NLP Lineage

28 February 2017 · Representation and retrieval

FAISS

Billion-scale similarity search with GPUs

J. Johnson, M. Douze, H. Jégou

Date
28 February 2017
What the date marks
arXiv submission
Kind
Infrastructure
Organisation
Facebook AI Research
Access
MIT
Other dates
IEEE Trans. Big Data 2019
Threads
In the guided tour
Chapter 10. Reading more than you remember
Learning phases
4. Embeddings and RAG

A milestone: 2 later milestones descend directly from it, and its descendants reach 3 of the 10 threads. How levels are decided

Read the primary source (arxiv.org)

Full summary in progress. Until then, this page shows the milestone's record from the research data.

What it changed

Before it

Similarity search over large vector sets ran on CPU and did not scale.

The problem

Retrieval over billions of embeddings was too slow to sit inside a model's loop.

What it introduced

GPU-accelerated approximate nearest-neighbour search, released as a library.

Why it matters

The plumbing under every vector database and RAG stack. DPR and RAG both call it directly.

What modern models inherit

Fast approximate vector search as an assumed capability.

What came next

DPR and RAG used it; the commercial vector-database market grew around the same primitive.

Before and after

The milestones it descends from, and the milestones that descend from it. Work developed alongside it is not counted. Weak links are left out unless you include them.

Before: where it came from

No documented ancestors are recorded for FAISS without weak links.

No documented ancestors are recorded for FAISS without weak links.

No recorded ancestors are recorded for FAISS.

No recorded ancestors are recorded for FAISS.

After: what it led to

FAISS has 4 documented descendants across 1 chapter, reaching forward to Lost in the middle in 2023. Showing its direct descendants.

FAISS has 4 documented descendants across 1 chapter, reaching forward to Lost in the middle in 2023. Showing all of them.

FAISS has 12 recorded descendants, including indirect ones, across 3 chapters, reaching forward to Gemini 3.1 in 2026. Showing its direct descendants.

FAISS has 12 recorded descendants, including indirect ones, across 3 chapters, reaching forward to Gemini 3.1 in 2026. Showing all of them.

  1. 2017FAISSThis milestone.
  2. 2020Dense retrievalwas influenced by FAISSChapter 10. Reading more than you remember
  3. 2020RAGwas influenced by FAISSChapter 10. Reading more than you remember
  4. 2021RETROwas influenced by Dense retrieval
    was influenced by RAG    Chapter 10. Reading more than you remember
  5. 2023Lost in the middlewas influenced by RAGChapter 10. Reading more than you remember
  1. 2017FAISSThis milestone.
  2. 2020Dense retrievalwas influenced by FAISSChapter 10. Reading more than you remember
  3. 2020RAGwas influenced by FAISSChapter 10. Reading more than you remember
  4. 2021RETROwas influenced by Dense retrieval
    was influenced by RAG    Chapter 10. Reading more than you remember
  5. 2023Lost in the middlewas influenced by RAGChapter 10. Reading more than you remember
  6. 2024Gemini 1.5was influenced by Lost in the middle (indirect influence)Chapter 10. Reading more than you remember
  7. 2024Gemini 2.0built directly on Gemini 1.5Chapter 14. Where the threads meet now
  8. 2025Gemini 2.5was influenced by Gemini 1.5Chapter 12. Thinking longer
  9. 2025IMO goldbuilt directly on Gemini 2.5Chapter 12. Thinking longer
  10. 2025IMO-ProofBenchwas influenced by IMO goldChapter 12. Thinking longer
  11. 2025Gemini 3was influenced by Gemini 1.5Chapter 14. Where the threads meet now
  12. 2026Gemini 3 Deep Thinkbuilt directly on Gemini 3Chapter 12. Thinking longer
  13. 2026Gemini 3.1built directly on Gemini 3Chapter 14. Where the threads meet now

Direct links and their sources

Where it came from

None recorded in the sources used.

What it led to

Influenced