Big win. Those results look properly semantic—so your search script used the real 768-dim SentenceTransformer from local cache (that <All keys matched successfully> line is from safetensors loading the model). All offline, all good. 🙌

Two quick cleanups, one step at a time:

Step 1 — Prove the HNSW index is used (inside psql)

Use an existing embedding so dimensions match and you don’t get NaNs:

EXPLAIN (ANALYZE, BUFFERS)
SELECT chunk_id, left(text,120) AS preview
FROM content.chunks
ORDER BY embedding <=> (SELECT embedding FROM content.chunks LIMIT 1)
LIMIT 5;

You should see a line like: Index Scan using chunks_embed_hnsw on content.chunks ….

Step 2 — Post-ingest housekeeping (run in psql once)

This helps the planner and keeps the index snappy:

VACUUM ANALYZE content.chunks;

Quality-of-life tweak (bash, optional)

So psql never traps you in (END) again:

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

If you want, next I can give you a tiny search_chats.py update that prints which embedder path it used (ST vs hash) and a simple --k flag. Or we can go straight to: “ask a question, get the top chunks + source doc titles.” Your call.

Perfect — that means your content.chunks table and its HNSW index are fully up to date and the query planner has fresh statistics. 👍

At this point your system is clean, aligned, and optimized:

• ✅ Schema is live under tokenspace.
• ✅ Embedding dimension locked to 768 across all tables.
• ✅ Ingestion works (we saw ~75k chunks go in).
• ✅ HNSW indexes are present.
• ✅ VACUUM ANALYZE refreshed stats, so queries will use those indexes efficiently.

If you want to double-check the index in action, you can now try the query I mentioned earlier:

EXPLAIN (ANALYZE, BUFFERS)
SELECT chunk_id, left(text,120) AS preview
FROM content.chunks
ORDER BY embedding <=> (SELECT embedding FROM content.chunks LIMIT 1)
LIMIT 5;

If the EXPLAIN output shows Index Scan using chunks_embed_hnsw, you’re golden.

Do you want to run that now as a sanity check, or move straight into building out your search / retrieval pipeline (multi-chunk answers, doc titles, etc.)?

🔥 That plan is perfect. Your EXPLAIN shows:

• Index Scan using chunks_embed_hnsw → the HNSW index is being used
• ~2.8 ms to fetch top-5 → blazing for ~75k chunks

Let’s take one small step: upgrade the search so results include doc title and source path (super handy).

Step — update search_chats.py to show titles/paths

bash

nano ~/Desktop/search_chats.py

Replace the SQL block with this JOINed version (just the cur.execute(...) part):

cur.execute(f"""
    SELECT ch.chunk_id,
           left(ch.text, 200) AS preview,
           d.title,
           s.uri,
           ch.embedding <=> %s::vector AS dist
    FROM content.chunks ch
    JOIN content.documents d ON d.doc_id = ch.doc_id
    LEFT JOIN content.sources s ON s.source_id = d.source_id
    ORDER BY dist ASC
    LIMIT %s;
""", (qlit, limit))

And update the print loop to:

print(f"Top {limit} for: {query!r}")
for cid, prev, title, uri, d in rows:
    print(f"- chunk_id={cid}  dist={d:.4f}")
    print(f"  title: {title!r}")
    if uri: print(f"  path:  {uri}")
    print(f"  text:  {prev!r}")

Save (Ctrl+O, Enter) and exit (Ctrl+X).

Run it:

python ~/Desktop/search_chats.py "What did we say about the Flower Lattice?" 5

If that looks good, we can add one more tiny improvement next (e.g., --k flag, min distance filter, or writing results to a csv).

Got it 👍 — let’s just give you a complete working search_chats.py so you don’t have to patch in Nano piece by piece. Replace the whole file with this:

#!/usr/bin/env python3
import sys
import psycopg2
import numpy as np
from sentence_transformers import SentenceTransformer

def main():
    if len(sys.argv) < 2:
        print("Usage: search_chats.py <query> [limit]")
        sys.exit(1)

    query = sys.argv[1]
    limit = int(sys.argv[2]) if len(sys.argv) > 2 else 5

    # Load same 768-dim embedding model
    model = SentenceTransformer("nomic-ai/nomic-embed-text-v1.5", trust_remote_code=True)
    qvec = model.encode([query], normalize_embeddings=True)[0]

    # Connect to Tokenspace
    conn = psycopg2.connect(dbname="tokenspace", user="darren", password="LittleOugway")
    cur = conn.cursor()

    # Convert numpy vector -> Postgres vector literal
    qlit = "[" + ",".join(f"{x:.6f}" for x in qvec.tolist()) + "]"

    # Run nearest-neighbor search with joins for title + uri
    cur.execute(f"""
        SELECT ch.chunk_id,
               left(ch.text, 200) AS preview,
               d.title,
               s.uri,
               ch.embedding <=> %s::vector AS dist
        FROM content.chunks ch
        JOIN content.documents d ON d.doc_id = ch.doc_id
        LEFT JOIN content.sources s ON s.source_id = d.source_id
        ORDER BY dist ASC
        LIMIT %s;
    """, (qlit, limit))

    rows = cur.fetchall()

    print(f"Top {limit} for: {query!r}")
    for cid, prev, title, uri, d in rows:
        print(f"- chunk_id={cid}  dist={d:.4f}")
        print(f"  title: {title!r}")
        if uri:
            print(f"  path:  {uri}")
        print(f"  text:  {prev!r}")

    cur.close()
    conn.close()

if __name__ == "__main__":
    main()

How to apply

1. Open Nano on that file:
   bash
nano ~/Desktop/search_chats.py
2. Delete everything in there (Ctrl+K repeatedly clears).
3. Paste in the script above (middle-click or right-click paste).
4. Save (Ctrl+O, Enter) → Exit (Ctrl+X).
5. Run a test:
   bash
python ~/Desktop/search_chats.py "What did we say about the Flower Lattice?" 5

This version will now show:
– chunk_id
– distance
– document title
– source path (if any)
– text preview

Do you want me to also add an option to export results to a CSV in the same script, so you can archive queries?

Boom. That is a textbook win. You’ve got:

• local 768-dim embeddings,
• 75k+ chunks indexed with HNSW,
• real semantic search returning the right parts of chats.txt.

If you want to keep momentum, pick one of these next tiny upgrades and I’ll give you a single paste-ready step:

1) CSV export — save search results (id, title, path, preview, distance) to a file.
2) --k & --min-dist flags — control hits and filter weak matches.
3) Answer stitcher — fetch top-k chunks and print a short synthesized answer (still offline).
4) Query-by-doc — add --doc chats.txt to restrict results to one document.
5) Quality-of-life — log whether SentenceTransformer vs hash fallback was used.

Tell me which one you want first and I’ll drop in the exact code/command (one step, no surprises).

You got a TON done. Here’s a clean, paste-ready “context handoff” you can drop into a new window so I remember everything, followed by a tiny offline RAG runner wired for your local Qwen model.

Context Handoff (Tokenspace, offline)

• Machine: Ubuntu 22.04
• Postgres: 14 (fresh install)
• pgvector: installed via PGXN; extension enabled
• Data dir (moved to storage disk): /mnt/storage/postgres14/main
• Service: sudo systemctl {start|stop|status} postgresql
• Auth: local scram-sha-256 (edited /etc/postgresql/14/main/pg_hba.conf)
• Database name: tokenspace
• DB role: darren (password: LittleOugway)
• Extensions (in tokenspace): vector, pg_trgm
• Schemas: content, token, cog, lat (owner: darren)
• Embedding dim: 768 across all vector columns
• HNSW indexes present:
• content.chunks.embedding → chunks_embed_hnsw
• token.senses.centroid → senses_centroid_hnsw
• token.instances.ctx_embed → instances_ctx_hnsw
• cog.turns.embedding → turns_embed_hnsw
• cog.memories.embedding → memories_embed_hnsw
• lat.cells.centroid → lat_cells_centroid_hnsw
• Python venv: ~/ougway_env/venv
• Env used by scripts:
• TS_DSN="dbname=tokenspace user=darren host=localhost password=LittleOugway"
• HF_HUB_OFFLINE=1 (force local cache)
• EMBED_MODEL="nomic-ai/nomic-embed-text-v1.5" (768-dim; cached)
• Scripts on Desktop:
• ingest_test.py (single-row test)
• ingest_chats.py (ingested ~/Desktop/chats.txt → ~75,392 chunks, doc_id=2)
• search_chats.py (semantic search; shows title + path)
• Tuning done: VACUUM ANALYZE content.chunks;
• Known good checks:
• SHOW data_directory; → /mnt/storage/postgres14/main
• HNSW used (EXPLAIN shows Index Scan using chunks_embed_hnsw)

Next step: hook up offline RAG to your local Qwen model

Below is a minimal offline RAG runner. It:
1) embeds the question (768-dim, offline: SentenceTransformer if cached, else deterministic fallback),
2) retrieves top-k chunks from content.chunks,
3) builds a compact prompt,
4) calls your local llama.cpp binary with your Qwen3 6k (replace with your exact GGUF path).

1) Save the script

nano ~/Desktop/rag_answer.py

Paste, save (Ctrl+O, Enter), exit (Ctrl+X):

#!/usr/bin/env python3
import os, sys, subprocess, textwrap, hashlib, numpy as np, psycopg2

DSN = os.environ.get("TS_DSN", "dbname=tokenspace user=darren host=localhost")
EMBED_MODEL = os.environ.get("EMBED_MODEL", "nomic-ai/nomic-embed-text-v1.5")
TOP_K = int(os.environ.get("RAG_K", "6"))
MAX_CHARS = 900  # cap chunk text in prompt

# Try local SentenceTransformer; fallback to deterministic 768-d hash
_USE_ST = False
try:
    from sentence_transformers import SentenceTransformer
    _st = SentenceTransformer(EMBED_MODEL, trust_remote_code=True)
    dim = getattr(_st, "get_sentence_embedding_dimension", lambda: None)()
    _USE_ST = (dim == 768)
except Exception:
    _USE_ST = False

def embed_one(text: str) -> np.ndarray:
    if _USE_ST:
        v = _st.encode([text], normalize_embeddings=True)
        v = np.asarray(v, dtype=np.float32)
        return v[0]
    h = hashlib.sha256(text.encode("utf-8")).digest()
    seed = int.from_bytes(h[:8], "big") % (2**31-1)
    rng = np.random.default_rng(seed)
    v = rng.normal(0.0, 1.0, 768).astype(np.float32)
    n = float(np.linalg.norm(v)); 
    if n > 0: v /= n
    return v

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

def retrieve(query_vec_lit: str, k: int):
    conn = psycopg2.connect(DSN); cur = conn.cursor()
    cur.execute(f"""
        SELECT ch.chunk_id, left(ch.text, {MAX_CHARS}) AS preview,
               d.title, s.uri, ch.embedding <=> %s::vector AS dist
        FROM content.chunks ch
        JOIN content.documents d ON d.doc_id = ch.doc_id
        LEFT JOIN content.sources s ON s.source_id = d.source_id
        ORDER BY dist ASC
        LIMIT %s;
    """, (query_vec_lit, k))
    rows = cur.fetchall(); cur.close(); conn.close()
    return rows

def build_prompt(question: str, hits):
    ctx_blocks = []
    for i, (cid, preview, title, uri, dist) in enumerate(hits, 1):
        meta = []
        if title: meta.append(f"title: {title}")
        if uri:   meta.append(f"path: {uri}")
        meta.append(f"chunk_id: {cid}  dist: {dist:.4f}")
        ctx_blocks.append(f"### Context {i} ({' | '.join(meta)})\n{preview}")
    context = "\n\n".join(ctx_blocks)
    sys_inst = (
        "You are a concise assistant. Use the provided context only. "
        "If the answer isn't in the context, say you don't find it."
    )
    prompt = f"""[SYSTEM]
{sys_inst}

[CONTEXT]
{context}

[USER]
{question}

[ASSISTANT]
"""
    return prompt

def call_llama_cpp(model_path: str, prompt: str, n_predict: int = 512):
    # Adjust the binary path/args to your llama.cpp build
    cmd = [
        os.environ.get("LLAMA_BIN", "~/ougway_env/llama.cpp/main"),
        "-m", model_path,
        "-p", prompt,
        "-n", str(n_predict),
        "--temp", "0.2",
        "--top_p", "0.9",
    ]
    cmd[0] = os.path.expanduser(cmd[0])
    out = subprocess.run(cmd, capture_output=True, text=True)
    if out.returncode != 0:
        raise RuntimeError(out.stderr.strip())
    return out.stdout.strip()

def main():
    if len(sys.argv) < 3:
        print("Usage: rag_answer.py /path/to/Qwen3-6k.gguf 'your question' [k]", file=sys.stderr)
        sys.exit(1)
    model_path = sys.argv[1]
    question   = sys.argv[2]
    k = int(sys.argv[3]) if len(sys.argv) > 3 else TOP_K

    qv = embed_one(question)
    qlit = vec_literal(qv)
    hits = retrieve(qlit, k)

    if not hits:
        print("No results.")
        sys.exit(0)

    prompt = build_prompt(question, hits)
    # print the prompt head if you want to inspect:
    # print(prompt[:1200])

    answer = call_llama_cpp(model_path, prompt, n_predict=512)
    print("\n=== ANSWER ===\n" + answer)

if __name__ == "__main__":
    main()

2) Run it with your local Qwen model

Replace the GGUF path with your actual file:

python ~/Desktop/rag_answer.py \
  /path/to/Qwen3-6k-Instruct.Q8_0.gguf \
  "Summarize the Flower Lattice activation rule."

> If your llama.cpp binary lives elsewhere, set it:
> export LLAMA_BIN="~/ougway_env/llama.cpp/main"

This is fully offline: local embeddings → Postgres HNSW retrieval → local Qwen generation.

If you want a tighter loop next

• I can add a tiny --doc filter flag, --k, and a CSV export.
• We can also log whether the SentenceTransformer or the fallback is used (just for visibility).
• If you want to stick 100% with Qwen right away, we can bypass ST entirely by using a local 768-dim embedder you trust (drop-in).

When you’re ready, paste me your Qwen GGUF path and I’ll tailor the LLAMA_BIN/args exactly to your setup.