Crate yvdb 

yvdb (an intro vector DB)

Small, educational vector database: single-node, in-memory store with disk durability, IVF bucket search, and a TOON query response format for LLM-friendly output.

What is in this version

Piece	What it does
In-memory store	Fast reads/writes while the server is running (Store in RAM).
WAL (data/wal.log)	Append-only log of inserts; replayed on startup so inserts survive restarts.
Binary snapshots (data/snapshots/snapshot-*.bin)	Periodic full saves using bincode + a 4-byte TOON magic header for file integrity.
IVF index	K-means centroids + buckets in src/store/mod.rs; queries scan the nearest bucket when the collection is large enough.
REST API (Axum)	Three flat POST routes (see below).
TOON export	POST /vectors/query returns plain text using a TOON-style layout (see below).

What is TOON (Token-Oriented Object Notation)?

TOON stands for Token-Oriented Object Notation. It is a text format meant to carry the same kind of data as JSON, but with fewer repeated words so LLM prompts stay smaller and easier to read.

Global spec (verified)

There is an official, public spec (not invented only for yvdb):

• Specification: github.com/toon-format/spec (Working Draft v3.0, 2025-11-24)
• Reference tooling: github.com/toon-format/toon (TypeScript encode/decode)
• Docs / overview: toonformat.dev

That spec defines a full grammar: line-oriented layout, array headers with lengths and field lists, delimiters, indentation for objects, and rules for quoting. It targets uniform tables (many rows, same columns) and LLM-facing payloads.

What yvdb implements (query results only)

yvdb uses the same idea (declare column names once, then row values only) but a small, fixed layout for vector search results — not the full spec parser.

	Official TOON spec	yvdb query response
Used where	General JSON ↔ TOON conversion	Only POST /vectors/query response body
Header style	Normative headers like key[N]{field1,field2}:	Project banner + fields: id, score, metadata + ---
Row style	Spec delimiter rules (comma/tab/pipe)	Comma-separated values per line
Requests	Typically JSON in	JSON in (unchanged)

Example yvdb query output (this is what the server returns):

[yvdb_query_results]
fields: id, score, metadata
---
item_abc, 1.000, {"category":"sports"}

Same information as JSON, different packaging:

{"results":[{"id":"item_abc","score":1.0,"metadata":{"category":"sports"}}]}

If you need strict compatibility with toon-format/spec, use their encoder on JSON; yvdb does not run that encoder today.

Benefits (why bother?)

1. Fewer tokens for LLMs — Column names (id, score, metadata) appear once in fields:, not on every row. That can reduce cost and noise when you paste search hits into a chat model.
2. Readable tables — Humans see a clear header and one line per hit, similar to CSV with a declared schema.
3. Still structured — Rows stay ordered; scores and metadata remain machine-parseable (metadata is still JSON text on each line).
4. JSON where it fits — Create, insert, errors, and health routes stay JSON because tools and clients already expect that shape.

Not the same as snapshot TOON bytes

Binary files under data/snapshots/ start with the four ASCII bytes TOON as a file magic marker (like a signature). That is only for detecting valid snapshot files on disk. It is not the text format above and is not human-readable.

Run

Use two terminals while learning: one runs the server (blocking), the other sends HTTP requests.

Terminal 1 — server

cd C:\Users\rcabe\Coding\qdrant-exp\yvdb
cargo run

Wait for: listening on 127.0.0.1:8080

RUST_LOG is optional; the server defaults to info if unset.

# optional: change bind address
$env:YVDB_BIND_ADDR="127.0.0.1:8080"
cargo run

Terminal 2 — client (PowerShell)

Prefer ConvertTo-Json + Invoke-RestMethod / Invoke-WebRequest on Windows (avoids curl.exe quoting issues).

---

Quick start: full workflow

Each step shows JSON (what the server expects) and PowerShell (recommended on Windows).

1) Create collection

Registers namespace demo2, dimension 4, metric cosine.

JSON:

{"name":"demo2","dimension":4,"metric":"cosine"}

PowerShell:

$body = @{ name = "demo2"; dimension = 4; metric = "cosine" } | ConvertTo-Json -Compress
Invoke-RestMethod -Uri "http://localhost:8080/collection/create" -Method POST -ContentType "application/json" -Body $body

Response: {"created":"demo2"} or {"exists":"demo2"} if the name is already loaded from disk.

2) Insert vectors (+ WAL)

Each insert is appended to data/wal.log then applied to memory.

JSON:

{
  "collection": "demo2",
  "records": [
    {"id":"item_abc","vector":[0.15,0.22,0.34,0.89],"metadata":{"category":"sports"}}
  ]
}

PowerShell:

$body = @{
  collection = "demo2"
  records = @(
    @{
      id = "item_abc"
      vector = @(0.15, 0.22, 0.34, 0.89)
      metadata = @{ category = "sports" }
    }
  )
} | ConvertTo-Json -Depth 5 -Compress

Invoke-RestMethod -Uri "http://localhost:8080/vectors/insert" -Method POST -ContentType "application/json" -Body $body

Response: {"inserted":1}

Batch must have 1 to YVDB_MAX_BATCH records (default 1024); empty or oversized batches return invalid batch size.

Vector length must match the collection dimension (4 here) or the server returns vector dimension mismatch.

3) Query → TOON

JSON:

{"collection":"demo2","vector":[0.15,0.22,0.34,0.89],"k":3}

PowerShell:

$body = @{ collection = "demo2"; vector = @(0.15, 0.22, 0.34, 0.89); k = 3 } | ConvertTo-Json -Compress
(Invoke-WebRequest -Uri "http://localhost:8080/vectors/query" -Method POST -ContentType "application/json" -Body $body -UseBasicParsing).Content

Example response (text/plain):

[yvdb_query_results]
fields: id, score, metadata
---
item_abc, 1.000, {"category":"sports"}

Use -UseBasicParsing on Invoke-WebRequest to skip PowerShell’s HTML parsing warning.

4) View (discovery, health, version)

Read-only GET routes; no body required.

Path	Purpose
GET /	Lists endpoint paths
GET /healthz	Liveness, uptime, and per-collection stats (collections array)
GET /version	Crate name and version

PowerShell:

Invoke-RestMethod -Uri "http://localhost:8080/" -Method GET
Invoke-RestMethod -Uri "http://localhost:8080/healthz" -Method GET
Invoke-RestMethod -Uri "http://localhost:8080/version" -Method GET

5) Restart → query without insert (persistence test)

1. Terminal 1: Ctrl+C to stop the server, then cargo run again.
2. Terminal 2: Run only the query PowerShell block from step 3 (no create/insert).

If you still see item_abc, WAL and/or snapshots restored your data from data/.

---

HTTP API (active routes)

Method	Path	Purpose
POST	/collection/create	Create a collection (name, dimension, metric).
POST	/vectors/insert	Insert or update records (WAL + memory).
POST	/vectors/query	Top-k search; response body is TOON text.
GET	/	Discovery JSON (lists endpoints).
GET	/healthz	Health check (JSON: status, uptime_secs, collections).
HEAD	/healthz	Same liveness as GET, no response body.
GET	/version	Package name and version.

POST /collection/create

JSON:

{"name":"demo2","dimension":4,"metric":"cosine"}

PowerShell:

$body = @{ name = "demo2"; dimension = 4; metric = "cosine" } | ConvertTo-Json -Compress
Invoke-RestMethod -Uri "http://localhost:8080/collection/create" -Method POST -ContentType "application/json" -Body $body

Response: {"created":"demo2"} or {"exists":"demo2"}.

Metrics: cosine, l2 (or euclidean).

POST /vectors/insert

JSON:

{
  "collection": "demo2",
  "records": [
    {"id":"item_abc","vector":[0.15,0.22,0.34,0.89],"metadata":{"category":"sports"}}
  ]
}

PowerShell:

$body = @{
  collection = "demo2"
  records = @(
    @{
      id = "item_abc"
      vector = @(0.15, 0.22, 0.34, 0.89)
      metadata = @{ category = "sports" }
    }
  )
} | ConvertTo-Json -Depth 5 -Compress

Invoke-RestMethod -Uri "http://localhost:8080/vectors/insert" -Method POST -ContentType "application/json" -Body $body

Response: {"inserted":1}

Batch must have 1 to YVDB_MAX_BATCH records (default 1024); empty or oversized batches return invalid batch size.

Do not send a single top-level id / vector without collection and records (that is not this API).

POST /vectors/query

JSON:

{"collection":"demo2","vector":[0.15,0.22,0.34,0.89],"k":3}

PowerShell:

$body = @{ collection = "demo2"; vector = @(0.15, 0.22, 0.34, 0.89); k = 3 } | ConvertTo-Json -Compress
(Invoke-WebRequest -Uri "http://localhost:8080/vectors/query" -Method POST -ContentType "application/json" -Body $body -UseBasicParsing).Content

Response (text/plain):

[yvdb_query_results]
fields: id, score, metadata
---
item_abc, 1.000, {"category":"sports"}

GET view routes

Path	Response
GET /	JSON map of endpoint paths
GET /healthz	{"status":"ok","uptime_secs":...,"collections":[...]}
GET /version	{"name":"yvdb","version":"0.1.4"}

PowerShell:

Invoke-RestMethod -Uri "http://localhost:8080/" -Method GET
Invoke-RestMethod -Uri "http://localhost:8080/healthz" -Method GET
Invoke-RestMethod -Uri "http://localhost:8080/version" -Method GET

Errors (JSON)

{"code":"bad_request","message":"vector dimension mismatch"}

Codes: bad_request, not_found, internal, timeout, method_not_allowed, payload_too_large.

---

Data and durability

While the server runs, all collections live in RAM. On disk:

Path	Format	Role
data/wal.log	JSON Lines	Every insert is logged; replayed after snapshots on startup.
data/snapshots/snapshot-<unix>.bin	TOON (4 bytes) + bincode	Periodic full snapshot (default every 30s).

Startup order: load latest snapshot → replay WAL → serve requests.

Stopping the server clears memory but does not delete data/ unless you remove it manually.

Reset local data (fresh start):

Remove-Item -Force .\data\wal.log -ErrorAction SilentlyContinue
Remove-Item -Force .\data\snapshots\* -ErrorAction SilentlyContinue

Note: POST /collection/create only writes memory until a snapshot runs or you insert (inserts go to the WAL). For a clean persistence test, insert at least one record before restarting.

---

Architecture (how modules connect)

HTTP (Axum main.rs)
  → routes.rs handlers
    → Store (in-memory, src/store/mod.rs)
         → IVF: centroids + buckets (K-means when enough vectors)
    → Wal append on insert (src/persist/wal.rs)
  → snapshot.rs: bincode + TOON header → data/snapshots/*.bin

---

Windows: sending JSON reliably

Recommended: hashtable + ConvertTo-Json:

$body = @{ name = "demo2"; dimension = 4; metric = "cosine" } | ConvertTo-Json -Compress
Invoke-RestMethod -Uri "http://localhost:8080/collection/create" -Method POST -ContentType "application/json" -Body $body

Alternative: write a UTF-8 file (no BOM) and use curl.exe --data-binary "@file.json".

Avoid curl.exe -d "{\"name\":...}" in PowerShell without --%; inner quotes are often stripped.

---

Scoring

• score is larger-is-better.
• cosine: similarity in roughly [-1, 1] (1.0 = same direction).
• l2: score = -distance (closer vectors score higher).

k == 0 is rejected. k greater than the number of records returns all available matches.

---

Env variables

Variable	Default	Meaning
YVDB_DATA_DIR	data	WAL and snapshot folder.
YVDB_BIND_ADDR	127.0.0.1:8080	Listen address.
YVDB_MAX_DIMENSION	4096	Max vector length.
YVDB_MAX_BATCH	1024	Max records per insert request.
YVDB_MAX_K	1000	Max k per query.
YVDB_SNAPSHOT_INTERVAL_SECS	30	Snapshot interval.
YVDB_SNAPSHOT_RETENTION	3	Snapshots kept on disk.
YVDB_SNAPSHOT_ON_SHUTDOWN	false	Write a snapshot when the server exits.
YVDB_WAL_ROTATE_MAX_BYTES	0	WAL rotation size (0 = off).
YVDB_MAX_REQUEST_BYTES	1048576	Max HTTP body size.
YVDB_REQUEST_TIMEOUT_MS	2000	Request timeout.

---

Notes

• Educational project: not a distributed production database.
• Old nested routes (/collections/{name}/upsert, etc.) and the adaptive heartbeat (min_score relaxation) were removed in v0.1.2.
• IVF builds lazily after a collection has more than 32 vectors; smaller collections use flat search.

Modules

api

HTTP route handlers and request/response types for the REST API.

app_state

Shared Axum handler state: store, WAL, config, and server start time.

config

Environment-driven server limits and paths (YVDB_* variables).

persist

WAL append log and bincode snapshots for disk durability.

store

In-memory vector collections, IVF index, and similarity search.