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class BaseEmbedding(TransformComponent, DispatcherSpanMixin): """Base class for embeddings.""" model_config = ConfigDict( protected_namespaces=("pydantic_model_",), arbitrary_types_allowed=True ) model_name: str = Field( default="unknown", description="The name of the embedding model." ) embed_batch_size: int = Field( default=DEFAULT_EMBED_BATCH_SIZE, description="The batch size for embedding calls.", gt=0, le=2048, ) callback_manager: CallbackManager = Field( default_factory=lambda: CallbackManager([]), exclude=True ) num_workers: Optional[int] = Field( default=None, description="The number of workers to use for async embedding calls.", ) @field_validator("callback_manager") @classmethod def check_callback_manager(cls, v: CallbackManager) -> CallbackManager: if v is None: return CallbackManager([]) return v @abstractmethod def _get_query_embedding(self, query: str) -> Embedding: """ Embed the input query synchronously. Subclasses should implement this method. Reference get_query_embedding's docstring for more information. """ @abstractmethod async def _aget_query_embedding(self, query: str) -> Embedding: """ Embed the input query asynchronously. Subclasses should implement this method. Reference get_query_embedding's docstring for more information. """ @dispatcher.span def get_query_embedding(self, query: str) -> Embedding: """ Embed the input query. When embedding a query, depending on the model, a special instruction can be prepended to the raw query string. For example, "Represent the question for retrieving supporting documents: ". If you're curious, other examples of predefined instructions can be found in embeddings/huggingface_utils.py. """ model_dict = self.to_dict() model_dict.pop("api_key", None) dispatcher.event( EmbeddingStartEvent( model_dict=model_dict, ) ) with self.callback_manager.event( CBEventType.EMBEDDING, payload={EventPayload.SERIALIZED: self.to_dict()} ) as event: query_embedding = self._get_query_embedding(query) event.on_end( payload={ EventPayload.CHUNKS: [query], EventPayload.EMBEDDINGS: [query_embedding], }, ) dispatcher.event( EmbeddingEndEvent( chunks=[query], embeddings=[query_embedding], ) ) return query_embedding @dispatcher.span async def aget_query_embedding(self, query: str) -> Embedding: """Get query embedding.""" model_dict = self.to_dict() model_dict.pop("api_key", None) dispatcher.event( EmbeddingStartEvent( model_dict=model_dict, ) ) with self.callback_manager.event( CBEventType.EMBEDDING, payload={EventPayload.SERIALIZED: self.to_dict()} ) as event: query_embedding = await self._aget_query_embedding(query) event.on_end( payload={ EventPayload.CHUNKS: [query], EventPayload.EMBEDDINGS: [query_embedding], }, ) dispatcher.event( EmbeddingEndEvent( chunks=[query], embeddings=[query_embedding], ) ) return query_embedding def get_agg_embedding_from_queries( self, queries: List[str], agg_fn: Optional[Callable[..., Embedding]] = None, ) -> Embedding: """Get aggregated embedding from multiple queries.""" query_embeddings = [self.get_query_embedding(query) for query in queries] agg_fn = agg_fn or mean_agg return agg_fn(query_embeddings) async def aget_agg_embedding_from_queries( self, queries: List[str], agg_fn: Optional[Callable[..., Embedding]] = None, ) -> Embedding: """Async get aggregated embedding from multiple queries.""" query_embeddings = [await self.aget_query_embedding(query) for query in queries] agg_fn = agg_fn or mean_agg return agg_fn(query_embeddings) @abstractmethod def _get_text_embedding(self, text: str) -> Embedding: """ Embed the input text synchronously. Subclasses should implement this method. Reference get_text_embedding's docstring for more information. """ async def _aget_text_embedding(self, text: str) -> Embedding: """ Embed the input text asynchronously. Subclasses can implement this method if there is a true async implementation. Reference get_text_embedding's docstring for more information. """ # Default implementation just falls back on _get_text_embedding return self._get_text_embedding(text) def _get_text_embeddings(self, texts: List[str]) -> List[Embedding]: """ Embed the input sequence of text synchronously. Subclasses can implement this method if batch queries are supported. """ # Default implementation just loops over _get_text_embedding return [self._get_text_embedding(text) for text in texts] async def _aget_text_embeddings(self, texts: List[str]) -> List[Embedding]: """ Embed the input sequence of text asynchronously. Subclasses can implement this method if batch queries are supported. """ return await asyncio.gather( *[self._aget_text_embedding(text) for text in texts] ) @dispatcher.span def get_text_embedding(self, text: str) -> Embedding: """ Embed the input text. When embedding text, depending on the model, a special instruction can be prepended to the raw text string. For example, "Represent the document for retrieval: ". If you're curious, other examples of predefined instructions can be found in embeddings/huggingface_utils.py. """ model_dict = self.to_dict() model_dict.pop("api_key", None) dispatcher.event( EmbeddingStartEvent( model_dict=model_dict, ) ) with self.callback_manager.event( CBEventType.EMBEDDING, payload={EventPayload.SERIALIZED: self.to_dict()} ) as event: text_embedding = self._get_text_embedding(text) event.on_end( payload={ EventPayload.CHUNKS: [text], EventPayload.EMBEDDINGS: [text_embedding], } ) dispatcher.event( EmbeddingEndEvent( chunks=[text], embeddings=[text_embedding], ) ) return text_embedding @dispatcher.span async def aget_text_embedding(self, text: str) -> Embedding: """Async get text embedding.""" model_dict = self.to_dict() model_dict.pop("api_key", None) dispatcher.event( EmbeddingStartEvent( model_dict=model_dict, ) ) with self.callback_manager.event( CBEventType.EMBEDDING, payload={EventPayload.SERIALIZED: self.to_dict()} ) as event: text_embedding = await self._aget_text_embedding(text) event.on_end( payload={ EventPayload.CHUNKS: [text], EventPayload.EMBEDDINGS: [text_embedding], } ) dispatcher.event( EmbeddingEndEvent( chunks=[text], embeddings=[text_embedding], ) ) return text_embedding @dispatcher.span def get_text_embedding_batch( self, texts: List[str], show_progress: bool = False, **kwargs: Any, ) -> List[Embedding]: """Get a list of text embeddings, with batching.""" cur_batch: List[str] = [] result_embeddings: List[Embedding] = [] queue_with_progress = enumerate( get_tqdm_iterable(texts, show_progress, "Generating embeddings") ) model_dict = self.to_dict() model_dict.pop("api_key", None) for idx, text in queue_with_progress: cur_batch.append(text) if idx == len(texts) - 1 or len(cur_batch) == self.embed_batch_size: # flush dispatcher.event( EmbeddingStartEvent( model_dict=model_dict, ) ) with self.callback_manager.event( CBEventType.EMBEDDING, payload={EventPayload.SERIALIZED: self.to_dict()}, ) as event: embeddings = self._get_text_embeddings(cur_batch) result_embeddings.extend(embeddings) event.on_end( payload={ EventPayload.CHUNKS: cur_batch, EventPayload.EMBEDDINGS: embeddings, }, ) dispatcher.event( EmbeddingEndEvent( chunks=cur_batch, embeddings=embeddings, ) ) cur_batch = [] return result_embeddings @dispatcher.span async def aget_text_embedding_batch( self, texts: List[str], show_progress: bool = False ) -> List[Embedding]: """Asynchronously get a list of text embeddings, with batching.""" num_workers = self.num_workers model_dict = self.to_dict() model_dict.pop("api_key", None) cur_batch: List[str] = [] callback_payloads: List[Tuple[str, List[str]]] = [] result_embeddings: List[Embedding] = [] embeddings_coroutines: List[Coroutine] = [] for idx, text in enumerate(texts): cur_batch.append(text) if idx == len(texts) - 1 or len(cur_batch) == self.embed_batch_size: # flush dispatcher.event( EmbeddingStartEvent( model_dict=model_dict, ) ) event_id = self.callback_manager.on_event_start( CBEventType.EMBEDDING, payload={EventPayload.SERIALIZED: self.to_dict()}, ) callback_payloads.append((event_id, cur_batch)) embeddings_coroutines.append(self._aget_text_embeddings(cur_batch)) cur_batch = [] # flatten the results of asyncio.gather, which is a list of embeddings lists nested_embeddings = [] if num_workers and num_workers > 1: nested_embeddings = await run_jobs( embeddings_coroutines, show_progress=show_progress, workers=self.num_workers, desc="Generating embeddings", ) else: if show_progress: try: from tqdm.asyncio import tqdm_asyncio nested_embeddings = await tqdm_asyncio.gather( *embeddings_coroutines, total=len(embeddings_coroutines), desc="Generating embeddings", ) except ImportError: nested_embeddings = await asyncio.gather(*embeddings_coroutines) else: nested_embeddings = await asyncio.gather(*embeddings_coroutines) result_embeddings = [ embedding for embeddings in nested_embeddings for embedding in embeddings ] for (event_id, text_batch), embeddings in zip( callback_payloads, nested_embeddings ): dispatcher.event( EmbeddingEndEvent( chunks=text_batch, embeddings=embeddings, ) ) self.callback_manager.on_event_end( CBEventType.EMBEDDING, payload={ EventPayload.CHUNKS: text_batch, EventPayload.EMBEDDINGS: embeddings, }, event_id=event_id, ) return result_embeddings def similarity( self, embedding1: Embedding, embedding2: Embedding, mode: SimilarityMode = SimilarityMode.DEFAULT, ) -> float: """Get embedding similarity.""" return similarity(embedding1=embedding1, embedding2=embedding2, mode=mode) def __call__(self, nodes: Sequence[BaseNode], **kwargs: Any) -> Sequence[BaseNode]: embeddings = self.get_text_embedding_batch( [node.get_content(metadata_mode=MetadataMode.EMBED) for node in nodes], **kwargs, ) for node, embedding in zip(nodes, embeddings): node.embedding = embedding return nodes async def acall( self, nodes: Sequence[BaseNode], **kwargs: Any ) -> Sequence[BaseNode]: embeddings = await self.aget_text_embedding_batch( [node.get_content(metadata_mode=MetadataMode.EMBED) for node in nodes], **kwargs, ) for node, embedding in zip(nodes, embeddings): node.embedding = embedding return nodes