Typo fix.

autogenhub · Nov 12, 2024 · d1e1234 · d1e1234
1 parent c5d437f
commit d1e1234
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Showing 2 changed files with 12 additions and 12 deletions.
diff --git a/test/agentchat/contrib/test_agent_optimizer.py b/test/agentchat/contrib/test_agent_optimizer.py
@@ -99,18 +99,18 @@ def test_step():
     user_proxy.initiate_chat(assistant, message=problem)
     optimizer.record_one_conversation(assistant.chat_messages_for_summary(user_proxy), is_satisfied=True)
 
-    register_for_llm, register_for_exector = optimizer.step()
+    register_for_llm, register_for_executor = optimizer.step()
 
     print("-------------------------------------")
     print("register_for_llm:")
     print(register_for_llm)
-    print("register_for_exector")
-    print(register_for_exector)
+    print("register_for_executor")
+    print(register_for_executor)
 
     for item in register_for_llm:
         assistant.update_function_signature(**item)
-    if len(register_for_exector.keys()) > 0:
-        user_proxy.register_function(function_map=register_for_exector)
+    if len(register_for_executor.keys()) > 0:
+        user_proxy.register_function(function_map=register_for_executor)
 
     print("-------------------------------------")
     print("Updated assistant.llm_config:")

diff --git a/website/blog/2023-12-23-AgentOptimizer/index.mdx b/website/blog/2023-12-23-AgentOptimizer/index.mdx
@@ -57,18 +57,18 @@ optimizer.record_one_conversation(history, is_satisfied=result)
 At each optimization iteration, it will return two fields register_for_llm and register_for_executor, which are subsequently utilized to update the assistant and UserProxy agents, respectively.
 
 ```python
-register_for_llm, register_for_exector = optimizer.step()
+register_for_llm, register_for_executor = optimizer.step()
 for item in register_for_llm:
     assistant.update_function_signature(**item)
-if len(register_for_exector.keys()) > 0:
-    user_proxy.register_function(function_map=register_for_exector)
+if len(register_for_executor.keys()) > 0:
+    user_proxy.register_function(function_map=register_for_executor)
 ```
 
 3. `reset_optimizer`:
 
 This method will reset the optimizer to the initial state, which is useful when you want to train the agent from scratch.
 
-**AgentOptimizer** includes mechanisms to check the (1) validity of the function and (2) code implementation before returning the register_for_llm, register_for_exector.
+**AgentOptimizer** includes mechanisms to check the (1) validity of the function and (2) code implementation before returning the register_for_llm, register_for_executor.
 Moreover, it also includes mechanisms to check whether each update is feasible, such as avoiding the removal of a function that is not in the current functions due to hallucination.
 
 ## Pseudocode for the optimization process
@@ -81,11 +81,11 @@ for i in range(EPOCH):
     is_correct = user_proxy.initiate_chat(assistant, message = problem)
     history = assistant.chat_messages_for_summary(user_proxy)
     optimizer.record_one_conversation(history, is_satisfied=is_correct)
-    register_for_llm, register_for_exector = optimizer.step()
+    register_for_llm, register_for_executor = optimizer.step()
     for item in register_for_llm:
         assistant.update_function_signature(**item)
-    if len(register_for_exector.keys()) > 0:
-        user_proxy.register_function(function_map=register_for_exector)
+    if len(register_for_executor.keys()) > 0:
+        user_proxy.register_function(function_map=register_for_executor)
 ```
 
 Given a prepared training dataset, the agents iteratively solve problems from the training set to obtain conversation history and statistical information.