An implementation plan for Project Spec-RAG. This roadmap is designed to maximize both your academic output (getting the paper accepted) and your career prospects (getting the interview).
This plan treats your project as an Enterprise-Grade AI System, moving from data engineering to advanced multimodal LLM fine-tuning.
# Project Spec-RAG: Cross-Modal Retrieval-Augmented Generation for Mass Spectrometry
**Objective:** Build a multimodal AI system that uses semantic retrieval to guide molecular generation, transitioning from academic baselines (T5) to state-of-the-art GenAI architectures (Llama 3/Gemma) to demonstrate full-stack AI engineering capability.
---
## 🏗️ System Architecture
**Flow:** `Input Spectrum` $\rightarrow$ `SpecBridge Encoder` $\rightarrow$ `Semantic Retrieval (RAG)` $\rightarrow$ `Multimodal Projector` $\rightarrow$ `LLM (Generation)`
| Component | Technology Stack | Resume Keywords |
| :--- | :--- | :--- |
| **Encoder** | SpecBridge (Current) | *Contrastive Learning, Representation Learning* |
| **Retrieval** | FAISS (HNSW Index) | *Vector Database, Semantic Search, HNSW* |
| **Model A (Baseline)** | MolT5 (Encoder-Decoder) | *Seq2Seq, Transformer, HuggingFace* |
| **Model B (Advanced)** | Llama-3-8B / Gemma-2B | *Decoder-only LLM, Instruction Tuning* |
| **Training** | PyTorch, LoRA/PEFT | *Parameter-Efficient Fine-Tuning, GPU Optimization* |
---
## 📅 Phase 1: The Semantic Retrieval Engine (Data Engineering)
**Goal:** Transform the static dataset into a queryable Vector Database.
**Timeframe:** Week 1
### 1.1 Data Preparation
* **Source:** Collect all unique SMILES from your training set (e.g., MassSpecGym/NIST).
* **Encoding:** Use the **Text Encoder** branch of SpecBridge (or ChemBERTa) to generate embeddings for every molecule.
* **Normalization:** Apply L2 normalization to allow for Cosine Similarity search.
### 1.2 Vector Indexing (FAISS)
* **Implementation:** Do not use flat search. Implement **HNSW (Hierarchical Navigable Small World)** indexing for scalability.
* **Deliverable:** A `.index` file containing 100k+ molecular vectors.
```python
import faiss
import numpy as np
# Resume Keyword: "Implemented HNSW Indexing for low-latency retrieval"
def build_index(embeddings):
d = embeddings.shape[1]
index = faiss.IndexHNSWFlat(d, 32) # M=32 neighbors
index.verbose = True
index.add(embeddings)
return index
1.3 Cross-Modal Retrieval Logic
- Task: Input a Spectrum SpecBridge Encoder Search Molecule Index.
- Validation: Verify that for a given spectrum, the "Ground Truth" molecule is within the Top-100 retrieved results (Recall@100).
🧪 Phase 2: The Baseline (MolT5 + RAG)
Goal: Establish a solid academic baseline using your current T5 stack. Timeframe: Week 2
2.1 Context Injection (Prompt Engineering)
- Strategy: Concatenate retrieved SMILES into the input text sequence.
- Input Format:
Input: <Spectrum_Token>
Context: Reference Molecules: [SMILES_1] [SMILES_2] [SMILES_3]
Target: [Ground_Truth_SMILES]
2.2 Fine-Tuning
- Action: Fine-tune MolT5-Base on this new dataset.
- Outcome: The model learns to "copy" structural motifs from the references rather than guessing blindly.
- Metric: Measure improvement in Tanimoto Similarity vs. the non-RAG SpecBridge.
🚀 Phase 3: The Career Booster (Llama-3 + LoRA)
Goal: Transition to modern GenAI architectures to make the resume "Headhunter-Proof." Timeframe: Week 3-4
3.1 The "LLaVA" Adapter (Multimodal Projector)
- Concept: Llama-3 cannot see spectrum embeddings (dim=768). You must project them to Llama's dimension (dim=4096).
- Implementation: Build a simple MLP Projector.
class SpecProjector(nn.Module):
def __init__(self, input_dim=768, llm_dim=4096):
super().__init__()
self.net = nn.Sequential(
nn.Linear(input_dim, llm_dim),
nn.GELU(),
nn.Linear(llm_dim, llm_dim)
)
3.2 Parameter-Efficient Fine-Tuning (PEFT)
Tooling: Use
bitsandbytes(for 4-bit quantization) andpeft(for LoRA).Config:
Load: Llama-3-8B (4-bit quantized).
Freeze: The Llama backbone.
Train: Only the Projector and LoRA Adapters (Attention layers).
Resume Win: "Fine-tuned Llama-3-8B on consumer hardware using QLoRA and custom multimodal adapters."
3.3 Instruction Tuning Data
- Format:
{
"role": "user",
"content": "Given the mass spectrum embedding <SPEC_EMB> and retrieved similar molecules <RAG_CONTEXT>, predict the exact structure."
},
{
"role": "assistant",
"content": "Based on the spectral features and reference structures, the molecule is <SMILES>."
}
🏆 Phase 4: Alignment (RLHF/DPO) [Optional / Advanced]
Goal: If you have time, optimize for specific chemical properties (e.g., Validity, QED).
Method: DPO (Direct Preference Optimization).
Data Construction:
(Winner): Ground Truth SMILES.
(Loser): A generated SMILES that is chemically invalid or has low spectral similarity.
Training: Use HuggingFace
TRL(Transformer Reinforcement Learning) library to align the Llama model to prefer valid molecules.
📝 Resume Strategy: How to list this?
Project: Spec-RAG (Multimodal GenAI & Search System)
- Designed a Retrieval-Augmented Generation (RAG) pipeline for scientific data, integrating FAISS for millisecond-latency cross-modal retrieval.
- Developed a Multimodal LLM by aligning a spectral encoder with Llama-3-8B using a custom MLP Projector and QLoRA fine-tuning.
- Engineered a Semantic Search engine using HNSW indexing, improving molecular generation accuracy by X% via in-context learning.
- Optimized inference throughput using 4-bit Quantization (AWQ) and vLLM strategies.