Using Embeddings for Smart Search in Your Web App

Introduction

Smart search aims to return results that are relevant to the intent behind a query, not just exact keyword matches. Embeddings offer a way to represent text as dense vectors that capture semantic meaning, enabling you to rank results by semantic similarity. This post walks through the why, the architecture, and a practical path to add embeddings-powered search to your web app.

What are embeddings?

Embeddings are high-dimensional numeric representations of text (or other data) where similar items are mapped to nearby points in vector space. They capture semantic relationships such as synonyms, context, and concept similarity. In search contexts, you compare the embedding of a user query with embeddings of indexed documents and retrieve those with the smallest distance (highest similarity).

How semantic search works

• Ingest and encode: Break content into chunks and convert each chunk into an embedding using a model.
• Index: Store embeddings in a vector store (a specialized database optimized for vector similarity search) along with metadata.
• Query: Encode the user query into an embedding.
• Retrieve: Find the top-k embeddings closest to the query embedding.
• Rank and present: Optionally rerank results using a more precise model or business rules, then display to the user.

Key benefits:

• Robust against synonyms and paraphrasing
• Handles longer documents by chunking
• Enables flexible, multi-modal or multi-turn search ideas in the future

Architecture overview

• Data ingestion: Source content (docs, FAQs, help articles) is chunked into digestible pieces.
• Embedding generation: A model converts text chunks to vectors.
• Vector store: Stores embeddings with metadata and supports nearest-neighbor search.
• API layer: Exposes a search endpoint for your frontend.
• Optional reranking: A cross-encoder or re-ranker model improves result quality.

Common tool choices:

• Embedding models: OpenAI embeddings, sentence-transformers, or other large language model (LLM) backed embeddings.
• Vector stores: Pinecone, Weaviate, Chroma, FAISS (local), RedisVector, or other vector databases.
• Frontend: Simple search input that calls your backend API, with result rendering and highlighting.

Data preparation and indexing

• Content segmentation: Break documents into logical chunks (e.g., paragraphs, sections) to improve granularity.
• Metadata: Include useful metadata (document_id, section, author, date) to help filtering and UX.
• Embedding generation: Generate embeddings for every chunk using your chosen model.
• Indexing: Upsert embeddings into your vector store with associated metadata.
• Refresh strategy: Recompute embeddings when content changes and prune stale vectors as needed.

Practical tips:

• Use a consistent chunk size (e.g., 200–500 tokens) to balance context with index size.
• Include both content and metadata in the index for richer search results.
• Consider caching embeddings for frequently asked queries to reduce latency and API usage.

Choosing models and vector stores

• Embedding models:
  • OpenAI embeddings are easy to get started with and work well across many domains.
  • Sentence-transformers (e.g., SBERT) are open-source and can run locally or on your own servers.
  • Tradeoffs: OpenAI tends to be simpler with strong general-purpose results but may incur higher per-call costs and latency; local models give more control and lower per-query cost but require compute and maintenance.
• Vector stores:
  • Fully managed: Pinecone, Weaviate, or similar services simplify scaling and operational concerns.
  • Open-source/local: FAISS, Chroma, or RedisVector offer control and potentially lower costs, at the expense of managing infrastructure.
• Ranking and reranking:
  • A lightweight nearest-neighbor search can be followed by a reranker (e.g., cross-encoder) for improved result quality on top results.

Implementation blueprint

• Backend responsibilities:
  • Endpoint to ingest content and build the vector index.
  • Endpoint to accept a user query, compute its embedding, search the vector store, and return results with metadata.
  • Optional: a reranking step for the top results.
• Frontend responsibilities:
  • Capture query input, show loading state, render results with highlighting.
  • Optional: show secondary results panels (e.g., most relevant docs, FAQs, or related topics).
• Performance considerations:
  • Latency: embeddings plus vector search impact user-perceived latency; consider caching and batching.
  • Costs: embedding-request costs can accumulate; implement rate limits and sensible chunking.
  • Privacy: ensure sensitive content is handled appropriately, especially if using third-party embedding services.

Implementation blueprint: a minimal back-end flow

• Step 1: Ingest content and create embeddings
  • Break content into chunks
  • For each chunk, obtain an embedding from your chosen model
  • Upsert to vector store with metadata
• Step 2: Build a search endpoint
  • Receive user query
  • Compute embedding for the query
  • Query vector store for top-k closest embeddings
  • Return results with metadata to the frontend
• Step 3: Optional reranking
  • Run a more expensive model on the top results to reorder by relevance
  • Return final results to user

Example: Lightweight search pipeline (pseudo-code)

• Ingest example (pseudo-code)

// ingest.js
const docs = [
  { id: "doc1", text: " ..." },
  { id: "doc2", text: " ..." },
  // ...
];

async function ingestAll(docs) {
  for (const d of docs) {
    const emb = await getEmbedding(d.text); // model-specific API
    vectorStore.upsert({ id: d.id, embedding: emb, metadata: { text: d.text, docId: d.id } });
  }
}

• Embedding helper (pseudo-code)

async function getEmbedding(text) {
  // Replace with your embedding model/API
  // Example: call to OpenAI or local model
  const response = await fetchEmbeddingAPI({ input: text });
  return response.embedding;
}

• Search endpoint (pseudo-code)

async function search(query, topK = 5) {
  const qEmb = await getEmbedding(query);
  const results = await vectorStore.query({ vector: qEmb, topK, includeMetadata: true });
  // Optional: rerank with a richer model
  const ranked = await rerankIfNeeded(results);
  return ranked;
}

• Frontend usage (pseudo-code)

async function onSearchSubmit(query) {
  showLoading();
  const results = await fetch('/search', { method: 'POST', body: JSON.stringify({ query }) });
  renderResults(results);
  hideLoading();
}

Note: Replace placeholder functions with your actual embedding API calls and vector store client methods.

Deployment and operations tips

• Start small: prototype with a single content domain to validate quality before scaling.
• Monitor latency and cost: measure end-to-end search latency and embedding costs; optimize by batching and caching common queries.
• Secure access: protect the search endpoint and limit access to sensitive data; consider per-user privacy if needed.
• Plan for data drift: content changes over time; implement a re-indexing schedule to keep embeddings fresh.
• UX considerations: show clear results with snippets, highlights, and relevant metadata; offer filtering and sort options.

Looking ahead

Embeddings enable flexible and powerful search experiences, from intent-aware results to cross-document relevance and even multi-turn conversations. As you evolve, you can layer additional features like knowledge-base linking, document summarization, or domain-specific re-rankers to further improve user satisfaction.

Conclusion

Embedding-based search unlocks semantic understanding in your web app, allowing users to find what they mean rather than what they typed. By structuring your data for semantic retrieval, selecting appropriate models and vector stores, and implementing a clean back-end and front-end flow, you can deliver a responsive, scalable smart search experience now—and iterate toward more advanced capabilities over time.