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🔨 refactor(Vectara.py): reorganize function parameters for better readability and maintainability

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🔧 chore(Vectara.py): add "embedding" parameter to the build function to support custom embeddings in VectaraComponent
This commit is contained in:
Gabriel Luiz Freitas Almeida 2023-08-09 15:47:22 -03:00
commit 9bbdd9465b
1 changed files with 5 additions and 1 deletions
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6
src/backend/langflow/components/vectorstores/Vectara.py
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@ -5,6 +5,7 @@ from langchain.vectorstores import Vectara
from langchain.schema import Document from langchain.schema import Document
from langchain.vectorstores.base import VectorStore from langchain.vectorstores.base import VectorStore
from langchain.schema import BaseRetriever from langchain.schema import BaseRetriever
from langchain.embeddings.base import Embeddings
class VectaraComponent(CustomComponent): class VectaraComponent(CustomComponent):
@ -21,14 +22,16 @@ class VectaraComponent(CustomComponent):
"vectara_api_key": {"display_name": "Vectara API Key", "password": True}, "vectara_api_key": {"display_name": "Vectara API Key", "password": True},
"code": {"show": False}, "code": {"show": False},
"documents": {"display_name": "Documents"}, "documents": {"display_name": "Documents"},
"embedding": {"display_name": "Embedding"},
} }
def build( def build(
self, self,
documents: Optional[Document],
vectara_customer_id: str, vectara_customer_id: str,
vectara_corpus_id: str, vectara_corpus_id: str,
vectara_api_key: str, vectara_api_key: str,
embedding: Optional[Embeddings] = None,
documents: Optional[Document] = None,
) -> Union[VectorStore, BaseRetriever]: ) -> Union[VectorStore, BaseRetriever]:
# If documents, then we need to create a Vectara instance using .from_documents # If documents, then we need to create a Vectara instance using .from_documents
if documents: if documents:
@ -37,6 +40,7 @@ class VectaraComponent(CustomComponent):
vectara_customer_id=vectara_customer_id, vectara_customer_id=vectara_customer_id,
vectara_corpus_id=vectara_corpus_id, vectara_corpus_id=vectara_corpus_id,
vectara_api_key=vectara_api_key, vectara_api_key=vectara_api_key,
embedding=embedding,
) )
return Vectara( return Vectara(