I'm building a pipeline that writes embeddings from Python, what should I choose?

If you’re writing embeddings from Python, the best choice usually depends on where they’re going:

Good default choices

• NumPy .npy / .npz
  Best if you want a simple, fast, Python-native format for local files.

  • Pros: very easy, preserves dtype/shape, fast read/write
  • Cons: not ideal for cross-language interoperability or database use

• Parquet
  Best if you’re storing embeddings in a data pipeline or analytics stack.

  • Pros: compact, columnar, works well with Spark/Polars/Pandas
  • Cons: embeddings are often arrays, so you may need to flatten or store as lists

• JSON / CSV
  Only if you need human readability or very simple interchange.

  • Pros: easy to inspect
  • Cons: large and slow for high-dimensional embeddings

If you want a vector database

Use the vector store’s native ingestion format/API:

• FAISS for local similarity search
• Milvus / Qdrant / Weaviate / Pinecone / Chroma if you want managed or scalable vector search

My recommendation

• For local Python-only pipelines: use NumPy .npy
• For production data pipelines: use Parquet
• For vector search: write directly to a vector database

If you tell me:

1. embedding dimension,
2. file-based vs database, and
3. whether you need cross-language support,
   I can recommend a precise format and Python library.