Support Vald secure connection (langchain-ai#13269)

**Description:** 
When using Vald, only insecure grpc connection was supported, so secure
connection is now supported.
In addition, grpc metadata can be added to Vald requests to enable
authentication with a token.

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docs/docs/integrations/vectorstores/vald.ipynb

@@ -149,6 +149,156 @@
    "source": [
     "db.max_marginal_relevance_search(query, k=2, fetch_k=10)"
    ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7dc7ce16-35af-49b7-8009-7eaadb7abbcb",
+   "metadata": {},
+   "source": [
+    "## Example of using secure connection\n",
+    "In order to run this notebook, it is necessary to run a Vald cluster with secure connection.\n",
+    "\n",
+    "Here is an example of a Vald cluster with the following configuration using [Athenz](https://github.com/AthenZ/athenz) authentication.\n",
+    "\n",
+    "ingress(TLS) -> [authorization-proxy](https://github.com/AthenZ/authorization-proxy)(Check athenz-role-auth in grpc metadata) -> vald-lb-gateway"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6894c02d-7a86-4600-bab1-f7e9cce79333",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import grpc\n",
+    "\n",
+    "with open(\"test_root_cacert.crt\", \"rb\") as root:\n",
+    "    credentials = grpc.ssl_channel_credentials(root_certificates=root.read())\n",
+    "\n",
+    "# Refresh is required for server use\n",
+    "with open(\".ztoken\", \"rb\") as ztoken:\n",
+    "    token = ztoken.read().strip()\n",
+    "\n",
+    "metadata = [(b\"athenz-role-auth\", token)]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "cc15c20b-485d-435e-a2ec-c7dcb9db40b5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.document_loaders import TextLoader\n",
+    "from langchain.embeddings import HuggingFaceEmbeddings\n",
+    "from langchain.text_splitter import CharacterTextSplitter\n",
+    "from langchain.vectorstores import Vald\n",
+    "\n",
+    "raw_documents = TextLoader(\"state_of_the_union.txt\").load()\n",
+    "text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)\n",
+    "documents = text_splitter.split_documents(raw_documents)\n",
+    "embeddings = HuggingFaceEmbeddings()\n",
+    "\n",
+    "db = Vald.from_documents(\n",
+    "    documents,\n",
+    "    embeddings,\n",
+    "    host=\"localhost\",\n",
+    "    port=443,\n",
+    "    grpc_use_secure=True,\n",
+    "    grpc_credentials=credentials,\n",
+    "    grpc_metadata=metadata,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "069b96c6-6db2-46ce-a820-24e8933156a0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "query = \"What did the president say about Ketanji Brown Jackson\"\n",
+    "docs = db.similarity_search(query, grpc_metadata=metadata)\n",
+    "docs[0].page_content"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8327accb-6776-4a20-a325-b5da92e3a049",
+   "metadata": {},
+   "source": [
+    "### Similarity search by vector"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d0ab2a97-83e4-490d-81a5-8aaa032d8811",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "embedding_vector = embeddings.embed_query(query)\n",
+    "docs = db.similarity_search_by_vector(embedding_vector, grpc_metadata=metadata)\n",
+    "docs[0].page_content"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f3f987bd-512e-4e29-acb3-e110e74b51a2",
+   "metadata": {},
+   "source": [
+    "### Similarity search with score"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "88dd39bc-8764-4a8c-ac89-06e2341aefa6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "docs_and_scores = db.similarity_search_with_score(query, grpc_metadata=metadata)\n",
+    "docs_and_scores[0]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fef1bd41-484e-4845-88a9-c7f504068db0",
+   "metadata": {},
+   "source": [
+    "### Maximal Marginal Relevance Search (MMR)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6cf08477-87b0-41ac-9536-52dec1c5d67f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "retriever = db.as_retriever(\n",
+    "    search_kwargs={\"search_type\": \"mmr\", \"grpc_metadata\": metadata}\n",
+    ")\n",
+    "retriever.get_relevant_documents(query, grpc_metadata=metadata)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f994fa57-53e4-4fe6-9418-59a5136c6fe8",
+   "metadata": {},
+   "source": [
+    "Or:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2111ce42-07c7-4ccc-bdbf-459165e3a410",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "db.max_marginal_relevance_search(query, k=2, fetch_k=10, grpc_metadata=metadata)"
+   ]
   }
  ],
  "metadata": {
@@ -167,7 +317,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.4"
+   "version": "3.11.4"
   }
  },
  "nbformat": 4,