Got it 👍 — thanks for being straight with me. I’ll keep them one per block from now on so you can paste/enter them exactly as-is.

Let’s go step by step in psql:

1. Switch to your omega DB

\c omega

2. Enable pgvector

CREATE EXTENSION IF NOT EXISTS vector;

3. Create content index

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

4. Create token index (senses)

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

5. Create token index (instances)

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

6. Create cog index (turns)

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

7. Create cog index (memories)

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

8. Verify they exist

\di+ *hnsw*

⚡ Each of those goes in alone, one after another, at the psql prompt.

Do you want me to also make you a ready-to-run create_indexes.sql file with no comments so you can just run:

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

instead of typing/pasting line by line?

You actually did create all five indexes — CREATE INDEX confirms that. The only hiccup was the check command: in psql, * isn’t a wildcard there. Use a regex-like pattern or just query the catalog.

Verify (run each line separately in psql, you’re already on omega)

\di+ idx_.*

\di+ idx_.*hnsw.*

SELECT schemaname, indexname, tablename
FROM pg_indexes
WHERE indexname LIKE '%hnsw%'
ORDER BY 1,2;

\d content.chunks

If those show the indexes, you’re golden. If not, we’ll fix.

Prefer a “can’t-mess-it-up” one-shot file?

Create a file on your machine and run it in one go. From the bash prompt (not inside psql):

cat > create_indexes.sql <<'SQL'
CREATE EXTENSION IF NOT EXISTS vector;

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);
SQL

Run it (whichever auth works for you):

# as postgres superuser:
sudo -u postgres psql -d omega -f create_indexes.sql

or, if you connect as your user:

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

Check:

sudo -u postgres psql -d omega -c "\di+ idx_.*hnsw.*"

—or inside psql:

\di+ idx_.*hnsw.*

Why your \di+ *hnsw* didn’t work

\d* patterns in psql use a regex-like matcher. * alone isn’t treated as “match anything”; use .* for “any chars” or just list by prefix: \di+ idx_.*.

Want me to hand you a one-shot ingestion+retrieval SQL/CLI bundle next (so you can test RAG end-to-end with a tiny text file)?

You did it—the indexes are there. Your \di+ *hnsw* check just didn’t match, but your catalog query proves it:

• ✅ content.chunks: chunks_embed_hnsw and idx_chunks_embed_hnsw
• ✅ token.senses: senses_centroid_hnsw and idx_senses_centroid_hnsw
• ✅ token.instances: instances_ctx_hnsw and idx_instances_ctx_hnsw
• ✅ cog.turns: turns_embed_hnsw and idx_turns_embed_hnsw
• ✅ cog.memories: memories_embed_hnsw and idx_memories_embed_hnsw
• ✅ lat.cells: lat_cells_centroid_hnsw

That means you now have duplicates (one from the schema + one we added). Duplicates won’t break anything, but they waste disk and slow writes. Let’s keep the original schema names (no idx_…) and drop the extras.

Drop the duplicate idx_… indexes (run each line in psql while connected to omega)

DROP INDEX CONCURRENTLY IF EXISTS content.idx_chunks_embed_hnsw;

DROP INDEX CONCURRENTLY IF EXISTS token.idx_senses_centroid_hnsw;

DROP INDEX CONCURRENTLY IF EXISTS token.idx_instances_ctx_hnsw;

DROP INDEX CONCURRENTLY IF EXISTS cog.idx_turns_embed_hnsw;

DROP INDEX CONCURRENTLY IF EXISTS cog.idx_memories_embed_hnsw;

Verify (simple way that always works)

SELECT schemaname, indexname, tablename
FROM pg_indexes
WHERE indexname LIKE '%hnsw%'
ORDER BY 1,2;

If you prefer a \di view:

\di+ *.hnsw*

(That *.hnsw* pattern matches “anything, dot, hnsw in the name”.)

Ready to test RAG end-to-end

Since your DB is omega, set the DSN and run the 1536-dim script I gave you.

At bash (not psql):

export TS_DSN="dbname=omega user=postgres host=localhost"
# or, if you use your Linux role:
# export TS_DSN="dbname=omega user=darren host=localhost"

export OPENAI_API_KEY="YOUR_KEY_HERE"

Create a tiny test file to ingest:

printf "The Flower Lattice activation rule: align nodes on 3-6-9 and apply phase-locked rotation.\n" > ~/Desktop/test_lattice.txt

Ingest it:

python rag_1536.py --ingest ~/Desktop/test_lattice.txt

Ask a question (just builds prompt):

python rag_1536.py --ask "What is the activation rule of the Flower Lattice?"

Ask and generate with your local model:

python rag_1536.py --ask "Summarize the activation rule." \
  --model ~/ougway_env/llama.cpp/models/qwen2.5-7b-instruct/Qwen2.5-7B-Instruct-Q8_0.gguf

You should see retrieved context from content.chunks and a model answer. If anything errors, paste the exact line and I’ll fix fast.

Boom — that’s a clean set. You now have exactly one HNSW per target table:

• content.chunks → chunks_embed_hnsw
• token.senses → senses_centroid_hnsw
• token.instances → instances_ctx_hnsw
• cog.turns → turns_embed_hnsw
• cog.memories → memories_embed_hnsw
• lat.cells → lat_cells_centroid_hnsw

All good. ✅

Next steps (no gotchas)

1) Set your DSN for the RAG script (bash, not psql)

export TS_DSN="dbname=omega user=postgres host=localhost"
# or, if you prefer your user:
# export TS_DSN="dbname=omega user=darren host=localhost"

2) Set embedding key (1536-dim path)

export OPENAI_API_KEY="YOUR_KEY_HERE"

3) Quick ingestion smoke test (bash)

printf "The Flower Lattice activation rule: align nodes on 3-6-9 and apply phase-locked rotation.\n" > ~/Desktop/test_lattice.txt
python rag_1536.py --ingest ~/Desktop/test_lattice.txt

4) Retrieval + prompt (bash)

python rag_1536.py --ask "What is the activation rule of the Flower Lattice?"

5) Full RAG (call your local model) (bash)

python rag_1536.py --ask "Summarize the activation rule." \
  --model ~/ougway_env/llama.cpp/models/qwen2.5-7b-instruct/Qwen2.5-7B-Instruct-Q8_0.gguf

Optional sanity checks (inside psql on omega)

List the HNSW indexes:

SELECT schemaname, indexname, tablename
FROM pg_indexes
WHERE indexname LIKE '%hnsw%'
ORDER BY 1,2;

See one table’s index definitions:

\d content.chunks

Tiny performance nudge (safe now, great later)

After you ingest a decent amount, run:

VACUUM ANALYZE;

…and (optionally) rebuild HNSW only after big batch loads (you already have them, so no action needed unless you want to DROP/CREATE around massive imports).

If you hit any error with the Python step, paste the exact line and I’ll patch it fast.

All good—couple quick fixes and you’ll be rolling.

0) Clean up the paused psql (optional)

You’ve got psql suspended (Stopped). Either kill it or exit cleanly:

fg
\q

1) Use python3 (not python)

On Ubuntu, call python3. Also make sure the script file exists (we haven’t created it on disk yet).

Create the rag_1536.py file

cat > ~/Desktop/rag_1536.py <<'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=omega user=postgres host=localhost")
EMBED_DIM = 1536
EMBED_MODEL = os.environ.get("EMBED_MODEL", "text-embedding-3-small")
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)

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

def embed_texts(texts):
    resp = client.embeddings.create(model=EMBED_MODEL, input=texts)
    vecs = np.array([d.embedding for d in resp.data], dtype=np.float32)
    vecs = vecs / (np.linalg.norm(vecs, axis=1, keepdims=True) + 1e-12)
    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):
    return "[" + ",".join(f"{x:.6f}" for x in vec.tolist()) + "]"

def load_text(path: Path) -> str:
    p = path.as_posix().lower()
    if p.endswith((".txt",".md",".html",".htm",".pdf",".odt")):
        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",".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"):
            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")
        return path.read_text(encoding="utf-8", errors="ignore")
    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

SQL_GET_OR_INS_SOURCE = """
WITH up AS (
  INSERT INTO content.sources(kind, uri)
  VALUES (%s, %s)
  ON CONFLICT DO NOTHING
  RETURNING source_id
)
SELECT source_id FROM up
UNION ALL
SELECT source_id FROM content.sources WHERE kind=%s AND uri=%s
LIMIT 1;
"""

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, 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"]
        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()
        print(f"Ingested doc_id={doc_id} chunks={len(chunks)}")
    finally:
        conn.close()

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_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

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:
        ingest_file(Path(args.ingest))

    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)
        else:
            print("Tip: pass --model /path/to/Qwen2.5-7B-Instruct-Q8_0.gguf to generate with llama.cpp")

if __name__ == "__main__":
    main()
PY
chmod +x ~/Desktop/rag_1536.py

2) Install Python deps (once)

sudo apt update
sudo apt install -y python3-pip
pip3 install --user psycopg2-binary numpy openai pdfplumber readability-lxml beautifulsoup4 lxml

If you plan to ingest .odt:

sudo apt install -y odt2txt

3) Make sure your env vars are set

You already set DSN:

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

And you need a real embedding key (not "YOUR_KEY_HERE"):

export OPENAI_API_KEY="sk-...real key..."

(If you’re using an OpenAI-compatible local server, set OPENAI_BASE_URL="http://localhost:port/v1" and OPENAI_API_KEY can be any token your server accepts.)

4) Try the ingest again

printf "The Flower Lattice activation rule: align nodes on 3-6-9 and apply phase-locked rotation.\n" > ~/Desktop/test_lattice.txt
python3 ~/Desktop/rag_1536.py --ingest ~/Desktop/test_lattice.txt

You should see: Ingested doc_id=… chunks=…

5) Retrieval (prompt only)

python3 ~/Desktop/rag_1536.py --ask "What is the activation rule of the Flower Lattice?"

6) Full RAG with your local model (optional)

python3 ~/Desktop/rag_1536.py --ask "Summarize the activation rule." \
  --model ~/ougway_env/llama.cpp/models/qwen2.5-7b-instruct/Qwen2.5-7B-Instruct-Q8_0.gguf

If any command errors, paste the exact line and message—I’ll adjust immediately.

All good—you accidentally tried to run bash commands inside psql, so Postgres tried to “execute” them as SQL. Here’s a foolproof, paste-safe path using Nano (since you know Ctrl-X). I’ll spell out every exit/confirm step.

A) Get back to the normal bash prompt

You’re sometimes in a suspended psql. Do this:

1) Bring psql to the foreground:

fg

2) Quit psql cleanly (you’ll see a prompt like omega=#):

\q

Now your prompt should end with $ (bash), e.g. darren@ougway:~/Desktop$

If fg says “no such job”, you’re already in bash—carry on.

B) Create the Python file (with Nano)

1) Open Nano to create the file:

nano ~/Desktop/rag_1536.py

2) Paste everything between the lines below into Nano:

────────── paste everything below this line ──────────

#!/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=omega user=postgres host=localhost")
EMBED_DIM = 1536
EMBED_MODEL = os.environ.get("EMBED_MODEL", "text-embedding-3-small")
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)

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

def embed_texts(texts):
    resp = client.embeddings.create(model=EMBED_MODEL, input=texts)
    vecs = np.array([d.embedding for d in resp.data], dtype=np.float32)
    vecs = vecs / (np.linalg.norm(vecs, axis=1, keepdims=True) + 1e-12)
    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):
    return "[" + ",".join(f"{x:.6f}" for x in vec.tolist()) + "]"

def load_text(path: Path) -> str:
    p = path.as_posix().lower()
    if p.endswith((".txt",".md",".html",".htm",".pdf",".odt")):
        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",".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"):
            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")
        return path.read_text(encoding="utf-8", errors="ignore")
    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

SQL_GET_OR_INS_SOURCE = """
WITH up AS (
  INSERT INTO content.sources(kind, uri)
  VALUES (%s, %s)
  ON CONFLICT DO NOTHING
  RETURNING source_id
)
SELECT source_id FROM up
UNION ALL
SELECT source_id FROM content.sources WHERE kind=%s AND uri=%s
LIMIT 1;
"""

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, 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"]
        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()
        print(f"Ingested doc_id={doc_id} chunks={len(chunks)}")
    finally:
        conn.close()

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_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

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:
        ingest_file(Path(args.ingest))

    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)
        else:
            print("Tip: pass --model /path/to/Qwen2.5-7B-Instruct-Q8_0.gguf to generate with llama.cpp")

if __name__ == "__main__":
    main()

────────── paste everything above this line ──────────

3) Save & exit Nano:
– Press Ctrl+O, then Enter to confirm the filename.
– Press Ctrl+X to exit Nano.

4) Make it executable:

chmod +x ~/Desktop/rag_1536.py

C) Install Python deps (once)

sudo apt update
sudo apt install -y python3-pip
pip3 install --user psycopg2-binary numpy openai pdfplumber readability-lxml beautifulsoup4 lxml
sudo apt install -y odt2txt  # optional, for .odt ingestion

D) Set the environment variables

export TS_DSN="dbname=omega user=postgres host=localhost"
export OPENAI_API_KEY="sk-REPLACE_WITH_REAL_KEY"

(To check they’re set: echo "$TS_DSN" and echo "${OPENAI_API_KEY:0:8}...")

E) Run a tiny ingestion + retrieval test

printf "The Flower Lattice activation rule: align nodes on 3-6-9 and apply phase-locked rotation.\n" > ~/Desktop/test_lattice.txt
python3 ~/Desktop/rag_1536.py --ingest ~/Desktop/test_lattice.txt
python3 ~/Desktop/rag_1536.py --ask "What is the activation rule of the Flower Lattice?"

Optional: if you want the model to answer via your local Qwen:

python3 ~/Desktop/rag_1536.py --ask "Summarize the activation rule." \
  --model ~/ougway_env/llama.cpp/models/qwen2.5-7b-instruct/Qwen2.5-7B-Instruct-Q8_0.gguf

What went wrong earlier (so you can spot it fast next time)

• The cat <<'PY' ... PY “here-doc” must be run in bash; you ran it in psql (omega=#) so Postgres tried to parse it and threw syntax error near "q".
• Ctrl+Z suspends a program (like psql) instead of quitting; use fg then \q to exit cleanly.
• In psql, only run SQL or backslash commands; in bash, run shell commands.

If anything above errors, paste the exact line and message. I’ll adjust immediately and keep commands single-purpose so they’re easy to follow.

You’re inside psql’s pager (less) again. Do this exactly:

Get out of the pager and psql

1) Press:
q
(you’ll return to a prompt like omega=# or postgres=#)

2) At the psql prompt, quit:
\q + Enter

3) Now you should be back at bash ($). If you’re unsure, run:
echo $0
(it should print bash)

(Optional) Stop this pager thing forever

From bash:

echo '\pset pager off' >> ~/.psqlrc

If you still have a suspended psql job lingering:

1) Show jobs:
jobs

2) For each psql job you see (e.g., %1), kill it:
kill %1
(then run jobs again; repeat if there are more)

Next: continue with the Python setup

When you’re at a $ prompt:

1) Open the file in Nano:
nano ~/Desktop/rag_1536.py

2) Paste the code I gave you (all of it).

3) Save & exit Nano:
– Ctrl+O, Enter
– Ctrl+X

4) Make it executable:
chmod +x ~/Desktop/rag_1536.py

5) Install deps (one-time):

sudo apt update
sudo apt install -y python3-pip
pip3 install --user psycopg2-binary numpy openai pdfplumber readability-lxml beautifulsoup4 lxml
sudo apt install -y odt2txt

6) Set env vars (replace with your real key):

export TS_DSN="dbname=omega user=postgres host=localhost"
export OPENAI_API_KEY="sk-REPLACE_WITH_REAL_KEY"

7) Quick test:

printf "The Flower Lattice activation rule: align nodes on 3-6-9 and apply phase-locked rotation.\n" > ~/Desktop/test_lattice.txt
python3 ~/Desktop/rag_1536.py --ingest ~/Desktop/test_lattice.txt
python3 ~/Desktop/rag_1536.py --ask "What is the activation rule of the Flower Lattice?"

If anything doesn’t behave, tell me exactly what you see on screen (the last few lines), and I’ll adjust the steps.

Got it — thanks for catching that before going further. Let’s slow this down and clear up what’s happening.

1. Why the OPENAI_API_KEY was in my instructions

That script I gave you uses an embedding model to turn text into vectors (1536-dimensional numbers) before putting them in Postgres.
The quick-and-dirty way is to call OpenAI’s hosted API (needs OPENAI_API_KEY), but you’re right: that means contacting OpenAI’s servers — which you explicitly don’t want. 👍

So, no worries — we’ll swap that out for a local embedding model. That way, nothing leaves your machine.

2. Your choices for embeddings

You need some embedding generator to fill the vector(1536) columns. Two good offline options:

• bge-small-en (384 dims) or bge-large-en (1024 dims) from HuggingFace.
• text-embedding-nomic-embed-text-v1 (768 or 1536 dims) — also available locally.

Since your schema is already locked at 1536 dimensions, the easiest local match is nomic-embed-text-v1.5 (1536-dim). It runs fine with CPU or GPU using the sentence-transformers Python library.

3. What we’ll change

Instead of using openai.Embeddings.create(...), we’ll load a local HuggingFace model and generate embeddings directly. That means:

• No API key.
• No internet.
• Just install sentence-transformers.

4. Adjusted plan

Install the embedding library

pip3 install --user sentence-transformers

Create a tiny test in bash

python3 - <<'PY'
from sentence_transformers import SentenceTransformer

# load a local model (downloads once, then cached)
model = SentenceTransformer("nomic-ai/nomic-embed-text-v1.5")

vec = model.encode(["The Flower Lattice activation rule"], normalize_embeddings=True)
print("Vector shape:", vec.shape)
print("First 8 numbers:", vec[0][:8])
PY

Expected output:

Vector shape: (1, 1536)
First 8 numbers: [0.00123 0.0456 ...]

That proves the local embeddings work and match the DB schema.

5. Next step

If you like this direction, I’ll rewrite the rag_1536.py script to use sentence-transformers instead of OpenAI’s API. That way, everything stays local and consistent with your 1536-dim schema.

👉 Do you want me to go ahead and drop in the local-only rewrite of the script for you? That will remove all references to OPENAI_API_KEY and make it purely offline.