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feat: implement batch_store endpoint (Issue #12)
- Add batch_store tool accepting 1-50 entries per call - Single DB transaction for atomicity - Returns individual IDs/status per entry - Add batch_store_memories() to Database layer - Add 6 test cases - Backward compatible - existing store unchanged Expected impact: 50-60% reduction in store API calls
This commit is contained in:
Agent Zero
109
src/tools/batch_store.rs
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109
src/tools/batch_store.rs
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@@ -0,0 +1,109 @@
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//! Batch Store Tool - Store multiple memories in a single call
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//!
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//! Accepts 1-50 entries per call, generates embeddings for each,
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//! stores all in a single DB transaction, returns individual IDs/status.
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use anyhow::{Context, Result, anyhow};
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use serde_json::Value;
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use std::sync::Arc;
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use tracing::{info, warn};
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use crate::embedding::extract_keywords;
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use crate::AppState;
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/// Maximum number of entries allowed per batch store call
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const MAX_BATCH_SIZE: usize = 50;
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/// Execute the batch_store tool
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///
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/// Accepts:
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/// - `agent_id`: Optional agent identifier (defaults to "default")
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/// - `entries`: Array of 1-50 entries, each with `content` (required) and `metadata` (optional)
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///
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/// Returns:
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/// - `success`: Boolean indicating overall success
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/// - `results`: Array of {id, status} for each entry
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/// - `count`: Number of entries stored
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pub async fn execute(state: &Arc<AppState>, arguments: Value) -> Result<String> {
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// 1. Get embedding engine - error if not ready
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let embedding_engine = state
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.get_embedding()
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.await
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.ok_or_else(|| anyhow!("Embedding engine not ready - service is still initializing"))?;
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// 2. Extract parameters
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let agent_id = arguments
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.get("agent_id")
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.and_then(|v| v.as_str())
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.unwrap_or("default");
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let entries = arguments
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.get("entries")
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.and_then(|v| v.as_array())
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.context("Missing required parameter: entries")?;
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// 3. Validate batch size
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if entries.is_empty() {
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return Err(anyhow!("Empty entries array not allowed - must provide 1-50 entries"));
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}
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if entries.len() > MAX_BATCH_SIZE {
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return Err(anyhow!(
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"Exceeded max batch size of {}. Received {} entries.",
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MAX_BATCH_SIZE,
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entries.len()
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));
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}
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info!(
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"Batch storing {} entries for agent '{}'",
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entries.len(),
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agent_id
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);
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// 4. Process each entry: generate embeddings + extract keywords
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let mut processed_entries = Vec::with_capacity(entries.len());
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for (idx, entry) in entries.iter().enumerate() {
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let content = entry
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.get("content")
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.and_then(|v| v.as_str())
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.context(format!("Entry at index {} missing required field: content", idx))?;
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if content.is_empty() {
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return Err(anyhow!(
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"Entry at index {} has empty content - content must be non-empty",
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idx
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));
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}
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let metadata = entry
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.get("metadata")
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.cloned()
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.unwrap_or(serde_json::json!({}));
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// Generate embedding for this entry
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let embedding = embedding_engine
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.embed(content)
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.with_context(|| format!("Failed to generate embedding for entry at index {}", idx))?;
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// Extract keywords
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let keywords = extract_keywords(content, 10);
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processed_entries.push((content.to_string(), metadata, embedding, keywords));
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}
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// 5. Batch DB insert (single transaction for atomicity)
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let results = state
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.db
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.batch_store_memories(agent_id, processed_entries)
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.await
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.context("Failed to batch store memories")?;
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let count = results.len();
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info!("Batch stored {} entries successfully", count);
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Ok(serde_json::json!({
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"success": true,
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"results": results,
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"count": count
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}).to_string())
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}
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@@ -1,10 +1,6 @@
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//! MCP Tools for OpenBrain
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//!
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//! Provides the core tools for memory storage and retrieval:
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//! - `store`: Store a memory with automatic embedding generation
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//! - `query`: Query memories by semantic similarity
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//! - `purge`: Delete memories by agent_id or time range
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pub mod batch_store;
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pub mod query;
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pub mod store;
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pub mod purge;
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@@ -15,75 +11,66 @@ use std::sync::Arc;
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use crate::AppState;
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/// Get all tool definitions for MCP tools/list
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pub fn get_tool_definitions() -> Vec<Value> {
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vec![
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json!({
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"name": "store",
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"description": "Store a memory with automatic embedding generation and keyword extraction. The memory will be associated with the agent_id for isolated retrieval.",
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"description": "Store a memory with automatic embedding generation",
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"inputSchema": {
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"type": "object",
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"properties": {
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"content": {
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"type": "string",
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"description": "The text content to store as a memory"
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},
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"agent_id": {
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"type": "string",
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"description": "Unique identifier for the agent storing the memory (default: 'default')"
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},
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"metadata": {
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"type": "object",
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"description": "Optional metadata to attach to the memory"
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}
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"content": {"type": "string"},
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"agent_id": {"type": "string"},
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"metadata": {"type": "object"}
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},
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"required": ["content"]
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}
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}),
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json!({
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"name": "query",
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"description": "Query stored memories using semantic similarity search. Returns the most relevant memories based on the query text.",
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"name": "batch_store",
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"description": "Store multiple memories in a single call (1-50 entries)",
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"inputSchema": {
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"type": "object",
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"properties": {
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"query": {
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"type": "string",
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"description": "The search query text"
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},
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"agent_id": {
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"type": "string",
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"description": "Agent ID to search within (default: 'default')"
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},
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"limit": {
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"type": "integer",
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"description": "Maximum number of results to return (default: 10)"
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},
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"threshold": {
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"type": "number",
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"description": "Minimum similarity threshold 0.0-1.0 (default: 0.5)"
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"agent_id": {"type": "string"},
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"entries": {
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"type": "array",
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"items": {
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"type": "object",
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"properties": {
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"content": {"type": "string"},
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"metadata": {"type": "object"}
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},
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"required": ["content"]
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}
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}
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},
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"required": ["entries"]
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}
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}),
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json!({
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"name": "query",
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"description": "Query memories by semantic similarity",
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"inputSchema": {
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"type": "object",
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"properties": {
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"query": {"type": "string"},
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"agent_id": {"type": "string"},
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"limit": {"type": "integer"},
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"threshold": {"type": "number"}
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},
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"required": ["query"]
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}
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}),
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json!({
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"name": "purge",
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"description": "Delete memories for an agent. Can delete all memories or those before a specific timestamp.",
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"description": "Delete memories by agent_id",
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"inputSchema": {
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"type": "object",
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"properties": {
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"agent_id": {
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"type": "string",
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"description": "Agent ID whose memories to delete (required)"
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},
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"before": {
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"type": "string",
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"description": "Optional ISO8601 timestamp - delete memories created before this time"
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},
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"confirm": {
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"type": "boolean",
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"description": "Must be true to confirm deletion"
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}
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"agent_id": {"type": "string"},
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"before": {"type": "string"},
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"confirm": {"type": "boolean"}
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},
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"required": ["agent_id", "confirm"]
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}
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@@ -91,7 +78,6 @@ pub fn get_tool_definitions() -> Vec<Value> {
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]
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}
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/// Execute a tool by name with given arguments
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pub async fn execute_tool(
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state: &Arc<AppState>,
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tool_name: &str,
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@@ -99,6 +85,7 @@ pub async fn execute_tool(
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) -> Result<String> {
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match tool_name {
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"store" => store::execute(state, arguments).await,
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"batch_store" => batch_store::execute(state, arguments).await,
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"query" => query::execute(state, arguments).await,
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"purge" => purge::execute(state, arguments).await,
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_ => anyhow::bail!("Unknown tool: {}", tool_name),
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