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Vector Databases & Embeddings

Project: Semantic Search Engine

Design a semantic search engine by understanding indexing, embeddings, retrieval, ranking, UX, and evaluation

40 min read· Project· Semantic Search· Hybrid Search· ChromaDB

Project: Semantic Search Engine

Semantic search finds results by meaning, not just exact keywords.

This project is not about pasting a full backend. It is about understanding the system you are building so you can implement it in any stack.

What you are building

text
documents -> chunks -> embeddings -> vector index -> query embedding -> ranked results

Then you improve it with metadata filters, hybrid keyword search, and evaluation.

Product goal

Build search that answers:

  • "find documents about refund exceptions"
  • "show similar troubleshooting guides"
  • "find policies related to vendor risk"
  • "search even when the user uses different words"

Core concepts

ConceptMeaning
embeddingvector representation of text meaning
chunksearchable piece of a document
vector indexdatabase optimized for nearest-neighbor search
metadatafilters such as author, date, product, team
rerankersecond pass that improves ordering
hybrid searchdense vector search plus keyword search

Step 1: prepare documents

Good search starts before embeddings.

Clean each document:

  • extract title and source
  • remove navigation boilerplate
  • preserve headings
  • keep source URL or file ID
  • split into meaningful chunks
  • attach metadata

Bad chunks create bad results. Avoid splitting in the middle of a table, paragraph, or procedure when possible.

Step 2: create embeddings

Each chunk becomes a vector.

Store:

  • chunk text
  • embedding vector
  • document ID
  • title
  • source
  • section heading
  • timestamp
  • access permissions

Permissions matter. Search should not return documents the user is not allowed to see.

Step 3: query flow

text
user query
  -> normalize query
  -> embed query
  -> retrieve top candidates
  -> apply filters
  -> rerank
  -> show snippets and sources

Step 4: result UX

A good search result shows:

  • title
  • short snippet
  • source
  • why it matched
  • date or version
  • action button to open source

Do not show only raw chunks. Users need context.

Step 5: evaluate search

Create a small test set:

QueryExpected result
"refund after 30 days"refund policy exception section
"reset SSO password"identity troubleshooting guide
"vendor breach process"security incident playbook

Measure:

  • recall at 5
  • precision at 5
  • whether the top result is useful
  • whether snippets explain the match
  • latency

Build checklist

  1. Index 20 to 50 documents.
  2. Search by vector similarity.
  3. Add metadata filters.
  4. Add keyword fallback.
  5. Add reranking.
  6. Add eval cases.
  7. Log failed searches.

Knowledge check

Q1: Why is chunking important for semantic search?

The search engine retrieves chunks, so chunk boundaries determine what evidence can be found.

Q2: Why should search enforce permissions?

Because embeddings and search results can leak private documents if access control is ignored.