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chore: Adjust MongoDB Atlas Vector Store parameters format

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joaoguilhermeS 2024-06-22 02:50:58 -03:00 • committed by Gabriel Luiz Freitas Almeida
commit 9eccd0c43b
1 changed files with 12 additions and 18 deletions
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30
src/backend/base/langflow/components/vectorstores/MongoDBAtlasVector.py
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@ -19,18 +19,13 @@ class MongoVectorStoreComponent(LCVectorStoreComponent):
StrInput(name="db_name", display_name="Database Name", required=True),
StrInput(name="collection_name", display_name="Collection Name", required=True),
StrInput(name="index_name", display_name="Index Name", required=True),
HandleInput(name="embedding", display_name="Embedding", input_types=["Embeddings"]),
MultilineInput(name="search_query", display_name="Search Query"),
DataInput(
name="vector_store_inputs",
display_name="Vector Store Inputs",
name="ingest_data",
display_name="Ingest Data",
is_list=True,
),
BoolInput(
name="add_to_vector_store",
display_name="Add to Vector Store",
info="If true, the Vector Store Inputs will be added to the Vector Store.",
),
MultilineInput(name="search_input", display_name="Search Input"),
HandleInput(name="embedding", display_name="Embedding", input_types=["Embeddings"]),
IntInput(
name="number_of_results",
display_name="Number of Results",
@ -55,13 +50,12 @@ class MongoVectorStoreComponent(LCVectorStoreComponent):
except Exception as e:
raise ValueError(f"Failed to connect to MongoDB Atlas: {e}")
if self.add_to_vector_store:
documents = []
for _input in self.vector_store_inputs or []:
if isinstance(_input, Data):
documents.append(_input.to_lc_document())
else:
documents.append(_input)
documents = []
for _input in self.ingest_data or []:
if isinstance(_input, Data):
documents.append(_input.to_lc_document())
else:
documents.append(_input)
if documents:
vector_store = MongoDBAtlasVectorSearch.from_documents(
@ -88,9 +82,9 @@ class MongoVectorStoreComponent(LCVectorStoreComponent):
vector_store = self._build_mongodb_atlas()
if self.search_input and isinstance(self.search_input, str):
if self.search_query and isinstance(self.search_query, str):
docs = vector_store.similarity_search(
query=self.search_input,
query=self.search_query,
k=self.number_of_results,
)
for doc in docs: