def internal_push_nlu(robot_code, version):
"""通知主进程nlu模型训练完毕，进行版本的切换
Args:
robot_code (str): 机器人唯一标识，ID
version (str): 机器人版本号
"""
url = "http://localhost:{}/xiaoyu/multi/nlu".format(SERVE_PORT)
data = {"robot_id": robot_code, "method": "checkout", "version": version}
post_rpc(url, data)
def faq_delete(robot_id, data):
"""删除faq引擎中的语料数据
Args:
robot_id (str): 机器人的唯一标识。
data (dict): 请求参数，里面包含需要删除的语料id
Examples:
>>> robot_id = "doctest_id"
>>> qids = {"faq_ids": ["id1"]}
>>> faq_delete(robot_id, qids)
{'status_code': 0}
"""
# TODO 这里后续要考虑如何删除similar questions
url = "http://{}/robot_manager/single/delete_items".format(FAQ_ENGINE_ADDR)
q_ids = data["faq_ids"]
if isinstance(q_ids, str):
q_ids = [q_ids]
# TODO 由于存储问题时默认会存储相似问题，这里需要把原问题关联的相似问题删除
# 这里处理方式有点打补丁的意思
all_ids = []
for qid in q_ids:
all_ids.extend(
[_build_sim_id(qid, i) for i in range(MAX_SIMILAR_QUESTIONS)])
q_ids.extend(all_ids)
request_data = {"q_ids": q_ids, "robot_code": robot_id}
return post_rpc(url, request_data)
def test_push():
data = {"robot_id": robot_code, "version": version}
response = post_rpc("http://127.0.0.1:{}/xiaoyu/push".format(serve_port),
data)
print(response)
def test_method_add():
requests_data = {
"robot_id": TEST_ROBOT_ID,
"method": "add",
"data": TEST_FAQ_DATA
}
response_data = post_rpc(URL, requests_data)
assert response_data["code"] == 200
def test_method_update():
faq_data = TEST_FAQ_DATA.copy()
requests_data = {
"robot_id": TEST_ROBOT_ID,
"method": "update",
"data": faq_data
}
response_data = post_rpc(URL, requests_data)
assert response_data["code"] == 200
def test_method_ask():
request_data = {
"robot_id": TEST_ROBOT_ID,
"method": "ask",
"data": {
"question": "苹果手机多少钱"
}
}
response_data = post_rpc(URL, request_data)
assert response_data["code"] == 200
def test_method_delete():
request_data = {
"robot_id": TEST_ROBOT_ID,
"method": "delete",
"data": {
"faq_ids": [TEST_FAQ_DATA[0]["faq_id"]]
}
}
response_data = post_rpc(URL, request_data)
assert response_data["code"] == 200
request_data = {
"robot_id": TEST_ROBOT_ID,
"method": "ask",
"data": {
"question": "Apple手机多少钱"
}
}
response_data = post_rpc(URL, request_data)
assert response_data["data"]["faq_id"] == UNK
def faq_ask(robot_id,
question,
faq_params={
"recommend_num": 5,
"perspective": FAQ_DEFAULT_PERSPECTIVE
}):
"""向faq引擎提问
Args:
robot_id (str): 机器人的唯一标识
question (str): 向机器人提问的问题
raw (bool, optional): 返回faq引擎的原始数据，还是返回解析后的答案数据。Default is False
faq_params (int, optional): faq相关参数
Examples:
>>> robot_id = "doctest_id"
>>> question = "你好"
>>> answer = faq_ask(robot_id, question)
>>> isinstance(answer, dict)
True
"""
url = "http://{}/robot_manager/single/ask".format(FAQ_ENGINE_ADDR)
request_data = {
"robot_code": robot_id,
"question": question,
}
request_data.update(faq_params)
response_data = post_rpc(url, request_data)["data"]
if response_data["answer_type"] == -1:
answer_data = {
"faq_id": UNK,
"title": "",
"similar_questions": [],
"related_quesions": [],
"key_words": [],
"effective_time": "",
"tags": [],
"answer": response_data["answer"],
"catagory": "",
}
else:
answer_data = json.loads(response_data["answer"])
# 相关问题加推荐问题的总数为指定数量
related_questions = answer_data.get("related_questions", [])
rec_num = faq_params["recommend_num"] - len(related_questions)
answer_data["recommendQuestions"] = response_data.get(
"recommendQuestions", [])[rec_num:]
answer_data["confidence"] = response_data.get("confidence", 0)
answer_data["hotQuestions"] = response_data.get("hotQuestions", [])
answer_data["recommendScores"] = response_data.get("recommendScores", [])
answer_data["reply_mode"] = response_data.get("reply_mode", "1")
answer_data["sms_content"] = response_data.get("sms_content", "")
return answer_data
def faq_push(robot_id):
"""
复制faq节点index
"""
if not MASTER_ADDR:
return {'status_code': 0}
target_robot_id = get_faq_master_robot_id(robot_id)
url = "http://{}/robot_manager/single/copy".format(FAQ_ENGINE_ADDR)
request_data = {
"robot_code": robot_id,
"target_robot_code": target_robot_id
}
return post_rpc(url, request_data)
def train_nlu():
with open(os.path.join(cwd, "assets/nlu_training_data.json")) as f:
nlu_data = json.load(f)
# 训练nlu模型
data = {
"robot_id": robot_code,
"method": "train",
"version": version,
"data": nlu_data
}
response = post_rpc(
"http://127.0.0.1:{}/xiaoyu/multi/nlu".format(serve_port), data)
print(response)
def faq_delete_all(robot_id):
"""删除特定机器人的所有语料
Args:
robot_id (str): 机器人的唯一标识
Examples:
>>> robot_id = "doctest_id"
>>> faq_delete_all(robot_id)
{'status_code': 0}
"""
url = "http://{}/robot_manager/single/delete_robot".format(FAQ_ENGINE_ADDR)
request_data = {"robot_code": robot_id}
return post_rpc(url, request_data)
def train_dialogue():
with open(os.path.join(cwd, "assets/dialogue_graph.json")) as f:
graph_data = json.load(f)
# 上传对话流程配置
data = {
"robot_id": robot_code,
"method": "train",
"version": version,
"data": graph_data
}
response = post_rpc(
"http://127.0.0.1:{}/xiaoyu/multi/graph".format(serve_port), data)
print(response)
def test_chat():
session_id = "test_chat_api"
data = {
"robotId": robot_code,
"userCode": "user1",
"sessionId": session_id,
"params": {
"归属地": "广州"
},
"userSays": "你好"
}
response = post_rpc(
"http://127.0.0.1:{}/api/v1/session/reply".format(serve_port), data)
print(response)
data = {
"robotId": robot_code,
"userCode": "user1",
"sessionId": session_id,
"userSays": "今天广州天气怎么样"
}
response = post_rpc(
"http://127.0.0.1:{}/api/v1/session/reply".format(serve_port), data)
print(response)
def faq_update(robot_id, data):
"""添加或者更新faq语料数据
Args:
robot_id (str): 机器人的唯一标识。
data (list): 需要存储的问题数据。
Return:
response (dict): faq服务器返回的信息
Examples:
>>> robot_id = "doctest_id"
>>> data = [
... {
... "faq_id": "id1",
... "title": "苹果手机多少钱",
... "similar_questions": [
... "Apple手机多少钱",
... "iphone多少钱"
... ],
... "related_quesions": [
... "ipad多少钱",
... "iwatch多少钱"
... ],
... "key_words": [
... "苹果",
... "Apple",
... "iphone"
... ],
... "effective_time": "2020-12-31",
... "tags": [
... "手机",
... "电子产品"
... ],
... "answer": "5400元",
... "catagory": "电子产品价格"
... }
... ]
>>> faq_update(robot_id, data)
{'status_code': 0}
"""
url = "http://{}/robot_manager/single/add_items".format(FAQ_ENGINE_ADDR)
documents = []
for item in data:
perspective = item.get("perspective", "") # 当视角参数为空字符串或者没有此参数时，设置为默认视角
doc = {
"answer": json.dumps(item, ensure_ascii=False),
"perspective":
perspective if perspective else FAQ_DEFAULT_PERSPECTIVE,
"question": item["title"],
"id": item["faq_id"],
"answer_id": item["faq_id"]
}
documents.append(doc)
for i, sim_q in enumerate(item.get("similar_questions", [])):
if i >= MAX_SIMILAR_QUESTIONS:
break
curdoc = copy.deepcopy(doc)
curdoc["question"] = sim_q
curdoc["id"] = _build_sim_id(curdoc["id"], i)
documents.append(curdoc)
request_data = {"documents": documents, "robot_code": robot_id}
return post_rpc(url, request_data)
def test_system():
# 删除机器人
data = {"robot_id": robot_code}
response = post_rpc("http://127.0.0.1:{}/xiaoyu/delete".format(serve_port),
data)
print(response)
serve_port = global_config["serve_port"]
cwd = os.path.abspath(os.path.dirname(__file__))
params = json.load(open(os.path.join(cwd, "params.json")))
session_id = "service_interact"
says = input("用户说：")
data = {
"robotId": params["robot_code"],
"userCode": "user1",
"params": params["params"],
"userSays": says,
"sessionId": session_id
}
response = post_rpc(
"http://127.0.0.1:{}/api/v1/session/reply".format(serve_port), data)
print(response)
while True:
says = input("用户说：")
data = {
"robotId": params["robot_code"],
"userCode": "user1",
"sessionId": session_id,
"userSays": says
}
response = post_rpc(
"http://127.0.0.1:{}/api/v1/session/reply".format(serve_port), data)
print(response)