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(improvement)(text2sql) add text2sql feature that only call LLM once, and add correponding configs and docs. (#102)

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Co-authored-by: shaoweigong <shaoweigong@tencent.com>
This commit is contained in:
codescracker
2023-09-20 10:22:20 +08:00
committed by GitHub
parent 6a5a95e543
commit c8ff37e304
9 changed files with 173 additions and 55 deletions
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1
chat/core/src/main/python/run_config.py
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@@ -15,6 +15,7 @@ CHROMA_DB_PERSIST_DIR = 'chm_db'
PRESET_QUERY_COLLECTION_NAME = "preset_query_collection"
TEXT2DSL_COLLECTION_NAME = "text2dsl_collection"
TEXT2DSL_FEW_SHOTS_EXAMPLE_NUM = 15
TEXT2DSL_IS_SHORTCUT = False
CHROMA_DB_PERSIST_PATH = os.path.join(PROJECT_DIR_PATH, CHROMA_DB_PERSIST_DIR)

32
chat/core/src/main/python/sql/constructor.py
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@@ -22,10 +22,8 @@ from util.text2vec import Text2VecEmbeddingFunction, hg_embedding
from util.chromadb_instance import client as chromadb_client, empty_chroma_collection_2
from run_config import TEXT2DSL_COLLECTION_NAME, TEXT2DSL_FEW_SHOTS_EXAMPLE_NUM
def reload_sql_example_collection(vectorstore:Chroma,
sql_examplars:List[Mapping[str, str]],
schema_linking_example_selector:SemanticSimilarityExampleSelector,
sql_example_selector:SemanticSimilarityExampleSelector,
example_nums:int
):
@@ -35,20 +33,16 @@ def reload_sql_example_collection(vectorstore:Chroma,
print("emptied sql_examples_collection size:", vectorstore._collection.count())
schema_linking_example_selector = SemanticSimilarityExampleSelector(vectorstore=sql_examples_vectorstore, k=example_nums,
input_keys=["question"],
example_keys=["table_name", "fields_list", "prior_schema_links", "question", "analysis", "schema_links"])
sql_example_selector = SemanticSimilarityExampleSelector(vectorstore=sql_examples_vectorstore, k=example_nums,
input_keys=["question"],
example_keys=["question", "current_date", "table_name", "schema_links", "sql"])
input_keys=["question"],
example_keys=["table_name", "fields_list", "prior_schema_links", "question", "analysis", "schema_links", "current_date", "sql"])
for example in sql_examplars:
schema_linking_example_selector.add_example(example)
sql_example_selector.add_example(example)
print("reloaded sql_examples_collection size:", vectorstore._collection.count())
return vectorstore, schema_linking_example_selector, sql_example_selector
return vectorstore, sql_example_selector
sql_examples_vectorstore = Chroma(collection_name=TEXT2DSL_COLLECTION_NAME,
@@ -57,22 +51,14 @@ sql_examples_vectorstore = Chroma(collection_name=TEXT2DSL_COLLECTION_NAME,
example_nums = TEXT2DSL_FEW_SHOTS_EXAMPLE_NUM
schema_linking_example_selector = SemanticSimilarityExampleSelector(vectorstore=sql_examples_vectorstore, k=example_nums,
input_keys=["question"],
example_keys=["table_name", "fields_list", "prior_schema_links", "question", "analysis", "schema_links"])
sql_example_selector = SemanticSimilarityExampleSelector(vectorstore=sql_examples_vectorstore, k=example_nums,
input_keys=["question"],
example_keys=["question", "current_date", "table_name", "schema_links", "sql"])
input_keys=["question"],
example_keys=["table_name", "fields_list", "prior_schema_links", "question", "analysis", "schema_links", "current_date", "sql"])
if sql_examples_vectorstore._collection.count() > 0:
print("examples already in sql_vectorstore")
print("init sql_vectorstore size:", sql_examples_vectorstore._collection.count())
if sql_examples_vectorstore._collection.count() < len(sql_examplars):
print("sql_examplars size:", len(sql_examplars))
sql_examples_vectorstore, schema_linking_example_selector, sql_example_selector = reload_sql_example_collection(sql_examples_vectorstore, sql_examplars, schema_linking_example_selector, sql_example_selector, example_nums)
print("added sql_vectorstore size:", sql_examples_vectorstore._collection.count())
else:
sql_examples_vectorstore, schema_linking_example_selector, sql_example_selector = reload_sql_example_collection(sql_examples_vectorstore, sql_examplars, schema_linking_example_selector, sql_example_selector, example_nums)
print("added sql_vectorstore size:", sql_examples_vectorstore._collection.count())
print("sql_examplars size:", len(sql_examplars))
sql_examples_vectorstore, sql_example_selector = reload_sql_example_collection(sql_examples_vectorstore, sql_examplars, sql_example_selector, example_nums)
print("added sql_vectorstore size:", sql_examples_vectorstore._collection.count())

12
chat/core/src/main/python/sql/examples_reload_run.py
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@@ -8,24 +8,22 @@ import json
sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
sys.path.append(os.path.dirname(os.path.abspath(__file__)))
from run_config import TEXT2DSL_FEW_SHOTS_EXAMPLE_NUM
from run_config import TEXT2DSL_FEW_SHOTS_EXAMPLE_NUM, TEXT2DSL_IS_SHORTCUT
from few_shot_example.sql_exampler import examplars as sql_examplars
from run_config import LLMPARSER_HOST
from run_config import LLMPARSER_PORT
from run_config import LLMPARSER_HOST, LLMPARSER_PORT
def text2dsl_setting_update(llm_parser_host:str, llm_parser_port:str,
sql_examplars:List[Mapping[str, str]], example_nums:int):
sql_examplars:List[Mapping[str, str]], example_nums:int, is_shortcut:bool):
url = f"http://{llm_parser_host}:{llm_parser_port}/query2sql_setting_update/"
print("url: ", url)
payload = {"sqlExamplars":sql_examplars, "exampleNums":example_nums}
payload = {"sqlExamplars":sql_examplars, "exampleNums":example_nums, "isShortcut":is_shortcut}
headers = {'content-type': 'application/json'}
response = requests.post(url, data=json.dumps(payload), headers=headers)
print(response.text)
if __name__ == "__main__":
arguments = sys.argv
text2dsl_setting_update(LLMPARSER_HOST, LLMPARSER_PORT,
sql_examplars, TEXT2DSL_FEW_SHOTS_EXAMPLE_NUM)
sql_examplars, TEXT2DSL_FEW_SHOTS_EXAMPLE_NUM, TEXT2DSL_IS_SHORTCUT)

34
chat/core/src/main/python/sql/output_parser.py
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@@ -10,4 +10,36 @@ def schema_link_parse(schema_link_output):
print(e)
schema_link_output = None
return schema_link_output
return schema_link_output
def combo_schema_link_parse(schema_linking_sql_combo_output: str):
try:
schema_linking_sql_combo_output = schema_linking_sql_combo_output.strip()
pattern = r'Schema_links:(\[.*?\])'
schema_links_match = re.search(pattern, schema_linking_sql_combo_output)
if schema_links_match:
schema_links = schema_links_match.group(1)
else:
schema_links = None
except Exception as e:
print(e)
schema_links = None
return schema_links
def combo_sql_parse(schema_linking_sql_combo_output: str):
try:
schema_linking_sql_combo_output = schema_linking_sql_combo_output.strip()
pattern = r'SQL:(.*)'
sql_match = re.search(pattern, schema_linking_sql_combo_output)
if sql_match:
sql = sql_match.group(1)
else:
sql = None
except Exception as e:
print(e)
sql = None
return sql

35
chat/core/src/main/python/sql/prompt_maker.py
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@@ -73,3 +73,38 @@ def sql_exampler(user_query: str,
schema_links=schema_link_str)
return sql_example_prompt
def schema_linking_sql_combo_examplar(user_query: str,
domain_name: str,
data_date : str,
fields_list: List[str],
prior_schema_links: Mapping[str,str],
example_selector: SemanticSimilarityExampleSelector) -> str:
prior_schema_links_str = '['+ ','.join(["""'{}'->{}""".format(k,v) for k,v in prior_schema_links.items()]) + ']'
example_prompt_template = PromptTemplate(input_variables=["table_name", "fields_list", "prior_schema_links", "current_date", "question", "analysis", "schema_links", "sql"],
template="Table {table_name}, columns = {fields_list}, prior_schema_links = {prior_schema_links}\nCurrent_date:{current_date}\n问题:{question}\n分析:{analysis} 所以Schema_links是:\nSchema_links:{schema_links}\nSQL:{sql}")
instruction = "# 根据数据库的表结构,参考先验信息,找出为每个问题生成SQL查询语句的schema_links,再根据schema_links为每个问题生成SQL查询语句"
schema_linking_sql_combo_prompt = "Table {table_name}, columns = {fields_list}, prior_schema_links = {prior_schema_links}\nCurrent_date:{current_date}\n问题:{question}\n分析: 让我们一步一步地思考。"
schema_linking_sql_combo_example_prompt_template = FewShotPromptTemplate(
example_selector=example_selector,
example_prompt=example_prompt_template,
example_separator="\n\n",
prefix=instruction,
input_variables=["table_name", "fields_list", "prior_schema_links", "current_date", "question"],
suffix=schema_linking_sql_combo_prompt
)
schema_linking_sql_combo_example_prompt = schema_linking_sql_combo_example_prompt_template.format(table_name=domain_name,
fields_list=fields_list,
prior_schema_links=prior_schema_links_str,
current_date=data_date,
question=user_query)
return schema_linking_sql_combo_example_prompt

85
chat/core/src/main/python/sql/run.py
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@@ -7,32 +7,37 @@ import sys
sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
sys.path.append(os.path.dirname(os.path.abspath(__file__)))
from sql.prompt_maker import schema_linking_exampler, sql_exampler
from sql.constructor import schema_linking_example_selector, sql_example_selector,sql_examples_vectorstore, reload_sql_example_collection
from sql.output_parser import schema_link_parse
from sql.prompt_maker import schema_linking_exampler, sql_exampler, schema_linking_sql_combo_examplar
from sql.constructor import sql_examples_vectorstore, sql_example_selector, reload_sql_example_collection
from sql.output_parser import schema_link_parse, combo_schema_link_parse, combo_sql_parse
from util.llm_instance import llm
from run_config import TEXT2DSL_IS_SHORTCUT
class Text2DSLAgent(object):
def __init__(self):
self.schema_linking_exampler = schema_linking_exampler
self.sql_exampler = sql_exampler
self.schema_linking_sql_combo_exampler = schema_linking_sql_combo_examplar
self.sql_examples_vectorstore = sql_examples_vectorstore
self.schema_linking_example_selector = schema_linking_example_selector
self.sql_example_selector = sql_example_selector
self.schema_link_parse = schema_link_parse
self.combo_schema_link_parse = combo_schema_link_parse
self.combo_sql_parse = combo_sql_parse
self.llm = llm
def update_examples(self, sql_examplars, example_nums):
self.sql_examples_vectorstore, self.schema_linking_example_selector, self.sql_example_selector = reload_sql_example_collection(self.sql_examples_vectorstore,
sql_examplars,
self.schema_linking_example_selector,
self.sql_example_selector,
example_nums)
self.is_shortcut = TEXT2DSL_IS_SHORTCUT
def update_examples(self, sql_examples, example_nums, is_shortcut):
self.sql_examples_vectorstore, self.sql_example_selector = reload_sql_example_collection(self.sql_examples_vectorstore,
sql_examples,
self.sql_example_selector,
example_nums)
self.is_shortcut = is_shortcut
def query2sql(self, query_text: str,
schema : Union[dict, None] = None,
@@ -53,14 +58,14 @@ class Text2DSLAgent(object):
model_name = schema['modelName']
fields_list = schema['fieldNameList']
schema_linking_prompt = self.schema_linking_exampler(query_text, model_name, fields_list, prior_schema_links, self.schema_linking_example_selector)
schema_linking_prompt = self.schema_linking_exampler(query_text, model_name, fields_list, prior_schema_links, self.sql_example_selector)
print("schema_linking_prompt->", schema_linking_prompt)
schema_link_output = self.llm(schema_linking_prompt)
schema_link_str = self.schema_link_parse(schema_link_output)
sql_prompt = self.sql_exampler(query_text, model_name, schema_link_str, current_date, self.sql_example_selector)
print("sql_prompt->", sql_prompt)
sql_output = llm(sql_prompt)
sql_output = self.llm(sql_prompt)
resp = dict()
resp['query'] = query_text
@@ -69,7 +74,7 @@ class Text2DSLAgent(object):
resp['priorSchemaLinking'] = linking
resp['dataDate'] = current_date
resp['schemaLinkingOutput'] = schema_link_output
resp['analysisOutput'] = schema_link_output
resp['schemaLinkStr'] = schema_link_str
resp['sqlOutput'] = sql_output
@@ -78,5 +83,57 @@ class Text2DSLAgent(object):
return resp
def query2sqlcombo(self, query_text: str,
schema : Union[dict, None] = None,
current_date: str = None,
linking: Union[List[Mapping[str, str]], None] = None
):
print("query_text: ", query_text)
print("schema: ", schema)
print("current_date: ", current_date)
print("prior_schema_links: ", linking)
if linking is not None:
prior_schema_links = {item['fieldValue']:item['fieldName'] for item in linking}
else:
prior_schema_links = {}
model_name = schema['modelName']
fields_list = schema['fieldNameList']
schema_linking_sql_combo_prompt = self.schema_linking_sql_combo_exampler(query_text, model_name, current_date, fields_list,
prior_schema_links, self.sql_example_selector)
print("schema_linking_sql_combo_prompt->", schema_linking_sql_combo_prompt)
schema_linking_sql_combo_output = self.llm(schema_linking_sql_combo_prompt)
schema_linking_str = self.combo_schema_link_parse(schema_linking_sql_combo_output)
sql_str = self.combo_sql_parse(schema_linking_sql_combo_output)
resp = dict()
resp['query'] = query_text
resp['model'] = model_name
resp['fields'] = fields_list
resp['priorSchemaLinking'] = prior_schema_links
resp['dataDate'] = current_date
resp['analysisOutput'] = schema_linking_sql_combo_output
resp['schemaLinkStr'] = schema_linking_str
resp['sqlOutput'] = sql_str
print("resp: ", resp)
return resp
def query2sql_run(self, query_text: str,
schema : Union[dict, None] = None,
current_date: str = None,
linking: Union[List[Mapping[str, str]], None] = None):
if self.is_shortcut:
return self.query2sqlcombo(query_text, schema, current_date, linking)
else:
return self.query2sql(query_text, schema, current_date, linking)
text2sql_agent = Text2DSLAgent()

9
chat/core/src/main/python/supersonic_llmparser.py
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@@ -51,7 +51,7 @@ async def din_query2sql(query_body: Mapping[str, Any]):
else:
linking = query_body['linking']
resp = text2sql_agent.query2sql(query_text=query_text,
resp = text2sql_agent.query2sql_run(query_text=query_text,
schema=schema, current_date=current_date, linking=linking)
return resp
@@ -70,7 +70,12 @@ async def query2sql_setting_update(query_body: Mapping[str, Any]):
else:
example_nums = query_body['exampleNums']
text2sql_agent.update_examples(sql_examplars=sql_examplars, example_nums=example_nums)
if 'isShortcut' not in query_body:
raise HTTPException(status_code=400, detail="isShortcut is not in query_body")
else:
is_shortcut = query_body['isShortcut']
text2sql_agent.update_examples(sql_examples=sql_examplars, example_nums=example_nums, is_shortcut=is_shortcut)
return "success"

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20
docs/userguides/text2sql_cn.md
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@@ -5,21 +5,25 @@ text2sql的功能实现，高度依赖对LLM的应用。通过LLM生成SQL的过
### **配置方式**
1. 样本池的配置。
- supersonic/chat/core/src/main/python/llm/few_shot_example/sql_exampler.py为样本池配置文件。用户可以以已有的样本作为参考，配置更贴近自身业务需求的样本，用于更好的引导LLM生成SQL。
- supersonic/chat/core/src/main/python/few_shot_example/sql_exampler.py 为样本池配置文件。用户可以以已有的样本作为参考，配置更贴近自身业务需求的样本，用于更好的引导LLM生成SQL。
2. 样本数量的配置。
- 在supersonic/chat/core/src/main/python/llm/run_config.py 中通过 TEXT2DSL_FEW_SHOTS_EXAMPLE_NUM 变量进行配置。
- 在 supersonic/chat/core/src/main/python/run_config.py 中通过 TEXT2DSL_FEW_SHOTS_EXAMPLE_NUM 变量进行配置。
- 默认值为15，为项目在内部实践后较优的经验值。样本少太少，对导致LLM在生成SQL的过程中缺少引导和示范，生成的SQL会更不稳定；样本太多，会增加生成SQL需要的时间和LLM的token消耗（或超过LLM的token上限）。
- <div align="left" >
3. SQL生成方式的配置
- 在 supersonic/chat/core/src/main/python/run_config.py 中通过 TEXT2DSL_IS_SHORTCUT 变量进行配置。
- 默认值为False；当为False时，会调用2次LLM生成SQL；当为True时，会只调用1次LLM生成SQL。相较于2次LLM调用生成的SQL，耗时会减少30-40%，token的消耗量会减少30%左右，但生成的SQL正确率会有所下降。
<div align="left" >
<img src=../images/text2sql_config.png width="70%"/>
<p>图1-1 样本数量的配置文件</p>
<p>图1-1 配置文件</p>
</div>
3. 运行中更新配置的脚本。
- 如果在启动项目后，用户需要对text2sql功能的相关配置进行调试，可以在修改相关配置文件后，通过脚本 supersonic/chat/core/src/main/python/bin/text2sql_resetting.sh 在项目运行中让配置生效。
### **运行中更新配置的脚本**
1. 如果在启动项目后，用户需要对text2sql功能的相关配置进行调试，可以在修改相关配置文件后，通过以下2种方式让配置在项目运行中让配置生效。
- 执行 supersonic-daemon.sh reload llmparser
- 执行 python examples_reload_run.py
### **FAQ**
1. 生成一个SQL需要消耗的的LLM token数量太多了，按照openAI对token的收费标准，生成一个SQL太贵了，可以少用一些token吗？
- 可以。 用户可以根据自身需求，如配置方式1.中所示，修改样本池中的样本，选用一些更加简短的样本。如配置方式2.中所示，减少使用的样本数量。
- 可以。 用户可以根据自身需求，如配置方式1.中所示，修改样本池中的样本，选用一些更加简短的样本。如配置方式2.中所示，减少使用的样本数量。配置方式3.中所示，只调用1次LLM生成SQL。
- 需要注意，样本和样本数量的选择对生成SQL的质量有很大的影响。过于激进的降低输入的token数量可能会降低生成SQL的质量。需要用户根据自身业务特点实测后进行平衡。