How do I use embeddings for semantic search in a document app?

To use embeddings for semantic search in a document app, the basic idea is:

1. Turn each document or chunk of text into a vector embedding
2. Store those vectors in a vector database or search index
3. Embed the user’s query the same way
4. Find the nearest vectors to the query vector
5. Return the matching documents/chunks

High-level flow

1) Ingest documents

Split documents into smaller chunks if they’re long, for example:

• 200–500 words per chunk
• with slight overlap between chunks

Why chunk?

• Better retrieval precision
• Long docs fit embedding model limits
• Makes results more relevant to specific questions

2) Generate embeddings

Use an embedding model to convert each chunk into a numeric vector.

Example:

• "How to reset my password" → [0.12, -0.44, ...]

The key property is that semantically similar text ends up near each other in vector space.

3) Store vectors with metadata

Save:

• the embedding vector
• chunk text
• document ID
• title
• page number
• tags, author, timestamp, etc.

Common storage options:

• PostgreSQL with pgvector
• Pinecone
• Weaviate
• Milvus
• Elasticsearch/OpenSearch with vector support
• FAISS for local/in-memory prototypes

4) Search by meaning

When a user searches:

1. Embed the query
2. Compute similarity against stored vectors
3. Return top matches

Typical similarity metrics:

• cosine similarity
• dot product
• Euclidean distance

Cosine similarity is common for semantic search.

Simple architecture

Document upload → split into chunks
→ embed chunks
→ store vectors + metadata

User search → embed query
→ vector similarity search
→ retrieve top chunks
→ optionally re-rank with an LLM or cross-encoder
→ show results

Example pseudocode

# index time
for doc in documents:
    chunks = chunk_text(doc.text)
    for chunk in chunks:
        vector = embed(chunk)
        vector_db.insert({
            "doc_id": doc.id,
            "chunk_text": chunk,
            "embedding": vector,
            "title": doc.title
        })

# search time
query_vector = embed(user_query)
results = vector_db.search(query_vector, top_k=5)

for r in results:
    print(r["title"], r["chunk_text"])

Practical tips

Chunking

• Keep chunks semantically coherent
• Add overlap to preserve context
• Avoid chunks that are too large or too tiny

Metadata filtering

Combine semantic search with filters:

• document type
• date range
• user permissions
• category

Example:

• “Find security docs from last month”

Hybrid search

Best results often come from combining:

• keyword search for exact matches
• embedding search for meaning

For example:

• search “car repair manual” should find “auto maintenance guide”
• but keyword search still helps with rare terms, product codes, and names

Re-ranking

After retrieving top 20 vector matches, use a more accurate model to re-rank the top results for better relevance.

Permissions

If your app has private documents, enforce access control before returning matches.

Common pitfalls

• Using full documents instead of chunks: hurts search quality
• Not updating embeddings when content changes
• Ignoring metadata filters
• Returning too many irrelevant matches
• Forgetting that embeddings don’t do exact keyword matching well
• Not handling access control

When semantic search is useful

It works well for:

• “Find docs about onboarding new hires”
• “Show me policies related to parental leave”
• “Where do we mention refund exceptions?”
• “Docs similar to this one”

Minimal implementation stack

A simple setup could be:

• Embeddings API or open-source embedding model
• PostgreSQL + pgvector
• Backend service to chunk/index/search
• Frontend search box

If you want, I can also give you:

1. a Python example with pgvector,
2. a Node.js example, or
3. a full architecture for a document search app.