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from utils import TEMP_DIR, message_dict
from haystack.dataclasses import ChatMessage
from haystack.components.generators.chat import OpenAIChatGenerator
import sqlite3
chat_generator = OpenAIChatGenerator(model="gpt-4o")
response = None
def example_question_generator(session_hash):
example_response = None
example_messages = [
ChatMessage.from_system(
"You are a helpful and knowledgeable agent who has access to an SQLite database which has a table called 'data_source'."
)
]
session_path = 'file_upload'
dir_path = TEMP_DIR / str(session_hash) / str(session_path)
connection = sqlite3.connect(f'{dir_path}/data_source.db')
print("Querying questions");
cur=connection.execute('select * from data_source')
columns = [i[0] for i in cur.description]
print("QUESTION COLUMNS")
print(columns)
cur.close()
connection.close()
example_messages.append(ChatMessage.from_user(text=f"""We have a SQLite database with the following {columns}.
We also have an AI agent with access to the same database that will be performing data analysis.
Please return an array of seven strings, each one being a question for our data analysis agent
that we can suggest that you believe will be insightful or helpful to a data analysis looking for
data insights. Return nothing more than the array of questions because I need that specific data structure
to process your response. No other response type or data structure will work."""))
example_response = chat_generator.run(messages=example_messages)
return example_response["replies"][0].text
def sql_example_question_generator(session_hash, db_tables, db_name):
example_response = None
example_messages = [
ChatMessage.from_system(
f"You are a helpful and knowledgeable agent who has access to an PostgreSQL database called {db_name}."
)
]
example_messages.append(ChatMessage.from_user(text=f"""We have a PostgreSQL database with the following tables: {db_tables}.
We also have an AI agent with access to the same database that will be performing data analysis.
Please return an array of seven strings, each one being a question for our data analysis agent
that we can suggest that you believe will be insightful or helpful to a data analysis looking for
data insights. Return nothing more than the array of questions because I need that specific data structure
to process your response. No other response type or data structure will work."""))
example_response = chat_generator.run(messages=example_messages)
return example_response["replies"][0].text
def doc_db_example_question_generator(session_hash, db_collections, db_name, db_schema):
example_response = None
example_messages = [
ChatMessage.from_system(
f"You are a helpful and knowledgeable agent who has access to an MongoDB NoSQL document database called {db_name}."
)
]
example_messages.append(ChatMessage.from_user(text=f"""We have a MongoDB NoSQL document database with the following collections: {db_collections}.
The schema of these collections is: {db_schema}.
We also have an AI agent with access to the same database that will be performing data analysis.
Please return an array of seven strings, each one being a question for our data analysis agent
that we can suggest that you believe will be insightful or helpful to a data analysis looking for
data insights. Return nothing more than the array of questions because I need that specific data structure
to process your response. No other response type or data structure will work."""))
example_response = chat_generator.run(messages=example_messages)
return example_response["replies"][0].text
def chatbot_with_fc(message, history, session_hash):
from functions import sqlite_query_func, table_generation_func, regression_func, scatter_chart_generation_func, \
line_chart_generation_func,bar_chart_generation_func,pie_chart_generation_func,histogram_generation_func
import tools.tools as tools
available_functions = {"sql_query_func": sqlite_query_func,"table_generation_func":table_generation_func,
"line_chart_generation_func":line_chart_generation_func,"bar_chart_generation_func":bar_chart_generation_func,
"scatter_chart_generation_func":scatter_chart_generation_func, "pie_chart_generation_func":pie_chart_generation_func,
"histogram_generation_func":histogram_generation_func,
"regression_func":regression_func }
session_path = 'file_upload'
dir_path = TEMP_DIR / str(session_hash) / str(session_path)
connection = sqlite3.connect(f'{dir_path}/data_source.db')
cur=connection.execute('select * from data_source')
columns = [i[0] for i in cur.description]
cur.close()
connection.close()
if message_dict[session_hash]['file_upload'] != None:
message_dict[session_hash]['file_upload'].append(ChatMessage.from_user(message))
else:
messages = [
ChatMessage.from_system(
f"""You are a helpful and knowledgeable agent who has access to an SQLite database which has a table called 'data_source' that contains the following columns: {columns}.
You also have access to a function, called table_generation_func, that can take a query.csv file generated from our sql query and returns an iframe that we should display in our chat window.
You also have access to a scatter plot function, called scatter_chart_generation_func, that can take a query.csv file generated from our sql query and uses plotly dictionaries to generate a scatter plot and returns an iframe that we should display in our chat window.
You also have access to a line chart function, called line_chart_generation_func, that can take a query.csv file generated from our sql query and uses plotly dictionaries to generate a line chart and returns an iframe that we should display in our chat window.
You also have access to a bar graph function, called line_chart_generation_func, that can take a query.csv file generated from our sql query and uses plotly dictionaries to generate a bar graph and returns an iframe that we should display in our chat window.
You also have access to a pie chart function, called pie_chart_generation_func, that can take a query.csv file generated from our sql query and uses plotly dictionaries to generate a pie chart and returns an iframe that we should display in our chat window.
You also have access to a histogram function, called histogram_generation_func, that can take a query.csv file generated from our sql query and uses plotly dictionaries to generate a histogram and returns an iframe that we should display in our chat window.
You also have access to a linear regression function, called regression_func, that can take a query.csv file generated from our sql query and a list of column names for our independent and dependent variables and return a regression data string and a regression chart which is returned as an iframe.
Could you please always display the generated charts, tables, and visualizations as part of your output?"""
)
]
messages.append(ChatMessage.from_user(message))
message_dict[session_hash]['file_upload'] = messages
response = chat_generator.run(messages=message_dict[session_hash]['file_upload'], generation_kwargs={"tools": tools.data_file_tools_call(session_hash)})
while True:
# if OpenAI response is a tool call
if response and response["replies"][0].meta["finish_reason"] == "tool_calls" or response["replies"][0].tool_calls:
function_calls = response["replies"][0].tool_calls
for function_call in function_calls:
message_dict[session_hash]['file_upload'].append(ChatMessage.from_assistant(tool_calls=[function_call]))
## Parse function calling information
function_name = function_call.tool_name
function_args = function_call.arguments
## Find the corresponding function and call it with the given arguments
function_to_call = available_functions[function_name]
function_response = function_to_call(**function_args, session_hash=session_hash, session_folder='file_upload')
print(function_name)
## Append function response to the messages list using `ChatMessage.from_tool`
message_dict[session_hash]['file_upload'].append(ChatMessage.from_tool(tool_result=function_response['reply'], origin=function_call))
response = chat_generator.run(messages=message_dict[session_hash]['file_upload'], generation_kwargs={"tools": tools.data_file_tools_call(session_hash)})
# Regular Conversation
else:
message_dict[session_hash]['file_upload'].append(response["replies"][0])
break
return response["replies"][0].text
def sql_chatbot_with_fc(message, history, session_hash, db_url, db_port, db_user, db_pass, db_name, db_tables):
from functions import sql_query_func, table_generation_func, regression_func, scatter_chart_generation_func, \
line_chart_generation_func,bar_chart_generation_func,pie_chart_generation_func,histogram_generation_func
import tools.tools as tools
available_functions = {"sql_query_func": sql_query_func,"table_generation_func":table_generation_func,
"line_chart_generation_func":line_chart_generation_func,"bar_chart_generation_func":bar_chart_generation_func,
"scatter_chart_generation_func":scatter_chart_generation_func, "pie_chart_generation_func":pie_chart_generation_func,
"histogram_generation_func":histogram_generation_func,
"regression_func":regression_func }
if message_dict[session_hash]['sql'] != None:
message_dict[session_hash]['sql'].append(ChatMessage.from_user(message))
else:
messages = [
ChatMessage.from_system(
f"""You are a helpful and knowledgeable agent who has access to an PostgreSQL database which has a series of tables called {db_tables}.
You also have access to a function, called table_generation_func, that can take a query.csv file generated from our sql query and returns an iframe that we should display in our chat window.
You also have access to a scatter plot function, called scatter_chart_generation_func, that can take a query.csv file generated from our sql query and uses plotly dictionaries to generate a scatter plot and returns an iframe that we should display in our chat window.
You also have access to a line chart function, called line_chart_generation_func, that can take a query.csv file generated from our sql query and uses plotly dictionaries to generate a line chart and returns an iframe that we should display in our chat window.
You also have access to a bar graph function, called line_chart_generation_func, that can take a query.csv file generated from our sql query and uses plotly dictionaries to generate a bar graph and returns an iframe that we should display in our chat window.
You also have access to a pie chart function, called pie_chart_generation_func, that can take a query.csv file generated from our sql query and uses plotly dictionaries to generate a pie chart and returns an iframe that we should display in our chat window.
You also have access to a histogram function, called histogram_generation_func, that can take a query.csv file generated from our sql query and uses plotly dictionaries to generate a histogram and returns an iframe that we should display in our chat window.
You also have access to a linear regression function, called regression_func, that can take a query.csv file generated from our sql query and a list of column names for our independent and dependent variables and return a regression data string and a regression chart which is returned as an iframe.
Could you please always display the generated charts, tables, and visualizations as part of your output?"""
)
]
messages.append(ChatMessage.from_user(message))
message_dict[session_hash]['sql'] = messages
response = chat_generator.run(messages=message_dict[session_hash]['sql'], generation_kwargs={"tools": tools.sql_tools_call(db_tables)})
while True:
# if OpenAI response is a tool call
if response and response["replies"][0].meta["finish_reason"] == "tool_calls" or response["replies"][0].tool_calls:
function_calls = response["replies"][0].tool_calls
for function_call in function_calls:
message_dict[session_hash]['sql'].append(ChatMessage.from_assistant(tool_calls=[function_call]))
## Parse function calling information
function_name = function_call.tool_name
function_args = function_call.arguments
## Find the corresponding function and call it with the given arguments
function_to_call = available_functions[function_name]
function_response = function_to_call(**function_args, session_hash=session_hash, db_url=db_url,
db_port=db_port, db_user=db_user, db_pass=db_pass, db_name=db_name, session_folder='sql')
print(function_name)
## Append function response to the messages list using `ChatMessage.from_tool`
message_dict[session_hash]['sql'].append(ChatMessage.from_tool(tool_result=function_response['reply'], origin=function_call))
response = chat_generator.run(messages=message_dict[session_hash]['sql'], generation_kwargs={"tools": tools.sql_tools_call(db_tables)})
# Regular Conversation
else:
message_dict[session_hash]['sql'].append(response["replies"][0])
break
return response["replies"][0].text
def doc_db_chatbot_with_fc(message, history, session_hash, db_connection_string, db_name, db_collections, db_schema):
from functions import doc_db_query_func, table_generation_func, regression_func, scatter_chart_generation_func, \
line_chart_generation_func,bar_chart_generation_func,pie_chart_generation_func,histogram_generation_func
import tools.tools as tools
available_functions = {"doc_db_query_func": doc_db_query_func,"table_generation_func":table_generation_func,
"line_chart_generation_func":line_chart_generation_func,"bar_chart_generation_func":bar_chart_generation_func,
"scatter_chart_generation_func":scatter_chart_generation_func, "pie_chart_generation_func":pie_chart_generation_func,
"histogram_generation_func":histogram_generation_func,
"regression_func":regression_func }
if message_dict[session_hash]['doc_db'] != None:
message_dict[session_hash]['doc_db'].append(ChatMessage.from_user(message))
else:
messages = [
ChatMessage.from_system(
f"""You are a helpful and knowledgeable agent who has access to an NoSQL MongoDB Document database which has a series of collections called {db_collections}.
The schema of these collections is: {db_schema}.
You also have access to a function, called table_generation_func, that can take a query.csv file generated from our sql query and returns an iframe that we should display in our chat window.
You also have access to a scatter plot function, called scatter_chart_generation_func, that can take a query.csv file generated from our sql query and uses plotly dictionaries to generate a scatter plot and returns an iframe that we should display in our chat window.
You also have access to a line chart function, called line_chart_generation_func, that can take a query.csv file generated from our sql query and uses plotly dictionaries to generate a line chart and returns an iframe that we should display in our chat window.
You also have access to a bar graph function, called line_chart_generation_func, that can take a query.csv file generated from our sql query and uses plotly dictionaries to generate a bar graph and returns an iframe that we should display in our chat window.
You also have access to a pie chart function, called pie_chart_generation_func, that can take a query.csv file generated from our sql query and uses plotly dictionaries to generate a pie chart and returns an iframe that we should display in our chat window.
You also have access to a histogram function, called histogram_generation_func, that can take a query.csv file generated from our sql query and uses plotly dictionaries to generate a histogram and returns an iframe that we should display in our chat window.
You also have access to a linear regression function, called regression_func, that can take a query.csv file generated from our sql query and a list of column names for our independent and dependent variables and return a regression data string and a regression chart which is returned as an iframe.
Could you please always display the generated charts, tables, and visualizations as part of your output?"""
)
]
messages.append(ChatMessage.from_user(message))
message_dict[session_hash]['doc_db'] = messages
response = chat_generator.run(messages=message_dict[session_hash]['doc_db'], generation_kwargs={"tools": tools.doc_db_tools_call(db_collections)})
while True:
# if OpenAI response is a tool call
if response and response["replies"][0].meta["finish_reason"] == "tool_calls" or response["replies"][0].tool_calls:
function_calls = response["replies"][0].tool_calls
for function_call in function_calls:
message_dict[session_hash]['doc_db'].append(ChatMessage.from_assistant(tool_calls=[function_call]))
## Parse function calling information
function_name = function_call.tool_name
function_args = function_call.arguments
## Find the corresponding function and call it with the given arguments
function_to_call = available_functions[function_name]
function_response = function_to_call(**function_args, session_hash=session_hash, connection_string=db_connection_string,
doc_db_name=db_name, session_folder='doc_db')
print(function_name)
## Append function response to the messages list using `ChatMessage.from_tool`
message_dict[session_hash]['doc_db'].append(ChatMessage.from_tool(tool_result=function_response['reply'], origin=function_call))
response = chat_generator.run(messages=message_dict[session_hash]['doc_db'], generation_kwargs={"tools": tools.doc_db_tools_call(db_collections)})
# Regular Conversation
else:
message_dict[session_hash]['doc_db'].append(response["replies"][0])
break
return response["replies"][0].text |