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Background

Vector Databases & Semantic Search

Build high-performance RAG systems and semantic search applications with Pinecone, Weaviate, ChromaDB, and Postgres with pgvector.

Vector Database Solutions We Deploy

Pinecone

Fully-managed vector database for production RAG systems with hybrid search, metadata filtering, and sub-100ms query latency at scale.

Weaviate

Open-source vector search engine with GraphQL API, multi-tenancy, and built-in vectorization modules for text, images, and custom data.

ChromaDB

Lightweight, embeddable vector database ideal for prototyping and small-to-medium scale deployments with simple Python/JavaScript APIs.

Postgres + pgvector

Leverage existing Postgres infrastructure for vector search with pgvector extension—ideal for hybrid SQL + vector queries and strict compliance.

Our Three-Layer Approach with Vector Databases

1

Advisory & Governance

Vector database selection, embedding model evaluation, and data architecture design.

  • • Vector DB selection (Pinecone vs Weaviate vs pgvector)
  • • Embedding model evaluation (OpenAI Ada, Cohere, open-source)
  • • Chunking strategy for document ingestion pipelines
  • • Metadata schema design for filtering and hybrid search
  • • Index configuration and similarity metrics (cosine, dot product, euclidean)

Example Deliverable:

Vector database architecture with embedding model comparison and chunking strategy

2

Build & Integrate

Production-ready RAG pipelines with vector search, metadata filtering, and reranking.

  • • Document ingestion pipelines (PDF, Word, HTML, Markdown)
  • • Embedding generation and upsert workflows
  • • Semantic search APIs with metadata pre-filtering
  • • Hybrid search combining vector similarity and keyword matching
  • • Reranking with cross-encoder models for improved precision

Example Deliverable:

Enterprise knowledge base with Pinecone and GPT-4 RAG pipeline

3

Operate & Scale

Monitor search quality, optimize index performance, and scale vector operations.

  • • Search relevance monitoring and quality metrics
  • • Index performance tuning and query optimization
  • • Embedding drift detection and reindexing workflows
  • • Cost optimization for managed vector databases
  • • Incremental updates and delta synchronization

Example Deliverable:

Vector search monitoring dashboard with relevance scoring and cost tracking

Use Cases from Our Implementations

Resume Matching for Recruiting Agents

Semantic search over candidate resumes using Pinecone with metadata filters for skills, experience, and location to power automated interview scheduling.

Tech:Pinecone, OpenAI Ada Embeddings, Metadata Filtering

Service Manual Q&A for Field Technicians

LlamaIndex + Weaviate-powered RAG system for natural language queries over equipment manuals, troubleshooting guides, and historical job notes.

Tech:Weaviate, LlamaIndex, Hybrid Search

Patient Record Search for Healthcare Outreach

Azure Postgres + pgvector for HIPAA-compliant semantic search over appointment history and clinical notes to power automated patient outreach campaigns with Azure OpenAI embeddings.

Tech:Azure Postgres, pgvector, Azure OpenAI Embeddings

Choosing the Right Vector Database

Use Pinecone When

  • • You need fully-managed infrastructure
  • • Scale to billions of vectors
  • • Sub-100ms query latency required
  • • Hybrid search with metadata filters

Use Weaviate When

  • • You want self-hosted control
  • • Need multi-tenancy isolation
  • • GraphQL API preferred
  • • Built-in vectorization modules

Use pgvector When

  • • Already using Postgres
  • • Strict data residency requirements
  • • Need SQL + vector hybrid queries
  • • Compliance-driven architecture

Ready to build semantic search into your application?

Let's discuss your data, scale requirements, and design the right vector database architecture for your RAG system.

Schedule Consultation