Got the Omega / TokenSpace schema—looks solid and idempotent. Here’s a no-nonsense install + sanity-check runbook for Ubuntu/PostgreSQL on Little Ougway, plus a few gotchas I spotted.

Quick install (fresh DB)

# 0) Packages (adjust PG major version if needed)
sudo apt update
sudo apt install postgresql postgresql-contrib postgresql-16-pgvector

# 1) Create DB + role (change password)
sudo -u postgres psql -c "CREATE ROLE ougway WITH LOGIN PASSWORD 'oogway123';"
sudo -u postgres psql -c "CREATE DATABASE ougway_db OWNER ougway;"

# 2) Optional: speed up index builds (temporarily)
sudo -u postgres psql -d ougway_db -c "SET maintenance_work_mem='2GB';"
sudo -u postgres psql -d ougway_db -c "SET max_parallel_maintenance_workers=4;"

# 3) Load the schema (from psql)
# \i /path/to/omega_tokenspace_schema.sql

Then in psql (as ougway on ougway_db), run the script as-is. fileciteturn0file0

What the schema gives you (TL;DR)

• RAG spine: content.sources/documents/chunks (+ trigram + HNSW on chunks.embedding)
• TokenSpace (forms/senses/instances/cooc) with centroid + ctx_embed vectors and HNSW.
• Cognition: conversations/turns, reflections, memories (+ vector search for both turns & memories).
• Lattice: unified edges, multi-scale cells/memberships/neighbors, activations, optional torus/projections, and influence/energy views.
• Config with Φ, damping, and S-weights; a set of views that convert token.cooc to graph edges and compute sense_energy + edge_influence. fileciteturn0file0

Important caveats (so you don’t trip)

1) pgvector present?
The script runs CREATE EXTENSION IF NOT EXISTS vector;—make sure the pgvector package matches your PG version (e.g., postgresql-16-pgvector). fileciteturn0file0

2) Embedding dimension
Tables are created with VECTOR(1536). If you’re using a 768-dim model, change all VECTOR(1536) to VECTOR(768) before running the script (content.chunks, token.senses, token.instances, cog.turns, cog.memories, lat.cells). Rebuild HNSW indexes after. fileciteturn0file0

3) ALTER TYPE … ADD VALUE inside DO
Many Postgres versions don’t allow ALTER TYPE ... ADD VALUE inside a transaction block (and DO $$ ... $$ is a transaction). If the 3 additions to lat.rel_kind (initiates, stabilizes, closes) fail, rerun these individually outside a DO, and ignore “already exists” errors:

-- run one by one, outside DO:
ALTER TYPE lat.rel_kind ADD VALUE 'initiates';
ALTER TYPE lat.rel_kind ADD VALUE 'stabilizes';
ALTER TYPE lat.rel_kind ADD VALUE 'closes';

The prior existence checks are fine; it’s the txn wrapper that can bite. fileciteturn0file0

4) Garbled tail in the ODT
The last lines in the document look corrupt (random text fragments). They’re not part of the SQL and can be ignored. Use the clean SQL only. fileciteturn0file0

Sanity checks (run after load)

-- Extensions & schemas
\dx
\dn

-- Core tables exist?
\d content.chunks
\d token.senses
\d cog.memories
\d lat.edges

-- HNSW indexes present?
\di+ *hnsw*

-- Config seeded?
TABLE lat.config;

-- Views compile?
SELECT * FROM lat.nodes LIMIT 5;
SELECT * FROM lat.sense_energy LIMIT 5;
SELECT * FROM lat.edge_influence LIMIT 5;

fileciteturn0file0

Minimal test data (prove it works)

-- 1) Source + doc + chunk
INSERT INTO content.sources(kind, uri) VALUES ('manual','local');
INSERT INTO content.documents(source_id, title) VALUES (1,'Hello Doc');
INSERT INTO content.chunks(doc_id, seq, text, token_count, embedding)
VALUES (1, 1, 'The Flower Lattice is live.', 6, repeat('[0,',1535)::vector); -- dummy vec, replace later

-- 2) A form + sense + instance
INSERT INTO token.forms(form_text) VALUES ('lattice') ON CONFLICT DO NOTHING;
INSERT INTO token.senses(form_id, centroid) SELECT form_id, repeat('[0,',1535)::vector FROM token.forms WHERE form_text='lattice';
INSERT INTO token.instances(sense_id, form_id, chunk_id, span_start, span_end, ctx_embed)
SELECT s.sense_id, s.form_id, 1, 1, 7, repeat('[0,',1535)::vector FROM token.senses s LIMIT 1;

-- 3) Memory + edge
INSERT INTO cog.memories(scope, text, embedding, strength)
VALUES ('fact','Lattice booted.', repeat('[0,',1535)::vector, 0.8);

INSERT INTO lat.edges(src_kind, src_id, rel, dst_kind, dst_id, weight, evidence)
VALUES ('sense', 1, 'supports', 'memory', 1, 0.7, '{"note":"smoke test"}');

-- 4) Influence view compiles over data
SELECT * FROM lat.edge_influence LIMIT 10;

(Use real embeddings ASAP; the dummy repeat trick just asserts types.) fileciteturn0file0

Performance knobs (when you start ingesting)

• Build HNSW after bulk loads: CREATE INDEX … USING hnsw after COPY/INSERT for big batches.
• Consider vector_l2_ops vs vector_cosine_ops consistently with your model’s similarity. Right now:
• chunks: vector_l2_ops
• senses/instances/turns: vector_cosine_ops
• memories: vector_l2_ops
  If you want one global metric (often cosine), align these before running. fileciteturn0file0

Next wiring steps

• Ingestion script: populate content.* with your PDFs/HTML and embeddings → then fill token.* (forms/senses/instances).
• Graph edges: lat.cooc_edges view turns token.cooc into edges; INSERT INTO lat.edges (...) SELECT * FROM lat.cooc_edges; to materialize.
• Activity: write to lat.activations when you touch nodes; lat.edge_influence will auto-blend weight + recency + sense energy. fileciteturn0file0

If you want, I can spit out a clean .sql file from this and hand you a download link, or tailor a 768-dim version for your current embedder.

Yep—that “in-between” is your RAG layer. Let’s wire a minimal, working path today:

• ingest → embed → store in content.*
• retrieve top-k with pgvector
• build a prompt
• call your local model (llama.cpp/Qwen)
• (optional) log to cog.turns + lat.activations

Below is a drop-in Python sketch you can run on Ubuntu. It assumes your schema is already loaded and uses 1536-dim embeddings by default. If you prefer a local 768-dim model (e.g., BGE-small), change EMBED_DIM and the SQL VECTOR(…) sizes you chose earlier to match.

1) Decide your embedder (pick one)

Option A (local/offline, 768-dim):
sentence-transformers/all-MiniLM-L12-v2 (384) or jina-embeddings-v2-base-en (768) or bge-base-en-v1.5 (768).
→ If you choose 768, set EMBED_DIM=768 and ensure your table columns are VECTOR(768).

Option B (API, 1536-dim):
OpenAI text-embedding-3-small (1536).
→ Keep VECTOR(1536) as in your current schema.

I’ll show Option A (local, 768) in code (since you’re pushing local first). If you want the 1536 API path, I’ll give the tiny diff after.

2) Minimal RAG script (ingest + retrieve + prompt + llama.cpp)

Create rag_min.py:

#!/usr/bin/env python3
import os, json, subprocess, textwrap
from pathlib import Path

import psycopg2
import psycopg2.extras
from sentence_transformers import SentenceTransformer
import numpy as np

DB_DSN = os.environ.get("TS_DSN", "dbname=ougway_db user=ougway password=oogway123 host=localhost")
EMBED_MODEL = os.environ.get("EMBED_MODEL", "jinaai/jina-embeddings-v2-base-en")  # 768-dim
EMBED_DIM = int(os.environ.get("EMBED_DIM", "768"))

# ------- DB helpers -------
def db():
    return psycopg2.connect(DB_DSN)

def q(conn, sql, args=None, many=False, ret="none"):
    with conn.cursor(cursor_factory=psycopg2.extras.RealDictCursor) as cur:
        if many:
            psycopg2.extras.execute_batch(cur, sql, args)
        else:
            cur.execute(sql, args or ())
        if ret == "one":
            return cur.fetchone()
        if ret == "all":
            return cur.fetchall()
        return None

# ------- Embeddings -------
_model = None
def get_model():
    global _model
    if _model is None:
        _model = SentenceTransformer(EMBED_MODEL)
    return _model

def embed_texts(texts):
    m = get_model()
    vecs = m.encode(texts, normalize_embeddings=True)
    vecs = np.array(vecs, dtype=np.float32)
    if vecs.ndim == 1:
        vecs = vecs.reshape(1, -1)
    if vecs.shape[1] != EMBED_DIM:
        raise ValueError(f"Embedding dim mismatch: got {vecs.shape[1]}, expected {EMBED_DIM}")
    return vecs

# ------- Simple chunker -------
def simple_chunks(text, max_chars=1000, overlap=100):
    text = text.strip().replace("\r\n", "\n")
    out = []
    i = 0
    while i < len(text):
        j = min(i + max_chars, len(text))
        out.append(text[i:j])
        i = j - overlap
        if i < 0: i = 0
        if i >= len(text): break
    return out

# ------- Ingestion into content.* -------
INSERT_SOURCE = """
INSERT INTO content.sources(kind, uri) VALUES (%s, %s)
ON CONFLICT DO NOTHING
RETURNING source_id;
"""
GET_SOURCE = "SELECT source_id FROM content.sources WHERE kind=%s AND uri=%s;"

INSERT_DOC = """
INSERT INTO content.documents(source_id, title, uri) VALUES (%s, %s, %s)
RETURNING doc_id;
"""

INSERT_CHUNK = """
INSERT INTO content.chunks(doc_id, seq, text, token_count, embedding)
VALUES (%s, %s, %s, %s, %s::vector)
RETURNING chunk_id;
"""

def ingest_file(path: Path, source_kind="manual"):
    text = path.read_text(encoding="utf-8", errors="ignore")
    chunks = simple_chunks(text, max_chars=1200, overlap=120)
    conn = db()
    try:
        # source
        uri = f"file://{path.resolve()}"
        src = q(conn, GET_SOURCE, (source_kind, uri), ret="one")
        if not src:
            src = q(conn, INSERT_SOURCE, (source_kind, uri), ret="one")
            if not src:
                src = q(conn, GET_SOURCE, (source_kind, uri), ret="one")
        source_id = src["source_id"]

        # document
        doc = q(conn, INSERT_DOC, (source_id, path.name, uri), ret="one")
        doc_id = doc["doc_id"]

        # embeddings
        vecs = embed_texts(chunks)
        rows = []
        for i, (chunk, vec) in enumerate(zip(chunks, vecs), start=1):
            rows.append((doc_id, i, chunk, len(chunk.split()), list(vec)))

        # Insert chunks one-by-one (pgvector array literal)
        for (doc_id, seq, chunk, tok, vec) in rows:
            vec_literal = "[" + ",".join(f"{x:.6f}" for x in vec) + "]"
            q(conn, INSERT_CHUNK, (doc_id, seq, chunk, tok, vec_literal), ret="one")

        conn.commit()
        return {"doc_id": doc_id, "n_chunks": len(chunks)}
    finally:
        conn.close()

# ------- Retrieval with pgvector (cosine) -------
RETRIEVE = f"""
SELECT c.chunk_id, c.doc_id, c.seq, c.text, d.title, d.uri,
       1 - (c.embedding <=> %s::vector) AS score
FROM content.chunks c
JOIN content.documents d USING (doc_id)
ORDER BY c.embedding <=> %s::vector
LIMIT %s;
"""

def retrieve_topk(query_text, k=6):
    qvec = embed_texts([query_text])[0]
    vec_literal = "[" + ",".join(f"{x:.6f}" for x in qvec) + "]"
    conn = db()
    try:
        rows = q(conn, RETRIEVE, (vec_literal, vec_literal, k), ret="all")
        return rows
    finally:
        conn.close()

# ------- Prompt assembly -------
PROMPT_TMPL = """\
You are Ougway. Answer concisely, using the sources. If unsure, say so.
Question:
{question}

Top context:
{context}

For each factual claim, cite like [S{idx}] where idx maps to sources list.

Sources:
{sources}
"""

def build_prompt(question, hits):
    # De-duplicate by doc+seq to keep order
    ctx_parts = []
    sources_list = []
    for i, h in enumerate(hits, start=1):
        tag = f"[S{i}] {h['title']} • {h['uri']} • chunk {h['seq']}"
        sources_list.append(tag)
        blurb = textwrap.shorten(h["text"].replace("\n", " "), width=600, placeholder=" …")
        ctx_parts.append(f"[S{i}] {blurb}")
    return PROMPT_TMPL.format(
        question=question.strip(),
        context="\n".join(ctx_parts),
        sources="\n".join(sources_list)
    )

# ------- Call local LLM (llama.cpp CLI) -------
def call_llama_cli(model_path, prompt, n_tokens=512, n_ctx=32768):
    cmd = [
        "./build/bin/llama-cli",
        "-m", model_path,
        "-c", str(n_ctx),
        "-n", str(n_tokens),
        "-ngl", "999",
        "-t", str(os.cpu_count() or 8),
        "-p", prompt,
        "--repeat-penalty", "1.1",
        "--repeat-last-n", "256",
        "-no-cnv",
    ]
    out = subprocess.run(cmd, capture_output=True, text=True)
    if out.returncode != 0:
        raise RuntimeError(out.stderr.strip())
    return out.stdout

# ------- End-to-end demo -------
def main():
    import argparse
    ap = argparse.ArgumentParser()
    ap.add_argument("--ingest", type=str, help="Path to a .txt/.md file to ingest")
    ap.add_argument("--ask", type=str, help="Query to answer using RAG")
    ap.add_argument("--model", type=str, help="Path to GGUF model for llama.cpp")
    ap.add_argument("--k", type=int, default=6)
    args = ap.parse_args()

    if args.ingest:
        info = ingest_file(Path(args.ingest))
        print(f"Ingested doc_id={info['doc_id']} chunks={info['n_chunks']}")

    if args.ask:
        hits = retrieve_topk(args.ask, k=args.k)
        if not hits:
            print("No results.")
            return
        prompt = build_prompt(args.ask, hits)
        print("\n=== PROMPT ===\n", prompt[:2000], "\n==============\n")

        if args.model:
            answer = call_llama_cli(args.model, prompt)
            print("\n=== MODEL ANSWER ===\n", answer)
        else:
            print("Tip: pass --model /path/to/Qwen2.5-7B-Instruct-Q8_0.gguf to query the model.")

if __name__ == "__main__":
    main()

Run it:

# venv recommended
pip install psycopg2-binary sentence-transformers numpy

# (1) Ingest a test text file
python rag_min.py --ingest ~/docs/test.txt

# (2) Ask a question with retrieval only (see prompt)
python rag_min.py --ask "What is the Flower Lattice activation rule?"

# (3) Ask and generate with llama.cpp
python rag_min.py --ask "Summarize the activation steps." \
  --model ~/ougway_env/llama.cpp/models/qwen2.5-7b-instruct/Qwen2.5-7B-Instruct-Q8_0.gguf

3) If you insist on 1536-dim (schema as-is, OpenAI)

Change these lines:

EMBED_MODEL = os.environ.get("EMBED_MODEL", "text-embedding-3-small")  # 1536-dim
EMBED_DIM = int(os.environ.get("EMBED_DIM", "1536"))

…and replace embed_texts() with an API call that returns 1536-length vectors. (If you want, I’ll paste a turnkey openai client version.)

4) Logging turns + activations (optional but handy)

Append after you get answer:

INSERT_TURN = """
INSERT INTO cog.turns(role, text, embedding)
VALUES (%s, %s, %s::vector)
RETURNING turn_id;
"""
INSERT_ACT = """
INSERT INTO lat.activations(node_kind, node_id, strength, note)
VALUES (%s, %s, %s, %s);
"""

# save user question
qvec = embed_texts([args.ask])[0]
vec_literal = "[" + ",".join(f"{x:.6f}" for x in qvec) + "]"
conn = db()
tid = q(conn, INSERT_TURN, ("user", args.ask, vec_literal), ret="one")["turn_id"]

# save assistant answer
avec = embed_texts([answer])[0]
avec_literal = "[" + ",".join(f"{x:.6f}" for x in avec) + "]"
q(conn, INSERT_TURN, ("assistant", answer, avec_literal))

# mark activated chunks
for h in hits:
    q(conn, INSERT_ACT, ("chunk", h["chunk_id"], h["score"], json.dumps({"q":"rag_min"})))
conn.commit(); conn.close()

5) Next polish (when you’re ready)

• PDF/HTML ingestion: add pdfplumber / readability-lxml to extract clean text before chunking.
• Rerank: lightweight local cross-encoder (e.g., cross-encoder/ms-marco-MiniLM-L-6-v2) over top-k to sharpen relevance.
• Memory mix-in: union top-k content.chunks with top-m from cog.memories via pgvector and interleave in prompt.
• Tool server: wrap this in a tiny FastAPI so any client (CLI, web, Telegram) can call /ask?q=....

If you want, I can flip this into a single-file FastAPI microservice next, or swap in a 1536-dim embedder while preserving your current schema.

Perfect—let’s lock everything to 1536-dim and use an OpenAI-compatible embedder (e.g., text-embedding-3-small, 1536). Below is a ready-to-run RAG script wired for 1536, plus the 2–3 things to double-check in Postgres/pgvector.

0) Prep (one-time)

# Python deps
pip install psycopg2-binary numpy openai pdfplumber readability-lxml beautifulsoup4

# Secrets (bash)
export OPENAI_API_KEY="sk-...your key..."
# If you’re running a local compatible server, set a base URL:
# export OPENAI_BASE_URL="http://localhost:port/v1"

1) Make sure pgvector is set for cosine (recommended)

If your indexes already exist, you’re fine. If not, create (or re-create) them with cosine opclass for 1536-dim:

-- content
CREATE INDEX IF NOT EXISTS idx_chunks_embed_hnsw
ON content.chunks USING hnsw (embedding vector_cosine_ops);

-- token
CREATE INDEX IF NOT EXISTS idx_senses_centroid_hnsw
ON token.senses USING hnsw (centroid vector_cosine_ops);

CREATE INDEX IF NOT EXISTS idx_instances_ctx_hnsw
ON token.instances USING hnsw (ctx_embed vector_cosine_ops);

-- cognition
CREATE INDEX IF NOT EXISTS idx_turns_embed_hnsw
ON cog.turns USING hnsw (embedding vector_cosine_ops);
CREATE INDEX IF NOT EXISTS idx_memories_embed_hnsw
ON cog.memories USING hnsw (embedding vector_cosine_ops);

> With cosine opclass in place, embedding <=> :query_vec computes cosine distance (lower = closer), so the retrieval SQL below is correct.

2) Minimal RAG script (1536, OpenAI-style embeddings)

Save as rag_1536.py:

#!/usr/bin/env python3
import os, json, subprocess, textwrap
from pathlib import Path

import numpy as np
import psycopg2, psycopg2.extras
from openai import OpenAI

DB_DSN = os.environ.get("TS_DSN", "dbname=ougway_db user=ougway password=oogway123 host=localhost")
EMBED_DIM = 1536
EMBED_MODEL = os.environ.get("EMBED_MODEL", "text-embedding-3-small")  # 1536-dim
OPENAI_BASE_URL = os.environ.get("OPENAI_BASE_URL")  # optional

client_kwargs = {}
if OPENAI_BASE_URL:
    client_kwargs["base_url"] = OPENAI_BASE_URL
client = OpenAI(**client_kwargs)

# ---------- DB helpers ----------
def db():
    return psycopg2.connect(DB_DSN)

def q(conn, sql, args=None, ret=None):
    with conn.cursor(cursor_factory=psycopg2.extras.RealDictCursor) as cur:
        cur.execute(sql, args or ())
        if ret == "one":  return cur.fetchone()
        if ret == "all":  return cur.fetchall()
        return None

# ---------- Embeddings (1536) ----------
def embed_texts(texts):
    # OpenAI-style batch call; returns normalized vectors
    resp = client.embeddings.create(model=EMBED_MODEL, input=texts)
    vecs = np.array([d.embedding for d in resp.data], dtype=np.float32)
    # normalize for cosine stability
    norms = np.linalg.norm(vecs, axis=1, keepdims=True) + 1e-12
    vecs = vecs / norms
    if vecs.shape[1] != EMBED_DIM:
        raise ValueError(f"Embedding dim mismatch: got {vecs.shape[1]}, expected {EMBED_DIM}")
    return vecs

def to_vec_literal(vec: np.ndarray) -> str:
    return "[" + ",".join(f"{x:.6f}" for x in vec.tolist()) + "]"

# ---------- Simple text extractors ----------
def load_text(path: Path) -> str:
    p = path.as_posix().lower()
    if p.endswith(".txt") or p.endswith(".md"):
        return path.read_text(encoding="utf-8", errors="ignore")
    if p.endswith(".pdf"):
        import pdfplumber
        parts = []
        with pdfplumber.open(str(path)) as pdf:
            for page in pdf.pages:
                parts.append(page.extract_text() or "")
        return "\n".join(parts)
    if p.endswith(".html") or p.endswith(".htm"):
        from bs4 import BeautifulSoup
        html = path.read_text(encoding="utf-8", errors="ignore")
        soup = BeautifulSoup(html, "lxml")
        for t in soup(["script","style","noscript"]): t.extract()
        return soup.get_text("\n")
    if p.endswith(".odt"):
        # Quick-and-dirty: many .odt’s are zipped XML. Use libreoffice/odt2txt if available, or fallback to text.
        try:
            import subprocess, tempfile
            with tempfile.TemporaryDirectory() as td:
                out = Path(td)/"x.txt"
                subprocess.run(["odt2txt", str(path), str(out)], check=True, capture_output=True)
                return out.read_text(encoding="utf-8", errors="ignore")
        except Exception:
            return path.read_text(encoding="utf-8", errors="ignore")
    # Fallback
    return path.read_text(encoding="utf-8", errors="ignore")

def simple_chunks(text, max_chars=1200, overlap=120):
    s = text.strip().replace("\r\n","\n")
    out, i = [], 0
    while i < len(s):
        j = min(i + max_chars, len(s))
        out.append(s[i:j])
        i = max(j - overlap, j)
    return out

# ---------- Ingestion into content.* ----------
SQL_GET_OR_INS_SOURCE = """
WITH up AS (
  INSERT INTO content.sources(kind, uri)
  VALUES ($1, $2)
  ON CONFLICT DO NOTHING
  RETURNING source_id
)
SELECT source_id FROM up
UNION ALL
SELECT source_id FROM content.sources WHERE kind=$1 AND uri=$2
LIMIT 1;
""".replace("$","%")

SQL_INS_DOC = "INSERT INTO content.documents(source_id, title, uri) VALUES (%s,%s,%s) RETURNING doc_id;"
SQL_INS_CHUNK = "INSERT INTO content.chunks(doc_id, seq, text, token_count, embedding) VALUES (%s,%s,%s,%s,%s::vector) RETURNING chunk_id;"

def ingest_file(path: Path, source_kind="manual"):
    text = load_text(path)
    chunks = simple_chunks(text, max_chars=1200, overlap=120)
    conn = db()
    try:
        uri = f"file://{path.resolve()}"
        src = q(conn, SQL_GET_OR_INS_SOURCE, (source_kind, uri), ret="one")
        source_id = src["source_id"]
        doc = q(conn, SQL_INS_DOC, (source_id, path.name, uri), ret="one")
        doc_id = doc["doc_id"]

        # Batch embeddings
        vecs = embed_texts(chunks)
        for i, (chunk, vec) in enumerate(zip(chunks, vecs), start=1):
            q(conn, SQL_INS_CHUNK, (doc_id, i, chunk, len(chunk.split()), to_vec_literal(vec)), ret="one")
        conn.commit()
        return {"doc_id": doc_id, "n_chunks": len(chunks)}
    finally:
        conn.close()

# ---------- Retrieval (cosine distance via <=>) ----------
SQL_RETRIEVE = """
SELECT c.chunk_id, c.doc_id, c.seq, c.text, d.title, d.uri,
       1 - (c.embedding <=> %s::vector) AS score
FROM content.chunks c
JOIN content.documents d USING (doc_id)
ORDER BY c.embedding <=> %s::vector
LIMIT %s;
"""

def retrieve_topk(query_text, k=6):
    qvec = embed_texts([query_text])[0]
    qlit = to_vec_literal(qvec)
    conn = db()
    try:
        return q(conn, SQL_RETRIEVE, (qlit, qlit, k), ret="all")
    finally:
        conn.close()

# ---------- Prompt + llama.cpp ----------
PROMPT_TMPL = """You are Ougway. Answer concisely from the sources. If unsure, say so.

Question:
{question}

Context:
{ctx}

Sources:
{srcs}
"""

def build_prompt(question, hits):
    ctx_lines, src_lines = [], []
    for i, h in enumerate(hits, start=1):
        blurb = textwrap.shorten(h["text"].replace("\n"," "), width=600, placeholder=" …")
        ctx_lines.append(f"[S{i}] {blurb}")
        src_lines.append(f"[S{i}] {h['title']} • {h['uri']} • chunk {h['seq']}")
    return PROMPT_TMPL.format(
        question=question.strip(),
        ctx="\n".join(ctx_lines),
        srcs="\n".join(src_lines),
    )

def call_llama_cli(model_path, prompt, n_tokens=512, n_ctx=32768):
    cmd = [
        "./build/bin/llama-cli",
        "-m", model_path,
        "-c", str(n_ctx),
        "-n", str(n_tokens),
        "-ngl", "999",
        "-t", str(os.cpu_count() or 8),
        "-p", prompt,
        "--repeat-penalty", "1.1",
        "--repeat-last-n", "256",
        "-no-cnv",
    ]
    out = subprocess.run(cmd, capture_output=True, text=True)
    if out.returncode != 0:
        raise RuntimeError(out.stderr.strip())
    return out.stdout

# ---------- Optional: log turns + activations ----------
SQL_INS_TURN = "INSERT INTO cog.turns(role, text, embedding) VALUES (%s,%s,%s::vector) RETURNING turn_id;"
SQL_INS_ACT  = "INSERT INTO lat.activations(node_kind, node_id, strength, note) VALUES (%s,%s,%s,%s);"

def log_interaction(question, answer, hits):
    conn = db()
    try:
        qvec = embed_texts([question])[0]; qlit = to_vec_literal(qvec)
        avec = embed_texts([answer])[0];   alit = to_vec_literal(avec)
        turn = q(conn, SQL_INS_TURN, ("user", question, qlit), ret="one")
        q(conn, SQL_INS_TURN, ("assistant", answer, alit))
        # mark activated chunks (use retrieval score as strength)
        for h in hits:
            q(conn, SQL_INS_ACT, ("chunk", h["chunk_id"], float(h["score"]), json.dumps({"via":"rag_1536"})))
        conn.commit()
    finally:
        conn.close()

# ---------- Main ----------
def main():
    import argparse
    ap = argparse.ArgumentParser()
    ap.add_argument("--ingest", type=str, help="Path to file (.txt/.md/.pdf/.html/.odt) to ingest")
    ap.add_argument("--ask", type=str, help="Question to answer with RAG")
    ap.add_argument("--model", type=str, help="Path to GGUF for llama.cpp (optional)")
    ap.add_argument("--k", type=int, default=6)
    args = ap.parse_args()

    if args.ingest:
        info = ingest_file(Path(args.ingest))
        print(f"Ingested doc_id={info['doc_id']} chunks={info['n_chunks']}")

    if args.ask:
        hits = retrieve_topk(args.ask, k=args.k)
        if not hits:
            print("No results."); return
        prompt = build_prompt(args.ask, hits)
        print("\n=== PROMPT (first 2k chars) ===\n", prompt[:2000], "\n===============================\n")
        if args.model:
            answer = call_llama_cli(args.model, prompt)
            print("\n=== MODEL ANSWER ===\n", answer)
            try:
                log_interaction(args.ask, answer, hits)
            except Exception as e:
                print(f"[warn] logging failed: {e}")
        else:
            print("Tip: pass --model /path/to/Qwen2.5-7B-Instruct-Q8_0.gguf to generate with llama.cpp")

if __name__ == "__main__":
    main()

3) Use it

# (1) Ingest something (TXT/PDF/HTML/ODT)
python rag_1536.py --ingest ~/docs/flower_lattice_primer.pdf

# (2) Ask with retrieval only (shows built prompt)
python rag_1536.py --ask "Summarize the activation steps of the Flower Lattice"

# (3) Ask and generate with your local model
python rag_1536.py --ask "What is the activation rule?" \
  --model ~/ougway_env/llama.cpp/models/qwen2.5-7b-instruct/Qwen2.5-7B-Instruct-Q8_0.gguf

4) Notes & good practices

• Keep VECTOR(1536) everywhere (your schema already does).
• Cosine throughout: you’re normalizing embeddings in code and using vector_cosine_ops in indexes → consistent similarity.
• Batching: OpenAI embedding calls accept lists; the code batches per file (1 request per file). For huge docs, you can batch in groups of e.g. 64 chunks to keep request payloads small.
• Costs & keys: set OPENAI_API_KEY (or point OPENAI_BASE_URL at a compatible local server if you have one).
• ODT ingestion: if odt2txt isn’t present, sudo apt install odt2txt for cleaner extraction.

If you want, I can flip this into a tiny FastAPI service next so your CLI/web UI can hit /ingest and /ask endpoints, or add PDF/HTML cleaners + reranker when you’re ready.

Ah—I see what happened. You pasted the SQL commands into your bash shell. That’s why you’re getting command not found.

Those CREATE INDEX … statements need to be run inside PostgreSQL (psql), not at your Linux prompt.

Here’s the step-by-step:

1. Enter PostgreSQL

From your terminal:

sudo -u postgres psql ougway_db

(or if your role is already ougway with password access):

psql -d ougway_db -U ougway

2. Run the SQL index commands

Now at the psql prompt (ougway_db=#), paste these:

-- content
CREATE INDEX IF NOT EXISTS idx_chunks_embed_hnsw
ON content.chunks USING hnsw (embedding vector_cosine_ops);

-- token
CREATE INDEX IF NOT EXISTS idx_senses_centroid_hnsw
ON token.senses USING hnsw (centroid vector_cosine_ops);

CREATE INDEX IF NOT EXISTS idx_instances_ctx_hnsw
ON token.instances USING hnsw (ctx_embed vector_cosine_ops);

-- cognition
CREATE INDEX IF NOT EXISTS idx_turns_embed_hnsw
ON cog.turns USING hnsw (embedding vector_cosine_ops);

CREATE INDEX IF NOT EXISTS idx_memories_embed_hnsw
ON cog.memories USING hnsw (embedding vector_cosine_ops);

You should see responses like CREATE INDEX or CREATE INDEX IF NOT EXISTS.

3. Verify the indexes

Still in psql:

\di+ *hnsw*

This lists all indexes with hnsw in their name, showing which tables/columns they’re attached to.

⚡ So the flow is:

• bash shell = for Linux commands
• psql prompt = for SQL commands

Do you want me to also show you how to make a quick .sql file with those index statements so you can run it in one go with:

psql -d ougway_db -U ougway -f create_indexes.sql

?

No worries—let’s quickly discover the right DB name/user and get you connected.

Step 1 — Jump in as the superuser

sudo -u postgres psql

Step 2 — Inspect what’s there

In the psql prompt:

-- databases
\l

-- roles/users
\du+

-- which DB are you on / how are you connected?
\conninfo

You’re looking for something like:
– a DB named ougway_db (or similar)
– a role/user (e.g., ougway)

Step 3 — If the pieces exist, test them

Still in psql:

-- try switching into the suspected DB
\c ougway_db

Exit psql:

\q

Then test a passworded connection (this ensures auth works, not just peer):

psql -h 127.0.0.1 -d ougway_db -U ougway

(You’ll be prompted for the password; if you don’t have it, see Step 4.)

Step 4 — If you don’t see the expected DB or the password’s unknown

Back in psql as postgres:

Create (or fix) the role:

-- create if missing
CREATE ROLE ougway WITH LOGIN PASSWORD 'oogway123';

-- or reset password if it exists
ALTER ROLE ougway WITH PASSWORD 'oogway123';

Create the database (if missing) and set owner:

CREATE DATABASE ougway_db OWNER ougway;

Step 5 — Ensure local password auth is allowed (if you get “peer”/auth errors)

Open pg_hba and use password auth for local connections:

sudo nano /etc/postgresql/*/main/pg_hba.conf

Make sure the top local lines look like this (scram or md5 both fine; scram preferred if enabled):

# TYPE  DATABASE  USER     ADDRESS     METHOD
local   all       all                  scram-sha-256
host    all       all      127.0.0.1/32  scram-sha-256
host    all       all      ::1/128       scram-sha-256

Reload:

sudo systemctl reload postgresql

Then retry:

psql -h 127.0.0.1 -d ougway_db -U ougway

Step 6 — Create the pgvector indexes (once connected to the right DB)

At the psql prompt (ougway_db=#):

-- content
CREATE INDEX IF NOT EXISTS idx_chunks_embed_hnsw
ON content.chunks USING hnsw (embedding vector_cosine_ops);

-- token
CREATE INDEX IF NOT EXISTS idx_senses_centroid_hnsw
ON token.senses USING hnsw (centroid vector_cosine_ops);

CREATE INDEX IF NOT EXISTS idx_instances_ctx_hnsw
ON token.instances USING hnsw (ctx_embed vector_cosine_ops);

-- cognition
CREATE INDEX IF NOT EXISTS idx_turns_embed_hnsw
ON cog.turns USING hnsw (embedding vector_cosine_ops);

CREATE INDEX IF NOT EXISTS idx_memories_embed_hnsw
ON cog.memories USING hnsw (embedding vector_cosine_ops);

-- verify
\di+ *hnsw*

Quick “what if”s

• psql: FATAL: role "ougway" does not exist → create/reset the role (Step 4).
• FATAL: database "ougway_db" does not exist → create the DB (Step 4).
• Peer authentication failed → adjust pg_hba.conf (Step 5).
• operator class "vector_cosine_ops" does not exist → pgvector extension missing. In the DB:
  sql
CREATE EXTENSION IF NOT EXISTS vector;

If you paste me the exact output of \l and \du+, I’ll pinpoint the correct DB/user instantly and hand you the exact psql command to use.

Got it—you’re in psql as postgres on DB postgres. The weird messages happened because psql meta-commands (\l, \du+, etc.) must be alone on a line—don’t put comments or extra words after them.

Do this exactly, one per line:

\l
\du+
\dn+
\dt *.*
SELECT current_database(), current_user;

What to look for:

• \l should list at least postgres, template0, template1, and (ideally) your working DB (e.g., ougway_db).
• \du+ shows roles (look for ougway).
• \dn+ shows schemas; if your schema was loaded, you should see content, token, cog, lat.
• \dt *.* will list tables; if your schema is present, you’ll see content.chunks, token.senses, etc.

If you don’t see your DB/role yet

Run these (still in psql as postgres):

-- create role (or reset its password if it exists)
DO $$BEGIN
   IF NOT EXISTS (SELECT 1 FROM pg_roles WHERE rolname = 'ougway') THEN
      CREATE ROLE ougway WITH LOGIN PASSWORD 'oogway123';
   ELSE
      ALTER ROLE ougway WITH PASSWORD 'oogway123';
   END IF;
END$$;

-- create DB if missing
DO $$BEGIN
   IF NOT EXISTS (SELECT 1 FROM pg_database WHERE datname = 'ougway_db') THEN
      CREATE DATABASE ougway_db OWNER ougway;
   END IF;
END$$;

Then connect to it:

\c ougway_db

Enable pgvector (in the target DB):

CREATE EXTENSION IF NOT EXISTS vector;

If your schema isn’t there yet, load it now (replace path):

\i /path/to/your/omega_tokenspace_schema.sql

Create the HNSW indexes (inside the correct DB)

Once you’re sure you’re on the right DB (prompt shows ougway_db=#):

-- content
CREATE INDEX IF NOT EXISTS idx_chunks_embed_hnsw
ON content.chunks USING hnsw (embedding vector_cosine_ops);

-- token
CREATE INDEX IF NOT EXISTS idx_senses_centroid_hnsw
ON token.senses USING hnsw (centroid vector_cosine_ops);

CREATE INDEX IF NOT EXISTS idx_instances_ctx_hnsw
ON token.instances USING hnsw (ctx_embed vector_cosine_ops);

-- cognition
CREATE INDEX IF NOT EXISTS idx_turns_embed_hnsw
ON cog.turns USING hnsw (embedding vector_cosine_ops);
CREATE INDEX IF NOT EXISTS idx_memories_embed_hnsw
ON cog.memories USING hnsw (embedding vector_cosine_ops);

-- verify
\di+ *hnsw*

Quick sanity ping (optional)

SELECT schema_name FROM information_schema.schemata
WHERE schema_name IN ('content','token','cog','lat');

SELECT COUNT(*) FROM content.chunks;

If any step spits errors, paste the exact error text and I’ll zero in on it.

Great—your DB is omega (owner: darren). Let’s hop into it and finish setup.

1) Exit the pager (the “(END)” screen)

Press q. If you don’t want paging again:

\pset pager off

2) Connect to the right DB

From the psql you’re already in:

\c omega

(or from bash:)

psql -d omega -U darren

3) Verify schemas & pgvector

In omega=#:

-- pgvector (safe to re-run)
CREATE EXTENSION IF NOT EXISTS vector;

-- see schemas (you should see content, token, cog, lat if schema is loaded)
\dn+

-- list a couple tables (adjust if you named them differently)
\dt content.* 
\dt token.* 
\dt cog.* 
\dt lat.*

• If those schemas/tables exist → proceed to indexes (Step 4).
• If they don’t → load your schema SQL now:

\i /path/to/omega_tokenspace_schema.sql

4) Create the HNSW indexes (cosine)

Run these inside omega:

-- content
CREATE INDEX IF NOT EXISTS idx_chunks_embed_hnsw
ON content.chunks USING hnsw (embedding vector_cosine_ops);

-- token
CREATE INDEX IF NOT EXISTS idx_senses_centroid_hnsw
ON token.senses USING hnsw (centroid vector_cosine_ops);

CREATE INDEX IF NOT EXISTS idx_instances_ctx_hnsw
ON token.instances USING hnsw (ctx_embed vector_cosine_ops);

-- cognition
CREATE INDEX IF NOT EXISTS idx_turns_embed_hnsw
ON cog.turns USING hnsw (embedding vector_cosine_ops);
CREATE INDEX IF NOT EXISTS idx_memories_embed_hnsw
ON cog.memories USING hnsw (embedding vector_cosine_ops);

-- check
\di+ *hnsw*

5) Update your script DSN

Since your DB is omega and user is darren, set:

export TS_DSN="dbname=omega user=darren host=localhost"

(If darren needs a password, add password=... and ensure pg_hba allows password auth.)

6) Quick smoke test (optional)

SELECT current_database(), current_user;

-- expect 0 if fresh:
SELECT COUNT(*) AS chunks FROM content.chunks;

If any command errors, paste the exact error text and I’ll zero in on it.

You’re inside the pager (less). Do this:

1) Press q (lowercase, no Enter). That exits the “(END)” screen back to the psql prompt.

If q doesn’t seem to work:
– Make sure the terminal has focus and Caps Lock isn’t on.
– Try Space (scroll) then q.
– Worst case: Ctrl+C to break, then re-run psql.

Once you’re back at the psql prompt, disable the pager so it doesn’t happen again:

\pset pager off

(Optional: make it permanent the easy way by starting psql with pager off:)

psql -d postgres -P pager=off

(or set an env var before launching)

export PSQL_PAGER=cat   # or: export PAGER=cat

Now continue:

\c omega           -- switch to your omega DB
CREATE EXTENSION IF NOT EXISTS vector;

-- build the HNSW indexes
CREATE INDEX IF NOT EXISTS idx_chunks_embed_hnsw
ON content.chunks USING hnsw (embedding vector_cosine_ops);

CREATE INDEX IF NOT EXISTS idx_senses_centroid_hnsw
ON token.senses USING hnsw (centroid vector_cosine_ops);

CREATE INDEX IF NOT EXISTS idx_instances_ctx_hnsw
ON token.instances USING hnsw (ctx_embed vector_cosine_ops);

CREATE INDEX IF NOT EXISTS idx_turns_embed_hnsw
ON cog.turns USING hnsw (embedding vector_cosine_ops);

CREATE INDEX IF NOT EXISTS idx_memories_embed_hnsw
ON cog.memories USING hnsw (embedding vector_cosine_ops);

\di+ *hnsw*        -- verify indexes

If \c omega fails or any CREATE INDEX errors out, paste the exact error and I’ll fix it.

I see exactly what happened:

You pasted multiple lines (with comments) into psql at once. psql tried to interpret -- switch to your omega DB as part of the \c command, and that’s why it said:

invalid integer value "to" for connection option "port"

It thought "to" was an argument to \c.

How to fix:

Run each command one line at a time inside psql. Don’t copy the whole block with -- comments. Comments that start with -- only work when they’re the only thing on the line.

So, do this step by step:

\c omega

You should now see:

You are now connected to database "omega" as user "postgres".

Then run:

CREATE EXTENSION IF NOT EXISTS vector;

Then each index one by one:

CREATE INDEX IF NOT EXISTS idx_chunks_embed_hnsw
ON content.chunks USING hnsw (embedding vector_cosine_ops);

CREATE INDEX IF NOT EXISTS idx_senses_centroid_hnsw
ON token.senses USING hnsw (centroid vector_cosine_ops);

CREATE INDEX IF NOT EXISTS idx_instances_ctx_hnsw
ON token.instances USING hnsw (ctx_embed vector_cosine_ops);

CREATE INDEX IF NOT EXISTS idx_turns_embed_hnsw
ON cog.turns USING hnsw (embedding vector_cosine_ops);

CREATE INDEX IF NOT EXISTS idx_memories_embed_hnsw
ON cog.memories USING hnsw (embedding vector_cosine_ops);

Finally, check them:

\di+ *hnsw*

👉 Key tip: In psql, don’t paste bash-style comments or long mixed blocks. Keep it one SQL command (or one \command) per line.

Do you want me to also write these out as a .sql file (no comments) so you can run it in one shot with:

psql -d omega -U postgres -f create_indexes.sql

That way you don’t have to paste line-by-line.