✨ feat(PromptRunner.py): add PromptRunner component to run a Chain with a given PromptTemplate

✨ feat(Vectara.py): add VectaraComponent to implement Vector Store using Vectara
🔧 chore(vectorstores): add empty __init__.py file to the vectorstores directory

src/backend/langflow/components/vectorstores/Vectara.py

@@ -0,0 +1,46 @@
+from typing import Optional, Union
+from langflow import CustomComponent
+
+from langchain.vectorstores import Vectara
+from langchain.schema import Document
+from langchain.vectorstores.base import VectorStore
+from langchain.schema import BaseRetriever
+
+
+class VectaraComponent(CustomComponent):
+    display_name: str = "Vectara"
+    description: str = "Implementation of Vector Store using Vectara"
+    documentation = (
+        "https://python.langchain.com/docs/integrations/vectorstores/vectara"
+    )
+    beta = True
+    # api key should be password = True
+    field_config = {
+        "vectara_customer_id": {"display_name": "Vectara Customer ID"},
+        "vectara_corpus_id": {"display_name": "Vectara Corpus ID"},
+        "vectara_api_key": {"display_name": "Vectara API Key", "password": True},
+        "code": {"show": False},
+        "documents": {"display_name": "Documents"},
+    }
+
+    def build(
+        self,
+        documents: Optional[Document],
+        vectara_customer_id: str,
+        vectara_corpus_id: str,
+        vectara_api_key: str,
+    ) -> Union[VectorStore, BaseRetriever]:
+        # If documents, then we need to create a Vectara instance using .from_documents
+        if documents:
+            return Vectara.from_documents(
+                documents=documents,
+                vectara_customer_id=vectara_customer_id,
+                vectara_corpus_id=vectara_corpus_id,
+                vectara_api_key=vectara_api_key,
+            )
+
+        return Vectara(
+            vectara_customer_id=vectara_customer_id,
+            vectara_corpus_id=vectara_corpus_id,
+            vectara_api_key=vectara_api_key,
+        )