{
 "schema_version": 1,
 "generated": "2026-10-31",
 "id": "momom101-rag-reactagent--study-hybrid-retrieval-and-citation-checks",
 "repo": "MoMoM101/RAG-ReActAgent",
 "repo_url": "https://github.com/MoMoM101/RAG-ReActAgent",
 "repo_slug": "momom101-rag-reactagent",
 "title": "Study how hybrid retrieval and citation checks are built",
 "page_title": "How to study how hybrid retrieval and citation checks are built with RAG Re Act Agent",
 "summary": "Read the retrieval, agent, and evaluation code to reuse the design in your own RAG project.",
 "who": "A developer building a RAG system who wants a worked reference for merging search results and verifying answer citations.",
 "problem": "Reading a RAG design from a blog post hides the details of fusion, deduplication, and citation checks.",
 "outcome": "A file-by-file summary of how semantic and keyword results are merged and how answers are verified.",
 "steps": [
  "`git clone https://github.com/MoMoM101/RAG-ReActAgent.git`",
  "`cd RAG-ReActAgent`",
  "Read backend/rag for loading, chunking, query classification, and retrieval, and backend/agent for the ReAct loop and answer verification.",
  "`pip install -r backend/requirements-dev.txt`",
  "Read docs/EVALUATION_REPRODUCIBILITY.md to see how the evaluation numbers were produced.",
  "Run the tests in backend/tests with pytest after the dev install. (inferred, check the README)"
 ],
 "agent_prompt": "Clone https://github.com/MoMoM101/RAG-ReActAgent and read backend/rag, backend/agent, and docs/EVALUATION_REPRODUCIBILITY.md. Produce a summary of how Qdrant and SQLite BM25 results are fused with weighted RRF and how answer citations are checked, with a file path for each claim. Expected output: the summary as a short list. Verify that every file path exists before reporting.",
 "agents": [
  "any"
 ],
 "difficulty": "Intermediate",
 "time": "30 minutes",
 "basis": "readme",
 "caveat": "Reading the code does not reproduce the reported numbers, which come from a fixed synthetic benchmark that includes a development subset rather than a strictly held-out test.",
 "category": "Work and operations",
 "tags": [
  "Work and operations"
 ],
 "lang": "python",
 "requirements": [
  "Docker and Docker Compose, or Python 3.12 and Node.js 22.22 or newer for a source install",
  "An OpenAI-compatible LLM service and an Embedding service, with an API key, for full knowledge Q&A"
 ],
 "risk": "none",
 "seen_in_the_wild": false,
 "mentioned_publicly": false,
 "risks": [],
 "best": false,
 "score": 0.8,
 "evidence": [],
 "url": "https://dedupedai.com/use-cases/momom101-rag-reactagent/study-hybrid-retrieval-and-citation-checks/",
 "md": "https://dedupedai.com/use-cases/momom101-rag-reactagent/study-hybrid-retrieval-and-citation-checks/index.md",
 "disclaimer": "Deduped lists open-source repositories that belong to other people. We do not own, maintain, audit, or vouch for any of them, and a repo appearing here is not an endorsement or a safety check. The use cases, steps, and prompts are written by AI and may be incomplete, outdated, or just wrong. Where a use case is marked \"Seen in the wild\", we link to public posts where someone described something similar, but we have not verified those posts and the use case itself is still our AI's own write-up. Use all of this at your own risk. Only run code, commands, or agents on systems and accounts you are authorized to use, and read the code and every command before you run it, especially anything that touches credentials, files, or the network. Nothing here is professional, security, legal, or financial advice. All trademarks and project names belong to their respective owners, and their use here does not imply affiliation. Content is provided as is, without warranties of any kind. Found a mistake, a safety problem, or something you want removed? Email hello@dedupedai.com and we will look at it promptly.",
 "api": "https://dedupedai.com/api/v1/usecases/momom101-rag-reactagent--study-hybrid-retrieval-and-citation-checks.json",
 "api_version": "v1"
}