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import os
import hydra
import aiflows
from aiflows.flow_launchers import FlowLauncher
from aiflows.backends.api_info import ApiInfo
from aiflows.utils.general_helpers import read_yaml_file
from aiflows import logging
from aiflows.flow_cache import CACHING_PARAMETERS, clear_cache
CACHING_PARAMETERS.do_caching = False # Set to True in order to disable caching
# clear_cache() # Uncomment this line to clear the cache
logging.set_verbosity_debug()
dependencies = [
{"url": "aiflows/AutoGPTFlowModule", "revision": os.getcwd()},
{"url": "aiflows/LCToolFlowModule", "revision": "main"},
]
from aiflows import flow_verse
flow_verse.sync_dependencies(dependencies)
if __name__ == "__main__":
# ~~~ Set the API information ~~~
# OpenAI backend
api_information = [ApiInfo(backend_used="openai",
api_key = os.getenv("OPENAI_API_KEY"))]
# Azure backend
# api_information = ApiInfo(backend_used = "azure",
# api_base = os.getenv("AZURE_API_BASE"),
# api_key = os.getenv("AZURE_OPENAI_KEY"),
# api_version = os.getenv("AZURE_API_VERSION") )
root_dir = "."
cfg_path = os.path.join(root_dir, "demo.yaml")
cfg = read_yaml_file(cfg_path)
cfg["flow"]["subflows_config"]["Controller"]["backend"]["api_infos"] = api_information
cfg["flow"]["subflows_config"]["Memory"]["backend"]["api_infos"] = api_information
# ~~~ Instantiate the Flow ~~~
flow_with_interfaces = {
"flow": hydra.utils.instantiate(cfg['flow'], _recursive_=False, _convert_="partial"),
"input_interface": (
None
if cfg.get( "input_interface", None) is None
else hydra.utils.instantiate(cfg['input_interface'], _recursive_=False)
),
"output_interface": (
None
if cfg.get( "output_interface", None) is None
else hydra.utils.instantiate(cfg['output_interface'], _recursive_=False)
),
}
# ~~~ Get the data ~~~
# data = {"id": 0, "goal": "Answer the following question: What is the population of Canada?"} # Uses wikipedia
# data = {"id": 0, "goal": "Answer the following question: Who was the NBA champion in 2023?"} # Uses duckduckgo
data = {"id": 0, "goal": "Answer the following question: What is the profession and date of birth of Michael Jordan?"}
# At first, we retrieve information about Michael Jordan the basketball player
# If we provide feedback, only in the first round, that we are not interested in the basketball player,
# but the statistician, and skip the feedback in the next rounds, we get the correct answer
# ~~~ Run inference ~~~
path_to_output_file = None
# path_to_output_file = "output.jsonl" # Uncomment this line to save the output to disk
_, outputs = FlowLauncher.launch(
flow_with_interfaces=flow_with_interfaces,
data=data,
path_to_output_file=path_to_output_file,
)
# ~~~ Print the output ~~~
flow_output_data = outputs[0]
print(flow_output_data)
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