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"""Skill: Interactive requirements gathering via guided questions.""" |
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import json |
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from video_processor.agent.skills.base import ( |
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AgentContext, |
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Artifact, |
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Skill, |
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register_skill, |
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) |
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from video_processor.utils.json_parsing import parse_json_from_response |
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class RequirementsChatSkill(Skill): |
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name = "requirements_chat" |
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description = "Interactive requirements gathering via guided questions" |
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def execute(self, context: AgentContext, **kwargs) -> Artifact: |
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"""Generate a structured requirements questionnaire.""" |
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stats = context.query_engine.stats() |
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entities = context.query_engine.entities() |
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parts = [ |
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"You are a requirements analyst. Based on the following " |
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"knowledge graph context, generate a requirements " |
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"gathering questionnaire.", |
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"", |
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"## Knowledge Graph Overview", |
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stats.to_text(), |
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"", |
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"## Entities", |
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entities.to_text(), |
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"", |
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"## Planning Entities", |
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] |
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for e in context.planning_entities: |
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parts.append(f"- {e}") |
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parts.append( |
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'\nGenerate a JSON object with a "questions" array. ' |
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"Each question should have:\n" |
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'- "id": string (e.g. "Q1")\n' |
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'- "category": "goals"|"constraints"|"priorities"|"scope"\n' |
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'- "question": string\n' |
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'- "context": string (why this matters)\n\n' |
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"Include 8-12 targeted questions.\n\n" |
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"Return ONLY the JSON." |
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) |
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prompt = "\n".join(parts) |
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response = context.provider_manager.chat(messages=[{"role": "user", "content": prompt}]) |
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parsed = parse_json_from_response(response) |
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content = json.dumps(parsed, indent=2) if not isinstance(parsed, str) else parsed |
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return Artifact( |
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name="Requirements Questionnaire", |
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content=content, |
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artifact_type="requirements", |
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format="json", |
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metadata={"stage": "questionnaire"}, |
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) |
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def gather_requirements(self, context: AgentContext, answers: dict) -> dict: |
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"""Take Q&A pairs and synthesize structured requirements.""" |
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stats = context.query_engine.stats() |
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qa_text = "" |
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for qid, answer in answers.items(): |
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qa_text += f"- {qid}: {answer}\n" |
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parts = [ |
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"You are a requirements analyst. Based on the knowledge " |
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"graph context and the answered questions, synthesize " |
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"structured requirements.", |
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"", |
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"## Knowledge Graph Overview", |
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stats.to_text(), |
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"", |
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"## Answers", |
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qa_text, |
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"Return a JSON object with:\n" |
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'- "goals": list of goal strings\n' |
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'- "constraints": list of constraint strings\n' |
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'- "priorities": list (ordered high to low)\n' |
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'- "scope": {"in_scope": [...], "out_of_scope": [...]}\n\n' |
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"Return ONLY the JSON.", |
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] |
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prompt = "\n".join(parts) |
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response = context.provider_manager.chat(messages=[{"role": "user", "content": prompt}]) |
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result = parse_json_from_response(response) |
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return result if isinstance(result, dict) else {"raw": result} |
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register_skill(RequirementsChatSkill()) |
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