Optimizing Vector Database Indexing for Better Semantic Retrieval Accuracy

HNSW (Hierarchical Navigable Small World) is basically the industry standard for vector indexing, but relying on default parameters is where most people kill their retrieval accuracy. I've spent the last month benchmarking semantic search across different embedding models—comparing OpenAI's text-embedding-3-small against Cohere and BGE-M3—and the "accuracy drop" most devs complain about isn't usually the model; it's the index configuration.

If you're seeing a high rate of false positives in your RAG pipeline, your efConstruction is likely too low. I ran a test set of 100k documents, and pushing efConstruction from 100 up to 400 significantly tightened the precision of my nearest neighbor searches, though it slowed down the indexing phase. The trade-off is brutal on build time, but for a static or slow-growing knowledge base, the retrieval gain is worth the compute.

The real headache starts when you compare how different LLMs handle the retrieved context. I noticed a recurring pattern: Claude 3.5 Sonnet is incredibly picky about the relevance of the retrieved chunks. If the vector DB returns "near-misses" (vectors that are mathematically close but semantically irrelevant), Claude often calls it out or ignores the noise. GPT-4o, on the other hand, tends to try and "force" a connection between the query and the retrieved noise, which leads to subtle hallucinations.

To fix this, I stopped relying solely on cosine similarity. I've been implementing a two-stage retrieval process: a fast HNSW search to grab the top 50 candidates, followed by a cross-encoder reranker (like BGE-Reranker) to prune that list down to the top 5.

Here is the basic logic I'm using to filter the candidates before passing them to the LLM:

# Pseudocode for two-stage retrieval
initial_results = vector_db.search(query_vector, limit=50) 
reranked_results = reranker.predict(
    [(query, doc.text) for doc in initial_results]
)
# Only keep docs with a reranker score > 0.7
final_context = [res for res, score in zip(initial_results, reranked_results) if score > 0.7]

Comparing the performance:

HNSW (Default)
Pros: Blazing fast, low latency.
Cons: Prone to "index drift" where the graph search misses the actual global optimum, leading to lower recall.

Flat Index (Brute Force)
Pros: 100% accuracy (exact nearest neighbor).
Cons: Linear time complexity; completely unusable once you hit a few hundred thousand vectors.

IVF-Flat (Inverted File Index)
Pros: Good middle ground for massive datasets.
Cons: Training phase is a pain, and if your data distribution shifts, you have to re-train the centroids or accuracy plummets.

One more thing: stop using 1536-dimensional vectors if you don't have to. I've found that using Matryoshka embeddings (like those from OpenAI or newer HuggingFace models) allows me to truncate vectors to 256 or 512 dimensions with minimal loss in retrieval accuracy, which drastically reduces the memory footprint of the index and speeds up the distance calculations. If you're running on limited RAM, this is the biggest win you can get without sacrificing the "semantic" feel of the search.