📝 #

Project Publication: Retrieval-Augmented Generation (RAG) Assistant

📌 #Abstract

This project is part of the Agentic AI Developer Certification (AAIDC) Module 1 and demonstrates the development of a Retrieval-Augmented Generation (RAG) Assistant. The assistant enhances AI reliability by combining document retrieval with natural language generation, producing accurate and context-aware responses.

🔍# Introduction

Large Language Models (LLMs) are powerful but often prone to hallucinations, generating incorrect or unverifiable answers. This project addresses that challenge by implementing a RAG architecture, grounding responses in actual documents. The goal is to showcase how retrieval mechanisms can be integrated with LLMs to build fact-based, trustworthy assistants.

⚙️# System Overview

The workflow of the RAG Assistant begins with document loading, where text files (.txt) are ingested from a designated folder. Once the documents are available, they undergo chunking, a process of splitting them into smaller segments. This step is crucial because it ensures that the content fits within token limits while also improving retrieval granularity.
Next, each chunk is converted into a semantic vector using OpenAI Embeddings. These embeddings capture the meaning of the text rather than just the raw words. Once generated, the embeddings are stored in Chroma, a vector database designed for fast and efficient similarity searches.
When a user submits a query, the system performs retrieval, pulling the most relevant chunks based on semantic similarity. These retrieved chunks are then passed as context to a large language model (LLM) such as GPT-3.5 via LangChain. Finally, the LLM generates a response that is both accurate and natural-sounding, grounded in the retrieved documents rather than hallucinated knowledge.

🛠 #Tools & Frameworks

This project integrates several modern AI frameworks and libraries to deliver the RAG pipeline. LangChain acts as the workflow orchestrator, connecting the components seamlessly. OpenAI Embeddings provide high-quality semantic vectorization of document chunks, while Chroma serves as the vector database, storing embeddings and enabling similarity-based retrieval. For the user interface, Streamlit is used to build an interactive and accessible front end that allows users to query the assistant in real time.

💻 #Installation Instructions

To install the RAG Assistant, start by cloning the official GitHub repository with git clone https://github.com/gone-vamshi233/rag-assistant.git and then navigate into the project folder. Next, create a virtual environment to keep dependencies isolated: on macOS and Linux, this can be done with python3 -m venv venv followed by source venv/bin/activate, while on Windows the commands are python -m venv venv and venv\Scripts\activate. Once the virtual environment is active, install the required dependencies using pip install -r requirements.txt. Finally, create a .env file in the project’s root directory and add your OpenAI API key in the format OPENAI_API_KEY=your_api_key_here. With these steps completed, the environment will be fully prepared to run the assistant.

🚀 #Usage Instructions

After completing the installation, using the assistant is simple. Place your text documents inside the data/ folder so they can be processed for retrieval. To start the application, run streamlit run app.py from the project directory, which will launch the interactive web interface in your browser. From there, you can type queries into the input box and receive context-aware answers grounded in your uploaded documents.

🛡 #Maintenance & Support Details

Maintaining the assistant involves a few simple steps. Whenever new documents need to be added, place them in the data/ folder and rebuild the embeddings to ensure they are included in retrieval. If the assistant produces incomplete or missing answers, double-check that all relevant documents have been uploaded and processed. For API-related issues, confirm that the .env file contains a valid OpenAI API key. These steps ensure smooth and reliable performance of the system.

✨ #Key Features

The RAG Assistant produces context-aware responses that are grounded in retrieved documents, ensuring accuracy and reliability. Its modular design provides flexibility, allowing components such as the language model, embeddings, or vector database to be swapped with minimal effort. The system is also highly scalable, supporting dynamic updates as new documents are added without disrupting existing workflows. Finally, it offers a user-friendly interface built with Streamlit, enabling intuitive and accessible querying for end users.

📚 #Example Use Case

A practical example of the RAG Assistant in action can be seen in an organizational knowledge base scenario. Suppose a company uploads its internal policies and guidelines into the system. When an employee queries, “What is the company’s leave policy for interns?” the assistant retrieves the relevant section directly from the uploaded documents and provides a concise, reliable answer. This not only saves time but also ensures that responses remain accurate and fact-based. Such a setup demonstrates the assistant’s usefulness in supporting enterprise knowledge management, educational resources, and research assistance.

🧪 #Evaluation & Best Practices

The assistant follows best practices to ensure reliable performance. Document chunking is used to optimize token usage and improve retrieval accuracy. The semantic similarity search mechanism ensures that even when queries are phrased differently, the most relevant information is still retrieved. Grounded answers significantly reduce the hallucinations that often occur in pure LLM-based systems. Moreover, the modular pipeline allows easy integration with new tools and APIs, making the system adaptable for evolving use cases.

🧩 #Embedding Model Choice Explanation

For semantic vectorization, the project uses OpenAI’s text-embedding-3-small model. This choice strikes a balance between computational efficiency and embedding quality, ensuring fast yet accurate retrieval. For response generation, GPT-3.5 is employed to produce fluent, context-aware answers. Together, this combination reduces hallucinations while maintaining high-quality outputs, making it well-suited for real-world applications where factual reliability is critical.

🧠 #Memory Mechanisms Explanation

The system incorporates lightweight memory mechanisms to keep responses grounded and context-aware. Retrieved document chunks act as temporary contextual memory for each query. For every user question, the system retrieves the top-k most relevant chunks from Chroma, ensuring that the LLM only considers the most important information.

This retrieved context is then injected into the prompt, enabling the LLM to generate an informed response. In longer conversations, the assistant can append previous query–response pairs, effectively building a short-term memory of the session. This design balances efficiency with contextual accuracy, avoiding unnecessary computational overhead while still maintaining continuity in dialogue.

⚠️ #Limitations

Despite its strengths, the system has certain limitations. Its effectiveness depends largely on the quality and coverage of the uploaded documents. If the dataset is incomplete, the assistant’s responses will also be limited. Performance can degrade when handling very large datasets or queries that are highly ambiguous. At present, the assistant is optimized for plain text files, though future iterations could extend support to multiple formats such as PDF or CSV.

📊 #Retrieval Evaluation

The retrieval performance of the system is evaluated across multiple metrics:

Precision: proportion of retrieved chunks that are truly relevant.

Recall: measures how many of the relevant chunks were successfully retrieved.

F1-score: harmonic mean of precision and recall.

In addition, the system tracks latency (time taken to retrieve the top-k chunks). Keeping latency low is important for smooth and responsive user experience.

🔚 #Conclusion

This project demonstrates how Retrieval-Augmented Generation (RAG) can enhance the reliability of large language models by grounding their outputs in real documents. With modular design, scalability, and an easy-to-use interface, the RAG Assistant can be applied in enterprise knowledge bases, education, and research. Future improvements will focus on adding support for multiple document formats and improving long-term memory mechanisms.

#Tags:
#RAG #AI #LLM #LangChain #VectorDatabase #Chroma #Streamlit #OpenAI #Embeddings