import re
from typing import Mapping, Optional, Any
import torch
import transformers
from transformers import AutoTokenizer, AutoModelForCausalLM
from huggingface_hub import snapshot_download
from langchain.embeddings import HuggingFaceInstructEmbeddings
from langchain.vectorstores import FAISS
from sentence_transformers import CrossEncoder
from qa_engine import logger, Config
from qa_engine.response import Response
from qa_engine.mocks import MockLocalBinaryModel
class HuggingFaceModel:
model_id: str = None
min_new_tokens: int = None
max_new_tokens: int = None
temperature: float = None
top_k: int = None
top_p: float = None
do_sample: bool = None
tokenizer: transformers.PreTrainedTokenizer = None
model: transformers.PreTrainedModel = None
def __init__(self, config: Config):
super().__init__()
self.model_id = config.question_answering_model_id
self.min_new_tokens = config.min_new_tokens
self.max_new_tokens = config.max_new_tokens
self.temperature = config.temperature
self.top_k = config.top_k
self.top_p = config.top_p
self.do_sample = config.do_sample
self.tokenizer = AutoTokenizer.from_pretrained(self.model_id)
self.model = AutoModelForCausalLM.from_pretrained(
self.model_id,
torch_dtype=torch.float16,
device_map="auto"
)
def _call(self, prompt: str, stop: Optional[list[str]] = None) -> str:
tokenized_prompt = self.tokenizer(
self.tokenizer.bos_token + prompt,
return_tensors="pt"
).to(self.model.device)
terminators = [
self.tokenizer.eos_token_id,
self.tokenizer.convert_tokens_to_ids("<|eot_id|>")
]
outputs = self.model.generate(
input_ids=tokenized_prompt.input_ids,
attention_mask=tokenized_prompt.attention_mask,
min_new_tokens=self.min_new_tokens,
max_new_tokens=self.max_new_tokens,
eos_token_id=terminators
)
response = outputs[0][tokenized_prompt.input_ids.shape[-1]:]
decoded_response = self.tokenizer.decode(response, skip_special_tokens=True)
return decoded_response
@property
def _identifying_params(self) -> Mapping[str, Any]:
return {'name_of_model': self.model_id}
@property
def _llm_type(self) -> str:
return self.model_id
class QAEngine():
"""
QAEngine class, used for generating answers to questions.
"""
def __init__(self, config: Config):
super().__init__()
self.config = config
self.question_answering_model_id=config.question_answering_model_id
self.embedding_model_id=config.embedding_model_id
self.index_repo_id=config.index_repo_id
self.prompt_template=config.prompt_template
self.use_docs_for_context=config.use_docs_for_context
self.num_relevant_docs=config.num_relevant_docs
self.add_sources_to_response=config.add_sources_to_response
self.use_messages_for_context=config.use_messages_in_context
self.debug=config.debug
self.first_stage_docs: int = 50
self.llm_model = self._get_model()
if self.use_docs_for_context:
logger.info(f'Downloading {self.index_repo_id}')
snapshot_download(
repo_id=self.index_repo_id,
allow_patterns=['*.faiss', '*.pkl'],
repo_type='dataset',
local_dir='indexes/run/'
)
logger.info('Loading embedding model')
embed_instruction = 'Represent the Hugging Face library documentation'
query_instruction = 'Query the most relevant piece of information from the Hugging Face documentation'
embedding_model = HuggingFaceInstructEmbeddings(
model_name=self.embedding_model_id,
embed_instruction=embed_instruction,
query_instruction=query_instruction
)
logger.info('Loading index')
self.knowledge_index = FAISS.load_local('./indexes/run/', embedding_model)
self.reranker = CrossEncoder('cross-encoder/ms-marco-MiniLM-L-12-v2')
def _get_model(self):
if self.question_answering_model_id == 'mock':
logger.warn('using mock model')
return MockLocalBinaryModel()
else:
logger.info('using transformers pipeline model')
return HuggingFaceModel(self.config)
@staticmethod
def _preprocess_input(question: str, context: str) -> str:
if '?' not in question:
question += '?'
# llama3 chatQA specific
messages = [
{"role": "user", "content": question}
]
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."
instruction = "Please give a full and complete answer for the question."
for item in messages:
if item['role'] == "user":
## only apply this instruction for the first user turn
item['content'] = instruction + " " + item['content']
break
conversation = '\n\n'.join([
"User: " + item["content"] if item["role"] == "user" else
"Assistant: " + item["content"] for item in messages
]) + "\n\nAssistant:"
inputs = system + "\n\n" + context + "\n\n" + conversation
return inputs
@staticmethod
def _postprocess_answer(answer: str) -> str:
'''
Preprocess the answer by removing unnecessary sequences and stop sequences.
'''
SEQUENCES_TO_REMOVE = [
'Factually: ', 'Answer: ', '<<SYS>>', '<</SYS>>', '[INST]', '[/INST]',
'<context>', '</context>', '<question>', '</question>',
]
SEQUENCES_TO_STOP = [
'User:', 'You:', 'Question:'
]
CHARS_TO_DEDUPLICATE = [
'\n', '\t', ' '
]
for seq in SEQUENCES_TO_REMOVE:
answer = answer.replace(seq, '')
for seq in SEQUENCES_TO_STOP:
if seq in answer:
answer = answer[:answer.index(seq)]
for char in CHARS_TO_DEDUPLICATE:
answer = re.sub(f'{char}+', f'{char}', answer)
answer = answer.strip()
return answer
def get_response(self, question: str, messages_context: str = '') -> Response:
"""
Generate an answer to the specified question.
Args:
question (str): The question to be answered.
messages_context (str, optional): The context to be used for generating the answer. Defaults to ''.
Returns:
response (Response): The Response object containing the generated answer and the sources of information
used to generate the response.
"""
response = Response()
context = ''
relevant_docs = ''
if self.use_messages_for_context and messages_context:
messages_context = f'\nPrevious questions and answers:\n{messages_context}'
context += messages_context
if self.use_docs_for_context:
logger.info('Retriving documents')
# messages context is used for better retrival
retrival_query = messages_context + question
relevant_docs = self.knowledge_index.similarity_search(
query=retrival_query,
k=self.first_stage_docs
)
cross_encoding_predictions = self.reranker.predict(
[(retrival_query, doc.page_content) for doc in relevant_docs]
)
relevant_docs = [
doc for _, doc in sorted(
zip(cross_encoding_predictions, relevant_docs),
reverse=True, key = lambda x: x[0]
)
]
relevant_docs = relevant_docs[:self.num_relevant_docs]
context += '\nExtracted documents:\n'
context += ''.join([doc.page_content for doc in relevant_docs])
metadata = [doc.metadata for doc in relevant_docs]
response.set_sources(sources=[str(m['source']) for m in metadata])
logger.info('Running LLM chain')
inputs = QAEngine._preprocess_input(question, context)
answer = self.llm_model._call(inputs)
answer_postprocessed = QAEngine._postprocess_answer(answer)
response.set_answer(answer_postprocessed)
logger.info('Received answer')
if self.debug:
logger.info('\n' + '=' * 100)
sep = '\n' + '-' * 100
logger.info(f'question len: {len(question)} {sep}')
logger.info(f'question: {question} {sep}')
logger.info(f'answer len: {len(response.get_answer())} {sep}')
logger.info(f'answer original: {answer} {sep}')
logger.info(f'answer postprocessed: {response.get_answer()} {sep}')
logger.info(f'{response.get_sources_as_text()} {sep}')
logger.info(f'messages_contex: {messages_context} {sep}')
logger.info(f'relevant_docs: {relevant_docs} {sep}')
logger.info(f'context len: {len(context)} {sep}')
logger.info(f'context: {context} {sep}')
return response