tomaarsen
commented
7 months ago Hello!
The issue originates in https://github.com/xlang-ai/instructor-embedding, which was created explicitly for Sentence Transformers 2.2.2. They haven't kept their code up to date with the recent Sentence Transformer updates, hence the failures. This is why HuggingFaceInstructEmbeddings fails while HuggingFaceEmbeddings and HuggingFaceBgeEmbeddings work.
A good solution would be to try this PR: https://github.com/xlang-ai/instructor-embedding/pull/112 with:
pip install git+https://github.com/SilasMarvin/instructor-embedding.git@silas-update-for-newer-sentence-transformersand the most recent sentence-transformers. That combination should work correctly.
- Tom Aarsen
BBC-Esq
SilasMarvin
This is a challenging issue that I've been working on...First, here is my entire script:
SCRIPT
``` import shutil import yaml import gc from langchain_community.docstore.document import Document from langchain_community.embeddings import HuggingFaceInstructEmbeddings, HuggingFaceEmbeddings, HuggingFaceBgeEmbeddings from langchain_community.vectorstores import TileDB from document_processor import load_documents, split_documents from loader_images import specify_image_loader import torch from utilities import validate_symbolic_links, my_cprint from pathlib import Path import os import logging from PySide6.QtCore import QDir import time import pickle logging.basicConfig( level=logging.INFO, format='%(name)s - %(pathname)s:%(lineno)s - %(funcName)s' ) logging.getLogger('chromadb.db.duckdb').setLevel(logging.WARNING) logging.getLogger('sentence_transformers').setLevel(logging.WARNING) class CreateVectorDB: def __init__(self, database_name): self.ROOT_DIRECTORY = Path(__file__).resolve().parent self.SOURCE_DIRECTORY = self.ROOT_DIRECTORY / "Docs_for_DB" self.PERSIST_DIRECTORY = self.ROOT_DIRECTORY / "Vector_DB" / database_name self.SAVE_JSON_DIRECTORY = self.ROOT_DIRECTORY / "Docs_for_DB" / database_name def load_config(self, root_directory): with open(root_directory / "config.yaml", 'r', encoding='utf-8') as stream: return yaml.safe_load(stream) def initialize_vector_model(self, embedding_model_name, config_data): EMBEDDING_MODEL_NAME = config_data.get("EMBEDDING_MODEL_NAME") compute_device = config_data['Compute_Device']['database_creation'] model_kwargs = {"device": compute_device} encode_kwargs = {'normalize_embeddings': False, 'batch_size': 8} if compute_device.lower() == 'cpu': encode_kwargs['batch_size'] = 2 else: batch_size_mapping = { 'sentence-t5-xxl': 1, ('instructor-xl', 'sentence-t5-xl'): 2, 'instructor-large': 3, ('jina-embedding-l', 'bge-large', 'gte-large', 'roberta-large', 'mxbai-embed-large-v1'): 4, 'jina-embedding-s': 9, ('bge-small', 'gte-small'): 10, ('MiniLM',): 20, } for key, value in batch_size_mapping.items(): if isinstance(key, tuple): if any(model_name_part in EMBEDDING_MODEL_NAME for model_name_part in key): encode_kwargs['batch_size'] = value break else: if key in EMBEDDING_MODEL_NAME: encode_kwargs['batch_size'] = value break my_cprint(f"Vector model initialized with a batch size of {encode_kwargs['batch_size']}", "blue") if "instructor" in embedding_model_name: embed_instruction = config_data['embedding-models']['instructor'].get('embed_instruction') query_instruction = config_data['embedding-models']['instructor'].get('query_instruction') encode_kwargs['show_progress_bar'] = True model = HuggingFaceInstructEmbeddings( model_name=embedding_model_name, model_kwargs=model_kwargs, embed_instruction=embed_instruction, query_instruction=query_instruction, encode_kwargs=encode_kwargs ) elif "bge" in embedding_model_name: query_instruction = config_data['embedding-models']['bge'].get('query_instruction') encode_kwargs['show_progress_bar'] = True model = HuggingFaceBgeEmbeddings( model_name=embedding_model_name, model_kwargs=model_kwargs, query_instruction=query_instruction, encode_kwargs=encode_kwargs ) else: model = HuggingFaceEmbeddings( model_name=embedding_model_name, show_progress=True, model_kwargs=model_kwargs, encode_kwargs=encode_kwargs ) return model, encode_kwargs def create_database(self, texts, embeddings): my_cprint("Creating vectors and database...\n\n NOTE:\n\nNOTE: The progress bar only relates to computing vectors, not inserting them into the database. Rest assured, after it reaches 100% it is still working unless you get an error message.\n", "yellow") start_time = time.time() if not self.PERSIST_DIRECTORY.exists(): self.PERSIST_DIRECTORY.mkdir(parents=True, exist_ok=True) db = TileDB.from_documents( documents=texts, embedding=embeddings, index_uri=str(self.PERSIST_DIRECTORY), allow_dangerous_deserialization=True, metric="euclidean", index_type="FLAT", ) print("Database created.") end_time = time.time() elapsed_time = end_time - start_time my_cprint("Database saved.", "cyan") print(f"Creation of vectors and inserting into the database took {elapsed_time:.2f} seconds.") def save_documents_to_json(self, json_docs_to_save): self.SAVE_JSON_DIRECTORY.mkdir(parents=True, exist_ok=True) for document in json_docs_to_save: document_hash = document.metadata.get('hash', None) if document_hash: json_filename = f"{document_hash}.json" json_file_path = self.SAVE_JSON_DIRECTORY / json_filename actual_file_path = document.metadata.get('file_path') if os.path.islink(actual_file_path): resolved_path = os.path.realpath(actual_file_path) document.metadata['file_path'] = resolved_path document_json = document.json(indent=4) with open(json_file_path, 'w', encoding='utf-8') as json_file: json_file.write(document_json) else: print("Warning: Document missing 'hash' in metadata. Skipping JSON creation.") def load_audio_documents(self, source_dir: Path = None) -> list: if source_dir is None: source_dir = self.SOURCE_DIRECTORY json_paths = [f for f in source_dir.iterdir() if f.suffix.lower() == '.json'] docs = [] for json_path in json_paths: try: with open(json_path, 'r', encoding='utf-8') as json_file: json_str = json_file.read() doc = Document.parse_raw(json_str) docs.append(doc) except Exception as e: my_cprint(f"Error loading {json_path}: {e}", "red") return docs def clear_docs_for_db_folder(self): for item in self.SOURCE_DIRECTORY.iterdir(): if item.is_file() or item.is_symlink(): try: item.unlink() except Exception as e: print(f"Failed to delete {item}: {e}") def run(self): config_data = self.load_config(self.ROOT_DIRECTORY) EMBEDDING_MODEL_NAME = config_data.get("EMBEDDING_MODEL_NAME") # load non-image/non-audio documents documents = load_documents(self.SOURCE_DIRECTORY) # load image documents image_documents = specify_image_loader() documents.extend(image_documents) json_docs_to_save = documents # load audio documents audio_documents = self.load_audio_documents() # Now calling the method internally documents.extend(audio_documents) if len(audio_documents) > 0: print(f"Loaded {len(audio_documents)} audio transcription(s)...") # split each document in the list of documents texts = split_documents(documents) # initialize vector model embeddings, encode_kwargs = self.initialize_vector_model(EMBEDDING_MODEL_NAME, config_data) # create database self.create_database(texts, embeddings) self.save_documents_to_json(json_docs_to_save) del embeddings.client del embeddings torch.cuda.empty_cache() gc.collect() my_cprint("Embedding model removed from memory.", "red") # clear ingest folder self.clear_docs_for_db_folder() print("Cleared all files and symlinks in Docs_for_DB folder.") ```This works fine when using
sentence-transformers==2.2.2. However, when I upgrade tosentence-transformers==2.6.1I get this error:ERROR
``` Traceback (most recent call last): File "D:\Scripts\ChromaDB-Plugin-for-LM-Studio\v4_3 - working\gui_tabs_databases.py", line 23, in run create_vector_db.run() # calls database_interactions.py ^^^^^^^^^^^^^^^^^^^^^^ File "D:\Scripts\ChromaDB-Plugin-for-LM-Studio\v4_3 - working\database_interactions.py", line 193, in run embeddings, encode_kwargs = self.initialize_vector_model(EMBEDDING_MODEL_NAME, config_data) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "D:\Scripts\ChromaDB-Plugin-for-LM-Studio\v4_3 - working\database_interactions.py", line 72, in initialize_vector_model model = HuggingFaceInstructEmbeddings( ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "D:\Scripts\ChromaDB-Plugin-for-LM-Studio\v4_3 - working\Lib\site-packages\langchain_community\embeddings\huggingface.py", line 153, in __init__ self.client = INSTRUCTOR( ^^^^^^^^^^^ File "D:\Scripts\ChromaDB-Plugin-for-LM-Studio\v4_3 - working\Lib\site-packages\sentence_transformers\SentenceTransformer.py", line 191, in __init__ modules = self._load_sbert_model( ^^^^^^^^^^^^^^^^^^^^^^^ TypeError: INSTRUCTOR._load_sbert_model() got an unexpected keyword argument 'token' ```I've verified that when using a
BGEmodel (viaHuggingFaceBgeEmbeddings),GTEmodel (viaHuggingFaceEmbeddings) andall-mpnet-base-v2(viaHuggingFaceEmbeddings) everything works fine. I've tried every which way to get it to work...Since I really like the "instructor" models in my program, this forces me to stay at
sentence-transformers==2.2.2or, alternatively, abandon them in order to upgrade so I can use newer models (e.g.mxbai-embed-large-v1). I wouldn't normally ask, but I've spend dozens of hours trying to solve this...ranging from usingSentenceTransformersdirectly pursuant to the API on your website to custom wrappers, etc.Can anyone help me and/or @tomaarsen in particular if he has time? I don't know if this is an issue for
sentence-transformersitself, its integration withHuggingFaceInstructEmbeddingsfrom Langchain, or just my code...Thanks in advance![EDIT] I am aware that Instructor models are unique in that the prompt is not included in pooling, as stated on your website's instructions/examples, and I DID examine
SentenceTransformersitself and see where you took that into account:(taken from version 2.6.0)
I just simply can't figure out why
HuggingFaceInstructEmbeddingsisn't working whileHuggingFaceEmbeddingsandHuggingFaceBgeEmbeddingswork fine when I pip install sentence-transformers above 2.2.2...This is literally the only issue that has stymied my program from upgrading the crucial dependency that is
sentence-transformers...Thanks again and love the repo!