namespaces

Author: [email protected]

docs/opengradient/client/client.md

@@ -35,14 +35,7 @@ def __init__(private_key: str, email: Optional[str] = None, password: Optio
 
 #### Variables
 
-* static `inference` : `Inference`
-* static `llm` : `LLM`
-* static `model_hub` : `ModelHub`
-* `alpha` - Access Alpha Testnet features.
-
-  Returns:
-    Alpha: Alpha namespace with workflow and ML model execution methods.
-
-  Example:
-    client = og.Client(...)
-    result = client.alpha.new_workflow(model_cid, input_query, input_tensor_name)
+* [**`inference`**](./onchain_inference): On-chain ONNX model inference via blockchain smart contracts.
+* [**`llm`**](./llm): LLM chat and completion via TEE-verified execution.
+* [**`model_hub`**](./model_hub): Model Hub for creating, versioning, and uploading ML models.
+* [**`alpha`**](./alpha): Alpha Testnet features including workflow management and ML model execution.

docs/opengradient/client/index.md

@@ -82,14 +82,7 @@ def __init__(private_key: str, email: Optional[str] = None, password: Optio
 
 #### Variables
 
-* static `inference` : `Inference`
-* static `llm` : `LLM`
-* static `model_hub` : `ModelHub`
-* `alpha` - Access Alpha Testnet features.
-
-  Returns:
-    Alpha: Alpha namespace with workflow and ML model execution methods.
-
-  Example:
-    client = og.Client(...)
-    result = client.alpha.new_workflow(model_cid, input_query, input_tensor_name)
+* [**`inference`**](./onchain_inference): On-chain ONNX model inference via blockchain smart contracts.
+* [**`llm`**](./llm): LLM chat and completion via TEE-verified execution.
+* [**`model_hub`**](./model_hub): Model Hub for creating, versioning, and uploading ML models.
+* [**`alpha`**](./alpha): Alpha Testnet features including workflow management and ML model execution.

docs/opengradient/index.md

@@ -140,17 +140,10 @@ def __init__(private_key: str, email: Optional[str] = None, password: Optio
 
 #### Variables
 
-* static `inference` : `Inference`
-* static `llm` : `LLM`
-* static `model_hub` : `ModelHub`
-* `alpha` - Access Alpha Testnet features.
-
-  Returns:
-    Alpha: Alpha namespace with workflow and ML model execution methods.
-
-  Example:
-    client = og.Client(...)
-    result = client.alpha.new_workflow(model_cid, input_query, input_tensor_name)
+* [**`inference`**](./onchain_inference): On-chain ONNX model inference via blockchain smart contracts.
+* [**`llm`**](./llm): LLM chat and completion via TEE-verified execution.
+* [**`model_hub`**](./model_hub): Model Hub for creating, versioning, and uploading ML models.
+* [**`alpha`**](./alpha): Alpha Testnet features including workflow management and ML model execution.
 
 ### `InferenceMode`
 

src/opengradient/client/client.py

@@ -10,6 +10,7 @@
     DEFAULT_OPENGRADIENT_LLM_STREAMING_SERVER_URL,
     DEFAULT_RPC_URL,
 )
+from .alpha import Alpha
 from .llm import LLM
 from .model_hub import ModelHub
 from .onchain_inference import Inference
@@ -31,8 +32,13 @@ class Client:
     """
 
     model_hub: ModelHub
+    """Model Hub for creating, versioning, and uploading ML models."""
+
     llm: LLM
+    """LLM chat and completion via TEE-verified execution."""
+
     inference: Inference
+    """On-chain ONNX model inference via blockchain smart contracts."""
 
     def __init__(
         self,
@@ -88,19 +94,8 @@ def __init__(
         self._alpha = None  # Lazy initialization for alpha namespace
 
     @property
-    def alpha(self):
-        """
-        Access Alpha Testnet features.
-
-        Returns:
-            Alpha: Alpha namespace with workflow and ML model execution methods.
-
-        Example:
-            client = og.Client(...)
-            result = client.alpha.new_workflow(model_cid, input_query, input_tensor_name)
-        """
+    def alpha(self) -> Alpha:
+        """Alpha Testnet features including workflow management and ML model execution."""
         if self._alpha is None:
-            from .alpha import Alpha
-
             self._alpha = Alpha(self._blockchain, self._wallet_account)
         return self._alpha

templates/text.mako

@@ -64,6 +64,21 @@ def format_for_list(docstring, depth=1):
   spaces = depth * 2 * ' '
   return re.compile(r'\n\n', re.MULTILINE).sub('\n\n' + spaces, docstring)
 
+def resolve_namespace_target(v):
+  """If v's type resolves to an opengradient class in a different module, return link target."""
+  result = [None]
+  var_mod = v.module.name if hasattr(v, 'module') and v.module else ''
+  def capture(dobj):
+    if not isinstance(dobj, pdoc.Module) and hasattr(dobj, 'module') and dobj.module is not None:
+      mod_parts = dobj.module.name.split('.')
+      if (len(mod_parts) >= 2 and mod_parts[0] == 'opengradient'
+          and dobj.module.name != var_mod):
+        result[0] = mod_parts[-1] if len(mod_parts) > 2 else mod_parts[1]
+    return '`{}`'.format(dobj.qualname.split('.')[-1])
+  if show_type_annotations:
+    v.type_annotation(link=capture)
+  return result[0]
+
 def linkify(text, mod):
   """Convert backtick-wrapped qualified names to markdown links."""
   cur_parts = mod.name.split('.')
@@ -185,14 +200,21 @@ prefix = qual + ' ' if qual else ''
 %>\
 % for v in vs:
 <%
-return_type = get_annotation(v.type_annotation, link=link)
-type_str = return_type if return_type else ''
 raw_desc = v.docstring.strip() if v.docstring else ''
 if raw_desc == 'The type of the None singleton.':
   raw_desc = ''
+ns_target = resolve_namespace_target(v)
+%>\
+% if ns_target:
+* [**`${v.name}`**](./${ns_target})${ ': ' + firstline(raw_desc) if raw_desc else ''}
+% else:
+<%
+return_type = get_annotation(v.type_annotation, link=link)
+type_str = return_type if return_type else ''
 desc = ' - ' + format_for_list(raw_desc, 1) if raw_desc else ''
 %>\
 * ${prefix}`${v.name}`${type_str}${desc}
+% endif
 % endfor
 </%def>\
 <%def name="show_module(module)">\