| from utils import TEMP_DIR, 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_generator(session_hash):
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| import sqlite3
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| example_response = None
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| example_messages = [
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| ChatMessage.from_system(
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| "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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| ]
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| dir_path = TEMP_DIR / str(session_hash)
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| connection = sqlite3.connect(f'{dir_path}/data_source.db')
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| print("Querying questions");
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| cur=connection.execute('select * from data_source')
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| columns = [i[0] for i in cur.description]
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| print("QUESTION COLUMNS")
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| print(columns)
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| cur.close()
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| connection.close()
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|
|
| example_messages.append(ChatMessage.from_user(text=f"""We have a SQLite database with the following {columns}.
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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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|
|
| example_response = chat_generator.run(messages=example_messages)
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|
|
| return example_response["replies"][0].text
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|
|
| def chatbot_with_fc(message, history, session_hash):
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| from functions import sqlite_query_func, table_generation_func, regression_func, scatter_chart_generation_func, \
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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
|
|
|
| available_functions = {"sql_query_func": sqlite_query_func,"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] != None:
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| message_dict[session_hash].append(ChatMessage.from_user(message))
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| else:
|
| 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'.
|
| 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 can 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 can 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 can 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 can 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 can 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 can 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."""
|
| )
|
| ]
|
| messages.append(ChatMessage.from_user(message))
|
| message_dict[session_hash] = messages
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|
|
| response = chat_generator.run(messages=message_dict[session_hash], generation_kwargs={"tools": tools.tools_call(session_hash)})
|
|
|
| while True:
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|
|
| 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:
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| message_dict[session_hash].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)
|
| print(function_name)
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|
|
| message_dict[session_hash].append(ChatMessage.from_tool(tool_result=function_response['reply'], origin=function_call))
|
| response = chat_generator.run(messages=message_dict[session_hash], generation_kwargs={"tools": tools.tools_call(session_hash)})
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|
|
|
|
| else:
|
| message_dict[session_hash].append(response["replies"][0])
|
| break
|
| return response["replies"][0].text
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|
|
|
|
|
|
