Roadmap: Personal Multimodal RAG (100 pages → Full Human History)

Roadmap: Personal Multimodal RAG (100 pages → Full Human History)

This document connects the current Quantum RAG demo work to two future targets:
1) MVP: 20–100 pages of text (practical, achievable now)
2) "Full human history" multimodal memory (theoretical north star)

It assumes limited/occasional quantum hardware access; quantum components are treated as algorithmic modules validated via simulation and resource estimation.

0) Current Work (What You Have Now)

Implemented assets (from quantum_rag_demo/)

• Grover-style retrieval simulation (Qiskit Aer) and demo results (ibm_quantum_results.json)
• Benchmark harness that records wall-clock times, operations counts, and complexity annotations (benchmark_comparison.py, benchmark_results.json)
• HAM-like associative memory implemented as complex-valued matrix accumulation (NumPy) (within benchmark_comparison.py)
• A clear validation roadmap (VALIDATION_ROADMAP.md) distinguishing theory vs simulation vs real-world

What this enables today

• Correctness validation of quantum circuits at small n
• Honest benchmarking of simulators (expected: slower than classical)
• A framework to compare hybrid pipelines (classical prefilter + quantum-ish refinement)

1) Target A: MVP for 100 Pages (Practical)

Goal

Build a robust, reproducible RAG system for ~10k–50k words (roughly 100–500 chunks) with:

• fast interactive retrieval
• measurable quality (recall@k, answer faithfulness)
• a clean path to multimodal later

Scope (MVP)

• Input: PDF/text documents only
• Storage: chunk store + metadata store
• Index: vector search + optional keyword/BM25
• Output: citations to source chunks + short synthesized answer

Architecture (MVP)

1. Ingestion
- Normalize text → chunk (e.g., 300–800 tokens)
- Metadata: doc_id, page range, timestamps, section headers
2. Embeddings
- Choose a stable embedding model (local or API). For now: any deterministic embedding is fine.
3. Indexing
- FAISS/HNSW (or similar) + optional BM25
4. Retrieval
- Hybrid: BM25 prefilter OR ANN prefilter → rerank
5. Generation
- RAG answer with citations + refusal when evidence is weak

What “quantum” means at MVP stage

• Treat quantum modules as pluggable scorers:
• Grover-style search → validate iteration counts / behavior (not speed)
• HAM-style associative memory → validate retrieval behavior + capacity constraints
• The MVP can ship with classical retrieval as the primary path, with quantum modules as experimental options.

Validation and benchmarking (MVP)

You can credibly achieve:

• Latency: p50/p95 retrieval times for 10–100 queries
• Quality: recall@k using synthetic or hand-labeled “ground truth” chunk IDs
• Reproducibility: deterministic dataset + seeded runs

Milestones (Target A)

A1 (1–2 weeks): Baseline RAG

• Ingest + chunk + embed + ANN index
• Baseline benchmark: naive scan vs ANN

A2 (1–2 weeks): Measurement harness

• Track p50/p95/p99, recall@k, memory, index build time
• Store results as JSON + plots

A3 (1–2 weeks): Hybrid retrieval

• Add reranker (cross-encoder or heuristic)
• Add metadata filters (page ranges, sections, recency)

A4 (1–2 weeks): Quantum-module integration (behavioral)

• Plug in quantum-ish scorer for rerank-on-K
• Validate with VALIDATION_ROADMAP.md Phase 1/2

Expected achievements (Target A)

• Real wall-clock wins vs naive scan (due to ANN + caching)
• Better answer quality with reranking + citations
• A well-instrumented harness to later test multimodal

2) Target B: “Full Human History” Multimodal Memory (Theoretical North Star)

Goal

A lifelong memory system that can ingest, compress, and retrieve from:

• text, email, web pages
• photos/video + visual events
• audio conversations + ASR transcripts
• sensor streams (location, biometrics, environment)

Key reality constraints

• Scale: raw data is enormous; retrieval must be hierarchical
• Privacy: default should be personal data vault, encryption, on-device first
• Cost/latency: most queries must hit summaries, not raw media
• Evaluation: ground truth is hard; require task-based evaluation and human feedback loops

Architecture (North Star)

Think in 5 layers:

1) Raw Multimodal Store (immutable)

• object storage for audio/video/images + hashes
• provenance, permissions, encryption

2) Event Segmentation Layer

• converts streams into “events” (time windows)
• e.g., meeting, commute, workout, reading session

3) Representation Layer (Embeddings + Symbols)

• per-modality embeddings (vision, audio, text)
• cross-modal alignment to a shared space
• extracted entities, topics, locations, people

4) Index Layer (Multi-index)

• Vector index (ANN)
• Keyword index (BM25)
• Graph index (entities/relations)
• Time index (temporal queries)

5) Memory Hierarchy + Compression

• hour/day/week/month summaries
• episodic memory (events)
• semantic memory (facts)
• procedural memory (habits)

Retrieval strategy (North Star)

• Query router decides which memory layer(s) to hit:
• fast path: summaries/semantic store
• drill-down path: events → raw artifacts
• “Evidence budget”: max retrieval cost per question
• Continual evaluation: did the retrieved evidence answer the query?

Where quantum could fit (theoretical)

If future quantum hardware becomes practical:

• Quantum kernels for similarity in high-dimensional spaces
• Grover-like search for unstructured candidate sets (still requires good oracles)
• Quantum-inspired linear algebra (often yields classical approximations that help today)

Important: for “full history,” the dominant engineering challenge is data modeling + indexing + summarization, not raw search speed.

3) Roadmap to Reach Target B (Practical steps that converge on the north star)

Phase B1: Text → Multimodal-ready schema (4–6 weeks)

Deliverables:

• Unified schema for Artifact, Event, Embedding, Entity, Summary
• Deterministic ingestion pipeline for text + PDFs
• Time-based event abstraction even for text (reading sessions / days)

Success criteria:

• Can answer: “What did I read last Tuesday about X?”

Phase B2: Add audio (ASR-first) (4–8 weeks)

Deliverables:

• Audio ingestion + speech-to-text transcripts
• Dual retrieval: transcript keyword + embedding retrieval
• Event segmentation by silence/speaker turns

Success criteria:

• Can answer: “What did I say about X in the last 2 weeks?” with citations to timestamps

Phase B3: Add images/video (8–12 weeks)

Deliverables:

• Keyframe extraction / scene segmentation
• Vision embeddings + captioning for searchability
• Link frames to events and timestamps

Success criteria:

• Can answer: “Show moments when I was at Y” / “find the slide with Z”

Phase B4: Summarization hierarchy + continuous learning (ongoing)

Deliverables:

• Daily/weekly/monthly summaries
• Entity graphs (people, places, projects)
• Feedback loop: user corrects summaries/retrieval; system learns preferences

Success criteria:

• Most queries answered from summaries (cost bounded), with drill-down on demand

4) Benchmarks for Both Targets (What to Measure)

Performance (system)

• Index build time
• Retrieval latency p50/p95
• Throughput (queries/sec)
• Storage footprint

Quality (retrieval)

• recall@k / MRR on labeled queries
• faithfulness: % answers with correct citations
• coverage: how often relevant evidence exists but wasn’t retrieved

“Lifelog” specific

• time-to-answer across memory layers (summary vs raw)
• compression ratio (raw → summaries)
• privacy constraints (on-device %, encrypted-at-rest %)

5) Honest Expectations

What you can likely achieve soon

• A strong 100-page RAG with reproducible benchmarks and clear quality metrics
• A multimodal-ready data model and event abstraction
• Audio search (ASR) with timestamp citations

What remains far away

• Any credible wall-clock “quantum advantage” on general retrieval
• Full-lifetime multimodal indexing without heavy summarization and tiered storage

6) Immediate Next Actions (Recommended)

1) For Target A (100 pages):

• Implement classical baseline (ANN) and lock down evaluation metrics (recall@k + p95 latency)

2) For Target B (north star):

• Start with an Event + Artifact schema and make all ingestions produce events

3) For quantum roadmap alignment:

• Keep quantum modules behind an interface and measure them as:
• correctness
• robustness under noise
• resource estimates (qubits/depth/shots)

References in this repo

• quantum_rag_demo/VALIDATION_ROADMAP.md — how to validate quantum-ish modules
• quantum_computing/QUANTUM_RAG_ADVANCED_CONCEPTS.md — what advanced concepts to implement
• quantum_rag_demo/benchmark_comparison.py — current benchmark harness & experimental components