From 1df20a8a6eb4ee2b7ef6a485ebacd52e0153ee65 Mon Sep 17 00:00:00 2001 From: Oded Falik Date: Thu, 9 Apr 2026 12:29:49 -0700 Subject: [PATCH 01/15] Add cloud backend: remote MCP server on Cloudflare Workers Move Paper Intelligence to the cloud using Cloudflare's infrastructure: - **Remote MCP server** via McpAgent (Streamable HTTP transport) - search, get_paper_info, list_papers tools - Bearer token auth - **Search**: grep + RAG + hybrid (faithful port of Python logic) - Workers AI embeddings (@cf/baai/bge-small-en-v1.5) - Vectorize for vector search - D1 for markdown grep + metadata - **Web UI** at root URL - Paper browser with markdown viewer - RAG chat with streaming (Claude + OpenAI) - Model selector, token-based auth - **Container setup** for Marker PDF conversion (ready, pending CF Containers beta) - **Migration script** to upload local papers to cloud Services: D1, R2, Vectorize, Workers AI, Durable Objects --- .gitignore | 4 + CLOUD_DESIGN.md | 297 +++ cloud/container/Dockerfile | 39 + cloud/container/requirements.txt | 3 + cloud/container/server.py | 87 + cloud/migrate.sh | 71 + cloud/package-lock.json | 4222 ++++++++++++++++++++++++++++++ cloud/package.json | 22 + cloud/schema.sql | 41 + cloud/setup.sh | 83 + cloud/src/chat.ts | 175 ++ cloud/src/container.ts | 18 + cloud/src/index.ts | 230 ++ cloud/src/mcp.ts | 184 ++ cloud/src/pipeline.ts | 391 +++ cloud/src/search.ts | 261 ++ cloud/src/types.ts | 63 + cloud/src/ui.ts | 283 ++ cloud/tsconfig.json | 18 + cloud/wrangler.jsonc | 65 + 20 files changed, 6557 insertions(+) create mode 100644 CLOUD_DESIGN.md create mode 100644 cloud/container/Dockerfile create mode 100644 cloud/container/requirements.txt create mode 100644 cloud/container/server.py create mode 100755 cloud/migrate.sh create mode 100644 cloud/package-lock.json create mode 100644 cloud/package.json create mode 100644 cloud/schema.sql create mode 100755 cloud/setup.sh create mode 100644 cloud/src/chat.ts create mode 100644 cloud/src/container.ts create mode 100644 cloud/src/index.ts create mode 100644 cloud/src/mcp.ts create mode 100644 cloud/src/pipeline.ts create mode 100644 cloud/src/search.ts create mode 100644 cloud/src/types.ts create mode 100644 cloud/src/ui.ts create mode 100644 cloud/tsconfig.json create mode 100644 cloud/wrangler.jsonc diff --git a/.gitignore b/.gitignore index b3d7f94..e924327 100644 --- a/.gitignore +++ b/.gitignore @@ -35,3 +35,7 @@ Thumbs.db # Model cache (large files) .cache/ *.onnx + +# Cloud worker +cloud/node_modules/ +cloud/.wrangler/ diff --git a/CLOUD_DESIGN.md b/CLOUD_DESIGN.md new file mode 100644 index 0000000..e0bd647 --- /dev/null +++ b/CLOUD_DESIGN.md @@ -0,0 +1,297 @@ +# Paper Intelligence Cloud + +## Background + +Paper Intelligence currently runs entirely locally: PDFs are stored on disk, Marker converts them to Markdown using local GPU/CPU, embeddings are generated with a local HuggingFace model (bge-small-en-v1.5), and vectors are stored in per-paper ChromaDB instances. The MCP server runs as a local process. + +This works, but has limitations: +- Papers are only available on the machine they were processed on +- Every machine needs the full ML stack (torch, marker, embedding models) +- No shared access between team members (Oded + Yue) +- Processing ties up local compute + +## The Idea + +Move storage, indexing, and search to Cloudflare. The local MCP server becomes a thin API client. Papers are accessible from anywhere, by anyone on the team. + +Strand has $250K in Cloudflare credits (YC perk) that are otherwise going mostly unused. This is a natural fit. + +## Architecture + +### Current (Local) + +``` +PDF on disk + → Marker (local, torch, GPU/MPS/CPU) → Markdown + images + → HuggingFace bge-small-en-v1.5 (local) → embeddings + → ChromaDB (local, per-paper SQLite) → vector storage + → Local grep + RAG → search results + → MCP server (local, stdio) → Claude/IDE +``` + +### Proposed (Cloud) + +``` +┌─────────────────────────────────────────────────────┐ +│ Cloudflare │ +│ │ +│ ┌──────────┐ ┌──────────────────┐ │ +│ │ R2 │ │ Container │ │ +│ │ (storage)│◄───│ (Marker on CPU) │ │ +│ │ │ │ standard-2/4 │ │ +│ │ - PDFs │ │ scale to zero │ │ +│ │ - .md │ └──────────────────┘ │ +│ │ - images │ │ +│ └────┬─────┘ ┌──────────────────┐ │ +│ │ │ Workers AI │ │ +│ │ │ (embeddings) │ │ +│ │ └────────┬─────────┘ │ +│ │ │ │ +│ │ ┌────────▼─────────┐ │ +│ │ │ Vectorize │ │ +│ │ │ (vector search) │ │ +│ │ └────────┬─────────┘ │ +│ │ │ │ +│ │ ┌────────▼─────────┐ │ +│ │ │ D1 (SQLite) │ │ +│ │ │ - metadata │ │ +│ │ │ - header index │ │ +│ │ └────────┬─────────┘ │ +│ │ │ │ +│ ┌────▼───────────────────▼─────────┐ │ +│ │ Worker (API) │ │ +│ │ POST /papers - upload │ │ +│ │ GET /papers/:id - status │ │ +│ │ POST /search - query │ │ +│ └──────────────┬───────────────────┘ │ +│ │ │ +└─────────────────┼────────────────────────────────────┘ + │ HTTPS +┌─────────────────▼────────────────────────────────────┐ +│ Local MCP Server (thin client) │ +│ - Proxies tool calls to Worker API │ +│ - Same MCP interface: search(), get_paper_info() │ +│ - No torch, no marker, no embeddings locally │ +└──────────────────────────────────────────────────────┘ +``` + +## Cloudflare Services Used + +| Service | Role | Why | +|---------|------|-----| +| **R2** | Object storage for PDFs, Markdown, images | S3-compatible, no egress fees, cheap | +| **Containers** | Runs Marker for PDF→Markdown conversion | Needs Python + torch (CPU). Scale to zero when idle | +| **Workers AI** | Generates embeddings | Replaces local HuggingFace model. No infra to manage | +| **Vectorize** | Vector database for semantic search | Replaces per-paper ChromaDB. Managed, scalable | +| **D1** | SQLite database for metadata + header index | Replaces per-paper JSON files. Queryable | +| **Workers** | API layer (HTTP endpoints) | Orchestrates everything. Request/response, no agent loop | + +## Processing Pipeline + +### Upload & Process (async, background) + +1. Client uploads PDF to Worker API +2. Worker stores PDF in R2 +3. Worker triggers Container (Marker on CPU) + - Container pulls PDF from R2 + - Marker converts to Markdown + extracts images + - Container writes .md + images back to R2 + - Container signals completion to Worker +4. Worker reads Markdown from R2 +5. Worker extracts header hierarchy → stores in D1 +6. Worker chunks Markdown (header-aware, 512 tokens, 50 overlap) +7. Worker calls Workers AI for embeddings +8. Worker stores vectors in Vectorize +9. Worker updates paper status in D1 + +### Search (sync, fast) + +1. Client sends query to Worker API +2. Worker runs in parallel: + - **Grep**: fetch Markdown from R2, regex/text search with header context + - **RAG**: embed query via Workers AI, search Vectorize for top-k +3. Worker deduplicates + merges results (same logic as current hybrid search) +4. Worker returns results with line numbers, header context, scores + +## Container Details (Marker) + +- **Instance type**: `standard-2` (1 vCPU, 6 GiB RAM, 12 GB disk) or `standard-4` (4 vCPU, 12 GiB RAM, 20 GB disk) +- **Runtime**: Python + torch (CPU) + marker-pdf +- **Scale to zero**: sleeps when idle, wakes on demand. Only pay for active processing +- **Image size concern**: torch CPU (~500MB) + Marker models + Python. Needs to fit in 12-20 GB disk. Should be fine but verify + +### CPU Performance Benchmarks (from local M-series Mac) + +| Paper Size | Pages | Time (CPU) | +|-----------|-------|------------| +| Large (Nature Medicine + supplements) | 85 | ~8 min | +| Medium (typical paper) | 15-20 | ~2-3 min (estimated) | +| Small (short paper) | 5-10 | ~1 min (estimated) | + +On Cloudflare `standard-4` (4 vCPU), expect similar or slightly slower times. PyTorch does use multiple cores for inference but it's not a linear speedup. + +This is acceptable — papers are processed once, then search is instant forever. + +## Data Model (D1) + +```sql +CREATE TABLE papers ( + id TEXT PRIMARY KEY, -- uuid + name TEXT NOT NULL, -- human-readable name + pdf_key TEXT NOT NULL, -- R2 key for PDF + markdown_key TEXT, -- R2 key for Markdown + status TEXT NOT NULL, -- 'uploading' | 'converting' | 'indexing' | 'embedding' | 'ready' | 'error' + error TEXT, -- error message if failed + page_count INTEGER, + markdown_length INTEGER, + chunk_count INTEGER, + created_at TEXT NOT NULL, + processed_at TEXT, + version TEXT NOT NULL -- paper-intelligence version for compat +); + +CREATE TABLE headers ( + id INTEGER PRIMARY KEY AUTOINCREMENT, + paper_id TEXT NOT NULL REFERENCES papers(id), + level INTEGER NOT NULL, -- 1-6 + text TEXT NOT NULL, + line_number INTEGER NOT NULL, + path TEXT NOT NULL, -- "Section > Subsection > ..." + FOREIGN KEY (paper_id) REFERENCES papers(id) +); + +CREATE TABLE images ( + id INTEGER PRIMARY KEY AUTOINCREMENT, + paper_id TEXT NOT NULL REFERENCES papers(id), + r2_key TEXT NOT NULL, + filename TEXT NOT NULL, + FOREIGN KEY (paper_id) REFERENCES papers(id) +); +``` + +## API Design (Worker) + +### `POST /papers` + +Upload a new paper for processing. + +``` +Content-Type: multipart/form-data +Body: { file: , name?: } + +Response 202: +{ id: "uuid", name: "paper-name", status: "converting" } +``` + +### `GET /papers/:id` + +Check paper status. + +``` +Response 200: +{ + id: "uuid", + name: "paper-name", + status: "ready", + page_count: 24, + chunk_count: 47, + created_at: "2026-04-09T...", + processed_at: "2026-04-09T..." +} +``` + +### `GET /papers` + +List all papers. + +``` +Response 200: +{ papers: [{ id, name, status, created_at }] } +``` + +### `POST /search` + +Search across papers. + +```json +{ + "query": "attention mechanism", + "mode": "hybrid", // "grep" | "rag" | "hybrid" + "top_k": 5, + "paper_ids": ["uuid1"], // optional, search subset + "case_sensitive": false, + "regex": false +} + +Response 200: +{ + "results": [ + { + "paper_id": "uuid", + "paper_name": "...", + "content": "matched text", + "line_number": 142, + "header_context": "Methods > Data", + "score": 0.89, + "match_type": "rag" + } + ], + "query": "attention mechanism", + "mode": "hybrid", + "papers_searched": 3 +} +``` + +### `DELETE /papers/:id` + +Delete a paper and all associated data (R2 objects, D1 rows, Vectorize vectors). + +## MCP Server Changes + +The local MCP server keeps the same tool interface but swaps the implementation: + +```python +# Before (local) +from paper_intelligence.tools.search import search +result = search(query="...", sources=["~/papers/foo.pdf"]) + +# After (cloud client) +import httpx +result = httpx.post("https://paper-intelligence..workers.dev/search", json={...}) +``` + +**Tools stay the same:** +- `search(query, sources, mode, top_k, ...)` → calls `POST /search` +- `get_paper_info(paper_dir)` → calls `GET /papers/:id` + +The `sources` parameter changes semantics — instead of local file paths, it accepts paper names or IDs. For backwards compatibility, if a local PDF path is passed, the MCP server uploads it first, waits for processing, then searches. + +## Auth + +Simple API key auth for now. Key stored in 1Password CLI Secrets vault, passed as `Authorization: Bearer ` header. The Worker validates it. + +No need for anything fancier with 2 users. + +## Migration Path + +1. **Phase 1**: Build the cloud backend (Worker + R2 + Container + Vectorize + D1) +2. **Phase 2**: Add cloud client mode to MCP server (flag to switch local vs cloud) +3. **Phase 3**: Re-process existing papers from ~/Documents/papers/ into cloud +4. **Phase 4**: Default to cloud, keep local as fallback + +Local mode stays intact — the cloud backend is additive, not a replacement. Useful for offline work or when you don't want to wait for network round trips. + +## What This Enables + +- **Shared paper library**: Oded and Yue search the same papers from any machine +- **No local ML stack**: New machines just need the thin MCP client, no torch/marker/embeddings +- **Process once, search everywhere**: Paper processed on upload, instantly searchable from any device +- **Burns Cloudflare credits on something useful**: R2 storage, Container compute, Workers AI embeddings, Vectorize queries + +## Open Questions + +- **Workers AI embedding model**: Which model to use? Need to check what's available and how it compares to bge-small-en-v1.5 quality-wise. Ideally something with similar or better retrieval quality. +- **Vectorize limits**: Check max vectors, dimensions, metadata size. Make sure it handles our scale (hundreds of papers, thousands of chunks). +- **Container cold start**: How long does it take a Marker container to wake from sleep? If it's 30+ seconds, might want to keep it warm during active use. +- **Markdown storage for grep**: Grep search needs the full Markdown. Fetching from R2 on every search adds latency. Could cache in D1 or use Workers KV, but Markdown can be large. Need to benchmark. +- **Image serving**: Do we need to serve extracted images? Current paper-intelligence references them in search results. If so, R2 presigned URLs work. diff --git a/cloud/container/Dockerfile b/cloud/container/Dockerfile new file mode 100644 index 0000000..c93f8e1 --- /dev/null +++ b/cloud/container/Dockerfile @@ -0,0 +1,39 @@ +# Marker PDF converter for Cloudflare Containers +# Target: linux/amd64 (CF Containers requirement) + +FROM python:3.11-slim + +WORKDIR /app + +# System deps for Marker (OCR, image processing) +RUN apt-get update && apt-get install -y --no-install-recommends \ + libgl1 \ + libglib2.0-0 \ + libsm6 \ + libxext6 \ + libxrender-dev \ + libgomp1 \ + && rm -rf /var/lib/apt/lists/* + +# Install PyTorch CPU-only (much smaller than full torch) +RUN pip install --no-cache-dir \ + torch torchvision \ + --index-url https://download.pytorch.org/whl/cpu + +# Install Marker + server deps +COPY requirements.txt . +RUN pip install --no-cache-dir -r requirements.txt + +# Pre-download Marker models into the image (avoids cold-start download) +RUN python -c "from marker.models import create_model_dict; create_model_dict()" \ + || echo "Model pre-download failed — will download on first request" + +COPY server.py . + +ENV PYTHONUNBUFFERED=1 +ENV TORCH_DEVICE=cpu + +EXPOSE 8080 + +# Use gunicorn with long timeout for large PDFs +CMD ["gunicorn", "--bind", "0.0.0.0:8080", "--timeout", "600", "--workers", "1", "server:app"] diff --git a/cloud/container/requirements.txt b/cloud/container/requirements.txt new file mode 100644 index 0000000..9e407d3 --- /dev/null +++ b/cloud/container/requirements.txt @@ -0,0 +1,3 @@ +marker-pdf>=1.0.0 +flask>=3.0.0 +gunicorn>=22.0.0 diff --git a/cloud/container/server.py b/cloud/container/server.py new file mode 100644 index 0000000..38305e8 --- /dev/null +++ b/cloud/container/server.py @@ -0,0 +1,87 @@ +"""Marker PDF conversion server for Cloudflare Containers. + +Accepts raw PDF bytes via POST /convert, runs Marker, returns markdown + images. +Models are pre-loaded on startup to avoid per-request download overhead. +""" + +import base64 +import io +import os +import tempfile + +from flask import Flask, request, jsonify + +os.environ["TORCH_DEVICE"] = "cpu" + +app = Flask(__name__) + +# Global converter — loaded once on startup +_converter = None + + +def get_converter(): + global _converter + if _converter is None: + from marker.converters.pdf import PdfConverter + from marker.models import create_model_dict + + print("Loading Marker models...", flush=True) + _converter = PdfConverter(artifact_dict=create_model_dict()) + print("Models loaded.", flush=True) + return _converter + + +@app.route("/health", methods=["GET"]) +def health(): + return jsonify({"status": "ok"}) + + +@app.route("/convert", methods=["POST"]) +def convert(): + pdf_data = request.get_data() + if not pdf_data: + return jsonify({"error": "No PDF data in request body"}), 400 + + with tempfile.NamedTemporaryFile(suffix=".pdf", delete=False) as f: + f.write(pdf_data) + pdf_path = f.name + + try: + from marker.output import text_from_rendered + + converter = get_converter() + rendered = converter(pdf_path) + markdown_text, _, images = text_from_rendered(rendered) + + image_list = [] + if images: + for name, data in images.items(): + if isinstance(data, bytes): + b64 = base64.b64encode(data).decode() + else: + buf = io.BytesIO() + data.save(buf, format="PNG") + b64 = base64.b64encode(buf.getvalue()).decode() + image_list.append({"name": name, "data": b64}) + + page_count = None + if hasattr(rendered, "metadata") and isinstance(rendered.metadata, dict): + page_count = rendered.metadata.get("page_count") + + return jsonify({ + "markdown": markdown_text, + "images": image_list, + "page_count": page_count, + }) + + except Exception as e: + return jsonify({"error": str(e)}), 500 + + finally: + os.unlink(pdf_path) + + +if __name__ == "__main__": + # Pre-load models before accepting requests + get_converter() + app.run(host="0.0.0.0", port=8080) diff --git a/cloud/migrate.sh b/cloud/migrate.sh new file mode 100755 index 0000000..fdf7e8e --- /dev/null +++ b/cloud/migrate.sh @@ -0,0 +1,71 @@ +#!/bin/bash +set -euo pipefail + +# Paper Intelligence Cloud — Migration Script +# Uploads locally processed papers to the cloud backend. +# +# Usage: ./migrate.sh [papers_dir] +# papers_dir: path to papers directory (default: ~/Documents/papers) + +PAPERS_DIR="${1:-$HOME/Documents/papers}" +API_URL="${PAPER_INTELLIGENCE_URL:-}" +API_TOKEN="${PAPER_INTELLIGENCE_TOKEN:-}" + +# Try to get API token from 1Password if not set +if [ -z "$API_TOKEN" ]; then + API_TOKEN=$(op read "op://CLI Secrets/paper-intelligence-api-token/token" 2>/dev/null || true) +fi + +if [ -z "$API_URL" ]; then + echo "Error: Set PAPER_INTELLIGENCE_URL to your Worker URL" + echo " export PAPER_INTELLIGENCE_URL=https://paper-intelligence..workers.dev" + exit 1 +fi + +if [ -z "$API_TOKEN" ]; then + echo "Error: Set PAPER_INTELLIGENCE_TOKEN or configure 1Password CLI" + exit 1 +fi + +echo "Migrating papers from: $PAPERS_DIR" +echo "Target: $API_URL" +echo "" + +SUCCESS=0 +SKIP=0 +FAIL=0 + +for dir in "$PAPERS_DIR"/*/; do + name=$(basename "$dir") + md="$dir/paper.md" + + if [ ! -f "$md" ]; then + echo "SKIP $name (no paper.md)" + SKIP=$((SKIP + 1)) + continue + fi + + echo -n "UPLOAD $name... " + + RESPONSE=$(curl -s -w "\n%{http_code}" -X POST "$API_URL/papers" \ + -H "Authorization: Bearer $API_TOKEN" \ + -H "Content-Type: application/json" \ + -d "$(jq -n --arg name "$name" --rawfile markdown "$md" \ + '{name: $name, markdown: $markdown}')") + + HTTP_CODE=$(echo "$RESPONSE" | tail -1) + BODY=$(echo "$RESPONSE" | sed '$d') + + if [ "$HTTP_CODE" = "202" ]; then + PAPER_ID=$(echo "$BODY" | jq -r '.id') + echo "OK (id: $PAPER_ID)" + SUCCESS=$((SUCCESS + 1)) + else + echo "FAIL (HTTP $HTTP_CODE)" + echo " $BODY" + FAIL=$((FAIL + 1)) + fi +done + +echo "" +echo "Done: $SUCCESS uploaded, $SKIP skipped, $FAIL failed" diff --git a/cloud/package-lock.json b/cloud/package-lock.json new file mode 100644 index 0000000..0bec92b --- /dev/null +++ b/cloud/package-lock.json @@ -0,0 +1,4222 @@ +{ + "name": "paper-intelligence-cloud", + "version": "0.1.0", + "lockfileVersion": 3, + "requires": true, + "packages": { + "": { + "name": "paper-intelligence-cloud", + "version": "0.1.0", + 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"typescript": "^5.7.0" + } +} diff --git a/cloud/schema.sql b/cloud/schema.sql new file mode 100644 index 0000000..1353dee --- /dev/null +++ b/cloud/schema.sql @@ -0,0 +1,41 @@ +-- Paper Intelligence Cloud — D1 Schema + +CREATE TABLE IF NOT EXISTS papers ( + id TEXT PRIMARY KEY, + name TEXT NOT NULL, + pdf_key TEXT, + markdown_key TEXT, + markdown_text TEXT, -- full markdown for grep search + status TEXT NOT NULL DEFAULT 'uploading', + error TEXT, + page_count INTEGER, + markdown_length INTEGER, + chunk_count INTEGER, + created_at TEXT NOT NULL DEFAULT (datetime('now')), + processed_at TEXT, + version TEXT NOT NULL DEFAULT '0.2.0' +); + +CREATE TABLE IF NOT EXISTS headers ( + id INTEGER PRIMARY KEY AUTOINCREMENT, + paper_id TEXT NOT NULL, + level INTEGER NOT NULL, + text TEXT NOT NULL, + line_number INTEGER NOT NULL, + path TEXT NOT NULL, + FOREIGN KEY (paper_id) REFERENCES papers(id) ON DELETE CASCADE +); + +CREATE TABLE IF NOT EXISTS images ( + id INTEGER PRIMARY KEY AUTOINCREMENT, + paper_id TEXT NOT NULL, + r2_key TEXT NOT NULL, + filename TEXT NOT NULL, + FOREIGN KEY (paper_id) REFERENCES papers(id) ON DELETE CASCADE +); + +CREATE INDEX IF NOT EXISTS idx_headers_paper ON headers(paper_id); +CREATE INDEX IF NOT EXISTS idx_headers_line ON headers(paper_id, line_number); +CREATE INDEX IF NOT EXISTS idx_images_paper ON images(paper_id); +CREATE INDEX IF NOT EXISTS idx_papers_status ON papers(status); +CREATE INDEX IF NOT EXISTS idx_papers_name ON papers(name); diff --git a/cloud/setup.sh b/cloud/setup.sh new file mode 100755 index 0000000..7d02424 --- /dev/null +++ b/cloud/setup.sh @@ -0,0 +1,83 @@ +#!/bin/bash +set -euo pipefail + +# Paper Intelligence Cloud — Setup Script +# Creates Cloudflare resources and deploys the Worker. +# +# Prerequisites: +# - npm install (in this directory) +# - wrangler login (authenticated to Cloudflare) +# - op CLI configured (for API token) + +echo "=== Paper Intelligence Cloud Setup ===" +echo "" + +# 1. Create D1 database +echo "Creating D1 database..." +D1_OUTPUT=$(npx wrangler d1 create paper-intelligence 2>&1) || true +echo "$D1_OUTPUT" + +# Extract database ID +D1_ID=$(echo "$D1_OUTPUT" | grep -o '"database_id": "[^"]*"' | head -1 | cut -d'"' -f4) +if [ -z "$D1_ID" ]; then + echo "Could not extract D1 database ID. If it already exists, find it with:" + echo " npx wrangler d1 list" + echo "Then update wrangler.jsonc manually." +else + echo "D1 database ID: $D1_ID" + # Update wrangler.jsonc + sed -i '' "s/\"database_id\": \"TODO\"/\"database_id\": \"$D1_ID\"/" wrangler.jsonc + echo "Updated wrangler.jsonc with D1 database ID." +fi +echo "" + +# 2. Create R2 bucket +echo "Creating R2 bucket..." +npx wrangler r2 bucket create paper-intelligence 2>&1 || echo "(may already exist)" +echo "" + +# 3. Create Vectorize index +echo "Creating Vectorize index..." +npx wrangler vectorize create paper-intelligence \ + --dimensions=384 \ + --metric=cosine 2>&1 || echo "(may already exist)" +echo "" + +# 4. Create metadata index for paper_id filtering +echo "Creating Vectorize metadata index..." +npx wrangler vectorize create-metadata-index paper-intelligence \ + --property-name=paper_id \ + --type=string 2>&1 || echo "(may already exist)" +echo "" + +# 5. Apply D1 schema +echo "Applying D1 schema..." +npx wrangler d1 execute paper-intelligence --file=schema.sql +echo "" + +# 6. Set API token secret +echo "Setting API_TOKEN secret..." +API_TOKEN=$(op read "op://CLI Secrets/paper-intelligence-api-token/token" 2>/dev/null || true) +if [ -n "$API_TOKEN" ]; then + echo "$API_TOKEN" | npx wrangler secret put API_TOKEN + echo "API_TOKEN set from 1Password." +else + echo "Could not read from 1Password. Set manually:" + echo " npx wrangler secret put API_TOKEN" +fi +echo "" + +# 7. Deploy +echo "Deploying Worker..." +npx wrangler deploy +echo "" + +echo "=== Setup complete ===" +echo "" +echo "MCP endpoint: https://paper-intelligence..workers.dev/mcp" +echo "" +echo "Add to Claude Code:" +echo " claude mcp add paper-intelligence-cloud \\" +echo " --transport http \\" +echo " --header 'Authorization: Bearer ' \\" +echo " https://paper-intelligence..workers.dev/mcp" diff --git a/cloud/src/chat.ts b/cloud/src/chat.ts new file mode 100644 index 0000000..95b0232 --- /dev/null +++ b/cloud/src/chat.ts @@ -0,0 +1,175 @@ +import type { Env } from "./types"; +import { search } from "./search"; + +/** + * RAG chat: search for relevant chunks, then stream an LLM response. + * Normalizes both OpenAI and Anthropic SSE formats to: data: {"text":"..."}\n\n + */ +export async function handleChat( + request: Request, + env: Env, +): Promise { + const body = (await request.json()) as { + message: string; + paper_ids?: string[]; + model?: string; + history?: { role: string; content: string }[]; + }; + + if (!body.message) { + return Response.json({ error: "Missing 'message'" }, { status: 400 }); + } + + const model = body.model || "claude-opus-4-6"; + const isOpenAI = + model.startsWith("gpt") || model.startsWith("o") || model.startsWith("chatgpt"); + + // Check API key availability + if (isOpenAI && !env.OPENAI_API_KEY) { + return Response.json( + { error: "OPENAI_API_KEY not configured" }, + { status: 500 }, + ); + } + if (!isOpenAI && !env.ANTHROPIC_API_KEY) { + return Response.json( + { error: "ANTHROPIC_API_KEY not configured" }, + { status: 500 }, + ); + } + + // RAG: search for relevant chunks + const searchResponse = await search( + env, + body.message, + "hybrid", + 10, + body.paper_ids || [], + false, + false, + ); + + // Build context from search results + const context = searchResponse.results + .map( + (r) => + `[${r.paper_name}] [${r.header_context || "General"}] (line ${r.line_number})\n${r.content}`, + ) + .join("\n\n---\n\n"); + + const systemPrompt = `You are a research paper assistant. Answer questions based on the following excerpts from the user's paper library. + +When referencing information, cite the paper name and section. If the excerpts don't contain enough information, say so. + +${context ? `--- Paper Excerpts ---\n\n${context}` : "(No relevant excerpts found)"}`; + + // Build messages + const history = body.history || []; + const messages = [ + ...history, + { role: "user" as const, content: body.message }, + ]; + + // Stream LLM response + let upstreamResponse: Response; + + if (isOpenAI) { + upstreamResponse = await fetch( + "https://api.openai.com/v1/chat/completions", + { + method: "POST", + headers: { + Authorization: `Bearer ${env.OPENAI_API_KEY}`, + "Content-Type": "application/json", + }, + body: JSON.stringify({ + model, + messages: [{ role: "system", content: systemPrompt }, ...messages], + stream: true, + }), + }, + ); + } else { + upstreamResponse = await fetch("https://api.anthropic.com/v1/messages", { + method: "POST", + headers: { + "x-api-key": env.ANTHROPIC_API_KEY!, + "anthropic-version": "2023-06-01", + "Content-Type": "application/json", + }, + body: JSON.stringify({ + model, + max_tokens: 8192, + system: systemPrompt, + messages, + stream: true, + }), + }); + } + + if (!upstreamResponse.ok) { + const err = await upstreamResponse.text(); + return Response.json( + { error: `LLM API error (${upstreamResponse.status}): ${err}` }, + { status: 502 }, + ); + } + + // Normalize the SSE stream + const normalized = upstreamResponse.body!.pipeThrough( + createNormalizer(isOpenAI ? "openai" : "anthropic"), + ); + + return new Response(normalized, { + headers: { + "Content-Type": "text/event-stream", + "Cache-Control": "no-cache", + Connection: "keep-alive", + }, + }); +} + +function createNormalizer( + provider: "openai" | "anthropic", +): TransformStream { + const encoder = new TextEncoder(); + const decoder = new TextDecoder(); + let buffer = ""; + + return new TransformStream({ + transform(chunk, controller) { + buffer += decoder.decode(chunk, { stream: true }); + const parts = buffer.split("\n"); + buffer = parts.pop() || ""; + + for (const line of parts) { + if (!line.startsWith("data: ")) continue; + const data = line.slice(6).trim(); + if (!data) continue; + if (data === "[DONE]") { + controller.enqueue(encoder.encode("data: [DONE]\n\n")); + continue; + } + try { + const parsed = JSON.parse(data); + let text = ""; + if (provider === "openai") { + text = parsed.choices?.[0]?.delta?.content || ""; + } else if (parsed.type === "content_block_delta") { + text = parsed.delta?.text || ""; + } + if (text) { + controller.enqueue( + encoder.encode(`data: ${JSON.stringify({ text })}\n\n`), + ); + } + } catch { + // skip unparseable lines + } + } + }, + flush(controller) { + controller.enqueue(encoder.encode("data: [DONE]\n\n")); + }, + }); +} diff --git a/cloud/src/container.ts b/cloud/src/container.ts new file mode 100644 index 0000000..08fa4d2 --- /dev/null +++ b/cloud/src/container.ts @@ -0,0 +1,18 @@ +import { Container } from "@cloudflare/containers"; + +export class MarkerContainer extends Container { + defaultPort = 8080; + sleepAfter = "5m"; + + override onStart(): void { + console.log("Marker container started"); + } + + override onStop(): void { + console.log("Marker container stopped"); + } + + override onError(error: unknown): void { + console.error("Marker container error:", error); + } +} diff --git a/cloud/src/index.ts b/cloud/src/index.ts new file mode 100644 index 0000000..80e45b3 --- /dev/null +++ b/cloud/src/index.ts @@ -0,0 +1,230 @@ +import type { Env, Paper } from "./types"; +import { processPaper, convertAndProcessPaper, deletePaper } from "./pipeline"; +import { PaperIntelligenceMCP } from "./mcp"; +import { handleChat } from "./chat"; +import { HTML } from "./ui"; +// Uncomment when Containers beta is enabled: +// export { MarkerContainer } from "./container"; + +export { PaperIntelligenceMCP }; + +export default { + async fetch( + request: Request, + env: Env, + ctx: ExecutionContext, + ): Promise { + const url = new URL(request.url); + + // CORS preflight + if (request.method === "OPTIONS") { + return new Response(null, { + headers: { + "Access-Control-Allow-Origin": "*", + "Access-Control-Allow-Methods": "GET, POST, DELETE, OPTIONS", + "Access-Control-Allow-Headers": "Authorization, Content-Type", + }, + }); + } + + // UI — no auth required (auth is in the UI itself) + if (url.pathname === "/" || url.pathname === "/index.html") { + return new Response(HTML, { + headers: { "Content-Type": "text/html; charset=utf-8" }, + }); + } + + // Auth check for all API routes + const authHeader = request.headers.get("Authorization"); + if (authHeader !== `Bearer ${env.API_TOKEN}`) { + return Response.json({ error: "Unauthorized" }, { status: 401 }); + } + + // MCP endpoint — Streamable HTTP + if (url.pathname.startsWith("/mcp")) { + return ( + PaperIntelligenceMCP.serve("/mcp") as { + fetch: (req: Request, env: Env, ctx: ExecutionContext) => Promise; + } + ).fetch(request, env, ctx); + } + + // Chat endpoint + if (url.pathname === "/chat" && request.method === "POST") { + return handleChat(request, env); + } + + // REST: Upload paper + if (url.pathname === "/papers" && request.method === "POST") { + return handleUpload(request, env, ctx); + } + + // REST: List papers + if (url.pathname === "/papers" && request.method === "GET") { + return handleListPapers(env); + } + + // REST: Paper content (markdown text for rendering) + const contentMatch = url.pathname.match( + /^\/papers\/([\w-]+)\/content$/, + ); + if (contentMatch && request.method === "GET") { + return handleGetPaperContent(contentMatch[1], env); + } + + // REST: Get paper + const paperMatch = url.pathname.match(/^\/papers\/([\w-]+)$/); + if (paperMatch && request.method === "GET") { + return handleGetPaper(paperMatch[1], env); + } + + // REST: Delete paper + if (paperMatch && request.method === "DELETE") { + return handleDeletePaper(paperMatch[1], env); + } + + return Response.json({ error: "Not found" }, { status: 404 }); + }, +} satisfies ExportedHandler; + +// --- REST Handlers --- + +async function handleUpload( + request: Request, + env: Env, + ctx: ExecutionContext, +): Promise { + const contentType = request.headers.get("Content-Type") ?? ""; + + let name: string; + let markdown: string | null = null; + let pdfData: ArrayBuffer | null = null; + + if (contentType.includes("application/json")) { + // JSON upload: { name, markdown } + const body = (await request.json()) as { + name?: string; + markdown?: string; + }; + if (!body.name) { + return Response.json( + { error: "Missing 'name' field" }, + { status: 400 }, + ); + } + name = body.name; + markdown = body.markdown ?? null; + } else if (contentType.includes("multipart/form-data")) { + // Multipart upload: PDF file + optional name + const formData = await request.formData(); + const file = formData.get("file") as File | null; + name = (formData.get("name") as string) ?? file?.name?.replace(/\.pdf$/i, "") ?? "unnamed"; + if (file) { + pdfData = await file.arrayBuffer(); + } + const md = formData.get("markdown") as string | null; + if (md) markdown = md; + } else { + return Response.json( + { error: "Content-Type must be application/json or multipart/form-data" }, + { status: 400 }, + ); + } + + if (!markdown && !pdfData) { + return Response.json( + { error: "Provide either 'markdown' text or a PDF 'file'" }, + { status: 400 }, + ); + } + + // Generate ID + const id = crypto.randomUUID(); + + // Insert paper record + await env.DB.prepare( + "INSERT INTO papers (id, name, status) VALUES (?, ?, ?)", + ) + .bind(id, name, markdown ? "indexing" : "uploading") + .run(); + + // Store PDF in R2 if provided + if (pdfData) { + const pdfKey = `papers/${id}/paper.pdf`; + await env.BUCKET.put(pdfKey, pdfData); + await env.DB.prepare("UPDATE papers SET pdf_key = ? WHERE id = ?") + .bind(pdfKey, id) + .run(); + } + + if (markdown) { + // Process immediately in background + ctx.waitUntil(processPaper(env, id, name, markdown)); + return Response.json({ id, name, status: "indexing" }, { status: 202 }); + } + + // PDF upload: convert via Marker container (when available) + if (env.MARKER_CONTAINER) { + ctx.waitUntil(convertAndProcessPaper(env, id, name, pdfData!)); + return Response.json( + { id, name, status: "converting" }, + { status: 202 }, + ); + } + + // Container not available — PDF stored, needs manual markdown upload + return Response.json( + { + id, + name, + status: "uploading", + message: + "PDF stored in R2. Marker container not yet enabled — re-upload with 'markdown' field, or enable Containers beta.", + }, + { status: 202 }, + ); +} + +async function handleListPapers(env: Env): Promise { + const { results } = await env.DB.prepare( + "SELECT id, name, status, page_count, chunk_count, created_at, processed_at FROM papers ORDER BY created_at DESC", + ).all(); + return Response.json({ papers: results }); +} + +async function handleGetPaper(id: string, env: Env): Promise { + const paper = await env.DB.prepare("SELECT * FROM papers WHERE id = ?") + .bind(id) + .first(); + if (!paper) { + return Response.json({ error: "Not found" }, { status: 404 }); + } + // Don't return full markdown_text in REST response + const { markdown_text: _, ...info } = paper; + return Response.json(info); +} + +async function handleGetPaperContent( + id: string, + env: Env, +): Promise { + const paper = await env.DB.prepare( + "SELECT id, name, markdown_text FROM papers WHERE id = ?", + ) + .bind(id) + .first<{ id: string; name: string; markdown_text: string | null }>(); + if (!paper) { + return Response.json({ error: "Not found" }, { status: 404 }); + } + return Response.json({ id: paper.id, name: paper.name, markdown: paper.markdown_text }); +} + +async function handleDeletePaper(id: string, env: Env): Promise { + try { + await deletePaper(env, id); + return Response.json({ success: true, id }); + } catch (err) { + const message = err instanceof Error ? err.message : String(err); + return Response.json({ error: message }, { status: 404 }); + } +} diff --git a/cloud/src/mcp.ts b/cloud/src/mcp.ts new file mode 100644 index 0000000..4941ec8 --- /dev/null +++ b/cloud/src/mcp.ts @@ -0,0 +1,184 @@ +import { McpAgent } from "agents/mcp"; +import { McpServer } from "@modelcontextprotocol/sdk/server/mcp.js"; +import { z } from "zod"; +import type { Env, Paper } from "./types"; +import { search } from "./search"; + +export class PaperIntelligenceMCP extends McpAgent { + server = new McpServer({ + name: "paper-intelligence", + version: "0.2.0", + }); + + async init() { + this.server.tool( + "search", + "Search across papers using text search (grep), semantic search (RAG), or both (hybrid). " + + "Returns matching passages with line numbers, section context, and relevance scores.", + { + query: z.string().describe("Search query text"), + mode: z + .enum(["grep", "rag", "hybrid"]) + .default("hybrid") + .describe("grep = exact text match, rag = semantic similarity, hybrid = both merged"), + top_k: z + .number() + .int() + .min(1) + .max(50) + .default(5) + .describe("Max results to return"), + sources: z + .array(z.string()) + .default([]) + .describe("Paper names or IDs to search (empty = search all)"), + case_sensitive: z + .boolean() + .default(false) + .describe("Case-sensitive grep matching"), + regex: z + .boolean() + .default(false) + .describe("Treat query as regular expression for grep"), + }, + async ({ query, mode, top_k, sources, case_sensitive, regex }) => { + const response = await search( + this.env, + query, + mode, + top_k, + sources, + case_sensitive, + regex, + ); + + return { + content: [ + { + type: "text" as const, + text: JSON.stringify(response, null, 2), + }, + ], + }; + }, + ); + + this.server.tool( + "get_paper_info", + "Get detailed information about a specific paper including processing status, " + + "section headers, chunk count, and metadata.", + { + source: z + .string() + .describe("Paper name or ID"), + }, + async ({ source }) => { + const UUID_RE = /^[0-9a-f]{8}-[0-9a-f]{4}-[0-9a-f]{4}-[0-9a-f]{4}-[0-9a-f]{12}$/i; + + let paper: Paper | null; + if (UUID_RE.test(source)) { + paper = await this.env.DB.prepare( + "SELECT * FROM papers WHERE id = ?", + ) + .bind(source) + .first(); + } else { + paper = await this.env.DB.prepare( + "SELECT * FROM papers WHERE LOWER(name) LIKE LOWER(?) LIMIT 1", + ) + .bind(`%${source}%`) + .first(); + } + + if (!paper) { + return { + content: [ + { + type: "text" as const, + text: JSON.stringify({ + success: false, + message: `Paper not found: ${source}`, + }), + }, + ], + }; + } + + // Get headers for this paper + const { results: headers } = await this.env.DB.prepare( + "SELECT level, text, line_number, path FROM headers WHERE paper_id = ? ORDER BY line_number", + ) + .bind(paper.id) + .all(); + + // Get image count + const imageRow = await this.env.DB.prepare( + "SELECT COUNT(*) as count FROM images WHERE paper_id = ?", + ) + .bind(paper.id) + .first<{ count: number }>(); + + return { + content: [ + { + type: "text" as const, + text: JSON.stringify( + { + success: true, + id: paper.id, + name: paper.name, + status: paper.status, + error: paper.error, + page_count: paper.page_count, + markdown_length: paper.markdown_length, + chunk_count: paper.chunk_count, + header_count: headers.length, + image_count: imageRow?.count ?? 0, + headers: headers.map((h) => ({ + level: h.level, + text: h.text, + line: h.line_number, + path: h.path, + })), + created_at: paper.created_at, + processed_at: paper.processed_at, + version: paper.version, + }, + null, + 2, + ), + }, + ], + }; + }, + ); + + this.server.tool( + "list_papers", + "List all papers in the library with their processing status.", + {}, + async () => { + const { results } = await this.env.DB.prepare( + "SELECT id, name, status, page_count, chunk_count, created_at, processed_at FROM papers ORDER BY created_at DESC", + ).all(); + + return { + content: [ + { + type: "text" as const, + text: JSON.stringify( + { + success: true, + papers: results, + total: results.length, + }, + null, + 2, + ), + }, + ], + }; + }, + ); + } +} diff --git a/cloud/src/pipeline.ts b/cloud/src/pipeline.ts new file mode 100644 index 0000000..2ec61aa --- /dev/null +++ b/cloud/src/pipeline.ts @@ -0,0 +1,391 @@ +import type { Env, FlatHeader, Chunk } from "./types"; + +const HEADER_PATTERN = /^(#{1,6})\s+(.+)$/; +const CHUNK_SIZE = 512; +const CHUNK_OVERLAP = 50; +const EMBEDDING_BATCH_SIZE = 100; +const VECTORIZE_BATCH_SIZE = 1000; +const MAX_METADATA_TEXT = 8000; // Vectorize has 10KB metadata limit per vector + +/** + * Extract markdown headers with hierarchical paths. + * Matches the Python MarkdownParser.build_flat_headers() logic. + */ +export function extractHeaders(markdown: string): FlatHeader[] { + const lines = markdown.split("\n"); + const headers: FlatHeader[] = []; + const stack: { level: number; text: string }[] = []; + + for (let i = 0; i < lines.length; i++) { + const match = lines[i].match(HEADER_PATTERN); + if (match) { + const level = match[1].length; + const text = match[2].trim(); + + // Pop stack until we find a parent (strictly lower level number = higher heading) + while (stack.length > 0 && stack[stack.length - 1].level >= level) { + stack.pop(); + } + + stack.push({ level, text }); + + headers.push({ + level, + text, + line: i + 1, // 1-indexed + path: stack.map((h) => h.text).join(" > "), + }); + } + } + + return headers; +} + +/** + * Find the nearest header at or before a given line number. + */ +export function getHeaderAtLine( + headers: FlatHeader[], + lineNumber: number, +): FlatHeader | null { + let result: FlatHeader | null = null; + for (const header of headers) { + if (header.line <= lineNumber) { + result = header; + } else { + break; + } + } + return result; +} + +/** + * Chunk markdown text into overlapping segments. + * Respects paragraph boundaries and tracks line numbers. + * Matches the Python MarkdownParser.chunk_text() logic: + * - chunk_size: 512 characters + * - chunk_overlap: 50 characters + */ +export function chunkText( + markdown: string, + headers: FlatHeader[], + chunkSize: number = CHUNK_SIZE, + chunkOverlap: number = CHUNK_OVERLAP, +): Chunk[] { + const lines = markdown.split("\n"); + + // Step 1: Split into paragraphs (groups of non-empty lines) + interface Paragraph { + text: string; + startLine: number; + endLine: number; + } + + const paragraphs: Paragraph[] = []; + let currentPara: string[] = []; + let currentStart = 1; + + for (let i = 0; i < lines.length; i++) { + const lineNum = i + 1; + if (lines[i].trim() === "") { + if (currentPara.length > 0) { + paragraphs.push({ + text: currentPara.join("\n"), + startLine: currentStart, + endLine: lineNum - 1, + }); + currentPara = []; + } + currentStart = lineNum + 1; + } else { + if (currentPara.length === 0) { + currentStart = lineNum; + } + currentPara.push(lines[i]); + } + } + if (currentPara.length > 0) { + paragraphs.push({ + text: currentPara.join("\n"), + startLine: currentStart, + endLine: lines.length, + }); + } + + // Step 2: Accumulate paragraphs into chunks + const chunks: Chunk[] = []; + let chunkText = ""; + let chunkStartLine: number | null = null; + let chunkEndLine = 0; + let prevSuffix = ""; + + for (const para of paragraphs) { + const wouldExceed = + chunkText.length > 0 && + chunkText.length + para.text.length + 2 > chunkSize; // +2 for "\n\n" + + if (wouldExceed) { + // Finalize current chunk + const header = getHeaderAtLine(headers, chunkStartLine ?? 1); + chunks.push({ + text: chunkText, + header_path: header?.path ?? "", + start_line: chunkStartLine ?? 1, + end_line: chunkEndLine, + chunk_index: chunks.length, + }); + + // Start new chunk with overlap from previous + prevSuffix = chunkText.slice(-chunkOverlap); + chunkText = prevSuffix + "\n\n" + para.text; + chunkStartLine = para.startLine; + chunkEndLine = para.endLine; + } else { + if (chunkText.length > 0) { + chunkText += "\n\n" + para.text; + } else { + chunkText = prevSuffix ? prevSuffix + para.text : para.text; + chunkStartLine = para.startLine; + } + chunkEndLine = para.endLine; + } + } + + // Don't forget last chunk + if (chunkText.length > 0) { + const header = getHeaderAtLine(headers, chunkStartLine ?? 1); + chunks.push({ + text: chunkText, + header_path: header?.path ?? "", + start_line: chunkStartLine ?? 1, + end_line: chunkEndLine, + chunk_index: chunks.length, + }); + } + + return chunks; +} + +/** + * Generate embeddings for text chunks using Workers AI. + * Handles batching (max 100 per API call). + */ +async function embedTexts(env: Env, texts: string[]): Promise { + const allEmbeddings: number[][] = []; + + for (let i = 0; i < texts.length; i += EMBEDDING_BATCH_SIZE) { + const batch = texts.slice(i, i + EMBEDDING_BATCH_SIZE); + const response = await env.AI.run("@cf/baai/bge-small-en-v1.5", { + text: batch, + }); + const result = response as { data: number[][] }; + allEmbeddings.push(...result.data); + } + + return allEmbeddings; +} + +/** + * Full processing pipeline: extract headers, chunk, embed, store. + * Called after markdown is available (either uploaded directly or converted from PDF). + */ +export async function processPaper( + env: Env, + paperId: string, + name: string, + markdown: string, +): Promise { + try { + // Update status: indexing + await env.DB.prepare( + "UPDATE papers SET status = 'indexing' WHERE id = ?", + ) + .bind(paperId) + .run(); + + // Store markdown in D1 for grep search + await env.DB.prepare( + "UPDATE papers SET markdown_text = ?, markdown_length = ? WHERE id = ?", + ) + .bind(markdown, markdown.length, paperId) + .run(); + + // Store markdown in R2 for archival + await env.BUCKET.put(`papers/${paperId}/paper.md`, markdown); + + // Extract headers + const headers = extractHeaders(markdown); + + // Store headers in D1 + if (headers.length > 0) { + // Batch insert headers + const stmt = env.DB.prepare( + "INSERT INTO headers (paper_id, level, text, line_number, path) VALUES (?, ?, ?, ?, ?)", + ); + const batch = headers.map((h) => + stmt.bind(paperId, h.level, h.text, h.line, h.path), + ); + // D1 batch limit is 100 statements + for (let i = 0; i < batch.length; i += 100) { + await env.DB.batch(batch.slice(i, i + 100)); + } + } + + // Update status: embedding + await env.DB.prepare( + "UPDATE papers SET status = 'embedding' WHERE id = ?", + ) + .bind(paperId) + .run(); + + // Chunk the markdown + const chunks = chunkText(markdown, headers); + + // Generate embeddings + const texts = chunks.map((c) => c.text); + const embeddings = await embedTexts(env, texts); + + // Store vectors in Vectorize + const vectors = chunks.map((chunk, i) => ({ + id: `${paperId}_${chunk.chunk_index}`, + values: embeddings[i], + metadata: { + paper_id: paperId, + paper_name: name, + text: chunk.text.slice(0, MAX_METADATA_TEXT), + header_path: chunk.header_path, + start_line: chunk.start_line, + end_line: chunk.end_line, + chunk_index: chunk.chunk_index, + }, + })); + + for (let i = 0; i < vectors.length; i += VECTORIZE_BATCH_SIZE) { + await env.VECTORIZE.upsert(vectors.slice(i, i + VECTORIZE_BATCH_SIZE)); + } + + // Update paper status to ready + await env.DB.prepare( + "UPDATE papers SET status = 'ready', chunk_count = ?, processed_at = datetime('now') WHERE id = ?", + ) + .bind(chunks.length, paperId) + .run(); + } catch (err) { + const message = err instanceof Error ? err.message : String(err); + await env.DB.prepare( + "UPDATE papers SET status = 'error', error = ? WHERE id = ?", + ) + .bind(message, paperId) + .run(); + throw err; + } +} + +/** + * Convert a PDF via the Marker container, then index + embed. + * Runs in background via ctx.waitUntil() — the upload returns 202 immediately. + */ +export async function convertAndProcessPaper( + env: Env, + paperId: string, + name: string, + pdfData: ArrayBuffer, +): Promise { + try { + await env.DB.prepare("UPDATE papers SET status = 'converting' WHERE id = ?") + .bind(paperId) + .run(); + + // Send PDF to Marker container + const containerId = env.MARKER_CONTAINER.idFromName("marker"); + const container = env.MARKER_CONTAINER.get(containerId); + const response = await container.fetch("http://container/convert", { + method: "POST", + body: pdfData, + headers: { "Content-Type": "application/pdf" }, + }); + + if (!response.ok) { + const error = await response.text(); + throw new Error(`Marker conversion failed (${response.status}): ${error}`); + } + + const result = (await response.json()) as { + markdown: string; + images: { name: string; data: string }[]; + page_count?: number; + }; + + // Store images in R2 + if (result.images.length > 0) { + const imgStmt = env.DB.prepare( + "INSERT INTO images (paper_id, r2_key, filename) VALUES (?, ?, ?)", + ); + const imgBatch: D1PreparedStatement[] = []; + for (const img of result.images) { + const key = `papers/${paperId}/images/${img.name}`; + // Decode base64 to binary + const binary = Uint8Array.from(atob(img.data), (c) => c.charCodeAt(0)); + await env.BUCKET.put(key, binary); + imgBatch.push(imgStmt.bind(paperId, key, img.name)); + } + for (let i = 0; i < imgBatch.length; i += 100) { + await env.DB.batch(imgBatch.slice(i, i + 100)); + } + } + + // Update page count if available + if (result.page_count) { + await env.DB.prepare("UPDATE papers SET page_count = ? WHERE id = ?") + .bind(result.page_count, paperId) + .run(); + } + + // Now index + embed the markdown + await processPaper(env, paperId, name, result.markdown); + } catch (err) { + const message = err instanceof Error ? err.message : String(err); + await env.DB.prepare( + "UPDATE papers SET status = 'error', error = ? WHERE id = ?", + ) + .bind(message, paperId) + .run(); + throw err; + } +} + +/** + * Delete all data for a paper: D1 rows, R2 objects, Vectorize vectors. + */ +export async function deletePaper(env: Env, paperId: string): Promise { + // Get paper info for cleanup + const paper = await env.DB.prepare("SELECT * FROM papers WHERE id = ?") + .bind(paperId) + .first(); + if (!paper) throw new Error(`Paper ${paperId} not found`); + + // Delete Vectorize vectors + const chunkCount = (paper.chunk_count as number) ?? 0; + if (chunkCount > 0) { + const ids = Array.from({ length: chunkCount }, (_, i) => `${paperId}_${i}`); + // Vectorize deleteByIds batch limit + for (let i = 0; i < ids.length; i += 1000) { + await env.VECTORIZE.deleteByIds(ids.slice(i, i + 1000)); + } + } + + // Delete R2 objects + const r2Objects = await env.BUCKET.list({ prefix: `papers/${paperId}/` }); + if (r2Objects.objects.length > 0) { + await env.BUCKET.delete(r2Objects.objects.map((o) => o.key)); + } + + // Delete D1 rows (cascade handles headers + images) + await env.DB.prepare("DELETE FROM headers WHERE paper_id = ?") + .bind(paperId) + .run(); + await env.DB.prepare("DELETE FROM images WHERE paper_id = ?") + .bind(paperId) + .run(); + await env.DB.prepare("DELETE FROM papers WHERE id = ?").bind(paperId).run(); +} diff --git a/cloud/src/search.ts b/cloud/src/search.ts new file mode 100644 index 0000000..c9a75eb --- /dev/null +++ b/cloud/src/search.ts @@ -0,0 +1,261 @@ +import type { Env, FlatHeader, SearchResult, SearchResponse } from "./types"; +import { extractHeaders, getHeaderAtLine } from "./pipeline"; + +function escapeRegex(str: string): string { + return str.replace(/[.*+?^${}()|[\]\\]/g, "\\$&"); +} + +/** + * Grep search: line-by-line regex matching on markdown text. + * Returns matches with surrounding context (±3 lines). + */ +function grepSearch( + paperId: string, + paperName: string, + markdown: string, + headers: FlatHeader[], + query: string, + options: { caseSensitive: boolean; regex: boolean }, +): SearchResult[] { + const flags = options.caseSensitive ? "g" : "gi"; + let pattern: RegExp; + try { + pattern = new RegExp( + options.regex ? query : escapeRegex(query), + flags, + ); + } catch { + return []; // invalid regex + } + + const lines = markdown.split("\n"); + const results: SearchResult[] = []; + + for (let i = 0; i < lines.length; i++) { + pattern.lastIndex = 0; + if (pattern.test(lines[i])) { + const lineNum = i + 1; + const contextStart = Math.max(0, i - 3); + const contextEnd = Math.min(lines.length - 1, i + 3); + const header = getHeaderAtLine(headers, lineNum); + + results.push({ + paper_id: paperId, + paper_name: paperName, + content: lines[i], + line_number: lineNum, + header_context: header?.path ?? "", + score: 0.5, // default score for grep in hybrid mode + match_type: "grep", + context: lines.slice(contextStart, contextEnd + 1).join("\n"), + }); + } + } + + return results; +} + +/** + * RAG search: embed query, search Vectorize for semantically similar chunks. + */ +async function ragSearch( + env: Env, + query: string, + paperIds: string[], + topK: number, +): Promise { + // Embed the query + const response = await env.AI.run("@cf/baai/bge-small-en-v1.5", { + text: [query], + }); + const result = response as { data: number[][] }; + const queryVector = result.data[0]; + + // Query Vectorize — handle multi-paper filtering + let allMatches: { id: string; score: number; metadata?: Record }[] = []; + + if (paperIds.length === 0) { + // Search all papers + const result = await env.VECTORIZE.query(queryVector, { + topK, + returnMetadata: "all", + }); + allMatches = result.matches; + } else if (paperIds.length === 1) { + // Single paper filter + const result = await env.VECTORIZE.query(queryVector, { + topK, + filter: { paper_id: paperIds[0] }, + returnMetadata: "all", + }); + allMatches = result.matches; + } else { + // Multiple papers: parallel queries, merge and re-sort + const queries = paperIds.map((pid) => + env.VECTORIZE.query(queryVector, { + topK, + filter: { paper_id: pid }, + returnMetadata: "all", + }), + ); + const results = await Promise.all(queries); + allMatches = results + .flatMap((r) => r.matches) + .sort((a, b) => b.score - a.score) + .slice(0, topK); + } + + return allMatches.map((m) => ({ + paper_id: (m.metadata?.paper_id as string) ?? "", + paper_name: (m.metadata?.paper_name as string) ?? "", + content: (m.metadata?.text as string) ?? "", + line_number: (m.metadata?.start_line as number) ?? 0, + end_line: (m.metadata?.end_line as number) ?? undefined, + header_context: (m.metadata?.header_path as string) ?? "", + score: m.score, + match_type: "rag" as const, + })); +} + +/** + * Resolve sources (names or IDs) to paper IDs. + */ +async function resolveSources( + db: D1Database, + sources: string[], +): Promise { + const UUID_RE = /^[0-9a-f]{8}-[0-9a-f]{4}-[0-9a-f]{4}-[0-9a-f]{4}-[0-9a-f]{12}$/i; + const ids: Set = new Set(); + + for (const source of sources) { + if (UUID_RE.test(source)) { + ids.add(source); + } else { + // Name-based lookup (case-insensitive substring) + const { results } = await db + .prepare( + "SELECT id FROM papers WHERE LOWER(name) LIKE LOWER(?) AND status = 'ready'", + ) + .bind(`%${source}%`) + .all(); + for (const row of results) { + ids.add(row.id as string); + } + } + } + + return [...ids]; +} + +/** + * Deduplicate results by content prefix (first 200 chars, lowercased). + * Matches the Python hybrid dedup logic. + */ +function deduplicateResults(results: SearchResult[]): SearchResult[] { + const seen = new Set(); + const unique: SearchResult[] = []; + + for (const r of results) { + const key = r.content.slice(0, 200).toLowerCase().trim(); + if (!seen.has(key)) { + seen.add(key); + unique.push(r); + } + } + + return unique; +} + +/** + * Main search function — supports grep, rag, and hybrid modes. + */ +export async function search( + env: Env, + query: string, + mode: "grep" | "rag" | "hybrid" = "hybrid", + topK: number = 5, + sources: string[] = [], + caseSensitive: boolean = false, + regex: boolean = false, +): Promise { + // Resolve sources to paper IDs + const paperIds = + sources.length > 0 ? await resolveSources(env.DB, sources) : []; + + // Get papers to search + let papers: { id: string; name: string; markdown_text: string }[]; + if (paperIds.length > 0) { + const placeholders = paperIds.map(() => "?").join(","); + const { results } = await env.DB.prepare( + `SELECT id, name, markdown_text FROM papers WHERE id IN (${placeholders}) AND status = 'ready'`, + ) + .bind(...paperIds) + .all(); + papers = results as typeof papers; + } else { + const { results } = await env.DB.prepare( + "SELECT id, name, markdown_text FROM papers WHERE status = 'ready'", + ).all(); + papers = results as typeof papers; + } + + if (papers.length === 0) { + return { + results: [], + num_results: 0, + papers_searched: 0, + query, + mode, + success: true, + }; + } + + const searchPaperIds = papers.map((p) => p.id); + let grepResults: SearchResult[] = []; + let ragResults: SearchResult[] = []; + + if (mode === "grep" || mode === "hybrid") { + // Run grep across all papers + for (const paper of papers) { + if (!paper.markdown_text) continue; + const headers = extractHeaders(paper.markdown_text); + const matches = grepSearch( + paper.id, + paper.name, + paper.markdown_text, + headers, + query, + { caseSensitive, regex }, + ); + grepResults.push(...matches); + } + } + + if (mode === "rag" || mode === "hybrid") { + ragResults = await ragSearch(env, query, searchPaperIds, topK); + } + + // Merge results based on mode + let results: SearchResult[]; + if (mode === "grep") { + results = grepResults.slice(0, topK); + } else if (mode === "rag") { + results = ragResults; + } else { + // Hybrid: RAG results first (by score), then grep results + const combined = [ + ...ragResults, + ...grepResults, + ]; + results = deduplicateResults(combined).slice(0, topK * 2); + } + + return { + results, + num_results: results.length, + papers_searched: papers.length, + query, + mode, + success: true, + }; +} diff --git a/cloud/src/types.ts b/cloud/src/types.ts new file mode 100644 index 0000000..d76118c --- /dev/null +++ b/cloud/src/types.ts @@ -0,0 +1,63 @@ +export interface Env { + DB: D1Database; + BUCKET: R2Bucket; + VECTORIZE: VectorizeIndex; + AI: Ai; + MCP_OBJECT: DurableObjectNamespace; + MARKER_CONTAINER: DurableObjectNamespace; + API_TOKEN: string; + OPENAI_API_KEY?: string; + ANTHROPIC_API_KEY?: string; +} + +export interface Paper { + id: string; + name: string; + pdf_key: string | null; + markdown_key: string | null; + markdown_text: string | null; + status: "uploading" | "converting" | "indexing" | "embedding" | "ready" | "error"; + error: string | null; + page_count: number | null; + markdown_length: number | null; + chunk_count: number | null; + created_at: string; + processed_at: string | null; + version: string; +} + +export interface FlatHeader { + level: number; + text: string; + line: number; + path: string; +} + +export interface Chunk { + text: string; + header_path: string; + start_line: number; + end_line: number; + chunk_index: number; +} + +export interface SearchResult { + paper_id: string; + paper_name: string; + content: string; + line_number: number; + end_line?: number; + header_context: string; + score: number; + match_type: "grep" | "rag"; + context?: string; +} + +export interface SearchResponse { + results: SearchResult[]; + num_results: number; + papers_searched: number; + query: string; + mode: string; + success: boolean; +} diff --git a/cloud/src/ui.ts b/cloud/src/ui.ts new file mode 100644 index 0000000..452a711 --- /dev/null +++ b/cloud/src/ui.ts @@ -0,0 +1,283 @@ +export const HTML = ` + + + + +Paper Intelligence + + + diff --git a/cloud/app/package-lock.json b/cloud/app/package-lock.json new file mode 100644 index 0000000..7a06576 --- /dev/null +++ b/cloud/app/package-lock.json @@ -0,0 +1,4865 @@ +{ + "name": "paper-intelligence-app", + "lockfileVersion": 3, + "requires": true, + "packages": { + "": { + "name": "paper-intelligence-app", + "dependencies": { + "@radix-ui/react-dialog": "^1.1.0", + 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+ "@vitejs/plugin-react": "^4.0.0", + "tailwindcss": "^4.0.0", + "typescript": "^5.7.0", + "vite": "^6.0.0" + } +} diff --git a/cloud/app/src/App.tsx b/cloud/app/src/App.tsx new file mode 100644 index 0000000..7d950b1 --- /dev/null +++ b/cloud/app/src/App.tsx @@ -0,0 +1,718 @@ +import { useState, useEffect, useCallback, useRef } from "react"; +import ReactMarkdown from "react-markdown"; +import rehypeRaw from "rehype-raw"; +import remarkGfm from "remark-gfm"; +import { + FileText, + BookOpen, + MessageSquare, + Upload, + Search, + Pencil, + X, +} from "lucide-react"; +import { + listPapers, + getPaperContent, + getPaperPdf, + updatePaperAlias, + uploadPapers, + streamChat, + type Paper, + type Header, +} from "./api"; +import { cn, fuzzyMatch, displayName } from "./lib/utils"; + +export function App() { + const [token, setToken] = useState( + () => localStorage.getItem("pi_token") || "", + ); + const [authed, setAuthed] = useState(!!token); + const [tokenInput, setTokenInput] = useState(""); + + function login() { + if (!tokenInput.trim()) return; + localStorage.setItem("pi_token", tokenInput.trim()); + setToken(tokenInput.trim()); + setAuthed(true); + } + + function logout() { + localStorage.removeItem("pi_token"); + setToken(""); + setAuthed(false); + } + + if (!authed) { + return ( +
+
+

+ Paper Intelligence +

+

+ Enter your API token to continue. +

+ setTokenInput(e.target.value)} + onKeyDown={(e) => e.key === "Enter" && login()} + autoFocus + /> + +
+
+ ); + } + + return
; +} + +function Main({ token, onLogout }: { token: string; onLogout: () => void }) { + const [papers, setPapers] = useState([]); + const [activePaperId, setActivePaperId] = useState(null); + const [filter, setFilter] = useState(""); + const [markdown, setMarkdown] = useState(null); + const [headers, setHeaders] = useState([]); + const [view, setView] = useState<"pdf" | "md">("pdf"); + const [pdfUrl, setPdfUrl] = useState(null); + const [showOutline, setShowOutline] = useState(false); + const [showChat, setShowChat] = useState(false); + const [showUpload, setShowUpload] = useState(false); + const [aliasModal, setAliasModal] = useState(null); + const [aliasValue, setAliasValue] = useState(""); + + const activePaper = papers.find((p) => p.id === activePaperId) || null; + + // Load papers + const loadPapers = useCallback(async () => { + try { + const list = await listPapers(); + setPapers(list); + } catch { + onLogout(); + } + }, [onLogout]); + + useEffect(() => { + loadPapers(); + }, [loadPapers]); + + // Poll for processing papers + useEffect(() => { + const processing = papers.some((p) => + ["uploading", "converting", "indexing", "embedding"].includes(p.status), + ); + if (!processing) return; + const id = setInterval(loadPapers, 3000); + return () => clearInterval(id); + }, [papers, loadPapers]); + + // Select paper + async function selectPaper(id: string) { + setActivePaperId(id); + setMarkdown(null); + setPdfUrl(null); + const paper = papers.find((p) => p.id === id); + setView(paper?.pdf_key ? "pdf" : "md"); + try { + const content = await getPaperContent(id); + setMarkdown(content.markdown); + setHeaders(content.headers); + if (paper?.pdf_key) { + const url = await getPaperPdf(id); + setPdfUrl(url); + } + } catch { + // error + } + } + + // Filter + fuzzy sort + const filteredPapers = filter.trim() + ? papers + .map((p) => ({ + p, + score: Math.max( + fuzzyMatch(displayName(p), filter), + fuzzyMatch(p.name, filter), + ), + })) + .filter((x) => x.score > 0) + .sort((a, b) => b.score - a.score) + .map((x) => x.p) + : papers; + + // Alias save + async function saveAlias() { + if (!aliasModal) return; + await updatePaperAlias(aliasModal.id, aliasValue); + setPapers((prev) => + prev.map((p) => + p.id === aliasModal.id ? { ...p, alias: aliasValue || null } : p, + ), + ); + setAliasModal(null); + } + + return ( +
+ {/* Header */} +
+

+ Paper Intelligence +

+ +
+ + {/* Sidebar */} + + + {/* Main area */} +
+ {/* Toolbar */} + {activePaper && ( +
+ + + + {displayName(activePaper)} + + + + {activePaper.pdf_key && ( +
+ + +
+ )} + +
+ )} + + {/* Content area */} +
+ {/* Outline */} + {showOutline && headers.length > 0 && ( +
+
+ Contents +
+ {headers.map((h, i) => ( +
{ + const el = document.getElementById(`heading-${i}`); + el?.scrollIntoView({ behavior: "smooth", block: "start" }); + }} + className={cn( + "py-1 text-[13px] leading-snug cursor-pointer text-muted hover:text-ink truncate", + h.level === 1 && "px-4", + h.level === 2 && "pl-7 pr-4 text-[12px]", + h.level === 3 && "pl-10 pr-4 text-[12px]", + h.level >= 4 && "pl-13 pr-4 text-[12px]", + )} + title={h.text} + > + {h.text} +
+ ))} +
+ )} + + {/* Content viewer */} + {!activePaper ? ( +
+ Select a paper to begin +
+ ) : view === "pdf" && pdfUrl ? ( +