initial implementation for vectordb

python/packages/autogen-ext/pyproject.toml

@@ -27,6 +27,7 @@ langchain = ["langchain_core~= 0.3.3"]
 azure = ["azure-core", "azure-identity"]
 docker = ["docker~=7.0"]
 openai = ["openai>=1.3"]
+chromadb = ["chromadb~=0.5.15", "sentence-transformers"]
 
 [tool.hatch.build.targets.wheel]
 packages = ["src/autogen_ext"]
@@ -56,4 +57,4 @@ test = "pytest -n auto"
 [tool.mypy]
 [[tool.mypy.overrides]]
 module = "docker.*"
-ignore_missing_imports = true
+ignore_missing_imports = true

python/packages/autogen-ext/src/autogen_ext/storage/__init__.py

@@ -0,0 +1,4 @@
+from ._chromadb import AsyncChromaVectorDB, ChromaVectorDB
+from ._factory import VectorDBFactory
+
+__all__ = ["ChromaVectorDB", "AsyncChromaVectorDB", "VectorDBFactory"]

python/packages/autogen-ext/src/autogen_ext/storage/_base.py

@@ -0,0 +1,369 @@
+from typing import (
+    Any,
+    Callable,
+    List,
+    Mapping,
+    Optional,
+    Protocol,
+    Sequence,
+    Tuple,
+    Union,
+    runtime_checkable,
+)
+
+from pydantic import BaseModel
+
+Metadata = Union[Mapping[str, Any], None]
+Vector = Union[Sequence[float], Sequence[int]]
+ItemID = Union[str, int]
+
+
+class Document(BaseModel):
+    """Define Document according to autogen 0.4 specifications."""
+
+    id: ItemID
+    content: Optional[str] = None
+    metadata: Optional[Metadata] = None
+    embedding: Optional[Vector] = None
+
+    model_config = {"arbitrary_types_allowed": True}
+
+
+"""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 AsyncVectorDB(Protocol):
+    """
+    Abstract class for async 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], Awaitable[Any]] | Create a collection in the vector database.
+        get_collection: Callable[[str], Awaitable[Any]] | Get the collection from the vector database.
+        delete_collection: Callable[[str], Awaitable[Any]] | Delete the collection from the vector database.
+        insert_docs: Callable[[List[Document], str, bool], Awaitable[None]] | Insert documents into the collection of the vector database.
+        update_docs: Callable[[List[Document], str], Awaitable[None]] | Update documents in the collection of the vector database.
+        delete_docs: Callable[[List[ItemID], str], Awaitable[None]] | Delete documents from the collection of the vector database.
+        retrieve_docs: Callable[[List[str], str, int, float], Awaitable[QueryResults]] | Retrieve documents from the collection of the vector database based on the queries.
+        get_docs_by_ids: Callable[[List[ItemID], str], Awaitable[List[Document]]] | Retrieve documents from the collection of the vector database based on the ids.
+    """
+
+    active_collection: Any = None
+    type: str = ""
+    embedding_function: Optional[Callable[..., Any]] = None  # embeddings = embedding_function(sentences)
+
+    async 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.
+        """
+        ...
+
+    async def get_collection(self, collection_name: Optional[str] = None) -> Any:
+        """
+        Get the collection from the vector database.
+
+        Args:
+            collection_name: Optional[str] | The name of the collection. Default is None.
+                If None, return the current active collection.
+
+        Returns:
+            Any | The collection object.
+        """
+        ...
+
+    async 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
+        """
+        ...
+
+    async def insert_docs(
+        self,
+        docs: List[Document],
+        collection_name: Optional[str] = None,
+        upsert: bool = False,
+        **kwargs: Any,
+    ) -> None:
+        """
+        Insert documents into the collection of the vector database.
+
+        Args:
+            docs: List[Document] | A list of documents. Each document is a Pydantic Document model.
+            collection_name: Optional[str] | The name of the collection. Default is None.
+            upsert: bool | Whether to update the document if it exists. Default is False.
+            kwargs: Dict[str, Any] | Additional keyword arguments.
+
+        Returns:
+            None
+        """
+        ...
+
+    async def update_docs(self, docs: List[Document], collection_name: Optional[str] = None, **kwargs: Any) -> None:
+        """
+        Update documents in the collection of the vector database.
+
+        Args:
+            docs: List[Document] | A list of documents.
+            collection_name: Optional[str] | The name of the collection. Default is None.
+            kwargs: Dict[str, Any] | Additional keyword arguments.
+
+        Returns:
+            None
+        """
+        ...
+
+    async def delete_docs(self, ids: List[ItemID], collection_name: Optional[str] = None, **kwargs: Any) -> 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: Optional[str] | The name of the collection. Default is None.
+            kwargs: Dict[str, Any] | Additional keyword arguments.
+
+        Returns:
+            None
+        """
+        ...
+
+    async def retrieve_docs(
+        self,
+        queries: List[str],
+        collection_name: Optional[str] = None,
+        n_results: int = 10,
+        distance_threshold: float = -1,
+        **kwargs: Any,
+    ) -> 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: Optional[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[str, Any] | Additional keyword arguments.
+
+        Returns:
+            QueryResults | The query results. Each query result is a list of list of tuples containing the document and
+                the distance.
+        """
+        ...
+
+    async def get_docs_by_ids(
+        self,
+        ids: Optional[List[ItemID]] = None,
+        collection_name: Optional[str] = None,
+        include: Optional[List[str]] = None,
+        **kwargs: Any,
+    ) -> List[Document]:
+        """
+        Retrieve documents from the collection of the vector database based on the ids.
+
+        Args:
+            ids: Optional[List[ItemID]] | A list of document ids. If None, will return all the documents. Default is None.
+            collection_name: Optional[str] | The name of the collection. Default is None.
+            include: Optional[List[str]] | The fields to include. Default is None.
+                If None, will include ["metadatas", "documents"], ids will always be included. This may differ
+                depending on the implementation.
+            kwargs: Dict[str, Any] | Additional keyword arguments.
+
+        Returns:
+            List[Document] | The results.
+        """
+        ...
+
+
+@runtime_checkable
+class VectorDB(Protocol):
+    """
+    Abstract class for synchronous vector database. A vector database is responsible for storing and retrieving documents.
+    For async support, use AsyncVectorDB instead.
+
+    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 = ""
+    embedding_function: Optional[Callable[[List[str]], List[List[float]]]] = (
+        None  # embeddings = embedding_function(sentences)
+    )
+
+    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: Optional[str] = None) -> Any:
+        """
+        Get the collection from the vector database.
+
+        Args:
+            collection_name: Optional[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: Optional[str] = None,
+        upsert: bool = False,
+        **kwargs: Any,
+    ) -> None:
+        """
+        Insert documents into the collection of the vector database.
+
+        Args:
+            docs: List[Document] | A list of documents. Each document is a Pydantic Document model.
+            collection_name: Optional[str] | The name of the collection. Default is None.
+            upsert: bool | Whether to update the document if it exists. Default is False.
+            kwargs: Dict[str, Any] | Additional keyword arguments.
+
+        Returns:
+            None
+        """
+        ...
+
+    def update_docs(self, docs: List[Document], collection_name: Optional[str] = None, **kwargs: Any) -> None:
+        """
+        Update documents in the collection of the vector database.
+
+        Args:
+            docs: List[Document] | A list of documents.
+            collection_name: Optional[str] | The name of the collection. Default is None.
+            kwargs: Dict[str, Any] | Additional keyword arguments.
+
+        Returns:
+            None
+        """
+        ...
+
+    def delete_docs(self, ids: List[ItemID], collection_name: Optional[str] = None, **kwargs: Any) -> 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: Optional[str] | The name of the collection. Default is None.
+            kwargs: Dict[str, Any] | Additional keyword arguments.
+
+        Returns:
+            None
+        """
+        ...
+
+    def retrieve_docs(
+        self,
+        queries: List[str],
+        collection_name: Optional[str] = None,
+        n_results: int = 10,
+        distance_threshold: float = -1,
+        **kwargs: Any,
+    ) -> 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: Optional[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[str, Any] | 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: Optional[List[ItemID]] = None,
+        collection_name: Optional[str] = None,
+        include: Optional[List[str]] = None,
+        **kwargs: Any,
+    ) -> List[Document]:
+        """
+        Retrieve documents from the collection of the vector database based on the ids.
+
+        Args:
+            ids: Optional[List[ItemID]] | A list of document ids. If None, will return all the documents. Default is None.
+            collection_name: Optional[str] | The name of the collection. Default is None.
+            include: Optional[List[str]] | The fields to include. Default is None.
+                If None, will include ["metadatas", "documents"], ids will always be included. This may differ
+                depending on the implementation.
+            kwargs: Dict[str, Any] | Additional keyword arguments.
+
+        Returns:
+            List[Document] | The results.
+        """
+        ...