提示词优化,迟到，请假
This commit is contained in:
yujj128
@@ -26,7 +26,7 @@ class OpenAICompatibleLLM(VannaBase):
|
||||
VannaBase.__init__(self, config=config_file)
|
||||
# default parameters - can be overrided using config
|
||||
self.temperature = 0.6
|
||||
self.max_tokens = 5000
|
||||
self.max_tokens = 10000
|
||||
|
||||
if "temperature" in config_file:
|
||||
self.temperature = config_file["temperature"]
|
||||
@@ -125,17 +125,21 @@ class OpenAICompatibleLLM(VannaBase):
|
||||
|
||||
def submit_prompt(self, prompt, **kwargs) -> str:
|
||||
if prompt is None:
|
||||
print("test1")
|
||||
raise Exception("Prompt is None")
|
||||
|
||||
if len(prompt) == 0:
|
||||
print("test2")
|
||||
raise Exception("Prompt is empty")
|
||||
print(prompt)
|
||||
|
||||
num_tokens = 0
|
||||
for message in prompt:
|
||||
num_tokens += len(message["content"]) / 4
|
||||
print("test3 {0}".format(num_tokens))
|
||||
|
||||
if kwargs.get("model", None) is not None:
|
||||
print("test4")
|
||||
model = kwargs.get("model", None)
|
||||
print(
|
||||
f"Using model {model} for {num_tokens} tokens (approx)"
|
||||
@@ -148,6 +152,7 @@ class OpenAICompatibleLLM(VannaBase):
|
||||
temperature=self.temperature,
|
||||
)
|
||||
elif kwargs.get("engine", None) is not None:
|
||||
print("test5")
|
||||
engine = kwargs.get("engine", None)
|
||||
print(
|
||||
f"Using model {engine} for {num_tokens} tokens (approx)"
|
||||
@@ -160,6 +165,7 @@ class OpenAICompatibleLLM(VannaBase):
|
||||
temperature=self.temperature,
|
||||
)
|
||||
elif self.config is not None and "engine" in self.config:
|
||||
print("test6")
|
||||
print(
|
||||
f"Using engine {self.config['engine']} for {num_tokens} tokens (approx)"
|
||||
)
|
||||
@@ -171,10 +177,11 @@ class OpenAICompatibleLLM(VannaBase):
|
||||
temperature=self.temperature,
|
||||
)
|
||||
elif self.config is not None and "model" in self.config:
|
||||
print("test7")
|
||||
print(
|
||||
f"Using model {self.config['model']} for {num_tokens} tokens (approx)"
|
||||
)
|
||||
print(self.config)
|
||||
print("config is ",self.config)
|
||||
response = self.client.chat.completions.create(
|
||||
model=self.config["model"],
|
||||
messages=prompt,
|
||||
@@ -194,6 +201,7 @@ class OpenAICompatibleLLM(VannaBase):
|
||||
# json=data
|
||||
# )
|
||||
else:
|
||||
print("test8")
|
||||
if num_tokens > 3500:
|
||||
model = "kimi"
|
||||
else:
|
||||
@@ -271,15 +279,26 @@ class OpenAICompatibleLLM(VannaBase):
|
||||
traceback.print_exc()
|
||||
raise e
|
||||
|
||||
def run_sql_2(self,sql):
|
||||
try:
|
||||
return self.run_sql(sql)
|
||||
except Exception as e:
|
||||
logger.error("run_sql failed {0}".format(sql))
|
||||
raise e
|
||||
|
||||
def generate_rewritten_question(self, last_question: str, new_question: str, **kwargs) -> str:
|
||||
logger.info(f"generate_rewritten_question---------------{new_question}")
|
||||
if last_question is None:
|
||||
return new_question
|
||||
|
||||
print("last question {0}".format(last_question))
|
||||
print("new question {0}".format(new_question))
|
||||
prompt = [
|
||||
self.system_message(
|
||||
"Your goal is to combine a sequence of questions into a singular question if they are related. If the second question does not relate to the first question and is fully self-contained, return the second question. Return just the new combined question with no additional explanations. The question should theoretically be answerable with a single SQL statement."),
|
||||
self.user_message(new_question),
|
||||
"你的目标是将一系列相关问题合并成一个单一的问题。"
|
||||
"合并准则一、如果第二个问题与第一个问题无关且本身是完整独立的，则直接返回第二个问题。"
|
||||
"合并准则二、如果第二个问题域第一个问题相关，且要基于第一个问题的前提，请合并两个问题为一个问题，只需返回合并后的新问题，不要添加任何额外解释。"
|
||||
"合并准则三、理论上，合并后的问题应该能够通过单个SQL语句来回答"),
|
||||
self.user_message("First question: " + last_question + "\nSecond question: " + new_question),
|
||||
]
|
||||
|
||||
return self.submit_prompt(prompt=prompt, **kwargs)
|
||||
@@ -312,6 +331,11 @@ class CustomQdrant_VectorStore(Qdrant_VectorStore):
|
||||
"model": self.embedding_model_name,
|
||||
"sentences": [data],
|
||||
}
|
||||
# request_body = {
|
||||
# "model": self.embedding_model_name,
|
||||
# "encoding_format": "float",
|
||||
# "input": [data],
|
||||
# }
|
||||
request_body.update(kwargs)
|
||||
|
||||
response = requests.post(
|
||||
@@ -325,7 +349,9 @@ class CustomQdrant_VectorStore(Qdrant_VectorStore):
|
||||
)
|
||||
result = response.json()
|
||||
embeddings = result['embeddings']
|
||||
# embeddings = result['data'][0]['embedding']
|
||||
return embeddings[0]
|
||||
# return embeddings
|
||||
|
||||
class CustomVanna(CustomQdrant_VectorStore, OpenAICompatibleLLM):
|
||||
def __init__(self, llm_config=None, vector_store_config=None):
|
||||
@@ -412,6 +438,64 @@ class TTLCacheWrapper:
|
||||
return False
|
||||
return self.get(id=id, field=field) is not None
|
||||
|
||||
def items(self):
|
||||
"""遍历所有未过期的缓存键值对"""
|
||||
current_time = time.time()
|
||||
items = []
|
||||
|
||||
for (id, field), expiry in self._expiry_times.items():
|
||||
# 检查是否过期
|
||||
if current_time <= expiry:
|
||||
value = self.get(id=id, field=field)
|
||||
if value is not None:
|
||||
items.append({
|
||||
'id': id,
|
||||
'field': field,
|
||||
'value': value,
|
||||
'expires_at': expiry,
|
||||
'time_left': expiry - current_time
|
||||
})
|
||||
|
||||
return items
|
||||
|
||||
def get_latest_by_id(self, id: str, limit: int = 1, field_filter: str = None):
|
||||
"""
|
||||
获取指定ID下时间最近的缓存项
|
||||
|
||||
Args:
|
||||
id: 要查询的ID
|
||||
limit: 返回最近几条记录，默认1条
|
||||
field_filter: 可选的字段过滤，如只获取特定字段
|
||||
|
||||
Returns:
|
||||
按时间倒序排列的缓存项列表
|
||||
"""
|
||||
current_time = time.time()
|
||||
matched_items = []
|
||||
|
||||
# 找出该ID下所有未过期的缓存项
|
||||
for (cache_id, field), expiry in self._expiry_times.items():
|
||||
if cache_id == id and current_time <= expiry:
|
||||
# 字段过滤
|
||||
if field_filter and field != field_filter:
|
||||
continue
|
||||
|
||||
value = self.get(id=id, field=field)
|
||||
if value is not None:
|
||||
matched_items.append({
|
||||
'id': id,
|
||||
'field': field,
|
||||
'value': value,
|
||||
'expires_at': expiry,
|
||||
'created_time': expiry - self.ttl, # 估算创建时间
|
||||
'time_left': expiry - current_time
|
||||
})
|
||||
|
||||
# 按过期时间倒序排列（最近创建的排在前面）
|
||||
matched_items.sort(key=lambda x: x['expires_at'], reverse=True)
|
||||
|
||||
return matched_items[:limit]
|
||||
|
||||
# 代理其他方法到原始cache
|
||||
def __getattr__(self, name):
|
||||
return getattr(self.cache, name)
|
||||
Reference in New Issue
Block a user