Inspiration
I wanted a private, offline-capable research assistant that keeps sensitive data under my control. Most existing assistants rely on cloud APIs, raising concerns about confidentiality, compliance, and data ownership. By combining local LLMs with retrieval-augmented generation (RAG), I set out to build a secure tool that can answer questions using my own documents without sending them anywhere.
What it does
Secure Research Assistant lets you:
- Upload and store documents locally (PDF, DOCX, TXT).
- Preprocess them into clean, searchable chunks.
- Convert content into embeddings and store in a local vector database (FAISS).
- Ask natural-language questions and get answers grounded in your documents.
- Use a 20B parameter local LLM (via LM Studio) for accurate, context-aware responses.
- View answers, response times, and source references through a Streamlit UI.
Everything runs locally—ensuring privacy and security.
How I built it
Backend: LM Studio hosting a 20B LLM model, exposed via local API. Document Pipeline: Python scripts for upload, preprocessing, chunking, and embedding generation. Vector Store: FAISS for fast similarity search and retrieval. RAG Workflow: Top-K relevant chunks are retrieved, inserted into prompts, and passed to the LLM for context-aware answers. Frontend: Streamlit app for file uploads, queries, answer display, and history tracking. Configuration: Environment variables and Docker setup to simplify installation and reproducibility.
Challenges I ran into
-Running a 20B model locally required careful resource management. -Splitting documents into meaningful chunks while respecting LLM context limits. -Preventing hallucinations when context was insufficient. -Handling PDF/Word formatting issues during preprocessing. -Designing a UI that shows transparency (sources, time taken) without clutter. Simplifying setup so others can use it easily.
Accomplishments that I'm proud of
-Built a complete RAG pipeline from document ingestion to LLM-generated answers. -Everything works fully offline—no data leaves the machine. -Flexible, configurable setup with open-source components. -A functional Streamlit UI that makes the tool user-friendly. Support for large local models (20B) with reasonable performance.
What I learned
-Practical RAG implementation: embeddings, vector DBs, and prompt design trade-offs. -Document parsing challenges and best practices for preprocessing. -Running large local models effectively and handling their resource constraints. -Designing transparent UIs for AI apps that foster trust.
- Engineering best practices for environment management, reproducibility, and optimization.
What's next for secure-research-assistant
-Smarter fallback handling when retrieved context is weak. -Support for scanned PDFs and OCR. -Hybrid search (lexical + semantic) for better retrieval accuracy. -Enhanced UI with chunk-level highlighting of sources. -Multiple LLM backend support (switch between models for speed/accuracy). -Incremental indexing and caching for faster updates. -Encryption and access control for stored data. -Packaging via Docker/desktop installer for easier deployment. -Scalability improvements to handle large document collections smoothly.
Built With
- llm
- lmstudio
- openai
