Launchstack – Open-source knowledge layer that index your docs for Claude MCP

Launchstack is a Next.js platform for role-based document management, AI-assisted Q&A, and predictive document analysis. It combines document upload, optional OCR, embeddings, and retrieval to help teams find gaps and act faster. - Clerk-based Employer/Employee authentication with role-aware middleware. - Document upload pipeline with optional OCR for scanned PDFs. - PostgreSQL + pgvector semantic retrieval for RAG workflows. - AI chat and predictive document analysis over uploaded content. - Agent guardrails with PII filtering, grounding checks, and confidence gating. - Supervisor agent that validates outputs against domain-specific rubrics. - Marketing pipeline with content generation for Reddit, X, LinkedIn, and Bluesky. - Optional web-enriched analysis with Tavily. - Optional reliability/observability via Inngest and LangSmith. Launchstack runs domain-specific analysis tailored to your document type: | Type | What It Detects | |---|---| | Contract | Missing exhibits, schedules, addendums, and supporting agreements | | Financial | Missing balance sheets, audit reports, income statements | | Technical | Missing specifications, manuals, diagrams, deliverables | | Compliance | Missing regulatory filings, certifications, policy documents | | Educational | Missing syllabi, handouts, readings, linked resources | | HR | Missing policies, forms, benefits materials, handbooks | | Research | Missing cited papers, datasets, supplementary materials | | General | Any document with cross-references and attachments | Each analysis type also extracts insights (deadlines, action items, resources, caveats) and runs chain-of-verification on high-priority predictions. Launchstack can ingest content exported from third-party tools. No API keys or OAuth setup required — export your data, upload the files, and the ingestion pipeline handles the rest. | Source | Export Method | Resulting Format | Launchstack Adapter | |---|---|---|---| | Notion | Settings > Export > Markdown & CSV | .md , .csv (ZIP) | TextAdapter, SpreadsheetAdapter | | Notion | Page > Export > HTML | .html | HtmlAdapter | | Google Docs | File > Download > Microsoft Word | .docx | DocxAdapter | | Google Sheets | File > Download > CSV or Excel | .csv , .xlsx | SpreadsheetAdapter | | Google Drive | Google Takeout (takeout.google.com) | .docx (ZIP) | DocxAdapter | | Slack | Workspace Settings > Import/Export > Export | .json (ZIP) | JsonExportAdapter | | GitHub | Code > Download ZIP | .md , .txt (ZIP) | TextAdapter | | GitHub | gh issue list --json ... | .json | JsonExportAdapter | | GitHub | gh pr list --json ... | .json | JsonExportAdapter | Notion - Open your Notion workspace. - Click the ... menu on a page, or go to Settings & members > Export for a full workspace export. - Select Markdown & CSV as the format and check Include subpages if needed. - Download the ZIP and upload it directly to Launchstack. Google Docs / Sheets - Open the document in Google Docs or Sheets. - Go to File > Download and choose Microsoft Word (.docx) or CSV / Excel (.xlsx). - Upload the downloaded file. For bulk exports, use Google Takeout to export your Drive as a ZIP. Slack - Go to Workspace Settings > Import/Export Data > Export. - Choose a date range and start the export. - Download the ZIP and upload it to Launchstack. Each channel's messages will be ingested as a separate document. GitHub - Repo docs: Click Code > Download ZIP on any GitHub repository. Upload the ZIP — all Markdown and text files will be ingested. - Issues: Install the GitHub CLI and run: Upload the resulting gh issue list --state all --limit 1000 --json number,title,body,state,labels,author,createdAt,closedAt,comments > issues.json issues.json file. - Pull requests: Run: Upload the resulting gh pr list --state all --limit 1000 --json number,title,body,state,labels,author,createdAt,mergedAt,comments > prs.json prs.json file. All uploaded content flows through the standard ingestion pipeline (chunking, embedding, RAG indexing) and becomes searchable alongside your other documents. Launchstack follows a three-layer modular architecture: block-beta columns 9 SLABEL["Services\nLayer"]:1 MKT["Marketing Engine\n─────────────\nTrend Analysis\nContent Generation\nWeb Scraping Jobs"]:2 LEG["Legal Services\n─────────────\nTemplate Library\nAuto-Fill & Clauses\nLegal Vault"]:2 ONB["Employee Onboarding\n─────────────\nOnboarding Agent\nQuizzes & Checks\nProgress Tracking"]:2 DOCR["Document Reasoning\n─────────────\nPage Index & TOC\nRLM Agent\nKnowledge Graph"]:2 space:9 TLABEL["Tools\nLayer"]:1 RAG["RAG Pipeline\n(BM25 + Vector)"]:2 WEB["Web Search\n(Tavily, Firecrawl)"]:2 REW["Doc Rewrite\n(Summarize, Refine)"]:2 TMPL["Template Engine\n(Form → PDF)"]:2 space:1 ING["Doc Ingestion\n(OCR, Chunk, Embed)"]:4 ENT["Entity Extraction\n(NER, Graph RAG)"]:4 space:9 PLABEL["Physical\nLayer"]:1 DB["PostgreSQL + pgvector\n─────────────\nEmbeddings Index\nDocument Structure\nKnowledge Graph\nDomain Tables"]:2 HOST["Hosting & Compute\n─────────────\nNext.js 15\nInngest Jobs\nAgent Hosting\nML Sidecar"]:2 EXT["External Services\n─────────────\nOCR Providers\nFile Storage (S3)\nClerk Auth + RBAC"]:2 KBS["Knowledge Bases\n─────────────\nCompany KB\nLegal Templates\nOnboarding Docs"]:2 %% Service → Tool edges MKT --> RAG MKT --> WEB MKT --> REW LEG --> RAG LEG --> REW LEG --> TMPL ONB --> RAG ONB --> REW DOCR --> RAG DOCR --> WEB DOCR --> REW DOCR --> ING DOCR --> ENT %% Tool → Physical edges RAG --> DB RAG --> KBS WEB --> HOST REW --> HOST TMPL --> EXT TMPL --> KBS ING --> DB ING --> EXT ING --> HOST ENT --> DB ENT --> HOST classDef layer fill:#1a1a2e,color:#eee,stroke:none classDef svc fill:#4A90D9,color:#fff,stroke:#2C5F8A,stroke-width:1px classDef tool fill:#F5A623,color:#fff,stroke:#C47D0E,stroke-width:1px classDef phys fill:#27AE60,color:#fff,stroke:#1E8449,stroke-width:1px class SLABEL,TLABEL,PLABEL layer class MKT,LEG,ONB,DOCR svc class RAG,WEB,REW,TMPL,ING,ENT tool class DB,HOST,EXT,KBS phys The platform is organized into: - Services Layer - Vertical business modules (Marketing, Legal, Onboarding, Document Reasoning) - Tools Layer - Reusable AI capabilities (RAG, Web Search, Document Processing, Entity Extraction) - Physical Layer - Infrastructure (PostgreSQL + pgvector, Next.js hosting, External services, Knowledge bases) All services operate within domain-partitioned boundaries enforced by Clerk RBAC. RAG queries are scoped by domain + company_id to ensure data isolation. - Next.js 15 + TypeScript - PostgreSQL + Drizzle ORM + pgvector - Clerk authentication - OpenAI + LangChain - UploadThing + optional OCR providers - Tailwind CSS - Node.js 18+ - pnpm - Docker + Docker Compose (recommended for local DB/full stack) - Git git clone cd pdr_ai_v2-2 pnpm install Create .env from .env.example and fill required values: DATABASE_URL NEXT_PUBLIC_CLERK_PUBLISHABLE_KEY CLERK_SECRET_KEY BLOB_READ_WRITE_TOKEN (Vercel Blob read/write token)OPENAI_API_KEY INNGEST_EVENT_KEY , as placeholder Optional integrations: NODE_ENV =development (for development, otherwise assumed to be production)UPLOADTHING_TOKEN TAVILY_API_KEY INNGEST_EVENT_KEY ,INNGEST_SIGNING_KEY AZURE_DOC_INTELLIGENCE_ENDPOINT ,AZURE_DOC_INTELLIGENCE_KEY LANDING_AI_API_KEY ,DATALAB_API_KEY LANGCHAIN_TRACING_V2 ,LANGCHAIN_API_KEY ,LANGCHAIN_PROJECT DEBUG_PERF (1 ortrue ) to enable dev perf logs for middleware and key auth/dashboard APIsSIDECAR_URL NEO4J_URI NEO4J_USERNAME NEO4J_PASSWORD Vercel Blob is used for storing uploaded documents. Both public and private stores are supported -- the upload logic auto-detects which mode the store uses and adapts automatically. - In the Vercel dashboard, go to Storage → Blob → Create Store. - Choose either Public or Private access. Both work: - Public stores produce URLs the browser can load directly (faster for previews). - Private stores keep files behind authentication; the app proxies content through /api/documents/[id]/content and/api/files/[id] so previews still work. - Generate a Read/Write token for the store and add it as BLOB_READ_WRITE_TOKEN in your environment (.env locally, or Vercel Project Settings for deploys). - Redeploy so the token is available at build and runtime. - Verify: sign in to the Employer Upload page, upload a small PDF, and confirm /api/upload-local returns avercel-storage.com URL without errors. pnpm db:push pnpm run dev Open http://localhost:3000 . make up spins up the entire stack behind a Caddy reverse proxy — no external cloud dependencies beyond API keys. Works for both local development and production. make up # start everything (foreground) make up-prod # start everything (detached, for servers) make down # stop containers make down-clean # stop + wipe volumes (fresh DB) make logs # tail all container logs Set DOMAIN in your .env : | Environment | .env setting | What happens | |---|---|---| | Local dev | DOMAIN=localhost (default) | HTTPS with self-signed cert | | Production | DOMAIN=yourdomain.com | HTTPS with auto-provisioned Let's Encrypt cert | All routes go through one domain — no port numbers to remember: | URL | What it serves | |---|---| https://yourdomain.com/ | Application | https://yourdomain.com/s3/... | File uploads/downloads (SeaweedFS) | https://yourdomain.com/inngest/ | Background job dashboard | | Container | Image | Role | |---|---|---| caddy | caddy:2-alpine | Reverse proxy, auto-HTTPS, only exposed service (ports 80/443) | db | pgvector/pgvector:pg16 | PostgreSQL with vector extension | migrate | Dockerfile | Pushes Drizzle schema on startup, then exits | seaweedfs | chrislusf/seaweedfs | S3-compatible file storage | app | Dockerfile | Production Next.js standalone server | inngest-dev | inngest/inngest | Background job processing + dashboard | Override defaults in .env before deploying: DOMAIN=yourdomain.com POSTGRES_PASSWORD= S3_ACCESS_KEY= S3_SECRET_KEY= Ensure ports 80 and 443 are open on your server. Caddy handles everything else — TLS provisioning, HTTP→HTTPS redirect, certificate renewal. - Deployment details (Docker, Vercel, VPS): docs/deployment.md - Feature workflows and architecture: docs/feature-workflows.md - Usage and API examples: docs/usage-examples.md - Observability and metrics: docs/observability.md - Manual testing (dev, post-PR): docs/manual-testing-guide.md POST /api/uploadDocument - upload and process document (OCR optional)POST /api/LangChain - document-grounded Q&APOST /api/agents/predictive-document-analysis - detect gaps and recommendationsGET /api/metrics - Prometheus metrics stream - Employee: view assigned documents, use AI chat/analysis. - Employer: upload/manage documents, categories, and employee access. pnpm db:studio pnpm db:push pnpm check pnpm lint pnpm typecheck pnpm build pnpm start - Notion API-key connector: Paste your Notion Internal Integration token in settings, select pages to sync. No OAuth required. Contributions welcome. - GitHub webhook sync: Automatically ingest new issues and PRs via repository webhooks. - Google Drive watch: Automatic re-sync when Google Docs are updated, using Drive push notifications. - Confirm Docker is running before DB startup. - If build issues occur: remove .next and reinstall dependencies. - If OCR UI is missing: verify OCR provider keys are configured. - If Docker image pull/build is corrupted: remove image and rebuild with --no-cache . - Create a feature branch. - Make changes and run pnpm check . - Open a pull request with test notes. Private and proprietary. Open an issue in this repository or contact the development team.