Add Upstash Vector Store support

docs/docs/migration/global-variables.mdx

@@ -105,6 +105,8 @@ The default list at the moment is:
 - PINECONE_API_KEY
 - SEARCHAPI_API_KEY
 - SERPAPI_API_KEY
+- UPSTASH_VECTOR_REST_URL
+- UPSTASH_VECTOR_REST_TOKEN
 - VECTARA_CUSTOMER_ID
 - VECTARA_CORPUS_ID
 - VECTARA_API_KEY

pyproject.toml

@@ -85,6 +85,7 @@ couchbase = "^4.2.1"
 youtube-transcript-api = "^0.6.2"
 markdown = "^3.6"
 langchain-chroma = "^0.1.1"
+upstash-vector = "^0.4.0"
 
 
 [tool.poetry.group.dev.dependencies]

src/backend/base/langflow/components/vectorsearch/UpstashSearch.py

@@ -0,0 +1,79 @@
+from typing import List, Optional
+
+from langchain_core.embeddings import Embeddings
+
+from langflow.components.vectorstores.base.model import LCVectorStoreComponent
+from langflow.components.vectorstores.Upstash import UpstashVectorStoreComponent
+from langflow.field_typing import Text
+from langflow.schema import Record
+
+
+class UpstashSearchComponent(UpstashVectorStoreComponent, LCVectorStoreComponent):
+ """
+ A custom component for implementing a Vector Store using Upstash.
+ """
+
+ display_name: str = "Upstash Search"
+ description: str = "Search an Upstash Vector Store for similar documents."
+
+ def build_config(self):
+ """
+ Builds the configuration for the component.
+
+ Returns:
+ - dict: A dictionary containing the configuration options for the component.
+ """
+ return {
+ "search_type": {
+ "display_name": "Search Type",
+ "options": ["Similarity", "MMR"],
+ },
+ "input_value": {"display_name": "Input"},
+ "inputs": {"display_name": "Input", "input_types": ["Document", "Record"]},
+ "embedding": {
+ "display_name": "Embedding",
+ "input_types": ["Embeddings"],
+ "info": "To use Upstash's embeddings, don't provide an embedding.",
+ },
+ "index_url": {
+ "display_name": "Index URL",
+ "info": "The URL of the Upstash index.",
+ },
+ "index_token": {
+ "display_name": "Index Token",
+ "info": "The token for the Upstash index.",
+ },
+ "number_of_results": {
+ "display_name": "Number of Results",
+ "info": "Number of results to return.",
+ "advanced": True,
+ },
+ "text_key": {
+ "display_name": "Text Key",
+ "info": "The key in the record to use as text.",
+ "advanced": True,
+ },
+ }
+
+ def build( # type: ignore[override]
+ self,
+ input_value: Text,
+ search_type: str,
+ text_key: str = "text",
+ index_url: Optional[str] = None,
+ index_token: Optional[str] = None,
+ embedding: Optional[Embeddings] = None,
+ number_of_results: int = 4,
+ ) -> List[Record]:
+ vector_store = super().build(
+ embedding=embedding,
+ text_key=text_key,
+ index_url=index_url,
+ index_token=index_token,
+ )
+ if not vector_store:
+ raise ValueError("Failed to load the Upstash Vector Store.")
+
+ return self.search_with_vector_store(
+ input_value=input_value, search_type=search_type, vector_store=vector_store, k=number_of_results
+ )