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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("") | |
| 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() | |