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import copy
import os
from pathlib import Path
from typing import Union, Any
from document_qa.grobid_processors import GrobidProcessor
from grobid_client.grobid_client import GrobidClient
from langchain.chains import create_extraction_chain, ConversationChain, ConversationalRetrievalChain
from langchain.chains.question_answering import load_qa_chain, stuff_prompt, refine_prompts, map_reduce_prompt, \
map_rerank_prompt
from langchain.prompts import SystemMessagePromptTemplate, HumanMessagePromptTemplate, ChatPromptTemplate
from langchain.retrievers import MultiQueryRetriever
from langchain.schema import Document
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.vectorstores import Chroma
from tqdm import tqdm
class DocumentQAEngine:
llm = None
qa_chain_type = None
embedding_function = None
embeddings_dict = {}
embeddings_map_from_md5 = {}
embeddings_map_to_md5 = {}
default_prompts = {
'stuff': stuff_prompt,
'refine': refine_prompts,
"map_reduce": map_reduce_prompt,
"map_rerank": map_rerank_prompt
}
def __init__(self,
llm,
embedding_function,
qa_chain_type="stuff",
embeddings_root_path=None,
grobid_url=None,
memory=None
):
self.embedding_function = embedding_function
self.llm = llm
self.memory = memory
self.chain = load_qa_chain(llm, chain_type=qa_chain_type)
if embeddings_root_path is not None:
self.embeddings_root_path = embeddings_root_path
if not os.path.exists(embeddings_root_path):
os.makedirs(embeddings_root_path)
else:
self.load_embeddings(self.embeddings_root_path)
if grobid_url:
self.grobid_url = grobid_url
grobid_client = GrobidClient(
grobid_server=self.grobid_url,
batch_size=1000,
coordinates=["p"],
sleep_time=5,
timeout=60,
check_server=True
)
self.grobid_processor = GrobidProcessor(grobid_client)
def load_embeddings(self, embeddings_root_path: Union[str, Path]) -> None:
"""
Load the embeddings assuming they are all persisted and stored in a single directory.
The root path of the embeddings containing one data store for each document in each subdirectory
"""
embeddings_directories = [f for f in os.scandir(embeddings_root_path) if f.is_dir()]
if len(embeddings_directories) == 0:
print("No available embeddings")
return
for embedding_document_dir in embeddings_directories:
self.embeddings_dict[embedding_document_dir.name] = Chroma(persist_directory=embedding_document_dir.path,
embedding_function=self.embedding_function)
filename_list = list(Path(embedding_document_dir).glob('*.storage_filename'))
if filename_list:
filenam = filename_list[0].name.replace(".storage_filename", "")
self.embeddings_map_from_md5[embedding_document_dir.name] = filenam
self.embeddings_map_to_md5[filenam] = embedding_document_dir.name
print("Embedding loaded: ", len(self.embeddings_dict.keys()))
def get_loaded_embeddings_ids(self):
return list(self.embeddings_dict.keys())
def get_md5_from_filename(self, filename):
return self.embeddings_map_to_md5[filename]
def get_filename_from_md5(self, md5):
return self.embeddings_map_from_md5[md5]
def query_document(self, query: str, doc_id, output_parser=None, context_size=4, extraction_schema=None,
verbose=False) -> (
Any, str):
# self.load_embeddings(self.embeddings_root_path)
if verbose:
print(query)
response = self._run_query(doc_id, query, context_size=context_size)
response = response['output_text'] if 'output_text' in response else response
if verbose:
print(doc_id, "->", response)
if output_parser:
try:
return self._parse_json(response, output_parser), response
except Exception as oe:
print("Failing to parse the response", oe)
return None, response
elif extraction_schema:
try:
chain = create_extraction_chain(extraction_schema, self.llm)
parsed = chain.run(response)
return parsed, response
except Exception as oe:
print("Failing to parse the response", oe)
return None, response
else:
return None, response
def query_storage(self, query: str, doc_id, context_size=4):
documents = self._get_context(doc_id, query, context_size)
context_as_text = [doc.page_content for doc in documents]
return context_as_text
def _parse_json(self, response, output_parser):
system_message = "You are an useful assistant expert in materials science, physics, and chemistry " \
"that can process text and transform it to JSON."
human_message = """Transform the text between three double quotes in JSON.\n\n\n\n
{format_instructions}\n\nText: \"\"\"{text}\"\"\""""
system_message_prompt = SystemMessagePromptTemplate.from_template(system_message)
human_message_prompt = HumanMessagePromptTemplate.from_template(human_message)
prompt_template = ChatPromptTemplate.from_messages([system_message_prompt, human_message_prompt])
results = self.llm(
prompt_template.format_prompt(
text=response,
format_instructions=output_parser.get_format_instructions()
).to_messages()
)
parsed_output = output_parser.parse(results.content)
return parsed_output
def _run_query(self, doc_id, query, context_size=4):
relevant_documents = self._get_context(doc_id, query, context_size)
response = self.chain.run(input_documents=relevant_documents,
question=query)
if self.memory:
self.memory.save_context({"input": query}, {"output": response})
return response
def _get_context(self, doc_id, query, context_size=4):
db = self.embeddings_dict[doc_id]
retriever = db.as_retriever(search_kwargs={"k": context_size})
relevant_documents = retriever.get_relevant_documents(query)
if self.memory and len(self.memory.buffer_as_messages) > 0:
relevant_documents.append(
Document(
page_content="""Following, the previous question and answers. Use these information only when in the question there are unspecified references:\n{}\n\n""".format(
self.memory.buffer_as_str))
)
return relevant_documents
def get_all_context_by_document(self, doc_id):
"""Return the full context from the document"""
db = self.embeddings_dict[doc_id]
docs = db.get()
return docs['documents']
def _get_context_multiquery(self, doc_id, query, context_size=4):
db = self.embeddings_dict[doc_id].as_retriever(search_kwargs={"k": context_size})
multi_query_retriever = MultiQueryRetriever.from_llm(retriever=db, llm=self.llm)
relevant_documents = multi_query_retriever.get_relevant_documents(query)
return relevant_documents
def get_text_from_document(self, pdf_file_path, chunk_size=-1, perc_overlap=0.1, include=(), verbose=False):
"""
Extract text from documents using Grobid, if chunk_size is < 0 it keeps each paragraph separately
"""
if verbose:
print("File", pdf_file_path)
filename = Path(pdf_file_path).stem
structure = self.grobid_processor.process_structure(pdf_file_path)
biblio = structure['biblio']
biblio['filename'] = filename.replace(" ", "_")
if verbose:
print("Generating embeddings for:", hash, ", filename: ", filename)
texts = []
metadatas = []
ids = []
if chunk_size < 0:
for passage in structure['passages']:
biblio_copy = copy.copy(biblio)
if len(str.strip(passage['text'])) > 0:
texts.append(passage['text'])
biblio_copy['type'] = passage['type']
biblio_copy['section'] = passage['section']
biblio_copy['subSection'] = passage['subSection']
metadatas.append(biblio_copy)
ids.append(passage['passage_id'])
else:
document_text = " ".join([passage['text'] for passage in structure['passages']])
# text_splitter = CharacterTextSplitter.from_tiktoken_encoder(
text_splitter = RecursiveCharacterTextSplitter.from_tiktoken_encoder(
chunk_size=chunk_size,
chunk_overlap=chunk_size * perc_overlap
)
texts = text_splitter.split_text(document_text)
metadatas = [biblio for _ in range(len(texts))]
ids = [id for id, t in enumerate(texts)]
if "biblio" in include:
biblio_metadata = copy.copy(biblio)
biblio_metadata['type'] = "biblio"
biblio_metadata['section'] = "header"
for key in ['title', 'authors', 'publication_year']:
if key in biblio_metadata:
texts.append("{}: {}".format(key, biblio_metadata[key]))
metadatas.append(biblio_metadata)
ids.append(key)
return texts, metadatas, ids
def create_memory_embeddings(self, pdf_path, doc_id=None, chunk_size=500, perc_overlap=0.1, include_biblio=False):
include = ["biblio"] if include_biblio else []
texts, metadata, ids = self.get_text_from_document(
pdf_path,
chunk_size=chunk_size,
perc_overlap=perc_overlap,
include=include)
if doc_id:
hash = doc_id
else:
hash = metadata[0]['hash']
if hash not in self.embeddings_dict.keys():
self.embeddings_dict[hash] = Chroma.from_texts(texts,
embedding=self.embedding_function,
metadatas=metadata,
collection_name=hash)
else:
# if 'documents' in self.embeddings_dict[hash].get() and len(self.embeddings_dict[hash].get()['documents']) == 0:
# self.embeddings_dict[hash].delete(ids=self.embeddings_dict[hash].get()['ids'])
self.embeddings_dict[hash].delete_collection()
self.embeddings_dict[hash] = Chroma.from_texts(texts,
embedding=self.embedding_function,
metadatas=metadata,
collection_name=hash)
self.embeddings_root_path = None
return hash
def create_embeddings(self, pdfs_dir_path: Path, chunk_size=500, perc_overlap=0.1, include_biblio=False):
input_files = []
for root, dirs, files in os.walk(pdfs_dir_path, followlinks=False):
for file_ in files:
if not (file_.lower().endswith(".pdf")):
continue
input_files.append(os.path.join(root, file_))
for input_file in tqdm(input_files, total=len(input_files), unit='document',
desc="Grobid + embeddings processing"):
md5 = self.calculate_md5(input_file)
data_path = os.path.join(self.embeddings_root_path, md5)
if os.path.exists(data_path):
print(data_path, "exists. Skipping it ")
continue
include = ["biblio"] if include_biblio else []
texts, metadata, ids = self.get_text_from_document(
input_file,
chunk_size=chunk_size,
perc_overlap=perc_overlap,
include=include)
filename = metadata[0]['filename']
vector_db_document = Chroma.from_texts(texts,
metadatas=metadata,
embedding=self.embedding_function,
persist_directory=data_path)
vector_db_document.persist()
with open(os.path.join(data_path, filename + ".storage_filename"), 'w') as fo:
fo.write("")
@staticmethod
def calculate_md5(input_file: Union[Path, str]):
import hashlib
md5_hash = hashlib.md5()
with open(input_file, 'rb') as fi:
md5_hash.update(fi.read())
return md5_hash.hexdigest().upper()
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