AI问诊系统开发架构解析：大模型 + 医疗知识库如何落地

用户层（小程序 / App / H5）
        ↓
问诊对话服务（Chat Service）
        ↓
AI能力层
   ├─ 大模型推理（LLM）
   ├─ 医疗知识库（RAG）
   ├─ 症状识别NLP
   ├─ 分诊规则引擎
        ↓
业务系统层
   ├─ 医生排班
   ├─ 挂号系统
   ├─ 电子病历
   ├─ 处方系统
   ├─ 支付系统

问题 → 向量检索 → 找到医学资料 → 拼接Prompt → 再交给大模型生成

from sentence_transformers import SentenceTransformer
import faiss
import numpy as np

model = SentenceTransformer("moka-ai/m3e-base")

docs = [
    "发烧超过38.5度持续三天建议就医",
    "胸闷胸痛可能与心血管疾病有关",
    "儿童咳嗽超过一周需排查肺炎"
]

embeddings = model.encode(docs)

index = faiss.IndexFlatL2(768)
index.add(np.array(embeddings).astype("float32"))

def search_knowledge(query):
    q_emb = model.encode([query])
    D, I = index.search(np.array(q_emb).astype("float32"), 3)
    return [docs[i] for i in I[0]]

def build_prompt(question, knowledge):
    context = "\n".join(knowledge)
    return f"""
你是一名专业医生助理，只能依据以下医学资料回答：

资料：
{context}

问题：
{question}

请给出安全、保守、医学合规的建议。
"""

import redis
import json

r = redis.Redis()

def save_session(uid, msg):
    key = f"chat:{uid}"
    history = r.get(key)
    history = json.loads(history) if history else []
    history.append(msg)
    r.set(key, json.dumps(history), ex=3600)

import re

def extract_symptoms(text):
    rules = {
        "发烧": r"发烧|高烧",
        "咳嗽": r"咳嗽|咳痰",
        "胸痛": r"胸痛|胸闷"
    }

    result = []
    for k, pattern in rules.items():
        if re.search(pattern, text):
            result.append(k)

    return result

["发烧", "咳嗽"]

TRIAGE_RULES = {
    ("发烧", "咳嗽"): "呼吸内科",
    ("胸痛",): "心内科",
}

def triage(symptoms):
    for rule, dept in TRIAGE_RULES.items():
        if all(s in symptoms for s in rule):
            return dept
    return "全科"

呼吸内科

from openai import OpenAI

client = OpenAI()

def ask_llm(prompt):
    res = client.chat.completions.create(
        model="gpt-4o-mini",
        messages=[{"role": "user", "content": prompt}],
        temperature=0.2
    )
    return res.choices[0].message.content

def chat(uid, question):

    knowledge = search_knowledge(question)

    prompt = build_prompt(question, knowledge)

    answer = ask_llm(prompt)

    symptoms = extract_symptoms(question)

    dept = triage(symptoms)

    return {
        "answer": answer,
        "department": dept
    }

AI建议：多喝水，观察体温变化，如持续发烧建议就医
推荐科室：呼吸内科