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Open-Retrievals is an easy-to-use python framework getting SOTA text embeddings, oriented to information retrieval and LLM retrieval augmented generation, based on PyTorch and Transformers.
- Contrastive learning enhanced embeddings
- LLM embeddings
Prerequisites
pip install transformers
pip install faiss-cpu
pip install peftWith pip
pip install open-retrievalsfrom retrievals import AutoModelForEmbedding, AutoModelForRetrieval
# Example list of documents
documents = [
"Open-retrievals is a text embedding libraries",
"I can use it simply with a SOTA RAG application.",
]
# This will trigger the model download and initialization
model_name_or_path = "sentence-transformers/all-MiniLM-L6-v2"
model = AutoModelForEmbedding(model_name_or_path)
embeddings = model.encode(documents)
len(embeddings) # Vector of 384 dimensionsBuild Index and Retrieval
from retrievals import AutoModelForEmbedding, AutoModelForRetrieval
sentences = ['A dog is chasing car.', 'A man is playing a guitar.']
model_name_or_path = "sentence-transformers/all-MiniLM-L6-v2"
model = AutoModelForEmbedding(model_name_or_path)
model.build_index(sentences)
matcher = AutoModelForRetrieval()
results = matcher.faiss_search("He plays guitar.")Rerank
from transformers import AutoTokenizer
from retrievals import RerankCollator, RerankModel, RerankTrainer, RerankDataset
train_dataset = RerankDataset(args=data_args)
tokenizer = AutoTokenizer.from_pretrained(model_args.tokenizer_name, use_fast=False)
model = RerankModel(
model_args.model_name_or_path,
pooling_method="mean"
)
optimizer = get_optimizer(model, lr=5e-5, weight_decay=1e-3)
lr_scheduler = get_scheduler(optimizer, num_train_steps=int(len(train_dataset) / 2 * 1))
trainer = RerankTrainer(
model=model,
args=training_args,
train_dataset=train_dataset,
data_collator=RerankCollator(tokenizer, max_length=data_args.query_max_len),
)
trainer.optimizer = optimizer
trainer.scheduler = lr_scheduler
trainer.train()RAG with LangChain
- Prerequisites
pip install langchain- Server
from retrievals.tools.langchain import LangchainEmbedding, LangchainReranker
from langchain.retrievers import ContextualCompressionRetriever
from langchain_community.vectorstores import Chroma as Vectorstore
class DenseRetrieval:
def __init__(self, persist_directory):
embeddings = LangchainEmbedding(model_name="BAAI/bge-large-zh-v1.5")
vectordb = Vectorstore(
persist_directory=persist_directory,
embedding_function=embeddings,
)
retrieval_args = {"search_type" :"similarity", "score_threshold": 0.15, "k": 30}
self.retriever = vectordb.as_retriever(retrieval_args)
reranker_args = {
"model": "../../inputs/bce-reranker-base_v1",
"top_n": 7,
"device": "cuda",
"use_fp16": True,
}
self.reranker = LangchainReranker(**reranker_args)
self.compression_retriever = ContextualCompressionRetriever(
base_compressor=self.reranker, base_retriever=self.retriever
)
def query(
self,
question: str
):
docs = self.compression_retriever.get_relevant_documents(question)
return docsUse Pretrained sentence embedding
from retrievals import AutoModelForEmbedding
sentences = ["Hello world", "How are you?"]
model_name_or_path = "sentence-transformers/all-MiniLM-L6-v2"
model = AutoModelForEmbedding(model_name_or_path, pooling_method="mean", normalize_embeddings=True)
sentence_embeddings = model.encode(sentences, convert_to_tensor=True)
print(sentence_embeddings)Finetune transformers by contrastive learning
from transformers import AutoTokenizer
from retrievals import AutoModelForEmbedding, AutoModelForRetrieval, RetrievalTrainer, PairCollator, TripletCollator
from retrievals.losses import ArcFaceAdaptiveMarginLoss, InfoNCE, SimCSE, TripletLoss
from retrievals.data import RetrievalDataset, RerankDataset
train_dataset = RetrievalDataset(args=data_args)
tokenizer = AutoTokenizer.from_pretrained(model_args.tokenizer_name, use_fast=False)
model = AutoModelForEmbedding(
model_args.model_name_or_path,
pooling_method="cls"
)
optimizer = get_optimizer(model, lr=5e-5, weight_decay=1e-3)
lr_scheduler = get_scheduler(optimizer, num_train_steps=int(len(train_dataset) / 2 * 1))
trainer = RetrievalTrainer(
model=model,
args=training_args,
train_dataset=train_dataset,
data_collator=TripletCollator(tokenizer, max_length=data_args.query_max_len),
loss_fn=TripletLoss(),
)
trainer.optimizer = optimizer
trainer.scheduler = lr_scheduler
trainer.train()Finetune LLM for embedding by Contrastive learning
from retrievals import AutoModelForEmbedding
model = AutoModelForEmbedding(
"mistralai/Mistral-7B-v0.1",
pooling_method='cls',
query_instruction=f'Instruct: Retrieve semantically similar text\nQuery: '
)Search by Cosine similarity/KNN
from retrievals import AutoModelForEmbedding, AutoModelForRetrieval
query_texts = ['A dog is chasing car.']
document_texts = ['A man is playing a guitar.', 'A bee is flying low']
model_name_or_path = "sentence-transformers/all-MiniLM-L6-v2"
model = AutoModelForEmbedding(model_name_or_path)
query_embeddings = model.encode(query_texts, convert_to_tensor=True)
document_embeddings = model.encode(document_texts, convert_to_tensor=True)
matcher = AutoModelForRetrieval(method='cosine')
dists, indices = matcher.similarity_search(query_embeddings, document_embeddings, top_k=1)
