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import json
import os
from typing import Any, Dict, List, Optional, TYPE_CHECKING
from .models import StrategicObjective
if TYPE_CHECKING:
from openai.types.chat import ChatCompletionUserMessageParam
def _alignment_label(score: float) -> str:
if score >= 0.75:
return "Strong"
if score >= 0.55:
return "Medium"
return "Weak"
class RAGEngine:
"""Retrieval-Augmented Generation (RAG) helper for improvement suggestions.
Responsibilities:
- Accept a strategic objective, current alignment score, and top-K retrieved action tasks
- Build a structured prompt with clear SYSTEM / CONTEXT / INSTRUCTIONS sections
- Optionally call an LLM (OpenAI API via env vars) and parse structured JSON
- Fallback to deterministic, rule-based suggestions if LLM is unavailable
"""
def __init__(self, model: Optional[str] = None) -> None:
# Model name can be overridden via env var OPENAI_MODEL
# Use a widely supported default; allow override via env or arg
self.model = model or os.environ.get("OPENAI_MODEL") or "gpt-4o-mini"
self.api_key = os.environ.get("OPENAI_API_KEY")
# ------------------------- Prompt Construction -------------------------
def build_prompt(
self,
strategy: StrategicObjective,
current_score: float,
retrieved_actions: List[Dict[str, Any]],
) -> ChatCompletionUserMessageParam:
system = "You are an AI business analyst."
actions_lines = []
for i, a in enumerate(retrieved_actions, start=1):
title = a.get("title") or a.get("metadata", {}).get("title") or "Action"
sim = a.get("similarity")
actions_lines.append(f"{i}. {title} (similarity: {sim:.2f})")
actions_block = "\n".join(actions_lines) if actions_lines else "(none)"
context = (
f"Strategic Objective:\n\n{strategy.title}\n\n"
f"Description:\n\n{strategy.description}\n\n"
f"Current Alignment Score:\n{current_score:.2f} ({_alignment_label(current_score)})\n\n"
f"Retrieved Action Tasks:\n{actions_block}"
)
instructions = (
"- Explain why alignment is at the current level\n"
"- Suggest 3 new action tasks\n"
"- Suggest 2 measurable KPIs\n"
"- Suggest timeline and ownership\n"
"- Keep suggestions realistic for AutoBridge"
)
response_format = (
"Respond in strict JSON with keys: "
"explanation (string), suggested_actions (string[3]), "
"kpis (string[2]), timeline_and_ownership (object with keys: owner, start, end), "
"risks (string[1..3])."
)
content = (
"SYSTEM:\n"
+ system
+ "\n\n"
+ "CONTEXT:\n"
+ context
+ "\n\n"
+ "INSTRUCTIONS:\n"
+ instructions
+ "\n\n"
+ "RESPONSE_FORMAT:\n"
+ response_format
)
return {
"role": "user",
"content": content,
}
# ------------------------- LLM Invocation -------------------------
def _call_openai(self, user_msg: ChatCompletionUserMessageParam) -> Optional[str]:
if not self.api_key:
print("RAGEngine: OPENAI_API_KEY not set; using fallback.")
return None
try:
from openai import OpenAI # type: ignore
client = OpenAI(api_key=self.api_key)
completion = client.chat.completions.create(
model=self.model,
messages=[
{
"role": "system",
"content": "You are an AI business analyst.",
},
user_msg,
],
)
print(f"RAGEngine: OpenAI call succeeded (model={self.model}).")
return completion.choices[0].message.content
except Exception as e:
print(f"RAGEngine: OpenAI call failed: {e!r}; using fallback.")
return None
# ------------------------- Parsing and Fallback -------------------------
def _parse_json(self, text: Optional[str]) -> Optional[Dict[str, Any]]:
if not text:
return None
# Try to locate first JSON object in text
text = text.strip()
start = text.find("{")
end = text.rfind("}")
if start != -1 and end != -1 and end > start:
snippet = text[start : end + 1]
try:
return json.loads(snippet)
except Exception:
pass
# Last resort
try:
return json.loads(text)
except Exception:
return None
def _fallback_rule_based(
self,
strategy: StrategicObjective,
current_score: float,
retrieved_actions: List[Dict[str, Any]],
) -> Dict[str, Any]:
label = _alignment_label(current_score)
# Simple templates for MSc-friendly determinism
if label == "Weak":
expl = (
"Current actions only partially address the strategy's outcomes; coverage is sparse "
"and lacks clear ownership/timelines, resulting in low similarity scores."
)
actions = [
"Publish a standardized landed-cost breakdown with supplier lane mapping",
"Automate ingestion of duties/freight/insurance components with validations",
"Launch monthly variance review with supplier scorecards and corrective actions",
]
kpis = [
"Cost variance across lanes < 3%",
"Data coverage of cost components ≥ 90% of SKUs",
]
timeline = {
"owner": "Finance Ops",
"start": "2026-02-15",
"end": "2026-05-31",
}
risks = [
"Supplier data quality or delays may limit transparency",
"Insufficient engineering capacity for integrations",
]
elif label == "Medium":
expl = (
"Alignment is improving but gaps remain in data completeness and process rigor; "
"consolidation and clearer deliverables would strengthen coverage."
)
actions = [
"Unify overlapping cost tools into a single authoritative calculator",
"Backfill historical cost data and set validation thresholds",
"Define escalation playbooks for clearance delays and exceptions",
]
kpis = [
"Audit pass rate ≥ 95%",
"Average clearance time < 24h",
]
timeline = {
"owner": "Operations",
"start": "2026-03-01",
"end": "2026-06-30",
}
risks = [
"Fragmented ownership across finance and operations",
]
else: # Strong
expl = (
"Actions strongly map to the strategy. Focus on monitoring, risk management, and "
"sustaining improvements through governance routines."
)
actions = [
"Introduce quarterly retrospectives with supplier and ops stakeholders",
"Automate anomaly detection for lane cost spikes",
"Publish transparency dashboards for leadership review",
]
kpis = [
"Early-warning alerts resolved within 48h",
"Quarterly savings achieved vs. target",
]
timeline = {
"owner": "Data Science",
"start": "2026-03-15",
"end": "2026-07-31",
}
risks = [
"Model drift or changing tariffs impacting reliability",
]
return {
"explanation": expl,
"suggested_actions": actions,
"kpis": kpis,
"timeline_and_ownership": timeline,
"risks": risks,
}
# ------------------------- Public API -------------------------
def generate(
self,
strategy: StrategicObjective,
current_score: float,
retrieved_actions: List[Dict[str, Any]],
) -> Dict[str, Any]:
user_msg = self.build_prompt(strategy, current_score, retrieved_actions)
text = self._call_openai(user_msg)
parsed = self._parse_json(text)
if parsed:
return parsed
return self._fallback_rule_based(strategy, current_score, retrieved_actions)
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