| |
| """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) |
|
|
