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0387a1c | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 | from __future__ import annotations
import asyncio
import json
from typing import Any, Dict, List, Optional
from .memory import ContextMessage
from .utils import get_logger
log = get_logger(__name__)
class AIEngine:
"""
OpenAI client wrapper with:
- async-friendly execution (OpenAI SDK is synchronous)
- retry w/ exponential backoff
- structured prompting for classification and reply drafting
"""
def __init__(self, *, api_key: str, model: str) -> None:
self.model = model
self.api_key = api_key
if not api_key or api_key == "your_openai_api_key_here":
self._client = None
log.warning("No valid OpenAI API key provided. Using dummy AI engine responses.")
return
# Lazy import so missing dependency errors are clearer.
from openai import OpenAI # type: ignore
self._client = OpenAI(api_key=api_key)
async def _call_with_retries(self, fn, *, attempts: int = 3) -> Any:
last_exc: Optional[BaseException] = None
for i in range(attempts):
try:
return await asyncio.to_thread(fn)
except Exception as e:
last_exc = e
delay_s = min(8.0, 0.6 * (2**i))
log.warning("OpenAI request failed (attempt %s/%s): %s", i + 1, attempts, e)
await asyncio.sleep(delay_s)
raise RuntimeError(f"OpenAI request failed after {attempts} attempts: {last_exc}") from last_exc
def _responses_supported(self) -> bool:
return hasattr(self._client, "responses")
async def classify_intent(self, *, subject: str, from_email: str, body: str) -> Dict[str, Any]:
if not self._client:
return {"intent": "General", "confidence": 1.0, "reasoning": "Dummy response due to missing API key."}
system = "You are a professional email assistant. Classify the email intent."
payload = {
"from": from_email,
"subject": subject,
"body": body,
"allowed_intents": ["Support", "Sales", "Spam", "General"],
}
messages: List[Dict[str, str]] = [
{"role": "system", "content": system},
{
"role": "user",
"content": (
"Classify the following email into exactly one of: Support, Sales, Spam, General.\n"
"Return STRICT JSON with keys: intent, confidence, reasoning.\n"
"confidence must be a number between 0 and 1.\n"
f"Email:\n{json.dumps(payload, ensure_ascii=False)}"
),
},
]
def _call() -> str:
if self._responses_supported():
resp = self._client.responses.create(
model=self.model,
input=messages,
response_format={"type": "json_object"},
)
return str(getattr(resp, "output_text", "") or "")
resp = self._client.chat.completions.create(
model=self.model,
messages=messages,
response_format={"type": "json_object"},
)
return str(resp.choices[0].message.content or "")
raw = (await self._call_with_retries(_call, attempts=3)).strip()
try:
data = json.loads(raw)
except Exception as e:
raise RuntimeError(f"Model did not return valid JSON: {raw}") from e
intent = str(data.get("intent", "General"))
confidence = float(data.get("confidence", 0.5))
reasoning = str(data.get("reasoning", "")).strip()
if intent not in {"Support", "Sales", "Spam", "General"}:
intent = "General"
confidence = max(0.0, min(1.0, confidence))
return {"intent": intent, "confidence": confidence, "reasoning": reasoning}
async def generate_reply(
self,
*,
subject: str,
from_email: str,
body: str,
tone: str,
context: List[ContextMessage],
) -> Dict[str, str]:
if not self._client:
return {"reply_subject": f"Re: {subject}", "reply_body": f"This is a dummy reply. You asked for a {tone} tone.\nNo valid OpenAI API key was provided."}
system = "You are a professional email assistant. Write polite, helpful, concise replies."
tone_hint = {
"formal": "Use a formal, business-appropriate tone.",
"casual": "Use a friendly, casual (but still professional) tone.",
"neutral": "Use a neutral professional tone.",
}.get(tone, "Use a neutral professional tone.")
messages: List[Dict[str, str]] = [{"role": "system", "content": system}]
if context:
context_text = "\n\n".join(f"{m.role.upper()}: {m.content}" for m in context)
messages.append({"role": "user", "content": f"Conversation context (most recent last):\n{context_text}"})
messages.append(
{
"role": "user",
"content": (
f"Email from: {from_email}\n"
f"Subject: {subject}\n\n"
f"Email content:\n{body}\n\n"
f"Desired tone: {tone} ({tone_hint})\n\n"
"Write a ready-to-send reply.\n"
"Output rules:\n"
"- First line: reply subject\n"
"- Blank line\n"
"- Then: reply body\n"
),
}
)
def _call() -> str:
if self._responses_supported():
resp = self._client.responses.create(model=self.model, input=messages)
return str(getattr(resp, "output_text", "") or "")
resp = self._client.chat.completions.create(model=self.model, messages=messages)
return str(resp.choices[0].message.content or "")
text = (await self._call_with_retries(_call, attempts=3)).strip()
if not text:
raise RuntimeError("Model returned empty reply.")
# Parse: first non-empty line as subject, rest as body.
lines = text.splitlines()
subject_line: Optional[str] = None
body_lines: List[str] = []
for i, ln in enumerate(lines):
if subject_line is None and ln.strip():
subject_line = ln.strip()
body_lines = lines[i + 1 :]
break
reply_subject = (subject_line or f"Re: {subject}").strip()
reply_body = "\n".join(body_lines).strip()
if not reply_body:
# If the model didn't follow formatting, use full text as body.
reply_subject = f"Re: {subject}".strip()
reply_body = text
return {"reply_subject": reply_subject, "reply_body": reply_body}
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