This AI SQL chatbot generates SQL code using AI, like ChatGPT for SQL Databases. Connect and chat with database in ChatGPT.
相关论文为：ChatDB: Augmenting LLMs with Databases as Their Symbolic Memory，代码已开源。论文地址：https://arxiv.org/abs/2306.03901 项目主页：https://chatdatabase.github.io 项目代码：https://github.com/huchenxucs/ChatDB 推特上一些知名的机器学习和自然语言处理研究者也对这项研究进行了宣传：与相...
Vanna is designed to connect to any database, LLM, and vector database. There's aVannaBaseabstract base class that defines some basic functionality. The package provides implementations for use with OpenAI and ChromaDB. You can easily extend Vanna to use your own LLM or vector database. See...
1、SQLDatabaseChain链 SQLDatabaseChain是langchain框架自带的数据库自然语言交互工具,其内部通过sqlalchemy来获取数据库的表名和表结构、字段信息,然后将数据库的信息和用户的自然语言请求一起发送给大模型进行分析,让大模型返回sql语句后,执行sql,并返回执行结果。 db_chain = SQLDatabaseChain.from_llm(llm, db,...
The views expressed in this paper are my own and do not necessarily reflect the views of Oracle. DatabaseMySQLOracle Databasesql Published at DZone with permission ofCORRADO DE BARI.See the original article here. Opinions expressed by DZone contributors are their own....
1.1 Multi chat! In this release i have added private chat!, Fixing #1 1.0 Initial Release This is the first stable release of mysql_websocket_chat. This project features basic websocket chat with configurable database setup to log chat message....
SQL数据库如MySQL或PostgreSQL非常适合结构化数据的存储和查询,适用于需要频繁访问和复杂查询的场景。 importmysql.connectormydb=mysql.connector.connect(host="localhost",user="yourusername",password="yourpassword",database="mydatabase")mycursor=mydb.cursor()sql="INSERT INTO chat_records (content) ...
图 2. VectotDB 应用流程。图源：https://www.pinecone.io/learn/vector-database/ 在这里其中对于 Vector DB 来说最主要的瓶颈还是在于第二步和第三步的索引，检索和压缩，因为 Vector DB 的目标和优势就是更好的处理大规模的数据：向量索引与检索，这就像是超级英雄的重要武器之一，是 Vector DB 处理大规模...
database="business", # 数据库名称 user="root", # 用户名 password="123456", # 密码 host="localhost", # 主机地址 port=3306, # 端口号 dialect="mysql" # 数据库方言 ) # 启动sql检查特性,自动修复错误sql db.enable_sql_check() # 设施返回GPT迭代思考的中间步骤 ...
Example 1: ‘‘‘json {"evidence": "released in the year 1945 refers to movie_release_year = 1945;", "conversations": [{"from": "human", "value": "\n I will ask you a question, then you should operate the MySQL database with sql to answer the question.\n You must response in...