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docs: add Faiss to Database intro

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Co-Authored-By: Claude <noreply@anthropic.com>
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Vinta Chen
2026-10-16 18:37:43 +08:00
co-authored by Claude
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@@ -5,6 +5,7 @@ How to choose:
- Analytical SQL over Parquet, CSV, or DataFrames: DuckDB
- Vector search in a prototype or notebook: Chroma
- ClickHouse's SQL dialect, or pandas code that runs on ClickHouse: chDB
- Similarity search over vectors you hold in NumPy arrays: Faiss
- Vectors stored with their raw images and text, on disk: LanceDB
- Vector search embedded in an app or an edge device: Zvec
- A small JSON document store for one script: TinyDB
@@ -15,6 +16,8 @@ chDB runs ClickHouse inside your process, so you get its SQL dialect without a C
Chroma's [in-memory client](https://docs.trychroma.com/docs/run-chroma/clients) is the quick way to try it in a notebook, and `PersistentClient` keeps the data on disk. Add text documents, and Chroma [embeds and indexes them for you](https://docs.trychroma.com/docs/overview/getting-started), so you can query with text too.
Faiss is a similarity search library, not a database. It [keeps no metadata with the vectors](https://arxiv.org/html/2401.08281#S6.SS1), only integer IDs, so you store your documents elsewhere and look them up by ID. Its [tutorial](https://github.com/facebookresearch/faiss/wiki/Getting-started) builds an index for the vector dimension, adds a float32 NumPy array, and calls `search()` for the nearest neighbors. `IndexFlatL2` returns exact results. To trade some accuracy for speed or memory, [pick an index by RAM and dataset size](https://github.com/facebookresearch/faiss/wiki/Guidelines-to-choose-an-index).
LanceDB starts as an [embedded library pointed at a local directory](https://docs.lancedb.com/quickstart). Its indexes live on disk, and a table [stores the raw data, metadata, and embeddings together](https://docs.lancedb.com/faq/faq-oss), which suits images and other multimodal data. Its [embedding function registry](https://docs.lancedb.com/embedding) generates vectors as you insert rows, and Python code can then query with text.
Zvec runs entirely in-process, with no server or daemon, and its docs pitch it [from prototypes to embedded apps and edge devices](https://zvec.org/en/docs/db/). Its [quickstart](https://zvec.org/en/docs/db/quickstart/) defines a schema of scalar and vector fields first, then combines vector search with filters on those scalar fields.