refactor: improve MLflow provider code based on PR feedback

  - refactor: simplify MLflow provider documentation per upstream feedback
  - refactor: improve import handling with module-level imports
  - refactor: use proper typing instead of type: ignore for mlflow client
  - refactor: use .split() instead of magic number [5:] for clarity
  - chore: remove manual test script per upstream request

  Changes:
  - Move mlflow imports to module level with graceful fallback
  - Replace  with proper  typing
  - Remove 7 duplicate imports throughout methods
  - Use  for better readability
  - Simplify documentation to essential information only

Signed-off-by: William Caban <[email protected]>

Author: williamcaban

docs/docs/providers/prompts/remote_mlflow.mdx

@@ -3,104 +3,7 @@ description: |
   [MLflow](https://mlflow.org/) is a remote provider for centralized prompt management and versioning
   using MLflow's Prompt Registry (available in MLflow 3.4+). It allows you to store, version, and manage
   prompts in a centralized MLflow server, enabling team collaboration and prompt lifecycle management.
-
-  ## Features
-  MLflow Prompts Provider supports:
-  - Create and store prompts with automatic versioning
-  - Retrieve prompts by ID and version
-  - Update prompts (creates new immutable versions)
-  - List all prompts or all versions of a specific prompt
-  - Set default version for a prompt
-  - Automatic variable extraction from templates
-  - Metadata storage and retrieval
-  - Centralized prompt management across teams
-
-  ## Key Capabilities
-  - **Version Control**: Immutable versioning ensures prompt history is preserved
-  - **Default Version Management**: Easily switch between prompt versions
-  - **Variable Auto-Extraction**: Automatically detects `{{ variable }}` placeholders
-  - **Metadata Tags**: Stores Llama Stack metadata for seamless integration
-  - **Team Collaboration**: Centralized MLflow server enables multi-user access
-
-  ## Usage
-
-  To use MLflow Prompts Provider in your Llama Stack project:
-
-  1. Install MLflow 3.4 or later
-  2. Start an MLflow server (local or remote)
-  3. Configure your Llama Stack project to use the MLflow provider
-  4. Start creating and managing prompts
-
-  ## Installation
-
-  Install MLflow using pip or uv:
-
-  ```bash
-  pip install 'mlflow>=3.4.0'
-  # or
-  uv pip install 'mlflow>=3.4.0'
-  ```
-
-  ## Quick Start
-
-  ### 1. Start MLflow Server
-
-  **Local server** (for development):
-  ```bash
-  mlflow server --host 127.0.0.1 --port 5555
-  ```
-
-  **Remote server** (for production):
-  ```bash
-  mlflow server --host 0.0.0.0 --port 5000 --backend-store-uri postgresql://user:pass@host/db
-  ```
-
-  ### 2. Configure Llama Stack
-
-  Add to your Llama Stack configuration:
-
-  ```yaml
-  prompts:
-    - provider_id: mlflow-prompts
-      provider_type: remote::mlflow
-      config:
-        mlflow_tracking_uri: http://localhost:5555
-        experiment_name: llama-stack-prompts
-  ```
-
-  ### 3. Use the Prompts API
-
-  ```python
-  from llama_stack_client import LlamaStackClient
-
-  client = LlamaStackClient(base_url="http://localhost:5000")
-
-  # Create a prompt
-  prompt = client.prompts.create(
-      prompt="Summarize the following text in {{ num_sentences }} sentences:\n\n{{ text }}",
-      variables=["num_sentences", "text"]
-  )
-  print(f"Created prompt: {prompt.prompt_id} (v{prompt.version})")
-
-  # Retrieve prompt
-  retrieved = client.prompts.get(prompt_id=prompt.prompt_id)
-  print(f"Retrieved: {retrieved.prompt}")
-
-  # Update prompt (creates version 2)
-  updated = client.prompts.update(
-      prompt_id=prompt.prompt_id,
-      prompt="Summarize in exactly {{ num_sentences }} sentences:\n\n{{ text }}",
-      version=1,
-      set_as_default=True
-  )
-  print(f"Updated to version: {updated.version}")
-
-  # List all prompts
-  prompts = client.prompts.list()
-  print(f"Found {len(prompts.data)} prompts")
-  ```
-
-  ## Documentation
+ 
   See [MLflow's documentation](https://mlflow.org/docs/latest/prompts.html) for more details about MLflow Prompt Registry.
 
 sidebar_label: Remote - MLflow

scripts/test_mlflow_prompts_manual.py

@@ -60,7 +60,7 @@ def to_mlflow_name(self, prompt_id: str) -> str:
             raise ValueError(f"Invalid prompt_id format: {prompt_id}. Expected format: pmpt_<48-hex-chars>")
 
         # Extract hex part (after "pmpt_" prefix)
-        hex_part = prompt_id[5:]
+        hex_part = prompt_id.split("pmpt_")[1]
 
         # Create MLflow name
         return f"{self.MLFLOW_NAME_PREFIX}{hex_part}"