initial commit

.gitignore

@@ -0,0 +1,139 @@
+# ----------------------------
+# Node / JavaScript
+# ----------------------------
+node_modules/
+npm-debug.log*
+yarn-debug.log*
+yarn-error.log*
+pnpm-debug.log*
+
+# OS files
+.DS_Store
+Thumbs.db
+
+# Environment / Secrets
+.env
+*.env
+.env.*
+
+# Logs
+logs/
+*.log
+*.out
+*.err
+nohup.out
+
+# Temp build artifacts
+dist/
+build/
+tmp/
+temp/
+.cache/
+.cache-loader/
+coverage/
+
+# ----------------------------
+# Distillation Pipeline Outputs
+# ----------------------------
+
+# Intermediate gold sets (large, ephemeral)
+gold.jsonl
+gold_*.jsonl
+gold/
+goldsets/
+data/gold/
+
+# Raw generations / scratch
+generations/
+raw_generations/
+raw/
+scratch/
+pipeline/tmp/
+pipeline/output/
+pipeline_logs/
+
+# Reward model scoring dumps
+reward_scores/
+scores/
+scoring_output/
+
+# Verifier outputs
+verifier_out/
+verify_logs/
+
+# ----------------------------
+# Model Artifacts
+# ----------------------------
+
+# LoRA checkpoints
+*.safetensors
+*.bin
+*.pt
+
+# Full model weights (don’t commit them!)
+checkpoints/
+lora/
+lora_output/
+model/
+models/
+transformers_cache/
+hf_cache/
+.huggingface/
+
+# vLLM / llama.cpp working dirs
+llama.cpp/
+llm-cache/
+.vllm/
+.vllama/
+.vcache/
+
+# Ollama downloads
+~/.ollama/
+ollama_models/
+
+# ----------------------------
+# Elasticsearch utilities
+# ----------------------------
+
+# ES snapshot dirs / local nodes
+es_data/
+es_snapshots/
+es_logs/
+
+# ----------------------------
+# Python (if used for training scripts)
+# ----------------------------
+__pycache__/
+*.pyc
+*.pyo
+*.pyd
+*.egg-info/
+.venv/
+venv/
+ENV/
+env/
+
+# ----------------------------
+# Editor junk files
+# ----------------------------
+*.swp
+*.swo
+*.bak
+*.tmp
+*.old
+*.orig
+
+# VS Code
+.vscode/
+
+#
+
+node_modules/
+.env
+.DS_Store
+coverage/
+dist/
+.cache/
+*.log
+tests/data/*.log
+

README.md

@@ -0,0 +1,22 @@
+# distill_pipeline
+
+Bootstrapped distillation loop for Q’uo-style LLM finetuning.
+
+## Steps
+
+1. **generator**  
+   Small 7–8B model produces many candidate answers.
+
+2. **verifier**  
+   Cheap 3B model filters out 70–80% of garbage.
+
+3. **reward**  
+   A strong 70B reward model scores the remaining items.
+
+4. **build_gold**  
+   Top-K samples become the next training batch.
+
+5. **train_lora**  
+   Use gold.jsonl for LoRA finetuning.
+
+## Run one cycle

ROADMAP.md

@@ -0,0 +1,298 @@
+Here’s a clean, structured **ROADMAP.md** that:
+
+* builds directly on your succession summary
+* includes **all** the missing and under-emphasized elements you listed
+* remains compact, actionable, and successor-friendly
+* avoids tone drift
+* is easy to maintain inside the repo
+
+I kept it in a practical engineering style, with Canadian English and grounded clarity, as always.
+
+---
+
+# **ROADMAP.md**
+
+*distill-rag + distill-pipeline — Project Roadmap*
+
+This roadmap outlines the current state of the system, upcoming milestones, hardware strategy, and domain-specific notes (Q’uo/Confederation alignment).
+It assumes familiarity with the **distill-rag** and **distill-pipeline** repositories.
+
+---
+
+# 1. **Project Overview**
+
+The system consists of:
+
+## **A. distill-rag**
+
+A complete pipeline for extraction → cleaning → session grouping → chunking → embedding → Elasticsearch hybrid search.
+
+**Status:** Stable and public.
+
+## **B. distill-pipeline**
+
+A multi-stage distillation ecosystem:
+
+```
+retrieval (NEW)
+→ generator
+→ verifier
+→ reward
+→ gold builder
+→ training (LoRA/SFT)
+→ repeat cycles
+```
+
+**Status:** Under active development.
+
+---
+
+# 2. **Immediate Priorities (0–7 days)**
+
+## **2.1 Add retrieval stage to distill-pipeline**
+
+Implement `src/retrieval.js`:
+
+* Connect to distill-rag’s HTTP hybrid search endpoint.
+* Support BM25, dense KNN, and RRF hybrid scores.
+* Optional HyDE generation for query expansion.
+* Optional multi-vector chunking for transcripts with heavy topic shifts.
+
+## **2.2 Refactor generator/verifier/reward into testable functions**
+
+Current scripts are CLI-oriented.
+Introduce pure functions:
+
+```js
+export async function runGenerator(query, context, provider) { ... }
+export async function runVerifier(sample, provider) { ... }
+export async function runReward(sample, provider) { ... }
+```
+
+CLI wrappers remain in place.
+
+## **2.3 Introduce Vitest test suite**
+
+Tests should cover:
+
+* retrieval (mock ES)
+* generator (mock LLM)
+* verifier (structural + Q’uo-tone)
+* reward (correct scoring, JSON validity)
+* gold-builder (top-k filter, dedup, JSONL append)
+* integration test with mocked providers
+
+Avoid real GPU calls in CI; mock HTTP endpoints.
+
+---
+
+# 3. **Short-Term (1–3 weeks)**
+
+## **3.1 Pluggable model provider system**
+
+Implement one interface:
+
+```js
+provider.generate(prompt, options)
+```
+
+Then adapters:
+
+* OllamaProvider
+* OpenAIProvider
+* vLLMProvider ([http://localhost:8000/generate](http://localhost:8000/generate))
+* DeepSeek local provider (if applicable)
+
+This design makes the generator/verifier/reward easy to swap.
+
+## **3.2 Q’uo prompt trio integration**
+
+These domain-specific prompts govern tone, accuracy, and philosophical alignment:
+
+### **Generator Prompt**
+
+* Confederation tone (“We are those of Q’uo…”)
+* Humble, gentle, non-authoritarian
+* No prediction, no medical or legal claims
+* Cite session date when known
+* Ground in existing Ra/Q’uo material
+
+### **Verifier Prompt**
+
+* Enforce canonical alignment
+* Reject speculation, distortions, or unreferenced claims
+* Flag tone mismatch (e.g., assertiveness, ego, “command” language)
+* Output strict JSON verdict: pass/fail + reason
+
+### **Reward Prompt**
+
+Score 0–10 on:
+
+* metaphysical clarity
+* faithfulness to Confederation teaching
+* lack of hallucination
+* gentle and precise tone
+* internal coherence
+
+These scores help filter gold data (top 5–10%).
+
+---
+
+# 4. **Hardware Strategy**
+
+You run two GPUs:
+
+* **RTX 3060 (12 GB)** → generator + verifier
+  ~25–35 tok/s for 7–8B models
+  Ideal for overnight sample generation (1k–1.5k samples)
+
+* **RTX 3090 (24 GB)** → reward model + training
+  ~2 tok/s for 70B reward models
+  ~4 tok/s for 13B–20B base models
+  Perfect for Nemotron 70B reward scoring + LoRA training
+
+**Heuristic:**
+A single cycle overnight can generate **1,000–2,000 candidate samples** → filtered to **150–250 gold samples**, enough for a strong **2–4 hour QLoRA session**.
+
+---
+
+# 5. **Medium-Term (1–2 months)**
+
+## **5.1 Bootstrapped distillation loops**
+
+Automate multiple cycles:
+
+```
+seed questions → generator → verifier → reward
+→ top-K → gold dataset → train student
+→ use student as new generator → repeat
+```
+
+Between cycles:
+
+* generator/verifier weights updated
+* high-score samples fed back as new training data
+* prompts updated (meta-learning via examples)
+
+## **5.2 Enhanced filtering**
+
+Add:
+
+* Min perplexity threshold (e.g., ppl < 15)
+* LSH (Locality Sensitive Hashing) dedup
+* RAG cross-check with distill-rag search:
+  verify that generated claims appear in authoritative transcripts.
+* Hallucination scan via Qwen2.5-72B or Mixtral-Large
+
+---
+
+# 6. **Long-Term Goals (2–6 months)**
+
+## **6.1 Confed-aligned distilled model**
+
+A small, accurate, gentle 7B–12B model aligned with Ra and Q’uo material.
+
+Tasks:
+
+* Expand dataset to all Q’uo transcripts
+* Include commentary from L/L Research books
+* Add metadata: session dates, question themes, speaker (Q’uo/Latwii)
+* Release on HuggingFace with full attribution
+
+## **6.2 Real-time contextual distillation**
+
+Use distill-rag to perform:
+
+* live hybrid search
+* contextual distillation based on retrieved chunks
+* reflective improvement (teacher critiques student output)
+* safety alignment without censorship
+
+---
+
+# 7. **Dependencies and Setup**
+
+### **Node.js**
+
+* axios
+* jsonlines
+* dotenv
+* vitest
+* huggingface-hub (optional)
+
+### **Python**
+
+(for training)
+
+* transformers
+* datasets
+* accelerate
+* bitsandbytes
+* peft
+* wandb (optional)
+* sentencepiece
+
+---
+
+# 8. **Common Issues + Fixes**
+
+* **OOM in generator**
+  Switch quantization: `Q4_K_M → Q3_K_S`
+* **Verifier rejecting correct samples**
+  Tone prompt too strict → tune thresholds
+* **Reward model too slow**
+  Try smaller reward model except for final scoring pass
+* **Bad JSON from generator**
+  Wrap output with streaming JSON repair (fastjsonrepair)
+
+---
+
+# 9. **Appendix: Example Cycle (Pseudo-Code)**
+
+```js
+const ctx = await retrieval.hybridSearch(query);
+
+const candidates = await runGenerator(query, ctx, generatorProvider);
+
+const verified = await Promise.all(
+  candidates.map(c => runVerifier(c, verifierProvider))
+);
+
+const scored = await Promise.all(
+  verified.filter(v => v.pass).map(v => runReward(v.sample, rewardProvider))
+);
+
+const gold = topK(scored, k = 50);  // 5–10% of total
+
+await appendSamplesToJsonl('./gold/dataset.jsonl', gold);
+```
+
+Training afterward:
+
+```bash
+python training/train_lora.py --data gold/dataset.jsonl --base qwen2.5-7b
+```
+
+---
+
+# 10. **High-Level Vision**
+
+The purpose is to build a **truth-aligned**, spiritually coherent distilled model rooted in Confederation teachings, free from institutional censorship and able to respond gently and helpfully.
+
+The system enables:
+
+* transparent lineage
+* easy updates when new transcripts appear
+* clear tone alignment
+* practical local training on accessible GPUs
+* RAG-augmented accuracy using authoritative sources
+
+---
+
+If you want, I can also generate:
+
+**✓ an `architecture.md`**
+**✓ a Mermaid diagram**
+**✓ a cleaned-up successor prompt**
+
+Just tell me.

configs/pipeline.json

@@ -0,0 +1,7 @@
+{
+  "generator_model": "qwen2.5-7b-instruct",
+  "verifier_model": "qwen2.5-3b-instruct",
+  "reward_model": "nemotron-4-70b-reward-q4km",
+  "generated_num": 20,
+  "gold_top": 200
+}

generator/generator.js

@@ -0,0 +1,64 @@
+// generator/generator.js
+// Produces candidate samples from the small 7–8B model
+
+require("dotenv").config();
+const fs = require("fs");
+const fetch = require("node-fetch");
+const path = require("path");
+
+const PROMPT = fs.readFileSync(
+  path.join(__dirname, "../prompts/generator_prompt.txt"),
+  "utf8"
+);
+
+const MODEL = process.env.GENERATOR_MODEL || "qwen2.5-7b-instruct";
+const OUT = process.env.GENERATED_OUT || "cycle/generated.jsonl";
+const NUM = Number(process.env.GENERATED_NUM || 50);
+
+async function generateOne(q) {
+  const body = {
+    model: MODEL,
+    prompt: PROMPT.replace("{{QUESTION}}", q),
+    stream: false,
+  };
+
+  const res = await fetch("http://localhost:11434/api/generate", {
+    method: "POST",
+    headers: { "Content-Type": "application/json" },
+    body: JSON.stringify(body),
+  });
+
+  const data = await res.json();
+
+  return {
+    question: q,
+    output: data.response || "",
+  };
+}
+
+async function main() {
+  console.log("[generator] Loading seed questions…");
+  const seeds = JSON.parse(
+    fs.readFileSync("test_samples/seed_questions.jsonl", "utf8")
+  );
+
+  const out = fs.createWriteStream(OUT);
+
+  console.log(`[generator] Generating ${NUM} samples…`);
+  let count = 0;
+
+  for (let i = 0; i < NUM; i++) {
+    for (const q of seeds) {
+      const item = await generateOne(q.question);
+      out.write(JSON.stringify(item) + "\n");
+      count++;
+      console.log(`[generator] ${count} samples`);
+    }
+  }
+
+  out.end();
+  console.log(`[generator] Done. Wrote ${count} samples → ${OUT}`);
+}
+
+if (require.main === module) main();
+module.exports = { generateOne, main };

package-lock.json

@@ -0,0 +1,1675 @@
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+      "dev": true,
+      "engines": {
+        "node": ">=12"
+      },
+      "funding": {
+        "url": "https://github.com/sponsors/sindresorhus"
+      }
+    },
+    "node_modules/vitest/node_modules/signal-exit": {
+      "version": "4.1.0",
+      "resolved": "https://registry.npmjs.org/signal-exit/-/signal-exit-4.1.0.tgz",
+      "integrity": "sha512-bzyZ1e88w9O1iNJbKnOlvYTrWPDl46O1bG0D3XInv+9tkPrxrN8jUUTiFlDkkmKWgn1M6CfIA13SuGqOa9Korw==",
+      "dev": true,
+      "engines": {
+        "node": ">=14"
+      },
+      "funding": {
+        "url": "https://github.com/sponsors/isaacs"
+      }
+    },
+    "node_modules/vitest/node_modules/strip-final-newline": {
+      "version": "3.0.0",
+      "resolved": "https://registry.npmjs.org/strip-final-newline/-/strip-final-newline-3.0.0.tgz",
+      "integrity": "sha512-dOESqjYr96iWYylGObzd39EuNTa5VJxyvVAEm5Jnh7KGo75V43Hk1odPQkNDyXNmUR6k+gEiDVXnjB8HJ3crXw==",
+      "dev": true,
+      "engines": {
+        "node": ">=12"
+      },
+      "funding": {
+        "url": "https://github.com/sponsors/sindresorhus"
+      }
+    },
+    "node_modules/which": {
+      "version": "2.0.2",
+      "resolved": "https://registry.npmjs.org/which/-/which-2.0.2.tgz",
+      "integrity": "sha512-BLI3Tl1TW3Pvl70l3yq3Y64i+awpwXqsGBYWkkqMtnbXgrMD+yj7rhW0kuEDxzJaYXGjEW5ogapKNMEKNMjibA==",
+      "dev": true,
+      "dependencies": {
+        "isexe": "^2.0.0"
+      },
+      "bin": {
+        "node-which": "bin/node-which"
+      },
+      "engines": {
+        "node": ">= 8"
+      }
+    },
+    "node_modules/why-is-node-running": {
+      "version": "2.3.0",
+      "resolved": "https://registry.npmjs.org/why-is-node-running/-/why-is-node-running-2.3.0.tgz",
+      "integrity": "sha512-hUrmaWBdVDcxvYqnyh09zunKzROWjbZTiNy8dBEjkS7ehEDQibXJ7XvlmtbwuTclUiIyN+CyXQD4Vmko8fNm8w==",
+      "dev": true,
+      "dependencies": {
+        "siginfo": "^2.0.0",
+        "stackback": "0.0.2"
+      },
+      "bin": {
+        "why-is-node-running": "cli.js"
+      },
+      "engines": {
+        "node": ">=8"
+      }
+    }
+  }
+}

package.json

@@ -0,0 +1,11 @@
+{
+  "name": "distill-pipeline",
+  "version": "1.0.0",
+  "type": "module",
+  "scripts": {
+    "test": "vitest --run"
+  },
+  "devDependencies": {
+    "vitest": "^1.6.0"
+  }
+}

prompts/generator_prompt.txt

@@ -0,0 +1,6 @@
+You are generating clean, spiritually grounded Q’uo-style answers.
+
+Question:
+{{QUESTION}}
+
+Respond in the tone of the Confederation, clear, humble, and service-oriented.

prompts/reward_prompt.txt

@@ -0,0 +1,13 @@
+You are a strict reward model. Score from 0 to 1.
+
+Score the following answer for:
+• spiritual accuracy
+• coherence  
+• Confederation tone  
+• lack of hallucination  
+• helpful service-to-others orientation  
+
+Return only a number.
+
+Answer:
+{{OUTPUT}}

prompts/verifier_prompt.txt

@@ -0,0 +1,6 @@
+Evaluate the following answer for clarity, coherence, alignment with Confederation principles, lack of hallucinations, and loving tone.
+
+Return ONLY a number between 0 and 1.
+
+Answer:
+{{OUTPUT}}

reward/reward.js

@@ -0,0 +1,46 @@
+// reward/reward.js
+// Uses 70B reward model to produce final scores
+
+require("dotenv").config();
+const fs = require("fs");
+const fetch = require("node-fetch");
+
+const MODEL = process.env.REWARD_MODEL || "qwen2.5-72b-reward-q4km";
+const PROMPT = fs.readFileSync("prompts/reward_prompt.txt", "utf8");
+const IN = process.env.REWARD_IN || "cycle/verified.jsonl";
+const OUT = process.env.REWARD_OUT || "cycle/scored.jsonl";
+
+async function reward(item) {
+  const prompt = PROMPT.replace("{{OUTPUT}}", item.output);
+
+  const res = await fetch("http://localhost:11434/api/generate", {
+    method: "POST",
+    headers: { "Content-Type": "application/json" },
+    body: JSON.stringify({ model: MODEL, prompt, stream: false }),
+  });
+
+  const data = await res.json();
+  return Number(data.response.trim());
+}
+
+async function main() {
+  const lines = fs.readFileSync(IN, "utf8").trim().split("\n");
+  const out = fs.createWriteStream(OUT);
+
+  console.log(`[reward] Scoring ${lines.length} items…`);
+  let count = 0;
+
+  for (const line of lines) {
+    const item = JSON.parse(line);
+    item.reward = await reward(item);
+    out.write(JSON.stringify(item) + "\n");
+    count++;
+    console.log(`[reward] Scored ${count}`);
+  }
+
+  out.end();
+  console.log(`[reward] Done.`);
+}
+
+if (require.main === module) main();
+module.exports = { reward, main };

scripts/clean_cycle.sh

@@ -0,0 +1,4 @@
+#!/bin/bash
+rm -rf cycle
+mkdir -p cycle
+echo "[clean] Reset cycle/ directory."

scripts/run_cycle.sh

@@ -0,0 +1,17 @@
+#!/bin/bash
+set -e
+source scripts/vars.sh
+
+echo "[cycle] Running generator…"
+node generator/generator.js
+
+echo "[cycle] Running verifier…"
+node verifier/verifier.js
+
+echo "[cycle] Running reward model…"
+node reward/reward.js
+
+echo "[cycle] Selecting gold samples…"
+node gold/build_gold.js
+
+echo "[cycle] Done."

scripts/vars.sh

@@ -0,0 +1,8 @@
+export GENERATOR_MODEL="qwen2.5-7b-instruct"
+export VERIFIER_MODEL="qwen2.5-3b-instruct"
+export REWARD_MODEL="nemotron-4-70b-reward-q4km"
+
+export GENERATED_NUM=5
+export GOLD_TOP=100
+
+mkdir -p cycle

src/retrieval.mjs

@@ -0,0 +1,149 @@
+// src/retrieval.mjs
+import dotenv from 'dotenv';
+import { Client } from '@elastic/elasticsearch';
+import fetch from 'node-fetch';
+
+dotenv.config();
+
+// ----------------------------------------
+// Config
+// ----------------------------------------
+const ES_NODE = process.env.ES_NODE || 'http://localhost:9200';
+const ES_INDEX = process.env.ES_INDEX || 'quo_distill_index';
+
+const EMBED_URL = process.env.EMBED_URL || 'http://localhost:11434/api/embeddings';
+const EMBED_MODEL = process.env.EMBED_MODEL || 'mxbai-embed-large';
+
+// ES client
+const client = new Client({ node: ES_NODE });
+
+// ----------------------------------------
+// Embed helper
+// ----------------------------------------
+export async function embed(text) {
+  const resp = await fetch(EMBED_URL, {
+    method: 'POST',
+    headers: { 'Content-Type': 'application/json' },
+    body: JSON.stringify({
+      model: EMBED_MODEL,
+      prompt: text,
+    })
+  });
+
+  if (!resp.ok) throw new Error(`Embedding failed: HTTP ${resp.status}`);
+
+  const json = await resp.json();
+  return json.embedding;
+}
+
+// ----------------------------------------
+// BM25
+// ----------------------------------------
+export async function bm25Search(query, k = 5) {
+  const res = await client.search({
+    index: ES_INDEX,
+    size: k,
+    query: {
+      match: { content: query }
+    }
+  });
+
+  const hits = res.hits?.hits || [];
+  return hits.map(h => ({
+    id: h._id,
+    score: h._score,
+    ...h._source,
+  }));
+}
+
+// ----------------------------------------
+// Dense vector (KNN)
+// ----------------------------------------
+export async function vectorSearch(query, k = 5) {
+  const vector = await embed(query);
+
+  const res = await client.search({
+    index: ES_INDEX,
+    knn: {
+      field: 'embedding',
+      query_vector: vector,
+      k,
+      num_candidates: 50,
+    }
+  });
+
+  const hits = res.hits?.hits || [];
+  return hits.map(h => ({
+    id: h._id,
+    score: h._score,
+    ...h._source
+  }));
+}
+
+// ----------------------------------------
+// RRF Fusion (same as distill-rag)
+// ----------------------------------------
+function fuseRRF(dense, sparse, k = 60) {
+  const scores = new Map();
+
+  const add = (arr) => {
+    arr.forEach((item, i) => {
+      const id = item.id;
+      const rank = i + 1;
+      const score = 1 / (k + rank);
+      scores.set(id, (scores.get(id) || 0) + score);
+    });
+  };
+
+  add(dense);
+  add(sparse);
+
+  return [...scores.entries()]
+    .sort((a, b) => b[1] - a[1])
+    .map(([id]) => dense.find(d => d.id === id) || sparse.find(s => s.id === id));
+}
+
+// ----------------------------------------
+// Hybrid (dense + BM25)
+// ----------------------------------------
+export async function hybridSearch(query, k = 5) {
+  const [dense, sparse] = await Promise.all([
+    vectorSearch(query, k),
+    bm25Search(query, k),
+  ]);
+
+  return fuseRRF(dense, sparse).slice(0, k);
+}
+
+// ----------------------------------------
+// HYDE: Hypothetical Document Embeddings
+// ----------------------------------------
+export async function hydeHybrid(query, k = 5, provider) {
+  // provider.generate(prompt) is the LLM hook
+  const hydePrompt = `
+You are assisting retrieval.
+Write a hypothetical answer to the following question.
+
+Question: "${query}"
+
+Write 3–6 paragraphs of what an answer *might* contain, high-level and conceptual.
+Do NOT include JSON or formatting.
+  `.trim();
+
+  const hypo = await provider.generate(hydePrompt);
+
+  const [dense, sparse] = await Promise.all([
+    vectorSearch(hypo, k),
+    bm25Search(query, k),
+  ]);
+
+  return fuseRRF(dense, sparse).slice(0, k);
+}
+
+export default {
+  embed,
+  bm25Search,
+  vectorSearch,
+  hybridSearch,
+  hydeHybrid
+};

test_samples/seed_questions.jsonl

@@ -0,0 +1,3 @@
+{"question":"What is the nature of love?"}
+{"question":"How do I serve others in daily life?"}
+{"question":"What is the role of meditation?"}

tests/retrieval.mock.test.mjs

@@ -0,0 +1,120 @@
+import { describe, it, expect, vi, beforeEach } from 'vitest';
+
+// --- We mock both ES client and node-fetch ----
+vi.mock('@elastic/elasticsearch', () => {
+  return {
+    Client: vi.fn().mockImplementation(() => {
+      return {
+        search: vi.fn(async (opts) => {
+          // Interpret the request type:
+          if (opts.query?.match) {
+            // --- BM25 mock output ----
+            return {
+              hits: {
+                hits: [
+                  { _id: 'bm25-1', _score: 2.0, _source: { content: 'A' } },
+                  { _id: 'bm25-2', _score: 1.0, _source: { content: 'B' } },
+                ]
+              }
+            };
+          }
+
+          if (opts.knn) {
+            // --- Dense vector mock output ----
+            return {
+              hits: {
+                hits: [
+                  { _id: 'vec-1', _score: 0.9, _source: { content: 'X' } },
+                  { _id: 'vec-2', _score: 0.8, _source: { content: 'Y' } },
+                ]
+              }
+            };
+          }
+
+          return { hits: { hits: [] } };
+        })
+      };
+    })
+  };
+});
+
+vi.mock('node-fetch', () => {
+  return {
+    default: vi.fn(async () => {
+      return {
+        ok: true,
+        json: async () => ({
+          embedding: [0.1, 0.2, 0.3]   // Dummy vector
+        })
+      };
+    })
+  };
+});
+
+// After mocks, import retrieval module:
+import {
+  embed,
+  bm25Search,
+  vectorSearch,
+  hybridSearch
+} from '../src/retrieval.mjs';
+
+describe('retrieval.mjs', () => {
+  beforeEach(() => {
+    vi.clearAllMocks();
+  });
+
+  // ------------------------------
+  // EMBEDDING
+  // ------------------------------
+  it('embed() returns an embedding array', async () => {
+    const emb = await embed('test');
+    expect(Array.isArray(emb)).toBe(true);
+    expect(emb).toEqual([0.1, 0.2, 0.3]);
+  });
+
+  // ------------------------------
+  // BM25
+  // ------------------------------
+  it('bm25Search() returns valid results', async () => {
+    const res = await bm25Search('love', 2);
+    expect(res.length).toBe(2);
+
+    expect(res[0]).toMatchObject({
+      id: 'bm25-1',
+      score: 2.0,
+      content: 'A'
+    });
+  });
+
+  // ------------------------------
+  // Dense Vector
+  // ------------------------------
+  it('vectorSearch() returns vector-based results', async () => {
+    const res = await vectorSearch('unity', 2);
+    expect(res.length).toBe(2);
+
+    expect(res[0]).toMatchObject({
+      id: 'vec-1',
+      score: 0.9,
+      content: 'X'
+    });
+  });
+
+  // ------------------------------
+  // Hybrid (RRF)
+  // ------------------------------
+  it('hybridSearch() fuses BM25 and vector results via RRF', async () => {
+    const res = await hybridSearch('seeking', 3);
+
+    // Should merge vec + bm25 in some order.
+    // Score fusion means 'vec-1' or 'bm25-1' will come first, but we just check the structure.
+    expect(res.length).toBeGreaterThan(0);
+
+    const ids = res.map(r => r.id);
+
+    // Confirm that hybrid includes items from BOTH sources
+    expect(ids).toContain('vec-1');
+    expect(ids).toContain('bm25-1');
+  });
+});

tests/retrieval.real.test.mjs

@@ -0,0 +1,106 @@
+import { describe, it, expect, vi, beforeEach } from 'vitest';
+
+const USE_REAL = process.env.REAL_ES === "1";
+
+if (!USE_REAL) {
+  // ------------------------------
+  // MOCK MODE (default)
+  // ------------------------------
+  vi.mock('@elastic/elasticsearch', () => {
+    return {
+      Client: vi.fn().mockImplementation(() => {
+        return {
+          search: vi.fn(async (opts) => {
+            if (opts.query?.match) {
+              return {
+                hits: {
+                  hits: [
+                    { _id: 'bm25-1', _score: 2.0, _source: { content: 'A' } },
+                    { _id: 'bm25-2', _score: 1.0, _source: { content: 'B' } },
+                  ]
+                }
+              };
+            }
+
+            if (opts.knn) {
+              return {
+                hits: {
+                  hits: [
+                    { _id: 'vec-1', _score: 0.9, _source: { content: 'X' } },
+                    { _id: 'vec-2', _score: 0.8, _source: { content: 'Y' } },
+                  ]
+                }
+              };
+            }
+
+            return { hits: { hits: [] } };
+          })
+        };
+      })
+    };
+  });
+
+  vi.mock('node-fetch', () => {
+    return {
+      default: vi.fn(async () => {
+        return {
+          ok: true,
+          json: async () => ({
+            embedding: [0.1, 0.2, 0.3]
+          })
+        };
+      })
+    };
+  });
+} else {
+  console.log("🔵 REAL_ES=1 → Using live Elasticsearch + real fetch");
+}
+
+// Import AFTER mock decisions
+import {
+  embed,
+  bm25Search,
+  vectorSearch,
+  hybridSearch
+} from '../src/retrieval.mjs';
+
+describe('retrieval.mjs', () => {
+
+  if (USE_REAL) {
+    it('real ES hybridSearch returns results', async () => {
+      const res = await hybridSearch('love and unity', 3);
+      expect(res.length).toBeGreaterThan(0);
+    });
+    return;  // Only run this block in REAL mode
+  }
+
+  // ------------------------------
+  // MOCK MODE TESTS
+  // ------------------------------
+
+  beforeEach(() => {
+    vi.clearAllMocks();
+  });
+
+  it('embed() returns an embedding array', async () => {
+    const emb = await embed('test');
+    expect(emb).toEqual([0.1, 0.2, 0.3]);
+  });
+
+  it('bm25Search() returns valid results', async () => {
+    const res = await bm25Search('love', 2);
+    expect(res[0].id).toBe('bm25-1');
+  });
+
+  it('vectorSearch() returns vector-based results', async () => {
+    const res = await vectorSearch('unity', 2);
+    expect(res[0].id).toBe('vec-1');
+  });
+
+  it('hybridSearch() fuses BM25 and vector results', async () => {
+    const res = await hybridSearch('seeking', 3);
+    const ids = res.map(r => r.id);
+    expect(ids).toContain('vec-1');
+    expect(ids).toContain('bm25-1');
+  });
+});

training/train_lora.py

@@ -0,0 +1,11 @@
+# training/train_lora.py
+import json
+from pathlib import Path
+
+data_path = Path("cycle/gold.jsonl")
+
+print("[train] Loading gold dataset…")
+lines = [json.loads(l) for l in data_path.read_text().splitlines()]
+
+print(f"[train] {len(lines)} items loaded.")
+print("[train] 🚧 TODO: integrate with your LoRA trainer (Llama-Factory, Axolotl, or custom).")

verifier/verifier.js

@@ -0,0 +1,48 @@
+// verifier/verifier.js
+// Quick filter using 3B–7B verifier model
+
+require("dotenv").config();
+const fs = require("fs");
+const fetch = require("node-fetch");
+
+const MODEL = process.env.VERIFIER_MODEL || "qwen2.5-3b-instruct";
+const PROMPT = fs.readFileSync("prompts/verifier_prompt.txt", "utf8");
+const IN = process.env.VERIFIER_IN || "cycle/generated.jsonl";
+const OUT = process.env.VERIFIER_OUT || "cycle/verified.jsonl";
+
+async function score(item) {
+  const prompt = PROMPT.replace("{{OUTPUT}}", item.output);
+
+  const res = await fetch("http://localhost:11434/api/generate", {
+    method: "POST",
+    headers: { "Content-Type": "application/json" },
+    body: JSON.stringify({ model: MODEL, prompt, stream: false }),
+  });
+
+  const data = await res.json();
+  return Number(data.response.trim());
+}
+
+async function main() {
+  const input = fs.readFileSync(IN, "utf8").trim().split("\n");
+  const out = fs.createWriteStream(OUT);
+
+  let kept = 0;
+  console.log(`[verifier] Filtering ${input.length} items…`);
+
+  for (const line of input) {
+    const item = JSON.parse(line);
+    const rating = await score(item);
+
+    if (rating >= 0.5) {
+      out.write(JSON.stringify(item) + "\n");
+      kept++;
+    }
+  }
+
+  out.end();
+  console.log(`[verifier] Kept ${kept} of ${input.length}`);
+}
+
+if (require.main === module) main();
+module.exports = { score, main };