How to Scale RAG for Millions of Documents for Your LLM

Scaling Retrieval-Augmented Generation (RAG) to handle millions of documents is a complex problem requiring optimizations in database architecture, indexing, retrieval, and data processing. Systems without thoughtful design will suffer from poor query performance, high latency, and storage inefficiencies.

Performance issues typically emerge when the document count exceeds a few million, with retrieval times slowing due to increased data fragmentation and longer query paths. Systems without thoughtful design will suffer from poor query performance, high latency, and storage inefficiencies. Here, we explore advanced strategies to handle these challenges effectively.

1. Vector Database Selection and Scaling

For datasets exceeding several million documents, choosing a vector database that can handle distributed indexing and querying is critical. Options like Weaviate, Pinecone, and PGVector provide horizontal scaling through sharding and replication. However, scaling vector databases is not straightforward due to their reliance on complex indexing structures like graphs.

Sharding improves scalability by dividing the data across multiple nodes, but distributed graph traversal introduces additional latency. Hybrid approaches, such as combining hierarchical graph indexing with cluster-based routing (e.g., IVF-PQ), can help reduce this overhead.

In a distributed setup, query engines must efficiently aggregate partial results from multiple nodes. This approach is similar to a map-reduce approach, where each node handles a segment of the query and returns its results to a central node for ranking.

Consider monitoring the load distribution across shards in real-time when dealing with extremely large datasets. Some vector database solutions provide built-in metrics and auto-scaling features to ensure that queries remain balanced and latency is minimized.

2. Optimizing Indexing Techniques

Hierarchical Navigable Small World (HNSW) is commonly used for ANN search but can become inefficient as data grows. Cluster-first indexing methods, such as IVF-PQ (Inverted File and Product Quantization), reduce the search space by routing queries to relevant clusters before full graph traversal.

Below is a method to balance speed and precision by using a multi-step cluster search:

import faiss
import numpy as np

# Generate 10 million embeddings with 512 dimensions
embeddings = np.random.rand(10_000_000, 512).astype('float32')

quantizer = faiss.IndexFlatL2(512)  
index_ivf = faiss.IndexIVFPQ(quantizer, 512, 1024, 64, 8)  # 1024 clusters, 64-byte PQ

index_ivf.train(embeddings[:100_000])  # Train on a sample of data
index_ivf.add(embeddings)

query_vector = np.random.rand(1, 512).astype('float32')
index_ivf.nprobe = 10
distances, indices = index_ivf.search(query_vector, k=5)

Training the index on a sample set reduces initial setup time while maintaining performance for large-scale queries.

Consider advanced quantization techniques like Optimized Product Quantization (OPQ) to reduce memory usage while maintaining high recall. Periodically retrain or refine your quantizer on updated data to keep index accuracy consistent over time.

3. Advanced Reranking Strategies

Embedding-based retrieval often returns noisy results. Incorporating reranking strategies improves relevance by considering multiple signals, such as document metadata and lexical similarity.

Reciprocal Rank Fusion (RRF) is a robust technique for aggregating multiple ranking sources. By assigning higher weights to top-ranked documents across models, RRF balances exploration and exploitation:

def reciprocal_rank_fusion(ranked_lists, top_k=10):
    fused_scores = {}
    for rank_list in ranked_lists:
        for position, (doc_id, _) in enumerate(rank_list):
            fused_scores[doc_id] = fused_scores.get(doc_id, 0) + 1 / (position + 1)

    return sorted(fused_scores.items(), key=lambda x: x[1], reverse=True)[:top_k]

# Aggregating ranked results from multiple models
ranked_lists = [
    [("doc1", 0.8), ("doc2", 0.6), ("doc3", 0.5)],
    [("doc3", 0.85), ("doc1", 0.65), ("doc4", 0.4)]
]
final_ranking = reciprocal_rank_fusion(ranked_lists)

Combining semantic search with BM25 or other lexical models often improves performance on heterogeneous datasets.

For even stronger reranking, experiment with cross-encoder or generative LLM-based re-rankers. These models score candidate passages with greater contextual understanding, often outperforming simple heuristic fusion methods.

4. Data Ingestion and Adaptive Chunking

Ingesting millions of documents requires efficient preprocessing, including content extraction, chunking, and metadata enrichment. A poor chunking strategy can result in embeddings that lose important context or exceed token limits during generation.

Adaptive chunking adjusts segment sizes dynamically based on the content type. For instance, structured documents like reports may require smaller chunks, while narrative text can use larger windows.

def adaptive_chunking(text, min_chunk_size=200, max_chunk_size=500, overlap=50):
    words = text.split()
    chunks = []
    start = 0

    while start < len(words):
        end = min(start + max_chunk_size, len(words))
        chunk = words[start:end]
        if len(chunk) >= min_chunk_size:
            chunks.append(" ".join(chunk))
        start = end - overlap

    return chunks

# Example usage
document_text = "This is a long document with multiple sections and paragraphs..."
chunks = adaptive_chunking(document_text)

This strategy ensures that each chunk remains within the optimal size for embedding generation while overlapping segments maintain context.

When determining ' max_chunk_size, ' keep track of your embedding or LLM token limits. Overly large chunks risk exceeding model constraints, while overly small chunks may dilute context.

5. Query Optimization with Smart Routing

Blindly querying millions of vectors is inefficient at scale. By pre-segmenting documents into logical groups (e.g., by topic or content type), queries can be routed to the most relevant clusters.

Hierarchical narrowing can further refine cluster-based routing, where each query step progressively reduces the candidate set. For example, a query might first identify the most relevant document clusters and then search within those clusters at the page or paragraph level.

Here's a simplified search process:

def hierarchical_search(query_vector, cluster_centers, index_by_cluster):
    cluster_distances = [(i, np.linalg.norm(query_vector - center)) for i, center in enumerate(cluster_centers)]
    cluster_distances.sort(key=lambda x: x[1])
    top_clusters = [cluster[0] for cluster in cluster_distances[:5]]

    results = []
    for cluster_id in top_clusters:
        index = index_by_cluster[cluster_id]
        distances, indices = index.search(query_vector, k=10)
        results.extend(zip(indices[0], distances[0]))

    results.sort(key=lambda x: x[1])
    return results[:10]

This approach balances performance and accuracy by limiting the search space at each stage.

6. Infrastructure and Parallel Processing

Embedding generation and indexing for millions of documents can take weeks without parallel processing. Distributed systems using Kubernetes or cloud-native services enable parallel execution of tasks across multiple nodes.

A scalable approach might involve:

1. Distributed embedding generation: Assign different document batches to GPU nodes.
2. Parallel indexing: Create local indices on each node, then merge them.
3. Temporary high-memory instances: Use these for resource-heavy tasks like clustering.

Infrastructure optimizations can reduce both time-to-completion and operational costs.

Leverage on-demand GPU instances for computationally intensive tasks like embedding generation or large-scale indexing merges. This allows you to ramp resources up or down, avoiding idle capacity and reducing overall costs.

Conclusion

Scaling RAG for millions of documents requires strategic decisions around vector databases, indexing, query optimization, and data pipelines. Advanced techniques like cluster-first indexing, adaptive chunking, and reciprocal rank fusion enhance performance and accuracy. By carefully tuning each component, you can build a scalable RAG system that delivers fast, relevant results even under heavy data loads.

With these strategies in place, your RAG system is equipped to handle the challenges of large-scale document retrieval and generation.