d8312c24a5
Pure function that assembles system prompt, cross-branch context, history, and images for _call_ai — no DB access, no LLM calls. Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
59 lines
2.0 KiB
Python
59 lines
2.0 KiB
Python
"""Branch-aware prompt builder — assembles AI context with cross-branch awareness.
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Pure function: takes data, returns dict matching _call_ai parameter names.
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No DB access, no LLM calls. The caller pre-fetches all data.
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Return keys: system_base, rag_context, history, new_message, images
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- system_base: stable system prompt (cached by Anthropic)
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- rag_context: cross-branch summaries + attachment descriptions (NOT cached)
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"""
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from typing import Any
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from app.services.assistant_chat_service import ASSISTANT_SYSTEM_PROMPT
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class BranchAwarePromptBuilder:
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"""Assembles prompt components for branch-aware AI calls."""
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def build(
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self,
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branch_messages: list[dict[str, Any]],
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sibling_summaries: str,
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session_context: str,
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attachments: list[dict[str, Any]],
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new_message: str,
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revival_context: str | None = None,
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token_budget: int = 100_000,
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) -> dict[str, Any]:
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"""Build prompt components for _call_ai.
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Returns dict with keys: system_base, rag_context, history, new_message, images.
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"""
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# 1. system_base — stable, cached across turns
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system_base = ASSISTANT_SYSTEM_PROMPT + "\n\n## Session Context\n" + session_context
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# 2. rag_context — changes per query, NOT cached
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rag_parts = []
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if revival_context:
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rag_parts.append(f"\n## Branch Revival\n{revival_context}")
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if sibling_summaries:
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rag_parts.append(sibling_summaries)
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rag_context = "\n".join(rag_parts)
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# 3. history — branch messages filtered to user/assistant
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history = []
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for msg in branch_messages:
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if msg.get("role") in ("user", "assistant"):
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history.append({"role": msg["role"], "content": msg["content"]})
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# 4. images
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images = attachments if attachments else None
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return {
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"system_base": system_base,
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"rag_context": rag_context,
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"history": history,
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"new_message": new_message,
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"images": images,
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}
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