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from utils import message_dict
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from haystack.dataclasses import ChatMessage
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from haystack.components.generators.chat import OpenAIChatGenerator
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chat_generator = OpenAIChatGenerator(model="gpt-4o")
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response = None
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def example_question_message(data_source, name, titles, schema):
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example_message_dict = {
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'file_upload' : ["You are a helpful and knowledgeable agent who has access to an SQLite database which has a table called 'data_source'.",
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f"""We have a SQLite database with the following {titles}.
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We also have an AI agent with access to the same database that will be performing data analysis.
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Please return an array of seven strings, each one being a question for our data analysis agent
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that we can suggest that you believe will be insightful or helpful to a data analysis looking for
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data insights. Return nothing more than the array of questions because I need that specific data structure
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to process your response. No other response type or data structure will work."""],
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'sql' : [f"You are a helpful and knowledgeable agent who has access to an MongoDB NoSQL document database called {name}.",
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f"""We have a PostgreSQL database with the following tables: {titles}.
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We also have an AI agent with access to the same database that will be performing data analysis.
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Please return an array of seven strings, each one being a question for our data analysis agent
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that we can suggest that you believe will be insightful or helpful to a data analysis looking for
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data insights. Return nothing more than the array of questions because I need that specific data structure
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to process your response. No other response type or data structure will work."""],
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'doc_db' : [f"You are a helpful and knowledgeable agent who has access to an MongoDB NoSQL document database called {name}.",
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f"""We have a MongoDB NoSQL document database with the following collections: {titles}.
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The schema of these collections is: {schema}.
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We also have an AI agent with access to the same database that will be performing data analysis.
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Please return an array of seven strings, each one being a question for our data analysis agent
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that we can suggest that you believe will be insightful or helpful to a data analysis looking for
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data insights. Return nothing more than the array of questions because I need that specific data structure
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to process your response. No other response type or data structure will work."""],
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'graphql' : [f"You are a helpful and knowledgeable agent who has access to an GraphQL API endpoint called {name}.",
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f"""We have a GraphQL API endpoint with the following types: {titles}.
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We also have an AI agent with access to the same GraphQL API endpoint that will be performing data analysis.
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Please return an array of seven strings, each one being a question for our data analysis agent
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that we can suggest that you believe will be insightful or helpful to a data analysis looking for
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data insights. Return nothing more than the array of questions because I need that specific data structure
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to process your response. No other response type or data structure will work."""]
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}
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return example_message_dict[data_source]
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def example_question_generator(session_hash, data_source, name, titles, schema):
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example_response = None
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example_message_list = example_question_message(data_source, name, titles, schema)
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example_messages = [
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ChatMessage.from_system(
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example_message_list[0]
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)
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]
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example_messages.append(ChatMessage.from_user(text=example_message_list[1]))
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example_response = chat_generator.run(messages=example_messages)
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return example_response["replies"][0].text
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def system_message(data_source, titles, schema=""):
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print("TITLES")
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print(titles)
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system_message_dict = {
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'file_upload' : 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: {titles}.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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Could you please always display the generated charts, tables, and visualizations as part of your output?""",
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'sql' : f"""You are a helpful and knowledgeable agent who has access to an PostgreSQL database which has a series of tables called {titles}.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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Could you please always display the generated charts, tables, and visualizations as part of your output?""",
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'doc_db' : f"""You are a helpful and knowledgeable agent who has access to a NoSQL MongoDB Document database which has a series of collections called {titles}.
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The schema of these collections is: {schema}.
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You also have access to a function, called table_generation_func, that can take a query.csv file generated from our MongoDB query and returns an iframe that we should display in our chat window.
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You also have access to a scatter plot function, called scatter_chart_generation_func, that can take a query.csv file generated from our MongoDB query and uses plotly dictionaries to generate a scatter plot and returns an iframe that we should display in our chat window.
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You also have access to a line chart function, called line_chart_generation_func, that can take a query.csv file generated from our MongoDB query and uses plotly dictionaries to generate a line chart and returns an iframe that we should display in our chat window.
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You also have access to a bar graph function, called line_chart_generation_func, that can take a query.csv file generated from our MongoDB query and uses plotly dictionaries to generate a bar graph and returns an iframe that we should display in our chat window.
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You also have access to a pie chart function, called pie_chart_generation_func, that can take a query.csv file generated from our MongoDB query and uses plotly dictionaries to generate a pie chart and returns an iframe that we should display in our chat window.
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You also have access to a histogram function, called histogram_generation_func, that can take a query.csv file generated from our MongoDB query and uses plotly dictionaries to generate a histogram and returns an iframe that we should display in our chat window.
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You also have access to a linear regression function, called regression_func, that can take a query.csv file generated from our MongoDB 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.
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Could you please always display the generated charts, tables, and visualizations as part of your output?""",
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'graphql' : f"""You are a helpful and knowledgeable agent who has access to a GraphQL API which has the following types: {titles}.
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We have also saved a schema.json file that contains the entire introspection query that we can use to find out more about each type before making a query.
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You also have access to a function, called table_generation_func, that can take a query.csv file generated from our GraphQL API query and returns an iframe that we should display in our chat window.
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You also have access to a scatter plot function, called scatter_chart_generation_func, that can take a query.csv file generated from our GraphQL API query and uses plotly dictionaries to generate a scatter plot and returns an iframe that we should display in our chat window.
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You also have access to a line chart function, called line_chart_generation_func, that can take a query.csv file generated from our GraphQL API query and uses plotly dictionaries to generate a line chart and returns an iframe that we should display in our chat window.
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You also have access to a bar graph function, called line_chart_generation_func, that can take a query.csv file generated from our GraphQL API query and uses plotly dictionaries to generate a bar graph and returns an iframe that we should display in our chat window.
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You also have access to a pie chart function, called pie_chart_generation_func, that can take a query.csv file generated from our GraphQL API query and uses plotly dictionaries to generate a pie chart and returns an iframe that we should display in our chat window.
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You also have access to a histogram function, called histogram_generation_func, that can take a query.csv file generated from our GraphQL API query and uses plotly dictionaries to generate a histogram and returns an iframe that we should display in our chat window.
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You also have access to a linear regression function, called regression_func, that can take a query.csv file generated from our GraphQL API 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.
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Could you please always display the generated charts, tables, and visualizations as part of your output?"""
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}
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return system_message_dict[data_source]
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def chatbot_func(message, history, session_hash, data_source, titles, schema, *args):
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from functions import table_generation_func, regression_func, scatter_chart_generation_func, \
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query_func, graphql_schema_query, graphql_csv_query, \
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line_chart_generation_func,bar_chart_generation_func,pie_chart_generation_func,histogram_generation_func
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import tools.tools as tools
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available_functions = {"query_func":query_func,"graphql_schema_query": graphql_schema_query,"graphql_csv_query": graphql_csv_query,
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"table_generation_func":table_generation_func,
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"line_chart_generation_func":line_chart_generation_func,"bar_chart_generation_func":bar_chart_generation_func,
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"scatter_chart_generation_func":scatter_chart_generation_func, "pie_chart_generation_func":pie_chart_generation_func,
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"histogram_generation_func":histogram_generation_func,
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"regression_func":regression_func }
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if message_dict[session_hash][data_source] != None:
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message_dict[session_hash][data_source].append(ChatMessage.from_user(message))
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else:
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messages = [
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ChatMessage.from_system(system_message(data_source, titles, schema))
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]
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messages.append(ChatMessage.from_user(message))
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message_dict[session_hash][data_source] = messages
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response = chat_generator.run(messages=message_dict[session_hash][data_source], generation_kwargs={"tools": tools.tools_call(session_hash, data_source, titles)})
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while True:
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if response and response["replies"][0].meta["finish_reason"] == "tool_calls" or response["replies"][0].tool_calls:
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function_calls = response["replies"][0].tool_calls
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for function_call in function_calls:
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message_dict[session_hash][data_source].append(ChatMessage.from_assistant(tool_calls=[function_call]))
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function_name = function_call.tool_name
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function_args = function_call.arguments
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function_to_call = available_functions[function_name]
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function_response = function_to_call(**function_args, session_hash=session_hash, session_folder=data_source, args=args)
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print(function_name)
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message_dict[session_hash][data_source].append(ChatMessage.from_tool(tool_result=function_response['reply'], origin=function_call))
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response = chat_generator.run(messages=message_dict[session_hash][data_source], generation_kwargs={"tools": tools.tools_call(session_hash, data_source, titles)})
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else:
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message_dict[session_hash][data_source].append(response["replies"][0])
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break
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return response["replies"][0].text |
