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Proposal 2025: FAISS Vector Search Integration for Drupal

Open on Drupal.org →
Created on 21 February 2025, about 2 months ago

Project Description

Drupal currently lacks support for FAISS (Facebook AI Similarity Search), a high-performance library designed for efficient vector-based similarity search. This project aims to develop a FAISS provider module that enables advanced content discovery, documentation deduplication, and AI-powered search capabilities within Drupal.

The solution will integrate with existing Drupal AI modules and Search API, providing a robust foundation for vector-based similarity search. By implementing FAISS as a local vector database, we can significantly improve content discovery while reducing dependency on external APIs.

Current Scenario / Pain Points

  • No native FAISS support in Drupal for vector similarity search
  • External API calls causing latency in embedding generation
  • Cost implications with third-party embedding services
  • Growing documentation duplication without automated detection
  • Performance bottlenecks in large-scale content similarity matching

Project Goal

    Functional Changes:
  • Develop a FAISS provider module (ai_provider_faiss) for vector storage and similarity search
  • Create an embedding pipeline using HuggingFace's all-MiniLM-L6-v2 model
  • Integrate with Search API for enhanced content discovery
  • Implement a dashboard for visualizing similarity scores and content relationships

Why This Matters

This integration will revolutionize how Drupal handles content discovery and similarity detection:

  • Cost Efficiency: Local vector operations eliminate expensive API calls
  • Performance: FAISS is optimized for rapid similarity search at scale
  • Independence: Reduced reliance on external services
  • Scalability: Handles millions of vectors efficiently

Future Impact

The FAISS provider will enable next-generation features in Drupal:

  • Intelligent Documentation Management
    • Automated duplicate detection
    • Content relationship mapping
    • Smart content suggestions
  • Enhanced Module Discovery
    • Similar module detection
    • Functionality matching
    • Better developer experience
  • AI-Powered Search
    • Semantic search capabilities
    • Context-aware results
    • Improved content relevance

Project Size

350 Hours

Project Difficulty

INTERMEDIATE

Project Skills/Prerequisites

  • Strong PHP and Drupal module development experience
  • Understanding of vector embeddings and similarity search concepts
  • Familiarity with Search API and HuggingFace integration
  • Experience with performance optimization and scalable solutions

Project Resources

🌱 Plan
Status

Active

Component

Organization

Created by

🇺🇸United States Stanzin

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