Verified claim · AI-ML · 100% confidence
FAISS introduced in: Johnson, Douze, Jégou 2017 — Facebook AI Similarity Search.
Last verified 2026-05-16 · Methodology veritas-v0.1 · 7ee9546a5a7d851e
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Structured fields
- Subject
- FAISS
- Predicate
introduced_in- Object
- Johnson, Douze, Jégou 2017 — Facebook AI Similarity Search
- Confidence
- 100%
- Tags
- faiss · facebook-ai · meta-ai · similarity-search · vector-search · 2017 · introduced_in
Sources (2)
[1] preprint · arXiv (Johnson, Douze, Jégou / Facebook AI Research) · 2017-02-28
Billion-scale similarity search with GPUs“Similarity search finds application in specialized database systems handling complex data such as images or videos, which are typically represented by high-dimensional features and require specific indexing structures. This paper tackles the problem of better utilizing GPUs for this task.”
[2] github release · Meta AI / Facebook AI Research · 2017-02-28
FAISS — official GitHub repository
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FAISS introduced in: Johnson, Douze, Jégou 2017 — Facebook AI Similarity Search. — SourceScore Claim 7ee9546a5a7d851e (verified 2026-05-16). https://sourcescore.org/api/v1/claims/7ee9546a5a7d851e.jsonEmbed this claim
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curl https://sourcescore.org/api/v1/claims/7ee9546a5a7d851e.jsonJavaScript / TypeScript
const r = await fetch("https://sourcescore.org/api/v1/claims/7ee9546a5a7d851e.json");
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// "FAISS introduced in: Johnson, Douze, Jégou 2017 — Facebook AI Similarity Search."Python
import httpx
r = httpx.get("https://sourcescore.org/api/v1/claims/7ee9546a5a7d851e.json")
envelope = r.json()
print(envelope["claim"]["statement"])
# "FAISS introduced in: Johnson, Douze, Jégou 2017 — Facebook AI Similarity Search."LangChain (retrieve-then-cite)
from langchain_core.tools import tool
import httpx
@tool
def get_faiss_fact() -> dict:
"""Fetch the verified SourceScore claim for FAISS."""
r = httpx.get("https://sourcescore.org/api/v1/claims/7ee9546a5a7d851e.json")
return r.json()