Spaces:
Running
Running
File size: 10,587 Bytes
e8ebf39 ae04b9d 8893df9 e8ebf39 b2f5314 e8ebf39 4d29d85 e8ebf39 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 |
import copy
import os
from pathlib import Path
from typing import Union, Any
from grobid_client.grobid_client import GrobidClient
from langchain.chains import create_extraction_chain
from langchain.chains.question_answering import load_qa_chain
from langchain.prompts import SystemMessagePromptTemplate, HumanMessagePromptTemplate, ChatPromptTemplate
from langchain.retrievers import MultiQueryRetriever
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.vectorstores import Chroma
from tqdm import tqdm
from document_qa.grobid_processors import GrobidProcessor
class DocumentQAEngine:
llm = None
qa_chain_type = None
embedding_function = None
embeddings_dict = {}
embeddings_map_from_md5 = {}
embeddings_map_to_md5 = {}
def __init__(self, llm, embedding_function, qa_chain_type="stuff", embeddings_root_path=None, grobid_url=None):
self.embedding_function = embedding_function
self.llm = llm
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)
return self.chain.run(input_documents=relevant_documents, question=query)
# return self.chain({"input_documents": relevant_documents, "question": prompt_chat_template}, return_only_outputs=True)
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)
return relevant_documents
def get_all_context_by_document(self, doc_id):
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, verbose=False):
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)]
return texts, metadatas, ids
def create_memory_embeddings(self, pdf_path, doc_id=None, chunk_size=500, perc_overlap=0.1):
texts, metadata, ids = self.get_text_from_document(pdf_path, chunk_size=chunk_size, perc_overlap=perc_overlap)
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)
self.embeddings_root_path = None
return hash
def create_embeddings(self, pdfs_dir_path: Path):
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
texts, metadata, ids = self.get_text_from_document(input_file, chunk_size=500, perc_overlap=0.1)
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()
|