Add Upstash Vector Store support (#2004)

* docs(migration): add UPSTASH_VECTOR_REST_URL and UPSTASH_VECTOR_REST_TOKEN to the list of default environment variables
pyproject.toml: add upstash-vector package as a dependency with version 0.4.0
src/backend/base/langflow/components/vectorsearch/UpstashSearch.py: create UpstashSearchComponent for implementing a Vector Store using Upstash
src/backend/base/langflow/components/vectorstores/Upstash.py: create UpstashVectorStoreComponent for implementing a Vector Store using Upstash
src/backend/base/langflow/services/settings/constants.py: add UPSTASH_VECTOR_REST_URL and UPSTASH_VECTOR_REST_TOKEN to the list of variables to get from the environment

* ✨ (UpstashSearch.py): Add support for 'number_of_results' and 'text_key' parameters in UpstashSearchComponent to enhance search functionality
♻️ (Upstash.py): Refactor UpstashVectorStoreComponent to include 'text_key' parameter for consistency and improved functionality

* ♻️ (Upstash.py): refactor UpstashVectorStoreComponent to improve code readability and maintainability by restructuring the instantiation of UpstashVectorStore instances based on conditions and adding support for adding documents directly to the instance.

* feat: Update langchain-core, langchainhub, langsmith, and requests dependencies to latest versions

Author: ogabrielluiz

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

poetry.lock

@@ -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]

pyproject.toml

@@ -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
+        )