systex_phase2/text_to_sql2.py

import re
from dotenv import load_dotenv
load_dotenv()

from langchain_community.utilities import SQLDatabase
import os
URI: str =  os.environ.get('SUPABASE_URI')
db = SQLDatabase.from_uri(URI)

# print(db.dialect)
# print(db.get_usable_table_names())
# db.run('SELECT * FROM "2022 清冊數據(GHG)" LIMIT 10;')

context = db.get_context()
# print(list(context))
# print(context["table_info"])

from langchain_core.prompts import FewShotPromptTemplate, PromptTemplate
from langchain.chains import create_sql_query_chain
from langchain_community.llms import Ollama

from langchain_community.tools.sql_database.tool import QuerySQLDataBaseTool
from operator import itemgetter

from langchain_core.output_parsers import StrOutputParser
from langchain_core.prompts import PromptTemplate
from langchain_core.runnables import RunnablePassthrough

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM
from transformers import AutoModelForCausalLM, AutoTokenizer,pipeline
import torch
from langchain_huggingface import HuggingFacePipeline

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM
# model_id = "defog/llama-3-sqlcoder-8b"
# tokenizer = AutoTokenizer.from_pretrained(model_id)
# sql_llm = HuggingFacePipeline.from_model_id(
#     model_id=model_id,
#     task="text-generation",
#     model_kwargs={"torch_dtype": torch.bfloat16},
#     pipeline_kwargs={"return_full_text": False},
#     device=0, device_map='cuda')

##########################################################################################
from langchain_community.chat_models import ChatOllama
# local_llm = "llama3-groq-tool-use:latest"
local_llm = "llama3-groq-tool-use:latest"
llm = ChatOllama(model=local_llm, temperature=0)
##########################################################################################
# model_id = "meta-llama/Meta-Llama-3.1-8B-Instruct"
# tokenizer = AutoTokenizer.from_pretrained(model_id)

# llm = HuggingFacePipeline.from_model_id(
#     model_id=model_id,
#     task="text-generation",
#     model_kwargs={"torch_dtype": torch.bfloat16},
#     pipeline_kwargs={"return_full_text": False,
#         "max_new_tokens": 512},
#     device=0, device_map='cuda')
# print(llm.pipeline)
# llm.pipeline.tokenizer.pad_token_id = llm.pipeline.model.config.eos_token_id[0]
##########################################################################################

# model = AutoModelForCausalLM.from_pretrained(model_id, load_in_4bit=True)

# pipe = pipeline("text-generation", model=model, tokenizer=tokenizer, max_new_tokens=500, top_k=50, temperature=0.1, 
#                 model_kwargs={"torch_dtype": torch.bfloat16, "return_full_text": False})
#, device="auto", load_in_4bit=True
# llm = HuggingFacePipeline(pipeline=pipe)

# llm = HuggingFacePipeline(pipeline=pipe)

# llm = Ollama(model = "llama3-groq-tool-use:latest", num_gpu=1)
def get_examples():
    examples = [
        {
            "input": "建準廣興廠去年的類別1總排放量是多少?",
            "query": """SELECT SUM("排放量(公噸CO2e)") AS "類別1總排放量"
                        FROM "104_112碳排放公開及建準資料"
                        WHERE "事業名稱" like '%建準%'
                        AND "事業名稱" like '%廣興廠%'
                        AND "類別" = '類別1-直接排放'
                        AND "盤查標準" = 'GHG'
                        AND "年度" = EXTRACT(YEAR FROM CURRENT_DATE)-1;""",
        },
        {
            "input": "建準台北辦事處2022年的能源間接排放總排放量是多少?",
            "query": """SELECT SUM("排放量(公噸CO2e)") AS "直接排放總排放量"
                        FROM "104_112碳排放公開及建準資料"
                        WHERE "事業名稱" like '%建準%'
                        AND "事業名稱" like '%台北辦事處%'
                        AND "類別" = '類別2-能源間接排放'
                        AND "盤查標準" = 'GHG'
                        AND "年度" = 2022;""",
        },
        {
            "input": "建準去年的固定燃燒總排放量是多少?",
            "query": """SELECT SUM("排放量(公噸CO2e)") AS "固定燃燒總排放量"
                        FROM "104_112碳排放公開及建準資料"
                        WHERE "事業名稱" like '%建準%'
                        AND "排放源" = '固定燃燒'
                        AND "盤查標準" = 'GHG'
                        AND "年度" = EXTRACT(YEAR FROM CURRENT_DATE)-1;""",
        },
        {
            "input": "台積電2022年的類別1總排放量是多少?",
            "query": """SELECT SUM("排放量(公噸CO2e)") AS "台積電2022年類別1總排放量"
                        FROM "104_112碳排放公開及建準資料"
                        WHERE "事業名稱" like '%台灣積體電路製造股份有限公司%'
                        AND "類別" = '類別1-直接排放'
                        AND "年度" = 2022;""",
        },


    ]

    return examples

def table_description():
    database_description = (
        "The database consists of following table: `104_112碳排放公開及建準資料`, `水電使用量(ISO)` and `水電使用量(GHG)`. "
        "This is a PostgreSQL database, so you need to use postgres-related queries.\n\n"
        "The `104_112碳排放公開及建準資料` table 描述了不同事業單位或廠房分別在 ISO 14064-1:2018 與 GHG Protocol 標準下的溫室氣體排放量，並依類別1至類別6劃分。"
        "It includes the following columns:\n"

        "- `年度`: 盤查年度\n"
        "- `類別`: 溫室氣體的排放類別，包含以下:\n"
        "   \t*類別1-直接排放\n"
        "   \t*類別2-能源間接排放\n"
        "   \t*類別3-運輸間接排放\n"
        "   \t*類別4-組織使用產品間接排放\n"
        "   \t*類別5-使用來自組織產品間接排放\n"
        "   \t*類別6\n"
        "- `排放源`: `類別`欄位進一步劃分的細項\n"
        "- `排放量(公噸CO2e)`: 溫室氣體排放量\n"
        "- `盤查標準`: ISO or GHG\n"
        

        "The `水電使用量(ISO)` and `水電使用量(GHG)` table 描述了不同廠房分別在 ISO 14064-1:2018 與 GHG Protocol 標準下的水電使用量，包含'外購電力 度數 (kwh)'與'自來水 度數 (立方公尺 m³)'。"
        "The `public.departments_table` table contains information about the various departments in the company. It includes:\n"
        "- `外購電力(灰電)`: 灰電(火力發電、核能發電等)的外購電力度數(kwh)\n"
        "- `外購電力(綠電)`: 綠電(太陽光電)的外購電力度數(kwh)\n"
        "- `自產電力(綠電)`: 綠電(太陽光電)的自產電力度數(kwh)\n"
        "- `用水量`: 自來水的使用度數(m³)\n\n"
    )

    return database_description

def write_query_chain(db, llm):

    template = """
    <|begin_of_text|>
    
    <|start_header_id|>system<|end_header_id|>
    Generate a SQL query to answer this question: `{input}`

    You are a PostgreSQL expert in ESG field. Given an input question, first create a syntactically correct PostgreSQL query to run, 
    then look at the results of the query and return the answer to the input question.\n\
    Unless the user specifies in the question a specific number of examples to obtain, query for at most {top_k} results using the LIMIT clause as per PostgreSQL. 
    You can order the results to return the most informative data in the database.\n\
    Never query for all columns from a table. You must query only the columns that are needed to answer the question. 
    Wrap each column name in  Quotation Mark (") to denote them as delimited identifiers.\n\
    
    ***Pay attention to only return query for PostgreSQL WITHOUT "```sql", And DO NOT content any other words.\n\
    ***Pay attention to only return PostgreSQL query and no premable or explanation.\n\
    <|eot_id|>
        
    <|begin_of_text|><|start_header_id|>user<|end_header_id|>
    DDL statements:
    {table_info}

    database description:
    {database_description}

    Provide ONLY PostgreSQL query and NO premable or explanation!
    Below are a number of examples of questions and their corresponding SQL queries.\n\
    
    <|eot_id|>
    
    <|start_header_id|>assistant<|end_header_id|>
    """
    # prompt_template = PromptTemplate.from_template(template)

    example_prompt = PromptTemplate.from_template("The following SQL query best answers the question `{input}`:\nSQL query: {query}")
    prompt = FewShotPromptTemplate(
        examples=get_examples(),
        example_prompt=example_prompt,
        prefix=template,
        suffix="User input: {input}\nSQL query: ",
        input_variables=["input", "top_k", "table_info"],
    )

    # llm = Ollama(model = "sqlcoder", num_gpu=1)
    # llm = HuggingFacePipeline(pipeline=pipe)
    
    
    write_query = create_sql_query_chain(llm, db, prompt)


    return write_query

def sql_to_nl_chain(llm):
    # llm = Ollama(model = "llama3.1", num_gpu=1)
    # llm = Ollama(model = "llama3.1:8b-instruct-q2_K", num_gpu=1)
    # llm = Ollama(model = "llama3-groq-tool-use:latest", num_gpu=1)
    answer_prompt = PromptTemplate.from_template(
        """
        <|begin_of_text|>
        <|begin_of_text|><|start_header_id|>system<|end_header_id|>
        Given the following user question, corresponding SQL query, and SQL result, answer the user question.
        根據使用者的問題、對應的 SQL 查詢和 SQL 結果，以繁體中文回答使用者問題。

        
        <|eot_id|>

        <|begin_of_text|><|start_header_id|>user<|end_header_id|>
        Question: {question}
        SQL Query: {query}
        SQL Result: {result}
        Answer: 
        <|eot_id|>
        
        <|start_header_id|>assistant<|end_header_id|>
        
        """
        )

    # llm = Ollama(model = "llama3-groq-tool-use:latest", num_gpu=1)
    chain = answer_prompt | llm | StrOutputParser()

    return chain

def get_query(db, question, selected_table, llm):
    write_query = write_query_chain(db, llm)
    query = write_query.invoke({"question": question, 'table_names_to_use': selected_table, "top_k": 1000, "table_info":context["table_info"], "database_description": table_description()})
    
    query = re.split('SQL query: ', query)[-1]
    query = query.replace("104_112碰排放公開及建準資料","104_112碳排放公開及建準資料")
    print(query)
    
    return query

def query_to_nl(db, question, query, llm):
    execute_query = QuerySQLDataBaseTool(db=db)
    result = execute_query.invoke(query)
    print(result)

    chain = sql_to_nl_chain(llm)
    answer = chain.invoke({"question": question, "query": query, "result": result})

    return answer

def run(db, question, selected_table, llm):

    write_query = write_query_chain(db, llm)
    query = write_query.invoke({"question": question, 'table_names_to_use': selected_table, "top_k": 1000, "table_info":context["table_info"], "database_description": table_description()})
    
    query = re.split('SQL query: ', query)[-1]
    query = query.replace("104_112碰排放公開及建準資料","104_112碳排放公開及建準資料")
    print(query)

    execute_query = QuerySQLDataBaseTool(db=db)
    result = execute_query.invoke(query)
    print(result)

    chain = sql_to_nl_chain(llm)
    answer = chain.invoke({"question": question, "query": query, "result": result})

    return query, result, answer


if __name__ == "__main__":
    import time
    
    start = time.time()
    
    selected_table = ['104_112碳排放公開及建準資料']
    question = "建準去年的固定燃燒總排放量是多少?"
    question = "台積電2022年的直接排放總排放量是多少?"
    query, result, answer = run(db, question, selected_table, llm)
    print("question: ", question)
    print("query: ", query)
    print("result: ", result)
    print("answer: ", answer)
    
    print(time.time()-start)