|
|
|
|
|
"""MedGenesis – OpenAI async helpers (summary + QA). |
|
|
|
|
|
Changes vs. legacy version |
|
|
~~~~~~~~~~~~~~~~~~~~~~~~~~ |
|
|
* Centralised **`_client()`** getter with singleton cache (avoids TLS overhead). |
|
|
* Exponential‑back‑off retry (2×, 4×) for transient 5xx. |
|
|
* Supports model override (`model="gpt-4o-mini"`, etc.). |
|
|
* Allows temperature & max_tokens tuning via kwargs. |
|
|
* Returns *str* (content) directly; orchestrator wraps if needed. |
|
|
""" |
|
|
from __future__ import annotations |
|
|
|
|
|
import os, asyncio, functools, time |
|
|
from typing import Any, Dict |
|
|
|
|
|
import openai |
|
|
|
|
|
openai.api_key = os.getenv("OPENAI_API_KEY") |
|
|
if not openai.api_key: |
|
|
raise RuntimeError("OPENAI_API_KEY not set in environment") |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
@functools.lru_cache(maxsize=1) |
|
|
def _client() -> openai.AsyncOpenAI: |
|
|
return openai.AsyncOpenAI(api_key=openai.api_key) |
|
|
|
|
|
|
|
|
async def _chat(messages: list[dict[str, str]], *, model: str, max_tokens: int, temperature: float = 0.2, retries: int = 3) -> str: |
|
|
delay = 2 |
|
|
for _ in range(retries): |
|
|
try: |
|
|
resp = await _client().chat.completions.create( |
|
|
model=model, |
|
|
messages=messages, |
|
|
max_tokens=max_tokens, |
|
|
temperature=temperature, |
|
|
) |
|
|
return resp.choices[0].message.content.strip() |
|
|
except openai.OpenAIError as e: |
|
|
if retries <= 1: |
|
|
raise |
|
|
await asyncio.sleep(delay) |
|
|
delay *= 2 |
|
|
|
|
|
return "[OpenAI request failed]" |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
async def ai_summarize(text: str, *, prompt: str | None = None, model: str = "gpt-4o", max_tokens: int = 350) -> str: |
|
|
"""LLM summariser tuned for biomedical search blobs.""" |
|
|
if not prompt: |
|
|
prompt = ( |
|
|
"Summarize the following biomedical search results. Highlight key findings, " |
|
|
"significant genes/drugs/trials, and suggest future research directions." |
|
|
) |
|
|
system = {"role": "system", "content": "You are an expert biomedical research assistant."} |
|
|
user = {"role": "user", "content": f"{prompt}\n\n{text}"} |
|
|
return await _chat([system, user], model=model, max_tokens=max_tokens) |
|
|
|
|
|
|
|
|
async def ai_qa(question: str, *, context: str = "", model: str = "gpt-4o", max_tokens: int = 350) -> str: |
|
|
"""One‑shot QA against provided *context*.""" |
|
|
system = {"role": "system", "content": "You are an advanced biomedical research agent."} |
|
|
user = {"role": "user", "content": f"Answer the question using the given context.\n\nQuestion: {question}\nContext: {context}"} |
|
|
return await _chat([system, user], model=model, max_tokens=max_tokens) |
|
|
|
