Semantic search and RAG require a database that understands meaning, not just keywords.
Vector database integration for semantic search and retrieval-augmented generation — Pinecone, pgvector, or Supabase pgvector for storing and querying embeddings. The embedding pipeline, similarity search, and the metadata filtering that makes AI-powered search precise.
Application that needs to search documents, products, or content by meaning rather than keywords — or an AI feature that needs to ground model responses in specific application data
Traditional databases search by exact or fuzzy text match. They return records where a field contains the query string. This works for structured data lookups but fails for:
Semantic search. A user searching "running shoes for bad knees" should find products tagged with "joint support," "cushioned sole," and "low impact." Keyword search returns nothing if none of those exact words appear in the query. Semantic search returns conceptually similar results.
RAG retrieval. A document Q&A system needs to find the 3-5 chunks of a document most relevant to the user's question, not the chunks that contain the exact query words. Vector similarity search returns the most semantically relevant chunks.
Recommendation. "Similar items" recommendations based on the meaning of product descriptions, articles, or user preferences — not just tag overlap.
How vector search works: the text is converted to a vector embedding (a list of ~1,500 numbers that represents its meaning in semantic space) via an embedding model (OpenAI text-embedding-3-small or similar). Queries are also embedded. The database returns vectors closest to the query vector — semantically similar content.
Vector database options:
pgvector. Postgres extension — available in Neon, Supabase, and standard Postgres. Best for applications already on Postgres that want to avoid adding another service. Performance adequate for most startup-scale needs.
Pinecone. Managed vector database purpose-built for high-scale similarity search. Better performance at large scale (millions of vectors). More complex setup.
Vector database implementation with embedding generation pipeline, similarity search, metadata filtering, and integration with the AI layer that uses the retrieved context
Embedding pipeline
Document chunking strategy (paragraph splits, token-aware splits). Embedding generation via OpenAI or Cohere. Batch embedding for large document sets.
pgvector setup
`CREATE EXTENSION vector`. `vector(1536)` column in the documents table. `ivfflat` index for approximate nearest neighbor search. Similarity search query.
Pinecone integration
Index creation with dimension configuration. Upsert with metadata. Similarity query with top-k results. Metadata filtering.
RAG retrieval
Query embedding. Similarity search. Context injection into the model prompt. Citations in the response.
Incremental indexing
Webhook or database trigger to embed new documents on creation. Re-embedding on content changes.
One honest number to start.
Fixed-scope, fixed-price. The number below is the starting point — final scope is built from your brief.
Vector database implementation with embedding generation pipeline, similarity search, metadata filtering, and integration with the AI layer that uses the retrieved context
Three steps, every time.
The same repeatable engagement on every project. No surprises, no mystery, no billable ambiguity.
Brief & discovery.
We send you questions, then get on a call. Output: a written scope with every step, feature, and integration listed.
Build & ship.
Fixed schedule, weekly reviews. No scope creep unless you change the scope — and if you do, we reprice it transparently.
Warranty & retainer.
30-day warranty on every launch. Most clients stay on a monthly retainer for ongoing features and maintenance.
Why Fixed-Price Matters Here
Vector database implementation scope is defined by the document corpus and the retrieval use cases. Fixed price.
Questions, answered.
pgvector for: up to ~1M vectors, queries under 100ms response time requirement, teams that want fewer services to manage. Pinecone for: millions of vectors, sub-10ms query requirements, applications where vector search is a primary feature. pgvector is the right choice for most startup-scale RAG implementations.
Algolia is keyword and typo-tolerant search with good ranking control. Semantic search understands meaning. For most product and content search, Algolia is simpler and more predictable. Use semantic/vector search when meaning matters more than keyword precision.
Part of the AI feature build. AI-powered application from $25k. Fixed-price.
Tell Ryel about your project.
Describe what you’re building and what outcome you need. You’ll have a written, fixed-price scope within the week.