Final Work Submission (GSoC 2025)
📅 November 01, 2025 – Final Work Submission
LLM4S – Embeddings, DBx, and Retrieval Engine
This page summarizes my complete Google Summer of Code 2025 contribution to the LLM4S project under the Scala Center.
The work focused on building a full, end-to-end retrieval pipeline consisting of:
Embedding Engine
DBx Vector Database System (Core → Mid → Full)
RAG-Ready Retrieval Layer (will update in future)
This is the final overview of phases, deliverables, documentation, and pull requests.
✅ Phase 1 — Embedding Engine
Document ingestion → chunking → embedding → similarity
Main Features
Multi-provider embedding system (OpenAI, VoyageAI)
Static model selection via
.envUniversalExtractor for:
PDF
DOCX
XLSX
TXT
URLs (HTML cleaning)
Natural sentence-aware chunking
Token-limit guards
Dual embeddings (document + query)
Cosine similarity scoring
Vector normalization + dimension registry
Retry / backoff / error-hardened API
Outcome:
A complete and reliable embedding pipeline forming the foundation for semantic search, pgvector storage, and future RAG.
PRs
#92 – OpenAI + VoyageAI Embedding Support
#100 – UniversalExtractor + Similarity Utils
#118 – Extended Voyage Models & Pre-RAG Improvements
✅ DBx-Core
Deliverables
Base pgvector schema
Embedding + metadata insertion
Dimension validation
Basic querying utilities
Duplicate detection (hash-based)
✅ DBx-Mid
Deliverables
IVFFLAT indexing
Optional HNSW indexing
JSONB metadata filters
Hybrid search (text + vector)
Query latency tuning
Batch read/write optimizations
✅ DBx-Full
Deliverables
Complete vector search engine
Query embedding → top-k retrieval
Similarity-based ranking
Metadata-aware scoring
RAG-ready chunk packaging
Agent memory integration
Outcome:
A stable, production-grade semantic memory layer fully integrated with LLM4S.
PRs
- PR #246 — DBx Core: Initial scaffolding for a provider-agnostic Vector Store layer
Phase 3 — Retrieval Engine (RAG Layer)
Query → embed → search DBx → build context → feed model
Main Deliverables
High-level retrieval API
Query embedding + similarity search
Top-k chunk selection
RAG context builder (merged snippets, metadata, scores)
Fallback logic (keyword → vector → hybrid)
Confidence-based chunk suppression
Agent memory fetch interface
Complete examples + documentation
Outcome:
LLM4S now supports full end-to-end retrieval-augmented generation with semantic search, pgvector storage, and multi-provider embeddings.
Final Architecture Overview
Document → Extraction → Chunking → Embedding
↓
DBx-Core (storage)
↓
DBx-Mid (indexing)
↓
DBx-Full (retrieval)
↓
RAG Context Builder → LLM/Agent
References
Project (repo root):
[GitHub: LLM4S repository]Embedding Engine code:
[GitHub: embeddings module path]UniversalExtractor.scala:
[GitHub: file link]
Some Detailed Pull Requests
Embedding Engine (Phase 1)
PR #83 — Embedding Support for OpenAI and VoyageAI
PR #100 — EmbedX: Universal Extractor & Similarity Support
PR #118 — PR3: Extended VoyageAI Models + Smarter Model Selection
PR #202 — EmbedX v2: Unified Embedding Pipeline & CLI Report
PR #242 — EmbedX Cleanup + PGVector Integration (Phase 2 Link)
PR #243 — EmbedX Cleanup + PGVector Integration (cont.)
DBx (Phase 2)
PR #239 — Text-Only Embeddings → Road to Vector DB
PR #246 — DBx-Core: Initial Scaffolding for Vector Store