Add a vectordb module

.github/workflows/contrib-openai.yml

@@ -53,7 +53,7 @@ jobs:
           AZURE_OPENAI_API_BASE: ${{ secrets.AZURE_OPENAI_API_BASE }}
           OAI_CONFIG_LIST: ${{ secrets.OAI_CONFIG_LIST }}
         run: |
-          coverage run -a -m pytest test/agentchat/contrib/test_retrievechat.py test/agentchat/contrib/test_qdrant_retrievechat.py
+          coverage run -a -m pytest test/agentchat/contrib/test_retrievechat.py test/agentchat/contrib/test_qdrant_retrievechat.py test/agentchat/contrib/vectordb
           coverage xml
       - name: Upload coverage to Codecov
         uses: codecov/codecov-action@v3

.github/workflows/contrib-tests.yml

@@ -60,11 +60,11 @@ jobs:
           fi
       - name: Test RetrieveChat
         run: |
-          pytest test/test_retrieve_utils.py test/agentchat/contrib/test_retrievechat.py test/agentchat/contrib/test_qdrant_retrievechat.py --skip-openai
+          pytest test/test_retrieve_utils.py test/agentchat/contrib/test_retrievechat.py test/agentchat/contrib/test_qdrant_retrievechat.py test/agentchat/contrib/vectordb --skip-openai
       - name: Coverage
         run: |
           pip install coverage>=5.3
-          coverage run -a -m pytest test/test_retrieve_utils.py test/agentchat/contrib/test_retrievechat.py test/agentchat/contrib/test_qdrant_retrievechat.py --skip-openai
+          coverage run -a -m pytest test/test_retrieve_utils.py test/agentchat/contrib/test_retrievechat.py test/agentchat/contrib/test_qdrant_retrievechat.py test/agentchat/contrib/vectordb --skip-openai
           coverage xml
       - name: Upload coverage to Codecov
         uses: codecov/codecov-action@v3

autogen/agentchat/contrib/vectordb/base.py

@@ -0,0 +1,209 @@
+from typing import Any, List, Mapping, Optional, Protocol, Sequence, Tuple, TypedDict, Union, runtime_checkable
+
+Metadata = Union[Mapping[str, Any], None]
+Vector = Union[Sequence[float], Sequence[int]]
+ItemID = Union[str, int]  # chromadb doesn't support int ids, VikingDB does
+
+
+class Document(TypedDict):
+    """A Document is a record in the vector database.
+
+    id: ItemID | the unique identifier of the document.
+    content: str | the text content of the chunk.
+    metadata: Metadata, Optional | contains additional information about the document such as source, date, etc.
+    embedding: Vector, Optional | the vector representation of the content.
+    """
+
+    id: ItemID
+    content: str
+    metadata: Optional[Metadata]
+    embedding: Optional[Vector]
+
+
+"""QueryResults is the response from the vector database for a query/queries.
+A query is a list containing one string while queries is a list containing multiple strings.
+The response is a list of query results, each query result is a list of tuples containing the document and the distance.
+"""
+QueryResults = List[List[Tuple[Document, float]]]
+
+
+@runtime_checkable
+class VectorDB(Protocol):
+    """
+    Abstract class for vector database. A vector database is responsible for storing and retrieving documents.
+
+    Attributes:
+        active_collection: Any | The active collection in the vector database. Make get_collection faster. Default is None.
+        type: str | The type of the vector database, chroma, pgvector, etc. Default is "".
+
+    Methods:
+        create_collection: Callable[[str, bool, bool], Any] | Create a collection in the vector database.
+        get_collection: Callable[[str], Any] | Get the collection from the vector database.
+        delete_collection: Callable[[str], Any] | Delete the collection from the vector database.
+        insert_docs: Callable[[List[Document], str, bool], None] | Insert documents into the collection of the vector database.
+        update_docs: Callable[[List[Document], str], None] | Update documents in the collection of the vector database.
+        delete_docs: Callable[[List[ItemID], str], None] | Delete documents from the collection of the vector database.
+        retrieve_docs: Callable[[List[str], str, int, float], QueryResults] | Retrieve documents from the collection of the vector database based on the queries.
+        get_docs_by_ids: Callable[[List[ItemID], str], List[Document]] | Retrieve documents from the collection of the vector database based on the ids.
+    """
+
+    active_collection: Any = None
+    type: str = ""
+
+    def create_collection(self, collection_name: str, overwrite: bool = False, get_or_create: bool = True) -> Any:
+        """
+        Create a collection in the vector database.
+        Case 1. if the collection does not exist, create the collection.
+        Case 2. the collection exists, if overwrite is True, it will overwrite the collection.
+        Case 3. the collection exists and overwrite is False, if get_or_create is True, it will get the collection,
+            otherwise it raise a ValueError.
+
+        Args:
+            collection_name: str | The name of the collection.
+            overwrite: bool | Whether to overwrite the collection if it exists. Default is False.
+            get_or_create: bool | Whether to get the collection if it exists. Default is True.
+
+        Returns:
+            Any | The collection object.
+        """
+        ...
+
+    def get_collection(self, collection_name: str = None) -> Any:
+        """
+        Get the collection from the vector database.
+
+        Args:
+            collection_name: str | The name of the collection. Default is None. If None, return the
+                current active collection.
+
+        Returns:
+            Any | The collection object.
+        """
+        ...
+
+    def delete_collection(self, collection_name: str) -> Any:
+        """
+        Delete the collection from the vector database.
+
+        Args:
+            collection_name: str | The name of the collection.
+
+        Returns:
+            Any
+        """
+        ...
+
+    def insert_docs(self, docs: List[Document], collection_name: str = None, upsert: bool = False, **kwargs) -> None:
+        """
+        Insert documents into the collection of the vector database.
+
+        Args:
+            docs: List[Document] | A list of documents. Each document is a TypedDict `Document`.
+            collection_name: str | The name of the collection. Default is None.
+            upsert: bool | Whether to update the document if it exists. Default is False.
+            kwargs: Dict | Additional keyword arguments.
+
+        Returns:
+            None
+        """
+        ...
+
+    def update_docs(self, docs: List[Document], collection_name: str = None, **kwargs) -> None:
+        """
+        Update documents in the collection of the vector database.
+
+        Args:
+            docs: List[Document] | A list of documents.
+            collection_name: str | The name of the collection. Default is None.
+            kwargs: Dict | Additional keyword arguments.
+
+        Returns:
+            None
+        """
+        ...
+
+    def delete_docs(self, ids: List[ItemID], collection_name: str = None, **kwargs) -> None:
+        """
+        Delete documents from the collection of the vector database.
+
+        Args:
+            ids: List[ItemID] | A list of document ids. Each id is a typed `ItemID`.
+            collection_name: str | The name of the collection. Default is None.
+            kwargs: Dict | Additional keyword arguments.
+
+        Returns:
+            None
+        """
+        ...
+
+    def retrieve_docs(
+        self,
+        queries: List[str],
+        collection_name: str = None,
+        n_results: int = 10,
+        distance_threshold: float = -1,
+        **kwargs,
+    ) -> QueryResults:
+        """
+        Retrieve documents from the collection of the vector database based on the queries.
+
+        Args:
+            queries: List[str] | A list of queries. Each query is a string.
+            collection_name: str | The name of the collection. Default is None.
+            n_results: int | The number of relevant documents to return. Default is 10.
+            distance_threshold: float | The threshold for the distance score, only distance smaller than it will be
+                returned. Don't filter with it if < 0. Default is -1.
+            kwargs: Dict | Additional keyword arguments.
+
+        Returns:
+            QueryResults | The query results. Each query result is a list of list of tuples containing the document and
+                the distance.
+        """
+        ...
+
+    def get_docs_by_ids(
+        self, ids: List[ItemID] = None, collection_name: str = None, include=None, **kwargs
+    ) -> List[Document]:
+        """
+        Retrieve documents from the collection of the vector database based on the ids.
+
+        Args:
+            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.
+            kwargs: dict | Additional keyword arguments.
+
+        Returns:
+            List[Document] | The results.
+        """
+        ...
+
+
+class VectorDBFactory:
+    """
+    Factory class for creating vector databases.
+    """
+
+    PREDEFINED_VECTOR_DB = ["chroma"]
+
+    @staticmethod
+    def create_vector_db(db_type: str, **kwargs) -> VectorDB:
+        """
+        Create a vector database.
+
+        Args:
+            db_type: str | The type of the vector database.
+            kwargs: Dict | The keyword arguments for initializing the vector database.
+
+        Returns:
+            VectorDB | The vector database.
+        """
+        if db_type.lower() in ["chroma", "chromadb"]:
+            from .chromadb import ChromaVectorDB
+
+            return ChromaVectorDB(**kwargs)
+        else:
+            raise ValueError(
+                f"Unsupported vector database type: {db_type}. Valid types are {VectorDBFactory.PREDEFINED_VECTOR_DB}."
+            )