Readme + demo

README.md

@@ -1,18 +1,64 @@
 ---
 license: mit
 ---
-## Description
 
-Replies with a fixed message.
+# Table of Contents
 
-## Configuration parameters
+* [fixed\_reply](#fixed_reply)
+  * [FixedReplyFlow](#fixed_reply.FixedReplyFlow)
+    * [run](#fixed_reply.FixedReplyFlow.run)
 
-fixed_reply (String): The string specifying the reply. Required parameter.
+<a id="fixed_reply"></a>
 
-## Input interface
+# fixed\_reply
 
-The Flow does not expect any input. 
+<a id="fixed_reply.FixedReplyFlow"></a>
 
-## Output interface
+## FixedReplyFlow Objects
+
+```python
+class FixedReplyFlow(AtomicFlow)
+```
+
+This class implements a FixedReplyFlow. It's used to reply with a fixed reply.
+
+*Configuration Parameters*:
+
+- `name` (str): The name of the flow.
+
+- `description` (str): A description of the flow. This description is used to generate the help message of the flow.
+
+- `fixed_reply` (str): The fixed reply to reply with.
+
+- The other configuration parameters are inherited from the default configuration of AtomicFlow (see AtomicFlow)
+
+*Input Interface*:
+
+- None
+
+Output Interface:
+
+- `fixed_reply` (str): The fixed reply.
+
+**Arguments**:
+
+- `\**kwargs` (`Dict[str, Any]`): The keyword arguments passed to the AtomicFlow constructor. Among these is the flow_config which should also contain the "fixed_reply" key.
+
+<a id="fixed_reply.FixedReplyFlow.run"></a>
+
+#### run
+
+```python
+def run(input_data: Dict[str, Any]) -> Dict[str, Any]
+```
+
+Runs the FixedReplyFlow. It's used to reply with a fixed reply.
+
+**Arguments**:
+
+- `input_data` (`Dict[str, Any]`): The input data dictionary
+
+**Returns**:
+
+`Dict[str, Any]`: The fixed reply
 
-fixed_reply (String): The string specifying the reply.

demo.yaml

@@ -0,0 +1,6 @@
+flow:
+  _target_: aiflows.FixedReplyFlowModule.FixedReplyFlow.instantiate_from_default_config
+  name: "FixedReplyFlow"
+  description: "A demo of the FixedReplyFlow."
+  fixed_reply: "This is a fixed reply."
+  

fixed_reply.py

@@ -4,6 +4,29 @@ from flows.base_flows import AtomicFlow
 
 
 class FixedReplyFlow(AtomicFlow):
+    """ This class implements a FixedReplyFlow. It's used to reply with a fixed reply.
+    
+    *Configuration Parameters*:
+    
+    - `name` (str): The name of the flow.
+    
+    - `description` (str): A description of the flow. This description is used to generate the help message of the flow.
+    
+    - `fixed_reply` (str): The fixed reply to reply with.
+    
+    - The other configuration parameters are inherited from the default configuration of AtomicFlow (see AtomicFlow)
+    
+    *Input Interface*:
+        
+    - None
+            
+    Output Interface:
+    
+    - `fixed_reply` (str): The fixed reply.
+            
+    :param \**kwargs: The keyword arguments passed to the AtomicFlow constructor. Among these is the flow_config which should also contain the "fixed_reply" key.
+    :type \**kwargs: Dict[str, Any]
+    """
     REQUIRED_KEYS_CONFIG = ["fixed_reply"]
 
     __default_flow_config = {
@@ -16,6 +39,13 @@ class FixedReplyFlow(AtomicFlow):
 
     def run(self,
             input_data: Dict[str, Any]) -> Dict[str, Any]:
+        """ Runs the FixedReplyFlow. It's used to reply with a fixed reply.
+        
+        :param input_data: The input data dictionary
+        :type input_data: Dict[str, Any]
+        :return: The fixed reply
+        :rtype: Dict[str, Any]
+        """
 
         return {"fixed_reply": self.flow_config["fixed_reply"]}
 

run.py

@@ -0,0 +1,58 @@
+import os
+
+import hydra
+
+from flows.flow_launchers import FlowLauncher
+from flows.utils.general_helpers import read_yaml_file
+
+from flows import logging
+from flows.flow_cache import CACHING_PARAMETERS
+
+CACHING_PARAMETERS.do_caching = False  # Set to True to enable caching
+# clear_cache() # Uncomment this line to clear the cache
+
+logging.set_verbosity_debug()
+logging.auto_set_dir()
+
+dependencies = [
+    {"url": "aiflows/FixedReplyFlowModule", "revision": os.getcwd()},
+]
+from flows import flow_verse
+flow_verse.sync_dependencies(dependencies)
+
+if __name__ == "__main__":
+
+    root_dir = "."
+    cfg_path = os.path.join(root_dir, "demo.yaml")
+    cfg = read_yaml_file(cfg_path)
+    flow_with_interfaces = {
+        "flow": hydra.utils.instantiate(cfg['flow'], _recursive_=False, _convert_="partial"),
+        "input_interface": (
+            None
+            if getattr(cfg, "input_interface", None) is None
+            else hydra.utils.instantiate(cfg['input_interface'], _recursive_=False)
+        ),
+        "output_interface": (
+            None
+            if getattr(cfg, "output_interface", None) is None
+            else hydra.utils.instantiate(cfg['output_interface'], _recursive_=False)
+        ),
+    }
+
+    # ~~~ Get the data ~~~
+    # This can be a list of samples
+    data = {"id": 0}  # Add your data here
+
+    # ~~~ Run inference ~~~
+    path_to_output_file = None
+    # path_to_output_file = "output.jsonl"  # Uncomment this line to save the output to disk
+
+    _, outputs = FlowLauncher.launch(
+        flow_with_interfaces=flow_with_interfaces,
+        data=data,
+        path_to_output_file=path_to_output_file,
+    )
+
+    # ~~~ Print the output ~~~
+    flow_output_data = outputs[0]
+    print(flow_output_data)