Back office ops · Production

Agentic RAG with ApertureDB and HuggingFace SmolAgents for research paper search

The problem

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.

First attempt

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.

Workflow diagram · grounded in source
1
Research question submitted
trigger
“question = "Why is calculating Higgs Boson decay important?"”
2
PDF fetch and content extraction
integration
“The fetch_paper_details function downloads the PDF and utilizes the partition feature of the unstructured library to identify the file type and extract structured content from the unstructured document”
3
Chunk embedding and vector storage
integration
“generate embeddings and store them in ApertureDB”
4
Semantic retrieval via DocumentRetrieverTool
ai_action
“Performs semantic search to fetch documents relevant to a given query”
5
Agent assesses retrieval results
validation
“Agents can evaluate their relevance and quality. If needed, they can re-query or adjust the search to improve outcomes”
6
Iterative query refinement
feedback_loop
“the agent critiques the results and formulates a new query to re-retrieve documents”
7
Agent generates final response
output
“agent_output = agent.run(question)”
Reported 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.

Reported stack
ApertureDBSmolAgentsOpenAILangChainUnstructuredTesseract OCRArXivgpt-3.5-turboHuggingFace
Source
https://mlops.community/blog/agentic-rag-with-aperturedb-and-huggingface-smolagents
Read source ↗

Frequently asked questions

What did this team achieve with this AI workflow?

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 tools did this team use?

ApertureDB, SmolAgents, OpenAI, LangChain, Unstructured, Tesseract OCR, ArXiv, gpt-3.5-turbo, HuggingFace.

What failed first in this deployment?

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 refi…

How is this back office ops AI workflow structured?

Research question submitted → PDF fetch and content extraction → Chunk embedding and vector storage → Semantic retrieval via DocumentRetrieverTool → Agent assesses retrieval results → Iterative query refinement → Agent generates final response.

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