Project: Advanced RAG System with LangChain

Duration: 5 min

This module delves into building an advanced Retrieval-Augmented Generation (RAG) system using LangChain. You will learn about vector databases, embeddings, chunking, reranking, and hybrid search techniques. Understanding these concepts is crucial for developing sophisticated AI applications that can retrieve and generate relevant information effectively.

Vector Databases and Embeddings

Vector databases store data in a vectorized format, allowing for efficient similarity searches. Embeddings are vector representations of data, typically text, that capture semantic meaning. Using embeddings, we can perform semantic search, enabling more accurate and relevant retrieval of information.

import numpy as np
from sentence_transformers import SentenceTransformer

# Load pre-trained model
model = SentenceTransformer('paraphrase-MiniLM-L6-v2')

# Sample text
texts = ['This is a sample text.', 'Another text for embedding.']

# Generate embeddings
embeddings = model.encode(texts)

print(embeddings)

Try it in Google Colab:

[[ 0.12345678 -0.23456789  0.3456789 ] [-0.456789   0.56789012 -0.6789012 ]]

Chunking and Reranking

Chunking involves breaking down large documents into smaller, manageable pieces. Reranking refines the order of retrieved documents based on their relevance to the query. This enhances the quality of the retrieved information by ensuring the most relevant chunks are presented first.

from langchain.text_splitter import RecursiveCharacterTextSplitter
from sentence_transformers import SentenceTransformer
import numpy as np

# Sample document
document = """
Machine learning is a subset of artificial intelligence that enables systems 
to learn and improve from experience without being explicitly programmed. 
Deep learning uses neural networks with multiple layers to process data.
Natural language processing focuses on understanding and generating human language.
"""

# Initialize text splitter (correct LangChain API)
splitter = RecursiveCharacterTextSplitter(
    chunk_size=100,
    chunk_overlap=20,
    separators=["\n\n", "\n", " ", ""]
)
chunks = splitter.split_text(document)

# Rerank chunks using semantic similarity
model = SentenceTransformer('paraphrase-MiniLM-L6-v2')
query = "deep learning neural networks"
query_embedding = model.encode(query)

chunk_embeddings = model.encode(chunks)
scores = np.dot(chunk_embeddings, query_embedding)

# Sort chunks by relevance
ranked_chunks = [chunk for _, chunk in sorted(
    zip(scores, chunks), 
    key=lambda x: x[0], 
    reverse=True
)]

print("Top chunk:", ranked_chunks[0])

💡 Tip: Use RecursiveCharacterTextSplitter for semantic coherence. The chunk_overlap parameter helps preserve context between chunks.

Key Concepts

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