refactor: address PR feedback - improve naming, error handling, and documentation

Address all feedback from PR #3962:

**Code Quality Improvements:**
- Rename `_uvicorn_run` → `_run_server` for accurate method naming
- Refactor error handling: move Gunicorn fallback logic from `_run_with_gunicorn` to caller
- Update comments to reflect both Uvicorn and Gunicorn behavior
- Update test mock from `_uvicorn_run` to `_run_server`

**Environment Variable:**
- Change `LLAMA_STACK_DISABLE_GUNICORN` → `LLAMA_STACK_ENABLE_GUNICORN`
- More intuitive positive logic (no double negatives)
- Defaults to `true` on Unix systems
- Clearer log messages distinguishing platform limitations vs explicit disable

**Documentation:**
- Remove unnecessary `uv sync --group unit --group test` from user docs
- Clarify SQLite limitations: "SQLite only allows one writer at a time"
- Accurate explanation: WAL mode enables concurrent reads but writes are serialized
- Strong recommendation for PostgreSQL in production with high traffic

**Architecture:**
- Better separation of concerns: `_run_with_gunicorn` just executes, caller handles fallback
- Exceptions propagate to caller for centralized decision making

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Co-Authored-By: Claude <noreply@anthropic.com>

docs/docs/distributions/self_hosted_distro/starter.md

@@ -90,7 +90,8 @@ On Unix-based systems (Linux, macOS), the server automatically uses Gunicorn wit
 **Important Notes**:
 
 - On Windows, the server automatically falls back to single-process Uvicorn.
-- **Database Race Condition**: When using multiple workers without `GUNICORN_PRELOAD=true`, you may encounter database initialization race conditions (e.g., "table already exists" errors) as multiple workers simultaneously attempt to create database tables. To avoid this issue in production, set `GUNICORN_PRELOAD=true` and ensure all dependencies are installed with `uv sync --group unit --group test`.
+- **Database Race Condition**: When using multiple workers without `GUNICORN_PRELOAD=true`, you may encounter database initialization race conditions (e.g., "table already exists" errors) as multiple workers simultaneously attempt to create database tables. To avoid this issue in production, set `GUNICORN_PRELOAD=true`.
+- **SQLite with Multiple Workers**: SQLite works with Gunicorn's multi-process mode for development and low-to-moderate traffic scenarios. The system automatically enables WAL (Write-Ahead Logging) mode and sets a 5-second busy timeout. However, **SQLite only allows one writer at a time** - even with WAL mode, write operations from multiple workers are serialized, causing workers to wait for database locks under concurrent write load. **For production deployments with high traffic or multiple workers, we strongly recommend using PostgreSQL or another production-grade database** for true concurrent write performance.
 
 **Example production configuration:**
 ```bash

docs/docs/distributions/starting_llama_stack_server.mdx

@@ -44,7 +44,9 @@ Configure Gunicorn behavior using environment variables:
 - `GUNICORN_MAX_REQUESTS_JITTER`: Randomize worker restart timing (default: `1000`)
 - `GUNICORN_PRELOAD`: Preload app before forking workers for memory efficiency (default: `true`, as set in `run.py` line 264)
 
-**Important**: When using multiple workers without `GUNICORN_PRELOAD=true`, you may encounter database initialization race conditions. To avoid this, set `GUNICORN_PRELOAD=true` and install all dependencies with `uv sync --group unit --group test`.
+**Important Notes**:
+- When using multiple workers without `GUNICORN_PRELOAD=true`, you may encounter database initialization race conditions. To avoid this, set `GUNICORN_PRELOAD=true`.
+- **SQLite with Multiple Workers**: SQLite works with Gunicorn's multi-process mode for development and low-to-moderate traffic scenarios. The system automatically enables WAL (Write-Ahead Logging) mode and sets a 5-second busy timeout. However, **SQLite only allows one writer at a time** - even with WAL mode, write operations from multiple workers are serialized, causing workers to wait for database locks under concurrent write load. **For production deployments with high traffic or multiple workers, we strongly recommend using PostgreSQL or another production-grade database** for true concurrent write performance.
 
 **Example production configuration:**
 ```bash

docs/docs/providers/agents/index.mdx

@@ -1,7 +1,7 @@
 ---
 description: "Agents
 
-    APIs for creating and interacting with agentic systems."
+APIs for creating and interacting with agentic systems."
 sidebar_label: Agents
 title: Agents
 ---
@@ -12,6 +12,6 @@ title: Agents
 
 Agents
 
-    APIs for creating and interacting with agentic systems.
+APIs for creating and interacting with agentic systems.
 
 This section contains documentation for all available providers for the **agents** API.

docs/docs/providers/batches/index.mdx

@@ -1,14 +1,14 @@
 ---
 description: "The Batches API enables efficient processing of multiple requests in a single operation,
-    particularly useful for processing large datasets, batch evaluation workflows, and
-    cost-effective inference at scale.
+particularly useful for processing large datasets, batch evaluation workflows, and
+cost-effective inference at scale.
 
-    The API is designed to allow use of openai client libraries for seamless integration.
+The API is designed to allow use of openai client libraries for seamless integration.
 
-    This API provides the following extensions:
-     - idempotent batch creation
+This API provides the following extensions:
+ - idempotent batch creation
 
-    Note: This API is currently under active development and may undergo changes."
+Note: This API is currently under active development and may undergo changes."
 sidebar_label: Batches
 title: Batches
 ---
@@ -18,14 +18,14 @@ title: Batches
 ## Overview
 
 The Batches API enables efficient processing of multiple requests in a single operation,
-    particularly useful for processing large datasets, batch evaluation workflows, and
-    cost-effective inference at scale.
+particularly useful for processing large datasets, batch evaluation workflows, and
+cost-effective inference at scale.
 
-    The API is designed to allow use of openai client libraries for seamless integration.
+The API is designed to allow use of openai client libraries for seamless integration.
 
-    This API provides the following extensions:
-     - idempotent batch creation
+This API provides the following extensions:
+ - idempotent batch creation
 
-    Note: This API is currently under active development and may undergo changes.
+Note: This API is currently under active development and may undergo changes.
 
 This section contains documentation for all available providers for the **batches** API.

docs/docs/providers/eval/index.mdx

@@ -1,7 +1,7 @@
 ---
 description: "Evaluations
 
-    Llama Stack Evaluation API for running evaluations on model and agent candidates."
+Llama Stack Evaluation API for running evaluations on model and agent candidates."
 sidebar_label: Eval
 title: Eval
 ---
@@ -12,6 +12,6 @@ title: Eval
 
 Evaluations
 
-    Llama Stack Evaluation API for running evaluations on model and agent candidates.
+Llama Stack Evaluation API for running evaluations on model and agent candidates.
 
 This section contains documentation for all available providers for the **eval** API.

docs/docs/providers/files/index.mdx

@@ -1,7 +1,7 @@
 ---
 description: "Files
 
-    This API is used to upload documents that can be used with other Llama Stack APIs."
+This API is used to upload documents that can be used with other Llama Stack APIs."
 sidebar_label: Files
 title: Files
 ---
@@ -12,6 +12,6 @@ title: Files
 
 Files
 
-    This API is used to upload documents that can be used with other Llama Stack APIs.
+This API is used to upload documents that can be used with other Llama Stack APIs.
 
 This section contains documentation for all available providers for the **files** API.

docs/docs/providers/inference/index.mdx

@@ -1,12 +1,12 @@
 ---
 description: "Inference
 
-    Llama Stack Inference API for generating completions, chat completions, and embeddings.
+Llama Stack Inference API for generating completions, chat completions, and embeddings.
 
-    This API provides the raw interface to the underlying models. Three kinds of models are supported:
-    - LLM models: these models generate \"raw\" and \"chat\" (conversational) completions.
-    - Embedding models: these models generate embeddings to be used for semantic search.
-    - Rerank models: these models reorder the documents based on their relevance to a query."
+This API provides the raw interface to the underlying models. Three kinds of models are supported:
+- LLM models: these models generate \"raw\" and \"chat\" (conversational) completions.
+- Embedding models: these models generate embeddings to be used for semantic search.
+- Rerank models: these models reorder the documents based on their relevance to a query."
 sidebar_label: Inference
 title: Inference
 ---
@@ -17,11 +17,11 @@ title: Inference
 
 Inference
 
-    Llama Stack Inference API for generating completions, chat completions, and embeddings.
+Llama Stack Inference API for generating completions, chat completions, and embeddings.
 
-    This API provides the raw interface to the underlying models. Three kinds of models are supported:
-    - LLM models: these models generate "raw" and "chat" (conversational) completions.
-    - Embedding models: these models generate embeddings to be used for semantic search.
-    - Rerank models: these models reorder the documents based on their relevance to a query.
+This API provides the raw interface to the underlying models. Three kinds of models are supported:
+- LLM models: these models generate "raw" and "chat" (conversational) completions.
+- Embedding models: these models generate embeddings to be used for semantic search.
+- Rerank models: these models reorder the documents based on their relevance to a query.
 
 This section contains documentation for all available providers for the **inference** API.

docs/docs/providers/safety/index.mdx

@@ -1,7 +1,7 @@
 ---
 description: "Safety
 
-    OpenAI-compatible Moderations API."
+OpenAI-compatible Moderations API."
 sidebar_label: Safety
 title: Safety
 ---
@@ -12,6 +12,6 @@ title: Safety
 
 Safety
 
-    OpenAI-compatible Moderations API.
+OpenAI-compatible Moderations API.
 
 This section contains documentation for all available providers for the **safety** API.