From 43a781b00160609002c30446c02e9947fe9af5ca Mon Sep 17 00:00:00 2001 From: joaoguilhermeS Date: Sat, 22 Jun 2024 17:41:04 -0300 Subject: [PATCH] chore: Updating Vectara Vector Store parameters format --- .../components/vectorstores/Vectara.py | 46 ++++++++----------- 1 file changed, 20 insertions(+), 26 deletions(-) diff --git a/src/backend/base/langflow/components/vectorstores/Vectara.py b/src/backend/base/langflow/components/vectorstores/Vectara.py index a48729eaa..f5461c04c 100644 --- a/src/backend/base/langflow/components/vectorstores/Vectara.py +++ b/src/backend/base/langflow/components/vectorstores/Vectara.py @@ -12,24 +12,19 @@ from langflow.schema import Data class VectaraVectorStoreComponent(LCVectorStoreComponent): display_name = "Vectara" description = "Vectara Vector Store with search capabilities" - documentation = "https://python.langchain.com/docs/modules/data_connection/vectorstores/integrations/vectara" + documentation = "https://python.langchain.com/v0.2/docs/integrations/vectorstores/vectara/" icon = "Vectara" inputs = [ StrInput(name="vectara_customer_id", display_name="Vectara Customer ID", required=True), StrInput(name="vectara_corpus_id", display_name="Vectara Corpus ID", required=True), SecretStrInput(name="vectara_api_key", display_name="Vectara API Key", required=True), + MultilineInput(name="search_query", display_name="Search Query"), DataInput( - name="vector_store_inputs", + name="ingest_data", display_name="Vector Store Inputs", 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"), IntInput( name="number_of_results", display_name="Number of Results", @@ -45,23 +40,22 @@ class VectaraVectorStoreComponent(LCVectorStoreComponent): def _build_vectara(self) -> Vectara: source = "Langflow" - 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: - return Vectara.from_documents( - documents=documents, - embedding=FakeEmbeddings(size=768), - vectara_customer_id=self.vectara_customer_id, - vectara_corpus_id=self.vectara_corpus_id, - vectara_api_key=self.vectara_api_key, - source=source, - ) + if documents: + return Vectara.from_documents( + documents=documents, + embedding=FakeEmbeddings(size=768), + vectara_customer_id=self.vectara_customer_id, + vectara_corpus_id=self.vectara_corpus_id, + vectara_api_key=self.vectara_api_key, + source=source, + ) return Vectara( vectara_customer_id=self.vectara_customer_id, @@ -73,9 +67,9 @@ class VectaraVectorStoreComponent(LCVectorStoreComponent): def search_documents(self) -> List[Data]: vector_store = self._build_vectara() - if self.search_input and isinstance(self.search_input, str) and self.search_input.strip(): + if self.search_query and isinstance(self.search_query, str) and self.search_query.strip(): docs = vector_store.similarity_search( - query=self.search_input, + query=self.search_query, k=self.number_of_results, )