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@@ -25,9 +25,9 @@ Developers can focus on building data apps and AI agents confidently, knowing th
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Spice is primarily used for:
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-**Data Federation**: SQL query across any database, data warehouse, or data lake. [Learn More](https://docs.spiceai.org/features/federated-queries).
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-**Data Federation**: SQL query across any database, data warehouse, or data lake. [Learn More](https://spiceai.org/docs/features/query-federation).
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-**Data Materialization and Acceleration**: Materialize, accelerate, and cache database queries. [Read the MaterializedView interview - Building a CDN for Databases](https://materializedview.io/p/building-a-cdn-for-databases-spice-ai)
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-**AI apps and agents**: An AI-database powering retrieval-augmented generation (RAG) and intelligent agents. [Learn More](https://github.com/spiceai/cookbook/tree/trunk/rag#readme).
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-**AI apps and agents**: An AI-database powering retrieval-augmented generation (RAG) and intelligent agents. [Learn More](https://github.com/spiceai/cookbook/blob/trunk/search_github_files/README.md).
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:::info[Watch 🎥]
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[CMU Databases - Accelerating Data and AI with Spice.ai Open-Source](https://www.youtube.com/watch?v=tyM-ec1lKfU)
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-**OpenAI-compatible API**: Connect to hosted models (OpenAI, Anthropic, xAI) or deploy locally (Llama, NVIDIA NIM). [AI Gateway Recipe](https://github.com/spiceai/cookbook/blob/trunk/openai_sdk/README.md)
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-**Federated Data Access**: Query using SQL and NSQL (text-to-SQL) across databases, data warehouses, and data lakes with advanced query push-down for fast retrieval across disparate data sources. [Federated SQL Query Recipe](https://github.com/spiceai/cookbook/blob/trunk/federation/README.md)
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-**Search and RAG**: Search and retrieve context with accelerated embeddings for retrieval-augmented generation (RAG) workflows. [Vector Search over GitHub Files](https://github.com/spiceai/cookbook/blob/trunk/search_github_files/README.md)
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-**LLM Memory and Observability**: Store and retrieve history and context for AI agents while gaining deep visibility into data flows, model performance, and traces. [LLM Memory Recipe](https://github.com/spiceai/cookbook/blob/trunk/llm-memory/README.md) | [Monitoring Features Documentation](https://docs.spiceai.org/features/monitoring)
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-**LLM Memory and Observability**: Store and retrieve history and context for AI agents while gaining deep visibility into data flows, model performance, and traces. [LLM Memory Recipe](https://github.com/spiceai/cookbook/blob/trunk/llm-memory/README.md) | [Monitoring Features Documentation](https://spiceai.org/docs/features/observability)
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