05_zotero_search_improvements.ipynb

05_zotero_search_improvements.ipynb

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      "source": "%ai reset",
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      "source": "from toolslm.download import read_docs\ndef read_gist(s): return read_docs('https://gist.githubusercontent.com/rbiswasfc/'+s)\nzotero_doc = read_gist(\"caa768a770c69cd93234b1f5be375c62/raw/221fd94736afd03351680effdfc2e42aaa5b2a6c/zotero.md\")",
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      "cell_type": "code",
      "source": "%%ai -c\nPlease go thorugh the $`zotero_doc` carefully and use it to answer following questions.",
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    {
      "metadata": {},
      "id": "2c47e1c8",
      "cell_type": "markdown",
      "source": "I've carefully reviewed the `zotero_doc`. What specific questions would you like me to answer about it?"
    },
    {
      "metadata": {
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      "id": "b2d63ad9",
      "cell_type": "code",
      "source": "%%ai\nWe are building a search app that leverages our internal (answerai) zotero library. \nTHe zotero library represents the kernel of our research interests.\nList down specific intents that the search app should be able to support?",
      "execution_count": 4,
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      "id": "7b2a6b03",
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      "source": "The search app should support these concrete intents:\n\n1. Find papers on a specific technique/topic (e.g., \"RAG over images\")\n2. Find benchmark datasets for a task (e.g., \"knowledge editing datasets\")\n3. Find critiques/limitations of a method (e.g., \"limitations of BERT for low-resource languages\")\n4. Identify applications in a different domain (e.g., \"transformers in time series forecasting\")\n5. Find papers comparing multiple approaches (e.g., \"CNN vs. transformer for image classification\")\n6. Recall a previously-seen paper \n7. Check if a method has been proposed before\n8. Find specific sections backing or refuting a claim\n9. Find explanations of a concept (e.g., \"intuitive explanation of contrastive learning\")\n10. Discover papers with code implementations (e.g., \"GAN implementations in PyTorch\")\n11. Locate survey papers on a topic (e.g., \"comprehensive survey of reinforcement learning\")\n12. Find real-world case studies (e.g., \"successful applications of federated learning\")"
    },
    {
      "metadata": {
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      "id": "60e7940b",
      "cell_type": "code",
      "source": "%%ai\nRewrite the above points with more thought and reflection",
      "execution_count": 5,
      "outputs": []
    },
    {
      "metadata": {},
      "id": "c7917bb6",
      "cell_type": "markdown",
      "source": "1. Technique/topic search: Find papers on specific AI methods (e.g., \"RAG over images\", \"few-shot learning in NLP\")\n\n2. Dataset discovery: Locate benchmark datasets for various AI tasks (e.g., \"knowledge editing datasets\", \"multi-modal sentiment analysis datasets\")\n\n3. Critical analysis: Find critiques, limitations, or failure cases of methods (e.g., \"limitations of BERT for low-resource languages\", \"adversarial attacks on vision transformers\")\n\n4. Cross-domain applications: Identify novel applications of techniques in different domains (e.g., \"transformers in time series forecasting\", \"GANs in drug discovery\")\n\n5. Comparative studies: Find papers comparing multiple approaches (e.g., \"CNN vs. transformer for image classification\", \"BERT vs. GPT for text generation\")\n\n6. Historical context: Trace the evolution of ideas or check if a method has been proposed before (e.g., \"early work on neural architecture search\", \"predecessors of attention mechanisms\")\n\n7. Evidence retrieval: Find specific sections or figures supporting or refuting claims (e.g., \"evidence for/against the lottery ticket hypothesis\")\n\n8. Concept explanation: Locate clear explanations or intuitive breakdowns of complex concepts (e.g., \"intuitive explanation of contrastive learning\", \"visual guide to diffusion models\")\n\n9. Implementation search: Discover papers with code implementations or practical guides (e.g., \"GAN implementations in PyTorch\", \"step-by-step guide to training large language models\")\n\n10. Literature review: Find comprehensive survey papers or meta-analyses on topics (e.g., \"survey of reinforcement learning\", \"meta-analysis of transfer learning techniques\")\n\n11. Real-world impact: Locate case studies or examples of successful real-world applications (e.g., \"applications of federated learning in healthcare\", \"NLP in financial forecasting\")\n\n12. Theoretical foundations: Find papers on the mathematical or theoretical underpinnings of methods (e.g., \"convergence proofs for stochastic gradient descent\", \"information theory in deep learning\")"
    },
    {
      "metadata": {
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      "id": "14af0344",
      "cell_type": "code",
      "source": "%%ai\nWhat aspects the search app need to excel at to deliver these?\nLet's think step by step. And list down top 5 required capabilities. Please provide your chain of thought first.",
      "execution_count": 6,
      "outputs": []
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      "source": "Chain of thought:\n\n1. Semantic understanding: The app needs to grasp the intent behind queries, not just match keywords. This is crucial for all intents, especially conceptual searches and cross-domain applications.\n\n2. Content parsing: To provide specific sections, figures, or code snippets, the app must effectively parse and index various parts of papers.\n\n3. Temporal awareness: For historical context and tracing idea evolution, the app needs to understand and sort papers chronologically.\n\n4. Multi-modal search: Given the importance of visual content in research papers, the app should handle both text and image-based queries.\n\n5. Relevance ranking: With a large library, surfacing the most relevant papers is crucial, especially for comparative studies and literature reviews.\n\n6. Domain adaptation: The app should understand domain-specific terminology and concepts to effectively handle cross-domain queries.\n\n7. Abstraction capability: For concept explanations and theoretical foundations, the app needs to identify and present high-level ideas.\n\n8. Fine-grained retrieval: To support evidence retrieval and implementation search, the app must locate specific parts within papers.\n\n9. Contextual understanding: The app should grasp the broader context of AI research to provide meaningful results for critical analysis and real-world impact queries.\n\n10. Update frequency: To remain useful, the app must regularly incorporate new papers into its index.\n\nTop 5 required capabilities:\n\n1. Advanced semantic search with domain-specific understanding\n2. Multi-modal indexing and retrieval (text and images)\n3. Fine-grained content parsing and retrieval\n4. Temporal-aware ranking and filtering\n5. Adaptive relevance ranking with context awareness"
    },
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      "id": "fe3c2419",
      "cell_type": "code",
      "source": "%%ai\nGreat. Convince us that we absolutely need these!",
      "execution_count": 7,
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      "source": "1. Advanced semantic search with domain-specific understanding:\n   This capability is crucial because AI research often involves complex, nuanced concepts. Simple keyword matching fails to capture the depth of queries like \"limitations of BERT for low-resource languages\" or \"intuitive explanation of contrastive learning\". The app must understand AI terminology, recognize synonyms and related concepts, and infer the user's intent. Without this, users would miss relevant papers that use different but semantically equivalent terms, severely limiting the app's utility for researchers.\n\n2. Multi-modal indexing and retrieval (text and images):\n   Many AI papers convey key ideas through figures, diagrams, and tables. The ability to search and retrieve based on both text and visual content is essential. For instance, a user might want to find papers with similar architecture diagrams or performance comparison charts. This capability enables researchers to quickly locate relevant visual information, which is often crucial for understanding complex methods or comparing results across papers.\n\n3. Fine-grained content parsing and retrieval:\n   AI papers are structured documents with abstracts, methods, results, and conclusions. The ability to parse and index at this granular level allows users to find specific information efficiently. For example, a researcher looking for \"evidence against the lottery ticket hypothesis\" needs the app to identify and retrieve relevant sections or paragraphs, not just entire papers. This capability dramatically reduces the time researchers spend manually scanning papers for relevant information.\n\n4. Temporal-aware ranking and filtering:\n   AI is a rapidly evolving field where the recency of information is often crucial. However, understanding the historical context of ideas is equally important. This capability allows users to trace the evolution of concepts, find seminal papers, or focus on the latest developments. It's essential for queries like \"early work on neural architecture search\" or for ensuring that literature reviews cover both foundational and cutting-edge papers.\n\n5. Adaptive relevance ranking with context awareness:\n   The relevance of a paper depends not just on its content, but on the user's context and the current state of the field. This capability ensures that the app provides results that are not only relevant to the query but also significant in the broader research context. For instance, when searching for \"GAN implementations in PyTorch\", it should prioritize papers that are widely cited, from reputable sources, or particularly innovative. This helps researchers focus on the most impactful and reliable information, crucial in a field with thousands of new papers published regularly.\n\nThese capabilities work synergistically to provide a powerful research tool. Without them, the app would struggle to meet the complex needs of AI researchers, potentially leading to missed insights, inefficient literature reviews, and a less comprehensive understanding of the field. By implementing these capabilities, the app becomes an indispensable aid in navigating the vast and rapidly growing body of AI research."
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      "id": "2f447bf6",
      "cell_type": "code",
      "source": "%%aio\nWe would like to improve on the search app that was mentioned in the `zotero_doc`. \nHow shall we go about it?",
      "execution_count": 8,
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      "source": "- To improve the search app mentioned in `zotero_doc`, we should focus on these key areas:\n\n1. Enhance semantic search:\n   - Upgrade from LanceDB Full-text search to a more advanced semantic search model\n   - Fine-tune the embedding model on AI research papers for domain-specific understanding\n\n2. Improve multi-modal capabilities:\n   - Extend Byaldi image search to index all pages of each paper, not just the first\n   - Integrate text and image search results for a unified ranking\n\n3. Implement fine-grained content parsing:\n   - Use Grobid for structured parsing of papers, as mentioned in the document\n   - Index and make searchable individual sections, tables, and figures\n\n4. Add temporal awareness:\n   - Incorporate publication dates into the ranking algorithm\n   - Allow filtering and sorting by date ranges\n\n5. Develop adaptive relevance ranking:\n   - Implement a learning-to-rank model that considers citation count, author reputation, and recency\n   - Incorporate user feedback and interaction data to improve rankings over time\n\n6. Expand citation traversal:\n   - Extend the citation traversal tool to cover more degrees of separation\n   - Implement forward citation tracking to find papers that cite the retrieved results"
    },
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      "metadata": {
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      "id": "89c5949d",
      "cell_type": "code",
      "source": "%%ai\nWhat needs to be done to make the app very effective? Remembert that we want a simple app that is accurate.\nThink first.",
      "execution_count": 9,
      "outputs": []
    },
    {
      "metadata": {},
      "id": "b6c0bc2c",
      "cell_type": "markdown",
      "source": "To create a simple yet accurate search app, we should focus on:\n\n1. Robust semantic search: This is the core functionality. Improving the embedding model and fine-tuning it on AI papers will significantly boost accuracy.\n\n2. Effective content parsing: Using Grobid for structured parsing will improve indexing quality without complicating the user interface.\n\n3. Smart ranking: Implementing a basic but effective ranking system that considers relevance, recency, and citation count.\n\n4. User feedback loop: Adding a simple mechanism for users to rate search results, using this data to continually improve rankings.\n\n5. Streamlined UI: Focusing on a clean, intuitive interface that allows users to easily refine searches and explore results.\n\nThese improvements target the most impactful areas while keeping the app simple and maintainable."
    },
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      "id": "1f7936f0",
      "cell_type": "code",
      "source": "%%ai\nLet's explore the last two points. What would be a smart yet minimal way of implementing.",
      "execution_count": 10,
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    },
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      "metadata": {},
      "id": "7e9b96cb",
      "cell_type": "markdown",
      "source": "For a smart yet minimal implementation of the user feedback loop and streamlined UI:\n\nUser feedback loop:\n1. Add a simple thumbs up/down button next to each search result\n2. Store feedback in a lightweight database (e.g., SQLite)\n3. Use a basic weighted average to adjust result rankings:\n   - Increase score for thumbs up, decrease for thumbs down\n   - Weight recent feedback more heavily\n4. Periodically retrain the ranking model using accumulated feedback\n\nStreamlined UI:\n1. Single search bar with autocomplete for common AI terms\n2. Results page with:\n   - Title, authors, publication date, and brief snippet for each result\n   - Expandable abstract view\n   - Quick filters: date range, paper type (e.g., conference, journal)\n3. \"More like this\" button on each result for related papers"
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