Q: Which vector database should I choose for my RAG system?

For small-scale (<100K documents): pgvector or ChromaDB for simplicity. For mid-scale (100K-1M): Pinecone or Weaviate for managed hosting and performance. For large-scale (1M+): Qdrant or Milvus for cost-effective scaling. The choice also depends on filtering needs, metadata support, and hybrid search capabilities.

Q: How does chunk size affect RAG performance?

Smaller chunks (256-512 tokens) improve retrieval precision but may lose context. Larger chunks (1024-2048 tokens) preserve context but reduce precision. Most production systems use 512-1024 tokens with overlap. For critical accuracy, implement a two-stage retrieval with small chunks for retrieval and parent documents for context.

Q: Should I use a hosted or self-hosted vector database?

Hosted (Pinecone, Weaviate Cloud) for teams without dedicated infrastructure engineers — lower operational burden but higher per-unit cost. Self-hosted (Qdrant, Milvus on Kubernetes) for teams with DevOps capabilities who need cost control at scale or have data residency requirements.

Q: How do I handle multilingual documents in a RAG system?

Use multilingual embedding models (e.g., Cohere multilingual, multilingual-e5-large). For critical accuracy, consider language-specific indexes. Query translation can also work: detect query language, retrieve from all language indexes, then rerank. Multilingual systems typically need 20-40% more compute.

Q: What is the cost of running a RAG system in production?

Monthly costs typically break down as: vector DB hosting ($70-$500), LLM API calls ($100-$2000 based on query volume), embedding API calls ($20-$200), compute for preprocessing ($50-$300), and storage ($20-$100). Total ranges from $300/month for small systems to $3000+/month for enterprise deployments.

Question 1

Which vector database should I choose for my RAG system?

Accepted Answer

For small-scale (<100K documents): pgvector or ChromaDB for simplicity. For mid-scale (100K-1M): Pinecone or Weaviate for managed hosting and performance. For large-scale (1M+): Qdrant or Milvus for cost-effective scaling. The choice also depends on filtering needs, metadata support, and hybrid search capabilities.

Question 2

How does chunk size affect RAG performance?

Accepted Answer

Smaller chunks (256-512 tokens) improve retrieval precision but may lose context. Larger chunks (1024-2048 tokens) preserve context but reduce precision. Most production systems use 512-1024 tokens with overlap. For critical accuracy, implement a two-stage retrieval with small chunks for retrieval and parent documents for context.

Question 3

Should I use a hosted or self-hosted vector database?

Accepted Answer

Hosted (Pinecone, Weaviate Cloud) for teams without dedicated infrastructure engineers — lower operational burden but higher per-unit cost. Self-hosted (Qdrant, Milvus on Kubernetes) for teams with DevOps capabilities who need cost control at scale or have data residency requirements.

Question 4

How do I handle multilingual documents in a RAG system?

Accepted Answer

Use multilingual embedding models (e.g., Cohere multilingual, multilingual-e5-large). For critical accuracy, consider language-specific indexes. Query translation can also work: detect query language, retrieve from all language indexes, then rerank. Multilingual systems typically need 20-40% more compute.

Question 5

What is the cost of running a RAG system in production?

Accepted Answer

Monthly costs typically break down as: vector DB hosting ($70-$500), LLM API calls ($100-$2000 based on query volume), embedding API calls ($20-$200), compute for preprocessing ($50-$300), and storage ($20-$100). Total ranges from $300/month for small systems to $3000+/month for enterprise deployments.

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Which vector database should I choose for my RAG system?

How does chunk size affect RAG performance?

Should I use a hosted or self-hosted vector database?

How do I handle multilingual documents in a RAG system?

What is the cost of running a RAG system in production?

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