o Tj @sdZddlmZddlmZdZeGdddZd"d d Zd#d dZd$ddZ d%ddZ d&d'ddZ d(ddZ d)dd Z d!S)*uSystem prompt + RAG context assembly, tuned for minimal token usage. Because QLoRA fine-tuning *internalizes* the behavioral rules (cite sources, use only context, no personalized advice), the served system prompt can be short — every token here is paid on EVERY request, so a compact prompt is a direct, permanent cost/energy saving. Training and serving use the same compact prompt to avoid a train/serve mismatch. Context assembly also minimizes input tokens: near-duplicate chunks are dropped and the context is capped to a token budget (lowest-scoring chunks fall off first). This is the application-layer analogue of the paper's sparse top-k context selection. ) annotations) dataclasszYou are a travel-insurance assistant. Use ONLY the CONTEXT; if the answer isn't there, say so and suggest a licensed advisor. Cite the passage number(s) you use, e.g. [1]. Never say which plan to buy. Be brief.c@s&eZdZUded<ded<ded<dS)RetrievedChunkstrtextsourcefloatscoreN)__name__ __module__ __qualname____annotations__rrsrc/agent/prompt.pyrs  rrrreturnintcCstdt|dS)zRCheap, dependency-free token estimate (~4 chars/token). Good enough for budgeting.N)maxlenrrrrestimate_tokens srcCsd|S)N )joinlowersplitrrrr _normalize%srchunkslist[RetrievedChunk]csLg}g}|D]}t|jtfdd|Drq|||q|S)zRDrop exact/subsumed duplicates (e.g. a table that also appears as flattened text).c3s(|]}|kp|vp|vVqdSNr).0sZnormrr /s&zdedup_chunks..N)rranyappend)rZkeptZ seen_normcrr"r dedup_chunks)s   r' budget_tokenscCsXt|dddd}gd}}|D]}t|j}|r |||kr q||||7}q|S)zUKeep highest-scoring chunks until the token budget is hit (always keep at least one).cSs|jSr)r )r&rrr8sz"fit_token_budget..T)keyreverserN)sortedrrr%)rr(Zorderedoutusedr&Zcostrrrfit_token_budget6s    r/TdedupboolcCs|rt|}t||S)z@Dedup + budget-trim retrieved chunks to minimize context tokens.N)r'r/)rr(r0rrrcompress_contextCs r2c CsP|sdSdg}t|dD]\}}|d|d|jd|jq d|S) z=Render retrieved chunks as a numbered, citable CONTEXT block.zCONTEXT: (none found)zCONTEXT:r[z] (z) z N) enumerater%rrstripr)rlinesir&rrrbuild_context_blockLs & r8query list[dict]cCs0t|}|d|d}dtdd|dgS)zIAssemble the chat messages for the LLM (kept terse to save input tokens).z Q: zH Answer from CONTEXT only; cite the passage number(s) you use, e.g. [1].system)ZroleZcontentuserN)r8r5 SYSTEM_PROMPT)r9rcontextZ user_contentrrrbuild_messagesVs r?N)rrrr)rrrr)rrrr)rrr(rrr)T)rrr(rr0r1rr)rrrr)r9rrrrr:)__doc__Z __future__rZ dataclassesrr=rrrr'r/r2r8r?rrrrs