I’m building a document search app and need recommendations on embeddings models and vector storage

Here’s a practical guide for choosing embeddings models and vector storage for a document search app.

1) Embeddings models: what to pick

Good default choices

If you want strong quality with minimal tuning:

• OpenAI text-embedding-3-small

  • Good cost/performance balance
  • Solid for semantic search over documents
  • Lower latency and cheaper than larger models

• OpenAI text-embedding-3-large

  • Better retrieval quality, especially for nuanced queries
  • More expensive, but worth it if search quality is critical

Strong open-source options

If you want to self-host or avoid vendor lock-in:

• bge-large-en / bge-base-en
  • Very good general-purpose retrieval embeddings
  • Popular for document search
• e5-large / e5-base
  • Strong for semantic retrieval
  • Works well with query/document prefixing patterns
• jina-embeddings-v2
  • Good multilingual support and practical performance

How to choose

• English-only, general doc search: text-embedding-3-small or bge-base-en
• Highest retrieval quality: text-embedding-3-large or bge-large-en
• Multilingual search: text-embedding-3-large or multilingual models like jina-embeddings-v2
• Self-hosted / open-source: BGE or E5 family

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2) Vector storage: what to use

Best hosted/vector DB options

• Pinecone
  • Very easy to operationalize
  • Good scaling and managed experience
• Weaviate
  • Strong feature set, hybrid search support
  • Good if you want metadata filtering + vector search
• Qdrant
  • Great performance, simple API, strong filtering
  • Excellent open-source and managed options

If you already use a database

• Postgres + pgvector
  • Best for simpler apps or when you want everything in one DB
  • Great if your scale is moderate
  • Easy metadata + relational queries
• MongoDB Atlas Vector Search
  • Useful if your app already lives in MongoDB
• Elasticsearch / OpenSearch
  • Good if you need strong keyword + vector hybrid search at scale

If you want local/prototyping

• FAISS
  • Fast and simple for experiments
  • Not a full database; you manage persistence and metadata separately

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3) Recommended pairings by scenario

Small-to-medium app, fast to ship

• Embeddings: text-embedding-3-small
• Storage: pgvector or Qdrant
• Why: simple, cheap, good enough for many document search apps

Higher search quality, production use

• Embeddings: text-embedding-3-large or bge-large-en
• Storage: Qdrant, Pinecone, or Weaviate
• Why: better retrieval and robust vector indexing

Enterprise / hybrid keyword + semantic search

• Embeddings: text-embedding-3-large or BGE
• Storage: OpenSearch or Elasticsearch
• Why: combines lexical and vector ranking well

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4) Practical architecture tips

Chunking matters as much as embeddings

For documents:

• Split into chunks of about 200–800 tokens
• Use overlap of ~10–20%
• Store:
  • chunk text
  • document ID
  • section/page info
  • metadata like title, author, date, tags

Use hybrid retrieval

Combine:

• vector similarity
• keyword/BM25 search
• optional reranker

This usually improves relevance a lot for document search.

Add a reranker if quality matters

A reranker can greatly improve top-k results:

• Cross-encoder rerankers
• Hosted rerankers
• Open-source rerankers like BGE reranker models

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5) My simple recommendation

If you want a safe default:

• Embeddings: text-embedding-3-small
• Vector store: Qdrant or pgvector
• Search strategy: hybrid keyword + vector
• Optional: reranker for top 20 results

If search quality is top priority:

• Embeddings: text-embedding-3-large
• Vector store: Qdrant, Pinecone, or Weaviate
• Add reranking

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6) Quick decision rule

• Need cheapest and simplest: pgvector
• Need managed scale: Pinecone
• Need best open-source option: Qdrant
• Need hybrid enterprise search: Elasticsearch/OpenSearch
• Need best overall retrieval quality: larger embeddings + reranker

If you want, I can also give you:

1. a decision matrix comparing these options,
2. a reference architecture for ingestion/search,
3. or a code example for one stack like Postgres + embeddings + retrieval.