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import json
def load_data(datapath):
print("loading data from %s" % datapath)
with open(datapath, "r") as f:
data_list = json.load(f)
return data_list
def reformat_question(turn_list, dataset_name):
## only take the lastest 7 turns
turn_list = turn_list[-7:]
assert turn_list[-1]['role'] == 'user'
# ChatRAG-Hi available datasets - all use long answer format
long_answer_dataset_list = ["doc2dial", "quac", "qrecc", "inscit", "doqa_movies", "doqa_travel", "doqa_cooking", "hybridial", "convfinqa"]
if dataset_name in long_answer_dataset_list:
for item in turn_list:
if item['role'] == 'user':
## only needs to add it on the first user turn
item['content'] = 'Please give a full and complete answer for the question. ' + item['content']
break
else:
raise Exception(f"Dataset '{dataset_name}' not supported in ChatRAG-Hi! Available datasets: {long_answer_dataset_list}")
question = ""
for item in turn_list:
if item["role"] == "user":
question += "User: " + item["content"] + "\n\n"
else:
assert item["role"] == "assistant"
question += "Assistant: " + item["content"] + "\n\n"
question += "Assistant:"
return question
def get_inputs(data_list, dataset_name, tokenizer, num_ctx, max_output_len, max_seq_length=4096):
system = "System: This is a chat between a user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions based on the context. The assistant should also indicate when the answer cannot be found in the context."
prompt_list = []
for item in data_list:
turn_list = item['messages']
question_formatted = reformat_question(turn_list, dataset_name)
ctx_list = ["title: " + ctx["title"] + ", source: " + ctx["text"] for ctx in item['ctxs'][:num_ctx]]
context = "\n\n".join(ctx_list)
context_tokens = tokenizer.encode(context)
question_tokens = tokenizer.encode(question_formatted)
system_tokens = tokenizer.encode(system)
if len(context_tokens) + len(question_tokens) + len(system_tokens) + max_output_len >= max_seq_length:
context_tokens = context_tokens[:max_seq_length - max_output_len - len(question_tokens) - len(system_tokens)]
context = tokenizer.decode(context_tokens, skip_special_tokens=True)
model_input = system + "\n\n" + context + "\n\n" + question_formatted
prompt_list.append(model_input)
return prompt_list
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