Merge branch 'microsoft:main' into main

.github/workflows/contrib-tests.yml

@@ -87,6 +87,10 @@ jobs:
           --health-retries 5
         ports:
           - 5432:5432
+      mongodb:
+        image: mongodb/mongodb-atlas-local:latest
+        ports:
+          - 27017:27017
     steps:
       - uses: actions/checkout@v4
       - name: Set up Python ${{ matrix.python-version }}
@@ -104,6 +108,9 @@ jobs:
       - name: Install pgvector when on linux
         run: |
           pip install -e .[retrievechat-pgvector]
+      - name: Install mongodb when on linux
+        run: |
+          pip install -e .[retrievechat-mongodb]
       - name: Install unstructured when python-version is 3.9 and on linux
         if: matrix.python-version == '3.9'
         run: |

autogen/agentchat/chat.py

@@ -107,6 +107,15 @@ def __find_async_chat_order(chat_ids: Set[int], prerequisites: List[Prerequisite
     return chat_order
 
 
+def _post_process_carryover_item(carryover_item):
+    if isinstance(carryover_item, str):
+        return carryover_item
+    elif isinstance(carryover_item, dict) and "content" in carryover_item:
+        return str(carryover_item["content"])
+    else:
+        return str(carryover_item)
+
+
 def __post_carryover_processing(chat_info: Dict[str, Any]) -> None:
     iostream = IOStream.get_default()
 
@@ -116,7 +125,7 @@ def __post_carryover_processing(chat_info: Dict[str, Any]) -> None:
             UserWarning,
         )
     print_carryover = (
-        ("\n").join([t for t in chat_info["carryover"]])
+        ("\n").join([_post_process_carryover_item(t) for t in chat_info["carryover"]])
         if isinstance(chat_info["carryover"], list)
         else chat_info["carryover"]
     )
@@ -153,7 +162,7 @@ def initiate_chats(chat_queue: List[Dict[str, Any]]) -> List[ChatResult]:
         For example:
             - `"sender"` - the sender agent.
             - `"recipient"` - the recipient agent.
-            - `"clear_history"  (bool) - whether to clear the chat history with the 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.

autogen/agentchat/contrib/vectordb/base.py

@@ -186,7 +186,8 @@ def get_docs_by_ids(
             ids: List[ItemID] | A list of document ids. If None, will return all the documents. Default is None.
             collection_name: str | The name of the collection. Default is None.
             include: List[str] | The fields to include. Default is None.
-                If None, will include ["metadatas", "documents"], ids will always be included.
+                If None, will include ["metadatas", "documents"], ids will always be included. This may differ
+                depending on the implementation.
             kwargs: dict | Additional keyword arguments.
 
         Returns:
@@ -200,7 +201,7 @@ class VectorDBFactory:
     Factory class for creating vector databases.
     """
 
-    PREDEFINED_VECTOR_DB = ["chroma", "pgvector", "qdrant"]
+    PREDEFINED_VECTOR_DB = ["chroma", "pgvector", "mongodb", "qdrant"]
 
     @staticmethod
     def create_vector_db(db_type: str, **kwargs) -> VectorDB:
@@ -222,6 +223,10 @@ def create_vector_db(db_type: str, **kwargs) -> VectorDB:
             from .pgvectordb import PGVectorDB
 
             return PGVectorDB(**kwargs)
+        if db_type.lower() in ["mdb", "mongodb", "atlas"]:
+            from .mongodb import MongoDBAtlasVectorDB
+
+            return MongoDBAtlasVectorDB(**kwargs)
         if db_type.lower() in ["qdrant", "qdrantdb"]:
             from .qdrant import QdrantVectorDB