{"enrichment":{"faq":[{"a":"rag-skills covers framework-specific patterns across the RAG stack. For LlamaIndex, focus on document chunking strategies and metadata preservation during ingestion. ChromaDB requires proper vector store configuration and deduplication to avoid redundant embeddings. Celery orchestrates async task queues for batch processing and worker scaling. Best practices include designing modular pipelines, implementing circuit breakers for embedding API resilience, and monitoring retrieval quality metrics.","q":"What are RAG best practices for LlamaIndex, ChromaDB, and Celery?"},{"a":"rag-skills teaches end-to-end RAG architecture setup. Start with LlamaIndex document ingestion pipelines that parse and chunk source materials while preserving metadata. Configure ChromaDB as your vector store, ensuring proper schema design and collection management. Use Celery to distribute embedding tasks across workers, preventing bottlenecks. Chain components together: documents \u2192 LlamaIndex processors \u2192 embeddings via Celery \u2192 ChromaDB storage \u2192 retrieval queries. Include error handling at each stage.","q":"How do you set up retrieval augmented generation with these frameworks?"},{"a":"rag-skills addresses LlamaIndex document chunking techniques that balance context preservation with retrieval granularity. Learn embedding performance optimization through batch processing with Celery, reducing API calls and latency. Strategies include semantic chunking, sliding windows, and metadata-aware segmentation. The skill covers embedding deduplication in ChromaDB to avoid storing duplicate vectors, and retrieval strategies that match query intent to chunk semantics for improved relevance.","q":"What document chunking and embedding strategies does rag-skills cover?"},{"a":"rag-skills provides ChromaDB vector store configuration guidance including collection setup, distance metrics (cosine, L2), and persistence options. Design schemas that capture document metadata for filtering during retrieval. Implement deduplication logic to prevent duplicate embeddings from consuming storage. Configure batch ingestion pipelines via Celery to handle large document sets efficiently. Monitor query performance and adjust embedding dimensions based on retrieval accuracy and latency trade-offs.","q":"How should I configure ChromaDB as a vector store for RAG?"},{"a":"rag-skills covers error handling and resilience patterns critical for production RAG systems. Circuit breaker patterns protect against embedding API failures by detecting repeated errors and failing fast rather than cascading timeouts. Implement retry logic with exponential backoff in Celery tasks. Add fallback strategies when embeddings fail, such as using cached embeddings or degraded retrieval modes. Monitor embedding service health and configure alerts for degradation.","q":"What resilience patterns like circuit breakers does rag-skills teach?"},{"a":"rag-skills teaches performance optimization across the RAG pipeline. For ingestion, use Celery batch processing to parallelize embedding generation across workers, reducing wall-clock time. In LlamaIndex, tune chunking parameters to balance context and retrieval speed. Optimize ChromaDB queries through indexing and metadata filtering to reduce search scope. Implement caching for frequently retrieved documents and embeddings. Monitor latency at each stage and profile bottlenecks to guide optimization priorities.","q":"How can I optimize document ingestion and retrieval performance?"}],"shadow_tags":["vector-database","document-ingestion","embedding-models","async-tasks","retrieval-patterns","performance-optimization","circuit-breaker","code-review-guide","framework-integration"],"summary_rewrite":"Master RAG implementation through framework-specific patterns for LlamaIndex document processing, ChromaDB vector storage, and Celery task orchestration. This skill covers ingestion workflows, retrieval strategies, embeddings with circuit breakers, and performance optimization techniques."},"files":[{"bytes":5309,"path":".claude/skills/rag-skills/SKILL.md","sha256":"fc038c5b359bff3a475e97894d0774e32713647aa863e7d82b0e0c4ad914a1ca","url":"https://skillfed.io/files/llama-farm/llamafarm/rag-skills/6319a16a/SKILL.md"}],"id":"llama-farm/llamafarm/rag-skills","links":{"html":"https://skillfed.io/llama-farm/llamafarm/rag-skills","md":"https://skillfed.io/llama-farm/llamafarm/rag-skills.md","repo":"https://github.com/llama-farm/llamafarm"},"meta":{"agents_supported":["claude-code"],"first_seen":"2026-07-28","forks":58,"language":"Python","last_updated":"2026-06-10","license":"Apache-2.0","name":"rag-skills","publisher":"llama-farm","stars":835},"relations":{"similar":[{"id":"llama-farm/llamafarm/python-skills"},{"id":"llama-farm/llamafarm/generate-subsystem-skills"},{"id":"llama-farm/llamafarm/server-skills"},{"id":"agentmc15/polymarket-trader/trading-strategies"},{"id":"llama-farm/llamafarm/runtime-skills"},{"id":"Mindrally/skills/llamaindex-development"},{"id":"arpitg1304/robotics-agent-skills/robotics-software-principles"},{"id":"foryourhealth111-pixel/Vibe-Skills/architecture-patterns"},{"id":"HermeticOrmus/LibreUIUX-Claude-Code/architecture-patterns"},{"id":"JosiahSiegel/claude-plugin-marketplace/python-type-hints"}]},"slug":{"owner":"llama-farm","repo":"llamafarm","skill":"rag-skills"},"version":"6319a16a"}