src/core.py

import os
from dotenv import load_dotenv
import pandas as pd
import io
from langchain.output_parsers import PydanticOutputParser
from langchain.prompts import ChatPromptTemplate
from langchain.tools import PythonAstREPLTool
from langchain.chat_models import ChatOpenAI
from langchain.schema.output_parser import StrOutputParser
from langchain.chat_models import ChatOpenAI
from src.types import TableMapping
from src.vars import NUM_ROWS_TO_RETURN
from src.prompt import DATA_SCIENTIST_PROMPT_STR, SPEC_WRITER_PROMPT_STR, ENGINEER_PROMPT_STR

load_dotenv()

DATA_DIR_PATH = os.path.join(os.path.dirname(__file__), 'data')
SYNTHETIC_DATA_DIR_PATH = os.path.join(DATA_DIR_PATH, 'synthetic')

# TODO: consider different models for different prompts, e.g. natural language prompt might be better with higher temperature
BASE_MODEL = ChatOpenAI(
    model_name='gpt-4',
    temperature=0,
)

def _get_data_str_from_df_for_prompt(df, num_rows_to_return=NUM_ROWS_TO_RETURN):
    return f'<df>\n{df.head(num_rows_to_return).to_markdown()}\n</df>'

def get_table_mapping(source_df, template_df):
    '''Use PydanticOutputParser to parse the output of the Data Scientist prompt into a TableMapping object.'''
    table_mapping_parser = PydanticOutputParser(pydantic_object=TableMapping)
    analyst_prompt = ChatPromptTemplate.from_template(
        template=DATA_SCIENTIST_PROMPT_STR, 
        partial_variables={'format_instructions': table_mapping_parser.get_format_instructions()},
    )
    mapping_chain = analyst_prompt | BASE_MODEL | table_mapping_parser
    table_mapping: TableMapping = mapping_chain.invoke({"source_1_csv_str": _get_data_str_from_df_for_prompt(source_df), "target_csv_str": _get_data_str_from_df_for_prompt(template_df)})
    return pd.DataFrame(table_mapping.dict()['table_mappings'])

def _sanitize_python_output(text: str):
    '''Remove markdown from python code, as prompt returns it.'''
    _, after = text.split("```python")
    return after.split("```")[0]

def generate_mapping_code(table_mapping_df) -> str:
    '''Chain two prompts together to generate python code from a table mapping: 1. technical spec writer, 2. python engineer'''
    writer_prompt = ChatPromptTemplate.from_template(SPEC_WRITER_PROMPT_STR)
    engineer_prompt = ChatPromptTemplate.from_template(ENGINEER_PROMPT_STR)
    
    writer_chain = writer_prompt | BASE_MODEL | StrOutputParser()
    engineer_chain = {"spec_str": writer_chain} | engineer_prompt | BASE_MODEL | StrOutputParser() | _sanitize_python_output
    return engineer_chain.invoke({"table_mapping": str(table_mapping_df.to_dict())})

def process_csv_text(temp_file):
    '''Process a CSV file into a dataframe, either from a string or a file.'''
    if isinstance(temp_file, str):
      df = pd.read_csv(io.StringIO(temp_file))
    else:
      df = pd.read_csv(temp_file.name)
    return df

def transform_source(source_df, code_text: str):
    '''Use PythonAstREPLTool to transform a source dataframe using python code.'''
    return PythonAstREPLTool(locals={'source_df': source_df}).run(code_text)