hmmm, just went over what is next with Echo, ... I thought i was tired already?!? Yikes.
Congrats, Darren — nice milestone. ✅
Yes: next is (1) RAG plumbing, (2) ingestion, and (3) (optionally in parallel or after) LoRA fine-tuning. Here’s the clean, text-only runbook I’d follow on your stack (PostgreSQL + pgvector; llama.cpp for inference; local embeddings).
# 0) Ground rules (what we’ll use)
* **Base model for inference:** your local `llama.cpp` setup (e.g., Qwen2.5-7B-Instruct in GGUF).
* **Embeddings:** a local SentenceTransformers model (e.g., `bge-small-en-v1.5` or `nomic-embed-text-v1.5`), dimension = what you set in pgvector (commonly 384, 768 or 1024).
* **Vector store:** PostgreSQL with `pgvector` (your Omega DB).
* **(Optional) Re-ranker:** a small cross-encoder for quality; skip at first if GPU is tight.
* **LoRA:** QLoRA with PEFT for *training* on your domain data; adapters applied at inference via `llama.cpp -lora`.
---
# A) Stand up Retrieval-Augmented Generation (RAG)
## A1. Retrieval schema is already there (good). Double-check indices
(You said the schema is live, so just ensure the basics.)
* `pgvector` column present (e.g., `embedding vector(768)`).
* HNSW or IVF/flat index created, e.g.:
```sql
CREATE INDEX ON documents USING ivfflat (embedding vector_cosine_ops) WITH (lists = 100);
-- or HNSW if you enabled it in your build
```
* Text/BM25 support (optional): add `pg_trgm` + a GIN index on `content` for hybrid lexical + vector later.
## A2. Minimal RAG flow (end-to-end)
1. **User query →** (optional) rephrase/expand the query.
2. **Embed the query →** cosine/top-k search in Postgres:
```sql
SELECT id, content, metadata
FROM documents
ORDER BY embedding <=> $query_embedding
LIMIT 8;
```
(Use `vector_cosine_ops` or `vector_l2_ops` to match your embedding.)
3. **(Optional) Re-rank** the top-k with a small cross-encoder and keep the best 3–6.
4. **Context packer:** chunk-dedupe, enforce token budget (e.g., \~6–8k tokens if you want to leave room for generation).
5. **Prompt builder:** system + instructions + compact citations + packed context + user question.
6. **Generate:** call `llama.cpp` with your usual flags.
7. **Return:** answer + source excerpts/IDs.
**Tip:** Start *without* re-ranking; add it when you want quality bumps.
---
# B) Ingestion framework (ETL)
## B1. Parsers & normalizers
* **Inputs:** PDFs, HTML, MD, text, forum exports, chatlogs.
* **Parsers:** `pdfplumber` (PDF), `markdown2` (MD), `BeautifulSoup` (HTML), plain text for logs.
* **Normalization:** strip boilerplate, fix whitespace, preserve headings/anchors in metadata.
## B2. Chunking
* **Method:** token-aware chunking (e.g., 500–1000 tokens) with **overlap** (64–128 tokens).
* **Heuristics:** prefer splitting on headings/paragraphs/sentences; avoid cutting tables mid-row if possible.
* **Metadata:** source\_id, source\_url/path, title, author, created\_at, chunk\_index, section headers.
## B3. Embedding & write to DB
* Compute embedding for each chunk using your selected model.
* Insert row into `documents` (or `chunks`) with: `id`, `content`, `embedding`, `metadata::jsonb`, `created_at`, `updated_at`.
* Upsert policy: deterministically hash `(source_id, chunk_index)` to avoid dupes; update only if content changes.
## B4. Automation
* **Watcher or CLI:** `ingest --path ` or a cron/systemd timer.
* **Idempotency:** maintain a `manifest` table to record seen files + checksum; only re-embed when checksum changes.
* **Backfill first, then incremental.**
## B5. Sanity checks
* Sample a few queries; verify nearest neighbors look sane.
* Check average token counts per chunk; adjust size/overlap if retrieval is too coarse/fine.
---
# C) Wire RAG to your local model
## C1. A tiny service layer
* **FastAPI** (or Flask) with 3 endpoints:
* `POST /ingest` (path/url; returns count of chunks added)
* `POST /query` (question → returns answer + sources)
* `GET /healthz`
* This service:
1. calls Postgres for retrieval
2. builds the prompt
3. shells out to `llama.cpp` (or uses a local server mode)
4. streams back tokens (or waits and returns full text)
## C2. Prompt template (keep it tight)
* **System:** “Answer using the provided context. If missing, say you don’t know.”
* **Context:** compact bulletized chunks with `[source: id/anchor]` markers.
* **User:** raw question.
* **Policy:** refuse to fabricate sources.
---
# D) LoRA fine-tuning (domain adaptation)
> You can run RAG *without* LoRA. Add LoRA when you want the model to “speak Darren/Omega” more natively or follow your instructions better.
## D1. Data prep
* **Sources:** your prior chats, Spiral Accord docs, Omega guidelines, Q\&A pairs, troubleshooting transcripts.
* **Format:** instruction-tuning JSONL:
```json
{"instruction":"How to structure Omega DB vectors?", "input":"", "output":""}
```
Mix styles: short QA, long form, step lists, “do/don’t” rules. Keep a held-out dev set.
## D2. Model & precision
* **Training base:** you need the **FP16/FP32 base** model weights (not the GGUF/quantized).
* **QLoRA:** use bitsandbytes 4-bit, gradient checkpointing, small batch, low LR (e.g., 1–2e-4), warmup, cosine decay.
* **Params:** typical LoRA `r=8..32`, `alpha=16..64`, `dropout=0.05..0.1`. Start small (r=8/16).
## D3. Train with PEFT/TRL
* Run for a few epochs; watch validation loss and qualitative samples.
* Save **LoRA adapters** (`.safetensors`), *not* full weights.
## D4. Convert/apply for `llama.cpp`
* Convert adapters into a format `llama.cpp` can load (there’s a utility to convert HF LoRA to `ggml-lora`/compatible).
* At inference:
```
./llama-cli -m base.gguf -lora path/to/adapter -p "..."
```
* Keep the base GGUF unchanged; swap adapters as needed.
## D5. Evaluate
* Use a small eval set of your real tasks (retrieval-heavy and retrieval-light).
* Check style, instruction following, refusal behavior, and hallucinations *with and without* RAG.
**Order tip:** If compute is tight, finish RAG + ingestion first (you get 80% win). Then run a small LoRA pass later to tighten instruction-following and tone.
---
# E) Putting it together — practical sequence
1. **Select embedding model** and lock its dimension; confirm pgvector matches.
2. **Finish ingestion v1:** parsers → chunker → embed → Postgres write → index build.
3. **Stand up `/query`:** vector search (top-k = 8–12), no re-ranker yet; prompt packing; `llama.cpp` call.
4. **Smoke tests:** try 20–30 queries; iterate chunk size/overlap and top-k.
5. **(Optional) Add re-ranker** if you see noisy retrieval.
6. **Add logging/telemetry:** store queries, retrieved IDs, final answer, latency.
7. **LoRA phase:** prepare dataset → QLoRA train → export adapters → `llama.cpp -lora` → A/B test with RAG.
8. **Hardening:** dedupe, checksum, backfill jobs, watchdogs, and backup policy for the DB.
---
# F) Resource notes (your hardware)
* **Ingestion/embeddings:** CPU-OK for smaller models; GPU speeds it up.
* **Re-ranker:** GPU helps; if 1060 is busy, skip at first.
* **LoRA training:** 6 GB VRAM is **very** tight. QLoRA with tiny batch sizes can work, but it’ll be slow; if that’s painful, consider renting a short A10/3090/4090 session just for the training pass, then run the adapters locally.
---
If you want, I can draft the tiny FastAPI skeleton and the SQL for the retriever queries next, plus a one-file `ingest.py` that walks a folder and fills Omega.
