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from langchain_community.llms import GooglePalm
from langchain.utilities import SQLDatabase
from langchain_experimental.sql import SQLDatabaseChain
from langchain.embeddings import HuggingFaceEmbeddings
from langchain.vectorstores import Chroma
from langchain.prompts import SemanticSimilarityExampleSelector
from langchain.chains.sql_database.prompt import PROMPT_SUFFIX, _mysql_prompt
from langchain.prompts.prompt import PromptTemplate
from langchain.prompts import FewShotPromptTemplate
import os
from few_shot import fewshots
from dotenv import load_dotenv
load_dotenv()
def get_few_shot_db_chain():
llm = GooglePalm(google_api_key=os.environ["google_api_key"], temperature=0.2)
db_user = "root"
db_password = "Kautilya1414"
db_host = 'localhost'
db_name = 'atliq_tshirts'
uri = f"mysql+pymysql://{db_user}:{db_password}@{db_host}/{db_name}"
db = SQLDatabase.from_uri(uri)
embeddings = HuggingFaceEmbeddings(model_name='sentence-transformers/all-MiniLM-L6-V2')
vectorize = [" ".join(example.values()) for example in fewshots]
vectorstore = Chroma.from_texts(vectorize, embeddings, metadatas=fewshots)
example_selector = SemanticSimilarityExampleSelector(vectorstore=vectorstore, k=2)
example_prompt = PromptTemplate(
input_variables=["Question", "SQLQuery", "SQLResult", "Answer"],
template="\nQuestion:{Question}\nSQLQuery:{SQLQuery}\nSQLResult:{SQLResult}\nAnswer:{Answer}"
)
few_shot_temp = FewShotPromptTemplate(example_selector=example_selector,
example_prompt=example_prompt,
prefix=_mysql_prompt,
suffix=PROMPT_SUFFIX,
input_variables=["input", "table_info", "top_k"])
new_chain = SQLDatabaseChain.from_llm(llm, db, verbose=True, prompt=few_shot_temp)
return new_chain
if __name__ == "__main__":
new_chain = get_few_shot_db_chain()
print(new_chain.run("how many white color Levi t-shirts we have?")) |