fix: reformatting...

autorag/nodes/retrieval/base.py

@@ -102,7 +102,10 @@ def cast_queries(queries: Union[str, List[str]]) -> List[str]:
 
 
 def evenly_distribute_passages(
-	ids: List[List[str]], scores: List[List[float]], contents: [List[List[str]]], top_k: int
+	ids: List[List[str]],
+	scores: List[List[float]],
+	contents: [List[List[str]]],
+	top_k: int,
 ) -> Tuple[List[str], List[float], List[str]]:
 	assert len(ids) == len(scores), "ids and scores must have same length."
 	query_cnt = len(ids)

autorag/nodes/retrieval/vectordb.py

@@ -27,7 +27,7 @@
 	flatten_apply,
 	result_to_dataframe,
 	pop_params,
-	fetch_contents,
+	# fetch_contents,
 	empty_cuda_cache,
 	convert_inputs_to_list,
 	make_batch,

autorag/vectordb/chroma.py

@@ -68,7 +68,9 @@ async def add(self, ids: List[str], texts: List[str]):
 		texts = self.truncated_inputs(texts)
 		text_embeddings = await self.embedding.aget_text_embedding_batch(texts)
 		if isinstance(self.collection, AsyncCollection):
-			await self.collection.add(ids=ids, embeddings=text_embeddings, documents=texts)
+			await self.collection.add(
+				ids=ids, embeddings=text_embeddings, documents=texts
+			)
 		else:
 			self.collection.add(ids=ids, embeddings=text_embeddings, documents=texts)
 

autorag/vectordb/milvus.py

@@ -68,7 +68,7 @@ def __init__(
 			field = FieldSchema(
 				name="vector", dtype=DataType.FLOAT_VECTOR, dim=dimension
 			)
-   			content = FieldSchema(
+			content = FieldSchema(
 				name="content", dtype=DataType.VARCHAR, max_length=65535
 			)
 			schema = CollectionSchema(fields=[pk, field, content])

autorag/vectordb/pinecone.py

@@ -62,8 +62,8 @@ async def add(self, ids: List[str], texts: List[str]):
 
 		metadatas = [{} for _ in texts]
 		for metadata, text in zip(metadatas, texts):
-            metadata[self.text_key] = text
-            
+			metadata[self.text_key] = text
+
 		vector_tuples = list(zip(ids, text_embeddings, metadatas))
 		batch_vectors = make_batch(vector_tuples, self.ingest_batch)
 

autorag/vectordb/qdrant.py

@@ -86,10 +86,13 @@ async def add(self, ids: List[str], texts: List[str]):
 
 		metadatas = [{} for _ in texts]
 		for metadata, text in zip(metadatas, texts):
-            metadata[self.text_key] = text
+			metadata[self.text_key] = text
 
 		points = list(
-			map(lambda x: PointStruct(id=x[0], vector=x[1], payload=x[2]), zip(ids, text_embeddings, metadatas))
+			map(
+				lambda x: PointStruct(id=x[0], vector=x[1], payload=x[2]),
+				zip(ids, text_embeddings, metadatas),
+			)
 		)
 
 		self.client.upload_points(
@@ -133,7 +136,9 @@ async def query(
 
 		search_queries = list(
 			map(
-				lambda x: SearchRequest(vector=x, limit=top_k, with_vector=True, with_payload=True),
+				lambda x: SearchRequest(
+					vector=x, limit=top_k, with_vector=True, with_payload=True
+				),
 				query_embeddings,
 			)
 		)
@@ -145,7 +150,10 @@ async def query(
 		# Extract IDs and distances
 		ids = [[str(hit.id) for hit in result] for result in search_result]
 		scores = [[hit.score for hit in result] for result in search_result]
-		contents = [[hit.payload.get(self.text_key) for hit in result] for result in search_result]
+		contents = [
+			[hit.payload.get(self.text_key) for hit in result]
+			for result in search_result
+		]
 
 		return ids, scores, contents