+mdb atlas vectordb [clean_final] (#3000)

* +mdb atlas

* Update test/agentchat/contrib/vectordb/test_mongodb.py

Co-authored-by: HRUSHIKESH DOKALA <96101829+Hk669@users.noreply.github.com>

* update test_mongodb.py; we dont need to do the assert .collection_name vs .name

* Try fix mongodb service

* Try fix mongodb service

* Update username and password

* Update autogen/agentchat/contrib/vectordb/mongodb.py

* closer --- but im not super thrilled about the solution...

* PYTHON-4506 Expanded tests and simplified vector search pipelines

* Update mongodb.py

* Update mongodb.py - Casey

* search_index_magic

index_name change; keeping track of lucene indexes is tricky

* Fix format

* Fix tests

* hacking trying to figure this out

* Streamline checks for indexes in construction and restructure tests

* Add tests for score_threshold, embedding inclusion, and multiple query tests

* refactored create_collection to meet base object requirements

* lint

* change the localhost port to 27017

* add test to check that no embedding is there unless explicitly provided

* Update logger

* Add test get docs with ids=None

* Rename and update notebook

* have index management include waiting behaviors

* Adds further optional waits or users and tests. Cleans up upsert.

* ensure the embedding size for multiple embedding inputs is equal to dimensions

* fix up tests and add configuration to ensure documents and indexes are READY for querying

* fix import failure

* adjust typing for 3.9

* fix up the notebook output

* changed language to communicate time taken on first init_chat call

* replace environment variable usage

---------

Co-authored-by: Fabian Valle <fabian.valle-simmons@mongodb.com>
Co-authored-by: HRUSHIKESH DOKALA <96101829+Hk669@users.noreply.github.com>
Co-authored-by: Li Jiang <bnujli@gmail.com>
Co-authored-by: Casey Clements <casey.clements@mongodb.com>
Co-authored-by: Jib <jib.adegunloye@mongodb.com>
Co-authored-by: Jib <Jibzade@gmail.com>
Co-authored-by: Cozypet <yanhan860711@gmail.com>

.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/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