批量添加ddl到向量数据库,添加documention,重写generate_rewritten_question
添加id,sql,question到上下文
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@@ -55,8 +55,7 @@ from vanna.flask import VannaFlaskApp
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vn = create_vana()
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vn = create_vana()
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app = VannaFlaskApp(vn,chart=False)
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app = VannaFlaskApp(vn,chart=False)
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init_vn(vn)
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init_vn(vn)
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cache = app.cache
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@app.flask_app.route("/api/v0/generate_sql_2", methods=["GET"])
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@app.flask_app.route("/api/v0/generate_sql_2", methods=["GET"])
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def generate_sql_2():
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def generate_sql_2():
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"""
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"""
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@@ -87,10 +86,14 @@ def generate_sql_2():
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if question is None:
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if question is None:
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return jsonify({"type": "error", "error": "No question provided"})
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return jsonify({"type": "error", "error": "No question provided"})
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id = cache.generate_id(question=question)
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#id = self.cache.generate_id(question=question)
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data = vn.generate_sql_2(question=question)
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data = vn.generate_sql_2(question=question)
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data['id'] =id
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sql = data["resp"]["sql"]
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print("sql:",sql)
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cache.set(id=id, field="question", value=question)
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cache.set(id=id, field="sql", value=sql)
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print("data---------------------------",data)
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return jsonify(data)
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return jsonify(data)
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@@ -2,6 +2,7 @@ from email.policy import default
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from typing import List
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from typing import List
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import orjson
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import orjson
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import pandas as pd
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from vanna.base import VannaBase
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from vanna.base import VannaBase
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from vanna.flask import MemoryCache
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from vanna.flask import MemoryCache
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from vanna.qdrant import Qdrant_VectorStore
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from vanna.qdrant import Qdrant_VectorStore
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@@ -18,8 +19,6 @@ from datetime import datetime
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class OpenAICompatibleLLM(VannaBase):
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class OpenAICompatibleLLM(VannaBase):
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def __init__(self, client=None, config_file=None):
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def __init__(self, client=None, config_file=None):
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VannaBase.__init__(self, config=config_file)
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VannaBase.__init__(self, config=config_file)
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self.cache = MemoryCache()
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# default parameters - can be overrided using config
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# default parameters - can be overrided using config
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self.temperature = 0.5
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self.temperature = 0.5
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self.max_tokens = 5000
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self.max_tokens = 5000
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@@ -170,8 +169,6 @@ class OpenAICompatibleLLM(VannaBase):
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[{'role': 'system', 'content': sys_char_temp}, {'role': 'user', 'content': user_char_temp}], **kwargs)
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[{'role': 'system', 'content': sys_char_temp}, {'role': 'user', 'content': user_char_temp}], **kwargs)
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print(llm_response2)
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print(llm_response2)
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result['chart'] = orjson.loads(extract_nested_json(llm_response2))
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result['chart'] = orjson.loads(extract_nested_json(llm_response2))
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result['id'] = self.cache.generate_id(question=question)
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print("result----------------------{0}".format(result))
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return result
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return result
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def generate_rewritten_question(self, last_question: str, new_question: str, **kwargs) -> str:
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def generate_rewritten_question(self, last_question: str, new_question: str, **kwargs) -> str:
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@@ -14,13 +14,13 @@ from service.cus_vanna_srevice import CustomVanna
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# )
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# )
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# """,
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# """,
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# ]
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# ]
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list_documentions = [
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# list_documentions = [
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"""
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# """
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gender 字段 0代表女性,1代表男性;
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# gender 字段 0代表女性,1代表男性;
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查询address时,尽量使用like查询,如:select * from db_user where address like '%北京%';
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# 查询address时,尽量使用like查询,如:select * from db_user where address like '%北京%';
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语法为sqlite语法;
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# 语法为sqlite语法;
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""",
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# """,
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]
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# ]
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table_ddls = [
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table_ddls = [
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"""
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"""
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CREATE TABLE 人员库表 (
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CREATE TABLE 人员库表 (
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