diff --git a/python/packages/autogen-ext/pyproject.toml b/python/packages/autogen-ext/pyproject.toml index f13843aabcf..5bb3da44856 100644 --- a/python/packages/autogen-ext/pyproject.toml +++ b/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 \ No newline at end of file diff --git a/python/packages/autogen-ext/src/autogen_ext/storage/__init__.py b/python/packages/autogen-ext/src/autogen_ext/storage/__init__.py new file mode 100644 index 00000000000..e9c14adfb67 --- /dev/null +++ b/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"] diff --git a/python/packages/autogen-ext/src/autogen_ext/storage/_base.py b/python/packages/autogen-ext/src/autogen_ext/storage/_base.py new file mode 100644 index 00000000000..dff99cf04ec --- /dev/null +++ b/python/packages/autogen-ext/src/autogen_ext/storage/_base.py @@ -0,0 +1,378 @@ +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, + **kwargs: Any, + ) -> 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. + kwargs: Dict[str, Any] | Additional keyword arguments for collection creation (e.g. schema). + + 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, **kwargs: Any + ) -> 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. + """ + ... diff --git a/python/packages/autogen-ext/src/autogen_ext/storage/_chromadb.py b/python/packages/autogen-ext/src/autogen_ext/storage/_chromadb.py new file mode 100644 index 00000000000..bd7c17085db --- /dev/null +++ b/python/packages/autogen-ext/src/autogen_ext/storage/_chromadb.py @@ -0,0 +1,784 @@ +import logging +import os +from typing import TYPE_CHECKING, Any, Callable, Dict, List, Literal, Optional, Sequence, Tuple, Union, cast + +from autogen_core.application.logging import TRACE_LOGGER_NAME +from chromadb import GetResult +from chromadb import QueryResult as ChromaQueryResult + +if TYPE_CHECKING: + from chromadb.api import AsyncClientAPI, ClientAPI + from chromadb.api.models.Collection import Collection + from chromadb.api.types import Embeddable, EmbeddingFunction + from chromadb.config import Settings + +from ._base import AsyncVectorDB, Document, ItemID, Metadata, QueryResults, Vector, VectorDB + +CHROMADB_MAX_BATCH_SIZE = int(os.environ.get("CHROMADB_MAX_BATCH_SIZE", 40000)) +logger = logging.getLogger(f"{TRACE_LOGGER_NAME}.{__name__}") + + +class ChromaVectorDB(VectorDB): + """ + A vector database that uses ChromaDB as the backend. + + .. note:: + + This class requires the :code:`chromadb` extra for the :code:`autogen-ext` package. + """ + + ChromaError = Exception # Default to Exception if chromadb is not installed + + def __init__( + self, + *, + client: Optional["ClientAPI"] = None, + path: Optional[str] = None, + embedding_function: Optional[ + Union[Callable[[List[str]], List[List[float]]], "EmbeddingFunction[Embeddable]"] + ] = None, + metadata: Optional[Dict[str, Any]] = None, + client_type: str = "persistent", + host: str = "localhost", + port: int = 8000, + **kwargs: Any, + ) -> None: + """ + Initialize the vector database. + + Args: + client: chromadb.Client | The client object of the vector database. Default is None. + If provided, it will use the client object directly and ignore other arguments. + path: Optional[str] | The path to the vector database. Required if client_type is 'persistent'. + embedding_function: Optional[Union[Callable, EmbeddingFunction]] | The embedding function used to generate the vector representation + of the documents. Default is None, SentenceTransformerEmbeddingFunction("all-MiniLM-L6-v2") will be used. + metadata: dict | The metadata of the vector database. Default is None. + client_type: str | The type of client to use. Can be 'persistent' or 'http'. Default is 'persistent'. + host: str | The host of the HTTP server. Default is 'localhost'. + port: int | The port of the HTTP server. Default is 8000. + kwargs: dict | Additional keyword arguments. + + Returns: + None + """ + try: + import chromadb + + if chromadb.__version__ < "0.5.0": + raise ImportError("Please upgrade chromadb to version 0.5.0 or later.") + from chromadb.api.types import IncludeEnum + from chromadb.errors import ChromaError + from chromadb.utils.embedding_functions.sentence_transformer_embedding_function import ( + SentenceTransformerEmbeddingFunction, + ) + + ChromaVectorDB.ChromaError = ChromaError # Set the class attribute + except ImportError as e: + raise RuntimeError( + "Missing dependencies for ChromaVectorDB. Please ensure the autogen-ext package was installed with the 'chromadb' extra." + ) from e + + self.IncludeEnum = IncludeEnum + self.embedding_function: "EmbeddingFunction[Any]" = ( # type: ignore + SentenceTransformerEmbeddingFunction("all-MiniLM-L6-v2") # type: ignore + if embedding_function is None + else cast("EmbeddingFunction[Any]", embedding_function) + ) + self.metadata = metadata + self.type = "chroma" + if client is not None: + self.client: "ClientAPI" = client + else: + if client_type == "persistent": + if path is None: + raise ValueError("Persistent client requires a 'path' to save the database.") + self.client = chromadb.PersistentClient(path=path, **kwargs) + elif client_type == "http": + self.client = chromadb.HttpClient(host=host, port=port, **kwargs) + else: + raise ValueError(f"Invalid client_type: {client_type}") + + self.active_collection: Optional["Collection"] = None + + def create_collection( + self, collection_name: str, overwrite: bool = False, get_or_create: bool = True, **kwargs: Any + ) -> "Collection": + """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 raises 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: + Collection | The collection object. + """ + try: + if self.active_collection and self.active_collection.name == collection_name: + collection = self.active_collection + else: + collection = self.client.get_collection( + name=collection_name, embedding_function=self.embedding_function + ) + except (ValueError, ChromaVectorDB.ChromaError): + collection = None + + if collection is None: + return self.client.create_collection( + name=collection_name, + embedding_function=kwargs.pop("embedding_function", self.embedding_function), + metadata=kwargs.pop("metadata", self.metadata), + data_loader=kwargs.pop("data_loader", None), + ) + elif overwrite: + self.client.delete_collection(name=collection_name) + return self.client.create_collection( + name=collection_name, + embedding_function=kwargs.pop("embedding_function", self.embedding_function), + metadata=kwargs.pop("metadata", self.metadata), + data_loader=kwargs.pop("data_loader", None), + ) + elif get_or_create: + return collection + else: + raise ValueError(f"Collection {collection_name} already exists.") + + def get_collection(self, collection_name: Optional[str] = None) -> "Collection": + """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: + Collection | The collection object. + """ + if collection_name is None: + if self.active_collection is None: + raise ValueError("No collection is specified.") + else: + logger.info( + f"No collection is specified. Using current active collection {self.active_collection.name}." + ) + else: + if not (self.active_collection and self.active_collection.name == collection_name): + self.active_collection = self.client.get_collection( + name=collection_name, embedding_function=self.embedding_function + ) + return self.active_collection + + def delete_collection(self, collection_name: str) -> None: + """ + Delete the collection from the vector database. + + Args: + collection_name: str | The name of the collection. + + Returns: + None + """ + self.client.delete_collection(name=collection_name) + if self.active_collection and self.active_collection.name == collection_name: + self.active_collection = None + + def _batch_insert( + self, + collection: "Collection", + embeddings: Optional[List[Any]] = None, + ids: Optional[List[str]] = None, + metadatas: Optional[List[Dict[str, Any]]] = None, + documents: Optional[List[str]] = None, + upsert: bool = False, + ) -> None: + batch_size = CHROMADB_MAX_BATCH_SIZE + for i in range(0, len(ids or []), batch_size): + end_idx = i + batch_size + collection_kwargs = { + "documents": documents[i:end_idx] if documents else None, + "ids": ids[i:end_idx] if ids else None, + "metadatas": metadatas[i:end_idx] if metadatas else None, + "embeddings": embeddings[i:end_idx] if embeddings else None, + } + if upsert: + collection.upsert(**collection_kwargs) # type: ignore + else: + collection.add(**collection_kwargs) # type: ignore + + 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 + """ + if not docs: + return + if docs[0].content is None and docs[0].embedding is None: + raise ValueError("Either document content or embedding is required.") + documents = [doc.content for doc in docs] if docs[0].content else None + ids = [str(doc.id) for doc in docs] + collection = self.get_collection(collection_name) + embeddings = [doc.embedding for doc in docs] if docs[0].embedding else None + if not embeddings and not documents: + raise ValueError("Either documents or embeddings must be provided.") + metadatas = [doc.metadata for doc in docs] if docs[0].metadata else None + self._batch_insert( + collection, + embeddings=embeddings, + ids=ids, + metadatas=metadatas, # type: ignore + documents=documents, # type: ignore + upsert=upsert, + ) + + 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 + """ + self.insert_docs(docs, collection_name=collection_name, upsert=True, **kwargs) + + def delete_docs(self, ids: Sequence[ItemID], collection_name: Optional[str] = None, **kwargs: Any) -> None: + """ + Delete documents from the collection of the vector database. + + Args: + ids: Sequence[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 + """ + collection = self.get_collection(collection_name) + collection.delete(ids=[str(id_) for id_ in ids] if ids else 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. + """ + collection = self.get_collection(collection_name) + if isinstance(queries, str): + queries = [queries] + results = collection.query( + query_texts=queries, + n_results=n_results, + ) + results_list = _chroma_results_to_query_results(results) + results_filtered = _filter_results_by_distance(results_list, distance_threshold) + return results_filtered + + def get_docs_by_ids( + self, + ids: Optional[Sequence[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[Sequence[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[IncludeEnum]] | The fields to include. Default is None. + If None, will include [IncludeEnum.metadatas, IncludeEnum.documents]. IDs are always included. + kwargs: Dict[str, Any] | Additional keyword arguments. + + Returns: + List[Document] | The results. + """ + if include is not None: + include_enums = [self.IncludeEnum(item) for item in include] + else: + include_enums = [self.IncludeEnum.metadatas, self.IncludeEnum.documents] + collection = self.get_collection(collection_name) + + results = collection.get(ids=[str(id_) for id_ in ids] if ids else None, include=include_enums) + results_list = _chroma_get_results_to_list_documents(results) + return results_list + + +class AsyncChromaVectorDB(AsyncVectorDB): + """ + An asynchronous vector database that uses ChromaDB as the backend. + + .. note:: + + This class requires the :code:`chromadb` extra for the :code:`autogen-ext` package. + """ + + ChromaError = Exception # Default to Exception if chromadb is not installed + + def __init__( + self, + *, + client: Optional["AsyncClientAPI"] = None, + embedding_function: Optional[ + Union[Callable[[List[str]], List[List[float]]], "EmbeddingFunction[Embeddable]"] + ] = None, + host: str = "localhost", + port: int = 8000, + ssl: bool = False, + headers: Optional[Dict[str, str]] = None, + settings: Optional["Settings"] = None, + tenant: str = "default_tenant", + database: str = "default_database", + **kwargs: Any, + ) -> None: + """ + Initialize the async vector database. + + Args: + client: chromadb.AsyncClientAPI | The client object of the vector database. Default is None. + If provided, it will use the client object directly and ignore other arguments. + embedding_function: Callable | The embedding function used to generate the vector representation + of the documents. Default is None. Must be provided for async client. + host: str | The host of the HTTP server. Default is 'localhost'. + port: int | The port of the HTTP server. Default is 8000. + ssl: bool | Whether to use SSL to connect to the Chroma server. Defaults to False. + headers: Optional[Dict[str, str]] | A dictionary of headers to send to the Chroma server. Defaults to None. + settings: Optional[Settings] | A dictionary of settings to communicate with the chroma server. + tenant: str | The tenant to use for this client. Defaults to "default_tenant". + database: str | The database to use for this client. Defaults to "default_database". + kwargs: dict | Additional keyword arguments. + + Returns: + None + """ + try: + import chromadb + + if chromadb.__version__ < "0.5.0": + raise ImportError("Please upgrade chromadb to version 0.5.0 or later.") + from chromadb.api.types import IncludeEnum + from chromadb.errors import ChromaError + from chromadb.utils.embedding_functions.sentence_transformer_embedding_function import ( + SentenceTransformerEmbeddingFunction, + ) + + AsyncChromaVectorDB.ChromaError = ChromaError # Set the class attribute + except ImportError as e: + raise RuntimeError( + "Missing dependencies for AsyncChromaVectorDB. Please ensure the autogen-ext package was installed with the 'chromadb' extra." + ) from e + + self.IncludeEnum = IncludeEnum + self.embedding_function: "EmbeddingFunction[Embeddable]" = ( # type: ignore + cast( + "EmbeddingFunction[Embeddable]", + embedding_function or SentenceTransformerEmbeddingFunction("all-MiniLM-L6-v2"), + ) + ) + self.type = "chroma" + if client is not None: + self.client: "AsyncClientAPI" = client + else: + self.client = chromadb.AsyncHttpClient( # type: ignore + host=host, + port=port, + ssl=ssl, + headers=headers, + settings=settings, + tenant=tenant, + database=database, + **kwargs, + ) + self.active_collection: Optional[Any] = None + + async def create_collection( + self, collection_name: str, overwrite: bool = False, get_or_create: bool = True, **kwargs: Any + ) -> 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 raises 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. + """ + try: + if self.active_collection and self.active_collection.name == collection_name: + collection = self.active_collection + else: + collection = await self.client.get_collection( + name=collection_name, embedding_function=self.embedding_function + ) + except (ValueError, AsyncChromaVectorDB.ChromaError): + collection = None + if collection is None: + return await self.client.create_collection( + name=collection_name, + embedding_function=kwargs.pop("embedding_function", self.embedding_function), + metadata=kwargs.pop("metadata", {}), + data_loader=kwargs.pop("data_loader", None), + ) + elif overwrite: + await self.client.delete_collection(name=collection_name) + return await self.client.create_collection( + name=collection_name, + embedding_function=kwargs.pop("embedding_function", self.embedding_function), + metadata=kwargs.pop("metadata", {}), + data_loader=kwargs.pop("data_loader", None), + ) + elif get_or_create: + return collection + else: + raise ValueError(f"Collection {collection_name} already exists.") + + 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. + """ + if collection_name is None: + if self.active_collection is None: + raise ValueError("No collection is specified.") + else: + logger.info( + f"No collection is specified. Using current active collection {self.active_collection.name}." + ) + else: + if not (self.active_collection and self.active_collection.name == collection_name): + self.active_collection = await self.client.get_collection( + name=collection_name, embedding_function=self.embedding_function + ) + return self.active_collection + + 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 + """ + await self.client.delete_collection(name=collection_name) + if self.active_collection and self.active_collection.name == collection_name: + self.active_collection = None + + async def _batch_insert( + self, + collection: Any, + embeddings: Optional[List[Any]] = None, + ids: Optional[List[str]] = None, + metadatas: Optional[List[Dict[str, Any]]] = None, + documents: Optional[List[str]] = None, + upsert: bool = False, + ) -> None: + batch_size = CHROMADB_MAX_BATCH_SIZE + for i in range(0, len(ids or []), batch_size): + end_idx = i + batch_size + collection_kwargs = { + "documents": documents[i:end_idx] if documents else None, + "ids": ids[i:end_idx] if ids else None, + "metadatas": metadatas[i:end_idx] if metadatas else None, + "embeddings": embeddings[i:end_idx] if embeddings else None, + } + if upsert: + await collection.upsert(**collection_kwargs) + else: + await collection.add(**collection_kwargs) + + 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 + """ + if not docs: + return + if docs[0].content is None and docs[0].embedding is None: + raise ValueError("Either document content or embedding is required.") + documents = [doc.content for doc in docs] if docs[0].content else None + ids = [str(doc.id) for doc in docs] + collection = await self.get_collection(collection_name) + embeddings = [doc.embedding for doc in docs] if docs[0].embedding else None + if not embeddings and not documents: + raise ValueError("Either documents or embeddings must be provided.") + metadatas = [doc.metadata for doc in docs] if docs[0].metadata else None + await self._batch_insert( + collection, + embeddings=embeddings, + ids=ids, + metadatas=metadatas, # type: ignore + documents=documents, # type: ignore + upsert=upsert, + ) + + 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 + """ + await self.insert_docs(docs, collection_name=collection_name, upsert=True, **kwargs) + + async def delete_docs(self, ids: Sequence[ItemID], collection_name: Optional[str] = None, **kwargs: Any) -> None: + """ + Delete documents from the collection of the vector database. + + Args: + ids: Sequence[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 + """ + collection = await self.get_collection(collection_name) + await collection.delete(ids=ids) + + 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. + """ + collection = await self.get_collection(collection_name) + + if isinstance(queries, str): + queries = [queries] + results = await collection.query( + query_texts=queries, + n_results=n_results, + ) + results_list = _chroma_results_to_query_results(results) + results_filtered = _filter_results_by_distance(results_list, distance_threshold) + return results_filtered + + async def get_docs_by_ids( + self, + ids: Optional[Sequence[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[Sequence[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[Sequence[IncludeEnum]] | The fields to include. Default is None. + If None, will include [IncludeEnum.metadatas, IncludeEnum.documents]. IDs are always included. + kwargs: Dict[str, Any] | Additional keyword arguments. + + Returns: + List[Document] | The results. + """ + collection = await self.get_collection(collection_name) + if include is not None: + include_enums = [self.IncludeEnum(item) for item in include] + else: + include_enums = [self.IncludeEnum.metadatas, self.IncludeEnum.documents] + results: GetResult = await collection.get(ids=ids, include=include_enums) + results_list = _chroma_get_results_to_list_documents(results) + return results_list + + +def _chroma_results_to_query_results( + data_dict: ChromaQueryResult, special_key: Literal["distances"] = "distances" +) -> QueryResults: + """Converts a ChromaDB query result into a list of lists of (Document, float) tuples. + + Args: + data_dict: A dictionary containing the results of a ChromaDB query. + special_key: The key in the dictionary containing the float values for each tuple (default is "distances"). + + Returns: + A list of lists, where each sublist corresponds to a query and contains tuples of (Document, float). + + Example: + data_dict = { + 'ids': [['1', '2'], ['3', '4']], + 'documents': [['doc1', 'doc2'], ['doc3', 'doc4']], + 'metadatas': [[{'meta': 'data1'}, {'meta': 'data2'}], [{'meta': 'data3'}, {'meta': 'data4'}]], + 'distances': [[0.1, 0.2], [0.3, 0.4]], + } + + results = _chroma_results_to_query_results(data_dict) + + # results will be: + # [ + # [ + # (Document(id='1', content='doc1', metadata={'meta': 'data1'}, embedding=None), 0.1), + # (Document(id='2', content='doc2', metadata={'meta': 'data2'}, embedding=None), 0.2), + # ], + # [ + # (Document(id='3', content='doc3', metadata={'meta': 'data3'}, embedding=None), 0.3), + # (Document(id='4', content='doc4', metadata={'meta': 'data4'}, embedding=None), 0.4), + # ], + # ] + """ + + if not data_dict or special_key not in data_dict or not data_dict.get(special_key): + return [] + + result: List[List[Tuple[Document, float]]] = [] + data_special_key: Optional[List[List[float]]] = data_dict[special_key] + + if data_special_key is None: + return result + + for i in range(len(data_special_key)): + sub_result: List[Tuple[Document, float]] = [] + ids_i = data_dict["ids"][i] + documents_list = data_dict.get("documents") + documents_i = documents_list[i] if documents_list else [None] * len(ids_i) # type: ignore + metadatas_list = data_dict.get("metadatas") + metadatas_i = metadatas_list[i] if metadatas_list else [None] * len(ids_i) # type: ignore + embeddings_list = data_dict.get("embeddings") + embeddings_i = embeddings_list[i] if embeddings_list else [None] * len(ids_i) + + for j in range(len(data_special_key[i])): + document = Document( + id=ids_i[j], + content=documents_i[j], + metadata=cast(Optional[Metadata], metadatas_i[j]), + embedding=cast(Optional[Vector], embeddings_i[j]), + ) + value = data_special_key[i][j] + sub_result.append((document, value)) + result.append(sub_result) + + return result + + +def _filter_results_by_distance(results: QueryResults, distance_threshold: float = -1) -> QueryResults: + """Filters results based on a distance threshold. + + Args: + results: QueryResults | The query results. List[List[Tuple[Document, float]]] + distance_threshold: The maximum distance allowed for results. + + Returns: + QueryResults | A filtered results containing only distances smaller than the threshold. + """ + + if distance_threshold > 0: + results = [[(key, value) for key, value in data if value < distance_threshold] for data in results] + + return results + + +def _chroma_get_results_to_list_documents(data_dict: GetResult) -> List[Document]: + """Converts a dictionary with list values to a list of Document. + + Args: + data_dict: A dictionary where keys map to lists or None. + + Returns: + List[Document] | The list of Document. + """ + results: List[Document] = [] + + num_items = len(data_dict["ids"]) + ids = data_dict["ids"] + documents = data_dict.get("documents") or [None] * num_items # type: ignore + metadatas = data_dict.get("metadatas") or [None] * num_items # type: ignore + embeddings = data_dict.get("embeddings") or [None] * num_items + + for i in range(num_items): + results.append( + Document( + id=ids[i], + content=documents[i], + metadata=metadatas[i], + embedding=cast(Vector, embeddings[i]), + ) + ) + return results diff --git a/python/packages/autogen-ext/src/autogen_ext/storage/_factory.py b/python/packages/autogen-ext/src/autogen_ext/storage/_factory.py new file mode 100644 index 00000000000..ab77a7091ab --- /dev/null +++ b/python/packages/autogen-ext/src/autogen_ext/storage/_factory.py @@ -0,0 +1,33 @@ +from typing import Any, Literal + +from ._base import VectorDB + + +class VectorDBFactory: + """ + Factory class for creating vector databases. + """ + + PREDEFINED_VECTOR_DB = ["chromadb"] + + @staticmethod + def create_vector_db(db_type: Literal["chromadb"], **kwargs: Any) -> VectorDB: + """ + Create a vector database. + + Args: + db_type: Literal["chroma", "chromadb"] | 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() == "chromadb": + from ._chromadb import ChromaVectorDB + + return ChromaVectorDB(**kwargs) # type: ignore + + else: + raise ValueError( + f"Unsupported vector database type: {db_type}. Valid types are {VectorDBFactory.PREDEFINED_VECTOR_DB}." + ) diff --git a/python/packages/autogen-ext/tests/storage/test_chroma_db.py b/python/packages/autogen-ext/tests/storage/test_chroma_db.py new file mode 100644 index 00000000000..9a11cfa88a7 --- /dev/null +++ b/python/packages/autogen-ext/tests/storage/test_chroma_db.py @@ -0,0 +1,141 @@ +from pathlib import Path +from typing import Generator + +import pytest +from autogen_ext.storage import ChromaVectorDB +from autogen_ext.storage._base import Document +from chromadb import Collection +from chromadb.config import Settings +from chromadb.errors import ChromaError + + +# Fixture for the synchronous database instance with function-level scope +@pytest.fixture(scope="function") +def db(tmp_path: Path) -> Generator[ChromaVectorDB, None, None]: + db_path = tmp_path / "test_db" + db_instance = ChromaVectorDB(path=str(db_path), settings=Settings(allow_reset=True)) + yield db_instance + # Teardown code + db_instance.client.reset() + + +# Fixture for unique collection names per test +@pytest.fixture(scope="function") +def collection_name(request: pytest.FixtureRequest) -> str: + return f"test_collection_{request.node.name}" # type: ignore + + +# Fixture to create and delete the collection around each test +@pytest.fixture(scope="function") +def collection(db: ChromaVectorDB, collection_name: str) -> Generator[Collection, None, None]: + collection = db.create_collection(collection_name, overwrite=True, get_or_create=True) + yield collection + db.delete_collection(collection_name) + + +def test_create_collection(db: ChromaVectorDB, collection_name: str) -> None: + collection = db.create_collection(collection_name, overwrite=True, get_or_create=True) + assert collection.name == collection_name + + +def test_delete_collection(db: ChromaVectorDB, collection_name: str) -> None: + # Create the collection first + db.create_collection(collection_name, overwrite=True, get_or_create=True) + db.delete_collection(collection_name) + with pytest.raises((ValueError, ChromaError)): + db.get_collection(collection_name) + + +def test_more_create_collection(db: ChromaVectorDB, collection_name: str) -> None: + # Ensure the collection is deleted at the start + try: + db.delete_collection(collection_name) + except (ValueError, ChromaError): + pass + + collection = db.create_collection(collection_name, overwrite=False, get_or_create=False) + assert collection.name == collection_name + with pytest.raises((ValueError, ChromaError)): + db.create_collection(collection_name, overwrite=False, get_or_create=False) + collection = db.create_collection(collection_name, overwrite=True, get_or_create=False) + assert collection.name == collection_name + collection = db.create_collection(collection_name, overwrite=False, get_or_create=True) + assert collection.name == collection_name + + +def test_get_collection(db: ChromaVectorDB, collection_name: str, collection: Collection) -> None: + retrieved_collection = db.get_collection(collection_name) + assert retrieved_collection.name == collection_name + + +def test_insert_docs(db: ChromaVectorDB, collection_name: str, collection: Collection) -> None: + docs = [ + Document(content="doc1", id="1"), + Document(content="doc2", id="2"), + Document(content="doc3", id="3"), + ] + db.insert_docs(docs, collection_name, upsert=False) + res = db.get_collection(collection_name).get(["1", "2"]) + assert res["documents"] == ["doc1", "doc2"] + + +def test_update_docs(db: ChromaVectorDB, collection_name: str, collection: Collection) -> None: + # Insert initial docs + initial_docs = [ + Document(content="doc1", id="1"), + Document(content="doc2", id="2"), + ] + db.insert_docs(initial_docs, collection_name, upsert=False) + + # Now update + updated_docs = [ + Document(content="doc11", id="1"), + Document(content="doc2", id="2"), + Document(content="doc3", id="3"), + ] + db.update_docs(updated_docs, collection_name) + res = db.get_collection(collection_name).get(["1", "2"]) + assert res["documents"] == ["doc11", "doc2"] + + +def test_delete_docs(db: ChromaVectorDB, collection_name: str, collection: Collection) -> None: + # Insert initial docs + initial_docs = [ + Document(content="doc1", id="1"), + Document(content="doc2", id="2"), + ] + db.insert_docs(initial_docs, collection_name, upsert=False) + + ids = ["1"] + db.delete_docs(ids, collection_name) + res = db.get_collection(collection_name).get(ids) + assert res["documents"] == [] + + +def test_retrieve_docs(db: ChromaVectorDB, collection_name: str, collection: Collection) -> None: + # Insert initial docs + initial_docs = [ + Document(content="doc2", id="2"), + Document(content="doc3", id="3"), + ] + db.insert_docs(initial_docs, collection_name, upsert=False) + + queries = ["doc2", "doc3"] + res = db.retrieve_docs(queries, collection_name) + assert [[r[0].id for r in rr] for rr in res] == [["2", "3"], ["3", "2"]] + res = db.retrieve_docs(queries, collection_name, distance_threshold=0.1) + assert [[r[0].id for r in rr] for rr in res] == [["2"], ["3"]] + + +def test_get_docs_by_ids(db: ChromaVectorDB, collection_name: str, collection: Collection) -> None: + # Insert initial docs + initial_docs = [ + Document(content="doc2", id="2"), + Document(content="doc3", id="3"), + ] + db.insert_docs(initial_docs, collection_name, upsert=False) + + res = db.get_docs_by_ids(["1", "2"], collection_name) + assert [r.id for r in res] == ["2"] + res = db.get_docs_by_ids(collection_name=collection_name) + assert [r.id for r in res] == ["2", "3"] diff --git a/python/uv.lock b/python/uv.lock index d1c9d588c3e..f70fca8fd40 100644 --- a/python/uv.lock +++ b/python/uv.lock @@ -4,8 +4,7 @@ resolution-markers = [ "python_full_version < '3.11'", "python_full_version == '3.11.*'", "python_full_version >= '3.12' and python_full_version < '3.12.4'", - "python_full_version < '3.13'", - "python_full_version >= '3.13'", + "python_full_version >= '3.12.4'", ] 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