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Open-retrievals simplifies text embeddings, retrievals, ranking, and RAG applications using PyTorch and Transformers. This user-friendly framework is designed for information retrieval and LLM-enhanced generation.

• Contrastive learning enhanced embeddings/ LLM embeddings
• Cross-encoder and ColBERT Rerank
• Fast RAG demo integrated with Langchain and LlamaIndex

Installation

Prerequisites

pip install transformers
pip install faiss-cpu  # if necessary
pip install peft  # if necessary

With pip

pip install open-retrievals

With source code

git clone https://github.com/LongxingTan/open-retrievals
cd open-retrievals
pip install -e .

Quick-start

Text embedding from Pretrained weights

from retrievals import AutoModelForEmbedding

sentences = [
    'query: how much protein should a female eat',
    'query: summit define',
    "passage: As a general guideline, the CDC's average requirement of protein for women ages 19 to 70 is 46 grams per day. But, as you can see from this chart, you'll need to increase that if you're expecting or training for a marathon. Check out the chart below to see how much protein you should be eating each day.",
    "passage: Definition of summit for English Language Learners. : 1  the highest point of a mountain : the top of a mountain. : 2  the highest level. : 3  a meeting or series of meetings between the leaders of two or more governments."
]
model_name_or_path = 'intfloat/e5-base-v2'
model = AutoModelForEmbedding.from_pretrained(model_name_or_path, pooling_method="mean")
embeddings = model.encode(sentences, normalize_embeddings=True, convert_to_tensor=True)
scores = (embeddings[:2] @ embeddings[2:].T) * 100
print(scores.tolist())

Index building for dense retrieval search

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"
index_path = './database/faiss/faiss.index'
model = AutoModelForEmbedding.from_pretrained(model_name_or_path)
model.build_index(sentences, index_path=index_path)

query_embed = model.encode("He plays guitar.")
matcher = AutoModelForRetrieval()
dists, indices = matcher.similarity_search(query_embed, index_path=index_path)
print(indices)

Rerank using pretrained weights

from retrievals import RerankModel

model_name_or_path: str = "BAAI/bge-reranker-base"
rerank_model = RerankModel.from_pretrained(model_name_or_path)
scores_list = rerank_model.compute_score(["In 1974, I won the championship in Southeast Asia in my first kickboxing match", "In 1982, I defeated the heavy hitter Ryu Long."])
print(scores_list)

RAG with LangChain integration

pip install langchain
pip install langchain_community
pip install chromadb

from retrievals.tools.langchain import LangchainEmbedding, LangchainReranker, LangchainLLM
from retrievals import RerankModel
from langchain.retrievers import ContextualCompressionRetriever
from langchain_community.vectorstores import Chroma as Vectorstore
from langchain.prompts.prompt import PromptTemplate
from langchain.chains import RetrievalQA

persist_directory = './database/faiss.index'
embed_model_name_or_path = "sentence-transformers/all-MiniLM-L6-v2"
rerank_model_name_or_path = "BAAI/bge-reranker-base"
llm_model_name_or_path = "microsoft/Phi-3-mini-128k-instruct"

embeddings = LangchainEmbedding(model_name_or_path=embed_model_name_or_path)
vectordb = Vectorstore(
    persist_directory=persist_directory,
    embedding_function=embeddings,
)
retrieval_args = {"search_type" :"similarity", "score_threshold": 0.15, "k": 10}
retriever = vectordb.as_retriever(**retrieval_args)

ranker = RerankModel.from_pretrained(rerank_model_name_or_path)
reranker = LangchainReranker(model=ranker, top_n=3)
compression_retriever = ContextualCompressionRetriever(
    base_compressor=reranker, base_retriever=retriever
)

llm = LangchainLLM(model_name_or_path=llm_model_name_or_path)

RESPONSE_TEMPLATE = """[INST]
<>
You are a helpful AI assistant. Use the following pieces of context to answer the user's question.<>
Anything between the following `context` html blocks is retrieved from a knowledge base.

    {context}

REMEMBER:
- If you don't know the answer, just say that you don't know, don't try to make up an answer.
- Let's take a deep breath and think step-by-step.

Question: {question}[/INST]
Helpful Answer:
"""

PROMPT = PromptTemplate(template=RESPONSE_TEMPLATE, input_variables=["context", "question"])

qa_chain = RetrievalQA.from_chain_type(
    llm,
    chain_type='stuff',
    retriever=compression_retriever,
    chain_type_kwargs={
        "verbose": True,
        "prompt": PROMPT,
    }
)

user_query = 'Introduce this'
response = qa_chain({"query": user_query})
print(response)

Text embedding model fine-tuned by contrastive learning

import torch.nn as nn
from datasets import load_dataset
from transformers import AutoTokenizer, AdamW, get_linear_schedule_with_warmup, TrainingArguments
from retrievals import AutoModelForEmbedding, RetrievalTrainer, PairCollator, TripletCollator
from retrievals.losses import ArcFaceAdaptiveMarginLoss, InfoNCE, SimCSE, TripletLoss

model_name_or_path: str = "sentence-transformers/paraphrase-multilingual-mpnet-base-v2"
batch_size: int = 128
epochs: int = 3

train_dataset = load_dataset('shibing624/nli_zh', 'STS-B')['train']
train_dataset = train_dataset.rename_columns({'sentence1': 'query', 'sentence2': 'document'})
tokenizer = AutoTokenizer.from_pretrained(model_name_or_path, use_fast=False)
model = AutoModelForEmbedding.from_pretrained(model_name_or_path, pooling_method="cls")
# model = model.set_train_type('pointwise')  # 'pointwise', 'pairwise', 'listwise'
optimizer = AdamW(model.parameters(), lr=5e-5)
num_train_steps=int(len(train_dataset) / batch_size * epochs)
scheduler = get_linear_schedule_with_warmup(optimizer, num_warmup_steps=0.05 * num_train_steps, num_training_steps=num_train_steps)

training_arguments = TrainingArguments(
    output_dir='./checkpoints',
    num_train_epochs=epochs,
    per_device_train_batch_size=batch_size,
    remove_unused_columns=False,
)
trainer = RetrievalTrainer(
    model=model,
    args=training_arguments,
    train_dataset=train_dataset,
    data_collator=PairCollator(tokenizer, query_max_length=128, document_max_length=128),
    loss_fn=InfoNCE(nn.CrossEntropyLoss(label_smoothing=0.05)),
)
trainer.optimizer = optimizer
trainer.scheduler = scheduler
trainer.train()

Fine-tuning of rerank models

from transformers import AutoTokenizer, TrainingArguments, get_cosine_schedule_with_warmup, AdamW
from retrievals import RerankCollator, RerankModel, RerankTrainer, RerankDataset

model_name_or_path: str = "microsoft/deberta-v3-base"
max_length: int = 128
learning_rate: float = 3e-5
batch_size: int = 4
epochs: int = 3

train_dataset = RerankDataset('./t2rank.json', positive_key='pos', negative_key='neg')
tokenizer = AutoTokenizer.from_pretrained(model_name_or_path, use_fast=False)
model = RerankModel.from_pretrained(model_name_or_path, pooling_method="mean")
optimizer = AdamW(model.parameters(), lr=learning_rate)
num_train_steps = int(len(train_dataset) / batch_size * epochs)
scheduler = get_cosine_schedule_with_warmup(optimizer, num_warmup_steps=0.05 * num_train_steps, num_training_steps=num_train_steps)

training_args = TrainingArguments(
    learning_rate=learning_rate,
    per_device_train_batch_size=batch_size,
    num_train_epochs=epochs,
    output_dir = './checkpoints',
    remove_unused_columns=False,
)
trainer = RerankTrainer(
    model=model,
    args=training_args,
    train_dataset=train_dataset,
    data_collator=RerankCollator(tokenizer, query_max_length=max_length, document_max_length=max_length),
)
trainer.optimizer = optimizer
trainer.scheduler = scheduler
trainer.train()

Semantic 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.from_pretrained(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)

Reference & Acknowledge

• sentence-transformers
• FlagEmbedding
• uniem
• BCEmbedding