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plug-and-play React component library for integrating DocSafe's AI-powered vector search and PDF citation highlighting directly into your application.","maintainers":[{"name":"bentejas","email":"bentejas@hotmail.com"},{"name":"bh1","email":"bhonda89@gmail.com"}],"readme":"# @adpharm/docsafe-search\n\nA plug-and-play React component library and backend utility for integrating DocSafe's AI-powered vector search and PDF citation highlighting directly into your application.\n\nThis package uses a **\"Dumb UI, Smart Server\"** architecture. It provides beautiful React components for your frontend and a secure search utility for your backend, ensuring your API keys are never exposed to the browser.\n\n## Installation\n\n```bash\nnpm install @adpharm/docsafe-search\n# or\nbun add @adpharm/docsafe-search\n```\n\n## 1. Prerequisites (Environment Variables)\n\nYour host application's backend must have the following environment variables set:\n\n```bash\nOPENAI_API_KEY=your_openai_key\nDOCSAFE_API_KEY=your_docsafe_db_key\n# DOCSAFE_DB_URL=https://db.docsafe-search.adpharm.digital (Optional: defaults to production)\n```\n\n## 2. Styling\n\nThe components are self-styling — no stylesheet import, no Tailwind setup, no wrapper required. Drop them in and they render with sensible defaults.\n\nTo match your app's brand, pass a `theme` prop. Every field is optional and merges over the defaults:\n\n```tsx\n<SearchBar\n  projectId=\"...\"\n  onSearch={handleSearch}\n  theme={{\n    colors: {\n      primary: \"#7c3aed\",\n      primaryHover: \"#6d28d9\",\n    },\n    radius: { lg: \"0.75rem\" },\n    fontFamily: \"Inter, sans-serif\",\n  }}\n/>\n```\n\nThe same `theme` prop is accepted by `SearchBar`, `ResultsPage`, and `PdfViewer`. By default `fontFamily` is `\"inherit\"`, so typography picks up your host application automatically.\n\nThe full default theme is exported as `defaultTheme` and its shape as `DocSafeTheme` if you want to derive your own.\n\n## 3. Backend Implementation\n\nCreate an API route in your host application to securely handle the vector search and LLM generation.\n\n**Example (Next.js App Router - `app/api/search/route.ts`):**\n\n```ts\nimport { runDocSafeSearch } from \"@adpharm/docsafe-search/server\";\n\nexport async function POST(req: Request) {\n  try {\n    const { query, projectId } = await req.json();\n\n    // This utility automatically reads your .env variables, performs the vector\n    // search, and asks OpenAI to generate an answer with citations.\n    const searchResults = await runDocSafeSearch(query, projectId);\n\n    return Response.json(searchResults);\n  } catch (error: any) {\n    return Response.json({ error: error.message }, { status: 500 });\n  }\n}\n```\n\n## 4. Frontend Implementation\n\nUse the exported UI components to build your search interface.\n\n**Example (React / Next.js Client Component):**\n\n```tsx\n\"use client\";\n\nimport { useState } from \"react\";\nimport {\n  SearchBar,\n  ResultsPage,\n  SearchResultData,\n} from \"@adpharm/docsafe-search\";\n\nexport default function DocumentSearch() {\n  const [data, setData] = useState<SearchResultData | null>(null);\n  const [isLoading, setIsLoading] = useState(false);\n  const [query, setQuery] = useState(\"\");\n  const [error, setError] = useState<string>();\n\n  const handleSearch = async (searchQuery: string, projectId: string) => {\n    setIsLoading(true);\n    setQuery(searchQuery);\n    setError(undefined);\n\n    try {\n      // Call your secure backend route\n      const response = await fetch(\"/api/search\", {\n        method: \"POST\",\n        headers: { \"Content-Type\": \"application/json\" },\n        body: JSON.stringify({ query: searchQuery, projectId }),\n      });\n\n      if (!response.ok) throw new Error(\"Search failed\");\n\n      const resultData = await response.json();\n      setData(resultData);\n    } catch (err: any) {\n      setError(err.message);\n    } finally {\n      setIsLoading(false);\n    }\n  };\n\n  // If we have data (or are loading data), show the Results layout.\n  // ResultsPage fills its parent — wrap it in a full-height container.\n  if (data || isLoading) {\n    return (\n      <div style={{ height: \"100vh\" }}>\n        <ResultsPage\n          query={query}\n          isLoading={isLoading}\n          data={data}\n          error={error}\n        />\n      </div>\n    );\n  }\n\n  // Otherwise, show the initial Search Bar\n  return (\n    <div className=\"flex items-center justify-center min-h-screen p-8\">\n      <div className=\"w-full max-w-3xl\">\n        <h1 className=\"text-3xl font-bold mb-8 text-center\">\n          Search Clinical Guidelines\n        </h1>\n        <SearchBar projectId=\"your-project-uuid-here\" onSearch={handleSearch} />\n      </div>\n    </div>\n  );\n}\n```\n\n## 5. Opening the PDF Viewer Directly (Without a Search)\n\nSometimes the host application already knows which document the user wants to see — for example, opening a file from a library list, an email link, or a bookmarked citation. For these cases the viewer can be driven imperatively, completely independent of the search flow.\n\n### Option A — `usePdfViewer` hook (recommended)\n\nThe hook exposes an `openDocument(...)` function and a `viewer` node you render wherever you want the PDF to appear (full page, drawer, modal, etc.).\n\n```tsx\n\"use client\";\n\nimport { usePdfViewer } from \"@adpharm/docsafe-search\";\n\nexport function DocumentLibrary({ docs }: { docs: { url: string; name: string }[] }) {\n  const { openDocument, close, viewer, isOpen } = usePdfViewer();\n\n  return (\n    <div className=\"flex h-screen\">\n      <ul className=\"w-64 border-r\">\n        {docs.map((d) => (\n          <li key={d.url}>\n            <button\n              onClick={() =>\n                openDocument({\n                  pdf_url: d.url,\n                  document_name: d.name,\n                  initial_page: 1, // optional\n                })\n              }\n            >\n              {d.name}\n            </button>\n          </li>\n        ))}\n      </ul>\n\n      <main className=\"flex-1 relative\">\n        {isOpen ? (\n          <>\n            <button onClick={close} className=\"absolute top-2 right-2 z-10\">Close</button>\n            {viewer}\n          </>\n        ) : (\n          <p>Select a document</p>\n        )}\n      </main>\n    </div>\n  );\n}\n```\n\n### Option B — Render `<PdfViewer />` directly with a `document` prop\n\nIf you'd rather manage the open document in your own state, pass it straight to the component:\n\n```tsx\nimport { PdfViewer, type PdfDocument } from \"@adpharm/docsafe-search\";\n\n<PdfViewer\n  document={{\n    pdf_url: \"https://...\",\n    document_name: \"Product Monograph.pdf\",\n    initial_page: 4,\n  }}\n/>\n```\n\n`PdfViewer` accepts either `document` (citation-free mode) or `activeCitation` (citation-driven mode), but not both.\n\n## 6. Asking Questions Inside an Open Document\n\nWhen the user is already looking at a specific document, they often want to query *within* it rather than across the whole project. Pass an `onAskQuestion` callback to `<PdfViewer />` (or to `usePdfViewer`) and the viewer renders a collapsible side panel: the user types a question, your backend runs a document-scoped vector search, and the returned citations are highlighted on the PDF.\n\n```tsx\nimport { usePdfViewer } from \"@adpharm/docsafe-search\";\n\nconst { openDocument, viewer } = usePdfViewer({\n  onAskQuestion: async (query, document) => {\n    const res = await fetch(\"/api/search-in-document\", {\n      method: \"POST\",\n      headers: { \"Content-Type\": \"application/json\" },\n      body: JSON.stringify({ query, documentId: document.document_id }),\n    });\n    return res.json();\n  },\n});\n```\n\nThe backend route should call the document-scoped server utility:\n\n```ts\nimport { runDocSafeSearchInDocument } from \"@adpharm/docsafe-search/server\";\n\nexport async function POST(req: Request) {\n  const { query, documentId } = await req.json();\n  return Response.json(await runDocSafeSearchInDocument(query, documentId));\n}\n```\n\nWhen opening a document, include its `document_id` so the callback can scope the search:\n\n```tsx\nopenDocument({\n  pdf_url: doc.pdf_url,\n  document_name: doc.title,\n  document_id: doc.id,\n});\n```\n\n## 7. Listing a Project's Documents\n\nFor building document-library UIs, the server exports `getHcpResources` and `getPatientResources`, which return each document with a presigned `pdf_url` and `thumbnail_url`:\n\n```ts\nimport {\n  getHcpResources,\n  getPatientResources,\n  type Resource,\n} from \"@adpharm/docsafe-search/server\";\n\nconst hcpDocs: Resource[] = await getHcpResources(projectId);\nconst patientDocs: Resource[] = await getPatientResources(projectId);\n```\n\nEach `Resource` includes `id`, `title`, `pdf_url`, `thumbnail_url`, `audience` (`\"hcp\" | \"patient\"`), and `language` (`\"en\" | \"fr\"`).\n\n","readmeFilename":"README.md"}