💾 Local Cosine Similarity

SLM Embeddings Server

Starts a local CPU-optimized embedding server to compute dense document and query vectors on standard hardware.

🚀 Overview & Capabilities

Starts a local CPU-optimized embedding server to compute dense document and query vectors on standard hardware.

Key Features

Loads quantized mini-LM or BGE embeddings locally
High-speed cosine similarity index built directly in memory
Provides local HTTP API endpoint for integration
Under 200 MB RAM memory usage footprint during idle states

💻 Installation

Install the local CPU-optimized package using pip:

Terminal

              # Install local CPU-optimized package
pip install slm-embeddings
            

🐙 Checkout from GitHub

Clone only this agent's folder from the monorepo using Git sparse-checkout — no need to download the full repository:

Option 1 — Sparse Checkout (Recommended)

Terminal — Git Sparse Checkout

              # 1. Create and enter a new directory
$ mkdir slm_embeddings && cd slm_embeddings

# 2. Initialise empty git repo and add remote
$ git init
$ git remote add origin https://github.com/t00114218-stack/SLMAgents.git

# 3. Enable sparse-checkout and set target folder
$ git sparse-checkout init --cone
$ git sparse-checkout set slm_embeddings

# 4. Pull only that agent's source
$ git pull origin main
            

Option 2 — Full Repository Clone

Terminal — Full Clone

              $ git clone https://github.com/t00114218-stack/SLMAgents.git
$ cd SLMAgents/slm_embeddings
            

💡 Tip: After checkout, install the package locally with pip install -e ./slm_embeddings to run in editable mode without publishing to PyPI.

⚙️ Configuration API

Constructor Parameters

Instantiate SLMEmbeddingsServer with performance options:

Parameter	Type / Default	Description
model_path	str \| None	Explicit path to ONNX model weights. If omitted, downloads standard checkpoints.
cache_dir	str \| None	Directory to store model weights offline. Defaults to `~/.cache/slm-embeddings/`. Also settable via SLM_EMBEDDINGS_SERVER_CACHE_DIR.
n_threads	int \| 4	CPU thread count for ONNX inference. Optimize for CPU core count. Also settable via SLM_EMBEDDINGS_SERVER_N_THREADS.

Methods

Method Signature	Return Type	Description
`embed(texts, system_prompt=None, user_input=None)`	`list[float]`	Generates dense vectors from standard string lists.

Method Parameters (Execution Customization)

All main execution methods accept optional system routing parameters:

Parameter	Type / Default	Description
system_prompt	str \| None	Optional custom system prompt instruction to override the default system template response parameters.
user_input	str \| None	Optional additional user-supplied target text variables or contextual keys.

Quick Start

from slm_embeddings import SLMEmbeddingsServer

server = SLMEmbeddingsServer()
vector = server.embed(
    ["sample test"],
    system_prompt="Calculate semantic weights",
    user_input="Normalized cosine distance"
)
print(vector)

Environment Variables

Configure agent parameters globally using environment values:

Environment Variable	Default	Purpose
SLM_EMBEDDINGS_SERVER_N_THREADS	4	Sets CPU inference execution threads.
SLM_EMBEDDINGS_SERVER_CACHE_DIR	~/.cache/slm-embeddings/	Default directory to store downloaded ONNX weights.

CPU Performance Tuning

To run the SLMEmbeddingsServer engine efficiently on CPU under 1.5 GB memory footprint:

Match Threads to Core Count: Set n_threads or SLM_EMBEDDINGS_SERVER_N_THREADS to match the physical CPU core count.
Sequential Processing: Avoid concurrent processing when batch files are large.
Garbage Collection: Clear variables and run gc.collect() to release model RAM blocks after execution.

Verified Input & Output Logs

Diagnostic execution console response running locally on CPU:

→ INPUT:
"sample test"

← OUTPUT:
"Vector dimension check: 1024"