diff --git a/adalflow/adalflow/components/retriever/lancedb_retriver.py b/adalflow/adalflow/components/retriever/lancedb_retriver.py index 68314411..9390452e 100644 --- a/adalflow/adalflow/components/retriever/lancedb_retriver.py +++ b/adalflow/adalflow/components/retriever/lancedb_retriver.py @@ -11,12 +11,24 @@ log = logging.getLogger(__name__) # Defined data types -LanceDBRetrieverDocumentEmbeddingType = Union[List[float], np.ndarray] # single embedding +LanceDBRetrieverDocumentEmbeddingType = Union[ + List[float], np.ndarray +] # single embedding LanceDBRetrieverDocumentsType = Sequence[LanceDBRetrieverDocumentEmbeddingType] + # Step 2: Define the LanceDBRetriever class -class LanceDBRetriever(Retriever[LanceDBRetrieverDocumentEmbeddingType, Union[str, List[str]]]): - def __init__(self, embedder: Embedder, dimensions: int, db_uri: str = "/tmp/lancedb", top_k: int = 5, overwrite: bool = True): +class LanceDBRetriever( + Retriever[LanceDBRetrieverDocumentEmbeddingType, Union[str, List[str]]] +): + def __init__( + self, + embedder: Embedder, + dimensions: int, + db_uri: str = "/tmp/lancedb", + top_k: int = 5, + overwrite: bool = True, + ): """ LanceDBRetriever is a retriever that leverages LanceDB to efficiently store and query document embeddings. @@ -39,13 +51,17 @@ def __init__(self, embedder: Embedder, dimensions: int, db_uri: str = "/tmp/lanc self.dimensions = dimensions # Define table schema with vector field for embeddings - schema = pa.schema([ - pa.field("vector", pa.list_(pa.float32(), list_size=self.dimensions)), - pa.field("content", pa.string()) - ]) + schema = pa.schema( + [ + pa.field("vector", pa.list_(pa.float32(), list_size=self.dimensions)), + pa.field("content", pa.string()), + ] + ) # Create or overwrite the table for storing documents and embeddings - self.table = self.db.create_table("documents", schema=schema, mode="overwrite" if overwrite else "append") + self.table = self.db.create_table( + "documents", schema=schema, mode="overwrite" if overwrite else "append" + ) def add_documents(self, documents: Sequence[Dict[str, Any]]): """ @@ -63,13 +79,18 @@ def add_documents(self, documents: Sequence[Dict[str, Any]]): embeddings = self.embedder(input=doc_texts).data # Format embeddings for LanceDB - data = [{"vector": embedding.embedding, "content": text} for embedding, text in zip(embeddings, doc_texts)] + data = [ + {"vector": embedding.embedding, "content": text} + for embedding, text in zip(embeddings, doc_texts) + ] # Add data to LanceDB table self.table.add(data) log.info(f"Added {len(documents)} documents to the index") - def retrieve(self, query: Union[str, List[str]], top_k: Optional[int] = None) -> List[RetrieverOutput]: + def retrieve( + self, query: Union[str, List[str]], top_k: Optional[int] = None + ) -> List[RetrieverOutput]: """. Retrieve top-k documents from LanceDB for a given query or queries. Args: @@ -83,11 +104,13 @@ def retrieve(self, query: Union[str, List[str]], top_k: Optional[int] = None) -> query = [query] if not query or (isinstance(query, str) and query.strip() == ""): - raise ValueError("Query cannot be empty.") + raise ValueError("Query cannot be empty.") # Check if table (index) exists before performing search if not self.table: - raise ValueError("The index has not been initialized or the table is missing.") + raise ValueError( + "The index has not been initialized or the table is missing." + ) query_embeddings = self.embedder(input=query).data output: List[RetrieverOutput] = [] @@ -105,9 +128,11 @@ def retrieve(self, query: Union[str, List[str]], top_k: Optional[int] = None) -> scores = results["_distance"].tolist() # Append results to output - output.append(RetrieverOutput( - doc_indices=indices, - doc_scores=scores, - query=query[0] if len(query) == 1 else query - )) + output.append( + RetrieverOutput( + doc_indices=indices, + doc_scores=scores, + query=query[0] if len(query) == 1 else query, + ) + ) return output diff --git a/adalflow/adalflow/utils/lazy_import.py b/adalflow/adalflow/utils/lazy_import.py index 831e9906..16ad8d1f 100644 --- a/adalflow/adalflow/utils/lazy_import.py +++ b/adalflow/adalflow/utils/lazy_import.py @@ -75,7 +75,7 @@ class OptionalPackages(Enum): ) LANCEDB = ( - "lancedb", + "lancedb", "Please install lancedb with: pip install lancedb .", ) diff --git a/adalflow/tests/test_lancedb_retriver.py b/adalflow/tests/test_lancedb_retriver.py index 59de2c82..874bb090 100644 --- a/adalflow/tests/test_lancedb_retriver.py +++ b/adalflow/tests/test_lancedb_retriver.py @@ -7,10 +7,12 @@ from unittest import mock from adalflow.core.types import EmbedderOutput, RetrieverOutput + # Helper function to create dummy embeddings def create_dummy_embeddings(num_embeddings, dim): return np.random.rand(num_embeddings, dim).astype(np.float32) + class TestLanceDBRetriever(unittest.TestCase): def setUp(self): self.dimensions = 128 @@ -21,7 +23,10 @@ def setUp(self): # Mock embedder to return dummy embeddings self.dummy_embeddings = create_dummy_embeddings(10, self.dimensions) self.embedder.return_value = EmbedderOutput( - data=[Mock(embedding=emb) for emb in self.dummy_embeddings[:len(self.single_query)]] + data=[ + Mock(embedding=emb) + for emb in self.dummy_embeddings[: len(self.single_query)] + ] ) with patch("lancedb.connect") as mock_db_connect: @@ -32,7 +37,7 @@ def setUp(self): embedder=self.embedder, dimensions=self.dimensions, db_uri="/tmp/lancedb", - top_k=self.top_k + top_k=self.top_k, ) def test_initialization(self): @@ -68,11 +73,10 @@ def test_retrieve_single_query(self): ) # Mock search results from LanceDB as pandas DataFrame - results_df = pd.DataFrame({ - "index": [0, 1, 2], - "_distance": [0.1, 0.2, 0.3] - }) - self.mock_table.search.return_value.limit.return_value.to_pandas.return_value = results_df + results_df = pd.DataFrame({"index": [0, 1, 2], "_distance": [0.1, 0.2, 0.3]}) + self.mock_table.search.return_value.limit.return_value.to_pandas.return_value = ( + results_df + ) result = self.retriever.retrieve(query) self.assertIsInstance(result[0], RetrieverOutput) @@ -91,11 +95,10 @@ def test_retrieve_multiple_queries(self): ) # Mock search results for each query - results_df = pd.DataFrame({ - "index": [0, 1, 2], - "_distance": [0.1, 0.2, 0.3] - }) - self.mock_table.search.return_value.limit.return_value.to_pandas.return_value = results_df + results_df = pd.DataFrame({"index": [0, 1, 2], "_distance": [0.1, 0.2, 0.3]}) + self.mock_table.search.return_value.limit.return_value.to_pandas.return_value = ( + results_df + ) result = self.retriever.retrieve(queries) self.assertEqual(len(result), len(queries)) @@ -106,10 +109,9 @@ def test_retrieve_multiple_queries(self): def test_retrieve_with_empty_query(self): # Mock the empty results DataFrame - self.mock_table.search.return_value.limit.return_value.to_pandas.return_value = pd.DataFrame({ - "index": [], - "_distance": [] - }) + self.mock_table.search.return_value.limit.return_value.to_pandas.return_value = pd.DataFrame( + {"index": [], "_distance": []} + ) def test_retrieve_with_no_index(self): empty_retriever = LanceDBRetriever( @@ -128,12 +130,10 @@ def test_overwrite_table_on_initialization(self): embedder=self.embedder, dimensions=self.dimensions, db_uri="/tmp/lancedb", - overwrite=True + overwrite=True, ) mock_db.create_table.assert_called_once_with( - "documents", - schema=mock.ANY, - mode="overwrite" + "documents", schema=mock.ANY, mode="overwrite" ) diff --git a/docs/poetry.lock b/docs/poetry.lock index 9c079273..c601dabf 100644 --- a/docs/poetry.lock +++ b/docs/poetry.lock @@ -1,4 +1,4 @@ -# This file is automatically @generated by Poetry 1.8.3 and should not be changed by hand. +# This file is automatically @generated by Poetry 1.8.4 and should not be changed by hand. 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