File size: 16,961 Bytes
e448d98 32f5b77 95c52e2 e448d98 c76addc e448d98 c76addc e448d98 c76addc e448d98 c76addc e448d98 4c67f45 e448d98 4c67f45 76271c7 3d87c18 32f5b77 4c67f45 e448d98 76271c7 ac253c3 e448d98 0614630 e448d98 0614630 e448d98 c76addc e448d98 c76addc e448d98 c76addc e448d98 c76addc e448d98 c76addc e448d98 c76addc e448d98 3d87c18 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 |
from utils import message_dict
from haystack.dataclasses import ChatMessage
from haystack.components.generators.chat import OpenAIChatGenerator
chat_generator = OpenAIChatGenerator(model="gpt-4o")
response = None
def example_question_message(data_source, name, titles, schema):
example_message_dict = {
'file_upload' : ["You are a helpful and knowledgeable agent who has access to an SQLite database which has a table called 'data_source'.",
f"""We have a SQLite database with the following {titles}.
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."""],
'sql' : [f"You are a helpful and knowledgeable agent who has access to an MongoDB NoSQL document database called {name}.",
f"""We have a PostgreSQL database with the following tables: {titles}.
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."""],
'doc_db' : [f"You are a helpful and knowledgeable agent who has access to an MongoDB NoSQL document database called {name}.",
f"""We have a MongoDB NoSQL document database with the following collections: {titles}.
The schema of these collections is: {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."""],
'graphql' : [f"You are a helpful and knowledgeable agent who has access to an GraphQL API endpoint called {name}.",
f"""We have a GraphQL API endpoint with the following types: {titles}.
We also have an AI agent with access to the same GraphQL API endpoint 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."""]
}
return example_message_dict[data_source]
def example_question_generator(session_hash, data_source, name, titles, schema):
example_response = None
example_message_list = example_question_message(data_source, name, titles, schema)
example_messages = [
ChatMessage.from_system(
example_message_list[0]
)
]
example_messages.append(ChatMessage.from_user(text=example_message_list[1]))
example_response = chat_generator.run(messages=example_messages)
return example_response["replies"][0].text
def system_message(data_source, titles, schema=""):
print("TITLES")
print(titles)
system_message_dict = {
'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}.
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?""",
'sql' : f"""You are a helpful and knowledgeable agent who has access to an PostgreSQL database which has a series of tables called {titles}.
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?""",
'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}.
The schema of these collections is: {schema}.
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.
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.
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.
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.
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.
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.
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.
Could you please always display the generated charts, tables, and visualizations as part of your output?""",
'graphql' : f"""You are a helpful and knowledgeable agent who has access to a GraphQL API which has the following types: {titles}.
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.
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.
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.
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.
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.
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.
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.
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.
Could you please always display the generated charts, tables, and visualizations as part of your output?"""
}
return system_message_dict[data_source]
def chatbot_func(message, history, session_hash, data_source, titles, schema, *args):
from functions import table_generation_func, regression_func, scatter_chart_generation_func, \
query_func, graphql_schema_query, graphql_csv_query, \
line_chart_generation_func,bar_chart_generation_func,pie_chart_generation_func,histogram_generation_func
import tools.tools as tools
available_functions = {"query_func":query_func,"graphql_schema_query": graphql_schema_query,"graphql_csv_query": graphql_csv_query,
"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][data_source] != None:
message_dict[session_hash][data_source].append(ChatMessage.from_user(message))
else:
messages = [
ChatMessage.from_system(system_message(data_source, titles, schema))
]
messages.append(ChatMessage.from_user(message))
message_dict[session_hash][data_source] = messages
response = chat_generator.run(messages=message_dict[session_hash][data_source], generation_kwargs={"tools": tools.tools_call(session_hash, data_source, titles)})
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][data_source].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=data_source, args=args)
print(function_name)
## Append function response to the messages list using `ChatMessage.from_tool`
message_dict[session_hash][data_source].append(ChatMessage.from_tool(tool_result=function_response['reply'], origin=function_call))
response = chat_generator.run(messages=message_dict[session_hash][data_source], generation_kwargs={"tools": tools.tools_call(session_hash, data_source, titles)})
# Regular Conversation
else:
message_dict[session_hash][data_source].append(response["replies"][0])
break
return response["replies"][0].text |