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Chroma is an open-source, AI-native embedding database designed for building sophisticated LLM applications. It allows users to store high-dimensional embeddings and associated metadata, facilitating powerful vector search and full-text filtering. Ideal for implementing Retrieval-Augmented Generation (RAG) systems, semantic search, and document retrieval in local or self-hosted environments, making it perfect for open-source AI projects and prototyping.

This skill guides the combination of vector similarity search and keyword-based matching to achieve superior information retrieval. It is essential when building advanced Retrieval-Augmented Generation (RAG) systems, custom search engines, or when standard search methods fail to capture both semantic meaning and specific term requirements.

Provides production-ready guidance for constructing retrieval-augmented generation pipelines, agent architectures, and LLMOps best practices, so teams can reliably deploy context-aware AI assistants with vector search, embeddings, and tool use.

Guides running Mistral AI secondary workflows for embeddings and function calling, covering semantic search, RAG pipelines, and tool-augmented chats.

Document describes structured prompts, chain-of-thought guidance, few-shot examples, system prompt design, and tooling advice for building reliable prompt engineering workflows in LLM-augmented products.

This skill provides comprehensive patterns and best practices for implementing efficient similarity search mechanisms using vector databases. It is essential for building advanced AI systems such as semantic search engines, implementing Retrieval-Augmented Generation (RAG), and developing recommendation systems. Use this when optimizing retrieval performance or scaling to massive volumes of vectors.

Summarizes the Test-Driven Development workflow with RED/GREEN/REFACTOR phases, the AAA pattern, prioritization cues, anti-patterns, and AI-augmented agent roles to guide implementation.