diff --git a/.github/workflows/_integration_test.yml b/.github/workflows/_integration_test.yml
index caf1f51..3df47bb 100644
--- a/.github/workflows/_integration_test.yml
+++ b/.github/workflows/_integration_test.yml
@@ -43,6 +43,8 @@ jobs:
           ASTRA_DB_API_ENDPOINT: ${{ secrets.ASTRA_DB_API_ENDPOINT }}
           ASTRA_DB_APPLICATION_TOKEN: ${{ secrets.ASTRA_DB_APPLICATION_TOKEN }}
           ASTRA_DB_KEYSPACE: ${{ secrets.ASTRA_DB_KEYSPACE }}
+          SHARED_SECRET_NAME_OPENAI: ${{ secrets.SHARED_SECRET_NAME_OPENAI }}
+          OPENAI_API_KEY: ${{ secrets.OPENAI_API_KEY }}
         run: |
           make integration_tests
 
diff --git a/.github/workflows/_release.yml b/.github/workflows/_release.yml
index b50db1f..d209715 100644
--- a/.github/workflows/_release.yml
+++ b/.github/workflows/_release.yml
@@ -156,13 +156,15 @@ jobs:
         run: make tests
         working-directory: ${{ inputs.working-directory }}
 
-#      - name: Run integration tests
-#        env:
-#          ASTRA_DB_API_ENDPOINT: ${{ secrets.ASTRA_DB_API_ENDPOINT }}
-#          ASTRA_DB_APPLICATION_TOKEN: ${{ secrets.ASTRA_DB_APPLICATION_TOKEN }}
-#          ASTRA_DB_KEYSPACE: ${{ secrets.ASTRA_DB_KEYSPACE }}
-#        run: make integration_tests
-#        working-directory: ${{ inputs.working-directory }}
+      - name: Run integration tests
+        env:
+          ASTRA_DB_API_ENDPOINT: ${{ secrets.ASTRA_DB_API_ENDPOINT }}
+          ASTRA_DB_APPLICATION_TOKEN: ${{ secrets.ASTRA_DB_APPLICATION_TOKEN }}
+          ASTRA_DB_KEYSPACE: ${{ secrets.ASTRA_DB_KEYSPACE }}
+          SHARED_SECRET_NAME_OPENAI: ${{ secrets.SHARED_SECRET_NAME_OPENAI }}
+          OPENAI_API_KEY: ${{ secrets.OPENAI_API_KEY }}
+        run: make integration_tests
+        working-directory: ${{ inputs.working-directory }}
 
       - name: Get minimum versions
         working-directory: ${{ inputs.working-directory }}
diff --git a/libs/astradb/Makefile b/libs/astradb/Makefile
index 6280a79..af92305 100644
--- a/libs/astradb/Makefile
+++ b/libs/astradb/Makefile
@@ -44,10 +44,10 @@ format format_diff:
 	poetry run ruff --select I --fix $(PYTHON_FILES)
 
 spell_check:
-	poetry run codespell --toml pyproject.toml
+	poetry run codespell --toml pyproject.toml -I codespell_ignore_words.txt
 
 spell_fix:
-	poetry run codespell --toml pyproject.toml -w
+	poetry run codespell --toml pyproject.toml -w -I codespell_ignore_words.txt
 
 check_imports: $(shell find langchain_astradb -name '*.py')
 	poetry run python ./scripts/check_imports.py $^
diff --git a/libs/astradb/codespell_ignore_words.txt b/libs/astradb/codespell_ignore_words.txt
new file mode 100644
index 0000000..0b3a7cd
--- /dev/null
+++ b/libs/astradb/codespell_ignore_words.txt
@@ -0,0 +1 @@
+Haa
diff --git a/libs/astradb/langchain_astradb/utils/astradb.py b/libs/astradb/langchain_astradb/utils/astradb.py
index 3ba98be..9321c49 100644
--- a/libs/astradb/langchain_astradb/utils/astradb.py
+++ b/libs/astradb/langchain_astradb/utils/astradb.py
@@ -19,6 +19,8 @@
 API_ENDPOINT_ENV_VAR = "ASTRA_DB_API_ENDPOINT"
 NAMESPACE_ENV_VAR = "ASTRA_DB_KEYSPACE"
 
+DEFAULT_VECTORIZE_SECRET_HEADER = "x-embedding-api-key"
+
 logger = logging.getLogger()
 
 
@@ -138,19 +140,29 @@ def __init__(
         collection_vector_service_options: Optional[
             CollectionVectorServiceOptions
         ] = None,
+        collection_embedding_api_key: Optional[str] = None,
     ) -> None:
         super().__init__(
             token, api_endpoint, astra_db_client, async_astra_db_client, namespace
         )
+        embedding_key_header = {
+            k: v
+            for k, v in {
+                DEFAULT_VECTORIZE_SECRET_HEADER: collection_embedding_api_key,
+            }.items()
+            if v is not None
+        }
         self.collection_name = collection_name
         self.collection = AstraDBCollection(
             collection_name=collection_name,
             astra_db=self.astra_db,
+            additional_headers=embedding_key_header,
         )
 
         self.async_collection = AsyncAstraDBCollection(
             collection_name=collection_name,
             astra_db=self.async_astra_db,
+            additional_headers=embedding_key_header,
         )
 
         if requested_indexing_policy is not None:
diff --git a/libs/astradb/langchain_astradb/vectorstores.py b/libs/astradb/langchain_astradb/vectorstores.py
index f2a7bf7..f154fee 100644
--- a/libs/astradb/langchain_astradb/vectorstores.py
+++ b/libs/astradb/langchain_astradb/vectorstores.py
@@ -156,6 +156,7 @@ def __init__(
         collection_vector_service_options: Optional[
             CollectionVectorServiceOptions
         ] = None,
+        collection_embedding_api_key: Optional[str] = None,
     ) -> None:
         """Wrapper around DataStax Astra DB for vector-store workloads.
 
@@ -181,8 +182,9 @@ def __init__(
         Args:
             embedding: the embeddings function or service to use.
                 This enables client-side embedding functions or calls to external
-                embedding providers. Only one of `embedding` or
-                `collection_vector_service_options` can be provided.
+                embedding providers. If `embedding` is provided, arguments
+                `collection_vector_service_options` and
+                `collection_embedding_api_key` cannot be provided.
             collection_name: name of the Astra DB collection to create/use.
             token: API token for Astra DB usage. If not provided, the environment
                 variable ASTRA_DB_APPLICATION_TOKEN is inspected.
@@ -220,10 +222,16 @@ def __init__(
                 (see docs.datastax.com/en/astra/astra-db-vector/api-reference/
                 data-api-commands.html#advanced-feature-indexing-clause-on-createcollection)
             collection_vector_service_options: specifies the use of server-side
-                embeddings within Astra DB. Only one of `embedding` or
-                `collection_vector_service_options` can be provided.
-                NOTE: This feature is under current development.
-
+                embeddings within Astra DB. If passing this parameter, `embedding`
+                cannot be provided.
+            collection_embedding_api_key: for usage of server-side embeddings
+                within Astra DB, with this parameter one can supply an API Key
+                that will be passed to Astra DB with each data request.
+                This is useful when the service is configured for the collection,
+                but no corresponding secret is stored within
+                Astra's key management system.
+                This parameter cannot be provided without
+                specifying `collection_vector_service_options`.
 
         Note:
             For concurrency in synchronous :meth:`~add_texts`:, as a rule of thumb, on a
@@ -242,7 +250,7 @@ def __init__(
             Remember you can pass concurrency settings to individual calls to
             :meth:`~add_texts` and :meth:`~add_documents` as well.
         """
-        # Embedding and collection_vector_service_options are mutually exclusive,
+        # Embedding and the server-side embeddings are mutually exclusive,
         # as both specify how to produce embeddings
         if embedding is None and collection_vector_service_options is None:
             raise ValueError(
@@ -256,6 +264,15 @@ def __init__(
                     can be provided."
             )
 
+        if (
+            collection_vector_service_options is None
+            and collection_embedding_api_key is not None
+        ):
+            raise ValueError(
+                "`collection_embedding_api_key` cannot be provided unless"
+                " `collection_vector_service_options` is also passed."
+            )
+
         self.embedding_dimension: Optional[int] = None
         self.embedding = embedding
         self.collection_name = collection_name
@@ -263,6 +280,7 @@ def __init__(
         self.api_endpoint = api_endpoint
         self.namespace = namespace
         self.collection_vector_service_options = collection_vector_service_options
+        self.collection_embedding_api_key = collection_embedding_api_key
         # Concurrency settings
         self.batch_size: int = batch_size or DEFAULT_BATCH_SIZE
         self.bulk_insert_batch_concurrency: int = (
@@ -305,6 +323,7 @@ def __init__(
             requested_indexing_policy=self.indexing_policy,
             default_indexing_policy=DEFAULT_INDEXING_OPTIONS,
             collection_vector_service_options=collection_vector_service_options,
+            collection_embedding_api_key=collection_embedding_api_key,
         )
         self.astra_db = self.astra_env.astra_db
         self.async_astra_db = self.astra_env.async_astra_db
diff --git a/libs/astradb/poetry.lock b/libs/astradb/poetry.lock
index 1bd4ea5..27ba834 100644
--- a/libs/astradb/poetry.lock
+++ b/libs/astradb/poetry.lock
@@ -1,4 +1,4 @@
-# This file is automatically @generated by Poetry 1.7.1 and should not be changed by hand.
+# This file is automatically @generated by Poetry 1.5.1 and should not be changed by hand.
 
 [[package]]
 name = "aiohttp"
@@ -112,13 +112,13 @@ frozenlist = ">=1.1.0"
 
 [[package]]
 name = "annotated-types"
-version = "0.6.0"
+version = "0.7.0"
 description = "Reusable constraint types to use with typing.Annotated"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "annotated_types-0.6.0-py3-none-any.whl", hash = "sha256:0641064de18ba7a25dee8f96403ebc39113d0cb953a01429249d5c7564666a43"},
-    {file = "annotated_types-0.6.0.tar.gz", hash = "sha256:563339e807e53ffd9c267e99fc6d9ea23eb8443c08f112651963e24e22f84a5d"},
+    {file = "annotated_types-0.7.0-py3-none-any.whl", hash = "sha256:1f02e8b43a8fbbc3f3e0d4f0f4bfc8131bcb4eebe8849b8e5c773f3a1c582a53"},
+    {file = "annotated_types-0.7.0.tar.gz", hash = "sha256:aff07c09a53a08bc8cfccb9c85b05f1aa9a2a6f23728d790723543408344ce89"},
 ]
 
 [package.dependencies]
@@ -148,13 +148,13 @@ trio = ["trio (>=0.23)"]
 
 [[package]]
 name = "astrapy"
-version = "1.1.0"
+version = "1.2.0"
 description = "AstraPy is a Pythonic SDK for DataStax Astra and its Data API"
 optional = false
 python-versions = "<4.0.0,>=3.8.0"
 files = [
-    {file = "astrapy-1.1.0-py3-none-any.whl", hash = "sha256:577151c3a9f8ed9c389402fad0b43ec17af9386974731622e2462908ca0e81a6"},
-    {file = "astrapy-1.1.0.tar.gz", hash = "sha256:287421bb293060967c4620b7a9de5949fe46391898768169dba370a7ec4676c4"},
+    {file = "astrapy-1.2.0-py3-none-any.whl", hash = "sha256:5d65242771934c38ebe16f330e9e517968c1437846dabdbe7e48470f7b1782e8"},
+    {file = "astrapy-1.2.0.tar.gz", hash = "sha256:6ce1b421d1ae21fe73373fa36048d8d56c775367886525504f01c48cbb742842"},
 ]
 
 [package.dependencies]
@@ -398,13 +398,13 @@ colorama = {version = "*", markers = "platform_system == \"Windows\""}
 
 [[package]]
 name = "codespell"
-version = "2.2.6"
+version = "2.3.0"
 description = "Codespell"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "codespell-2.2.6-py3-none-any.whl", hash = "sha256:9ee9a3e5df0990604013ac2a9f22fa8e57669c827124a2e961fe8a1da4cacc07"},
-    {file = "codespell-2.2.6.tar.gz", hash = "sha256:a8c65d8eb3faa03deabab6b3bbe798bea72e1799c7e9e955d57eca4096abcff9"},
+    {file = "codespell-2.3.0-py3-none-any.whl", hash = "sha256:a9c7cef2501c9cfede2110fd6d4e5e62296920efe9abfb84648df866e47f58d1"},
+    {file = "codespell-2.3.0.tar.gz", hash = "sha256:360c7d10f75e65f67bad720af7007e1060a5d395670ec11a7ed1fed9dd17471f"},
 ]
 
 [package.extras]
@@ -424,21 +424,6 @@ files = [
     {file = "colorama-0.4.6.tar.gz", hash = "sha256:08695f5cb7ed6e0531a20572697297273c47b8cae5a63ffc6d6ed5c201be6e44"},
 ]
 
-[[package]]
-name = "dataclasses-json"
-version = "0.6.6"
-description = "Easily serialize dataclasses to and from JSON."
-optional = false
-python-versions = "<4.0,>=3.7"
-files = [
-    {file = "dataclasses_json-0.6.6-py3-none-any.whl", hash = "sha256:e54c5c87497741ad454070ba0ed411523d46beb5da102e221efb873801b0ba85"},
-    {file = "dataclasses_json-0.6.6.tar.gz", hash = "sha256:0c09827d26fffda27f1be2fed7a7a01a29c5ddcd2eb6393ad5ebf9d77e9deae8"},
-]
-
-[package.dependencies]
-marshmallow = ">=3.18.0,<4.0.0"
-typing-inspect = ">=0.4.0,<1"
-
 [[package]]
 name = "deprecation"
 version = "2.1.0"
@@ -791,12 +776,11 @@ optional = false
 python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*, !=3.5.*, !=3.6.*"
 files = [
     {file = "jsonpointer-2.4-py2.py3-none-any.whl", hash = "sha256:15d51bba20eea3165644553647711d150376234112651b4f1811022aecad7d7a"},
-    {file = "jsonpointer-2.4.tar.gz", hash = "sha256:585cee82b70211fa9e6043b7bb89db6e1aa49524340dde8ad6b63206ea689d88"},
 ]
 
 [[package]]
 name = "langchain"
-version = "0.2.0rc2"
+version = "0.2.1"
 description = "Building applications with LLMs through composability"
 optional = false
 python-versions = ">=3.8.1,<4.0"
@@ -806,9 +790,8 @@ develop = false
 [package.dependencies]
 aiohttp = "^3.8.3"
 async-timeout = {version = "^4.0.0", markers = "python_version < \"3.11\""}
-dataclasses-json = ">= 0.5.7, < 0.7"
-langchain-core = ">=0.1.52,<0.3"
-langchain-text-splitters = ">=0.0.1,<0.1"
+langchain-core = "^0.2.0"
+langchain-text-splitters = "^0.2.0"
 langsmith = "^0.1.17"
 numpy = "^1"
 pydantic = ">=1,<3"
@@ -828,7 +811,7 @@ embeddings = ["sentence-transformers (>=2,<3)"]
 extended-testing = ["aiosqlite (>=0.19.0,<0.20.0)", "aleph-alpha-client (>=2.15.0,<3.0.0)", "anthropic (>=0.3.11,<0.4.0)", "arxiv (>=1.4,<2.0)", "assemblyai (>=0.17.0,<0.18.0)", "atlassian-python-api (>=3.36.0,<4.0.0)", "beautifulsoup4 (>=4,<5)", "bibtexparser (>=1.4.0,<2.0.0)", "cassio (>=0.1.0,<0.2.0)", "chardet (>=5.1.0,<6.0.0)", "cohere (>=4,<6)", "couchbase (>=4.1.9,<5.0.0)", "dashvector (>=1.0.1,<2.0.0)", "databricks-vectorsearch (>=0.21,<0.22)", "datasets (>=2.15.0,<3.0.0)", "dgml-utils (>=0.3.0,<0.4.0)", "esprima (>=4.0.1,<5.0.0)", "faiss-cpu (>=1,<2)", "feedparser (>=6.0.10,<7.0.0)", "fireworks-ai (>=0.9.0,<0.10.0)", "geopandas (>=0.13.1,<0.14.0)", "gitpython (>=3.1.32,<4.0.0)", "google-cloud-documentai (>=2.20.1,<3.0.0)", "gql (>=3.4.1,<4.0.0)", "hologres-vector (>=0.0.6,<0.0.7)", "html2text (>=2020.1.16,<2021.0.0)", "javelin-sdk (>=0.1.8,<0.2.0)", "jinja2 (>=3,<4)", "jq (>=1.4.1,<2.0.0)", "jsonschema (>1)", "langchain-openai (>=0.1,<0.2)", "lxml (>=4.9.3,<6.0)", "markdownify (>=0.11.6,<0.12.0)", "motor (>=3.3.1,<4.0.0)", "msal (>=1.25.0,<2.0.0)", "mwparserfromhell (>=0.6.4,<0.7.0)", "mwxml (>=0.3.3,<0.4.0)", "newspaper3k (>=0.2.8,<0.3.0)", "numexpr (>=2.8.6,<3.0.0)", "openai (<2)", "openai (<2)", "openapi-pydantic (>=0.3.2,<0.4.0)", "pandas (>=2.0.1,<3.0.0)", "pdfminer-six (>=20221105,<20221106)", "pgvector (>=0.1.6,<0.2.0)", "praw (>=7.7.1,<8.0.0)", "psychicapi (>=0.8.0,<0.9.0)", "py-trello (>=0.19.0,<0.20.0)", "pymupdf (>=1.22.3,<2.0.0)", "pypdf (>=3.4.0,<4.0.0)", "pypdfium2 (>=4.10.0,<5.0.0)", "pyspark (>=3.4.0,<4.0.0)", "rank-bm25 (>=0.2.2,<0.3.0)", "rapidfuzz (>=3.1.1,<4.0.0)", "rapidocr-onnxruntime (>=1.3.2,<2.0.0)", "rdflib (==7.0.0)", "requests-toolbelt (>=1.0.0,<2.0.0)", "rspace_client (>=2.5.0,<3.0.0)", "scikit-learn (>=1.2.2,<2.0.0)", "sqlite-vss (>=0.1.2,<0.2.0)", "streamlit (>=1.18.0,<2.0.0)", "sympy (>=1.12,<2.0)", "telethon (>=1.28.5,<2.0.0)", "timescale-vector (>=0.0.1,<0.0.2)", "tqdm (>=4.48.0)", "upstash-redis (>=0.15.0,<0.16.0)", "xata (>=1.0.0a7,<2.0.0)", "xmltodict (>=0.13.0,<0.14.0)"]
 javascript = ["esprima (>=4.0.1,<5.0.0)"]
 llms = ["clarifai (>=9.1.0)", "cohere (>=4,<6)", "huggingface_hub (>=0,<1)", "manifest-ml (>=0.0.1,<0.0.2)", "nlpcloud (>=1,<2)", "openai (<2)", "openlm (>=0.0.5,<0.0.6)", "torch (>=1,<3)", "transformers (>=4,<5)"]
-openai = ["openai (<2)", "tiktoken (>=0.3.2,<0.6.0)"]
+openai = ["openai (<2)", "tiktoken (>=0.7,<1.0)"]
 qdrant = ["qdrant-client (>=1.3.1,<2.0.0)"]
 text-helpers = ["chardet (>=5.1.0,<6.0.0)"]
 
@@ -836,12 +819,12 @@ text-helpers = ["chardet (>=5.1.0,<6.0.0)"]
 type = "git"
 url = "https://github.com/langchain-ai/langchain.git"
 reference = "HEAD"
-resolved_reference = "be15740084e31d6850808e08ccb005f652664a48"
+resolved_reference = "cccc8fbe2fe59bde0846875f67aa046aeb1105a3"
 subdirectory = "libs/langchain"
 
 [[package]]
 name = "langchain-core"
-version = "0.2.0rc1"
+version = "0.2.2rc1"
 description = "Building applications with LLMs through composability"
 optional = false
 python-versions = ">=3.8.1,<4.0"
@@ -863,12 +846,12 @@ extended-testing = ["jinja2 (>=3,<4)"]
 type = "git"
 url = "https://github.com/langchain-ai/langchain.git"
 reference = "HEAD"
-resolved_reference = "be15740084e31d6850808e08ccb005f652664a48"
+resolved_reference = "cccc8fbe2fe59bde0846875f67aa046aeb1105a3"
 subdirectory = "libs/core"
 
 [[package]]
 name = "langchain-text-splitters"
-version = "0.0.2"
+version = "0.2.0"
 description = "LangChain text splitting utilities"
 optional = false
 python-versions = ">=3.8.1,<4.0"
@@ -876,7 +859,7 @@ files = []
 develop = false
 
 [package.dependencies]
-langchain-core = ">=0.1.28,<0.3"
+langchain-core = "^0.2.0"
 
 [package.extras]
 extended-testing = ["beautifulsoup4 (>=4.12.3,<5.0.0)", "lxml (>=4.9.3,<6.0)"]
@@ -885,18 +868,18 @@ extended-testing = ["beautifulsoup4 (>=4.12.3,<5.0.0)", "lxml (>=4.9.3,<6.0)"]
 type = "git"
 url = "https://github.com/langchain-ai/langchain.git"
 reference = "HEAD"
-resolved_reference = "be15740084e31d6850808e08ccb005f652664a48"
+resolved_reference = "cccc8fbe2fe59bde0846875f67aa046aeb1105a3"
 subdirectory = "libs/text-splitters"
 
 [[package]]
 name = "langsmith"
-version = "0.1.59"
+version = "0.1.63"
 description = "Client library to connect to the LangSmith LLM Tracing and Evaluation Platform."
 optional = false
 python-versions = "<4.0,>=3.8.1"
 files = [
-    {file = "langsmith-0.1.59-py3-none-any.whl", hash = "sha256:445e3bc1d3baa1e5340cd979907a19483b9763a2ed37b863a01113d406f69345"},
-    {file = "langsmith-0.1.59.tar.gz", hash = "sha256:e748a89f4dd6aa441349143e49e546c03b5dfb43376a25bfef6a5ca792fe1437"},
+    {file = "langsmith-0.1.63-py3-none-any.whl", hash = "sha256:7810afdf5e3f3b472fc581a29371fb96cd843dde2149e048d1b9610325159d1e"},
+    {file = "langsmith-0.1.63.tar.gz", hash = "sha256:a609405b52f6f54df442a142cbf19ab38662d54e532f96028b4c546434d4afdf"},
 ]
 
 [package.dependencies]
@@ -904,25 +887,6 @@ orjson = ">=3.9.14,<4.0.0"
 pydantic = ">=1,<3"
 requests = ">=2,<3"
 
-[[package]]
-name = "marshmallow"
-version = "3.21.2"
-description = "A lightweight library for converting complex datatypes to and from native Python datatypes."
-optional = false
-python-versions = ">=3.8"
-files = [
-    {file = "marshmallow-3.21.2-py3-none-any.whl", hash = "sha256:70b54a6282f4704d12c0a41599682c5c5450e843b9ec406308653b47c59648a1"},
-    {file = "marshmallow-3.21.2.tar.gz", hash = "sha256:82408deadd8b33d56338d2182d455db632c6313aa2af61916672146bb32edc56"},
-]
-
-[package.dependencies]
-packaging = ">=17.0"
-
-[package.extras]
-dev = ["marshmallow[tests]", "pre-commit (>=3.5,<4.0)", "tox"]
-docs = ["alabaster (==0.7.16)", "autodocsumm (==0.2.12)", "sphinx (==7.3.7)", "sphinx-issues (==4.1.0)", "sphinx-version-warning (==1.1.2)"]
-tests = ["pytest", "pytz", "simplejson"]
-
 [[package]]
 name = "multidict"
 version = "6.0.5"
@@ -1438,7 +1402,6 @@ files = [
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+    {file = "watchdog-4.0.1-cp39-cp39-macosx_10_9_x86_64.whl", hash = "sha256:667f3c579e813fcbad1b784db7a1aaa96524bed53437e119f6a2f5de4db04235"},
+    {file = "watchdog-4.0.1-cp39-cp39-macosx_11_0_arm64.whl", hash = "sha256:d10a681c9a1d5a77e75c48a3b8e1a9f2ae2928eda463e8d33660437705659682"},
+    {file = "watchdog-4.0.1-pp310-pypy310_pp73-macosx_10_9_x86_64.whl", hash = "sha256:0144c0ea9997b92615af1d94afc0c217e07ce2c14912c7b1a5731776329fcfc7"},
+    {file = "watchdog-4.0.1-pp310-pypy310_pp73-macosx_11_0_arm64.whl", hash = "sha256:998d2be6976a0ee3a81fb8e2777900c28641fb5bfbd0c84717d89bca0addcdc5"},
+    {file = "watchdog-4.0.1-pp38-pypy38_pp73-macosx_10_9_x86_64.whl", hash = "sha256:e7921319fe4430b11278d924ef66d4daa469fafb1da679a2e48c935fa27af193"},
+    {file = "watchdog-4.0.1-pp38-pypy38_pp73-macosx_11_0_arm64.whl", hash = "sha256:f0de0f284248ab40188f23380b03b59126d1479cd59940f2a34f8852db710625"},
+    {file = "watchdog-4.0.1-pp39-pypy39_pp73-macosx_10_9_x86_64.whl", hash = "sha256:bca36be5707e81b9e6ce3208d92d95540d4ca244c006b61511753583c81c70dd"},
+    {file = "watchdog-4.0.1-pp39-pypy39_pp73-macosx_11_0_arm64.whl", hash = "sha256:ab998f567ebdf6b1da7dc1e5accfaa7c6992244629c0fdaef062f43249bd8dee"},
+    {file = "watchdog-4.0.1-py3-none-manylinux2014_aarch64.whl", hash = "sha256:dddba7ca1c807045323b6af4ff80f5ddc4d654c8bce8317dde1bd96b128ed253"},
+    {file = "watchdog-4.0.1-py3-none-manylinux2014_armv7l.whl", hash = "sha256:4513ec234c68b14d4161440e07f995f231be21a09329051e67a2118a7a612d2d"},
+    {file = "watchdog-4.0.1-py3-none-manylinux2014_i686.whl", hash = "sha256:4107ac5ab936a63952dea2a46a734a23230aa2f6f9db1291bf171dac3ebd53c6"},
+    {file = "watchdog-4.0.1-py3-none-manylinux2014_ppc64.whl", hash = "sha256:6e8c70d2cd745daec2a08734d9f63092b793ad97612470a0ee4cbb8f5f705c57"},
+    {file = "watchdog-4.0.1-py3-none-manylinux2014_ppc64le.whl", hash = "sha256:f27279d060e2ab24c0aa98363ff906d2386aa6c4dc2f1a374655d4e02a6c5e5e"},
+    {file = "watchdog-4.0.1-py3-none-manylinux2014_s390x.whl", hash = "sha256:f8affdf3c0f0466e69f5b3917cdd042f89c8c63aebdb9f7c078996f607cdb0f5"},
+    {file = "watchdog-4.0.1-py3-none-manylinux2014_x86_64.whl", hash = "sha256:ac7041b385f04c047fcc2951dc001671dee1b7e0615cde772e84b01fbf68ee84"},
+    {file = "watchdog-4.0.1-py3-none-win32.whl", hash = "sha256:206afc3d964f9a233e6ad34618ec60b9837d0582b500b63687e34011e15bb429"},
+    {file = "watchdog-4.0.1-py3-none-win_amd64.whl", hash = "sha256:7577b3c43e5909623149f76b099ac49a1a01ca4e167d1785c76eb52fa585745a"},
+    {file = "watchdog-4.0.1-py3-none-win_ia64.whl", hash = "sha256:d7b9f5f3299e8dd230880b6c55504a1f69cf1e4316275d1b215ebdd8187ec88d"},
+    {file = "watchdog-4.0.1.tar.gz", hash = "sha256:eebaacf674fa25511e8867028d281e602ee6500045b57f43b08778082f7f8b44"},
 ]
 
 [package.extras]
@@ -1894,4 +1835,4 @@ multidict = ">=4.0"
 [metadata]
 lock-version = "2.0"
 python-versions = ">=3.8.1,<4.0"
-content-hash = "b84212079a0b3a10ad7759bca1557e81458e4dd7c26fba596323382d3763edd7"
+content-hash = "eab37187d7130cd3a5b98edbd5e3e85e67e7db8d050a2f3077a6ad07a90166c9"
diff --git a/libs/astradb/pyproject.toml b/libs/astradb/pyproject.toml
index d466804..ea550f2 100644
--- a/libs/astradb/pyproject.toml
+++ b/libs/astradb/pyproject.toml
@@ -13,7 +13,7 @@ license = "MIT"
 [tool.poetry.dependencies]
 python = ">=3.8.1,<4.0"
 langchain-core = ">=0.1.31,<0.3"
-astrapy = "^1"
+astrapy = "^1.2"
 numpy = "^1"
 
 [tool.poetry.group.test]
diff --git a/libs/astradb/testing.env.sample b/libs/astradb/testing.env.sample
new file mode 100644
index 0000000..2edbbd4
--- /dev/null
+++ b/libs/astradb/testing.env.sample
@@ -0,0 +1,11 @@
+export ASTRA_DB_APPLICATION_TOKEN="AstraCS:aaabbbccc..."
+export ASTRA_DB_API_ENDPOINT="https://0123...-region.apps.astra.datastax.com"
+export ASTRA_DB_KEYSPACE="default_keyspace"
+
+# required to test vectorize with SHARED_SECRET
+export SHARED_SECRET_NAME_OPENAI="NAME_SUPPLIED_IN_ASTRA_KMS"
+# required to test vectorize with HEADER
+export OPENAI_API_KEY="sk-aaabbbccc..."
+
+# Also invoke pytest prepending `NVIDIA_VECTORIZE_AVAILABLE=1` if
+# the database supports server-side nVidia embeddings as well
\ No newline at end of file
diff --git a/libs/astradb/tests/integration_tests/test_vectorstores.py b/libs/astradb/tests/integration_tests/test_vectorstores.py
index 7faddf8..07a82f8 100644
--- a/libs/astradb/tests/integration_tests/test_vectorstores.py
+++ b/libs/astradb/tests/integration_tests/test_vectorstores.py
@@ -34,16 +34,60 @@
 
 COLLECTION_NAME_DIM2 = "lc_test_d2"
 COLLECTION_NAME_DIM2_EUCLIDEAN = "lc_test_d2_eucl"
-COLLECTION_NAME_VECTORIZE = "lc_test_vectorize"
+COLLECTION_NAME_VECTORIZE_OPENAI = "lc_test_vec_openai"
+COLLECTION_NAME_VECTORIZE_OPENAI_HEADER = "lc_test_vec_openai_h"
+COLLECTION_NAME_VECTORIZE_NVIDIA = "lc_test_nvidia"
 
 MATCH_EPSILON = 0.0001
 
+# For the time being, prod-regions described only
+OPENAI_VECTORIZE_REGIONS_MAP = {
+    "prod": {"us-east-2", "westus3", "us-east1"},  # resp. aws, azure, gcp
+}
+
+openai_vectorize_options = CollectionVectorServiceOptions(
+    provider="openai",
+    model_name="text-embedding-3-small",
+    authentication={
+        "providerKey": f"{os.environ.get('SHARED_SECRET_NAME_OPENAI', '')}.providerKey",
+    },
+)
+openai_vectorize_options_header = CollectionVectorServiceOptions(
+    provider="openai",
+    model_name="text-embedding-3-small",
+)
+nvidia_vectorize_options = CollectionVectorServiceOptions(
+    provider="nvidia",
+    model_name="NV-Embed-QA",
+)
+
+
+def is_nvidia_vector_service_available() -> bool:
+    # For the time being, this is manually controlled
+    if os.environ.get("NVIDIA_VECTORIZE_AVAILABLE"):
+        try:
+            return int(os.environ["NVIDIA_VECTORIZE_AVAILABLE"]) != 0
+        except Exception:
+            return False
+    else:
+        return False
 
-def is_vector_service_available() -> bool:
+
+def is_openai_vector_service_available() -> bool:
+    env: str
+    if "astra.datastax.com" in os.environ.get("ASTRA_DB_API_ENDPOINT", ""):
+        env = "prod"
+    else:
+        env = "other"
+    openai_vectorize_regions = OPENAI_VECTORIZE_REGIONS_MAP.get(env, set())
     return all(
         [
-            "us-west-2" in os.environ.get("ASTRA_DB_API_ENDPOINT", ""),
-            "astra-dev.datastax.com" in os.environ.get("ASTRA_DB_API_ENDPOINT", ""),
+            any(
+                openai_region in os.environ.get("ASTRA_DB_API_ENDPOINT", "")
+                for openai_region in openai_vectorize_regions
+            ),
+            "astra.datastax.com" in os.environ.get("ASTRA_DB_API_ENDPOINT", ""),
+            os.environ.get("SHARED_SECRET_NAME_OPENAI"),
         ]
     )
 
@@ -176,31 +220,77 @@ def vectorize_store(
     astradb_credentials: AstraDBCredentials,
 ) -> Iterable[AstraDBVectorStore]:
     """
-    astra db vector store with server-side embeddings using the nvidia model
+    astra db vector store with server-side embeddings using openai + shared_secret
     """
-    # Only available in dev us-west-2 now
-    if not is_vector_service_available():
-        pytest.skip("vectorize unavailable")
+    if not is_openai_vector_service_available():
+        pytest.skip("vectorize/openai unavailable")
 
-    options = CollectionVectorServiceOptions(
-        provider="nvidia", model_name="NV-Embed-QA"
+    v_store = AstraDBVectorStore(
+        collection_vector_service_options=openai_vectorize_options,
+        collection_name=COLLECTION_NAME_VECTORIZE_OPENAI,
+        **astradb_credentials,
     )
+    v_store.clear()
+
+    yield v_store
+
+    # explicitly delete the collection to avoid max collection limit
+    v_store.delete_collection()
+
+
+@pytest.fixture(scope="function")
+def vectorize_store_w_header(
+    astradb_credentials: AstraDBCredentials,
+) -> Iterable[AstraDBVectorStore]:
+    """
+    astra db vector store with server-side embeddings using openai + header
+    """
+    if not is_openai_vector_service_available():
+        pytest.skip("vectorize/openai service unavailable")
+
+    if not os.environ.get("OPENAI_API_KEY"):
+        pytest.skip("OpenAI key not available")
+
     v_store = AstraDBVectorStore(
-        collection_vector_service_options=options,
-        collection_name=COLLECTION_NAME_VECTORIZE,
+        collection_vector_service_options=openai_vectorize_options_header,
+        collection_name=COLLECTION_NAME_VECTORIZE_OPENAI_HEADER,
+        collection_embedding_api_key=os.environ["OPENAI_API_KEY"],
         **astradb_credentials,
     )
     v_store.clear()
 
     yield v_store
 
-    # explicilty delete the collection to avoid max collection limit
+    # explicitly delete the collection to avoid max collection limit
+    v_store.delete_collection()
+
+
+@pytest.fixture(scope="function")
+def vectorize_store_nvidia(
+    astradb_credentials: AstraDBCredentials,
+) -> Iterable[AstraDBVectorStore]:
+    """
+    astra db vector store with server-side embeddings using the nvidia model
+    """
+    if not is_nvidia_vector_service_available():
+        pytest.skip("vectorize/nvidia unavailable")
+
+    v_store = AstraDBVectorStore(
+        collection_vector_service_options=nvidia_vectorize_options,
+        collection_name=COLLECTION_NAME_VECTORIZE_NVIDIA,
+        **astradb_credentials,
+    )
+    v_store.clear()
+
+    yield v_store
+
+    # explicitly delete the collection to avoid max collection limit
     v_store.delete_collection()
 
 
 @pytest.mark.skipif(not _has_env_vars(), reason="Missing Astra DB env. vars")
 class TestAstraDBVectorStore:
-    def test_astradb_vectorstore_create_delete(
+    def test_astradb_vectorstore_create_delete_sync(
         self, astradb_credentials: AstraDBCredentials
     ) -> None:
         """Create and delete."""
@@ -235,18 +325,15 @@ def test_astradb_vectorstore_create_delete(
             v_store_2.clear()
 
     @pytest.mark.skipif(
-        not is_vector_service_available(), reason="vectorize unavailable"
+        not is_openai_vector_service_available(), reason="vectorize unavailable"
     )
-    def test_astradb_vectorstore_create_delete_vectorize(
+    def test_astradb_vectorstore_create_delete_vectorize_sync(
         self, astradb_credentials: AstraDBCredentials
     ) -> None:
         """Create and delete with vectorize option."""
-        options = CollectionVectorServiceOptions(
-            provider="nvidia", model_name="NV-Embed-QA"
-        )
         v_store = AstraDBVectorStore(
-            collection_vector_service_options=options,
-            collection_name=COLLECTION_NAME_VECTORIZE,
+            collection_vector_service_options=openai_vectorize_options,
+            collection_name=COLLECTION_NAME_VECTORIZE_OPENAI,
             **astradb_credentials,
         )
         v_store.add_texts(["Sample 1"])
@@ -280,18 +367,15 @@ async def test_astradb_vectorstore_create_delete_async(
             await v_store_2.aclear()
 
     @pytest.mark.skipif(
-        not is_vector_service_available(), reason="vectorize unavailable"
+        not is_openai_vector_service_available(), reason="vectorize unavailable"
     )
     async def test_astradb_vectorstore_create_delete_vectorize_async(
         self, astradb_credentials: AstraDBCredentials
     ) -> None:
         """Create and delete with vectorize option."""
-        options = CollectionVectorServiceOptions(
-            provider="nvidia", model_name="NV-Embed-QA"
-        )
         v_store = AstraDBVectorStore(
-            collection_vector_service_options=options,
-            collection_name=COLLECTION_NAME_VECTORIZE,
+            collection_vector_service_options=openai_vectorize_options,
+            collection_name=COLLECTION_NAME_VECTORIZE_OPENAI,
             **astradb_credentials,
         )
         await v_store.adelete_collection()
@@ -300,7 +384,7 @@ async def test_astradb_vectorstore_create_delete_vectorize_async(
         SKIP_COLLECTION_DELETE,
         reason="Collection-deletion tests are suppressed",
     )
-    def test_astradb_vectorstore_pre_delete_collection(
+    def test_astradb_vectorstore_pre_delete_collection_sync(
         self, astradb_credentials: AstraDBCredentials
     ) -> None:
         """Use of the pre_delete_collection flag."""
@@ -369,7 +453,7 @@ async def test_astradb_vectorstore_pre_delete_collection_async(
         finally:
             await v_store.adelete_collection()
 
-    def test_astradb_vectorstore_from_x(
+    def test_astradb_vectorstore_from_x_sync(
         self, astradb_credentials: AstraDBCredentials
     ) -> None:
         """from_texts and from_documents methods."""
@@ -414,27 +498,23 @@ def test_astradb_vectorstore_from_x(
                 v_store_2.clear()
 
     @pytest.mark.skipif(
-        not is_vector_service_available(), reason="vectorize unavailable"
+        not is_openai_vector_service_available(), reason="vectorize unavailable"
     )
-    def test_astradb_vectorstore_from_x_vectorize(
+    def test_astradb_vectorstore_from_x_vectorize_sync(
         self, astradb_credentials: AstraDBCredentials
     ) -> None:
         """from_texts and from_documents methods with vectorize."""
-        options = CollectionVectorServiceOptions(
-            provider="nvidia", model_name="NV-Embed-QA"
-        )
-
         AstraDBVectorStore(
-            collection_vector_service_options=options,
-            collection_name=COLLECTION_NAME_VECTORIZE,
+            collection_vector_service_options=openai_vectorize_options,
+            collection_name=COLLECTION_NAME_VECTORIZE_OPENAI,
             **astradb_credentials,
         ).clear()
 
         # from_texts
         v_store = AstraDBVectorStore.from_texts(
             texts=["Hi", "Ho"],
-            collection_vector_service_options=options,
-            collection_name=COLLECTION_NAME_VECTORIZE,
+            collection_vector_service_options=openai_vectorize_options,
+            collection_name=COLLECTION_NAME_VECTORIZE_OPENAI,
             **astradb_credentials,
         )
         try:
@@ -448,8 +528,8 @@ def test_astradb_vectorstore_from_x_vectorize(
                 Document(page_content="Hee"),
                 Document(page_content="Hoi"),
             ],
-            collection_vector_service_options=options,
-            collection_name=COLLECTION_NAME_VECTORIZE,
+            collection_vector_service_options=openai_vectorize_options,
+            collection_name=COLLECTION_NAME_VECTORIZE_OPENAI,
             **astradb_credentials,
         )
         try:
@@ -504,20 +584,17 @@ async def test_astradb_vectorstore_from_x_async(
                 await v_store_2.aclear()
 
     @pytest.mark.skipif(
-        not is_vector_service_available(), reason="vectorize unavailable"
+        not is_openai_vector_service_available(), reason="vectorize unavailable"
     )
-    async def test_astradb_vectorstore_from_x_async_vectorize(
+    async def test_astradb_vectorstore_from_x_vectorize_async(
         self, astradb_credentials: AstraDBCredentials
     ) -> None:
         """from_texts and from_documents methods with vectorize."""
         # from_text with vectorize
-        options = CollectionVectorServiceOptions(
-            provider="nvidia", model_name="NV-Embed-QA"
-        )
         v_store = await AstraDBVectorStore.afrom_texts(
             texts=["Haa", "Huu"],
-            collection_vector_service_options=options,
-            collection_name=COLLECTION_NAME_VECTORIZE,
+            collection_vector_service_options=openai_vectorize_options,
+            collection_name=COLLECTION_NAME_VECTORIZE_OPENAI,
             **astradb_credentials,
         )
         try:
@@ -533,8 +610,8 @@ async def test_astradb_vectorstore_from_x_async_vectorize(
                 Document(page_content="HeeH"),
                 Document(page_content="HooH"),
             ],
-            collection_vector_service_options=options,
-            collection_name=COLLECTION_NAME_VECTORIZE,
+            collection_vector_service_options=openai_vectorize_options,
+            collection_name=COLLECTION_NAME_VECTORIZE_OPENAI,
             **astradb_credentials,
         )
         try:
@@ -544,8 +621,16 @@ async def test_astradb_vectorstore_from_x_async_vectorize(
         finally:
             await v_store_2.adelete_collection()
 
-    @pytest.mark.parametrize("vector_store", ["store_someemb", "vectorize_store"])
-    def test_astradb_vectorstore_crud(
+    @pytest.mark.parametrize(
+        "vector_store",
+        [
+            "store_someemb",
+            "vectorize_store",
+            "vectorize_store_w_header",
+            "vectorize_store_nvidia",
+        ],
+    )
+    def test_astradb_vectorstore_crud_sync(
         self, vector_store: str, request: pytest.FixtureRequest
     ) -> None:
         """Basic add/delete/update behaviour."""
@@ -605,7 +690,15 @@ def test_astradb_vectorstore_crud(
         res4 = vstore.similarity_search("ww", k=1, filter={"k": "w"})
         assert res4[0].metadata["ord"] == 205
 
-    @pytest.mark.parametrize("vector_store", ["store_someemb", "vectorize_store"])
+    @pytest.mark.parametrize(
+        "vector_store",
+        [
+            "store_someemb",
+            "vectorize_store",
+            "vectorize_store_w_header",
+            "vectorize_store_nvidia",
+        ],
+    )
     async def test_astradb_vectorstore_crud_async(
         self, vector_store: str, request: pytest.FixtureRequest
     ) -> None:
@@ -666,7 +759,9 @@ async def test_astradb_vectorstore_crud_async(
         res4 = await vstore.asimilarity_search("ww", k=1, filter={"k": "w"})
         assert res4[0].metadata["ord"] == 205
 
-    def test_astradb_vectorstore_mmr(self, store_parseremb: AstraDBVectorStore) -> None:
+    def test_astradb_vectorstore_mmr_sync(
+        self, store_parseremb: AstraDBVectorStore
+    ) -> None:
         """
         MMR testing. We work on the unit circle with angle multiples
         of 2*pi/20 and prepare a store with known vectors for a controlled
@@ -719,7 +814,7 @@ def _v_from_i(i: int, N: int) -> str:
         res_i_vals = {doc.metadata["i"] for doc in res1}
         assert res_i_vals == {0, 4}
 
-    def test_astradb_vectorstore_mmr_vectorize_unsupported(
+    def test_astradb_vectorstore_mmr_vectorize_unsupported_sync(
         self, vectorize_store: AstraDBVectorStore
     ) -> None:
         """
@@ -737,7 +832,15 @@ async def test_astradb_vectorstore_mmr_vectorize_unsupported_async(
         with pytest.raises(ValueError):
             await vectorize_store.amax_marginal_relevance_search("aa", k=2, fetch_k=3)
 
-    @pytest.mark.parametrize("vector_store", ["store_someemb", "vectorize_store"])
+    @pytest.mark.parametrize(
+        "vector_store",
+        [
+            "store_someemb",
+            "vectorize_store",
+            "vectorize_store_w_header",
+            "vectorize_store_nvidia",
+        ],
+    )
     def test_astradb_vectorstore_metadata(
         self, vector_store: str, request: pytest.FixtureRequest
     ) -> None:
@@ -804,7 +907,7 @@ def test_astradb_vectorstore_metadata(
         assert {doc.page_content for doc in res4} == {"q", "r"}
 
     @pytest.mark.parametrize("vector_store", ["store_parseremb"])
-    def test_astradb_vectorstore_similarity_scale(
+    def test_astradb_vectorstore_similarity_scale_sync(
         self, vector_store: str, request: pytest.FixtureRequest
     ) -> None:
         """Scale of the similarity scores."""
@@ -845,7 +948,15 @@ async def test_astradb_vectorstore_similarity_scale_async(
         sco_near, sco_far = scores
         assert abs(1 - sco_near) < MATCH_EPSILON and abs(sco_far) < MATCH_EPSILON
 
-    @pytest.mark.parametrize("vector_store", ["store_someemb", "vectorize_store"])
+    @pytest.mark.parametrize(
+        "vector_store",
+        [
+            "store_someemb",
+            "vectorize_store",
+            "vectorize_store_w_header",
+            "vectorize_store_nvidia",
+        ],
+    )
     def test_astradb_vectorstore_massive_delete(
         self, vector_store: str, request: pytest.FixtureRequest
     ) -> None:
@@ -894,7 +1005,7 @@ def test_astradb_vectorstore_delete_collection(
         with pytest.raises(ValueError):
             _ = v_store.similarity_search("hah", k=10)
 
-    def test_astradb_vectorstore_custom_params(
+    def test_astradb_vectorstore_custom_params_sync(
         self, astradb_credentials: AstraDBCredentials
     ) -> None:
         """Custom batch size and concurrency params."""
@@ -1057,7 +1168,7 @@ def test_astradb_vectorstore_metrics(
             else:
                 vstore_euc.clear()
 
-    def test_astradb_vectorstore_indexing(self) -> None:
+    def test_astradb_vectorstore_indexing_sync(self) -> None:
         """
         Test that the right errors/warnings are issued depending
         on the compatibility of on-DB indexing settings and the requested ones.
diff --git a/libs/astradb/tests/unit_tests/test_astra_db_environment.py b/libs/astradb/tests/unit_tests/test_astra_db_environment.py
index 22ffb89..62f6cbc 100644
--- a/libs/astradb/tests/unit_tests/test_astra_db_environment.py
+++ b/libs/astradb/tests/unit_tests/test_astra_db_environment.py
@@ -17,7 +17,7 @@ def test_initialization(self) -> None:
 
         # clean environment
         if TOKEN_ENV_VAR in os.environ:
-            del os.environ["TOKEN_ENV_VAR"]
+            del os.environ[TOKEN_ENV_VAR]
         if API_ENDPOINT_ENV_VAR in os.environ:
             del os.environ[API_ENDPOINT_ENV_VAR]
         if NAMESPACE_ENV_VAR in os.environ: