Install and schema setup — Part 3

Yep—those errors are exactly what we expected:

• vector can only be created by superuser.
• You also need CREATE privilege on the database to create schemas.

We’ll fix both, then re-run your schema as darren. One step at a time.

Step 1 — Create the extensions as superuser

bash

sudo -u postgres psql -d tokenspace -c "CREATE EXTENSION IF NOT EXISTS vector;"

bash

sudo -u postgres psql -d tokenspace -c "CREATE EXTENSION IF NOT EXISTS pg_trgm;"

(If either says it already exists, that’s fine.)

Step 2 — Make sure you own the DB and can create schemas

bash

sudo -u postgres psql -c "ALTER DATABASE tokenspace OWNER TO darren;"

bash

sudo -u postgres psql -d tokenspace -c "GRANT CREATE ON DATABASE tokenspace TO darren;"

Step 3 — Apply the schema again as darren

bash

psql -U darren -d tokenspace

psql

\pset pager off
\i '/home/darren/Desktop/TokenSpace Database Schema 768dim final.txt'

If anything errors again, paste the last 10–15 lines and we’ll fix the next permission quickly.

🎉 Excellent — clean PostgreSQL, pgvector in place, database renamed to tokenspace, and your full schema applied as your own role (darren). That’s exactly what we wanted.

At this point, your database is ready to ingest and query locally — no outside services required. ✅

Next logical steps (when you’re ready)

1. Quick sanity check
   Run this inside psql:
   sql
\dn
\dt content.*
\dt token.*
\dt cog.*
\dt lat.*
   That will show all four schemas and the tables inside each. You should see dozens of objects created.

2. First ingestion test
   We can wire up a tiny Python script to:
   – load a 768-dim embedding model (local, like nomic-ai/nomic-embed-text-v1.5),
   – encode a sentence,
   – insert it into content.chunks.

3. Query sanity
   Then do a quick nearest-neighbors search with HNSW to make sure retrieval works.

Do you want me to prepare the sanity check queries now so you can quickly see all your tables and confirm the schema is live?

Hell yes — that’s a clean build. 🙌

Let’s keep it truly one step at a time.

Next step: confirm the HNSW vector indexes exist

At the tokenspace=> prompt, run this single command:

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

Paste the output, and I’ll tell you if we need to create any indexes (one-by-one) or if we’re good to move on to a tiny ingest test.

That’s exactly what we wanted to see — all six vector indexes are there and online. ✅

What HNSW means

HNSW = Hierarchical Navigable Small World graph.

It’s a clever algorithm for fast approximate nearest-neighbor search.

• Imagine you have thousands or millions of embeddings (each a 768-dimensional vector).
• If you search them naively, you’d compare your query vector to every single row → slow.
• With HNSW, Postgres organizes them into a graph structure that makes “who’s closest to me?” queries extremely fast — like skipping through a map rather than walking every street.
• pgvector implements HNSW as an index type, so you can do queries like:

sql
SELECT chunk_id, text
FROM content.chunks
ORDER BY embedding <=> '[0.01, 0.02, ...]'::vector
LIMIT 5;
and it will return the nearest embeddings in milliseconds instead of minutes.

So right now, your tokenspace DB can:
– Store embeddings (vector(768) fields).
– Search them quickly with HNSW indexes.
– Keep them organized across content, tokens, cognition, and lattice.

👉 Next, we can do a tiny ingestion test: insert one sentence with an embedding, then search it back to prove the pipeline works.

Do you want me to prep that minimal ingestion test script (Python + SQL), or would you like to try just manually inserting a fake vector first to see it in action?

Yep—let’s do it super step-by-step.

Step 1 — leave psql

At the tokenspace=> prompt, type:

\q

Tell me when you’re back at a $ bash prompt, and I’ll give you the next single step.

Perfect. We’ll do a tiny, fully offline ingestion test—no external downloads, no models. We’ll use a deterministic local function to turn text into a 768-dim vector (via hashing) just to prove the DB + HNSW pipeline works. Then later you can swap in your local model.

We’ll go one step at a time.

Step 1 — activate your Python venv

bash

source ~/ougway_env/venv/bin/activate

(Your prompt should show (venv) on the left. If that errors, tell me and I’ll adjust.)

Step 2 — install the two Python deps we need

bash

pip install --upgrade pip
pip install psycopg2-binary numpy

Step 3 — set DB connection env vars (temporary, for this shell only)

> You can change the password string if you changed it earlier.
bash

export TS_DSN="dbname=tokenspace user=darren host=localhost password=LittleOugway"

Step 4 — create the test script

bash

nano ~/Desktop/ingest_test.py

Paste this entire file, then save (Ctrl+O, Enter) and exit (Ctrl+X):

#!/usr/bin/env python3
import os, sys, time, hashlib
from math import sqrt
import numpy as np
import psycopg2
import psycopg2.extras

DSN = os.environ.get("TS_DSN", "dbname=tokenspace user=darren host=localhost")

def text2vec768(text: str) -> np.ndarray:
    """
    Pure-local, deterministic 768-dim embedding using a hash seed.
    This is just for pipeline testing (not a real semantic model).
    """
    h = hashlib.sha256(text.encode("utf-8")).digest()
    seed = int.from_bytes(h[:8], "big", signed=False) % (2**31-1)
    rng = np.random.default_rng(seed)
    v = rng.normal(loc=0.0, scale=1.0, size=768).astype(np.float32)
    # L2-normalize
    n = float(np.linalg.norm(v))
    if n > 0:
        v /= n
    return v

def vec_literal(v: np.ndarray) -> str:
    """Format as pgvector literal: '[0.1, 0.2, ...]'"""
    return "[" + ",".join(f"{x:.6f}" for x in v.tolist()) + "]"

def main():
    doc_title = "Test Doc: Flower Lattice"
    chunk_text = "The Flower Lattice activation rule: align nodes on 3-6-9 and apply phase-locked rotation."
    query_text = "What is the activation rule of the Flower Lattice?"

    # compute vectors
    v_chunk = text2vec768(chunk_text)
    v_query = text2vec768(query_text)

    print("Chunk vector dim:", v_chunk.shape[0])
    print("Query vector dim:", v_query.shape[0])

    # connect
    conn = psycopg2.connect(DSN)
    conn.autocommit = False
    cur = conn.cursor()

    try:
        # Insert a source (manual), document, and one chunk
        cur.execute("""
            INSERT INTO content.sources(kind, uri, meta)
            VALUES('manual', NULL, '{}'::jsonb)
            RETURNING source_id;
        """)
        source_id = cur.fetchone()[0]

        cur.execute("""
            INSERT INTO content.documents(source_id, external_id, title, authored_at, meta)
            VALUES(%s, NULL, %s, now(), '{}'::jsonb)
            RETURNING doc_id;
        """, (source_id, doc_title))
        doc_id = cur.fetchone()[0]

        cur.execute(f"""
            INSERT INTO content.chunks(doc_id, seq, text, token_count, embedding, lang, tags, meta)
            VALUES (%s, %s, %s, %s, %s::vector, 'en', ARRAY['test'], '{{}}'::jsonb)
            RETURNING chunk_id;
        """, (
            doc_id, 0, chunk_text, len(chunk_text.split()),
            vec_literal(v_chunk)
        ))
        chunk_id = cur.fetchone()[0]

        conn.commit()
        print(f"Inserted chunk_id={chunk_id} in doc_id={doc_id} (source_id={source_id})")

        # simple KNN query using the query vector
        cur.execute(f"""
            SELECT chunk_id, left(text, 120) AS preview,
                   embedding <=> %s::vector AS dist
            FROM content.chunks
            ORDER BY dist ASC
            LIMIT 3;
        """, (vec_literal(v_query),))

        rows = cur.fetchall()
        print("\nTop matches:")
        for r in rows:
            print(f"- chunk_id={r[0]}, dist={r[2]:.4f}, text='{r[1]}'")

    except Exception as e:
        conn.rollback()
        print("ERROR:", e, file=sys.stderr)
        raise
    finally:
        cur.close()
        conn.close()

if __name__ == "__main__":
    main()

Step 5 — run it

bash

python ~/Desktop/ingest_test.py

Expected:
– It prints Chunk vector dim: 768, Query vector dim: 768.
– Shows an Inserted chunk_id=... line.
– Then prints “Top matches” with your inserted chunk as the nearest hit (dist small).

Paste the last ~10 lines of output you see. If anything errors, I’ll fix it right away.

haha yesss — “face-locked rotation” achieved 😄

That output means the end-to-end pipeline (insert → HNSW query) works. The 0.9835 “dist” is high only because we used a quick hash-based fake embedding; it’s just to prove the plumbing.

Wanna keep it one step at a time? Here’s the next tiny step to confirm Postgres is actually using the HNSW index for KNN:

Step — verify the HNSW index is used

bash

psql -U darren -d tokenspace

psql

EXPLAIN (ANALYZE, BUFFERS)
SELECT chunk_id, left(text,120) AS preview
FROM content.chunks
ORDER BY embedding <=> '[0.0,0.0,0.0]'::vector
LIMIT 5;

You should see a plan line like Index Scan using chunks_embed_hnsw on content.chunks …. Paste the first ~10 lines of that plan and we’ll confirm it’s good.

If that looks good, we can choose what you want to do next:

• Option A (stay fully offline now): keep using the test hasher so you can wire up the rest of the RAG flow (documents → chunks → retrieval).
• Option B (upgrade embeddings locally): switch the test script to a real local 768-dim model (still offline once the model files are on disk). I’ll give you paste-ready steps either to (1) download once and cache, or (2) point to a local folder you’ve already copied over.

Good catch — that error is just because we gave Postgres a 3-dimensional vector literal ([0.0,0.0,0.0]) but your column is 768-dimensional. Let’s use an existing embedding so the dimensions match.

Try this (at the tokenspace=> prompt)

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 plan line like:

Index Scan using chunks_embed_hnsw on content.chunks ...

Paste the first ~10 lines of the plan if you want me to confirm.

FYI: how to make a 768-dim zero vector (if you ever need it)

EXPLAIN (ANALYZE, BUFFERS)
SELECT chunk_id, left(text,120) AS preview
FROM content.chunks
ORDER BY embedding <=> array_fill(0.0::float4, ARRAY[768])::vector
LIMIT 5;

(That builds a 768-length float array and casts it to vector.)

Perfect. We’ll do a test ingestion of ~/Desktop/chats.txt using the same offline hash-based 768-dim embedding (no outside services). One step at a time.

Step 1 — make sure the file is there

bash

ls -lh ~/Desktop/chats.txt

If that shows the file, we’ll create a tiny ingester next.

Love it—let’s ingest ~/Desktop/chats.txt fully offline. We’ll try your local 768-dim SentenceTransformer if it’s already cached; if not, we’ll automatically fall back to a deterministic hash embed (still 768-dim) so nothing reaches outside.

We’ll go one step at a time.

1) (You’re already in the venv) Set DB connection + offline flag

bash

export TS_DSN="dbname=tokenspace user=darren host=localhost password=LittleOugway"
export HF_HUB_OFFLINE=1

> HF_HUB_OFFLINE=1 forces SentenceTransformers to use local cache only.

2) Create the ingester script

bash

nano ~/Desktop/ingest_chats.py

Paste this entire script, then Ctrl+O, Enter, Ctrl+X:

#!/usr/bin/env python3
import os, sys, hashlib, math, pathlib, time
from typing import List, Tuple
import numpy as np
import psycopg2

DSN = os.environ.get("TS_DSN", "dbname=tokenspace user=darren host=localhost")

# --- Try local SentenceTransformer (no network). Fallback to hash embedding. ---
_EMBED_MODEL = os.environ.get("EMBED_MODEL", "nomic-ai/nomic-embed-text-v1.5")  # 768-dim
_USE_ST = False
try:
    from sentence_transformers import SentenceTransformer
    _st_model = SentenceTransformer(_EMBED_MODEL, trust_remote_code=True)
    if hasattr(_st_model, "get_sentence_embedding_dimension"):
        dim = _st_model.get_sentence_embedding_dimension()
        _USE_ST = (dim == 768)
    else:
        _USE_ST = False
except Exception as e:
    _USE_ST = False

def embed_texts(texts: List[str]) -> np.ndarray:
    """Return (n,768) float32 embeddings, offline only."""
    if _USE_ST:
        vecs = _st_model.encode(texts, normalize_embeddings=True)
        vecs = np.asarray(vecs, dtype=np.float32)
        if vecs.shape[1] != 768:
            raise ValueError(f"Model dim {vecs.shape[1]} != 768")
        return vecs
    # fallback: deterministic 768-d hash “embedding” (not semantic, just plumbing)
    out = np.zeros((len(texts), 768), dtype=np.float32)
    for i, t in enumerate(texts):
        h = hashlib.sha256(t.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
        out[i] = v
    return out

# --- Simple chunking: paragraphs -> soft wrap to ~900 chars with 150 overlap ---
def para_split(text: str) -> List[str]:
    parts, buf = [], []
    for line in text.splitlines():
        if line.strip() == "":
            if buf:
                parts.append("\n".join(buf).strip())
                buf = []
        else:
            buf.append(line)
    if buf:
        parts.append("\n".join(buf).strip())
    return [p for p in parts if p]

def wrap_chunks(paras: List[str], max_chars=900, overlap=150) -> List[str]:
    chunks = []
    for p in paras:
        if len(p) <= max_chars:
            chunks.append(p)
        else:
            start = 0
            while start < len(p):
                end = min(len(p), start + max_chars)
                chunks.append(p[start:end])
                if end == len(p): break
                start = max(0, end - overlap)
    return chunks

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

def main():
    if len(sys.argv) != 2:
        print("Usage: ingest_chats.py /path/to/chats.txt", file=sys.stderr)
        sys.exit(1)

    path = pathlib.Path(sys.argv[1]).expanduser().resolve()
    if not path.exists():
        print(f"File not found: {path}", file=sys.stderr)
        sys.exit(2)

    text = path.read_text(encoding="utf-8", errors="ignore")
    paras = para_split(text)
    chunks = wrap_chunks(paras, max_chars=900, overlap=150)
    print(f"File: {path.name}  | paragraphs: {len(paras)}  | chunks: {len(chunks)}")
    if not chunks:
        print("Nothing to ingest.", file=sys.stderr); sys.exit(3)

    # Batch embed for memory friendliness
    batch = 64
    embeddings = []
    for i in range(0, len(chunks), batch):
        batch_texts = chunks[i:i+batch]
        embeddings.append(embed_texts(batch_texts))
        print(f" Embedded {min(i+batch, len(chunks))}/{len(chunks)}", end="\r")
    print()
    emb = np.vstack(embeddings).astype(np.float32)

    # Connect and insert
    conn = psycopg2.connect(DSN)
    conn.autocommit = False
    cur = conn.cursor()
    try:
        # source + document
        cur.execute("""
            INSERT INTO content.sources(kind, uri, meta)
            VALUES('file', %s, '{}'::jsonb)
            RETURNING source_id;
        """, (str(path),))
        source_id = cur.fetchone()[0]

        cur.execute("""
            INSERT INTO content.documents(source_id, external_id, title, authored_at, meta)
            VALUES(%s, %s, %s, now(), '{}'::jsonb)
            RETURNING doc_id;
        """, (source_id, None, path.name))
        doc_id = cur.fetchone()[0]

        # insert chunks
        for seq, (txt, vec) in enumerate(zip(chunks, emb)):
            cur.execute(f"""
                INSERT INTO content.chunks
                    (doc_id, seq, text, token_count, embedding, lang, tags, meta)
                VALUES
                    (%s, %s, %s, %s, %s::vector, 'en', ARRAY['chat'], '{{}}'::jsonb);
            """, (doc_id, seq, txt, len(txt.split()), vec_literal(vec)))
            if (seq+1) % 200 == 0:
                conn.commit()
                print(f" Inserted {seq+1}/{len(chunks)} rows")

        conn.commit()
        print(f"Done. doc_id={doc_id}, total chunks={len(chunks)}")

        # quick KNN sanity: use the first chunk as a query
        cur.execute("""
            SELECT chunk_id, left(text,120) AS preview,
                   embedding <=> (SELECT embedding FROM content.chunks WHERE doc_id=%s AND seq=0) AS dist
            FROM content.chunks
            WHERE doc_id=%s
            ORDER BY dist ASC
            LIMIT 5;
        """, (doc_id, doc_id))
        rows = cur.fetchall()
        print("\nNearest to chunk 0:")
        for cid, prev, d in rows:
            print(f"- chunk_id={cid}, dist={d:.4f}, text='{prev}'")

    except Exception as e:
        conn.rollback()
        raise
    finally:
        cur.close()
        conn.close()

if __name__ == "__main__":
    main()

3) Run it on your chats file

bash

python ~/Desktop/ingest_chats.py ~/Desktop/chats.txt

What you should see:
– A line like File: chats.txt | paragraphs: N | chunks: M
– Progress embedding
– Inserts with occasional “Inserted … rows”
– Final Done. doc_id=…, total chunks=…
– A short “Nearest to chunk 0” list

If you see an error about sentence_transformers, the script will fall back automatically to the hash embedding (you’ll still get 768-d vectors and the ingest will succeed). If anything else barks, paste the last ~15 lines and I’ll adjust the script immediately.

That’s a huge win 🎉

✅ You got 75k+ chunks safely into tokenspace with local 768-dim vectors
✅ Ingestion pipeline runs end-to-end, fully offline
✅ DB integrity (HNSW index + schema) held up without errors

The reason you’re seeing dist=0.0000 and duplicates like “You said:” is that the fallback hash embed (if SentenceTransformers wasn’t available) produces perfectly reproducible vectors for repeated short strings—so identical phrases collapse to identical embeddings. That’s expected for hash-based mode.

👉 If your SentenceTransformer model did run (sometimes it logs Vector shape: (1, 768) when loaded), then you’d see small non-zero distances. Either way, the plumbing is now proven solid.

Next steps we can do (when you’re ready):

1. Confirm embedding mode:
   – Check if your run used ST or fallback (we can log that explicitly).
2. Test retrieval on real questions:
   sql
SELECT chunk_id, left(text,200), embedding <=> '[...]'::vector
FROM content.chunks
ORDER BY embedding <=> '[...]'::vector
LIMIT 5;
   (with an actual query vector).
3. Build a query wrapper script: Python CLI to let you type a question and get back top-N chunks.
4. Index maintenance: ANALYZE; and vacuuming for performance.

So yes—it’s good to go. 🚀

Want me to help you add a simple search.py script so you can type a question (like “What did we say about the Flower Lattice?”) and see what comes back?

Ah, I see what happened — in your SQL you literally pasted [...], but Postgres expects a real numeric vector (all 768 floats). That’s why it said invalid input syntax.

You’ve got two ways to test:

1. Use a trivial numeric vector directly in SQL

For example, a 768-dim zero vector (shortened here to save space):

SELECT chunk_id, left(text,200), embedding <=> array_fill(0.0::real, ARRAY[768])::vector
FROM content.chunks
ORDER BY embedding <=> array_fill(0.0::real, ARRAY[768])::vector
LIMIT 5;

This uses array_fill to generate 768 zeros and casts to vector. That’ll return something, but it’s not meaningful search—just a sanity check.

2. Do a real semantic query through Python

The normal way is to embed your question with the same SentenceTransformer you used at ingestion. Example snippet:

from sentence_transformers import SentenceTransformer
import psycopg2

# connect
conn = psycopg2.connect(dbname="tokenspace", user="darren", password="LittleOugway", host="localhost")
cur = conn.cursor()

# load same model as ingestion
model = SentenceTransformer("sentence-transformers/all-MiniLM-L6-v2")

# turn query into vector
query = "What did we say about the Flower Lattice?"
qvec = model.encode([query])[0].tolist()

# SQL search
cur.execute("""
    SELECT chunk_id, left(text,200), embedding <=> %s::vector AS dist
    FROM content.chunks
    ORDER BY embedding <=> %s::vector
    LIMIT 5;
""", (qvec, qvec))

for row in cur.fetchall():
    print(row)

That will actually give you closest matching chunks by meaning.

So:
– If you just want to test the DB mechanics → use the array_fill(0.0, [768]) SQL trick.
– If you want a real search → we should run it through Python with SentenceTransformers.

👉 Do you want me to write you a ready-to-run search.py script so you can query your tokenspace interactively from the terminal?

Great progress. Two quick things:

Why you saw NaN

You compared against a zero vector. Cosine distance with a zero vector is undefined → NaN. Easy fix: compare against any real 768-dim embedding from your table (or embed a real query via Python).

Quick SQL sanity (uses an existing embedding so it’s non-zero)

At the tokenspace=> prompt run this one query:

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 Index Scan using chunks_embed_hnsw ... in the plan, confirming the HNSW index is used.

Real search (offline) — script that matches your ingestion embedder

We’ll query using the same embedding function as your ingester: try local SentenceTransformers with HF_HUB_OFFLINE=1, else fall back to the deterministic hash embedding. One step at a time.

1) Make sure you’re in your venv

bash

source ~/ougway_env/venv/bin/activate

2) Set env (offline + DB)

bash

export TS_DSN="dbname=tokenspace user=darren host=localhost password=LittleOugway"
export HF_HUB_OFFLINE=1
export EMBED_MODEL="nomic-ai/nomic-embed-text-v1.5"

3) Create the search script

bash

nano ~/Desktop/search_chats.py

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

#!/usr/bin/env python3
import os, sys, 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")

# Try local SentenceTransformer (offline). Fallback to hash embedding.
_USE_ST = False
try:
    from sentence_transformers import SentenceTransformer
    _st_model = SentenceTransformer(EMBED_MODEL, trust_remote_code=True)
    dim = getattr(_st_model, "get_sentence_embedding_dimension", lambda: None)()
    _USE_ST = (dim == 768)
except Exception:
    _USE_ST = False

def embed_query(text: str) -> np.ndarray:
    if _USE_ST:
        v = _st_model.encode([text], normalize_embeddings=True)
        v = np.asarray(v, dtype=np.float32)
        if v.shape[1] != 768:
            raise ValueError(f"Model dim {v.shape[1]} != 768")
        return v[0]
    # deterministic hash-based 768-d vector (offline, plumbing only)
    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 main():
    if len(sys.argv) < 2:
        print("Usage: search_chats.py 'your question here' [limit]", file=sys.stderr)
        sys.exit(1)
    query = sys.argv[1]
    limit = int(sys.argv[2]) if len(sys.argv) > 2 else 5

    qv = embed_query(query)
    qlit = vec_literal(qv)

    conn = psycopg2.connect(DSN)
    cur = conn.cursor()
    cur.execute(f"""
        SELECT chunk_id, left(text, 200) AS preview,
               embedding <=> %s::vector AS dist
        FROM content.chunks
        ORDER BY dist ASC
        LIMIT %s;
    """, (qlit, limit))
    rows = cur.fetchall()
    cur.close(); conn.close()

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

if __name__ == "__main__":
    main()

4) Run a search

bash

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

You’ll get top matches with distances. If SentenceTransformers isn’t cached locally, it’ll silently use the hash fallback (still 768-dim, still offline). Later, if you want semantic results, we can place the model files on disk and it’ll use them automatically (no external calls).

If you want, we can also log which embedder was used (ST vs hash) with a one-line print—just say the word.