repos remain available as independent remotes - this monorepo
pulls them in via subtree, the originals are untouched.
90 lines
3.4 KiB
Python
90 lines
3.4 KiB
Python
"""Structured LLM prompts for document enrichment.
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Provides the system prompt that instructs the LLM to return structured JSON
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with entities, categories, tags, and action items.
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"""
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ENRICHMENT_SYSTEM_PROMPT = """\
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You are a knowledge management assistant. Analyze the following text extracted \
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from a document and return structured metadata as JSON.
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Return a JSON object with exactly these fields:
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- "title": A concise, descriptive title for this note (max 80 chars)
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- "category": One of "meeting", "project", "decision", "inbox"
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- "tags": List of relevant topic tags (lowercase, no # prefix)
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- "entities": List of detected entities, each with:
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- "type": One of "person", "project", "date", "action_item"
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- "value": The extracted text value
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- "confidence": Float between 0.0 and 1.0 indicating detection confidence
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- "position": Approximate character offset where entity appears (integer or null)
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- "action_items": List of action items, each with:
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- "description": What needs to be done
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- "assignee": Person responsible (string or null)
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- "deadline": Date in ISO 8601 format, e.g. "2024-03-22" (string or null)
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- "summary": A 1-2 sentence summary of the document (string or null)
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Rules:
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- Be precise with entity detection. Only mark entities you are confident about.
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- For people, use their full name as the value.
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- For projects, use the project name as commonly referenced.
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- For dates, use ISO 8601 format (YYYY-MM-DD) as the value.
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- Assign confidence scores honestly: 0.9+ for clearly stated entities, 0.7-0.9 for \
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likely entities, below 0.7 for uncertain ones.
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- Category "meeting" for meeting notes, agendas, minutes.
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- Category "project" for project-specific documentation.
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- Category "decision" for recorded decisions, ADRs.
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- Category "inbox" for anything that doesn't clearly fit the above.
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Context: This is a personal knowledge base for a project manager working across \
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multiple business projects.{context_section}
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"""
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STRICT_RETRY_PROMPT = """\
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Your previous response was not valid JSON. Please respond with ONLY a valid JSON \
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object matching the schema described in the system prompt. No markdown, no code fences, \
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no explanation — just the raw JSON object.
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"""
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def build_system_prompt(
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known_projects: list[str] | None = None,
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known_people: list[str] | None = None,
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) -> str:
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"""Build the system prompt with optional context about known entities.
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Args:
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known_projects: List of known project names for context.
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known_people: List of known people names for context.
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Returns:
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Complete system prompt string.
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"""
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context_parts: list[str] = []
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if known_projects:
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projects_str = ", ".join(known_projects)
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context_parts.append(f"Known projects: {projects_str}.")
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if known_people:
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people_str = ", ".join(known_people)
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context_parts.append(f"Known people: {people_str}.")
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if context_parts:
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context_section = "\n" + " ".join(context_parts)
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else:
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context_section = ""
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return ENRICHMENT_SYSTEM_PROMPT.format(context_section=context_section)
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def build_user_prompt(extracted_text: str) -> str:
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"""Build the user message containing the extracted document text.
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Args:
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extracted_text: The raw text extracted from the document.
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Returns:
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User prompt string.
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"""
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return f"Analyze this document and return the structured JSON:\n\n{extracted_text}"
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