rag-and-vector-search
DevelopmentUse when building RAG systems, implementing semantic/hybrid search, selecting vector databases, tuning retrieval quality, or choosing chunking and embedding strategies.
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RAG and Vector Search
Embedding Model Selection
| Model | Dims | Best For |
|---|---|---|
| text-embedding-3-large | 3072 | Highest accuracy (OpenAI); supports Matryoshka dim reduction |
| text-embedding-3-small | 1536 | Cost-effective default (OpenAI) |
| voyage-3 | 1024 | Code, legal, finance domains (best retrieval quality) |
| gte-Qwen2-7B-instruct | 3584 | Best open-source; instruction-tuned |
| bge-large-en-v1.5 | 1024 | Strong open-source English, smaller footprint |
| all-MiniLM-L6-v2 | 384 | Fast/lightweight, prototyping |
| multilingual-e5-large | 1024 | Multi-language (requires query/passage prefixes) |
Matryoshka Embeddings
Models like text-embedding-3-large support dimension reduction: truncate vectors to 256/512/1024 dims with minimal quality loss. Reduces storage 3-12x. Test recall at target dimension before committing.
Never mix embedding models in the same index -- vectors from different models are incompatible.
Chunking Decisions
| Strategy | When |
|---|---|
| Token-based (512-1000) | Default; predictable size |
| Semantic/header-based | Markdown/structured docs; preserves logical units |
| Recursive character | Unstructured text; LangChain default |
| Parent-child | Need small chunks for retrieval precision, large for LLM context |
- Chunk size: 500-1000 tokens default; smaller for precision, larger for context
- Overlap: 10-20% to avoid losing boundary context
- Always test chunk size impact on retrieval quality for your specific corpus
Distance Metrics
| Metric | When |
|---|---|
| Cosine | Default; works with normalized embeddings |
| Dot Product | When magnitude carries meaning |
| Euclidean (L2) | Raw/unnormalized embeddings |
Index Selection by Scale
| Vector Count | Index Type | Notes |
|---|---|---|
| < 10K | Flat (exact) | No approximation needed |
| 10K-1M | HNSW | Good recall/speed tradeoff |
| 1M-100M | HNSW + INT8 quantization | Reduces memory ~4x |
| > 100M | IVF + PQ or DiskANN | Trades recall for scale |
HNSW Tuning
| Scale | M | efConstruction | efSearch (95% recall) | efSearch (99% recall) |
|---|---|---|---|---|
| < 100K | 16 | 100 | 64 | 128 |
| < 1M | 32 | 200 | 128 | 256 |
| > 1M | 48 | 256 | 128 | 256 |
Higher M = better recall but more memory. Memory per vector: dimensions * bytes_per_dim + M * 2 * 4 bytes.
Vector Database Selection
| DB | Strength | Best For |
|---|---|---|
| pgvector | Already using Postgres; hybrid FTS+vector | Small-medium scale, simplicity |
| Qdrant | Filtering, quantization, Rust perf | Production workloads needing metadata filters |
| Weaviate | GraphQL API, multi-modal, hybrid built-in | Multi-modal search, rapid prototyping |
| Pinecone | Fully managed, zero ops | Teams without infra capacity |
| Turbopuffer | S3-backed, cost-effective at scale | Large-scale with cold storage economics |
| Elasticsearch 8.x | Existing ES stack; native RRF | Hybrid search with mature text search |
Retrieval Architecture
Hybrid Search (Preferred for Production)
Combine dense (vector) + sparse (BM25/FTS) retrieval. Two fusion approaches:
- RRF (Reciprocal Rank Fusion): Works well without tuning, robust default. Score = sum of
1/(k + rank)across result lists, k=60. - Linear combination: More control but requires tuning alpha. Normalize scores before combining.
Reranking (Always Worth It)
- Retrieve 20-50 candidates with hybrid search
- Rerank with cross-encoder (e.g.,
cross-encoder/ms-marco-MiniLM-L-6-v2) - Cohere Rerank API: managed option, supports
rerank-english-v3.0and multilingual - ColBERT / late-interaction: token-level matching, better for long documents than bi-encoder reranking
- Return top 3-5 to LLM
- For diversity: use MMR (
lambda_mult=0.5balances relevance vs diversity)
pgvector + FTS Pattern
Store embeddings and tsvector in same table. Use CTE with RRF to combine vector similarity rank and text search rank in a single query.
Advanced RAG Patterns
GraphRAG
Build knowledge graph from documents, then traverse graph relationships during retrieval. Best for corpora with rich entity relationships (legal, biomedical, enterprise docs). Use with Neo4j or networkx.
Contextual Retrieval (Anthropic Pattern)
Prepend chunk-specific context before embedding: "This chunk is from section X of document Y and discusses Z." Improves retrieval by 20-67% on benchmarks. Compute once at index time.
Proposition-Based Chunking
Decompose documents into atomic propositions ("The Eiffel Tower is in Paris", "It was completed in 1889") instead of fixed-size chunks. Better precision for fact-lookup tasks. Higher indexing cost.
Late Chunking
Embed the full document first (using long-context model), then pool token embeddings into chunks. Preserves cross-chunk context that gets lost with chunk-then-embed.
RAG Pipeline Opinions
Retrieval
- Multi-query retrieval: generate 3-5 query variations for better recall on ambiguous questions
- Parent document retriever: index small chunks, return parent context to LLM
- Contextual compression: extract only relevant portions from retrieved docs before sending to LLM
- Metadata filtering: always index source, timestamp, category; filter at query time to reduce noise
Generation
- Always include citation markers ([1], [2]) in prompt template
- Ask for confidence score; instruct model to say "I don't have enough information" when context is insufficient
- Evaluate groundedness: NLI-based check that response is entailed by retrieved context
Evaluation Metrics
- Retrieval: Precision@K, Recall@K, MRR, NDCG
- Generation: Groundedness (NLI), faithfulness, answer relevance
- Test retrieval and generation independently; don't just evaluate end-to-end
Quantization Tradeoffs
| Type | Size Reduction | Recall Impact | When |
|---|---|---|---|
| FP16 | 2x | Negligible | Default for GPU |
| INT8 scalar | 4x | < 1% loss | Production default |
| Product Quantization | 16-32x | 2-5% loss | Memory-constrained, > 100M vectors |
| Binary | 32x | Significant | First-pass candidate filtering only |
Memory Estimation
total_bytes = num_vectors * (dimensions * bytes_per_dim + M * 2 * 4)
Example: 1M vectors, 1536 dims, FP32, M=16 = ~6.1 GB vectors + ~128 MB index overhead.
Cross-References
- ai-ml:llm-application-patterns -- prompt engineering, agent patterns, production deployment
- ai-ml:structured-output-patterns -- extracting structured data from retrieved documents
- ai-ml:embedding-and-representation-learning -- embedding models, fine-tuning for retrieval