Advanced Retrieval Pattern

Overview

The Advanced Retrieval pattern significantly improves upon naive RAG through sophisticated techniques that better understand query intent and document relevance. This pattern implements several key architectural improvements:

Architectural Diagram

                +-------------------+       +-------------------+       +-------------------+
                |                   |       |                   |       |                   |
                |    User Query     | ----> | Query Processor   | ----> | Dual-Encoder      |
                |                   |       |                   |       |                   |
                +-------------------+       +-------------------+       +-------------------+
                        ^                         |                           |
                        |                         v                           v
                        |                 +-------------------+       +-------------------+
                        +-----------------|  Knowledge Base   |       | Cross-Encoder     |
                                          |                   |       |                   |
                                          +-------------------+       +-------------------+
                        ^                         |                           |
                        |                         v                           v
                        |                 +-------------------+       +-------------------+
                        +-----------------|  Ranking Model    |       |  Final Response   |
                                          |                   |       |                   |
                                          +-------------------+       +-------------------+
                

Key Enhancements

1. Dense Retrieval: State-of-the-art embeddings for semantic understanding
2. Query Processing: Multi-stage expansion and refinement
3. Context Awareness: Document re-ranking based on query context
4. Dynamic Filtering: Adaptive document selection
5. Relevance Scoring: Cross-encoder based precision scoring

Data Flow

1. Query is received and preprocessed
2. Query is expanded and refined
3. Initial retrieval using dual-encoder
4. Re-ranking using cross-encoder
5. Final document selection and response generation

Implementation Example

from sentence_transformers import SentenceTransformer
from sklearn.metrics.pairwise import cosine_similarity
import numpy as np

class AdvancedRetriever:
    def __init__(self, knowledge_base):
        self.model = SentenceTransformer('all-MiniLM-L6-v2')
        self.knowledge_base = knowledge_base
        self.embeddings = self.model.encode(knowledge_base)
        
    def retrieve(self, query, top_k=5):
        # Encode query
        query_embedding = self.model.encode(query)
        
        # Calculate similarities
        similarities = cosine_similarity(
            [query_embedding],
            self.embeddings
        )[0]
        
        # Get top-k documents
        top_indices = np.argsort(similarities)[-top_k:][::-1]
        return [self.knowledge_base[i] for i in top_indices]

# Usage
knowledge_base = [...]  # Your document collection
retriever = AdvancedRetriever(knowledge_base)
results = retriever.retrieve("What is RAG?")
            

When to Use

• Enterprise-grade question answering systems
• Applications requiring high precision retrieval
• Systems handling diverse and complex queries
• When working with large, heterogeneous document collections
• When computational resources support advanced processing

Performance Considerations

• Latency increases with retrieval complexity
• Requires more computational resources than naive RAG
• Benefits from GPU acceleration for embedding models
• May require caching strategies for production deployment