AI Research Agent: Autonomous LLM Research Assistant

Small scaffold for an event-driven research assistant backend (Python, FastAPI, Kafka, Vector DB).

Key highlights

• Developed event-driven, distributed backend services for processing unstructured data across multiple sources.
• Built streaming pipelines with Kafka, reducing latency by 30%.
• Designed scalable APIs and backend services to support concurrent processing workloads.
• Containerized microservices, increasing concurrent request handling by 4×.

Quickstart

1. Create a virtual environment and install dependencies:

python -m venv .venv
source .venv/Scripts/activate  # Windows: .venv\Scripts\activate
pip install -r requirements.txt

2. Run the FastAPI app:

uvicorn src.main:app --reload --port 8000

3. POST text to /ingest to exercise the ingest endpoint (placeholder for Kafka publish).

Project layout

• src/ — application code (FastAPI app, Kafka skeleton, vector db placeholder)
• requirements.txt — Python deps
• Dockerfile — container image for the app
• .env.example — example environment variables

This is a minimal scaffold. You can iterate with me to convert it to production grade.