📚 Vector DB Document Retrieval

SLM RAG

Local Retrieval-Augmented Generation engine for offline document indexing, dense vector search, and grounded Q&A.

🚀 Overview & Capabilities

Local Retrieval-Augmented Generation engine for offline document indexing, dense vector search, and grounded Q&A.

Key Features

Offline ONNX embedding generation
Local vector similarity indexing
Grounded factual document question answering
Strict context window enforcement

💻 Installation

Install the local package using pip:

Terminal

              $pip install slm-rag

🐙 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_rag && cd slm_rag

# 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_rag

# 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_rag
            

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

⚙️ Configuration API

Constructor Parameters

Instantiate SLMRag with performance and runtime options:

Parameter	Type / Default	Description
`model_path`	str \| None	Local ONNX model path. Default: None.
`temperature`	float \| 0.0	Sampling temperature. Default: 0.0.
`top_p`	float \| 0.9	Nucleus sampling threshold. Default: 0.9.
`max_tokens`	int \| 256	Maximum output token limit. Default: 256.
`n_threads`	int \| 4	CPU threads. Default: 4.

Methods

Method Signature	Return Type	Description
`answer(question, chunks, ...)`	str \| dict	Retrieves matching context chunks and generates factual response.

Execution Parameters

Complete list of execution parameters accepted by the primary agent method:

Parameter	Type / Default	Description
`question`	str	User question string.
`chunks`	list[str] \| str	Document context text or chunk list.
`instruction`	str \| None	Extraction instruction guideline. Default: None.
`system_prompt`	str \| None	System prompt instruction. Default: None.
`user_input`	str \| None	Contextual key values. Default: None.
`temperature`	float \| 0.0	Sampling temperature. Default: 0.0.
`top_p`	float \| 0.9	Nucleus sampling probability. Default: 0.9.
`max_tokens`	int \| 256	Maximum token limit. Default: 256.

Quick Start

Python Example

              from slm_rag import SLMRag

rag = SLMRag(temperature=0.0, top_p=0.9, max_tokens=256)
res = rag.answer(
    question="What is total Q3 net revenue?",
    chunks="Q3 net revenue reached $1.25M., Due date: Sept 2026",
    instruction="Extract exact numerical totals",
    system_prompt="Factual document extraction",
    user_input="Currency: USD",
    temperature=0.0,
    top_p=0.9,
    max_tokens=256
)
print(res)
            

🔍 Verified Output Logs

Diagnostic execution console output running locally on CPU:

Output Console

              → INPUT:
Question: What is total Q3 net revenue?

← OUTPUT:
{
  'status': '200 OK',
  'answer': 'Based on documents: Q3 net revenue is $1.25M USD.',
  'retrieved_chunks': 2
}
            