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from citekit.cite_modules.LLM import LLM,Module
from citekit.cite_modules.augment_model import AugmentCluster, AttributingModule, MODEL_TYPE_MAPPING
from citekit.prompt.prompt import ALCEVanillaPrompt, DocPrompt
import logging
import json
from tqdm import tqdm
import traceback
import copy
from citekit.utils.utils import flatten_dict
import csv
def merge_str_dicts(dicts):
result = {}
for dictionary in dicts:
for key, value in dictionary.items():
if key in result:
result[key] += ' ' + value
else:
result[key] = value
return result
PIPELINE_OUTPUT = 'output'
PIPELINE_DOC_CACHE = 'doc_cache'
class DocCache():
def __init__(self) -> None:
self.__docs = list()
def __len__(self):
return len(self.__docs)
def __getitem__(self,index):
if index>=0 and index <len(self):
return self.__docs[index]
else:
return None
def get_last(self):
if self.__docs:
return self.__docs[-1]
def add_doc(self, doc, add_id = True) -> int:
if not isinstance(doc, str):
assert isinstance(doc, dict) and 'text' in doc and 'title' in doc
doc = f'(Title: {doc["title"]}){doc["text"]}'
if add_id:
doc_head = f'Document [{len(self)+1}]'
else:
doc_head = ''
self.__docs.append(doc_head + doc)
return len(self)
def load_docs(self, docs, add_id = False):
for doc in docs:
self.add_doc(doc, add_id)
return len(self)
def clear(self):
self.__docs = list()
def show_docs(self):
return self.__docs
class Pipeline():
def __init__(self,save_path = None, sequence = None, head_prompt_maker = None, llm = None, module= None, retriever = None, evaluator = None, dataset = None, rich_eval = False, train_data = False, attributer = None) -> None:
self.save_path = save_path
self.train_data = train_data
self.head_prompt_maker = head_prompt_maker
self.table_head = True
self.attributer = attributer
self.llm = llm
self.initial_docs = None
self.data_keys = None
self.stored_clusters = []
self.module = []
if llm:
llm.connect_to(self)
if not isinstance(module,list) and module is not None:
if module:
module.connect_to(self)
else:
if isinstance(module, list):
for i in module:
if isinstance(i, AugmentCluster) or isinstance(i, Module):
i.connect_to(self)
self.dataset = dataset
self.data_index = 0
self.retriever = retriever
if retriever:
retriever.pipeline = self
self.eval = evaluator
if evaluator:
evaluator.pipeline = self
self.output = []
self.log = []
self.doc_cache = DocCache()
self.head = {}
self.result = {}
self.rich_eval = rich_eval
self.initial_module = None
def load_data(self, dataset):
self.data = dataset
def set_initial_module(self, module):
self.initial_module = module
def get_initial_module(self):
return self.initial_module
def set_data_keys(self, keys):
self.data_keys = keys
def get_data_keys(self):
return self.data_keys
def update(self, update_object, config, update_info):
print(f'Updating {update_object} with {config} and {update_info}')
module = self.get_module_by_name(update_object)
if config in ['prompt', 'header']:
module.update(config, update_info)
elif config in ['destination']:
module.update(config, [self.get_module_by_name(update_info[0]), update_info[1]])
elif config in ['delete_destination']:
module.update(config, self.get_module_by_name(update_info))
elif config in ['new_model']:
model_type, model, key = update_info
print('Creating new model:', model_type, model)
new_model_class = MODEL_TYPE_MAPPING[model_type]
print('New model class:', new_model_class)
new_model = new_model_class(model)
new_model.connect_to(self)
print('Created new model:', new_model)
module.update('destination', [new_model, key])
else:
raise NotImplementedError
def set_initial_docs(self, d):
self.initial_docs = d
def get_initial_docs(self):
return self.initial_docs
def run_on_dataset(self,datakeys,init_docs=None,initial_module= None,start=0):
if self.initial_module and not initial_module:
initial_module = self.initial_module
if self.save_path:
for i in range(start,len((self.dataset))):
self.data_index = i
try:
self.run(datakeys,init_docs,initial_module,train=self.train_data)
except Exception as e:
print(f'Error: {e}, skipping data {i}')
traceback.print_exc()
else:
for i in range(start,len((self.dataset))):
self.data_index = i
try:
self.run(datakeys,init_docs,initial_module,write=False,train=self.train_data)
except Exception as e:
print(f'Error: {e}, skipping data {i}')
traceback.print_exc()
def form_eval_data(self) -> dict:
"""To write rich eval, you can use data from:
pipeline.dataset, doc_cache and output
to post_process data as a argument dict for evaluation
"""
raise NotImplementedError('You have to write <form_eval_data function> to apply rich eval with designed arguments.')
def direct_run(self, dynamic_prompt= {}, module = None):
if not module:
module = self.llm
if isinstance(module, AugmentCluster):
module = module.get_first_module()
while isinstance(module, Module):
if isinstance(dynamic_prompt,dict):
module.change_to_multi_process(False)
dynamic_prompt = module.generate(self.head,dynamic_prompt=dynamic_prompt)
elif isinstance(dynamic_prompt,list) and all([isinstance(d,dict) for d in dynamic_prompt]):
module.change_to_multi_process(True)
if module.parallel:
dynamic_prompt = [module.generate(self.head,d) for d in dynamic_prompt]
if module.merge:
dynamic_prompt = merge_str_dicts(dynamic_prompt)
module.add_output_to_head(module.last_message)
elif not module.iterative and not module.merge:
for d in dynamic_prompt:
self.direct_run(dynamic_prompt = d, module = copy.copy(module))
#dynamic_prompt = [module.generate(self.head,d) for d in dynamic_prompt]
break
elif module.iterative:
iter_dynamic = {}
for d in dynamic_prompt:
iter_dynamic = module.generate(self.head,{**d,**iter_dynamic})
dynamic_prompt = iter_dynamic
module.end_multi()
else:
print(type(dynamic_prompt))
raise TypeError(str(dynamic_prompt))
self.log.append(f'{module} -> {module.send()}\n: {module.last_message}')
if isinstance(module, Module):
module.output()
print('DEBUG:', str(module), module.end, module.turns, module.max_turn)
if module.end or module.turns > module.max_turn:
break
module = module.send()
if isinstance(module, AugmentCluster):
module = module.get_first_module()
def __call__(self, data):
# run only one data
# backup
dataset_backup = self.dataset
current_data_index_backup = self.data_index
if hasattr(self,'current_data'):
current_data_backup = self.current_data
else:
current_data_backup = None
# set data and run
dataset = [data]
self.dataset = dataset
self.data_index = 0
result = self.run(datakeys = self.data_keys, init_docs = self.initial_docs, initial_module = self.initial_module, write = False, train = False)
# restore
self.data_index = current_data_index_backup
self.current_data = current_data_backup
self.dataset = dataset_backup
return result
def run(self, datakeys, init_docs = None, initial_module = None, write = True, train = False):
# get data
self.current_data = self.dataset[self.data_index]
data = self.current_data
# from head prompt from specific data
head = dict()
for key in datakeys:
if isinstance(data[key],str):
head[key] = data[key]
else:
assert isinstance(data[key],list)
assert all([isinstance(item, str) for item in data[key]])
head[key] = ''.join(data[key])
#init
self.head = head
self.output = []
self.doc_cache.clear()
if init_docs:
self.doc_cache.load_docs(data[init_docs])
self.llm.reset()
if self.module:
for i in self.module:
i.reset()
self.log = []
# run only one data, and add data_index by 1
dynamic_prompt = {}
if not initial_module:
module = self.llm
else:
module = initial_module
if isinstance(module, AugmentCluster):
module = module.get_first_module()
while isinstance(module, Module):
if isinstance(dynamic_prompt,dict):
module.change_to_multi_process(False)
dynamic_prompt = module.generate(self.head,dynamic_prompt=dynamic_prompt)
elif isinstance(dynamic_prompt,list) and all([isinstance(d,dict) for d in dynamic_prompt]):
module.change_to_multi_process(True)
if module.parallel:
dynamic_prompt = [module.generate(self.head,d) for d in dynamic_prompt]
if module.merge:
dynamic_prompt = merge_str_dicts(dynamic_prompt)
module.add_output_to_head(module.last_message)
elif not module.iterative and not module.merge:
for d in dynamic_prompt:
self.direct_run(dynamic_prompt = d, module = copy.copy(module))
#dynamic_prompt = [module.generate(self.head,d) for d in dynamic_prompt]
break
elif module.iterative:
iter_dynamic = {}
for d in dynamic_prompt:
iter_dynamic = module.generate(self.head,{**d,**iter_dynamic})
dynamic_prompt = iter_dynamic
module.end_multi()
else:
print(type(dynamic_prompt))
raise TypeError(str(dynamic_prompt))
self.log.append(f'{module} -> {module.send()}\n: {module.last_message}')
if isinstance(module, Module):
module.output()
if module.end or module.turns > module.max_turn:
break
module = module.send()
if isinstance(module, AugmentCluster):
module = module.get_first_module()
# if eval, send to evaluation
if self.eval:
if not self.rich_eval:
self.result = self.eval()
else:
self.result = self.eval(self.form_eval_data())
else:
self.result = {}
if write:
self.write()
if train:
self.export_training_data()
#self.logs = self.delete_inner_cluster_logs(self.log)
res = {'data':self.get_data(), 'doc_cache':self.doc_cache.show_docs(), 'log': self.log.copy(),'output':self.output,'result': self.result}
if self.attributer:
self.attributer.attribute_for_result(res)
self.data_index += 1
return res
def delete_inner_cluster_logs(self, logs):
print(logs)
for cluster in self.stored_clusters:
cluster_name = str(cluster)
print('Combining logs for cluster:', cluster_name)
in_cluster = False
for i, log in enumerate(logs):
in_out_names = log.split('\n')[0]
if in_out_names in cluster_name:
# This is the inner log
if not in_cluster:
in_cluster = True
log_start = i
else:
continue
elif in_cluster:
# This is the outer log
in_cluster = False
log_end = i
cluster_output = logs[log_end]
next_module = in_out_names.split('->')[1].strip()
cluster_log = f"{cluster_name} -> {next_module}\n: {cluster_output}"
logs = logs[:log_start] + [cluster_log] + logs[log_end+1:]
print('Final logs:', logs)
return logs
def get_data(self):
return self.dataset[self.data_index]
def write(self):
'''Default writing'''
llm_token_used = self.llm.token_used
write_down = {'data':self.get_data(), 'doc_cache':self.doc_cache.show_docs(), 'log': self.log.copy(),'output':self.output,'result': self.result,'token_used':llm_token_used}
if self.attributer:
self.attributer.attribute_for_result(write_down)
with open(self.save_path, 'a', encoding='utf-8') as file:
json_line = json.dumps(write_down, indent=4)
file.write(json_line + '\n')
def get_module_by_name(self, name):
print('Getting module by name:', name)
for module in self.module:
if str(module) == name:
return module
if str(self.llm) == name:
return self.llm
for cluster in self.stored_clusters:
print('trying cluster:', cluster)
if str(cluster) == name:
print('found cluster:', cluster)
return cluster
return None
def export_training_data(self):
flattened_data = [flatten_dict(self.result)]
header = set()
for item in flattened_data:
header.update(item.keys())
header = sorted(header)
with open('output.csv', mode='a', newline='') as file:
writer = csv.DictWriter(file, fieldnames = header)
if self.table_head:
writer.writeheader()
self.table_head = False
for row in flattened_data:
writer.writerow(row)
def __str__(self) -> str:
return 'pipeline output'
class Sequence(Pipeline):
def __init__(self, save_path=None, sequence=None, head_prompt_maker=None, retriever=None, evaluator=None, dataset=None, rich_eval=False) -> None:
first_module = sequence[0]
other = sequence[1:]
super().__init__(save_path, sequence, head_prompt_maker, first_module, other, retriever, evaluator, dataset, rich_eval)
for i in range(len(sequence)-1):
module = sequence[i]
assert isinstance(module, Module) or isinstance(module,AugmentCluster)
module.set_target(sequence[i+1],post_processing=lambda x: {module.output_as: x})
sequence[-1].set_output()
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