可以到 gitclone.com/aiit/chat/ 试一下,比如问这个问题“从用户表(users)中按姓名模糊查出所有记录,并按姓名字头排序”,无论是glm4、llama3.2、gemma2、qwen2.5、phi3.5、mistral-small、deepseek-coder-v2(上面那个链接里都有),都会生成正确的SQL,一般还会生成用python调用此
我们实现函数 generate_sql ,利用 LLM 的 tool calling 能力,将用户原始的自然语言提问,转化成结构化的标准 SQL。具体函数实现如下: 代码语言:javascript 代码运行次数:0 运行 AI代码解释 def generate_sql(db_schema: str, query: str) -> str: """ 调用LLM,利用工具调用能力,生成SQL语句 :param db_schema...
- Generate Final Answer: Use if answer to User's question can be given with MEMORY - Calculator: Use this tool to solve mathematical problems. - Query_Database: Write an SQL Query to query the Database. ANSWER FORMAT ```json { "tool_name": "Calculator" } ``` [/INST] User: How m...
lmql.run_sync(query_string,model=lmql.model("local:llama.cpp:zephyr-7b-beta.Q4_K_M.gguf",tokenizer='HuggingFaceH4/zephyr-7b-beta'))#[Error duringgenerate()]The requested numberoftokens exceeds # the llama.cpp model's context size.Please specify a higher n_ctx value. 1. 2. 3. 4. ...
Text-to-SQL refers to the task defined as " given a relational database D and a natural language sentence S that describes a question on D , generate an SQL query Q over D that expresses S ". Several LLM-based text-to-SQL tools, that is, text-to-SQL tools that explore Large ...
modal run src.finetune_sql --data-dir "data_sql" --model-dir "model_sql" 微调脚本会执行以下步骤: 将数据集拆分为训练和验证拆分 train_val = data["train"].train_test_split(test_size=val_set_size, shuffle=True, seed=42) train_data = train_val["train"].shuffle().map(generate_and_to...
# Connect to MySQL conn = mysql.connector.connect( host="localhost", user="root", password="Your MySQL Password", database="SalesOrderSchema" ) cursor = conn.cursor() # Generate and insert data for _ in range(100): # Let's say we want to generate 100 records ...
简单来说,假如你有海量的图关联场景,你用非图的数据库写查询语句(像是上图 SQL 部分)。虽然理论上 SQL 是可以实现多跳的查询,或是查询是两点之间任意的路径,但往往这个查询语言不好写,并且响应速度满足不了业务需求。简单来说,非常痛苦。 雪人图 而图数据库便是面向连接的存储,像雪人兄弟的跳转,其实就是 O(...
We generate a SQL query (possibly from an LLM). We run both the "gold" query and the generated query on their respective database to obtain 2 dataframes with the results. We compare the 2 dataframes using an "exact" and a "subset" match. TODO add link to blogpost. ...
简单来说,假如你有海量的图关联场景,你用非图的数据库写查询语句(像是上图 SQL 部分)。虽然理论上 SQL 是可以实现多跳的查询,或是查询是两点之间任意的路径,但往往这个查询语言不好写,并且响应速度满足不了业务需求。简单来说,非常痛苦。 而图数据库便是面向连接的存储,像雪人兄弟的跳转,其实就是 O (1) 的...