Database Engineering / Vector Databases
Vector Database Engineering
Semantic search, recommendations, and RAG all depend on a vector database that returns the right results quickly. We help you choose between pgvector, Pinecone, Weaviate, Qdrant, and others, design the embedding and indexing setup, and tune retrieval quality and cost.
This Is For You If...
You're adding semantic search or RAG and need to choose a vector store
Retrieval results are irrelevant, and you can't tell if it's the embeddings, chunking, or index
Vector search has become slow or expensive as your data grew
You want vectors in your existing PostgreSQL instead of another service
Which Area Matches Your Need?
Ask Yourself | Points Toward |
Are you choosing a vector database? | Vector Store Selection |
Are results irrelevant or inconsistent? | Retrieval Quality Tuning |
Is search slow or costly at scale? | Index & Performance Tuning |
Do you need filters, permissions, or multi-tenancy? | Hybrid & Filtered Search |
Vector Database Services We Offer
Vector Store Selection
pgvector, Pinecone, Weaviate, Qdrant, Milvus, or Elasticsearch — chosen for your data size, filtering needs, hosting, and budget.
Retrieval Quality Tuning
We evaluate embeddings, chunking, and similarity settings against real queries so results are measurably relevant.
Index & Performance Tuning
HNSW and IVF index settings, dimensionality, and quantization tuned for the right balance of speed, recall, and cost.
Hybrid & Filtered Search
Combining vector and keyword search, metadata filters, and per-tenant isolation for production applications.
What We Need From You
What to Share | Why It Helps |
What users search for or ask | Defines what "relevant" means for evaluation |
Data type and volume | Shapes chunking, embedding, and index choices |
Current setup, if any | Lets us diagnose rather than rebuild |
Hosting and cost constraints | Narrows the right vector store |
Common Mistakes to Avoid
Instinct | Why It Doesn't Help | Do This Instead |
Picking a vector database first | The bigger quality factors are embeddings and chunking | Evaluate retrieval end to end |
No evaluation set | Changes can't be measured, only felt | Build a small set of real queries with expected results |
Pure vector search for everything | Misses exact terms, codes, and names | Use hybrid search where exact matches matter |
Ignoring permissions in retrieval | Users can see data they shouldn't | Apply filters and tenant isolation in the query |
How It Works
1. Share the use case
Tell us what users search for and what data you have
2. We build an evaluation set
Real queries with expected results
3. We design the setup
Store, embeddings, chunking, and index settings
4. We tune
We measure relevance, latency, and cost and adjust
5. We hand over
Working retrieval plus the evaluation to keep it honest
Why Codersarts
We measure retrieval quality instead of judging it by eye, so you know whether a change actually improved results. Recommendations are based on your data and constraints, not on any single vendor.
Turnaround Time
A selection and evaluation setup is typically delivered within a week. Tuning and production rollout are scoped from the first results.
FAQ
Is pgvector enough?
For many products, yes — especially if you already run PostgreSQL. We test it against your data before recommending something else.
Do you build full RAG systems?
Yes — see RAG Systems.
Can you reduce our vector search cost?
Often, through index settings, quantization, and right-sized hosting.
Is our project kept confidential?
Yes. Your data, code, and results stay confidential, and an NDA is available on request.
Related Services
Is Your Vector Search Returning the Wrong Results?
Get retrieval that is measured, tuned, and production-ready.