Atlas

Atlas is a local, two-stage Retrieval-Augmented Generation (RAG) system.

How It Works

User Query
    │
    ▼
[Stage 1] Bi-encoder Vector Search
    │  Returns top-N candidate documents
    ▼
[Stage 2] Cross-Encoder Re-ranking
    │  Scores each (query, document) pair jointly, selects top-K
    ▼
[Stage 3] LLM Generation
    │  Generates an answer from the re-ranked context
    ▼
Answer

Prerequisites

• Ollama installed and running.
• Python 3.10 or higher.

Installation

1. Clone the repository and navigate to the directory.
2. Install dependencies:

   pip install -r requirements.txt

3. Pull the required LLM:

   ollama pull llama3.2:1b

Running the Project

python main.py

Running the Evaluation

python eval/run_eval.py

Evaluation Results

Metric	Retrieval Only	Retrieval + Re-rank
Hit Rate	100%	100%
MRR	0.466	0.733 (+57%)
Latency	~95 ms	~6.5 s (CPU)

The cross-encoder improved our Mean Reciprocal Rank (MRR) by 57%, proving it is significantly better at putting the single most factual document at the #1 spot for the LLM.