back_office_ops · workflow
Serverless semantic search over academic papers using AWS Lambda, Qdrant, and GPT-3.5-turbo
The author needed a scalable, serverless way to perform semantic search over academic papers without provisioning or managing server infrastructure.
How it works
Common implementation structure
How this type of workflow is generally built, generalized across documented cases — not tied to any one vendor's stack. Click any stage to read what happens there. Specific products that implement these stages appear in “Tools commonly seen” below.
Stage 1 · Academic paper fetch and chunk
Selecting a paper number fetches the academic paper and splits it into chunks with page content, source, and page metadata.
Tools used
QdrantLangChainOpenAIGPT-3.5-turboAWS ECRAPI GatewayStreamlitDockerAWS CloudWatch
Outcome
A working serverless RAG application was built allowing users to ask questions about academic papers via a Streamlit interface backed by AWS Lambda, Qdrant, and GPT-3.5-turbo, described as scalable and efficient.
Grounding & classification
Source type: technical build writeup
17 fields verified against source quotes, 1 dropped as unverifiable.
knowledge searchragknowledge basesource backedtools describedworkflow describededucationtechnical build writeupback office opsrag answering