rag-implementation
RAG Implementation Patterns
Purpose: Provide complete RAG pipeline templates, chunking strategies, vector database schemas, and retrieval patterns for building production-ready RAG systems with Vercel AI SDK.
Activation Triggers:
- Building RAG (Retrieval Augmented Generation) systems
- Implementing semantic search functionality
- Creating AI-powered knowledge bases
- Document ingestion and embedding generation
- Vector database integration
- Hybrid search (vector + keyword) implementation
More from vanman2024/ai-dev-marketplace
document-parsers
Multi-format document parsing tools for PDF, DOCX, HTML, and Markdown with support for LlamaParse, Unstructured.io, PyPDF2, PDFPlumber, and python-docx. Use when parsing documents, extracting text from PDFs, processing Word documents, converting HTML to text, extracting tables from documents, building RAG pipelines, chunking documents, or when user mentions document parsing, PDF extraction, DOCX processing, table extraction, OCR, LlamaParse, Unstructured.io, or document ingestion.
31fastapi-auth-patterns
Implement and validate FastAPI authentication strategies including JWT tokens, OAuth2 password flows, OAuth2 scopes for permissions, and Supabase integration. Use when implementing authentication, securing endpoints, handling user login/signup, managing permissions, integrating OAuth providers, or when user mentions JWT, OAuth2, Supabase auth, protected routes, access control, role-based permissions, or authentication errors.
14stt-integration
ElevenLabs Speech-to-Text transcription workflows with Scribe v1 supporting 99 languages, speaker diarization, and Vercel AI SDK integration. Use when implementing audio transcription, building STT features, integrating speech-to-text, setting up Vercel AI SDK with ElevenLabs, or when user mentions transcription, STT, Scribe v1, audio-to-text, speaker diarization, or multi-language transcription.
14model-routing-patterns
Model routing configuration templates and strategies for cost optimization, speed optimization, quality optimization, and intelligent fallback chains. Use when building AI applications with OpenRouter, implementing model routing strategies, optimizing API costs, setting up fallback chains, implementing quality-based routing, or when user mentions model routing, cost optimization, fallback strategies, model selection, intelligent routing, or dynamic model switching.
13ai-content-generation
AI-powered content and image generation using content-image-generation MCP with Google Imagen 3/4, Veo 2/3, Claude Sonnet, and Gemini 2.0. Use when generating marketing content, creating hero images, building blog posts, generating product descriptions, creating videos, optimizing AI prompts, estimating generation costs, or when user mentions Imagen, Veo, AI content, AI images, content generation, image generation, video generation, marketing copy, or Google AI.
12pgvector-setup
Configure pgvector extension for vector search in Supabase - includes embedding storage, HNSW/IVFFlat indexes, hybrid search setup, and AI-optimized query patterns. Use when setting up vector search, building RAG systems, configuring semantic search, creating embedding storage, or when user mentions pgvector, vector database, embeddings, semantic search, or hybrid search.
11