c3d31c06e2
- Auth/roles (no self-reg), admin user provision, JWT - Analyze: sales kit + initial pain-fit from form/upload - Persona generator: 15 personas (5/tier) w/ pain variety, negotiation, init mode, channel, latent/revealable, wrong_text special - Chat simulator: per-mode initiation, one-shot, hidden signals, judge-LLM debrief+coaching - Trainee loop: win/lose board, weak-areas, user-generated personas - Admin analytics; EN+TH Vue SPA served by Flask - Deploy: Dockerfile, docker-compose, README, eng-log + HANDOFF - Tests (mock LLM): m0/m1/routes/e2e all pass
97 lines
3.6 KiB
Python
97 lines
3.6 KiB
Python
"""Analyzer: extracts a Sales Kit (product facts) + initial pain-fit from inputs.
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Product data is used primarily to derive pains that persona generation can build
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against. The result also carries a `scenario` prompt that frames persona creation.
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"""
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from __future__ import annotations
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from typing import Any
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from ..llm import LLMClient
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SALES_KIT_SYSTEM = """You are an expert ecommerce/B2B analyst. Given product information
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(typed in a form and/or extracted from uploaded files), produce a structured Sales Kit.
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Rules:
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- Output ONLY valid JSON with the exact keys requested.
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- Pain-fit: judge which pains / customer pain categories the product can PLAUSIBLY solve,
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and clearly distinguish "strong fit" from "partial / weak fit".
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- The product info is only initial grounding; personas may be reused across similar products.
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- If some fields are unknown, leave them as empty lists / empty strings (never invent specifics).
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Output schema:
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{
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"productName": string,
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"category": string,
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"valueProps": [string],
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"features": [string],
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"pricingAnchors": [string],
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"targetAudience": { "segment": string, "demographics": string, "useCases": [string] },
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"objectionHandlers": [string],
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"initialPainFit": [
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{ "pain": string, "fit": "strong"|"partial"|"weak", "evidence": string }
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],
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"scenarioFrame": string
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}
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The scenarioFrame is a one-paragraph description of the selling situation (who the seller,
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what channel, target segment) that will frame persona creation.
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"""
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class Analyzer:
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def __init__(self, llm: LLMClient) -> None:
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self.llm = llm
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def analyze(
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self,
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*,
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product: str = "",
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segment: str = "",
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description: str = "",
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file_text: str = "",
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channel: str = "facebook",
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) -> dict[str, Any]:
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# Build the merged product context (form wins over file text)
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product_src = product.strip() or file_text.strip() or ""
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context = (
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f"PRODUCT (form/typed):\n{product}\n\n" if product.strip() else ""
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)
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if segment.strip():
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context += f"INITIAL CUSTOMER SEGMENT:\n{segment}\n\n"
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if description.strip():
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context += f"ADDITIONAL DESCRIPTION / SCENARIO:\n{description}\n\n"
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if file_text.strip():
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context += f"UPLOADED FILE CONTENT:\n{file_text[:12000]}\n"
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if not context.strip():
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raise ValueError("no product information provided (form or file)")
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user_prompt = (
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f"Channel: {channel}\n\n"
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f"Analyze the following and return the Sales Kit JSON:\n\n{context}"
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)
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result = self.llm.complete_json(
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SALES_KIT_SYSTEM, user_prompt, temperature=0.2, max_tokens=5000
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)
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# Normalize shape defensively
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result.setdefault("productName", product_src[:200] or "Untitled product")
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result.setdefault("category", "")
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result.setdefault("valueProps", [])
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result.setdefault("features", [])
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result.setdefault("pricingAnchors", [])
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result.setdefault("targetAudience", {
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"segment": segment or "",
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"demographics": "",
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"useCases": [],
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})
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result.setdefault("objectionHandlers", [])
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result.setdefault("initialPainFit", [])
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result.setdefault("scenarioFrame", description or "")
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for k in ("valueProps", "features", "pricingAnchors", "objectionHandlers"):
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if not isinstance(result[k], list):
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result[k] = []
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if not isinstance(result.get("initialPainFit"), list):
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result["initialPainFit"] = []
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return result
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