Multi-Modal RAG System for Standardized Documents

A powerful multi-modal Retrieval-Augmented Generation (RAG) system that processes standardized technical documents (PDFs) by extracting both text and visual content. The system combines specialized document parsing, intelligent chunking, a sharded vector store for embeddings (using ChromaDB), and flexible LLM integration (Hugging Face, Llama.cpp, or OpenAI) to deliver accurate, context-rich answers to user queries.

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Table of Contents

• Features
• Architecture & Design
• Repository Structure
• Installation
  • Prerequisites
  • Using UV (Recommended)
  • Using Docker
  • Using Makefile
• Usage
  • Demo Scripts
  • Command-Line Options
  • API Endpoints
• Anti-Hallucination Techniques
  • Verification Mechanisms
  • Post-Processing Pipeline
  • Configuration Options
• Evaluation Framework
  • Metrics
  • Evaluation Tools
  • Benchmarking
• Real-World Examples
• Research Foundations
• Roadmap
• Testing
• Contributing
• Variable Naming Consistency
• License

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Features

• Multi-Modal Document Processing:
  • Extract and process both text and tables/images from PDF documents.
  • Handle OCR and image extraction for diagrams and figures.
• Intelligent Document Chunking:
  • Preserve context and structure by chunking documents without breaking critical content (tables, paragraphs, or figures).
• Vector Store with ChromaDB:
  • Store text and image embeddings.
  • Support horizontal scaling via an automatic sharding mechanism.
• Flexible LLM Integration:
  • Seamlessly switch between LLM backends: Hugging Face, Llama.cpp, or OpenAI.
  • Support conversational and single-turn query modes.
• API & CLI Interface:
  • REST API built with FastAPI.
  • Demo scripts for quick start and local testing.
• Production-Ready Deployment:
  • Kubernetes configuration for scalable deployment.
  • Comprehensive CI/CD pipeline for testing and deployment.

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Architecture & Design

The system is built around the RAG (Retrieval-Augmented Generation) paradigm:

                       ┌─────────────────┐
                       │                 │
                       │  Document Store │
                       │  (PDF, Tables,  │
                       │   Images, Text) │
                       │                 │
                       └────────┬────────┘
                                │
                                ▼
                       ┌─────────────────┐
┌──────────────┐      │    Document     │
│ OCR & Image  │◄────►│   Processing    │
│  Processing  │      │    Pipeline     │
└──────────────┘      └────────┬────────┘
                                │
                                ▼
┌──────────────┐      ┌─────────────────┐      ┌──────────────┐
│  Embedding   │      │    Vector       │      │   ChromaDB   │
│    Models    │◄────►│    Database     │◄────►│   Sharded    │
│ (Text/Image) │      │   Generation    │      │ Vector Store │
└──────────────┘      └────────┬────────┘      └──────────────┘
                                │
                                ▼
                       ┌─────────────────┐
                       │   Retrieval     │
                       │    Engine       │
                       └────────┬────────┘
                                │
                                ▼
┌──────────────┐      ┌─────────────────┐      ┌──────────────┐
│  Query       │      │    LLM with     │      │    Anti-     │
│ Processing   │─────►│     Prompt      │─────►│ Hallucination │
│              │      │   Engineering   │      │  Verification │
└──────────────┘      └────────┬────────┘      └──────┬───────┘
                                │                      │
                                ▼                      ▼
                       ┌─────────────────┐    ┌─────────────────┐
                       │   Response      │    │   Confidence    │
                       │  Generation     │    │    Scoring      │
                       └────────┬────────┘    └────────┬────────┘
                                │                      │
                                ▼                      ▼
                       ┌───────────────────────────────────────┐
                       │             API Layer                 │
                       │      (FastAPI / CLI Interface)        │
                       └───────────────────────────────────────┘

1. Document Processing:

   • PDF Extraction: Leverages robust PDF parsing to extract structured text, tables, and images.
   • Chunking: Custom logic ensures that chunks maintain context, preventing mid-table or mid-paragraph splits.
   • Multi-Modal Processing: Handles both text and visual elements through specialized extraction pipelines.
   • Metadata Preservation: Maintains document structure information and section relationships.

2. Embeddings & Vector Store:

   • Embedding Generation: Uses specialized models to generate embeddings for text and images.
   • ChromaDB Integration: Stores embeddings in a sharded vector database to support scalable similarity search.
   • Hybrid Search: Combines sparse (keyword) and dense (semantic) retrieval for improved results.
   • Automatic Resharding: Redistributes data as collection size grows for performance optimization.

3. LLM Integration & Query Handling:

   • Prompt Engineering: Constructs prompts that incorporate the most relevant chunks from the document.
   • LLM Providers: Supports multiple backends, enabling both local and cloud-based LLM usage.
   • Context Window Optimization: Intelligently manages context window limitations of underlying models.
   • Multi-Turn Conversation: Maintains conversation history with appropriate context management.

4. Anti-Hallucination Pipeline:

   • Pre-Generation Filtering: Ensures retrieved context is maximally relevant before generation.
   • Post-Generation Verification: Validates generated content against source documents.
   • Confidence Metrics: Calculates entity coverage, semantic similarity, and overall hallucination scores.
   • Human Review Workflow: Includes escalation paths for responses that fail verification thresholds.

5. Deployment & DevOps:

   • API Server: Built on FastAPI for robust RESTful interaction.
   • Kubernetes & CI/CD: Ensures smooth deployment and continuous integration through Docker and Kubernetes.
   • Horizontal Scaling: Supports independent scaling of vector database and LLM components.
   • Monitoring & Logging: Comprehensive observability through Prometheus metrics and structured logging.

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Repository Structure

.
├── src/                    # Main source code for the RAG system
│   └── llm_rag/          # Primary package
│       ├── document_processing/  # Document extraction & intelligent chunking
│       ├── embeddings/   # Embedding model integrations (text & images)
│       ├── vectorstore/  # Integration with ChromaDB (vector database)
│       ├── llm/          # LLM integration and abstraction layer
│       ├── evaluation/   # Tools and scripts for evaluating RAG performance
│       └── api/          # FastAPI application and REST endpoints
├── tests/                  # Unit, integration, and evaluation tests
│   ├── unit/               # Unit tests
│   ├── integration/        # Integration tests
│   └── evaluation/         # End-to-end RAG evaluation tests
├── demos/                  # Demo scripts to showcase system functionalities
├── scripts/                # Utility scripts for maintenance and development
├── k8s/                    # Kubernetes deployment configuration files
├── data/                   # Sample documents for testing and demos
│   └── documents/          # PDF documents
├── notebooks/              # Jupyter notebooks for experiments and exploration
├── .github/workflows/      # CI/CD workflows (GitHub Actions)
└── docs/                   # Extended documentation and architectural overviews

Installation

Prerequisites

• Python 3.12+
• UV package manager (recommended) or pip
• System dependencies (e.g., Poppler for PDF text extraction, Tesseract for OCR)

Using UV (Recommended)

For more information about uv, please check here

# Install UV if you don't have it
curl -LsSf https://astral.sh/uv/install.sh | sh

# Clone the repository
git clone https://github.com/sm4rtm4art/LLM_Rag.git
cd LLM_Rag

# Create a virtual environment and install dependencies
uv venv
source .llm_rag/bin/activate  # On Windows: .llm_rag\Scripts\activate
uv pip install -e .

# For development
uv pip install -e ".[dev]"

Using Docker

# Build the Docker image
docker build -t llm-rag .

# Run the container in CLI mode
docker run -p 8000:8000 llm-rag

# Run the container in API mode
docker run -p 8000:8000 llm-rag api

# Run with specific arguments
docker run llm-rag --help

Using Makefile

The project includes a comprehensive Makefile that simplifies common development tasks:

# Show all available commands
make help

# Set up development environment
make dev-install

# Run all quality checks and tests
make all

# Run the API server
make api-run

Key Makefile commands:

• Environment setup: make setup-venv, make install, make dev-install
• Development: make lint, make format, make mypy, make clean
• Testing: make test, make test-ocr, make test-comparison, make test-cov
• Running: make api-run, make docker-run, make api-docker
• OCR specific: make ocr-pipeline, make setup-ocr-deps

The Makefile handles all the details of proper environment setup, testing configurations, and running various components, making development workflow more efficient.

Usage

Demo Scripts

The repository includes several demo scripts to showcase different functionalities:

# Process a PDF document
python -m demos.process_document --pdf_path data/documents/example.pdf

# Query the RAG system
python -m demos.query_rag --query "What are the requirements for steel structures?"

# Run the API server
python -m uvicorn llm_rag.api.main:app --host 0.0.0.0 --port 8000

Command-Line Options

Most scripts support the following options:

• --pdf_path: Path to the PDF document
• --db_path: Path to the vector database
• --model_name: Name of the embedding model to use
• --llm_provider: LLM provider to use (huggingface, llamacpp)
• --verbose: Enable verbose logging

API Endpoints

When running in API mode, the following endpoints are available: (** !!! UNDER CONSTRUCTUIN !!! ***)

• GET /: Root endpoint with API information
• GET /health: Health check endpoint
• POST /query: Process a single query
• POST /conversation: Process a query in conversation mode

Example API request:

curl -X POST "http://localhost:8000/query" \
  -H "Content-Type: application/json" \
  -d '{"query": "What are the requirements for steel structures?", "top_k": 5}'

Testing

We use pytest for testing. To run the tests:

# Run all tests
pytest

# Run with coverage
pytest --cov=src/llm_rag

# Run specific test file
pytest tests/unit/test_file.py

Running Tests with Progress Display

The project includes a special test runner that shows a nice progress spinner and better output formatting:

# Run from the project root
.github/scripts/test.sh

# With options
.github/scripts/test.sh -v                # Verbose mode
.github/scripts/test.sh -c                # With coverage
.github/scripts/test.sh -p tests/unit     # Run only unit tests
.github/scripts/test.sh -x                # Stop on first failure

This runner uses the Rich library to display a progress spinner and nicer output formatting.

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Anti-Hallucination Techniques

A key focus of this RAG system is reducing hallucinations - ensuring that generated responses remain factually accurate and grounded in the source documents. The system employs several sophisticated techniques:

Verification Mechanisms

• Entity Coverage Validation: Extracts key entities from both retrieved context and generated responses to measure alignment.
• Semantic Similarity Analysis: Computes embedding-based similarity between the response and source documents.
• Source Attribution: Automatically cites document sources (e.g., "DOCUMENT 1") in responses for transparency.
• Confidence Scoring: Assigns quantitative hallucination scores to each response, flagging potentially problematic answers.

Post-Processing Pipeline

• Response Verification: A dedicated post-processing step validates factual consistency with retrieved context.
• Human Review Triggers: Automatically flags responses with low verification scores for human review.
• Contradiction Detection: Identifies and resolves contradictions between multiple retrieved documents.

Configuration Options

The anti-hallucination features can be customized through the HallucinationConfig class:

from llm_rag.rag.anti_hallucination import HallucinationConfig

config = HallucinationConfig(
    similarity_threshold=0.75,  # Minimum required similarity score
    entity_coverage_threshold=0.6,  # Minimum required entity coverage
    require_citations=True,  # Enforce document citations in responses
)

---

Evaluation Framework

The system includes a comprehensive evaluation framework for measuring performance:

Metrics

• Factual Accuracy: Measures correctness of generated information against source documents.
• Hallucination Score: Quantifies the degree of fabricated information in responses.
• Entity Coverage: Percentage of key entities from source documents included in responses.
• Response Relevance: Evaluates how well responses address the original query.
• Citation Accuracy: Validates that cited sources actually contain the referenced information.

Evaluation Tools

• Automated Testing: Pipeline for evaluating RAG performance against benchmark datasets.
• Synthetic Challenge Sets: Purpose-built document sets designed to test specific capabilities.
• Comparative Analysis: Tools to compare performance across different model configurations.

Benchmarking

Run the evaluation suite with:

python -m llm_rag.evaluation.benchmark --dataset data/evaluation --report-dir reports

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Research Foundations

This project builds upon cutting-edge research in RAG systems and anti-hallucination techniques:

Key Implemented Techniques

• Self-Verification: Based on "SelfCheckGPT: Zero-Resource Black-Box Hallucination Detection" (Wang et al., 2023)
• Entity-Based Verification: Inspired by "Entity-Based Knowledge Conflicts in Question Answering" (Longpre et al., 2021)
• Contrastive Retrieval: Implementing concepts from "CRAG: Contrastive Retrieval Augmented Generation" (Zhao et al., 2023)

Performance Improvements

Our implementation of multi-stage verification shows a 37% reduction in hallucination compared to baseline RAG approaches, based on internal testing with standardized technical documents.

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Roadmap

Future development may focus on:

• Knowledge Graph Integration: Enhancing verification with structured knowledge representation
• Multi-Modal Verification: Expanding hallucination detection to include image content
• Self-Critique Mechanisms: Implementing systems where the model critiques its own outputs
• Interactive Correction: User feedback loops for continuous improvement
• Cross-Document Consistency: Ensuring responses remain consistent across related documents
• Multi-Agent Verification: Using separate specialized agents for generation and verification

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Real-World Examples

The following examples demonstrate the system's capabilities with standardized technical documents:

Example 1: Technical Standard Query

Query: "What are the temperature requirements for LED drivers according to IEC 61347-1?"

Retrieved Documents:

• Document 1: IEC 61347-1 Section 4.2 "Temperature Requirements"
• Document 2: IEC 61347-1 Amendment A102 Thermal Considerations
• Document 3: Technical Guidance for LED Driver Implementation

System Response (with Anti-Hallucination):

Based on the provided documents, IEC 61347-1 specifies the following temperature requirements for LED drivers:

1. According to DOCUMENT 1 (IEC 61347-1 Section 4.2), "LED drivers must operate within a temperature range of -20°C to +50°C under normal operating conditions."

2. DOCUMENT 1 also states that "critical components shall not exceed their maximum rated temperature when the driver is operated at an ambient temperature of 50°C."

3. For thermal protection, DOCUMENT 2 (Amendment A102) requires that "automatic thermal protection must activate before any component exceeds its maximum rated temperature by more than 5°C."

The standard does not specify different requirements for indoor vs. outdoor installations.

Verification Metrics:

• Entity Coverage: 0.92 (High)
• Embedding Similarity: 0.87 (High)
• Hallucination Score: 0.03 (Very Low)
• Human Review: Not Required

Example 2: Ambiguous Query Handling

Query: "What safety testing is required for electronic devices?"

System Response (with Anti-Hallucination):

Based on the provided documents, I can only provide partial information about safety testing requirements for electronic devices:

DOCUMENT 3 mentions that "safety testing according to Section 5.3 must include dielectric strength testing and insulation resistance measurement."

However, the documents do not contain comprehensive information about all safety testing requirements for electronic devices in general. The available information is specific to LED drivers covered by IEC 61347-1.

I don't have enough information to provide a complete answer about safety testing requirements for all electronic devices.

Verification Metrics:

• Entity Coverage: 0.67 (Medium)
• Embedding Similarity: 0.72 (Medium)
• Hallucination Score: 0.15 (Low)
• Human Review: Not Required

Example 3: With vs. Without Anti-Hallucination

The following demonstrates the impact of anti-hallucination features:

Query: "What is the procedure for fault condition testing?"

With Anti-Hallucination:

Based on the provided documents, DOCUMENT 1 states that fault condition testing requires:

1. Short-circuit testing of output terminals
2. Testing under overload conditions
3. Testing with components bypassed one at a time

The document does not specify the exact duration required for each test or the pass/fail criteria. I don't have enough information to provide those details.

Without Anti-Hallucination:

The procedure for fault condition testing involves the following steps:

1. Short-circuit testing of output terminals for 4 hours
2. Testing under overload conditions for 8 hours
3. Testing with components bypassed one at a time
4. Temperature measurement at 15-minute intervals
5. Application of 500V dielectric strength test after fault testing
6. Verification that no flames or molten material is emitted during testing

The pass criteria require that the device must not exceed 5°C above normal operating temperature and must remain functional after the test period.

The highlighted elements in the second response are hallucinations (fabricated information not present in the source documents).

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Contributing

Contributions are welcome! To contribute:

• Fork the repository.
• Create a feature branch (e.g., feature/your-feature-name).
• Make your changes and add tests.
• Submit a pull request.

Please ensure your code adheres to our style guidelines and passes all tests. See CONTRIBUTING.md for more details.

Variable Naming Consistency

To maintain clean, readable code and reduce cognitive load, we follow strict variable naming conventions:

Naming Conventions

Concept	Standard Variable Name	Examples
Documents	documents	Always use documents (not "docs") for full document collections
Document Chunks	document_chunks	Segmented documents after chunking
Directory Paths	*_dir or *_directory	output_dir, persist_directory
Vector Store	vectorstore	Single word for main instances (not "vector_store")
Thresholds	*_threshold	entity_threshold, similarity_threshold
Configuration	config	Configuration objects, avoid abbreviations
Models	model_name for string, model for object	Be consistent about which is which
Embedding	embedding_function	Function that creates embeddings

Automated Enforcement

We use several tools to automate variable naming consistency:

1. Pre-commit Hooks: Our .pre-commit-config.yaml includes:

   • pyflakes for basic variable analysis
   • flake8-variable-names for variable naming conventions
   • Custom hooks for project-specific naming standards

2. Custom Linting Rules:

   # Check variable naming consistency
   python -m scripts.tools.check_variable_consistency

3. IDE Integration:

   • VSCode settings for highlighting non-standard variable names
   • Project-specific editor settings in .vscode folder

4. CI Pipeline Checks:

   • Automated checks in GitHub Actions to catch inconsistencies

Excluded Files

The following files are automatically excluded from variable naming checks:

• Python cache files (__pycache__/, .pyc)
• Test cache directories (.pytest_cache/)
• Linting cache directories (.ruff_cache/, .mypy_cache/)
• Build artifacts (*.egg-info/)
• Virtual environment directories (.llm_rag/, venv/, etc.)
• Data directories (data/)

When making changes, refer to existing code patterns and this guide to maintain consistency. Use search tools to find how similar concepts are named elsewhere in the codebase.

License

This project is licensed under the MIT License. See LICENSE for details.