fix cluttered api referances docs (#2069)

Author: priyansh4320

autogen/agentchat/assistant_agent.py

@@ -19,10 +19,10 @@ class AssistantAgent(ConversableAgent):
 
     AssistantAgent is a subclass of ConversableAgent configured with a default system message.
     The default system message is designed to solve a task with LLM,
-    including suggesting python code blocks and debugging.
-    `human_input_mode` is default to "NEVER"
-    and `code_execution_config` is default to False.
-    This agent doesn't execute code by default, and expects the user to execute the code.
+    including suggesting python code blocks and debugging. \n
+    `human_input_mode` is default to "NEVER" \n
+    and `code_execution_config` is default to False. \n
+    This agent doesn't execute code by default, and expects the user to execute the code. \n
     """
 
     DEFAULT_SYSTEM_MESSAGE = """You are a helpful AI assistant.
@@ -52,20 +52,20 @@ def __init__(
         **kwargs: Any,
     ):
         """Args:
-        name (str): agent name.
-        system_message (str): system message for the ChatCompletion inference.
+        - name (str): agent name. \n
+        - system_message (str): system message for the ChatCompletion inference. \n
             Please override this attribute if you want to reprogram the agent.
-        llm_config (dict or False or None): llm inference configuration.
-            Please refer to [OpenAIWrapper.create](https://docs.ag2.ai/latest/docs/api-reference/autogen/OpenAIWrapper/#autogen.OpenAIWrapper.create)
-            for available options.
-        is_termination_msg (function): a function that takes a message in the form of a dictionary
+        - llm_config (dict or False or None): llm inference configuration. \n
+            Please refer to [OpenAIWrapper.create](https://docs.ag2.ai/latest/docs/api-reference/autogen/OpenAIWrapper/#autogen.OpenAIWrapper.create) \n
+            for available options. \n
+        - is_termination_msg (function): a function that takes a message in the form of a dictionary
             and returns a boolean value indicating if this received message is a termination message.
-            The dict can contain the following keys: "content", "role", "name", "function_call".
-        max_consecutive_auto_reply (int): the maximum number of consecutive auto replies.
+            The dict can contain the following keys: "content", "role", "name", "function_call". \n
+        - max_consecutive_auto_reply (int): the maximum number of consecutive auto replies.
             default to None (no limit provided, class attribute MAX_CONSECUTIVE_AUTO_REPLY will be used as the limit in this case).
-            The limit only plays a role when human_input_mode is not "ALWAYS".
-        **kwargs (dict): Please refer to other kwargs in
-            [ConversableAgent](https://docs.ag2.ai/latest/docs/api-reference/autogen/ConversableAgent).
+            The limit only plays a role when human_input_mode is not "ALWAYS". \n
+        - **kwargs (dict): Please refer to other kwargs in
+            [ConversableAgent](https://docs.ag2.ai/latest/docs/api-reference/autogen/ConversableAgent). \n
         """
         super().__init__(
             name,

autogen/agentchat/chat.py

@@ -154,37 +154,36 @@ def initiate_chats(chat_queue: list[dict[str, Any]]) -> list[ChatResult]:
     """Initiate a list of chats.
 
     Args:
-        chat_queue (List[Dict]): A list of dictionaries containing the information about the chats.
-
+        chat_queue (List[Dict]): A list of dictionaries containing the information about the chats.\n
             Each dictionary should contain the input arguments for
-            [`ConversableAgent.initiate_chat`](../ConversableAgent#initiate-chat).
-            For example:
-                - `"sender"` - the sender agent.
-                - `"recipient"` - the recipient agent.
-                - `"clear_history"` (bool) - whether to clear the chat history with the agent.
-                Default is True.
-                - `"silent"` (bool or None) - (Experimental) whether to print the messages in this
-                conversation. Default is False.
-                - `"cache"` (Cache or None) - the cache client to use for this conversation.
-                Default is None.
-                - `"max_turns"` (int or None) - maximum number of turns for the chat. If None, the chat
-                will continue until a termination condition is met. Default is None.
-                - `"summary_method"` (str or callable) - a string or callable specifying the method to get
-                a summary from the chat. Default is DEFAULT_summary_method, i.e., "last_msg".
-                - `"summary_args"` (dict) - a dictionary of arguments to be passed to the summary_method.
-                Default is {}.
-                - `"message"` (str, callable or None) - if None, input() will be called to get the
-                initial message.
-                - `**context` - additional context information to be passed to the chat.
-                - `"carryover"` - It can be used to specify the carryover information to be passed
-                to this chat. If provided, we will combine this carryover with the "message" content when
-                generating the initial chat message in `generate_init_message`.
-                - `"finished_chat_indexes_to_exclude_from_carryover"` - It can be used by specifying a list of indexes of the finished_chats list,
-                from which to exclude the summaries for carryover. If 'finished_chat_indexes_to_exclude_from_carryover' is not provided or an empty list,
-                then summary from all the finished chats will be taken.
+            [`ConversableAgent.initiate_chat`](../ConversableAgent#initiate-chat).\n
+            For example:\n
+                - `"sender"` - the sender agent.\n
+                - `"recipient"` - the recipient agent.\n
+                - `"clear_history"` (bool) - whether to clear the chat history with the agent.\n
+                  Default is True.\n
+                - `"silent"` (bool or None) - (Experimental) whether to print the messages in this\n
+                  conversation. Default is False.\n
+                - `"cache"` (Cache or None) - the cache client to use for this conversation.\n
+                  Default is None.\n
+                - `"max_turns"` (int or None) - maximum number of turns for the chat. If None, the chat\n
+                  will continue until a termination condition is met. Default is None.\n
+                - `"summary_method"` (str or callable) - a string or callable specifying the method to get\n
+                  a summary from the chat. Default is DEFAULT_summary_method, i.e., "last_msg".\n
+                - `"summary_args"` (dict) - a dictionary of arguments to be passed to the summary_method.\n
+                  Default is {}.\n
+                - `"message"` (str, callable or None) - if None, input() will be called to get the\n
+                  initial message.\n
+                - `**context` - additional context information to be passed to the chat.\n
+                - `"carryover"` - It can be used to specify the carryover information to be passed\n
+                  to this chat. If provided, we will combine this carryover with the "message" content when\n
+                  generating the initial chat message in `generate_init_message`.\n
+                - `"finished_chat_indexes_to_exclude_from_carryover"` - It can be used by specifying a list of indexes of the finished_chats list,\n
+                  from which to exclude the summaries for carryover. If 'finished_chat_indexes_to_exclude_from_carryover' is not provided or an empty list,\n
+                  then summary from all the finished chats will be taken.\n
 
     Returns:
-        (list): a list of ChatResult objects corresponding to the finished chats in the chat_queue.
+        (list): a list of ChatResult objects corresponding to the finished chats in the chat_queue.\n
     """
     consolidate_chat_info(chat_queue)
     _validate_recipients(chat_queue)

autogen/agentchat/contrib/captainagent/captainagent.py

@@ -149,25 +149,26 @@ def __init__(
         description: str | None = DEFAULT_DESCRIPTION,
         **kwargs: Any,
     ):
-        """Args:
-        name (str): agent name.
-        system_message (str): system message for the ChatCompletion inference.
-            Please override this attribute if you want to reprogram the agent.
-        llm_config (LLMConfig or dict or False): llm inference configuration.
-            Please refer to [OpenAIWrapper.create](https://docs.ag2.ai/latest/docs/api-reference/autogen/OpenAIWrapper/#autogen.OpenAIWrapper.create) for available options.
-        is_termination_msg (function): a function that takes a message in the form of a dictionary
-            and returns a boolean value indicating if this received message is a termination message.
-            The dict can contain the following keys: "content", "role", "name", "function_call".
-        max_consecutive_auto_reply (int): the maximum number of consecutive auto replies.
-            default to None (no limit provided, class attribute MAX_CONSECUTIVE_AUTO_REPLY will be used as the limit in this case).
-            The limit only plays a role when human_input_mode is not "ALWAYS".
-        agent_lib (str): the path or a JSON file of the agent library for retrieving the nested chat instantiated by CaptainAgent.
-        tool_lib (str): the path to the tool library for retrieving the tools used in the nested chat instantiated by CaptainAgent.
-        nested_config (dict): the configuration for the nested chat instantiated by CaptainAgent.
-            A full list of keys and their functionalities can be found in [docs](https://docs.ag2.ai/latest/docs/user-guide/reference-agents/captainagent).
-        agent_config_save_path (str): the path to save the generated or retrieved agent configuration.
-        **kwargs (dict): Please refer to other kwargs in
-            [ConversableAgent](https://github.com/ag2ai/ag2/blob/main/autogen/agentchat/conversable_agent.py#L74).
+        """
+        Args:\n
+        name (str): agent name.\n
+        system_message (str): system message for the ChatCompletion inference.\n
+            Please override this attribute if you want to reprogram the agent.\n
+        llm_config (LLMConfig or dict or False): llm inference configuration.\n
+            Please refer to [OpenAIWrapper.create](https://docs.ag2.ai/latest/docs/api-reference/autogen/OpenAIWrapper/#autogen.OpenAIWrapper.create) for available options.\n
+        is_termination_msg (function): a function that takes a message in the form of a dictionary\n
+            and returns a boolean value indicating if this received message is a termination message.\n
+            The dict can contain the following keys: "content", "role", "name", "function_call".\n
+        max_consecutive_auto_reply (int): the maximum number of consecutive auto replies.\n
+            default to None (no limit provided, class attribute MAX_CONSECUTIVE_AUTO_REPLY will be used as the limit in this case).\n
+            The limit only plays a role when human_input_mode is not "ALWAYS".\n
+        agent_lib (str): the path or a JSON file of the agent library for retrieving the nested chat instantiated by CaptainAgent.\n
+        tool_lib (str): the path to the tool library for retrieving the tools used in the nested chat instantiated by CaptainAgent.\n
+        nested_config (dict): the configuration for the nested chat instantiated by CaptainAgent.\n
+            A full list of keys and their functionalities can be found in [docs](https://docs.ag2.ai/latest/docs/user-guide/reference-agents/captainagent).\n
+        agent_config_save_path (str): the path to save the generated or retrieved agent configuration.\n
+        **kwargs (dict): Please refer to other kwargs in\n
+            [ConversableAgent](https://github.com/ag2ai/ag2/blob/main/autogen/agentchat/conversable_agent.py#L74).\n
         """
         super().__init__(
             name,

autogen/agentchat/contrib/graph_rag/graph_rag_capability.py

@@ -14,10 +14,10 @@
 class GraphRagCapability(AgentCapability):
     """A graph-based RAG capability uses a graph query engine to give a conversable agent the graph-based RAG ability.
 
-    An agent class with graph-based RAG capability could
-    1. create a graph in the underlying database with input documents.
-    2. retrieved relevant information based on messages received by the agent.
-    3. generate answers from retrieved information and send messages back.
+    An agent class with graph-based RAG capability could:\n
+    1. create a graph in the underlying database with input documents.\n
+    2. retrieved relevant information based on messages received by the agent.\n
+    3. generate answers from retrieved information and send messages back.\n
 
     For example,
     ```python

autogen/agentchat/contrib/graph_rag/neo4j_graph_query_engine.py

@@ -28,23 +28,24 @@
 
 @require_optional_import("llama_index", "neo4j")
 class Neo4jGraphQueryEngine:
-    """This class serves as a wrapper for a property graph query engine backed by LlamaIndex and Neo4j,
-    facilitating the creating, connecting, updating, and querying of LlamaIndex property graphs.
-
-    It builds a property graph Index from input documents,
-    storing and retrieving data from the property graph in the Neo4j database.
-
-    It extracts triplets, i.e., [entity] -> [relationship] -> [entity] sets,
-    from the input documents using llamIndex extractors.
-
-    Users can provide custom entities, relationships, and schema to guide the extraction process.
-
-    If strict is True, the engine will extract triplets following the schema
-    of allowed relationships for each entity specified in the schema.
-
-    It also leverages LlamaIndex's chat engine which has a conversation history internally to provide context-aware responses.
-
-    For usage, please refer to example notebook/agentchat_graph_rag_neo4j.ipynb
+    """
+    This class serves as a wrapper for a property graph query engine backed by LlamaIndex and Neo4j,\n
+    facilitating the creating, connecting, updating, and querying of LlamaIndex property graphs.\n
+    \n
+    It builds a property graph Index from input documents,\n
+    storing and retrieving data from the property graph in the Neo4j database.\n
+    \n
+    It extracts triplets, i.e., [entity] -> [relationship] -> [entity] sets,\n
+    from the input documents using llamIndex extractors.\n
+    \n
+    Users can provide custom entities, relationships, and schema to guide the extraction process.\n
+    \n
+    If strict is True, the engine will extract triplets following the schema\n
+    of allowed relationships for each entity specified in the schema.\n
+    \n
+    It also leverages LlamaIndex's chat engine which has a conversation history internally to provide context-aware responses.\n
+    \n
+    For usage, please refer to example notebook/agentchat_graph_rag_neo4j.ipynb\n
     """
 
     def __init__(  # type: ignore[no-any-unimported]

autogen/agentchat/contrib/rag/query_engine.py

@@ -29,18 +29,18 @@ def init_db(
         """Initialize the database with the input documents or records.
 
         This method initializes database with the input documents or records.
-        Usually, it takes the following steps:
-        1. connecting to a database.
-        2. insert records
-        3. build indexes etc.
+        Usually, it takes the following steps:\n
+        1. connecting to a database.\n
+        2. insert records.\n
+        3. build indexes etc.\n
 
-        Args:
-            new_doc_dir (Optional[Union[Path, str]]): A directory containing documents to be ingested.
-            new_doc_paths_or_urls (Optional[Sequence[Union[Path, str]]]): A list of paths or URLs to documents to be ingested.
-            *args: Any additional arguments
-            **kwargs: Any additional keyword arguments
-        Returns:
-            bool: True if initialization is successful, False otherwise
+        Args:\n
+            new_doc_dir (Optional[Union[Path, str]]): A directory containing documents to be ingested.\n
+            new_doc_paths_or_urls (Optional[Sequence[Union[Path, str]]]): A list of paths or URLs to documents to be ingested.\n
+            *args: Any additional arguments\n
+            **kwargs: Any additional keyword arguments\n
+        Returns:\n
+            bool: True if initialization is successful, False otherwise\n
         """
         ...