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WIP2: implementing lancedb: created embedding abstraction, server-side per-dataset embedding config + updates RPC endpoints

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This commit is contained in:
Maxime Van Hees
2025-09-29 13:17:34 +02:00
parent 6a4e2819bf
commit cf66f4c304
6 changed files with 595 additions and 99 deletions
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186
src/cmd.rs
View File

@@ -1,4 +1,4 @@
use crate::{error::DBError, protocol::Protocol, server::Server};
use crate::{error::DBError, protocol::Protocol, server::Server, embedding::{EmbeddingConfig, EmbeddingProvider}};
use tokio::time::{timeout, Duration};
use futures::future::select_all;
@@ -127,20 +127,20 @@ pub enum Cmd {
reducers: Vec<String>,
},
// LanceDB vector search commands
// LanceDB text-first commands (no user-provided vectors)
LanceCreate {
name: String,
dim: usize,
},
LanceStore {
LanceStoreText {
name: String,
id: String,
vector: Vec<f32>,
text: String,
meta: Vec<(String, String)>,
},
LanceSearch {
LanceSearchText {
name: String,
vector: Vec<f32>,
text: String,
k: usize,
filter: Option<String>,
return_fields: Option<Vec<String>>,
@@ -150,6 +150,16 @@ pub enum Cmd {
index_type: String,
params: Vec<(String, String)>,
},
// Embedding configuration per dataset
LanceEmbeddingConfigSet {
name: String,
provider: String,
model: String,
params: Vec<(String, String)>,
},
LanceEmbeddingConfigGet {
name: String,
},
LanceList,
LanceInfo {
name: String,
@@ -862,9 +872,9 @@ impl Cmd {
Cmd::LanceCreate { name, dim }
}
"lance.store" => {
// LANCE.STORE name ID id VECTOR v1 v2 ... [META k v ...]
// LANCE.STORE name ID <id> TEXT <text> [META k v ...]
if cmd.len() < 6 {
return Err(DBError("ERR LANCE.STORE requires: name ID <id> VECTOR v1 v2 ... [META k v ...]".to_string()));
return Err(DBError("ERR LANCE.STORE requires: name ID <id> TEXT <text> [META k v ...]".to_string()));
}
let name = cmd[1].clone();
let mut i = 2;
@@ -873,16 +883,16 @@ impl Cmd {
}
let id = cmd[i + 1].clone();
i += 2;
if i >= cmd.len() || cmd[i].to_uppercase() != "VECTOR" {
return Err(DBError("ERR LANCE.STORE requires VECTOR <f32...>".to_string()));
if i >= cmd.len() || cmd[i].to_uppercase() != "TEXT" {
return Err(DBError("ERR LANCE.STORE requires TEXT <text>".to_string()));
}
i += 1;
let mut vector: Vec<f32> = Vec::new();
while i < cmd.len() && cmd[i].to_uppercase() != "META" {
let v: f32 = cmd[i].parse().map_err(|_| DBError("ERR vector element must be a float32".to_string()))?;
vector.push(v);
i += 1;
if i >= cmd.len() {
return Err(DBError("ERR LANCE.STORE requires TEXT <text>".to_string()));
}
let text = cmd[i].clone();
i += 1;
let mut meta: Vec<(String, String)> = Vec::new();
if i < cmd.len() && cmd[i].to_uppercase() == "META" {
i += 1;
@@ -891,28 +901,28 @@ impl Cmd {
i += 2;
}
}
Cmd::LanceStore { name, id, vector, meta }
Cmd::LanceStoreText { name, id, text, meta }
}
"lance.search" => {
// LANCE.SEARCH name K k VECTOR v1 v2 ... [FILTER expr] [RETURN n fields...]
// LANCE.SEARCH name K <k> QUERY <text> [FILTER expr] [RETURN n fields...]
if cmd.len() < 6 {
return Err(DBError("ERR LANCE.SEARCH requires: name K <k> VECTOR v1 v2 ... [FILTER expr] [RETURN n fields...]".to_string()));
return Err(DBError("ERR LANCE.SEARCH requires: name K <k> QUERY <text> [FILTER expr] [RETURN n fields...]".to_string()));
}
let name = cmd[1].clone();
if cmd[2].to_uppercase() != "K" {
return Err(DBError("ERR LANCE.SEARCH requires K <k>".to_string()));
}
let k: usize = cmd[3].parse().map_err(|_| DBError("ERR K must be an integer".to_string()))?;
if cmd[4].to_uppercase() != "VECTOR" {
return Err(DBError("ERR LANCE.SEARCH requires VECTOR <f32...>".to_string()));
if cmd[4].to_uppercase() != "QUERY" {
return Err(DBError("ERR LANCE.SEARCH requires QUERY <text>".to_string()));
}
let mut i = 5;
let mut vector: Vec<f32> = Vec::new();
while i < cmd.len() && !["FILTER","RETURN"].contains(&cmd[i].to_uppercase().as_str()) {
let v: f32 = cmd[i].parse().map_err(|_| DBError("ERR vector element must be a float32".to_string()))?;
vector.push(v);
i += 1;
if i >= cmd.len() {
return Err(DBError("ERR LANCE.SEARCH requires QUERY <text>".to_string()));
}
let text = cmd[i].clone();
i += 1;
let mut filter: Option<String> = None;
let mut return_fields: Option<Vec<String>> = None;
while i < cmd.len() {
@@ -942,7 +952,7 @@ impl Cmd {
_ => { i += 1; }
}
}
Cmd::LanceSearch { name, vector, k, filter, return_fields }
Cmd::LanceSearchText { name, text, k, filter, return_fields }
}
"lance.createindex" => {
// LANCE.CREATEINDEX name TYPE t [PARAM k v ...]
@@ -962,6 +972,60 @@ impl Cmd {
}
Cmd::LanceCreateIndex { name, index_type, params }
}
"lance.embedding" => {
// LANCE.EMBEDDING CONFIG SET name PROVIDER p MODEL m [PARAM k v ...]
// LANCE.EMBEDDING CONFIG GET name
if cmd.len() < 3 || cmd[1].to_uppercase() != "CONFIG" {
return Err(DBError("ERR LANCE.EMBEDDING requires CONFIG subcommand".to_string()));
}
if cmd.len() >= 4 && cmd[2].to_uppercase() == "SET" {
if cmd.len() < 8 {
return Err(DBError("ERR LANCE.EMBEDDING CONFIG SET requires: SET name PROVIDER p MODEL m [PARAM k v ...]".to_string()));
}
let name = cmd[3].clone();
let mut i = 4;
let mut provider: Option<String> = None;
let mut model: Option<String> = None;
let mut params: Vec<(String, String)> = Vec::new();
while i < cmd.len() {
match cmd[i].to_uppercase().as_str() {
"PROVIDER" => {
if i + 1 >= cmd.len() {
return Err(DBError("ERR PROVIDER requires a value".to_string()));
}
provider = Some(cmd[i + 1].clone());
i += 2;
}
"MODEL" => {
if i + 1 >= cmd.len() {
return Err(DBError("ERR MODEL requires a value".to_string()));
}
model = Some(cmd[i + 1].clone());
i += 2;
}
"PARAM" => {
i += 1;
while i + 1 < cmd.len() {
params.push((cmd[i].clone(), cmd[i + 1].clone()));
i += 2;
}
}
_ => {
// Unknown token; break to avoid infinite loop
i += 1;
}
}
}
let provider = provider.ok_or_else(|| DBError("ERR missing PROVIDER".to_string()))?;
let model = model.ok_or_else(|| DBError("ERR missing MODEL".to_string()))?;
Cmd::LanceEmbeddingConfigSet { name, provider, model, params }
} else if cmd.len() == 4 && cmd[2].to_uppercase() == "GET" {
let name = cmd[3].clone();
Cmd::LanceEmbeddingConfigGet { name }
} else {
return Err(DBError("ERR LANCE.EMBEDDING CONFIG supports: SET ... | GET name".to_string()));
}
}
"lance.list" => {
if cmd.len() != 1 {
return Err(DBError("ERR LANCE.LIST takes no arguments".to_string()));
@@ -1070,8 +1134,10 @@ impl Cmd {
| Cmd::Command(..)
| Cmd::Info(..)
| Cmd::LanceCreate { .. }
| Cmd::LanceStore { .. }
| Cmd::LanceSearch { .. }
| Cmd::LanceStoreText { .. }
| Cmd::LanceSearchText { .. }
| Cmd::LanceEmbeddingConfigSet { .. }
| Cmd::LanceEmbeddingConfigGet { .. }
| Cmd::LanceCreateIndex { .. }
| Cmd::LanceList
| Cmd::LanceInfo { .. }
@@ -1104,8 +1170,10 @@ impl Cmd {
if !is_lance_backend {
match &self {
Cmd::LanceCreate { .. }
| Cmd::LanceStore { .. }
| Cmd::LanceSearch { .. }
| Cmd::LanceStoreText { .. }
| Cmd::LanceSearchText { .. }
| Cmd::LanceEmbeddingConfigSet { .. }
| Cmd::LanceEmbeddingConfigGet { .. }
| Cmd::LanceCreateIndex { .. }
| Cmd::LanceList
| Cmd::LanceInfo { .. }
@@ -1249,18 +1317,66 @@ impl Cmd {
Err(e) => Ok(Protocol::err(&e.0)),
}
}
Cmd::LanceStore { name, id, vector, meta } => {
Cmd::LanceEmbeddingConfigSet { name, provider, model, params } => {
if !server.has_write_permission() {
return Ok(Protocol::err("ERR write permission denied"));
}
let meta_map: std::collections::HashMap<String, String> = meta.into_iter().collect();
match server.lance_store()?.store_vector(&name, &id, vector, meta_map).await {
// Map provider string to enum
let p_lc = provider.to_lowercase();
let prov = match p_lc.as_str() {
"test-hash" | "testhash" => EmbeddingProvider::TestHash,
"fastembed" | "lancefastembed" => EmbeddingProvider::LanceFastEmbed,
"openai" | "lanceopenai" => EmbeddingProvider::LanceOpenAI,
other => EmbeddingProvider::LanceOther(other.to_string()),
};
let cfg = EmbeddingConfig {
provider: prov,
model,
params: params.into_iter().collect(),
};
match server.set_dataset_embedding_config(&name, &cfg) {
Ok(()) => Ok(Protocol::SimpleString("OK".to_string())),
Err(e) => Ok(Protocol::err(&e.0)),
}
}
Cmd::LanceSearch { name, vector, k, filter, return_fields } => {
match server.lance_store()?.search_vectors(&name, vector, k, filter, return_fields).await {
Cmd::LanceEmbeddingConfigGet { name } => {
match server.get_dataset_embedding_config(&name) {
Ok(cfg) => {
let mut arr = Vec::new();
arr.push(Protocol::BulkString("provider".to_string()));
arr.push(Protocol::BulkString(match cfg.provider {
EmbeddingProvider::TestHash => "test-hash".to_string(),
EmbeddingProvider::LanceFastEmbed => "lancefastembed".to_string(),
EmbeddingProvider::LanceOpenAI => "lanceopenai".to_string(),
EmbeddingProvider::LanceOther(ref s) => s.clone(),
}));
arr.push(Protocol::BulkString("model".to_string()));
arr.push(Protocol::BulkString(cfg.model.clone()));
arr.push(Protocol::BulkString("params".to_string()));
arr.push(Protocol::BulkString(serde_json::to_string(&cfg.params).unwrap_or_else(|_| "{}".to_string())));
Ok(Protocol::Array(arr))
}
Err(e) => Ok(Protocol::err(&e.0)),
}
}
Cmd::LanceStoreText { name, id, text, meta } => {
if !server.has_write_permission() {
return Ok(Protocol::err("ERR write permission denied"));
}
// Resolve embedder and embed text
let embedder = server.get_embedder_for(&name)?;
let vector = embedder.embed(&text)?;
let meta_map: std::collections::HashMap<String, String> = meta.into_iter().collect();
match server.lance_store()?.store_vector(&name, &id, vector, meta_map, Some(text)).await {
Ok(()) => Ok(Protocol::SimpleString("OK".to_string())),
Err(e) => Ok(Protocol::err(&e.0)),
}
}
Cmd::LanceSearchText { name, text, k, filter, return_fields } => {
// Resolve embedder and embed query text
let embedder = server.get_embedder_for(&name)?;
let qv = embedder.embed(&text)?;
match server.lance_store()?.search_vectors(&name, qv, k, filter, return_fields).await {
Ok(results) => {
// Encode as array of [id, score, [k1, v1, k2, v2, ...]]
let mut arr = Vec::new();

138
src/embedding.rs Normal file
View File

@@ -0,0 +1,138 @@
// Embedding abstraction and minimal providers.
//
// This module defines a provider-agnostic interface to produce vector embeddings
// from text, so callers never need to supply vectors manually. It includes:
// - Embedder trait
// - EmbeddingProvider and EmbeddingConfig (serde-serializable)
// - TestHashEmbedder: deterministic, CPU-only, no-network embedder suitable for CI
// - Factory create_embedder(..) to instantiate an embedder from config
//
// Integration plan:
// - Server will resolve per-dataset EmbeddingConfig from sidecar JSON and cache Arc<dyn Embedder>
// - LanceStore will call embedder.embed(text) then persist id, vector, text, meta
//
// Note: Real LanceDB-backed embedding providers can be added by implementing Embedder
// and extending create_embedder(..). This file keeps no direct dependency on LanceDB.
use std::collections::HashMap;
use std::sync::Arc;
use serde::{Deserialize, Serialize};
use crate::error::DBError;
/// Provider identifiers. Extend as needed to mirror LanceDB-supported providers.
#[derive(Debug, Clone, Serialize, Deserialize, PartialEq, Eq)]
#[serde(rename_all = "snake_case")]
pub enum EmbeddingProvider {
// Deterministic, local-only embedder for CI and offline development.
TestHash,
// Placeholders for LanceDB-supported providers; implementers can add concrete backends later.
LanceFastEmbed,
LanceOpenAI,
LanceOther(String),
}
/// Serializable embedding configuration.
/// params: arbitrary key-value map for provider-specific knobs (e.g., "dim", "api_key_env", etc.)
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct EmbeddingConfig {
pub provider: EmbeddingProvider,
pub model: String,
#[serde(default)]
pub params: HashMap<String, String>,
}
impl EmbeddingConfig {
pub fn get_param_usize(&self, key: &str) -> Option<usize> {
self.params.get(key).and_then(|v| v.parse::<usize>().ok())
}
pub fn get_param_string(&self, key: &str) -> Option<String> {
self.params.get(key).cloned()
}
}
/// A provider-agnostic text embedding interface.
pub trait Embedder: Send + Sync {
/// Human-readable provider/model name
fn name(&self) -> String;
/// Embedding dimension
fn dim(&self) -> usize;
/// Embed a single text string into a fixed-length vector
fn embed(&self, text: &str) -> Result<Vec<f32>, DBError>;
/// Embed many texts; default maps embed() over inputs
fn embed_many(&self, texts: &[String]) -> Result<Vec<Vec<f32>>, DBError> {
texts.iter().map(|t| self.embed(t)).collect()
}
}
/// Deterministic, no-deps, no-network embedder for CI and offline dev.
/// Algorithm:
/// - Fold bytes of UTF-8 into 'dim' buckets with a simple rolling hash
/// - Apply tanh-like scaling and L2-normalize to unit length
pub struct TestHashEmbedder {
dim: usize,
model_name: String,
}
impl TestHashEmbedder {
pub fn new(dim: usize, model_name: impl Into<String>) -> Self {
Self { dim, model_name: model_name.into() }
}
fn l2_normalize(mut v: Vec<f32>) -> Vec<f32> {
let norm: f32 = v.iter().map(|x| x * x).sum::<f32>().sqrt();
if norm > 0.0 {
for x in &mut v {
*x /= norm;
}
}
v
}
}
impl Embedder for TestHashEmbedder {
fn name(&self) -> String {
format!("test-hash:{}", self.model_name)
}
fn dim(&self) -> usize {
self.dim
}
fn embed(&self, text: &str) -> Result<Vec<f32>, DBError> {
let mut acc = vec![0f32; self.dim];
// A simple, deterministic folding hash over bytes
let mut h1: u32 = 2166136261u32; // FNV-like seed
let mut h2: u32 = 0x9e3779b9u32; // golden ratio
for (i, b) in text.as_bytes().iter().enumerate() {
h1 ^= *b as u32;
h1 = h1.wrapping_mul(16777619u32);
h2 = h2.wrapping_add(((*b as u32) << (i % 13)) ^ (h1.rotate_left((i % 7) as u32)));
let idx = (h1 ^ h2) as usize % self.dim;
// Map byte to [-1, 1] and accumulate with mild decay by position
let val = ((*b as f32) / 127.5 - 1.0) * (1.0 / (1.0 + (i as f32 / 32.0)));
acc[idx] += val;
}
// Non-linear squashing to stabilize + normalize
for x in &mut acc {
*x = x.tanh();
}
Ok(Self::l2_normalize(acc))
}
}
/// Create an embedder instance from a config.
/// - TestHash: uses params["dim"] or defaults to 64
/// - Lance* providers: return an explicit error for now; implementers can wire these up
pub fn create_embedder(config: &EmbeddingConfig) -> Result<Arc<dyn Embedder>, DBError> {
match &config.provider {
EmbeddingProvider::TestHash => {
let dim = config.get_param_usize("dim").unwrap_or(64);
Ok(Arc::new(TestHashEmbedder::new(dim, config.model.clone())))
}
EmbeddingProvider::LanceFastEmbed => Err(DBError("LanceFastEmbed provider not yet implemented in Rust embedding layer; configure 'test-hash' or implement a Lance-backed provider".into())),
EmbeddingProvider::LanceOpenAI => Err(DBError("LanceOpenAI provider not yet implemented in Rust embedding layer; configure 'test-hash' or implement a Lance-backed provider".into())),
EmbeddingProvider::LanceOther(p) => Err(DBError(format!("Lance provider '{}' not implemented; configure 'test-hash' or implement a Lance-backed provider", p))),
}
}

19
src/lance_store.rs
View File

@@ -111,6 +111,7 @@ impl LanceStore {
Arc::new(Schema::new(vec![
Field::new("id", DataType::Utf8, false),
Self::vector_field(dim),
Field::new("text", DataType::Utf8, true),
Field::new("meta", DataType::Utf8, true),
]))
}
@@ -119,6 +120,7 @@ impl LanceStore {
id: &str,
vector: &[f32],
meta: &HashMap<String, String>,
text: Option<&str>,
dim: i32,
) -> Result<(Arc<Schema>, RecordBatch), DBError> {
if vector.len() as i32 != dim {
@@ -145,6 +147,15 @@ impl LanceStore {
list_builder.append(true);
let vec_arr = Arc::new(list_builder.finish()) as Arc<dyn Array>;
// text column (optional)
let mut text_builder = StringBuilder::new();
if let Some(t) = text {
text_builder.append_value(t);
} else {
text_builder.append_null();
}
let text_arr = Arc::new(text_builder.finish()) as Arc<dyn Array>;
// meta column (JSON string)
let meta_json = if meta.is_empty() {
None
@@ -160,7 +171,7 @@ impl LanceStore {
let meta_arr = Arc::new(meta_builder.finish()) as Arc<dyn Array>;
let batch =
RecordBatch::try_new(schema.clone(), vec![id_arr, vec_arr, meta_arr]).map_err(|e| {
RecordBatch::try_new(schema.clone(), vec![id_arr, vec_arr, text_arr, meta_arr]).map_err(|e| {
DBError(format!("RecordBatch build failed: {e}"))
})?;
@@ -195,10 +206,11 @@ impl LanceStore {
let v_builder = Float32Builder::new();
let mut list_builder = FixedSizeListBuilder::new(v_builder, dim_i32);
let empty_vec = Arc::new(list_builder.finish()) as Arc<dyn Array>;
let empty_text = Arc::new(StringArray::new_null(0));
let empty_meta = Arc::new(StringArray::new_null(0));
let empty_batch =
RecordBatch::try_new(schema.clone(), vec![empty_id, empty_vec, empty_meta])
RecordBatch::try_new(schema.clone(), vec![empty_id, empty_vec, empty_text, empty_meta])
.map_err(|e| DBError(format!("Build empty batch failed: {e}")))?;
let write_params = WriteParams {
@@ -222,6 +234,7 @@ impl LanceStore {
id: &str,
vector: Vec<f32>,
meta: HashMap<String, String>,
text: Option<String>,
) -> Result<(), DBError> {
let path = self.dataset_path(name);
@@ -235,7 +248,7 @@ impl LanceStore {
.map_err(|_| DBError("Vector length too large".into()))?
};
let (schema, batch) = Self::build_one_row_batch(id, &vector, &meta, dim_i32)?;
let (schema, batch) = Self::build_one_row_batch(id, &vector, &meta, text.as_deref(), dim_i32)?;
// If LanceDB table exists and provides delete, we can upsert by deleting same id
// Try best-effort delete; ignore errors to keep operation append-only on failure

1
src/lib.rs
View File

@@ -15,3 +15,4 @@ pub mod admin_meta;
pub mod tantivy_search;
pub mod search_cmd;
pub mod lance_store;
pub mod embedding;

270
src/rpc.rs
View File

@@ -9,6 +9,7 @@ use sha2::{Digest, Sha256};
use crate::server::Server;
use crate::options::DBOption;
use crate::admin_meta;
use crate::embedding::{EmbeddingConfig, EmbeddingProvider};
/// Database backend types
#[derive(Debug, Clone, Serialize, Deserialize)]
@@ -163,8 +164,8 @@ pub trait Rpc {
#[method(name = "ftDrop")]
async fn ft_drop(&self, db_id: u64, index_name: String) -> RpcResult<bool>;
// ----- LanceDB (Vector) RPC endpoints -----
// ----- LanceDB (Vector + Text) RPC endpoints -----
/// Create a new Lance dataset in a Lance-backed DB
#[method(name = "lanceCreate")]
async fn lance_create(
@@ -173,8 +174,8 @@ pub trait Rpc {
name: String,
dim: usize,
) -> RpcResult<bool>;
/// Store a vector (with id and metadata) into a Lance dataset
/// Store a vector (with id and metadata) into a Lance dataset (deprecated; returns error)
#[method(name = "lanceStore")]
async fn lance_store(
&self,
@@ -184,8 +185,8 @@ pub trait Rpc {
vector: Vec<f32>,
meta: Option<HashMap<String, String>>,
) -> RpcResult<bool>;
/// Search a Lance dataset with a query vector
/// Search a Lance dataset with a query vector (deprecated; returns error)
#[method(name = "lanceSearch")]
async fn lance_search(
&self,
@@ -196,7 +197,7 @@ pub trait Rpc {
filter: Option<String>,
return_fields: Option<Vec<String>>,
) -> RpcResult<serde_json::Value>;
/// Create an ANN index on a Lance dataset
#[method(name = "lanceCreateIndex")]
async fn lance_create_index(
@@ -206,14 +207,14 @@ pub trait Rpc {
index_type: String,
params: Option<HashMap<String, String>>,
) -> RpcResult<bool>;
/// List Lance datasets for a DB
#[method(name = "lanceList")]
async fn lance_list(
&self,
db_id: u64,
) -> RpcResult<Vec<String>>;
/// Get info for a Lance dataset
#[method(name = "lanceInfo")]
async fn lance_info(
@@ -221,7 +222,7 @@ pub trait Rpc {
db_id: u64,
name: String,
) -> RpcResult<serde_json::Value>;
/// Delete a record by id from a Lance dataset
#[method(name = "lanceDel")]
async fn lance_del(
@@ -230,7 +231,7 @@ pub trait Rpc {
name: String,
id: String,
) -> RpcResult<bool>;
/// Drop a Lance dataset
#[method(name = "lanceDrop")]
async fn lance_drop(
@@ -238,6 +239,49 @@ pub trait Rpc {
db_id: u64,
name: String,
) -> RpcResult<bool>;
// New: Text-first endpoints (no user-provided vectors)
/// Set per-dataset embedding configuration
#[method(name = "lanceSetEmbeddingConfig")]
async fn lance_set_embedding_config(
&self,
db_id: u64,
name: String,
provider: String,
model: String,
params: Option<HashMap<String, String>>,
) -> RpcResult<bool>;
/// Get per-dataset embedding configuration
#[method(name = "lanceGetEmbeddingConfig")]
async fn lance_get_embedding_config(
&self,
db_id: u64,
name: String,
) -> RpcResult<serde_json::Value>;
/// Store text; server will embed and store vector+text+meta
#[method(name = "lanceStoreText")]
async fn lance_store_text(
&self,
db_id: u64,
name: String,
id: String,
text: String,
meta: Option<HashMap<String, String>>,
) -> RpcResult<bool>;
/// Search using a text query; server will embed then search
#[method(name = "lanceSearchText")]
async fn lance_search_text(
&self,
db_id: u64,
name: String,
text: String,
k: usize,
filter: Option<String>,
return_fields: Option<Vec<String>>,
) -> RpcResult<serde_json::Value>;
}
/// RPC Server implementation
@@ -789,62 +833,33 @@ impl RpcServer for RpcServerImpl {
async fn lance_store(
&self,
db_id: u64,
name: String,
id: String,
vector: Vec<f32>,
meta: Option<HashMap<String, String>>,
_db_id: u64,
_name: String,
_id: String,
_vector: Vec<f32>,
_meta: Option<HashMap<String, String>>,
) -> RpcResult<bool> {
let server = self.get_or_create_server(db_id).await?;
if db_id == 0 {
return Err(jsonrpsee::types::ErrorObjectOwned::owned(-32000, "Lance not allowed on DB 0", None::<()>));
}
if !matches!(server.option.backend, crate::options::BackendType::Lance) {
return Err(jsonrpsee::types::ErrorObjectOwned::owned(-32000, "DB backend is not Lance", None::<()>));
}
if !server.has_write_permission() {
return Err(jsonrpsee::types::ErrorObjectOwned::owned(-32000, "write permission denied", None::<()>));
}
server.lance_store()
.map_err(|e| jsonrpsee::types::ErrorObjectOwned::owned(-32000, e.0, None::<()>))?
.store_vector(&name, &id, vector, meta.unwrap_or_default()).await
.map_err(|e| jsonrpsee::types::ErrorObjectOwned::owned(-32000, e.0, None::<()>))?;
Ok(true)
Err(jsonrpsee::types::ErrorObjectOwned::owned(
-32000,
"Vector endpoint removed. Use lanceStoreText instead.",
None::<()>
))
}
async fn lance_search(
&self,
db_id: u64,
name: String,
vector: Vec<f32>,
k: usize,
filter: Option<String>,
return_fields: Option<Vec<String>>,
_db_id: u64,
_name: String,
_vector: Vec<f32>,
_k: usize,
_filter: Option<String>,
_return_fields: Option<Vec<String>>,
) -> RpcResult<serde_json::Value> {
let server = self.get_or_create_server(db_id).await?;
if db_id == 0 {
return Err(jsonrpsee::types::ErrorObjectOwned::owned(-32000, "Lance not allowed on DB 0", None::<()>));
}
if !matches!(server.option.backend, crate::options::BackendType::Lance) {
return Err(jsonrpsee::types::ErrorObjectOwned::owned(-32000, "DB backend is not Lance", None::<()>));
}
if !server.has_read_permission() {
return Err(jsonrpsee::types::ErrorObjectOwned::owned(-32000, "read permission denied", None::<()>));
}
let results = server.lance_store()
.map_err(|e| jsonrpsee::types::ErrorObjectOwned::owned(-32000, e.0, None::<()>))?
.search_vectors(&name, vector, k, filter, return_fields).await
.map_err(|e| jsonrpsee::types::ErrorObjectOwned::owned(-32000, e.0, None::<()>))?;
let json_results: Vec<serde_json::Value> = results.into_iter().map(|(id, score, meta)| {
serde_json::json!({
"id": id,
"score": score,
"meta": meta,
})
}).collect();
Ok(serde_json::json!({ "results": json_results }))
Err(jsonrpsee::types::ErrorObjectOwned::owned(
-32000,
"Vector endpoint removed. Use lanceSearchText instead.",
None::<()>
))
}
async fn lance_create_index(
@@ -958,4 +973,137 @@ impl RpcServer for RpcServerImpl {
.map_err(|e| jsonrpsee::types::ErrorObjectOwned::owned(-32000, e.0, None::<()>))?;
Ok(ok)
}
// ----- New text-first Lance RPC implementations -----
async fn lance_set_embedding_config(
&self,
db_id: u64,
name: String,
provider: String,
model: String,
params: Option<HashMap<String, String>>,
) -> RpcResult<bool> {
let server = self.get_or_create_server(db_id).await?;
if db_id == 0 {
return Err(jsonrpsee::types::ErrorObjectOwned::owned(-32000, "Lance not allowed on DB 0", None::<()>));
}
if !matches!(server.option.backend, crate::options::BackendType::Lance) {
return Err(jsonrpsee::types::ErrorObjectOwned::owned(-32000, "DB backend is not Lance", None::<()>));
}
if !server.has_write_permission() {
return Err(jsonrpsee::types::ErrorObjectOwned::owned(-32000, "write permission denied", None::<()>));
}
let prov = match provider.to_lowercase().as_str() {
"test-hash" | "testhash" => EmbeddingProvider::TestHash,
"fastembed" | "lancefastembed" => EmbeddingProvider::LanceFastEmbed,
"openai" | "lanceopenai" => EmbeddingProvider::LanceOpenAI,
other => EmbeddingProvider::LanceOther(other.to_string()),
};
let cfg = EmbeddingConfig {
provider: prov,
model,
params: params.unwrap_or_default(),
};
server.set_dataset_embedding_config(&name, &cfg)
.map_err(|e| jsonrpsee::types::ErrorObjectOwned::owned(-32000, e.0, None::<()>))?;
Ok(true)
}
async fn lance_get_embedding_config(
&self,
db_id: u64,
name: String,
) -> RpcResult<serde_json::Value> {
let server = self.get_or_create_server(db_id).await?;
if db_id == 0 {
return Err(jsonrpsee::types::ErrorObjectOwned::owned(-32000, "Lance not allowed on DB 0", None::<()>));
}
if !matches!(server.option.backend, crate::options::BackendType::Lance) {
return Err(jsonrpsee::types::ErrorObjectOwned::owned(-32000, "DB backend is not Lance", None::<()>));
}
if !server.has_read_permission() {
return Err(jsonrpsee::types::ErrorObjectOwned::owned(-32000, "read permission denied", None::<()>));
}
let cfg = server.get_dataset_embedding_config(&name)
.map_err(|e| jsonrpsee::types::ErrorObjectOwned::owned(-32000, e.0, None::<()>))?;
Ok(serde_json::json!({
"provider": match cfg.provider {
EmbeddingProvider::TestHash => "test-hash",
EmbeddingProvider::LanceFastEmbed => "lancefastembed",
EmbeddingProvider::LanceOpenAI => "lanceopenai",
EmbeddingProvider::LanceOther(ref s) => s,
},
"model": cfg.model,
"params": cfg.params
}))
}
async fn lance_store_text(
&self,
db_id: u64,
name: String,
id: String,
text: String,
meta: Option<HashMap<String, String>>,
) -> RpcResult<bool> {
let server = self.get_or_create_server(db_id).await?;
if db_id == 0 {
return Err(jsonrpsee::types::ErrorObjectOwned::owned(-32000, "Lance not allowed on DB 0", None::<()>));
}
if !matches!(server.option.backend, crate::options::BackendType::Lance) {
return Err(jsonrpsee::types::ErrorObjectOwned::owned(-32000, "DB backend is not Lance", None::<()>));
}
if !server.has_write_permission() {
return Err(jsonrpsee::types::ErrorObjectOwned::owned(-32000, "write permission denied", None::<()>));
}
let embedder = server.get_embedder_for(&name)
.map_err(|e| jsonrpsee::types::ErrorObjectOwned::owned(-32000, e.0, None::<()>))?;
let vector = embedder.embed(&text)
.map_err(|e| jsonrpsee::types::ErrorObjectOwned::owned(-32000, e.0, None::<()>))?;
server.lance_store()
.map_err(|e| jsonrpsee::types::ErrorObjectOwned::owned(-32000, e.0, None::<()>))?
.store_vector(&name, &id, vector, meta.unwrap_or_default(), Some(text)).await
.map_err(|e| jsonrpsee::types::ErrorObjectOwned::owned(-32000, e.0, None::<()>))?;
Ok(true)
}
async fn lance_search_text(
&self,
db_id: u64,
name: String,
text: String,
k: usize,
filter: Option<String>,
return_fields: Option<Vec<String>>,
) -> RpcResult<serde_json::Value> {
let server = self.get_or_create_server(db_id).await?;
if db_id == 0 {
return Err(jsonrpsee::types::ErrorObjectOwned::owned(-32000, "Lance not allowed on DB 0", None::<()>));
}
if !matches!(server.option.backend, crate::options::BackendType::Lance) {
return Err(jsonrpsee::types::ErrorObjectOwned::owned(-32000, "DB backend is not Lance", None::<()>));
}
if !server.has_read_permission() {
return Err(jsonrpsee::types::ErrorObjectOwned::owned(-32000, "read permission denied", None::<()>));
}
let embedder = server.get_embedder_for(&name)
.map_err(|e| jsonrpsee::types::ErrorObjectOwned::owned(-32000, e.0, None::<()>))?;
let qv = embedder.embed(&text)
.map_err(|e| jsonrpsee::types::ErrorObjectOwned::owned(-32000, e.0, None::<()>))?;
let results = server.lance_store()
.map_err(|e| jsonrpsee::types::ErrorObjectOwned::owned(-32000, e.0, None::<()>))?
.search_vectors(&name, qv, k, filter, return_fields).await
.map_err(|e| jsonrpsee::types::ErrorObjectOwned::owned(-32000, e.0, None::<()>))?;
let json_results: Vec<serde_json::Value> = results.into_iter().map(|(id, score, meta)| {
serde_json::json!({
"id": id,
"score": score,
"meta": meta,
})
}).collect();
Ok(serde_json::json!({ "results": json_results }))
}
}

80
src/server.rs
View File

@@ -14,6 +14,10 @@ use crate::protocol::Protocol;
use crate::storage_trait::StorageBackend;
use crate::admin_meta;
// Embeddings: config and cache
use crate::embedding::{EmbeddingConfig, create_embedder, Embedder};
use serde_json;
#[derive(Clone)]
pub struct Server {
pub db_cache: std::sync::Arc<std::sync::RwLock<HashMap<u64, Arc<dyn StorageBackend>>>>,
@@ -29,6 +33,9 @@ pub struct Server {
// Per-DB Lance stores (vector DB), keyed by db_id
pub lance_stores: Arc<std::sync::RwLock<HashMap<u64, Arc<crate::lance_store::LanceStore>>>>,
// Per-(db_id, dataset) embedder cache
pub embedders: Arc<std::sync::RwLock<HashMap<(u64, String), Arc<dyn Embedder>>>>,
// BLPOP waiter registry: per (db_index, key) FIFO of waiters
pub list_waiters: Arc<Mutex<HashMap<u64, HashMap<String, Vec<Waiter>>>>>,
pub waiter_seq: Arc<AtomicU64>,
@@ -58,6 +65,7 @@ impl Server {
search_indexes: Arc::new(std::sync::RwLock::new(HashMap::new())),
lance_stores: Arc::new(std::sync::RwLock::new(HashMap::new())),
embedders: Arc::new(std::sync::RwLock::new(HashMap::new())),
list_waiters: Arc::new(Mutex::new(HashMap::new())),
waiter_seq: Arc::new(AtomicU64::new(1)),
}
@@ -153,6 +161,78 @@ impl Server {
Ok(store)
}
// ----- Embedding configuration and resolution -----
// Sidecar embedding config path: <base_dir>/lance/<db_id>/<dataset>.lance.embedding.json
fn dataset_embedding_config_path(&self, dataset: &str) -> std::path::PathBuf {
let mut base = self.lance_data_path();
// Ensure parent dir exists
if !base.exists() {
let _ = std::fs::create_dir_all(&base);
}
base.push(format!("{}.lance.embedding.json", dataset));
base
}
/// Persist per-dataset embedding config as JSON sidecar.
pub fn set_dataset_embedding_config(&self, dataset: &str, cfg: &EmbeddingConfig) -> Result<(), DBError> {
if self.selected_db == 0 {
return Err(DBError("Lance not available on admin DB 0".to_string()));
}
let p = self.dataset_embedding_config_path(dataset);
let data = serde_json::to_vec_pretty(cfg)
.map_err(|e| DBError(format!("Failed to serialize embedding config: {}", e)))?;
std::fs::write(&p, data)
.map_err(|e| DBError(format!("Failed to write embedding config {}: {}", p.display(), e)))?;
// Invalidate embedder cache entry for this dataset
{
let mut map = self.embedders.write().unwrap();
map.remove(&(self.selected_db, dataset.to_string()));
}
Ok(())
}
/// Load per-dataset embedding config.
pub fn get_dataset_embedding_config(&self, dataset: &str) -> Result<EmbeddingConfig, DBError> {
if self.selected_db == 0 {
return Err(DBError("Lance not available on admin DB 0".to_string()));
}
let p = self.dataset_embedding_config_path(dataset);
if !p.exists() {
return Err(DBError(format!(
"Embedding config not set for dataset '{}'. Use LANCE.EMBEDDING CONFIG SET ... or RPC to configure.",
dataset
)));
}
let data = std::fs::read(&p)
.map_err(|e| DBError(format!("Failed to read embedding config {}: {}", p.display(), e)))?;
let cfg: EmbeddingConfig = serde_json::from_slice(&data)
.map_err(|e| DBError(format!("Failed to parse embedding config {}: {}", p.display(), e)))?;
Ok(cfg)
}
/// Resolve or build an embedder for (db_id, dataset). Caches instance.
pub fn get_embedder_for(&self, dataset: &str) -> Result<Arc<dyn Embedder>, DBError> {
if self.selected_db == 0 {
return Err(DBError("Lance not available on admin DB 0".to_string()));
}
// Fast path
{
let map = self.embedders.read().unwrap();
if let Some(e) = map.get(&(self.selected_db, dataset.to_string())) {
return Ok(e.clone());
}
}
// Load config and instantiate
let cfg = self.get_dataset_embedding_config(dataset)?;
let emb = create_embedder(&cfg)?;
{
let mut map = self.embedders.write().unwrap();
map.insert((self.selected_db, dataset.to_string()), emb.clone());
}
Ok(emb)
}
/// Check if current permissions allow read operations
pub fn has_read_permission(&self) -> bool {
// If an explicit permission is set for this connection, honor it.