Merge branch 'main' into k8s-code-executor

.github/workflows/contrib-tests.yml

@@ -612,3 +612,43 @@ jobs:
         with:
           file: ./coverage.xml
           flags: unittests
+
+  BedrockTest:
+    runs-on: ${{ matrix.os }}
+    strategy:
+      fail-fast: false
+      matrix:
+        os: [ubuntu-latest, macos-latest, windows-2019]
+        python-version: ["3.9", "3.10", "3.11", "3.12"]
+        exclude:
+          - os: macos-latest
+            python-version: "3.9"
+    steps:
+      - uses: actions/checkout@v4
+        with:
+          lfs: true
+      - name: Set up Python ${{ matrix.python-version }}
+        uses: actions/setup-python@v5
+        with:
+          python-version: ${{ matrix.python-version }}
+      - name: Install packages and dependencies for all tests
+        run: |
+          python -m pip install --upgrade pip wheel
+          pip install pytest-cov>=5
+      - name: Install packages and dependencies for Amazon Bedrock
+        run: |
+          pip install -e .[boto3,test]
+      - name: Set AUTOGEN_USE_DOCKER based on OS
+        shell: bash
+        run: |
+          if [[ ${{ matrix.os }} != ubuntu-latest ]]; then
+            echo "AUTOGEN_USE_DOCKER=False" >> $GITHUB_ENV
+          fi
+      - name: Coverage
+        run: |
+          pytest test/oai/test_bedrock.py --skip-openai
+      - name: Upload coverage to Codecov
+        uses: codecov/codecov-action@v3
+        with:
+          file: ./coverage.xml
+          flags: unittests

.github/workflows/dotnet-build.yml

@@ -51,7 +51,7 @@ jobs:
     strategy:
       fail-fast: false
       matrix:
-        os: [ ubuntu-latest, macos-latest, windows-latest ]
+        os: [ ubuntu-latest, macos-latest ]
         python-version: ["3.11"]
     runs-on: ${{ matrix.os }}
     timeout-minutes: 30

CONTRIBUTORS.md

@@ -20,9 +20,10 @@
 | Beibin Li | [BeibinLi](https://github.com/BeibinLi) | Microsoft Research | alt-models | Yes | |
 | Gagan Bansal | [gagb](https://github.com/gagb) | Microsoft Research |  Complex Tasks | | |
 | Adam Fourney | [afourney](https://github.com/afourney) | Microsoft Research |  Complex Tasks | | |
-| Ricky Loynd | [rickyloyn-microsoft](https://github.com/rickyloynd-microsoft) | Microsoft Research |  Learning | | |
+| Ricky Loynd | [rickyloynd-microsoft](https://github.com/rickyloynd-microsoft) | Microsoft Research | Teachability | | |
 | Eric Zhu | [ekzhu](https://github.com/ekzhu) | Microsoft Research |  Infra | | |
 | Jack Gerrits | [jackgerrits](https://github.com/jackgerrits) | Microsoft Research |  Infra | | |
+| David Luong | [David Luong](https://github.com/DavidLuong98) | Microsoft | AutoGen.Net | | |
 
 
 ## I would like to join this list. How can I help the project?

autogen/agentchat/contrib/capabilities/transform_messages.py

@@ -1,9 +1,8 @@
 import copy
 from typing import Dict, List
 
-from autogen import ConversableAgent
-
 from ....formatting_utils import colored
+from ...conversable_agent import ConversableAgent
 from .transforms import MessageTransform
 
 

autogen/agentchat/contrib/capabilities/transforms.py

@@ -445,3 +445,95 @@ def _compress_text(self, text: str) -> Tuple[str, int]:
     def _validate_min_tokens(self, min_tokens: Optional[int]):
         if min_tokens is not None and min_tokens <= 0:
             raise ValueError("min_tokens must be greater than 0 or None")
+
+
+class TextMessageContentName:
+    """A transform for including the agent's name in the content of a message."""
+
+    def __init__(
+        self,
+        position: str = "start",
+        format_string: str = "{name}:\n",
+        deduplicate: bool = True,
+        filter_dict: Optional[Dict] = None,
+        exclude_filter: bool = True,
+    ):
+        """
+        Args:
+            position (str): The position to add the name to the content. The possible options are 'start' or 'end'. Defaults to 'start'.
+            format_string (str): The f-string to format the message name with. Use '{name}' as a placeholder for the agent's name. Defaults to '{name}:\n' and must contain '{name}'.
+            deduplicate (bool): Whether to deduplicate the formatted string so it doesn't appear twice (sometimes the LLM will add it to new messages itself). Defaults to True.
+            filter_dict (None or dict): A dictionary to filter out messages that you want/don't want to compress.
+                If None, no filters will be applied.
+            exclude_filter (bool): If exclude filter is True (the default value), messages that match the filter will be
+                excluded from compression. If False, messages that match the filter will be compressed.
+        """
+
+        assert isinstance(position, str) and position is not None
+        assert position in ["start", "end"]
+        assert isinstance(format_string, str) and format_string is not None
+        assert "{name}" in format_string
+        assert isinstance(deduplicate, bool) and deduplicate is not None
+
+        self._position = position
+        self._format_string = format_string
+        self._deduplicate = deduplicate
+        self._filter_dict = filter_dict
+        self._exclude_filter = exclude_filter
+
+        # Track the number of messages changed for logging
+        self._messages_changed = 0
+
+    def apply_transform(self, messages: List[Dict]) -> List[Dict]:
+        """Applies the name change to the message based on the position and format string.
+
+        Args:
+            messages (List[Dict]): A list of message dictionaries.
+
+        Returns:
+            List[Dict]: A list of dictionaries with the message content updated with names.
+        """
+        # Make sure there is at least one message
+        if not messages:
+            return messages
+
+        messages_changed = 0
+        processed_messages = copy.deepcopy(messages)
+        for message in processed_messages:
+            # Some messages may not have content.
+            if not transforms_util.is_content_right_type(
+                message.get("content")
+            ) or not transforms_util.is_content_right_type(message.get("name")):
+                continue
+
+            if not transforms_util.should_transform_message(message, self._filter_dict, self._exclude_filter):
+                continue
+
+            if transforms_util.is_content_text_empty(message["content"]) or transforms_util.is_content_text_empty(
+                message["name"]
+            ):
+                continue
+
+            # Get and format the name in the content
+            content = message["content"]
+            formatted_name = self._format_string.format(name=message["name"])
+
+            if self._position == "start":
+                if not self._deduplicate or not content.startswith(formatted_name):
+                    message["content"] = f"{formatted_name}{content}"
+
+                    messages_changed += 1
+            else:
+                if not self._deduplicate or not content.endswith(formatted_name):
+                    message["content"] = f"{content}{formatted_name}"
+
+                    messages_changed += 1
+
+        self._messages_changed = messages_changed
+        return processed_messages
+
+    def get_logs(self, pre_transform_messages: List[Dict], post_transform_messages: List[Dict]) -> Tuple[str, bool]:
+        if self._messages_changed > 0:
+            return f"{self._messages_changed} message(s) changed to incorporate name.", True
+        else:
+            return "No messages changed to incorporate name.", False

autogen/agentchat/contrib/vectordb/qdrant.py

@@ -93,7 +93,7 @@ def __init__(
             kwargs: dict | Additional keyword arguments.
         """
         self.client: QdrantClient = client or QdrantClient(location=":memory:")
-        self.embedding_function = FastEmbedEmbeddingFunction() or embedding_function
+        self.embedding_function = embedding_function or FastEmbedEmbeddingFunction()
         self.collection_options = collection_options
         self.content_payload_key = content_payload_key
         self.metadata_payload_key = metadata_payload_key

autogen/agentchat/groupchat.py

@@ -5,7 +5,7 @@
 import re
 import sys
 from dataclasses import dataclass, field
-from typing import Callable, Dict, List, Literal, Optional, Tuple, Union
+from typing import Any, Callable, Dict, List, Literal, Optional, Tuple, Union
 
 from ..code_utils import content_str
 from ..exception_utils import AgentNameConflict, NoEligibleSpeaker, UndefinedNextAgent
@@ -17,6 +17,12 @@
 from .chat import ChatResult
 from .conversable_agent import ConversableAgent
 
+try:
+    # Non-core module
+    from .contrib.capabilities import transform_messages
+except ImportError:
+    transform_messages = None
+
 logger = logging.getLogger(__name__)
 
 
@@ -76,6 +82,8 @@ def custom_speaker_selection_func(
         of times until a single agent is returned or it exhausts the maximum attempts.
         Applies only to "auto" speaker selection method.
         Default is 2.
+    - select_speaker_transform_messages: (optional) the message transformations to apply to the nested select speaker agent-to-agent chat messages.
+        Takes a TransformMessages object, defaults to None and is only utilised when the speaker selection method is "auto".
     - select_speaker_auto_verbose: whether to output the select speaker responses and selections
         If set to True, the outputs from the two agents in the nested select speaker chat will be output, along with
         whether the responses were successful, or not, in selecting an agent
@@ -132,6 +140,7 @@ def custom_speaker_selection_func(
     The names are case-sensitive and should not be abbreviated or changed.
     The only names that are accepted are {agentlist}.
     Respond with ONLY the name of the speaker and DO NOT provide a reason."""
+    select_speaker_transform_messages: Optional[Any] = None
     select_speaker_auto_verbose: Optional[bool] = False
     role_for_select_speaker_messages: Optional[str] = "system"
 
@@ -249,6 +258,20 @@ def __post_init__(self):
         elif self.max_retries_for_selecting_speaker < 0:
             raise ValueError("max_retries_for_selecting_speaker must be greater than or equal to zero")
 
+        # Load message transforms here (load once for the Group Chat so we don't have to re-initiate it and it maintains the cache across subsequent select speaker calls)
+        self._speaker_selection_transforms = None
+        if self.select_speaker_transform_messages is not None:
+            if transform_messages is not None:
+                if isinstance(self.select_speaker_transform_messages, transform_messages.TransformMessages):
+                    self._speaker_selection_transforms = self.select_speaker_transform_messages
+                else:
+                    raise ValueError("select_speaker_transform_messages must be None or MessageTransforms.")
+            else:
+                logger.warning(
+                    "TransformMessages could not be loaded, the 'select_speaker_transform_messages' transform"
+                    "will not apply."
+                )
+
         # Validate select_speaker_auto_verbose
         if self.select_speaker_auto_verbose is None or not isinstance(self.select_speaker_auto_verbose, bool):
             raise ValueError("select_speaker_auto_verbose cannot be None or non-bool")
@@ -655,6 +678,10 @@ def validate_speaker_name(recipient, messages, sender, config) -> Tuple[bool, Un
         else:
             start_message = messages[-1]
 
+        # Add the message transforms, if any, to the speaker selection agent
+        if self._speaker_selection_transforms is not None:
+            self._speaker_selection_transforms.add_to_agent(speaker_selection_agent)
+
         # Run the speaker selection chat
         result = checking_agent.initiate_chat(
             speaker_selection_agent,
@@ -749,6 +776,10 @@ def validate_speaker_name(recipient, messages, sender, config) -> Tuple[bool, Un
         else:
             start_message = messages[-1]
 
+        # Add the message transforms, if any, to the speaker selection agent
+        if self._speaker_selection_transforms is not None:
+            self._speaker_selection_transforms.add_to_agent(speaker_selection_agent)
+
         # Run the speaker selection chat
         result = await checking_agent.a_initiate_chat(
             speaker_selection_agent,
@@ -1264,11 +1295,10 @@ def resume(
             if not message_speaker_agent and message["name"] == self.name:
                 message_speaker_agent = self
 
-            # Add previous messages to each agent (except their own messages and the last message, as we'll kick off the conversation with it)
+            # Add previous messages to each agent (except the last message, as we'll kick off the conversation with it)
             if i != len(messages) - 1:
                 for agent in self._groupchat.agents:
-                    if agent.name != message["name"]:
-                        self.send(message, self._groupchat.agent_by_name(agent.name), request_reply=False, silent=True)
+                    self.send(message, self._groupchat.agent_by_name(agent.name), request_reply=False, silent=True)
 
                 # Add previous message to the new groupchat, if it's an admin message the name may not match so add the message directly
                 if message_speaker_agent:
@@ -1310,7 +1340,7 @@ def resume(
     async def a_resume(
         self,
         messages: Union[List[Dict], str],
-        remove_termination_string: Union[str, Callable[[str], str]],
+        remove_termination_string: Union[str, Callable[[str], str]] = None,
         silent: Optional[bool] = False,
     ) -> Tuple[ConversableAgent, Dict]:
         """Resumes a group chat using the previous messages as a starting point, asynchronously. Requires the agents, group chat, and group chat manager to be established

autogen/coding/func_with_reqs.py

@@ -6,7 +6,7 @@
 from dataclasses import dataclass, field
 from importlib.abc import SourceLoader
 from textwrap import dedent, indent
-from typing import Any, Callable, Generic, List, TypeVar, Union
+from typing import Any, Callable, Generic, List, Set, TypeVar, Union
 
 from typing_extensions import ParamSpec
 
@@ -159,12 +159,12 @@ def _build_python_functions_file(
     funcs: List[Union[FunctionWithRequirements[Any, P], Callable[..., Any], FunctionWithRequirementsStr]]
 ) -> str:
     # First collect all global imports
-    global_imports = set()
+    global_imports: Set[str] = set()
     for func in funcs:
         if isinstance(func, (FunctionWithRequirements, FunctionWithRequirementsStr)):
-            global_imports.update(func.global_imports)
+            global_imports.update(map(_import_to_str, func.global_imports))
 
-    content = "\n".join(map(_import_to_str, global_imports)) + "\n\n"
+    content = "\n".join(global_imports) + "\n\n"
 
     for func in funcs:
         content += _to_code(func) + "\n\n"

autogen/logger/file_logger.py

@@ -18,6 +18,7 @@
 if TYPE_CHECKING:
     from autogen import Agent, ConversableAgent, OpenAIWrapper
     from autogen.oai.anthropic import AnthropicClient
+    from autogen.oai.bedrock import BedrockClient
     from autogen.oai.cohere import CohereClient
     from autogen.oai.gemini import GeminiClient
     from autogen.oai.groq import GroqClient
@@ -215,6 +216,7 @@ def log_new_client(
             | TogetherClient
             | GroqClient
             | CohereClient
+            | BedrockClient
         ),
         wrapper: OpenAIWrapper,
         init_args: Dict[str, Any],

autogen/logger/sqlite_logger.py

@@ -19,6 +19,7 @@
 if TYPE_CHECKING:
     from autogen import Agent, ConversableAgent, OpenAIWrapper
     from autogen.oai.anthropic import AnthropicClient
+    from autogen.oai.bedrock import BedrockClient
     from autogen.oai.cohere import CohereClient
     from autogen.oai.gemini import GeminiClient
     from autogen.oai.groq import GroqClient
@@ -402,6 +403,7 @@ def log_new_client(
             TogetherClient,
             GroqClient,
             CohereClient,
+            BedrockClient,
         ],
         wrapper: OpenAIWrapper,
         init_args: Dict[str, Any],