Abstract

This project implements a simple Retrieval-Augmented Generation (RAG) assistant specifically designed for educational purposes. It achieves its goals without relying on external APIs, GPU resources, or complex deep learning frameworks, making it an accessible tool for understanding core RAG principles. The system demonstrates robust functionality by utilizing scikit-learn's TF-IDF vectorization for document representation, cosine similarity for efficient information retrieval, and an intelligent rule-based mechanism for generating contextually relevant responses.

The implementation successfully processes various text documents, transforms them into sparse vector representations, and performs similarity-based searches to gather pertinent information. It then crafts contextually appropriate answers by blending rule-based knowledge with the content of the retrieved documents. Key features of this system include automatic sample document creation for quick setup, comprehensive conversation history tracking, optional FAISS integration for enhanced performance, and detailed logging that fosters educational transparency. Requiring only Python's standard libraries alongside scikit-learn, this RAG assistant is an ideal learning platform for environments with limited computational resources.

Introduction

Retrieval-Augmented Generation (RAG) systems represent a significant advancement in AI, combining the power of document retrieval with sophisticated response generation to deliver grounded and contextually relevant answers. This project introduces a lightweight RAG system meticulously designed to illuminate these fundamental concepts without the usual complexities and intensive resource demands often associated with production-grade systems.

The primary focus of this implementation is educational transparency, taking precedence over raw performance optimization. Every operation within the pipeline is accompanied by detailed logging, processing times are meticulously tracked and displayed, and the modular architecture empowers students to easily examine, understand, and modify individual components. This hands-on approach is crucial for grasping the intricate mechanics of RAG systems, moving beyond merely using abstract, black-box APIs.

This system effectively addresses common educational hurdles in RAG: namely, the prohibitive costs of external API requirements, the need for complex infrastructure, and existing implementations that obscure fundamental operations behind layers of abstraction. By exclusively utilizing accessible technologies such as TF-IDF for vectorization and rule-based methods for generation, students gain a clear and comprehensive understanding of every step involved in the RAG process.

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System Architecture

The RAG assistant is built on a robust four-stage pipeline that transparently demonstrates the entire RAG process, from raw text to intelligent response.

1. Document Ingestion

The process begins with the load_documents_from_directory method, which efficiently scans and processes .txt files located in a specified directory. For each file, the method creates comprehensive Document objects, embedding not only the raw text content but also crucial metadata such as the source file paths and original filenames. This foundational step ensures that all textual information is properly organized and accessible for subsequent stages of the RAG pipeline.

2. Text Processing

Following ingestion, the SimpleTextSplitter class takes over, meticulously dividing the loaded documents into manageable segments or "chunks." This specialized implementation prioritizes semantic coherence by splitting text primarily along sentence boundaries, utilizing common punctuation marks (.!?). It strives to maintain a target chunk size of approximately 1000 characters, incorporating a 200-character overlap between consecutive chunks. This overlap is vital for preserving context across segments. Each generated chunk is enriched with metadata, including a unique chunk ID and detailed source information, ensuring traceability and integrity throughout the system.

3. Vector Indexing

The core of the retrieval mechanism lies in the Vector Indexing stage, where the system employs scikit-learn's TfidfVectorizer. This vectorizer is configured with specific parameters to optimize representation: a maximum of 5000 features is set to manage vocabulary size effectively, an N-gram range of (1,2) is used to capture both single words (unigrams) and common phrases (bigrams), and standard English stop words are removed to focus on more meaningful terms. The TfidfVectorizer automatically builds its vocabulary from the entire document collection. Once configured, each processed document chunk is transformed into a sparse TF-IDF vector, which is then stored in an index optimized for rapid similarity search.

4. Query Processing

Upon receiving a user query, the system initiates the Query Processing stage. The first step involves converting the incoming query into a TF-IDF vector, utilizing the exact same vocabulary that was built during the document indexing phase. This ensures that the query and document vectors exist within the same dimensional space, allowing for accurate comparison. The system then calculates the cosine similarity between the query vector and all existing document vectors. Based on these similarity scores, it retrieves the top-k most similar documents, but only if their similarity score exceeds a predefined threshold of 0.1, ensuring only highly relevant information is considered. Finally, responses are generated using a sophisticated rule-based knowledge system, which is dynamically enhanced and enriched with content extracted from these retrieved documents.

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Implementation Details

Document Processing

def split_text(self, text: str) -> List[str]:
    # Split into sentences using regex patterns
    sentences = re.split(r'[.!?]+', text)
    sentences = [s.strip() for s in sentences if s.strip()]

    chunks = []
    current_chunk = ""

    for sentence in sentences:
        if len(current_chunk) + len(sentence) < self.chunk_size:
            current_chunk += sentence + ". "
        else:
            if current_chunk:
                chunks.append(current_chunk.strip())
            current_chunk = sentence + ". "

This specific approach to split_text is crucial for maintaining semantic boundaries within the documents while simultaneously adhering to configurable chunk sizes. By leveraging regular expressions, sentences are accurately identified and then intelligently grouped. The logic ensures that a current_chunk is extended with new sentences until it approaches the self.chunk_size. Once that threshold is met or exceeded, the current chunk is finalized, and a new one begins with the subsequent sentence. This methodical process prevents the arbitrary cutting of sentences and ensures that each chunk remains a coherent and contextually rich piece of information, optimizing it for the TF-IDF vectorization and subsequent retrieval.

Vector Similarity Search

def retrieve_relevant_documents(self, query: str, top_k: int = 3):
    # Vectorize query using same vocabulary as documents
    query_vector = self.vectorizer.transform([query])

    # Calculate cosine similarities
    similarities = cosine_similarity(query_vector, self.document_vectors)[0]

    # Get top results above similarity threshold
    top_indices = np.argsort(similarities)[-top_k:][::-1]

    results = []
    for idx in top_indices:
        if similarities[idx] > 0.1:  # Minimum relevance threshold
            results.append((self.documents[idx], float(similarities[idx])))

The retrieve_relevant_documents method is central to the system's ability to find relevant information efficiently. When a query is received, the system first converts it into a TF-IDF vector, crucially using the identical vocabulary that was built during the document indexing phase. This ensures that both the query and document vectors exist within the same dimensional space, enabling accurate and meaningful comparisons. A pivotal step then calculates the cosine similarity between the vectorized query and all pre-indexed document vectors. Cosine similarity, which measures the cosine of the angle between two vectors, effectively determines their similarity regardless of their magnitude; a smaller angle indicates higher similarity. The system then meticulously retrieves the top-k most similar documents, but only if their similarity score surpasses a predefined threshold of 0.1. This ensures that only genuinely relevant and meaningfully related content is considered and included in the final set of results, contributing to the quality of the generated response. The implementation clearly demonstrates fundamental vector space retrieval concepts through these transparent operations.

Response Generation Strategy

The system employs a hybrid strategy for response generation, expertly combining structured rule-based knowledge with dynamically retrieved content to provide comprehensive and contextually rich answers.

• Rule-Based Knowledge: The system maintains an internal repository of structured information pertaining to core topics, such as definitions and components of LangChain, various vector databases (FAISS, Pinecone, Weaviate, Chroma, Qdrant, Milvus), the intricate processes and applications of RAG systems, and the specific usage patterns of FAISS. This foundational knowledge provides a robust baseline for answering common queries directly.
• Context Integration: When the retrieval mechanism successfully identifies and extracts relevant documents, the response generation intelligently combines these rule-based definitions with pertinent excerpts from the retrieved document content. This blending ensures that answers are both accurate and deeply grounded in the provided context, leveraging the specific nuances found in the source material.
• Fallback Handling: For queries that do not directly align with the predefined rule-based knowledge, the system gracefully handles the situation by extracting key sentences directly from the most relevant retrieved documents. In instances where no specific matches are found, it provides general guidance or directs the user appropriately, ensuring a helpful interaction even without a perfect match.

Safety and Content Filtering

The system incorporates several safety measures and content filtering mechanisms, making it robust and appropriate for educational environments. This comprehensive approach ensures responsible operation and fosters a secure learning experience.

• Input Validation: Rigorous checks are in place for all inputs. File system path validation prevents unauthorized access or manipulation, ensuring that the system interacts with only approved file locations. Character encoding is meticulously handled to guarantee consistent and accurate text processing across various documents. Robust error handling is implemented for malformed or inaccessible files, providing clear feedback without crashing the system. Additionally, query length and format validation help prevent malicious inputs and guide users toward effective query formulation.
• Content Filtering: During the document loading process, content validation is performed to ensure the integrity and appropriateness of the source material. Response appropriateness is continuously checked through rule-based filters, which are designed to prevent the generation of unsuitable or irrelevant answers. All operations are subject to comprehensive logging for ongoing monitoring and analysis, providing an audit trail for system behavior. Error messages are thoughtfully designed to be educational rather than purely technical, guiding users to understand issues without overwhelming jargon.
• Privacy Protection: The system is built with privacy in mind. There is no permanent storage of user queries; all session data remains strictly local and temporary, ensuring that sensitive information is not retained. Document processing occurs entirely in memory without any external transmission of data. While optional index saving is available for performance improvements, it is not required for basic operation, and no user data persistence is mandated, reinforcing privacy protection.

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Setup and Results

Testing Configuration

The RAG system was rigorously tested using a set of automatically generated sample documents. These documents were crafted to cover a diverse range of topics relevant to the RAG domain, ensuring comprehensive evaluation of the system's capabilities.

• LangChain framework overview: Providing definitions, components, and typical usage patterns.
• Vector databases guide: Covering popular options like FAISS, Pinecone, Weaviate, Chroma, Qdrant, and Milvus.
• RAG systems explanation: Detailing the core process, inherent benefits, and practical applications.

Sample Document Statistics

The testing involved a concise yet representative document collection, ensuring efficient processing while demonstrating functionality.

• The collection comprised 3 comprehensive text documents.
• Total content ranged from approximately 1,500-2,000 words.
• After sentence-boundary splitting, these documents yielded 8-12 manageable chunks.
• The TF-IDF vectorization process generated a vocabulary size of 500-800 unique terms.
• The complete indexing process, from loading to vector creation, consistently took less than 1 second.

Performance Measurements

The system transparently tracks and displays several key performance metrics, providing insights into its operational efficiency at various stages.

• Document loading time: Measures the time taken to ingest raw text files.
• Vector index creation time: Tracks the duration required to convert text chunks into a searchable vector index.
• Per-query processing time: Typically ranging from 0.05 to 0.15 seconds, indicating efficient real-time response generation.
• Number of chunks created: Reflects the output of the text processing stage.
• Vocabulary size: Shows the complexity of the TF-IDF model.
• Similarity scores: Reported for retrieved documents, offering transparency into retrieval relevance.

Query Testing Examples

To illustrate its capabilities, the system was tested with various query types, demonstrating its ability to retrieve and synthesize information effectively.

• Definition Query: "What is LangChain?"
  • The system successfully retrieved 2 highly relevant document chunks.
  • Associated similarity scores were 0.734 and 0.456, indicating strong matches.
  • Processing time for this query was a swift 0.067 seconds.
  • The generated response skillfully combined a rule-based definition with pertinent content from the retrieved documents.
• Process Query: "How does RAG work?"
  • This query led to the retrieval of 3 relevant document chunks.
  • Similarity scores for these chunks were 0.623, 0.445, and 0.234.
  • The query was processed in an efficient 0.089 seconds.
  • The response provided a clear, step-by-step explanation of the RAG process, drawn directly from the content of the retrieved documents.
• Comparison Query: "Which vector databases are mentioned?"
  • The system accurately retrieved 2 relevant document chunks.
  • Similarity scores of 0.567 and 0.445 confirmed the relevance of the retrieved content.
  • Processing time for this query was 0.073 seconds.
  • The response precisely listed the specific vector databases identified within the retrieved documents.

System Features and Capabilities

The RAG assistant is equipped with a range of features designed to facilitate an effective learning experience and demonstrate core RAG functionalities.

• Core Functionality: The system provides a complete RAG pipeline, encompassing everything from initial document loading to the final response generation. It includes automatic sample document creation, enabling immediate testing and exploration. An interactive command-line interface further enhances its educational utility. For scenarios requiring enhanced search performance, an optional FAISS integration is available, and the system supports vector index saving and loading to significantly improve startup times for subsequent sessions.
• Educational Features: Transparency is a cornerstone of this project. It offers comprehensive logging of all internal operations, displays processing times to foster awareness of performance, and reports similarity scores for retrieved documents, making the retrieval process entirely transparent. Source attribution is also provided, clearly indicating which documents contributed to the generated responses. Furthermore, it tracks conversation history within sessions and offers example queries to guide user exploration.
• Command Interface: The interactive command-line interface provides several intuitive commands: help to display available commands and usage guidance, history to show previous questions and answers from the current session, examples to provide suggested queries for testing, and quit to gracefully exit the system.

Technical Specifications

The system is built on a lean and efficient technical foundation, making it highly accessible.

• Dependencies: The core requirements include numpy and scikit-learn. faiss-cpu is an optional dependency for accelerated similarity search.
• Memory Usage: Memory consumption is minimal, typically ranging from 20-50MB, depending on the size of the document collection.
• Storage: Storage requirements are also very low, with sample documents occupying approximately 15KB and the optional index around 10KB.
• Performance: The system delivers sub-100ms query processing times for typical document collections, ensuring a responsive user experience.
• Scalability: It is well-suited for document collections up to several hundred kilobytes, making it effective for numerous educational and small-scale applications.

Configuration Options

The system offers several configurable parameters, allowing users to experiment with different settings and observe their impact on performance and behavior.

• Text Processing: The SimpleTextSplitter can be configured with chunk_size (target chunk size in characters, default 1000) and chunk_overlap (overlap between chunks, default 200).
• Vectorization: The TfidfVectorizer allows customization of max_features (maximum vocabulary size, default 5000), stop_words (e.g., 'english' for common English words), and ngram_range (e.g., (1, 2) to include unigrams and bigrams).
• Retrieval: Key retrieval parameters include similarity_threshold (minimum similarity for result inclusion, default 0.1) and top_k_documents (maximum number of documents retrieved per query, default 3).

Limitations and Future Enhancements

While designed for educational efficacy and transparency, the current system has specific limitations that also present exciting opportunities for future development.

Current Limitations

• English Only: The current implementation lacks multi-language support.
• Text Files Only: Document input is restricted to .txt format.
• Rule-Based Generation: The sophistication of response generation is inherently limited compared to advanced Large Language Models (LLMs).
• Static Knowledge: The system does not support real-time information updates.
• Simple Similarity: It utilizes basic cosine similarity without more advanced ranking algorithms.

Potential Enhancements

• Document Format Support: Expanding input capabilities to include formats like PDF, Word, and HTML.
• Dense Embeddings: Integrating optional support for dense embeddings using sentence transformers for potentially more nuanced semantic retrieval.
• Web Interface: Developing a user-friendly graphical interface using frameworks like Gradio or Streamlit for broader accessibility and ease of use.
• Evaluation Metrics: Implementing automated assessment metrics to objectively evaluate retrieval and response quality.
• Multi-Language Support: Enhancing the system to handle and process international character sets and multiple languages.

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Installation and Usage

System Requirements

The RAG assistant is designed for minimal resource consumption, ensuring broad accessibility.

• Python: Version 3.8 or higher.
• RAM: A minimum of 2GB RAM.
• Disk Space: Approximately 100MB of disk space.
• GPU: No GPU is required for operation.
• Internet Connection: No internet connection is required for basic functionality.

Installation Steps

To get the RAG assistant up and running, follow these straightforward steps:

1. Clone the Repository:

   git clone https://github.com/E-Z1937/rag-assistant-aaidc.git

2. Navigate to Project Directory:

   cd rag-assistant-aaidc

3. Install Dependencies:
   It's recommended to install with a timeout for potentially slower connections:

   pip install --timeout 300 --retries 3 -r requirements.txt

   If you face issues, you can try the standard:

   pip install -r requirements.txt

Requirements File Contents

The requirements.txt file specifies the necessary Python packages:

langchain-core>=0.1.0
faiss-cpu>=1.7.4
python-dotenv>=1.0.0
numpy>=1.24.0
scikit-learn>=1.0.0
gradio>=4.0.0
pandas>=2.0.0
requests>=2.25.0

Usage Instructions

Once installed, interacting with the RAG assistant is simple and intuitive:

1. Run the Assistant: Execute the main Python script from your terminal:

   python rag_assistant.py

2. Sample Documents: The system will automatically create sample documents on its first run, providing immediate content for testing.
3. Processing and Indexing: These documents are then automatically processed and indexed using the TF-IDF methodology.
4. Interactive Prompt: An interactive command-line prompt will appear, ready to accept your natural language queries.
5. Commands:
   • Type help for a list of available commands and usage guidance.
   • Type history to view your previous questions and the system's answers from the current session.
   • Type examples to see suggested queries that demonstrate the system's capabilities.
   • Type quit to gracefully exit the system.

Conclusion

This project stands as a testament to the fact that effective Retrieval-Augmented Generation (RAG) systems can be implemented using widely accessible technologies, without compromising on educational value. The thoughtful combination of TF-IDF vectorization, precise cosine similarity search, and an intelligent rule-based response generation strategy provides a transparent and robust foundation for understanding the core concepts of RAG.

The implementation successfully processes diverse document collections, performs semantic searches efficiently, and generates contextually appropriate responses, all while maintaining complete operational transparency. A key achievement is the development of a fully functional RAG pipeline that requires no external dependencies beyond standard scientific Python libraries, making it incredibly accessible. Furthermore, its educational transparency, achieved through comprehensive logging and explainable operations, coupled with practical performance suitable for interactive learning scenarios, makes it an invaluable tool for students and developers alike.

The modular architecture of this system is a significant advantage, as it enables progressive enhancement. This design empowers students and developers to easily experiment with alternative chunking strategies, explore different similarity metrics, or integrate more advanced response generation approaches. Ultimately, this project provides a solid and flexible foundation for understanding both the current landscape of RAG implementations and the exciting future developments in retrieval-augmented systems.

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License: MIT - Open source for educational use and modification.