Hermes Pipeline
37 lines
2.3 KiB
JSON
37 lines
2.3 KiB
JSON
{
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"name": "literature-review-with-traceable-ai-evidence",
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"version": "1.0.0",
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"goal": "Enable researchers to ingest documents, asynchronously index them, and interact with multi-agent AI to answer questions with verifiable citations to original text.",
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"inputs": [
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"Documents in PDF, Office, image, or text formats",
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"Research questions or topics of interest",
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"Optional: user model configuration via .env or settings"
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],
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"steps": [
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"Upload documents to a project (via desktop app or web UI)",
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"System asynchronously converts Office docs to PDF if needed, runs OCR to extract text with coordinates, chunks and embeds into vector store",
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"User starts a main research session or creates exploration branches without waiting for indexing to finish",
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"Leader agent receives query and delegates subtasks to researcher, reviewer, writer subagents",
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"Subagents perform hybrid retrieval and rerank to find relevant chunks with source coordinates",
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"Agents synthesize answers and return evidence with clickable citations that highlight original pages",
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"User verifies conclusions by navigating to cited source locations and can save notes to research memory or mind map"
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],
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"outputs": [
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"AI-generated answers with traceable evidence (coordinates, page highlights)",
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"Research session history with branches",
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"Indexed document library for future queries",
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"Mind maps or structured notes",
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"Persistent run events for resuming sessions"
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],
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"failure_modes": [
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"Missing local Office/LibreOffice converter causes document conversion failure",
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"First-time model download may be slow or require network",
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"OCR may have low confidence on poor quality scans",
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"Retrieval might miss context if chunking splits semantics",
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"Multi-agent coordination could produce conflicting intermediate results"
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],
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"confidence": 0.85,
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"explanation": "The README describes PaperSage's core workflow: asynchronous document ingestion with OCR/indexing, followed by multi-agent question answering with cited evidence. This process is not tied to the specific codebase and can be reused as a general literature review methodology for any document-centric research using RAG and agent collaboration.",
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"source_repo": "https://github.com/0verL1nk/PaperSage.git",
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"score": 1.0
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} |