diff --git a/python/packages/autogen-ext/pyproject.toml b/python/packages/autogen-ext/pyproject.toml index f13843aabcf..8d16a40c8e8 100644 --- a/python/packages/autogen-ext/pyproject.toml +++ b/python/packages/autogen-ext/pyproject.toml @@ -4,7 +4,7 @@ build-backend = "hatchling.build" [project] name = "autogen-ext" -version = "0.4.0.dev2" +version = "0.4.0dev1" license = {file = "LICENSE-CODE"} description = "AutoGen extensions library" readme = "README.md" @@ -15,7 +15,7 @@ classifiers = [ "Operating System :: OS Independent", ] dependencies = [ - "autogen-core==0.4.0.dev2", + "autogen-core==0.4.0dev1", ] @@ -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..523de3f21c4 --- /dev/null +++ b/python/packages/autogen-ext/src/autogen_ext/storage/__init__.py @@ -0,0 +1,4 @@ +from ._chromadb import ChromaVectorDB +from ._factory import VectorDBFactory + +__all__ = ["ChromaVectorDB", "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..2e73c61e515 --- /dev/null +++ b/python/packages/autogen-ext/src/autogen_ext/storage/_base.py @@ -0,0 +1,371 @@ +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[[List[str]], List[List[float]]]] = ( + 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: Sequence[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: Sequence[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: Sequence[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: Sequence[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[Sequence[ItemID]] = None, + collection_name: Optional[str] = None, + include: Optional[Sequence[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: Sequence[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: Sequence[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: Sequence[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: Sequence[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[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[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..61872885917 --- /dev/null +++ b/python/packages/autogen-ext/src/autogen_ext/storage/_chromadb.py @@ -0,0 +1,688 @@ +# python\packages\autogen-ext\src\autogen_ext\storage\_chromadb.py + +import logging +import os +from typing import TYPE_CHECKING, Any, Callable, Dict, List, Optional, Sequence, Union + +from autogen_core.application.logging import TRACE_LOGGER_NAME + +if TYPE_CHECKING: + from chromadb.api import AsyncClientAPI, Client + from chromadb.api.models.Collection import Collection + from chromadb.config import Settings + +from ._base import AsyncVectorDB, Document, ItemID, QueryResults, VectorDB +from ._utils import chroma_results_to_query_results, filter_results_by_distance + +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["Client"] = None, + path: Optional[str] = None, + embedding_function: Optional[Callable[[List[str]], List[List[float]]]] = 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: Callable | 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.") + import chromadb.utils.embedding_functions as ef + from chromadb.errors import ChromaError + + 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.client: "Client" = client + self.embedding_function = ( + ef.SentenceTransformerEmbeddingFunction("all-MiniLM-L6-v2") + if embedding_function is None + else embedding_function + ) + self.metadata = metadata if metadata else {} + self.type = "chroma" + if not self.client: + 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 + ) -> "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=self.embedding_function, + metadata=self.metadata, + ) + elif overwrite: + self.client.delete_collection(name=collection_name) + return self.client.create_collection( + name=collection_name, + embedding_function=self.embedding_function, + metadata=self.metadata, + ) + 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.") + if docs[0].id is None: + raise ValueError("The document id 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, + documents=documents, + upsert=upsert, + ) + + def update_docs(self, docs: Sequence[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: 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 + """ + collection = self.get_collection(collection_name) + collection.delete(ids=ids) + + 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["contents"] = results.pop("documents") + results = chroma_results_to_query_results(results) + results = filter_results_by_distance(results, distance_threshold) + return results + + @staticmethod + def _chroma_get_results_to_list_documents(data_dict: Dict[str, Any]) -> 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] = [] + keys = [key for key in data_dict if data_dict[key] is not None] + + for i in range(len(data_dict[keys[0]])): + doc_dict = {} + for key in data_dict.keys(): + if data_dict[key] is not None and len(data_dict[key]) > i: + doc_dict[key[:-1]] = data_dict[key][i] + results.append(Document(**doc_dict)) # type: ignore + return results + + 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 are always included. + kwargs: Dict[str, Any] | Additional keyword arguments. + + Returns: + List[Document] | The results. + """ + collection = self.get_collection(collection_name) + if include is None: + include = ["metadatas", "documents"] + results = collection.get(ids=ids, include=include) + results = self._chroma_get_results_to_list_documents(results) + return results + + +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: "AsyncClientAPI", + embedding_function: Optional[Callable[[List[str]], List[List[float]]]] = 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.errors import ChromaError + + 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.client: "AsyncClientAPI" = client + self.embedding_function = embedding_function + if self.embedding_function is None: + raise ValueError("An embedding function must be provided for AsyncChromaVectorDB.") + self.type = "chroma" + if not self.client: + self.client = chromadb.AsyncHttpClient( + host=host, + port=port, + ssl=ssl, + headers=headers, + settings=settings, + tenant=tenant, + database=database, + **kwargs, + ) + self.active_collection: Optional["Collection"] = None + + 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 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=self.embedding_function, + metadata={}, + ) + elif overwrite: + await self.client.delete_collection(name=collection_name) + return await self.client.create_collection( + name=collection_name, + embedding_function=self.embedding_function, + metadata={}, + ) + 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.") + if docs[0].id is None: + raise ValueError("The document id 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, + documents=documents, + 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: 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 + """ + 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["contents"] = results.pop("documents") + results = chroma_results_to_query_results(results) + results = filter_results_by_distance(results, distance_threshold) + return results + + @staticmethod + def _chroma_get_results_to_list_documents(data_dict: Dict[str, Any]) -> 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 = [] + keys = [key for key in data_dict if data_dict[key] is not None] + + for i in range(len(data_dict[keys[0]])): + doc_dict = {} + for key in data_dict.keys(): + if data_dict[key] is not None and len(data_dict[key]) > i: + doc_dict[key[:-1]] = data_dict[key][i] + results.append(Document(**doc_dict)) # type: ignore + return results + + 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 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 None: + include = ["metadatas", "documents"] + results = await collection.get(ids=ids, include=include) + results = self._chroma_get_results_to_list_documents(results) + 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..ba9f2d04749 --- /dev/null +++ b/python/packages/autogen-ext/src/autogen_ext/storage/_factory.py @@ -0,0 +1,33 @@ +from typing import 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) -> 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) + + else: + raise ValueError( + f"Unsupported vector database type: {db_type}. Valid types are {VectorDBFactory.PREDEFINED_VECTOR_DB}." + ) diff --git a/python/packages/autogen-ext/src/autogen_ext/storage/_utils.py b/python/packages/autogen-ext/src/autogen_ext/storage/_utils.py new file mode 100644 index 00000000000..fe4c08e9edf --- /dev/null +++ b/python/packages/autogen-ext/src/autogen_ext/storage/_utils.py @@ -0,0 +1,93 @@ +from typing import Any, Dict, List, Optional, Tuple + +from ._base import QueryResults + + +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_results_to_query_results( + data_dict: Dict[str, Optional[List[List[Any]]]], special_key: str = "distances" +) -> List[List[Tuple[Dict[str, Any], float]]]: + """Converts a dictionary with list-of-list values to a list of tuples. + + Args: + data_dict: A dictionary where keys map to lists of lists or None. + special_key: str | The key in the dictionary containing the special values + for each tuple. + + Returns: + List[List[Tuple[Dict[str, Any], float]]] | A list of tuples, where each tuple contains + a sub-dictionary with some keys from the original dictionary and the value from the + special_key. + + Example: + data_dict = { + "key1s": [[1, 2, 3], [4, 5, 6], [7, 8, 9]], + "key2s": [["a", "b", "c"], ["c", "d", "e"], ["e", "f", "g"]], + "key3s": None, + "key4s": [["x", "y", "z"], ["1", "2", "3"], ["4", "5", "6"]], + "distances": [[0.1, 0.2, 0.3], [0.4, 0.5, 0.6], [0.7, 0.8, 0.9]], + } + + results = [ + [ + ({"key1": 1, "key2": "a", "key4": "x"}, 0.1), + ({"key1": 2, "key2": "b", "key4": "y"}, 0.2), + ({"key1": 3, "key2": "c", "key4": "z"}, 0.3), + ], + [ + ({"key1": 4, "key2": "c", "key4": "1"}, 0.4), + ({"key1": 5, "key2": "d", "key4": "2"}, 0.5), + ({"key1": 6, "key2": "e", "key4": "3"}, 0.6), + ], + [ + ({"key1": 7, "key2": "e", "key4": "4"}, 0.7), + ({"key1": 8, "key2": "f", "key4": "5"}, 0.8), + ({"key1": 9, "key2": "g", "key4": "6"}, 0.9), + ], + ] + """ + + if not data_dict or special_key not in data_dict or not data_dict[special_key]: + return [] + + keys: List[str] = [ + key + for key in data_dict + if key != special_key + and data_dict[key] is not None + and isinstance(data_dict[key], list) + and len(data_dict[key]) > 0 + and isinstance(data_dict[key][0], list) + ] + result: List[List[Tuple[Dict[str, Any], float]]] = [] + data_special_key = data_dict[special_key] + + assert data_special_key is not None + + for i in range(len(data_special_key)): + sub_result: List[Tuple[Dict[str, Any], float]] = [] + for j, distance in enumerate(data_special_key[i]): + sub_dict: Dict[str, Any] = {} + for key in keys: + if len(data_dict[key]) > i and len(data_dict[key][i]) > j: + sub_dict[key[:-1]] = data_dict[key][i][j] # remove 's' at the end from key + sub_result.append((sub_dict, distance)) + result.append(sub_result) + + return result 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..5be4e06a014 --- /dev/null +++ b/python/packages/autogen-ext/tests/storage/test_chroma_db.py @@ -0,0 +1,80 @@ +from autogen_ext.storage._base import Document +import pytest +from autogen_ext.storage import ChromaVectorDB +from chromadb.errors import ChromaError + + +# @pytest.mark.skipif(skip, reason="dependency is not installed") +def test_chromadb(): + # test create collection + db = ChromaVectorDB(path=".db") + collection_name = "test_collection" + collection = db.create_collection(collection_name, overwrite=True, get_or_create=True) + assert collection.name == collection_name + + # test_delete_collection + db.delete_collection(collection_name) + pytest.raises((ValueError, ChromaError), db.get_collection, collection_name) + + # test more create collection + collection = db.create_collection(collection_name, overwrite=False, get_or_create=False) + assert collection.name == collection_name + 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 + + # test_get_collection + collection = db.get_collection(collection_name) + assert collection.name == collection_name + + # test_insert_docs + 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"] + + # test_update_docs + docs = [Document(content="doc11", id="1"), Document(content="doc2", id="2"), Document(content="doc3", id="3")] + db.update_docs(docs, collection_name) + res = db.get_collection(collection_name).get(["1", "2"]) + assert res["documents"] == ["doc11", "doc2"] + + # test_delete_docs + ids = ["1"] + collection_name = "test_collection" + db.delete_docs(ids, collection_name) + res = db.get_collection(collection_name).get(ids) + assert res["documents"] == [] + + # test_retrieve_docs + queries = ["doc2", "doc3"] + collection_name = "test_collection" + 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"]] + + # test_get_docs_by_ids + res = db.get_docs_by_ids(["1", "2"], collection_name) + assert [r.id for r in res] == ["2"] # "1" has been deleted + res = db.get_docs_by_ids(collection_name=collection_name) + assert [r.id for r in res] == ["2", "3"] + + # test _chroma_get_results_to_list_documents + data_dict = { + "key1s": [1, 2, 3], + "key2s": ["a", "b", "c"], + "key3s": None, + "key4s": ["x", "y", "z"], + } + + results = [ + {"key1": 1, "key2": "a", "key4": "x"}, + {"key1": 2, "key2": "b", "key4": "y"}, + {"key1": 3, "key2": "c", "key4": "z"}, + ] + assert db._chroma_get_results_to_list_documents(data_dict) == results # type: ignore diff --git a/python/uv.lock b/python/uv.lock index ed20c3784d7..02bba38dfa1 100644 --- a/python/uv.lock +++ b/python/uv.lock @@ -16,12 +16,18 @@ resolution-markers = [ "(python_full_version < '3.11' and platform_machine != 'aarch64' and platform_system != 'Darwin') or (python_full_version < '3.11' and platform_system != 'Darwin' and platform_system != 'Linux')", "(python_full_version == '3.11.*' and platform_machine != 'aarch64' and platform_system != 'Darwin') or (python_full_version == '3.11.*' and platform_system != 'Darwin' and platform_system != 'Linux')", "(python_full_version >= '3.12' and python_full_version < '3.12.4' and platform_machine != 'aarch64' and platform_system != 'Darwin') or (python_full_version >= '3.12' and python_full_version < '3.12.4' and platform_system != 'Darwin' and platform_system != 'Linux')", - "python_full_version < '3.13' and platform_system == 'Darwin'", - "python_full_version >= '3.13' and platform_system == 'Darwin'", - "python_full_version < '3.13' and platform_machine == 'aarch64' and platform_system == 'Linux'", - "python_full_version >= '3.13' and platform_machine == 'aarch64' and platform_system == 'Linux'", - "(python_full_version < '3.13' and platform_machine != 'aarch64' and platform_system != 'Darwin') or (python_full_version < '3.13' and platform_system != 'Darwin' and platform_system != 'Linux')", - "(python_full_version >= '3.13' and platform_machine != 'aarch64' and platform_system != 'Darwin') or (python_full_version >= '3.13' and platform_system != 'Darwin' and platform_system != 'Linux')", + "python_full_version < '3.11' and platform_system == 'Darwin'", + "python_full_version == '3.11.*' and platform_system == 'Darwin'", + "python_full_version >= '3.12' and python_full_version < '3.12.4' and platform_system == 'Darwin'", + "python_full_version >= '3.12.4' and platform_system == 'Darwin'", + "python_full_version < '3.11' and platform_machine == 'aarch64' and platform_system == 'Linux'", + "python_full_version == '3.11.*' and platform_machine == 'aarch64' and platform_system == 'Linux'", + "python_full_version >= '3.12' and python_full_version < '3.12.4' and platform_machine == 'aarch64' and platform_system == 'Linux'", + "python_full_version >= '3.12.4' and platform_machine == 'aarch64' and platform_system == 'Linux'", + "(python_full_version < '3.11' and platform_machine != 'aarch64' and platform_system != 'Darwin') or (python_full_version < '3.11' and platform_system != 'Darwin' and platform_system != 'Linux')", + "(python_full_version == '3.11.*' and platform_machine != 'aarch64' and platform_system != 'Darwin') or (python_full_version == '3.11.*' and platform_system != 'Darwin' and platform_system != 'Linux')", + "(python_full_version >= '3.12' and python_full_version < '3.12.4' and platform_machine != 'aarch64' and platform_system != 'Darwin') or (python_full_version >= '3.12' and python_full_version < '3.12.4' and platform_system != 'Darwin' and platform_system != 'Linux')", + "(python_full_version >= '3.12.4' and platform_machine != 'aarch64' and platform_system != 'Darwin') or (python_full_version >= '3.12.4' and platform_system != 'Darwin' and platform_system != 'Linux')", ] [manifest] @@ -309,6 +315,18 @@ wheels = [ { url = "https://files.pythonhosted.org/packages/b7/7b/7bf42178d227b26d3daf94cdd22a72a4ed5bf235548c4f5aea49c51c6458/arxiv-2.1.3-py3-none-any.whl", hash = "sha256:6f43673ab770a9e848d7d4fc1894824df55edeac3c3572ea280c9ba2e3c0f39f", size = 11478 }, ] +[[package]] +name = "asgiref" +version = "3.8.1" +source = { registry = "https://pypi.org/simple" } +dependencies = [ + { name = "typing-extensions", marker = "python_full_version < '3.11'" }, +] +sdist = { url = "https://files.pythonhosted.org/packages/29/38/b3395cc9ad1b56d2ddac9970bc8f4141312dbaec28bc7c218b0dfafd0f42/asgiref-3.8.1.tar.gz", hash = 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