An intelligent research assistant that retrieves, summarizes, and generates insights from large text sources using Retrieval-Augmented Generation (RAG) and LLMs.
This project demonstrates how to build an end-to-end AI system that can understand context, answer questions, and provide factual summaries using modern machine learning pipelines.
-
Retrieval-Augmented Generation (RAG) — combines vector database retrieval with LLM reasoning for accurate responses.
-
Chunking and Embeddings — text data is chunked and embedded for efficient semantic search.
-
Vector Database Integration — uses Chroma to store and retrieve embeddings.
-
LLM Integration — powered by Hugging Face Transformers (Flan-T5 / Gemma) for generation.
-
Prompt Engineering Layer — uses few-shot, Chain-of-Thought (CoT), and Tree-of-Thought (ToT) prompting strategies.
-
Fast Inference Pipeline — optimized with caching, quantization, and memory-efficient loading.
-
Guardrails — adds data safety filters and fallback prompts to prevent harmful outputs.
Category Tools Used Languages Python Frameworks PyTorch, Transformers, LangChain Database Chroma / FAISS (for vector search) Backend Flask / FastAPI Deployment Docker, Hugging Face Spaces / AWS
Data Source → Text Cleaning → Chunking → Embeddings → Vector DB ↓ Query Processing ↓ RAG Pipeline → LLM Response ↓ Flask/FastAPI Deployment
⚙️ Installation
git clone https://github.com//ai-research-assistant.git cd ai-research-assistant
python -m venv .venv source .venv/bin/activate # On Windows: .venv\Scripts\activate
pip install -r requirements.txt
python app.py
-
Add retrieval feedback loops for continuous learning.
-
Integrate more evaluation datasets (TruthfulQA, MMLU).
-
Enhance UI for interactive research query submission.
-
Deploy on AWS Lambda / ECS for scalable inference.
Then open your browser at http://127.0.0.1:5000 and start asking research questions.
Example query:
“Summarize the key challenges in deploying LLMs at scale.”