mirror of
https://github.com/csunny/DB-GPT.git
synced 2025-07-29 14:57:35 +00:00
fix:web knowledge upload bug
This commit is contained in:
aries_ckt
commit
c219c7776e
@ -5,7 +5,6 @@ from pilot.scene.base import ChatScene
|
||||
from pilot.common.sql_database import Database
|
||||
from pilot.configs.config import Config
|
||||
from pilot.scene.chat_db.auto_execute.prompt import prompt
|
||||
from pilot.base_modules.agent.commands.command_mange import ApiCall
|
||||
|
||||
CFG = Config()
|
||||
|
||||
@ -38,7 +37,6 @@ class ChatWithDbAutoExecute(BaseChat):
|
||||
|
||||
self.database = CFG.LOCAL_DB_MANAGE.get_connect(self.db_name)
|
||||
self.top_k: int = 200
|
||||
self.api_call = ApiCall(display_registry=CFG.command_disply)
|
||||
|
||||
def generate_input_values(self):
|
||||
"""
|
||||
@ -71,12 +69,6 @@ class ChatWithDbAutoExecute(BaseChat):
|
||||
}
|
||||
return input_values
|
||||
|
||||
def stream_plugin_call(self, text):
|
||||
text = text.replace("\n", " ")
|
||||
print(f"stream_plugin_call:{text}")
|
||||
return self.api_call.run_display_sql(text, self.database.run_to_df)
|
||||
|
||||
#
|
||||
# def do_action(self, prompt_response):
|
||||
# print(f"do_action:{prompt_response}")
|
||||
# return self.database.run(prompt_response.sql)
|
||||
def do_action(self, prompt_response):
|
||||
print(f"do_action:{prompt_response}")
|
||||
return self.database.run(prompt_response.sql)
|
||||
|
||||
@ -8,51 +8,24 @@ from pilot.scene.chat_db.auto_execute.example import sql_data_example
|
||||
|
||||
CFG = Config()
|
||||
|
||||
PROMPT_SCENE_DEFINE = "You are a SQL expert. "
|
||||
|
||||
_PROMPT_SCENE_DEFINE_EN = "You are a database expert. "
|
||||
_PROMPT_SCENE_DEFINE_ZH = "你是一个数据库专家. "
|
||||
|
||||
_DEFAULT_TEMPLATE_EN = """
|
||||
_DEFAULT_TEMPLATE = """
|
||||
Given an input question, create a syntactically correct {dialect} sql.
|
||||
Table structure information:
|
||||
{table_info}
|
||||
Constraint:
|
||||
1. You can only use the table provided in the table structure information to generate sql. If you cannot generate sql based on the provided table structure, please say: "The table structure information provided is not enough to generate sql query." It is prohibited to fabricate information at will.
|
||||
2. Do not query columns that do not exist. Pay attention to which column is in which table.
|
||||
3. Replace the corresponding sql into the sql field in the returned result
|
||||
4. Unless the user specifies in the question a specific number of examples he wishes to obtain, always limit the query to a maximum of {top_k} results.
|
||||
5. Please output the Sql content in the following format to execute the corresponding SQL to display the data:<api-call><name>response_table</name><args><sql>SQL Query to run</sql></args></api-call>
|
||||
Please make sure to respond as following format:
|
||||
thoughts summary to say to user.<api-call><name>response_table</name><args><sql>SQL Query to run</sql></args></api-call>
|
||||
|
||||
|
||||
Unless the user specifies in his question a specific number of examples he wishes to obtain, always limit your query to at most {top_k} results.
|
||||
Use as few tables as possible when querying.
|
||||
Only use the following tables schema to generate sql:
|
||||
{table_info}
|
||||
Be careful to not query for columns that do not exist. Also, pay attention to which column is in which table.
|
||||
|
||||
Question: {input}
|
||||
|
||||
Respond in JSON format as following format:
|
||||
{response}
|
||||
Ensure the response is correct json and can be parsed by Python json.loads
|
||||
"""
|
||||
|
||||
_DEFAULT_TEMPLATE_ZH = """
|
||||
给定一个输入问题，创建一个语法正确的 {dialect} sql。
|
||||
已知表结构信息:
|
||||
{table_info}
|
||||
|
||||
约束:
|
||||
1. 只能使用表结构信息中提供的表来生成 sql，如果无法根据提供的表结构中生成 sql ，请说：“提供的表结构信息不足以生成 sql 查询。” 禁止随意捏造信息。
|
||||
2. 不要查询不存在的列，注意哪一列位于哪张表中。
|
||||
3.将对应的sql替换到返回结果中的sql字段中
|
||||
4.除非用户在问题中指定了他希望获得的具体示例数量，否则始终将查询限制为最多 {top_k} 个结果。
|
||||
|
||||
请务必按照以下格式回复：
|
||||
对用户说的想法摘要。<api-call><name>response_table</name><args><sql>要运行的 SQL</sql></args></api-call>
|
||||
|
||||
问题:{input}
|
||||
"""
|
||||
|
||||
_DEFAULT_TEMPLATE = (
|
||||
_DEFAULT_TEMPLATE_EN if CFG.LANGUAGE == "en" else _DEFAULT_TEMPLATE_ZH
|
||||
)
|
||||
|
||||
PROMPT_SCENE_DEFINE = (
|
||||
_PROMPT_SCENE_DEFINE_EN if CFG.LANGUAGE == "en" else _PROMPT_SCENE_DEFINE_ZH
|
||||
)
|
||||
|
||||
RESPONSE_FORMAT_SIMPLE = {
|
||||
"thoughts": "thoughts summary to say to user",
|
||||
"sql": "SQL Query to run",
|
||||
@ -60,7 +33,7 @@ RESPONSE_FORMAT_SIMPLE = {
|
||||
|
||||
PROMPT_SEP = SeparatorStyle.SINGLE.value
|
||||
|
||||
PROMPT_NEED_NEED_STREAM_OUT = True
|
||||
PROMPT_NEED_NEED_STREAM_OUT = False
|
||||
|
||||
# Temperature is a configuration hyperparameter that controls the randomness of language model output.
|
||||
# A high temperature produces more unpredictable and creative results, while a low temperature produces more common and conservative output.
|
||||
@ -70,7 +43,7 @@ PROMPT_TEMPERATURE = 0.5
|
||||
prompt = PromptTemplate(
|
||||
template_scene=ChatScene.ChatWithDbExecute.value(),
|
||||
input_variables=["input", "table_info", "dialect", "top_k", "response"],
|
||||
# response_format=json.dumps(RESPONSE_FORMAT_SIMPLE, ensure_ascii=False, indent=4),
|
||||
response_format=json.dumps(RESPONSE_FORMAT_SIMPLE, ensure_ascii=False, indent=4),
|
||||
template_define=PROMPT_SCENE_DEFINE,
|
||||
template=_DEFAULT_TEMPLATE,
|
||||
stream_out=PROMPT_NEED_NEED_STREAM_OUT,
|
||||
|
||||
Loading…
Reference in New Issue
Block a user