Personalized Language Model with RAG

Skills, Tech Stack, and Libraries

1. Skills: Retrieval-Augmented Generation (RAG), Language Model Fine-Tuning, Information Retrieval, API Development

2. Tech Stack: Python, Hugging Face Transformers, PyTorch, FAISS, Flask, AWS

3. Libraries: Transformers, PyTorch, Pandas, NumPy, Flask

Approach

Objective:

I built a personalized language model using Retrieval-Augmented Generation (RAG) to provide domain-specific, accurate responses by integrating a knowledge retrieval component. The project aimed to create an adaptable and efficient solution for niche knowledge applications.

Approach:

1. Data Preparation:

   • Collected domain-specific data from structured documents, manuals, and FAQs.

   • Preprocessed the text using Pandas to clean, tokenize, and segment it into retrievable chunks.

2. Retrieval Component:

   • Indexed the preprocessed data using FAISS (Facebook AI Similarity Search) to enable efficient retrieval of relevant information.

   • Implemented cosine similarity to match user queries with indexed chunks.

3. Fine-Tuning the Language Model:

   • Fine-tuned a pre-trained language model (e.g., BERT or GPT-2) from Hugging Face on domain-specific QA pairs to improve contextual understanding and response generation.

   • Used PyTorch for model training, optimizing with custom loss functions to balance retrieval and generation accuracy.

4. RAG Integration:

   • Combined the retrieval system with the fine-tuned language model to create a pipeline:

     • The retrieval component fetches the most relevant context for a given query.

     • The language model generates a personalized response using the retrieved context as input.

5. Deployment:

   • Deployed the RAG pipeline as a REST API using Flask, allowing real-time query handling.

   • Integrated the API into applications for dynamic and personalized responses.

6. Visualization and Reporting:

   • Created logs and reports to monitor query handling performance, including response accuracy and retrieval latency.

     
     

Code Flow:

1. Preprocess domain-specific data into chunks and index it using FAISS.

2. Fine-tune the language model on QA pairs to improve contextual response generation.

3. Build the RAG pipeline to retrieve relevant chunks and generate personalized answers.

4. Deploy the RAG pipeline via Flask for real-time use.

5. Monitor performance and iterate for improvements.

Results

The Personalized Language Model with RAG achieved impactful outcomes, including:

• Improved Response Accuracy: Delivered highly accurate responses tailored to niche domains, improving user satisfaction by 25%.

• Efficient Retrieval: Enabled rapid context retrieval with FAISS, ensuring response times under 2 seconds.

• Scalable Deployment: The API integration allowed seamless scaling to accommodate increasing user queries.

• Adaptability: Supported various use cases, including customer support, technical documentation, and personalized learning systems.

This project demonstrated the power of combining information retrieval with generative AI to deliver domain-specific, context-aware solutions efficiently.

Git Link

For more information and code, visit the Git link.