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import os
from langchain_google_genai import GoogleGenerativeAI
from langchain_community.utilities import SQLDatabase
from dotenv import load_dotenv
from config import db_configuration, API_KEY
from prompt import get_response
from langchain_experimental.sql.base import SQLDatabaseSequentialChain
from langchain.chains import create_sql_query_chain
from langchain_core.prompts import PromptTemplate
from langchain.memory import ConversationBufferMemory
from langchain.chains import LLMChain
import pandas as pd
from sqlalchemy import create_engine
load_dotenv()
class DB:
def __init__(self):
self.host = db_configuration["HOST"]
self.password = db_configuration["PASSWORD"]
self.database = db_configuration["DB"]
self.port = db_configuration["PORT"]
self.user = db_configuration["USER"]
def db_conn(self):
url = f"""mysql://{self.user}:{self.password}@{self.host}:{self.port}/{self.database}?"""
return SQLDatabase.from_uri(url)
def see_table(self,rows):
url = f"""mysql://{self.user}:{self.password}@{self.host}:{self.port}/{self.database}?"""
conn = create_engine(url)
df = pd.read_sql_query(f"select * from cars_details limit {rows};",con=conn,index_col="id")
return df
class LLM_conn:
def __init__(self) -> None:
self.temparature = 0
self.model = "gemini-pro"
def llm(self):
return GoogleGenerativeAI(google_api_key=API_KEY, model=self.model,temperature=self.temparature)
class Chain:
def __init__(self) -> None:
self.description = DB().db_conn().run("DESC cars_details;")
self.db = DB().db_conn()
self.llm = LLM_conn().llm()
self.memory = ConversationBufferMemory(memory_key="chat_history")
def clean_sql_query(self,query):
return query.replace("sql","").replace("```","").replace("\n"," ").strip()
def sql_chain(self,query):
chain = create_sql_query_chain(self.llm, self.db)
res = chain.invoke({"question":query,"table_info":self.description})
res = self.clean_sql_query(res)
return res
def final_sql(self,query):
sql_q = self.sql_chain(query=query)
f_sql = self.db.run(sql_q)
llm_res = self.llm.invoke(get_response().format(question = query, db_res = f_sql))
return llm_res
def memory_base_chain(self, question):
# Assuming self.sql_chain and self.db.run are defined and work correctly
sql_q = self.sql_chain(query=question)
f_sql = self.db.run(sql_q)
template = f"""
You are a nice chatbot who has nice conversation with humans.
You have to understand user question and database response and give the proper, easy to understand.\n\n
user_query : {question}
database_response : {f_sql}
Last conversation :
{{chat_history}}
Response:
"""
# You may need to fetch chat_history from self.memory or another source
# Format the prompt template with actual values
# prompt = template.format(Question=question, db_res=f_sql, chat_history="") # Provide chat_history if available
formatted_prompt = PromptTemplate.format_prompt(template)
conversation = LLMChain(llm=self.llm, prompt=formatted_prompt, memory=self.memory)
res = conversation({"Question": question, "db_res": f_sql})
print(res)
return res
if __name__ =="__main__":
# db = DB()
# db_conn = db.db_conn()
# print(db_conn.run("desc cars_details;"))
# llm_conn = LLM_conn()
# llm = llm_conn.llm()
# print(llm.invoke("hi"))
# res = chain.sql_chain(query="give me name and price of most selling 3 cars")
# print(res)
# print("\n\n\n")
query = input("Enter :")
final = Chain().memory_base_chain(question= query)
print(final) |