Hello world !

from langchain_core.output_parsers.openai_tools import PydanticToolsParser
from langchain_core.prompts import ChatPromptTemplate
from langchain_core.pydantic_v1 import BaseModel, Field
from app import mysql, llm
from openai_module import init_openai
from operator import itemgetter

from langchain.chains import create_sql_query_chain
from langchain_core.runnables import RunnablePassthrough



class Table(BaseModel):
"""Table in SQL database."""

name: str = Field(description="Name of table in SQL database.")


def invoke_table_chain(user_input):
try:
table_names = "\n".join(mysql.get_usable_table_names())

system = f"""Return the names of ALL the SQL tables that MIGHT be relevant to the user question. \
The tables are:

{table_names}

Remember to include ALL POTENTIALLY RELEVANT tables, even if you're not sure that they're needed.
"""

prompt = ChatPromptTemplate.from_messages(
[
("system", system),
("human", f"{user_input}"),
]
)

llm_with_tools = llm.bind_tools([Table])
output_parser = PydanticToolsParser(tools=[Table])
table_chain = prompt | llm_with_tools | output_parser
table_chain.invoke({"input": f'{user_input}'})
query_chain = create_sql_query_chain(llm, mysql)
# Convert "question" key to the "input" key expected by current table_chain.
table_chain = {"input": itemgetter(f"{user_input}")} | table_chain
# Set table_names_to_use using table_chain.
full_chain = RunnablePassthrough.assign(table_names_to_use=table_chain) | query_chain
print(f"full_chain: \n{full_chain}\n")
query = full_chain.invoke({"question": f'{user_input}'})
return query
except Exception as ex:
print(f"In invoke_table_chain() => {ex}")
return None

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