@ -57,18 +57,22 @@ max_length = {
" ada " : 2049
}
def count_tokens ( model_name , text ) :
return len ( encodings [ model_name ] . encode ( text ) )
def get_max_context_length ( model_name ) :
return max_length [ model_name ]
def get_token_ids_for_task_parsing ( model_name ) :
text = ''' { " task " : " text-classification " , " token-classification " , " text2text-generation " , " summarization " , " translation " , " question-answering " , " conversational " , " text-generation " , " sentence-similarity " , " tabular-classification " , " object-detection " , " image-classification " , " image-to-image " , " image-to-text " , " text-to-image " , " visual-question-answering " , " document-question-answering " , " image-segmentation " , " text-to-speech " , " text-to-video " , " automatic-speech-recognition " , " audio-to-audio " , " audio-classification " , " canny-control " , " hed-control " , " mlsd-control " , " normal-control " , " openpose-control " , " canny-text-to-image " , " depth-text-to-image " , " hed-text-to-image " , " mlsd-text-to-image " , " normal-text-to-image " , " openpose-text-to-image " , " seg-text-to-image " , " args " , " text " , " path " , " dep " , " id " , " <GENERATED>- " } '''
res = encodings [ model_name ] . encode ( text )
res = list ( set ( res ) )
return res
def get_token_ids_for_choose_model ( model_name ) :
text = ''' { " id " : " reason " } '''
res = encodings [ model_name ] . encode ( text )
@ -76,13 +80,7 @@ def get_token_ids_for_choose_model(model_name):
return res
#########
parser = argparse . ArgumentParser ( )
parser . add_argument ( " --config " , type = str , default = " swarms/agents/workers/multi_modal_workers/omni_agent/config.yml " )
parser . add_argument ( " --mode " , type = str , default = " cli " )
@ -183,7 +181,7 @@ if inference_mode!="huggingface":
r = requests . get ( Model_Server + " /running " )
if r . status_code != 200 :
raise ValueError ( message )
except :
except BaseException :
raise ValueError ( message )
@ -222,6 +220,7 @@ elif "HUGGINGFACE_ACCESS_TOKEN" in os.environ and os.getenv("HUGGINGFACE_ACCESS_
else :
raise ValueError ( f " Incorrect HuggingFace token. Please check your { args . config } file. " )
def convert_chat_to_completion ( data ) :
messages = data . pop ( ' messages ' , [ ] )
tprompt = " "
@ -243,6 +242,7 @@ def convert_chat_to_completion(data):
data [ ' max_tokens ' ] = data . get ( ' max_tokens ' , max ( get_max_context_length ( LLM ) - count_tokens ( LLM_encoding , final_prompt ) , 1 ) )
return data
def send_request ( data ) :
api_key = data . pop ( " api_key " )
api_type = data . pop ( " api_type " )
@ -269,6 +269,7 @@ def send_request(data):
else :
return response . json ( ) [ " choices " ] [ 0 ] [ " message " ] [ " content " ] . strip ( )
def replace_slot ( text , entries ) :
for key , value in entries . items ( ) :
if not isinstance ( value , str ) :
@ -276,6 +277,7 @@ def replace_slot(text, entries):
text = text . replace ( " {{ " + key + " }} " , value . replace ( ' " ' , " ' " ) . replace ( ' \n ' , " " ) )
return text
def find_json ( s ) :
s = s . replace ( " \' " , " \" " )
start = s . find ( " { " )
@ -284,21 +286,24 @@ def find_json(s):
res = res . replace ( " \n " , " " )
return res
def field_extract ( s , field ) :
try :
field_rep = re . compile ( f ' { field } .*?:.*? " (.*?) " ' , re . IGNORECASE )
extracted = field_rep . search ( s ) . group ( 1 ) . replace ( " \" " , " \' " )
except :
except BaseException :
field_rep = re . compile ( f ' { field } : \ * " (.*?) " ' , re . IGNORECASE )
extracted = field_rep . search ( s ) . group ( 1 ) . replace ( " \" " , " \' " )
return extracted
def get_id_reason ( choose_str ) :
reason = field_extract ( choose_str , " reason " )
id = field_extract ( choose_str , " id " )
choose = { " id " : id , " reason " : reason }
return id . strip ( ) , reason . strip ( ) , choose
def record_case ( success , * * args ) :
if success :
f = open ( " logs/log_success.jsonl " , " a " )
@ -308,6 +313,7 @@ def record_case(success, **args):
f . write ( json . dumps ( log ) + " \n " )
f . close ( )
def image_to_bytes ( img_url ) :
img_byte = io . BytesIO ( )
img_url . split ( " . " ) [ - 1 ]
@ -315,6 +321,7 @@ def image_to_bytes(img_url):
img_data = img_byte . getvalue ( )
return img_data
def resource_has_dep ( command ) :
args = command [ " args " ]
for _ , v in args . items ( ) :
@ -322,6 +329,7 @@ def resource_has_dep(command):
return True
return False
def fix_dep ( tasks ) :
for task in tasks :
args = task [ " args " ]
@ -335,6 +343,7 @@ def fix_dep(tasks):
task [ " dep " ] = [ - 1 ]
return tasks
def unfold ( tasks ) :
flag_unfold_task = False
try :
@ -361,6 +370,7 @@ def unfold(tasks):
return tasks
def chitchat ( messages , api_key , api_type , api_endpoint ) :
data = {
" model " : LLM ,
@ -371,6 +381,7 @@ def chitchat(messages, api_key, api_type, api_endpoint):
}
return send_request ( data )
def parse_task ( context , input , api_key , api_type , api_endpoint ) :
demos_or_presteps = parse_task_demos_or_presteps
messages = json . loads ( demos_or_presteps )
@ -404,6 +415,7 @@ def parse_task(context, input, api_key, api_type, api_endpoint):
}
return send_request ( data )
def choose_model ( input , task , metas , api_key , api_type , api_endpoint ) :
prompt = replace_slot ( choose_model_prompt , {
" input " : input ,
@ -454,6 +466,7 @@ def response_results(input, results, api_key, api_type, api_endpoint):
}
return send_request ( data )
def huggingface_model_inference ( model_id , data , task ) :
task_url = f " https://api-inference.huggingface.co/models/ { model_id } " # InferenceApi does not yet support some tasks
inference = InferenceApi ( repo_id = model_id , token = config [ " huggingface " ] [ " token " ] )
@ -586,6 +599,7 @@ def huggingface_model_inference(model_id, data, task):
result = { " generated audio " : f " /audios/ { name } . { type } " }
return result
def local_model_inference ( model_id , data , task ) :
task_url = f " { Model_Server } /models/ { model_id } "
@ -732,6 +746,7 @@ def get_model_status(model_id, url, headers, queue = None):
queue . put ( ( model_id , False , None ) )
return False
def get_avaliable_models ( candidates , topk = 5 ) :
all_available_models = { " local " : [ ] , " huggingface " : [ ] }
threads = [ ]
@ -766,6 +781,7 @@ def get_avaliable_models(candidates, topk=5):
return all_available_models
def collect_result ( command , choose , inference_result ) :
result = { " task " : command }
result [ " inference result " ] = inference_result
@ -945,6 +961,7 @@ def run_task(input, command, results, api_key, api_type, api_endpoint):
results [ id ] = collect_result ( command , choose , inference_result )
return True
def chat_huggingface ( messages , api_key , api_type , api_endpoint , return_planning = False , return_results = False ) :
start = time . time ( )
context = messages [ : - 1 ]
@ -1032,6 +1049,7 @@ def chat_huggingface(messages, api_key, api_type, api_endpoint, return_planning
logger . info ( f " response: { response } " )
return answer
def test ( ) :
# single round examples
inputs = [
@ -1055,6 +1073,7 @@ def test():
]
chat_huggingface ( messages , API_KEY , API_TYPE , API_ENDPOINT , return_planning = False , return_results = False )
def cli ( ) :
messages = [ ]
print ( " Welcome to Jarvis! A collaborative system that consists of an LLM as the controller and numerous expert models as collaborative executors. Jarvis can plan tasks, schedule Hugging Face models, generate friendly responses based on your requests, and help you with many things. Please enter your request (`exit` to exit). " )
Kye
1 year ago