back_office_ops · workflow
Agentic RAG with ApertureDB and HuggingFace SmolAgents for research paper search
Researchers are overwhelmed by the volume of academic papers that keyword search cannot navigate effectively, and vanilla RAG's single retrieval step produces flawed or hallucinated responses when retrieved documents are irrelevant or incomplete.
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 · Research question submitted
A research question is submitted to the ToolCallingAgent to begin the retrieval pipeline.
Tools used
ApertureDBSmolAgentsOpenAILangChain · partnerUnstructuredTesseract OCRArXivgpt-3.5-turbo
Outcome
The guide produces a working agentic RAG system capable of handling academic paper retrieval with iterative query refinement, addressing vanilla RAG's hallucination and relevance-matching limitations.
What failed first
Vanilla RAG fails because semantic similarity uses only the user query as a reference point, causing misaligned results when query phrasing doesn't match document structure, and it has no mechanism to critique or refine retrieval when it fails.
Grounding & classification
Source type: technical build writeup
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agentic workflowai agentdata extractionknowledge searchragknowledge basebuilder submittedsource backedtools describedworkflow describededucationtechnical build writeupback office opsagentic task executionrag answering