Show how to use LangChain to incorporate Residual Vector Quantization (RVQ) with code examples

To integrate Residual Vector Quantization (RVQ) with LangChain, we will need to go through a few essential steps:

1. Set up the environment with LangChain.
2. Integrate RVQ into the workflow, where we quantize embeddings or any vector representations.
3. Use LangChain’s components (like document loaders and retrievers) to utilize the quantized embeddings for tasks like summarization or question answering.

Let’s walk through the steps:

Step 1: Install Dependencies

You need to install LangChain, PyTorch (for RVQ implementation), and any other dependencies. You can install them using pip:

pip install langchain transformers torch numpy

Step 2: Initialize LangChain

We will begin by setting up LangChain with a basic chain to load documents and summarize them. Afterward, we will integrate RVQ into this chain.

from langchain.chains import DocumentChain
from langchain.schema import Document
from langchain.prompts import PromptTemplate
from langchain.llms import OpenAI
from langchain.document_loaders import TextLoader

# Initialize LLM for LangChain
llm = OpenAI(temperature=0, openai_api_key="your-api-key")

# Load a document (for example, from a local text file)
loader = TextLoader("your_document.txt")
documents = loader.load()

# Create a simple prompt template for summarization
prompt_template = "Summarize the following document: {text}"
prompt = PromptTemplate(input_variables=["text"], template=prompt_template)

# Create a document chain to summarize the document using LangChain
doc_chain = DocumentChain(prompt_template=prompt, llm=llm)

# Run the chain
output = doc_chain.run(input_documents=documents)
print(output)

This code sets up a LangChain DocumentChain to summarize a document. It uses the OpenAI model as the language model, and it loads a document from a file.

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Step 3: Integrating RVQ for Embedding Quantization

We now need to define the Residual Vector Quantization (RVQ) process and apply it to the embeddings we get from LangChain. We will apply RVQ to quantize embeddings before passing them to the LLM in the chain.

Let’s define a simple RVQ quantization process. Here, we will assume that we are quantizing embeddings generated by a transformer model like BERT.

import torch
import numpy as np

# Simulate a simple residual vector quantization function
def rvq_quantize(embedding, codebooks):
    residual = embedding.clone()
    indices = []

    # Apply RVQ across multiple stages (simplified version)
    for stage in range(len(codebooks)):
        codebook = codebooks[stage]
        distances = torch.cdist(residual.unsqueeze(0), codebook.unsqueeze(0)).squeeze(0)
        closest_idx = torch.argmin(distances, dim=0)
        indices.append(closest_idx)
        residual -= codebook[closest_idx]

    return indices

def create_codebooks(embed_dim, codebook_size, num_stages):
    # Create random codebooks for RVQ (normally, they would be learned)
    return [torch.randn(codebook_size, embed_dim, requires_grad=True) for _ in range(num_stages)]

# Example codebooks creation
embed_dim = 768  # Size of BERT embeddings
codebook_size = 256  # Number of entries in the codebook
num_stages = 4  # Number of stages for RVQ

codebooks = create_codebooks(embed_dim, codebook_size, num_stages)

# Example embedding (e.g., from a transformer model)
embedding = torch.randn(embed_dim)  # Example random embedding (e.g., from BERT output)

# Quantize the embedding using RVQ
quantized_indices = rvq_quantize(embedding, codebooks)
print("Quantized Indices:", quantized_indices)

In this simplified example:

• We create codebooks for RVQ.
• We simulate an embedding (normally, you would get this from a model like BERT).
• The function rvq_quantize quantizes the embedding using residuals across multiple stages of quantization.

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Step 4: Integrating RVQ with LangChain for Document Processing

To integrate RVQ-based quantization into LangChain’s document processing, we will modify the document processing pipeline. Instead of using the raw embeddings, we will pass the quantized embeddings through the chain. Here’s how we can modify the LangChain example:

from langchain.embeddings import HuggingFaceEmbeddings
from langchain.vectorstores import FAISS

# Define a custom embedding class that integrates RVQ-based quantization
class RVQEmbeddings(HuggingFaceEmbeddings):
    def __init__(self, model_name="bert-base-uncased", codebooks=None, *args, **kwargs):
        super().__init__(model_name=model_name, *args, **kwargs)
        self.codebooks = codebooks
    
    def embed_documents(self, texts):
        embeddings = super().embed_documents(texts)  # Get the embeddings from the HuggingFace model
        quantized_embeddings = []

        for embedding in embeddings:
            # Quantize each embedding using RVQ
            quantized_embedding = rvq_quantize(torch.tensor(embedding), self.codebooks)
            quantized_embeddings.append(quantized_embedding)
        
        return quantized_embeddings

# Create RVQ-based embeddings
rvq_embeddings = RVQEmbeddings(codebooks=codebooks)

# Use RVQ embeddings with LangChain to load documents and store in a FAISS vector store
document_chain = DocumentChain(prompt_template=prompt, llm=llm)

# Assume we have documents from the loader as before
# Apply RVQ embeddings to documents
texts = [doc.page_content for doc in documents]
quantized_embeddings = rvq_embeddings.embed_documents(texts)

# Use FAISS for fast similarity search with quantized embeddings
faiss_store = FAISS.from_embeddings(quantized_embeddings)

# Now you can use LangChain to answer questions from documents using quantized embeddings
faiss_search_chain = document_chain | faiss_store.as_retriever()

# Query the chain
query = "What is the summary of this document?"
response = faiss_search_chain.run(input_query=query)
print(response)

Key Components:

• RVQEmbeddings class: A custom subclass of HuggingFaceEmbeddings that integrates RVQ. It quantizes the embeddings using the provided codebooks.
• faiss_store: FAISS is used for efficient similarity search, and it works with the quantized embeddings generated through RVQ.
• faiss_search_chain: This combines LangChain’s DocumentChain with the FAISS retriever, allowing you to answer questions based on the quantized embeddings.

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Conclusion

Here’s a summary of the steps for incorporating RVQ into LangChain:

1. Setup LangChain with basic document loading and processing.
2. Implement RVQ: Define a quantization method to process embeddings.
3. Custom Embedding Class: Use RVQ to modify the embeddings used by LangChain.
4. Integrate RVQ into Document Search: Use quantized embeddings for tasks like question answering or document retrieval, incorporating FAISS for similarity search.

This pipeline quantizes the embeddings using RVQ and allows you to perform tasks like document summarization or question answering based on the quantized representations.