LocalRAG

This project implements and evaluates a Retrieval-Augmented Generation (RAG) pipeline optimized for performance on consumer hardware, supporting multiple local language models and diverse datasets.

Project Context

This repository contains the core components of a RAG system that:

1. Establishes a functional RAG system using locally executable models.
2. Evaluates its performance thoroughly across retrieval quality, generation quality, and latency metrics.
3. Delivers a fully reproducible foundation that others can extend with further performance‑improvement techniques.

System Overview

The system uses LangChain to orchestrate a complete RAG pipeline with support for multiple models and datasets:

• Datasets: Supports ClapNQ, TriviaQA, and HotpotQA datasets from Hugging Face.

• Embedding: Uses intfloat/e5-base-v2 for document and query embedding

• Vector Storage: Chroma for efficient local passage indexing and retrieval, with separate indices for each dataset.

• Generation: Multiple models via Ollama, including:

  • gemma3:1b, gemma3:4b, gemma3:12b
  • llama3.2:1b, llama3.2:3b, llama3.1:8b
  • olmo2:7b
  • Any other models available through Ollama

• GPU Utilization: Maximizes GPU usage with ollama_num_gpu=999 setting to offload as many model layers as possible to the GPU.

• Evaluation: Comprehensive metrics including:

  • Retrieval: NDCG@10, Precision@10, Recall@10 (using ranx)
  • Generation: ROUGE scores, Unanswerable Accuracy (using evaluate)
  • End-to-End RAG Quality: Faithfulness, Answer Relevancy (using RAGAs with OpenAI's gpt-4.1-mini-2025-04-14 API)
  • Performance: Retrieval and Generation Latency (mean, median, P95, P99)

Note on Evaluation Model: While the goal is local execution, RAGAs evaluation using local models proved prohibitively resource-intensive on consumer hardware. Therefore, RAGAs evaluation currently utilizes the OpenAI API for faster and more reliable results. The core generation pipeline remains local.

Prerequisites

Software

• Python 3.10 or higher
• Git
• Ollama (ollama.com) installed and running
• An OpenAI API key (optional, for RAGAs evaluation only)

Hardware

• Tested on M3 Max with 48GB RAM, but should run (potentially slower) on less powerful machines
• At least 20GB of free disk space (for models, dependencies, and vector stores)

Data

• All datasets are automatically downloaded from Hugging Face:
  • ClapNQ: PrimeQA/clapnq and PrimeQA/clapnq_passages
  • TriviaQA: trivia_qa (config: "rc")
  • HotpotQA: hotpot_qa (config: "distractor")
• Original ClapNQ qrels files are included in the repository under data/retrieval/dev/

Setup

1. Clone the repository:

   git clone https://github.com/patek1/LocalRAG.git
   cd LocalRAG

2. Create and activate a virtual environment:

   python3 -m venv .venv
   source .venv/bin/activate

3. Install dependencies:

   • For development and running:

     pip install -r requirements.txt

   • For exact reproducibility (after a successful run): A frozen environment is provided in requirements_frozen.txt:

     pip install -r requirements_frozen.txt

4. Install and setup Ollama:

   • Download and install Ollama from ollama.com.
   • Pull the model(s) you want to evaluate:

     ollama pull gemma3:1b
     ollama pull gemma3:12b
     ollama pull llama3.1:8b
     # ... or any other Ollama models

   • Ensure the Ollama application is running or start the server:

     ollama serve & # (Optional, runs in background)

   • Verify Ollama is running and the models are present:

     ollama list

5. Set up OpenAI API key (optional, for RAGAs evaluation):

   • Copy the example environment file:

     cp .env.example .env

   • Edit the .env file and add your OpenAI API key:

     OPENAI_API_KEY="sk-your-api-key-here"
     

   • Note: Using the OpenAI API incurs costs based on usage. If no API key is provided, the system will gracefully skip RAGAs evaluation.

6. Prepare Data:

   • Process and index each dataset you want to evaluate:

     python scripts/create_index.py --dataset ClapNQ
     python scripts/create_index.py --dataset TriviaQA
     python scripts/create_index.py --dataset HotpotQA

   • This will:
     1. Download the datasets from Hugging Face
     2. Process them into standardized formats
     3. Create the necessary files in data/processed_corpora/ and data/processed_questions/
     4. Build vector indices in vectorstores/

Configuration

Key parameters are defined in src/config.py:

• Settings: Main configuration class with system-wide parameters
• DatasetConfig: Dataset-specific configurations (Hugging Face IDs, field mappings, etc.)
• FieldMapping: Maps between raw dataset fields and standardized pipeline fields

The most important settings are now accessible via command-line arguments to the scripts.

Usage

Two-Step Workflow

The LocalRAG pipeline now uses a two-step workflow:

1. Index each dataset (once per dataset):

   source .venv/bin/activate
   python scripts/create_index.py --dataset <DATASET_NAME>

2. Run evaluations with different models:

   source .venv/bin/activate
   python src/main.py --model <MODEL_TAG> --dataset <DATASET_NAME> [--limit N]

Command-Line Options

scripts/create_index.py

• --dataset <DATASET_NAME>: (Required) Specifies the dataset to process and index (e.g., "ClapNQ", "TriviaQA", "HotpotQA")
• --reindex: (Optional) Forces re-processing and re-indexing of the dataset, even if an index already exists
• --debug: (Optional) Limits the dataset size for faster processing during development/testing

Example: Create or update the HotpotQA index:

python scripts/create_index.py --dataset HotpotQA --reindex

src/main.py

• --model <MODEL_TAG>: (Required) Specifies the Ollama model to use (e.g., "gemma3:1b", "gemma3:12b", "llama3.1:8b")
• --dataset <DATASET_NAME>: (Required) Specifies the dataset to use (must be indexed first)
• --limit <N>: (Optional) Process only N random questions from the dataset. Useful for quick testing and evaluation on a subset.

Example: Evaluate gemma3:12b on TriviaQA with 50 questions:

python src/main.py --model gemma3:12b --dataset TriviaQA --limit 50

Notes:

1. Ensure that you have previously indexed the dataset using create_index.py before running main.py
2. The same --limit N value with the same dataset will always use the same subset of questions for reproducibility
3. GPU utilization is automatically maximized with the ollama_num_gpu=999 setting

Expected Output

1. Vector Store: Dataset-specific Chroma indices created in vectorstores/<dataset_name>_corpus/.

2. Results Files: JSON files saved in the results/<model_tag_safe>/<dataset_name>/ directory:

   • <N>_quality_metrics.json: Contains retrieval (NDCG@10, P@10, R@10), generation (ROUGE, Unanswerable Accuracy), and RAGAs metrics for a run with N questions.
   • latency_metrics.json: Contains mean, median, P95, P99 latency for retrieval and generation steps, along with the source_subset_size indicating the number of questions used to generate these stats.

Architecture

The project follows a modular structure:

• src/: Core source code modules:

  • config.py: Central configuration management with dataset configs
  • data_loader/: Dataset loading and processing for all supported datasets
  • embedding.py: Embedding model with MPS acceleration
  • vector_store.py: Chroma vector store management
  • indexing.py: Corpus indexing pipeline with batch processing
  • prompting.py: Prompt formatting for the generator LLM
  • generation.py: Ollama LLM initialization with GPU optimization
  • pipeline.py: RAG chain orchestration
  • eval/: Evaluation metrics modules
  • utils.py: Common utilities
  • main.py: Main execution script with CLI argument handling

• scripts/: Utility scripts:

  • create_index.py: Dataset processing and indexing script

• data/: Data directory structure:

  • processed_corpora/: Standardized corpus files (generated)
  • processed_questions/: Standardized question files (generated)
  • subsets/: Question ID subsets for reproducible evaluation (generated)
  • Original ClapNQ files used by the ClapNQ processor

• vectorstores/: Chroma vector indices for corpus retrieval (generated)

• results/: Results organized by model and dataset (generated)

Note: Directories marked as "(generated)" are created during execution and are not included in the repository.

Metrics

The evaluation pipeline calculates and reports:

1. Retrieval Quality (via ranx):
   • NDCG@10
   • Precision@10
   • Recall@10
2. Answer Quality (via evaluate and custom logic):
   • ROUGE-1, ROUGE-2, ROUGE-L (F-measure) for answerable questions.
   • Unanswerable Accuracy: Percentage of unanswerable questions correctly identified as "unanswerable".
   • Note: For datasets without explicit unanswerables (TriviaQA, HotpotQA), this reports "N/A".
3. RAG Quality (via RAGAs using OpenAI, when API key is available):
   • Faithfulness: How factually consistent the generated answer is with the retrieved context.
   • Answer Relevancy: How relevant the generated answer is to the original question.
4. Performance:
   • Retrieval Latency (Mean, Median, P95, P99) in seconds.
   • Generation Latency (Mean, Median, P95, P99) in seconds.

Future Work

This project provides a robust framework for evaluating various RAG configurations. Future work could include:

• Implementing additional datasets beyond the current three
• Exploring advanced retrieval techniques
• Adding more sophisticated prompt engineering
• Implementing more comprehensive evaluation metrics

Contact

If you have questions or would like to discuss extending this project, feel free to contact me: Mischa Büchel, [email protected]