3527e732d4
Production-ready Next.js boilerplate with: - Runtime env validation (fail-fast on missing vars) - Feature-gated config (S3, Stripe, email, OAuth) - Docker + Coolify deployment pipeline - PostgreSQL + pgvector, MinIO S3, Better Auth - TypeScript strict mode (no ignoreBuildErrors) - i18n (en/es), AI modules, billing, monitoring Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
136 lines
6.8 KiB
Markdown
136 lines
6.8 KiB
Markdown
---
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title: Chat with PDF
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description: Engage in conversations with your PDF documents using AI to extract insights and answer questions.
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url: /ai/docs/pdf
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---
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# Chat with PDF
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The [Chat with PDF](https://ai.turbostarter.dev/pdf) demo application enables intelligent interaction with document content through a conversational AI interface. Upload PDF files and instantly engage in natural dialogue about their contents, asking questions, requesting summaries, and extracting key information with remarkable accuracy.
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<AIAppShowcase id="pdf" />
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## Features
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Transform how you interact with document content through these powerful capabilities:
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<Cards>
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<Card title="PDF upload">
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Easily upload PDF files directly into the application for analysis.
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</Card>
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<Card title="Contextual conversation">
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Chat with an AI that understands the content of your uploaded PDF, providing
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relevant answers based on the text.
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</Card>
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<Card title="Information extraction">
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Quickly find specific information, key points, or summaries within the
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document through natural language queries.
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</Card>
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<Card title="Source highlighting (coming soon)">
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Visualize exactly which document sections informed the AI's responses with
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precise source highlighting.
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</Card>
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<Card title="Multi-document intelligence (coming soon)">
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Conduct sophisticated conversations spanning multiple uploaded documents,
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enabling cross-document analysis and comparison.
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</Card>
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</Cards>
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## Setup
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To implement the "Chat with PDF" application in your project, configure these essential backend services:
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<Cards>
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<Card href="/ai/docs/database" title="Database">
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Set up PostgreSQL with the `pgvector` extension to efficiently store
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conversation history, document metadata, and vector embeddings for semantic
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search.
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</Card>
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<Card href="/ai/docs/storage" title="Storage">
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Configure S3-compatible cloud storage for secure management of uploaded PDF
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documents.
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</Card>
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</Cards>
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You'll also need to obtain API keys for both the conversational AI models and the embedding models used for text processing.
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## AI models
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This application leverages two complementary AI model types working together:
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1. **Large Language Models (LLMs):** Provide sophisticated natural language understanding to interpret your questions and generate contextually appropriate responses based on document content.
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2. **Embedding Models:** Convert document text segments into numerical vector representations that enable efficient semantic similarity search and [Retrieval-Augmented Generation (RAG)](https://en.wikipedia.org/wiki/Retrieval-augmented_generation).
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Configure the providers for the models you wish to use:
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<Cards>
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<Card href="/ai/docs/openai" title="OpenAI" description="Utilize GPT models for conversational AI and advanced embedding models for vector representation." icon={<OpenAI />} />
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<Card href="/ai/docs/anthropic" title="Anthropic" description="Implement Claude models for sophisticated reasoning and nuanced document understanding." icon={<Anthropic />} />
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<Card href="/ai/docs/google" title="Google AI" description="Leverage Gemini models for powerful conversational capabilities with document content." icon={<Google />} />
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<Card href="/ai/docs/replicate" title="Replicate" description="Access diverse open-source embedding models for flexible implementation options." icon={<Replicate />} />
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</Cards>
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For comprehensive configuration details, consult the [AI SDK documentation](https://sdk.vercel.ai/docs) covering provider setup and model selection.
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## Data persistence
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The application stores data related to chats, documents, and embeddings to provide a persistent experience.
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<Card href="/ai/docs/database" title="Database" description="Learn more about database services in TurboStarter AI." />
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Application data is organized within a dedicated PostgreSQL schema named `pdf`:
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* `chats`: captures essential metadata for each document-specific conversation session.
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* `messages`: stores all user queries and AI responses within conversation threads.
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* `documents`: maintains comprehensive tracking of uploaded PDF files, including filenames and storage locations.
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* `embeddings`: contains text segments extracted from PDFs along with their vector representations (using [`pgvector`](https://github.com/pgvector/pgvector)'s `vector` data type). To optimize similarity searches critical for RAG processing, the system creates an index (`embeddingIndex` using [HNSW](https://github.com/pgvector/pgvector#hnsw)) on the `embedding` column.
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<Card href="/ai/docs/storage" title="Storage" description="Learn more about cloud storage services in TurboStarter AI." />
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The PDF files uploaded by users are securely stored in your configured [cloud storage](/ai/docs/storage) bucket. The `path` field in the `documents` table maintains the precise reference to each file's location.
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## Structure
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The "Chat with PDF" feature is architected across the monorepo for optimal organization and code reuse:
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### Core
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The `@turbostarter/ai` package (`packages/ai`) contains the essential logic under `modules/pdf`:
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* Comprehensive types, validation schemas, and constants specific to PDF processing
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* Advanced document parsing, text segmentation, and embedding generation utilities
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* Core API logic for managing conversations, performing RAG-based lookups, and interacting with LLMs
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* Database operations for storing and retrieving conversations, documents, and embeddings
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* Shared utilities for managing PDF file uploads and downloads
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### API
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The `packages/api` package defines the backend API endpoints using [Hono](https://hono.dev/):
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* `src/modules/ai/pdf/pdf.router.ts`: implements Hono RPC routes for document upload and conversation management, handles input validation, applies middleware (authentication, credit management), and invokes the core functionality from `@turbostarter/ai`.
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### Web
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The [Next.js](https://nextjs.org/) application (`apps/web`) delivers an intuitive user interface:
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* `src/app/[locale]/(apps)/pdf/**`: contains the Next.js App Router pages and layouts for the document conversation experience
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* `src/components/pdf/**`: houses reusable React components specific to the PDF interaction UI (document upload, conversation interface, message display)
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### Mobile
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The [Expo](https://expo.dev/)/[React Native](https://reactnative.dev/) application (`apps/mobile`) provides a native mobile experience:
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* `src/app/pdf/**`: defines the screens for the mobile document conversation interface
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* `src/components/pdf/**`: contains React Native components optimized for mobile document interaction
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* **API integration**: utilizes the same Hono RPC client (`packages/api`) as the web app for consistent backend communication
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This architecture ensures that core AI processing and data handling logic is shared across platforms, while enabling optimized UI implementations tailored to each environment.
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