functions/query_functions.py

from typing import List
from typing import AnyStr
from haystack import component
import pandas as pd
from pandasql import sqldf
pd.set_option('display.max_rows', None)
pd.set_option('display.max_columns', None)
pd.set_option('display.width', None)
pd.set_option('display.max_colwidth', None)
import sqlite3
import psycopg2
from pymongo import MongoClient
import pymongoarrow.monkey
import json
import pluck
from utils import TEMP_DIR
import ast

@component
class SQLiteQuery:

    def __init__(self, sql_database: str):
      self.connection = sqlite3.connect(sql_database, check_same_thread=False)

    @component.output_types(results=List[str], queries=List[str])
    def run(self, queries: AnyStr, session_hash):
        print("ATTEMPTING TO RUN SQLITE QUERY")
        dir_path = TEMP_DIR / str(session_hash)
        results = []
        result = pd.read_sql(queries, self.connection)
        result.to_csv(f'{dir_path}/file_upload/query.csv', index=False)
        column_names = list(result.columns)
        results.append(f"{result}")
        self.connection.close()
        return {"results": results, "queries": queries, "csv_columns": column_names}
    
@component
class PostgreSQLQuery:

    def __init__(self, url: str, sql_port: int, sql_user: str, sql_pass: str, sql_db_name: str):
      self.connection = psycopg2.connect(
            database=sql_db_name,
            user=sql_user,
            password=sql_pass,
            host=url,  # e.g., "localhost" or an IP address
            port=sql_port  # default is 5432
        )

    @component.output_types(results=List[str], queries=List[str])
    def run(self, queries: AnyStr, session_hash):
        print("ATTEMPTING TO RUN POSTGRESQL QUERY")
        dir_path = TEMP_DIR / str(session_hash)
        results = []
        result = pd.read_sql_query(queries, self.connection)
        result.to_csv(f'{dir_path}/sql/query.csv', index=False)
        column_names = list(result.columns)
        results.append(f"{result}")
        self.connection.close()
        return {"results": results, "queries": queries, "csv_columns": column_names}

@component
class DocDBQuery:

    def __init__(self, connection_string: str, doc_db_name: str):
      client = MongoClient(connection_string)

      self.client = client
      self.connection = client[doc_db_name]

    @component.output_types(results=List[str], queries=List[str])
    def run(self, aggregation_pipeline: List[str], db_collection,  session_hash):
        pymongoarrow.monkey.patch_all()
        print("ATTEMPTING TO RUN MONGODB QUERY")
        dir_path = TEMP_DIR / str(session_hash)
        results = []
        print(aggregation_pipeline)

        aggregation_pipeline = aggregation_pipeline.replace(" ", "")

        false_replace = [':false', ': false']
        false_value = ':False'   
        true_replace = [':true', ': true']
        true_value = ':True'

        for replace in false_replace:
            aggregation_pipeline = aggregation_pipeline.replace(replace, false_value)
        for replace in true_replace:
            aggregation_pipeline = aggregation_pipeline.replace(replace, true_value)

        query_list = ast.literal_eval(aggregation_pipeline)

        print("QUERY List")
        print(query_list)
        print(db_collection)
        
        db = self.connection
        collection = db[db_collection]

        print(collection)
        docs = collection.aggregate_pandas_all(query_list)
        print("DATA FRAME COMPLETE")
        docs.to_csv(f'{dir_path}/doc_db/query.csv', index=False)
        column_names = list(docs.columns)
        print("CSV COMPLETE")
        results.append(f"{docs}") 
        self.client.close()
        return {"results": results, "queries": aggregation_pipeline, "csv_columns": column_names}
    
@component
class GraphQLQuery:

    def __init__(self):

      self.connection = pluck

    @component.output_types(results=List[str], queries=List[str])
    def run(self, graphql_query, graphql_api_string, graphql_api_token, graphql_token_header, session_hash):
        print("ATTEMPTING TO RUN GRAPHQL QUERY")
        dir_path = TEMP_DIR / str(session_hash)
        results = []

        headers = {"Content-Type": "application/json"}
        if graphql_token_header and graphql_api_token:
          headers[graphql_token_header] = graphql_api_token

        print(graphql_query)

        response = self.connection.execute(url=graphql_api_string, headers=headers, query=graphql_query, column_names="short")

        if response.errors:
           raise ValueError(response.errors)
        elif response.data:
          print("DATA FRAME COMPLETE")
          print(response)
          response_frame = response.frames['default']
          print("RESPONSE FRAME")
          #print(response_frame)

          response_frame.to_csv(f'{dir_path}/graphql/query.csv', index=False)
          column_names = list(response_frame.columns)
          print("CSV COMPLETE")
          results.append(f"{response_frame}") 
          return {"results": results, "queries": graphql_query, "csv_columns": column_names}
    
def query_func(queries:AnyStr, session_hash, session_folder, args, **kwargs):
    try:
      print("QUERY")
      print(queries)
      if session_folder == "file_upload":
        dir_path = TEMP_DIR / str(session_hash)
        sql_query = SQLiteQuery(f'{dir_path}/file_upload/data_source.db')
        result = sql_query.run(queries, session_hash)
      elif session_folder == "sql":
        sql_query = PostgreSQLQuery(args[0], args[1], args[2], args[3], args[4])
        result = sql_query.run(queries, session_hash)
      elif session_folder == 'doc_db':
        doc_db_query = DocDBQuery(args[0], args[1])
        result = doc_db_query.run(queries, kwargs['db_collection'], session_hash)
      elif session_folder == 'graphql':
        graphql_object = GraphQLQuery()
        result = graphql_object.run(queries, args[0], args[1], args[2], session_hash)
      print("RESULT")
      print(result["csv_columns"])
      if len(result["results"][0]) > 1000:
        print("QUERY TOO LARGE")
        return {"reply": f"""query result too large to be processed by llm, the query results are in our query.csv file. 

                The column names of this query.csv file are: {result["csv_columns"]}. 

                If you need to display the results directly, perhaps use the table_generation_func function."""}
      else:   
        return {"reply": result["results"][0]}

    except Exception as e:
      reply = f"""There was an error running the {session_folder} Query = {queries}

              The error is {e},

              You should probably try again.

              """
      print(reply)
      return {"reply": reply}
    
def graphql_schema_query(graphql_type: AnyStr, session_hash, **kwargs):
    dir_path = TEMP_DIR / str(session_hash)
    try:
      with open(f'{dir_path}/graphql/schema.json', 'r') as file:
        data = json.load(file)

      types_list = data["types"]
      result = list(filter(lambda item: item["name"] == graphql_type, types_list))

      print("SCHEMA RESULT")
      print(graphql_type)
      print(str(result))

      return {"reply": str(result)}

    except Exception as e:
      reply = f"""There was an error querying our schema.json file with the type:{graphql_type}

              The error is {e},

              You should probably try again.

              """
      print(reply)
      return {"reply": reply}

def graphql_csv_query(csv_query: AnyStr, session_hash, **kwargs):
    dir_path = TEMP_DIR / str(session_hash)
    try:
      query = pd.read_csv(f'{dir_path}/graphql/query.csv')
      query.Name = 'query'
      print("GRAPHQL CSV QUERY")
      print(csv_query)
      queried_df = sqldf(csv_query, locals())
      print(queried_df)
      column_names = list(queried_df.columns)
      queried_df.to_csv(f'{dir_path}/graphql/query.csv', index=False)

      if len(queried_df) > 1000:
        print("CSV QUERY TOO LARGE")
        return {"reply": f"""The new query results are in our query.csv file. 

                The column names of this query.csv file are: {column_names}. 

                If you need to display the results directly, perhaps use the table_generation_func function."""}
      else:
        return {"reply": str(queried_df)}

    except Exception as e:
      reply = f"""There was an error querying our query.csv file with the query:{csv_query}

              The error is {e},

              You should probably try again.

              """
      print(reply)
      return {"reply": reply}