Improving docs

website/docs/deployment/kubernetes/index.md

@@ -15,6 +15,7 @@ tags:
 
 ```bash
 helm repo add spiceai https://helm.spiceai.org
+helm repo update
 helm upgrade --install spiceai spiceai/spiceai
 ```
 

website/docs/faq/index.md

@@ -9,11 +9,21 @@ sidebar_position: 2
 
 ## 1. What is Spice?
 
-Spice is a portable runtime written in Rust that provides a unified SQL interface for developers to materialize, accelerate, and query data from various sources, including databases, data warehouses, and data lakes. It acts as an application-specific, tier-optimized Database CDN, bringing data closer to applications for faster and more efficient access.
+Spice is an open-source SQL query and AI compute engine, written in Rust, for data-driven apps and agents.
+
+Spice provides three industry standard APIs in a lightweight, portable runtime (single ~140 MB binary):
+
+1. **SQL Query APIs**: HTTP, Arrow Flight, Arrow Flight SQL, ODBC, JDBC, and ADBC.
+2. **OpenAI-Compatible APIs**: HTTP APIs compatible the OpenAI SDK, AI SDK with local model serving (CUDA/Metal accelerated) and gateway to hosted models.
+3. **Iceberg Catalog REST APIs**: A unified Iceberg Catalog API.
 
 ## 2. Why should I use Spice?
 
-Spice simplifies querying data from one or more sources by enabling developers to co-locate datasets with their applications or machine learning models. With support for in-memory Arrow records, SQLite/DuckDB, and PostgreSQL, Spice accelerates queries with high concurrency and low latency. This makes it ideal for use cases requiring fast, reliable, and cost-efficient data access.
+Spice is primarily used for:
+
+- **Data Federation**: SQL query across any database, data warehouse, or data lake. [Learn More](https://docs.spiceai.org/features/federated-queries).
+- **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)
+- **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).
 
 ## 3. How is Spice different?
 
@@ -67,4 +77,4 @@ Spice is highly flexible and supports multiple deployment configurations:
 
 ## 12. How can I get started?
 
-Visit the [Spice.ai Quickstart Guide](https://docs.spiceai.org/quickstart/) to set up the runtime, connect to data sources, and start querying in minutes. Comprehensive examples and step-by-step instructions are available to help you get the most out of Spice.
+Visit the [Spice.ai Getting Started Guide](https://spiceai.org/docs/getting-started/) to set up the runtime, connect to data sources, and start querying in minutes. Comprehensive examples and step-by-step instructions are available to help you get the most out of Spice.

website/docs/features/data-acceleration/constraints.md

@@ -62,7 +62,6 @@ datasets:
 
 ## Limitations
 
-- **Not supported for in-memory Arrow:** The default in-memory Arrow acceleration engine does not support constraints. Use [DuckDB](/docs/components/data-accelerators/duckdb.md), [SQLite](/docs/components/data-accelerators/duckdb.md), or [PostgreSQL](/docs/components/data-accelerators/postgres/index.md) as the acceleration engine to enable constraint checking.
 - **Single on_conflict target supported**: Only a single `on_conflict` target can be specified, unless all `on_conflict` targets are specified with drop.
 
   - <details>

website/docs/getting-started/spicepods.md

@@ -23,7 +23,7 @@ A Spicepod is described by a YAML manifest file, typically named `spicepod.yaml`
 - **Metadata:** Basic information about the Spicepod, such as its name and version.
 - **Datasets:** Definitions of datasets that are used or produced within the Spicepod.
 - **Catalogs:** Definitions of catalogs that are used within the Spicepod.
-- **Models:** Definitions of ML models that the Spicepod manages, including their sources and associated datasets.
+- **Models:** Definitions of language or traditional ML models that the Spicepod manages, including their sources and associated datasets.
 - **Secrets:** Configuration for any secret stores used within the Spicepod.
 
 ## Example Manifest
@@ -34,17 +34,17 @@ kind: Spicepod
 name: my_spicepod
 
 datasets:
-  - from: spice.ai/spiceai/quickstart
-    name: qs
+  - from: spice.ai/spiceai/quickstart/datasets/taxi_trips
+    name: taxi_trips
     acceleration:
       enabled: true
-      refresh_mode: append
 
 models:
-  - from: file://model_path.onnx
-    name: my_model
-    datasets:
-      - qs
+  - from: openai:gpt-4o-mini
+    name: openai_model
+    params:
+      openai_api_key: ${ env:OPENAI_API_KEY }
+      tools: auto
 
 secrets:
   - from: env
@@ -59,17 +59,21 @@ kind: Spicepod
 name: another_spicepod
 
 datasets:
-  - from: spice.ai/spiceai/sample
+  - from: databricks:spiceai_demo.public.dataset
     name: sample_ds
+    params:
+      mode: delta_lake
+      databricks_endpoint: dbc-a1b2345c-d6e7.cloud.databricks.com
+      databricks_token: ${secrets:my_token}
+      databricks_aws_access_key_id: ${secrets:aws_access_key_id}
+      databricks_aws_secret_access_key: ${secrets:aws_secret_access_key}
     acceleration:
       enabled: true
       refresh_mode: full
 
 models:
-  - from: file://another_model_path.onnx
-    name: another_model
-    datasets:
-      - sample_ds
+  - from: huggingface.co/microsoft/Phi-3.5-mini-instruct
+    name: phi
 
 secrets:
   - from: env

website/docs/index.mdx

@@ -16,7 +16,7 @@ import ThemeBasedImage from '@site/src/components/ThemeBasedImage';
 
 Spice provides three industry standard APIs in a lightweight, portable runtime (single ~140 MB binary):
 
-1. **SQL Query APIs**: Arrow Flight, Arrow Flight SQL, ODBC, JDBC, and ADBC.
+1. **SQL Query APIs**: HTTP, Arrow Flight, Arrow Flight SQL, ODBC, JDBC, and ADBC.
 2. **OpenAI-Compatible APIs**: HTTP APIs compatible the OpenAI SDK, AI SDK with local model serving (CUDA/Metal accelerated) and gateway to hosted models.
 3. **Iceberg Catalog REST APIs**: A unified Iceberg Catalog API.
 
@@ -36,23 +36,27 @@ Spice is primarily used for:
 
 Spice is built-on industry leading technologies including [Apache DataFusion](https://datafusion.apache.org), Apache Arrow, Arrow Flight, SQLite, and DuckDB. If you want to build with DataFusion or using DuckDB, Spice provides a simple, flexible, and production-ready engine you can just use.
 
-<ThemeBasedImage
-  width='600'
-  alt='OGP'
-  lightSrc='https://github.com/spiceai/spiceai/assets/80174/7d93ae32-d6d8-437b-88d3-d64fe089e4b7'
-  darkSrc='https://github.com/spiceai/spiceai/assets/80174/7d93ae32-d6d8-437b-88d3-d64fe089e4b7'
-/>
+<div style={{display: 'flex', justifyContent: 'center'}}>
+  <ThemeBasedImage
+    width='600'
+    alt='OGP'
+    lightSrc='https://github.com/spiceai/spiceai/assets/80174/7d93ae32-d6d8-437b-88d3-d64fe089e4b7'
+    darkSrc='https://github.com/spiceai/spiceai/assets/80174/7d93ae32-d6d8-437b-88d3-d64fe089e4b7'
+  />
+</div>
 
 ## Why Spice?
 
 Spice makes it fast and easy to query data from one or more sources using SQL. You can co-locate a managed dataset with your application or machine learning model, and accelerate it with Arrow in-memory, SQLite/DuckDB, or with attached PostgreSQL for fast, high-concurrency, low-latency queries. Accelerated engines give you flexibility and control over query cost and performance.
 
-<ThemeBasedImage
-  width='800'
-  alt='Before Spice'
-  lightSrc='https://github.com/spiceai/spiceai/assets/80174/29e4421d-8942-4f2a-8397-e9d4fdeda36b'
-  darkSrc='https://github.com/spiceai/spiceai/assets/80174/29e4421d-8942-4f2a-8397-e9d4fdeda36b'
-/>
+<div style={{display: 'flex', justifyContent: 'center'}}>
+  <ThemeBasedImage
+    width='800'
+    alt='Before Spice'
+    lightSrc='https://github.com/spiceai/spiceai/assets/80174/29e4421d-8942-4f2a-8397-e9d4fdeda36b'
+    darkSrc='https://github.com/spiceai/spiceai/assets/80174/29e4421d-8942-4f2a-8397-e9d4fdeda36b'
+  />
+</div>
 
 Spice simplifies building data-driven AI applications and agents by making it fast and easy to query, federate, and accelerate data from one or more sources using SQL, while grounding AI in real-time, reliable data. Co-locate datasets with apps and AI models to power AI feedback loops, enable RAG and search, and deliver fast, low-latency data-query and AI-inference with full control over cost and performance.
 
@@ -132,7 +136,7 @@ Limited = Partial or restricted support
 
 - **Is Spice a CDN for databases?** Yes, a common use-case for Spice is as a CDN for different data sources. Using CDN concepts, Spice enables you to ship (load) a working set of your database (or data lake, or data warehouse) where it's most frequently accessed, like from a data-intensive application or for AI context.
 
-### Watch a 30-sec BI dashboard acceleration demo
+### Watch a quick BI dashboard acceleration demo
 
 <div style={{display: 'flex', justifyContent: 'center', marginBottom: '15px'}}>
   <ReactPlayer

website/docs/installation.md

@@ -40,7 +40,7 @@ For deployment options, such as to Kubernetes, see [`Deployment`](./deployment/i
 
 ## Direct Download
 
-x86_x64 and ARM binaries for linux, Windows, and macOS are available for download from GitHub at [github.com/spiceai/spiceai/releases](https://github.com/spiceai/spiceai/releases).
+Binaries for Linux, Windows, and macOS are available for download from GitHub at [github.com/spiceai/spiceai/releases](https://github.com/spiceai/spiceai/releases).
 
 ## Building Spice from Source
 
@@ -123,9 +123,10 @@ export PATH="$PATH:$HOME/.spice/bin"
 ```
 
 ### Build with Hardware Acceleration
+
 Spice OSS supports running both local language models and embedding models on dedicated hardware. This is for models from either Huggingface or models located locally.
 
-Currently, neither CUDA nor metal have dedicated Dockerfiles or release binaries.
+The Spice CLI will automatically detect and download the appropriate runtime binary with hardware acceleration if available.
 
 #### CUDA Support