diff --git a/.github/workflows/run_tests.yml b/.github/workflows/run_tests.yml
index de5897bb..751e9a0f 100644
--- a/.github/workflows/run_tests.yml
+++ b/.github/workflows/run_tests.yml
@@ -64,6 +64,7 @@ jobs:
         GCP_PROJECT_ID: ${{ secrets.GCP_PROJECT_ID }}
         COHERE_API_KEY: ${{ secrets.COHERE_API_KEY }}
         MISTRAL_API_KEY: ${{ secrets.MISTRAL_API_KEY }}
+        VOYAGE_API_KEY: ${{ secrets.VOYAGE_API_KEY }}
         AZURE_OPENAI_API_KEY: ${{secrets.AZURE_OPENAI_API_KEY}}
         AZURE_OPENAI_ENDPOINT: ${{secrets.AZURE_OPENAI_ENDPOINT}}
         AZURE_OPENAI_DEPLOYMENT_NAME: ${{secrets.AZURE_OPENAI_DEPLOYMENT_NAME}}
@@ -86,6 +87,7 @@ jobs:
         GCP_PROJECT_ID: ${{ secrets.GCP_PROJECT_ID }}
         COHERE_API_KEY: ${{ secrets.COHERE_API_KEY }}
         MISTRAL_API_KEY: ${{ secrets.MISTRAL_API_KEY }}
+        VOYAGE_API_KEY: ${{ secrets.VOYAGE_API_KEY }}
         AZURE_OPENAI_API_KEY: ${{secrets.AZURE_OPENAI_API_KEY}}
         AZURE_OPENAI_ENDPOINT: ${{secrets.AZURE_OPENAI_ENDPOINT}}
         AZURE_OPENAI_DEPLOYMENT_NAME: ${{secrets.AZURE_OPENAI_DEPLOYMENT_NAME}}
diff --git a/README.md b/README.md
index a576ce5c..e0a52804 100644
--- a/README.md
+++ b/README.md
@@ -49,7 +49,7 @@ Install `redisvl` into your Python (>=3.8) environment using `pip`:
 ```bash
 pip install redisvl
 ```
-> For more detailed instructions, visit the [installation guide](https://docs.redisvl.com/en/latest/overview/installation.html).
+> For more detailed instructions, visit the [installation guide](https://docs.redisvl.com/en/stable/overview/installation.html).
 
 ## Setting up Redis
 
@@ -71,7 +71,7 @@ Choose from multiple Redis deployment options:
 
 
 ## 🗃️ Redis Index Management
-1. [Design a schema for your use case](https://docs.redisvl.com/en/latest/user_guide/getting_started_01.html#define-an-indexschema) that models your dataset with built-in Redis  and indexable fields (*e.g. text, tags, numerics, geo, and vectors*). [Load a schema](https://docs.redisvl.com/en/latest/user_guide/getting_started_01.html#example-schema-creation) from a YAML file:
+1. [Design a schema for your use case](https://docs.redisvl.com/en/stable/user_guide/getting_started_01.html#define-an-indexschema) that models your dataset with built-in Redis  and indexable fields (*e.g. text, tags, numerics, geo, and vectors*). [Load a schema](https://docs.redisvl.com/en/stable/user_guide/getting_started_01.html#example-schema-creation) from a YAML file:
     ```yaml
     index:
       name: user-idx
@@ -121,7 +121,7 @@ Choose from multiple Redis deployment options:
     })
     ```
 
-2. [Create a SearchIndex](https://docs.redisvl.com/en/latest/user_guide/getting_started_01.html#create-a-searchindex) class with an input schema and client connection in order to perform admin and search operations on your index in Redis:
+2. [Create a SearchIndex](https://docs.redisvl.com/en/stable/user_guide/getting_started_01.html#create-a-searchindex) class with an input schema and client connection in order to perform admin and search operations on your index in Redis:
     ```python
     from redis import Redis
     from redisvl.index import SearchIndex
@@ -133,10 +133,10 @@ Choose from multiple Redis deployment options:
     # Create the index in Redis
     index.create()
     ```
-    > Async compliant search index class also available: [AsyncSearchIndex](https://docs.redisvl.com/en/latest/api/searchindex.html#redisvl.index.AsyncSearchIndex).
+    > Async compliant search index class also available: [AsyncSearchIndex](https://docs.redisvl.com/en/stable/api/searchindex.html#redisvl.index.AsyncSearchIndex).
 
-3. [Load](https://docs.redisvl.com/en/latest/user_guide/getting_started_01.html#load-data-to-searchindex)
-and [fetch](https://docs.redisvl.com/en/latest/user_guide/getting_started_01.html#fetch-an-object-from-redis) data to/from your Redis instance:
+3. [Load](https://docs.redisvl.com/en/stable/user_guide/getting_started_01.html#load-data-to-searchindex)
+and [fetch](https://docs.redisvl.com/en/stable/user_guide/getting_started_01.html#fetch-an-object-from-redis) data to/from your Redis instance:
     ```python
     data = {"user": "john", "credit_score": "high", "embedding": [0.23, 0.49, -0.18, 0.95]}
 
@@ -151,7 +151,7 @@ and [fetch](https://docs.redisvl.com/en/latest/user_guide/getting_started_01.htm
 
 Define queries and perform advanced searches over your indices, including the combination of vectors, metadata filters, and more.
 
-- [VectorQuery](https://docs.redisvl.com/en/latest/api/query.html#vectorquery) - Flexible vector queries with customizable filters enabling semantic search:
+- [VectorQuery](https://docs.redisvl.com/en/stable/api/query.html#vectorquery) - Flexible vector queries with customizable filters enabling semantic search:
 
     ```python
     from redisvl.query import VectorQuery
@@ -179,24 +179,25 @@ Define queries and perform advanced searches over your indices, including the co
     results = index.query(query)
     ```
 
-- [RangeQuery](https://docs.redisvl.com/en/latest/api/query.html#rangequery) - Vector search within a defined range paired with customizable filters
-- [FilterQuery](https://docs.redisvl.com/en/latest/api/query.html#filterquery) - Standard search using filters and the full-text search
-- [CountQuery](https://docs.redisvl.com/en/latest/api/query.html#countquery) - Count the number of indexed records given attributes
+- [RangeQuery](https://docs.redisvl.com/en/stable/api/query.html#rangequery) - Vector search within a defined range paired with customizable filters
+- [FilterQuery](https://docs.redisvl.com/en/stable/api/query.html#filterquery) - Standard search using filters and the full-text search
+- [CountQuery](https://docs.redisvl.com/en/stable/api/query.html#countquery) - Count the number of indexed records given attributes
 
-> Read more about building [advanced Redis queries](https://docs.redisvl.com/en/latest/user_guide/hybrid_queries_02.html).
+> Read more about building [advanced Redis queries](https://docs.redisvl.com/en/stable/user_guide/hybrid_queries_02.html).
 
 
 ## 🔧  Utilities
 
 ### Vectorizers
 Integrate with popular embedding providers to greatly simplify the process of vectorizing unstructured data for your index and queries:
-- [AzureOpenAI](https://docs.redisvl.com/en/latest/api/vectorizer.html#azureopenaitextvectorizer)
-- [Cohere](https://docs.redisvl.com/en/latest/api/vectorizer.html#coheretextvectorizer)
-- [Custom](https://docs.redisvl.com/en/latest/api/vectorizer.html#customtextvectorizer)
-- [GCP VertexAI](https://docs.redisvl.com/en/latest/api/vectorizer.html#vertexaitextvectorizer)
-- [HuggingFace](https://docs.redisvl.com/en/latest/api/vectorizer.html#hftextvectorizer)
-- [Mistral](https://docs.redisvl.com/en/latest/api/vectorizer/html#mistralaitextvectorizer)
-- [OpenAI](https://docs.redisvl.com/en/latest/api/vectorizer.html#openaitextvectorizer)
+- [AzureOpenAI](https://docs.redisvl.com/en/stable/api/vectorizer.html#azureopenaitextvectorizer)
+- [Cohere](https://docs.redisvl.com/en/stable/api/vectorizer.html#coheretextvectorizer)
+- [Custom](https://docs.redisvl.com/en/stable/api/vectorizer.html#customtextvectorizer)
+- [GCP VertexAI](https://docs.redisvl.com/en/stable/api/vectorizer.html#vertexaitextvectorizer)
+- [HuggingFace](https://docs.redisvl.com/en/stable/api/vectorizer.html#hftextvectorizer)
+- [Mistral](https://docs.redisvl.com/en/stable/api/vectorizer/html#mistralaitextvectorizer)
+- [OpenAI](https://docs.redisvl.com/en/stable/api/vectorizer.html#openaitextvectorizer)
+- [VoyageAI](https://docs.redisvl.com/en/stable/api/vectorizer/html#voyageaitextvectorizer)
 
 ```python
 from redisvl.utils.vectorize import CohereTextVectorizer
@@ -215,11 +216,11 @@ embeddings = co.embed_many(
 )
 ```
 
-> Learn more about using [vectorizers]((https://docs.redisvl.com/en/latest/user_guide/vectorizers_04.html)) in your embedding workflows.
+> Learn more about using [vectorizers]((https://docs.redisvl.com/en/stable/user_guide/vectorizers_04.html)) in your embedding workflows.
 
 
 ### Rerankers
-[Integrate with popular reranking providers](https://docs.redisvl.com/en/latest/user_guide/rerankers_06.html) to improve the relevancy of the initial search results from Redis
+[Integrate with popular reranking providers](https://docs.redisvl.com/en/stable/user_guide/rerankers_06.html) to improve the relevancy of the initial search results from Redis
 
 
 
@@ -229,7 +230,7 @@ We're excited to announce the support for **RedisVL Extensions**. These modules
 *Have an idea for another extension? Open a PR or reach out to us at applied.ai@redis.com. We're always open to feedback.*
 
 ### LLM Semantic Caching
-Increase application throughput and reduce the cost of using LLM models in production by leveraging previously generated knowledge with the [`SemanticCache`](https://docs.redisvl.com/en/latest/api/cache.html#semanticcache).
+Increase application throughput and reduce the cost of using LLM models in production by leveraging previously generated knowledge with the [`SemanticCache`](https://docs.redisvl.com/en/stable/api/cache.html#semanticcache).
 
 ```python
 from redisvl.extensions.llmcache import SemanticCache
@@ -256,11 +257,11 @@ print(response[0]["response"])
 >>> Paris
 ```
 
-> Learn more about [semantic caching]((https://docs.redisvl.com/en/latest/user_guide/llmcache_03.html)) for LLMs.
+> Learn more about [semantic caching]((https://docs.redisvl.com/en/stable/user_guide/llmcache_03.html)) for LLMs.
 
 ### LLM Session Management
 
-Improve personalization and accuracy of LLM responses by providing user chat history as context. Manage access to the session data using recency or relevancy, *powered by vector search* with the [`SemanticSessionManager`](https://docs.redisvl.com/en/latest/api/session_manager.html).
+Improve personalization and accuracy of LLM responses by providing user chat history as context. Manage access to the session data using recency or relevancy, *powered by vector search* with the [`SemanticSessionManager`](https://docs.redisvl.com/en/stable/api/session_manager.html).
 
 ```python
 from redisvl.extensions.session_manager import SemanticSessionManager
@@ -292,7 +293,7 @@ session.get_relevant("weather", top_k=1)
 ```stdout
 >>> [{"role": "user", "content": "what is the weather going to be today?"}]
 ```
-> Learn more about [LLM session management]((https://docs.redisvl.com/en/latest/user_guide/session_manager_07.html)).
+> Learn more about [LLM session management]((https://docs.redisvl.com/en/stable/user_guide/session_manager_07.html)).
 
 
 ### LLM Semantic Routing
@@ -329,7 +330,7 @@ router("Hi, good morning")
 ```stdout
 >>> RouteMatch(name='greeting', distance=0.273891836405)
 ```
-> Learn more about [semantic routing](https://docs.redisvl.com/en/latest/user_guide/semantic_router_08.html).
+> Learn more about [semantic routing](https://docs.redisvl.com/en/stable/user_guide/semantic_router_08.html).
 
 ## 🖥️ Command Line Interface
 Create, destroy, and manage Redis index configurations from a purpose-built CLI interface: `rvl`.
@@ -345,7 +346,7 @@ Commands:
         stats       Obtain statistics about an index
 ```
 
-> Read more about [using the CLI](https://docs.redisvl.com/en/latest/user_guide/cli.html).
+> Read more about [using the CLI](https://docs.redisvl.com/en/stable/user_guide/cli.html).
 
 ## 🚀 Why RedisVL?
 
@@ -359,9 +360,9 @@ The Redis Vector Library bridges the gap between the AI-native developer ecosyst
 ## 😁 Helpful Links
 
 For additional help, check out the following resources:
- - [Getting Started Guide](https://docs.redisvl.com/en/latest/user_guide/getting_started_01.html)
- - [API Reference](https://docs.redisvl.com/en/latest/api/index.html)
- - [Example Gallery](https://docs.redisvl.com/en/latest/examples/index.html)
+ - [Getting Started Guide](https://docs.redisvl.com/en/stable/user_guide/getting_started_01.html)
+ - [API Reference](https://docs.redisvl.com/en/stable/api/index.html)
+ - [Example Gallery](https://docs.redisvl.com/en/stable/examples/index.html)
  - [Redis AI Recipes](https://github.com/redis-developer/redis-ai-resources)
  - [Official Redis Vector API Docs](https://redis.io/docs/interact/search-and-query/advanced-concepts/vectors/)
 
diff --git a/docs/api/reranker.rst b/docs/api/reranker.rst
index 06bfc12c..3515b0ae 100644
--- a/docs/api/reranker.rst
+++ b/docs/api/reranker.rst
@@ -3,7 +3,7 @@ Rerankers
 ***********
 
 CohereReranker
-================
+==============
 
 .. _coherereranker_api:
 
@@ -15,7 +15,7 @@ CohereReranker
 
 
 HFCrossEncoderReranker
-========================
+======================
 
 .. _hfcrossencoderreranker_api:
 
@@ -23,4 +23,16 @@ HFCrossEncoderReranker
 
 .. autoclass:: HFCrossEncoderReranker
    :show-inheritance:
-   :members:
\ No newline at end of file
+   :members:
+
+
+VoyageAIReranker
+================
+
+.. _voyageaireranker_api:
+
+.. currentmodule:: redisvl.utils.rerank.voyageai
+
+.. autoclass:: VoyageAIReranker
+   :show-inheritance:
+   :members:
diff --git a/docs/api/vectorizer.rst b/docs/api/vectorizer.rst
index f53b22d6..6bc2d6de 100644
--- a/docs/api/vectorizer.rst
+++ b/docs/api/vectorizer.rst
@@ -61,6 +61,7 @@ CohereTextVectorizer
    :show-inheritance:
    :members:
 
+
 BedrockTextVectorizer
 =====================
 
@@ -72,6 +73,7 @@ BedrockTextVectorizer
    :show-inheritance:
    :members:
 
+
 CustomTextVectorizer
 ====================
 
@@ -81,4 +83,16 @@ CustomTextVectorizer
 
 .. autoclass:: CustomTextVectorizer
    :show-inheritance:
-   :members:
\ No newline at end of file
+   :members:
+
+
+VoyageAITextVectorizer
+======================
+
+.. _voyageaitextvectorizer_api:
+
+.. currentmodule:: redisvl.utils.vectorize.text.voyageai
+
+.. autoclass:: VoyageAITextVectorizer
+   :show-inheritance:
+   :members:
diff --git a/docs/user_guide/rerankers_06.ipynb b/docs/user_guide/rerankers_06.ipynb
index adfc2af8..47327f17 100644
--- a/docs/user_guide/rerankers_06.ipynb
+++ b/docs/user_guide/rerankers_06.ipynb
@@ -13,6 +13,7 @@
     "\n",
     "- A re-ranker that uses pre-trained [Cross-Encoders](https://sbert.net/examples/applications/cross-encoder/README.html) which can use models from [Hugging Face cross encoder models](https://huggingface.co/cross-encoder) or Hugging Face models that implement a cross encoder function ([example: BAAI/bge-reranker-base](https://huggingface.co/BAAI/bge-reranker-base)).\n",
     "- The [Cohere /rerank API](https://docs.cohere.com/docs/rerank-2).\n",
+    "- The [VoyageAI /rerank API](https://docs.voyageai.com/docs/reranker).\n",
     "\n",
     "Before running this notebook, be sure to:\n",
     "1. Have installed ``redisvl`` and have that environment active for this notebook.\n",
@@ -306,7 +307,100 @@
     "for result, score in zip(results, scores):\n",
     "    print(score, \" -- \", result)"
    ]
-  }
+  },
+
+  {
+    "cell_type": "markdown",
+    "metadata": {},
+    "source": [
+     "### Using the VoyageAI Reranker\n",
+     "\n",
+     "To initialize the VoyageAI reranker you'll need to install the voyaeai library and provide the right VoyageAI API Key."
+    ]
+   },
+   {
+    "cell_type": "code",
+    "execution_count": 32,
+    "metadata": {
+     "metadata": {}
+    },
+    "outputs": [],
+    "source": [
+     "#!pip install voyageai"
+    ]
+   },
+   {
+    "cell_type": "code",
+    "execution_count": 33,
+    "metadata": {
+     "metadata": {}
+    },
+    "outputs": [],
+    "source": [
+     "import getpass\n",
+     "\n",
+     "# setup the API Key\n",
+     "api_key = os.environ.get(\"VOYAGE_API_KEY\") or getpass.getpass(\"Enter your VoyageAI API key: \")"
+    ]
+   },
+   {
+    "cell_type": "code",
+    "execution_count": 34,
+    "metadata": {
+     "metadata": {}
+    },
+    "outputs": [],
+    "source": [
+     "from redisvl.utils.rerank import VoyageAIReranker\n",
+     "\n",
+     "reranker = VoyageAIReranker(model=\"rerank-lite-1\", limit=3, api_config={\"api_key\": api_key})",
+     "# Please check the available models at https://docs.voyageai.com/docs/reranker"
+    ]
+   },
+    {
+    "cell_type": "markdown",
+    "metadata": {},
+    "source": [
+     "### Rerank documents with VoyageAIReranker\n",
+     "\n",
+     "Below we will use the `VoyageAIReranker` to rerank and also truncate the list of\n",
+     "documents above based on relevance to the initial query."
+    ]
+   },
+   {
+    "cell_type": "code",
+    "execution_count": 35,
+    "metadata": {
+     "metadata": {}
+    },
+    "outputs": [],
+    "source": [
+     "results, scores = reranker.rank(query=query, docs=docs)"
+    ]
+   },
+   {
+    "cell_type": "code",
+    "execution_count": 36,
+    "metadata": {
+     "metadata": {}
+    },
+    "outputs": [
+     {
+      "name": "stdout",
+      "output_type": "stream",
+      "text": [
+       "0.796875  --  Washington, D.C. (also known as simply Washington or D.C., and officially as the District of Columbia) is the capital of the United States. It is a federal district. The President of the USA and many major national government offices are in the territory. This makes it the political center of the United States of America.\n",
+       "0.578125  --  Charlotte Amalie is the capital and largest city of the United States Virgin Islands. It has about 20,000 people. The city is on the island of Saint Thomas.\n",
+       "0.5625  --  Carson City is the capital city of the American state of Nevada. At the 2010 United States Census, Carson City had a population of 55,274.\n"
+      ]
+     }
+    ],
+    "source": [
+     "for result, score in zip(results, scores):\n",
+     "    print(score, \" -- \", result)"
+    ]
+   }
+ 
  ],
  "metadata": {
   "kernelspec": {
diff --git a/docs/user_guide/vectorizers_04.ipynb b/docs/user_guide/vectorizers_04.ipynb
index 06bd198f..ad34672e 100644
--- a/docs/user_guide/vectorizers_04.ipynb
+++ b/docs/user_guide/vectorizers_04.ipynb
@@ -15,6 +15,7 @@
     "5. Mistral AI\n",
     "6. Amazon Bedrock\n",
     "7. Bringing your own vectorizer\n",
+    "8. VoyageAI\n",
     "\n",
     "Before running this notebook, be sure to\n",
     "1. Have installed ``redisvl`` and have that environment active for this notebook.\n",
@@ -496,6 +497,76 @@
    ]
   },
   {
+    "cell_type": "markdown",
+    "metadata": {},
+    "source": [
+     "### VoyageAI\n",
+     "\n",
+     "[VoyageAI](https://dash.voyageai.com/) allows you to implement language AI into your product. The `VoyageAITextVectorizer` makes it simple to use RedisVL with the embeddings models at VoyageAI. For this you will need to install `voyageai`.\n",
+     "\n",
+     "```bash\n",
+     "pip install voyageai\n",
+     "```"
+    ]
+   },
+   {
+    "cell_type": "code",
+    "execution_count": 11,
+    "metadata": {},
+    "outputs": [],
+    "source": [
+     "import getpass\n",
+     "# setup the API Key\n",
+     "api_key = os.environ.get(\"VOYAGE_API_KEY\") or getpass.getpass(\"Enter your VoyageAI API key: \")"
+    ]
+   },
+   {
+    "cell_type": "markdown",
+    "metadata": {},
+    "source": [
+     "\n",
+     "Special attention needs to be paid to the `input_type` parameter for each `embed` call. For example, for embedding \n",
+     "queries, you should set `input_type='query'`; for embedding documents, set `input_type='document'`. See\n",
+     "more information [here](https://docs.voyageai.com/docs/embeddings)"
+    ]
+   },
+   {
+    "cell_type": "code",
+    "execution_count": 12,
+    "metadata": {},
+    "outputs": [
+     {
+      "name": "stdout",
+      "output_type": "stream",
+      "text": [
+       "Vector dimensions:  1024\n",
+       "[0.015814896672964096, 0.046988241374492645, -0.00518248463049531, -0.05383478105068207, -0.015586535446345806, -0.0837097093462944, 0.03744547441601753, -0.007797810714691877, 0.00717928446829319, 0.06857716292142868]\n",
+       "Vector dimensions:  1024\n",
+       "[0.006725038401782513, 0.01441393606364727, -0.030212024226784706, -0.06782275438308716, -0.021446991711854935, -0.07667966187000275, 0.01804908737540245, -0.015767497941851616, -0.02152789570391178, 0.049741245806217194]\n"
+      ]
+     }
+    ],
+    "source": [
+     "from redisvl.utils.vectorize import VoyageAITextVectorizer\n",
+     "\n",
+     "# create a vectorizer\n",
+     "vo = VoyageAITextVectorizer(\n",
+     "    model=\"voyage-law-2\",  # Please check the available models at https://docs.voyageai.com/docs/embeddings\n",
+     "    api_config={\"api_key\": api_key},\n",
+     ")\n",
+     "\n",
+     "# embed a search query\n",
+     "test = vo.embed(\"This is a test sentence.\", input_type='query')\n",
+     "print(\"Vector dimensions: \", len(test))\n",
+     "print(test[:10])\n",
+     "\n",
+     "# embed a document\n",
+     "test = vo.embed(\"This is a test sentence.\", input_type='document')\n",
+     "print(\"Vector dimensions: \", len(test))\n",
+     "print(test[:10])"
+    ]
+   },
+   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
diff --git a/poetry.lock b/poetry.lock
index bf582bb6..1a3167fb 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -1,4 +1,4 @@
-# This file is automatically @generated by Poetry 1.8.5 and should not be changed by hand.
+# This file is automatically @generated by Poetry 1.8.3 and should not be changed by hand.
 
 [[package]]
 name = "accessible-pygments"
@@ -18,6 +18,140 @@ pygments = ">=1.5"
 dev = ["pillow", "pkginfo (>=1.10)", "playwright", "pre-commit", "setuptools", "twine (>=5.0)"]
 tests = ["hypothesis", "pytest"]
 
+[[package]]
+name = "aiohappyeyeballs"
+version = "2.4.4"
+description = "Happy Eyeballs for asyncio"
+optional = true
+python-versions = ">=3.8"
+files = [
+    {file = "aiohappyeyeballs-2.4.4-py3-none-any.whl", hash = "sha256:a980909d50efcd44795c4afeca523296716d50cd756ddca6af8c65b996e27de8"},
+    {file = "aiohappyeyeballs-2.4.4.tar.gz", hash = "sha256:5fdd7d87889c63183afc18ce9271f9b0a7d32c2303e394468dd45d514a757745"},
+]
+
+[[package]]
+name = "aiohttp"
+version = "3.11.11"
+description = "Async http client/server framework (asyncio)"
+optional = true
+python-versions = ">=3.9"
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+
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 [[package]]
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 version = "0.7.16"
@@ -323,17 +457,17 @@ css = ["tinycss2 (>=1.1.0,<1.5)"]
 
 [[package]]
 name = "boto3"
-version = "1.35.92"
+version = "1.35.97"
 description = "The AWS SDK for Python"
 optional = true
 python-versions = ">=3.8"
 files = [
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+botocore = ">=1.35.97,<1.36.0"
 jmespath = ">=0.7.1,<2.0.0"
 s3transfer = ">=0.10.0,<0.11.0"
 
@@ -342,21 +476,21 @@ crt = ["botocore[crt] (>=1.21.0,<2.0a0)"]
 
 [[package]]
 name = "botocore"
-version = "1.35.92"
+version = "1.35.97"
 description = "Low-level, data-driven core of boto 3."
 optional = true
 python-versions = ">=3.8"
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 [package.dependencies]
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 python-dateutil = ">=2.1,<3.0.0"
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 ]
 
 [package.extras]
@@ -580,13 +714,13 @@ colorama = {version = "*", markers = "platform_system == \"Windows\""}
 
 [[package]]
 name = "cohere"
-version = "5.13.5"
+version = "5.13.6"
 description = ""
 optional = true
 python-versions = "<4.0,>=3.9"
 files = [
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 version = "2024.12.0"
@@ -1135,13 +1370,13 @@ requests = ["requests (>=2.20.0,<3.0.0.dev0)"]
 
 [[package]]
 name = "google-cloud-aiplatform"
-version = "1.75.0"
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 description = "Vertex AI API client library"
 optional = true
 python-versions = ">=3.8"
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 pydantic = "<3"
 shapely = "<3.0.0dev"
+typing-extensions = "*"
 
 [package.extras]
 autologging = ["mlflow (>=1.27.0,<=2.16.0)"]
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@@ -2550,8 +2786,8 @@ files = [
 
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@@ -2576,6 +2812,110 @@ docs = ["sphinx"]
 gmpy = ["gmpy2 (>=2.1.0a4)"]
 tests = ["pytest (>=4.6)"]
 
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 [package.dependencies]
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+    {file = "SQLAlchemy-2.0.37-cp39-cp39-win_amd64.whl", hash = "sha256:4b12885dc85a2ab2b7d00995bac6d967bffa8594123b02ed21e8eb2205a7584b"},
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+    {file = "sqlalchemy-2.0.37.tar.gz", hash = "sha256:12b28d99a9c14eaf4055810df1001557176716de0167b91026e648e65229bffb"},
 ]
 
 [package.dependencies]
-greenlet = {version = "!=0.4.17", markers = "python_version < \"3.13\" and (platform_machine == \"aarch64\" or platform_machine == \"ppc64le\" or platform_machine == \"x86_64\" or platform_machine == \"amd64\" or platform_machine == \"AMD64\" or platform_machine == \"win32\" or platform_machine == \"WIN32\")"}
+greenlet = {version = "!=0.4.17", markers = "python_version < \"3.14\" and (platform_machine == \"aarch64\" or platform_machine == \"ppc64le\" or platform_machine == \"x86_64\" or platform_machine == \"amd64\" or platform_machine == \"AMD64\" or platform_machine == \"win32\" or platform_machine == \"WIN32\")"}
 typing-extensions = ">=4.6.0"
 
 [package.extras]
@@ -5268,13 +5699,13 @@ test = ["argcomplete (>=3.0.3)", "mypy (>=1.7.0)", "pre-commit", "pytest (>=7.0,
 
 [[package]]
 name = "transformers"
-version = "4.47.1"
+version = "4.48.0"
 description = "State-of-the-art Machine Learning for JAX, PyTorch and TensorFlow"
 optional = true
 python-versions = ">=3.9.0"
 files = [
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-    {file = "transformers-4.47.1.tar.gz", hash = "sha256:6c29c05a5f595e278481166539202bf8641281536df1c42357ee58a45d0a564a"},
+    {file = "transformers-4.48.0-py3-none-any.whl", hash = "sha256:6d3de6d71cb5f2a10f9775ccc17abce9620195caaf32ec96542bd2a6937f25b0"},
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 ]
 
 [package.dependencies]
@@ -5291,16 +5722,16 @@ tqdm = ">=4.27"
 
 [package.extras]
 accelerate = ["accelerate (>=0.26.0)"]
-agents = ["Pillow (>=10.0.1,<=15.0)", "accelerate (>=0.26.0)", "datasets (!=2.5.0)", "diffusers", "opencv-python", "sentencepiece (>=0.1.91,!=0.1.92)", "torch"]
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+agents = ["Pillow (>=10.0.1,<=15.0)", "accelerate (>=0.26.0)", "datasets (!=2.5.0)", "diffusers", "opencv-python", "sentencepiece (>=0.1.91,!=0.1.92)", "torch (>=2.0)"]
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 audio = ["kenlm", "librosa", "phonemizer", "pyctcdecode (>=0.4.0)"]
 benchmark = ["optimum-benchmark (>=0.3.0)"]
-codecarbon = ["codecarbon (==1.2.0)"]
+codecarbon = ["codecarbon (>=2.8.1)"]
 deepspeed = ["accelerate (>=0.26.0)", "deepspeed (>=0.9.3)"]
-deepspeed-testing = ["GitPython (<3.1.19)", "accelerate (>=0.26.0)", "beautifulsoup4", "cookiecutter (==1.7.3)", "datasets (!=2.5.0)", "deepspeed (>=0.9.3)", "dill (<0.3.5)", "evaluate (>=0.2.0)", "faiss-cpu", "nltk (<=3.8.1)", "optuna", "parameterized", "protobuf", "psutil", "pydantic", "pytest (>=7.2.0,<8.0.0)", "pytest-rich", "pytest-timeout", "pytest-xdist", "rjieba", "rouge-score (!=0.0.7,!=0.0.8,!=0.1,!=0.1.1)", "ruff (==0.5.1)", "sacrebleu (>=1.4.12,<2.0.0)", "sacremoses", "sentencepiece (>=0.1.91,!=0.1.92)", "tensorboard", "timeout-decorator"]
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+dev-torch = ["GitPython (<3.1.19)", "Pillow (>=10.0.1,<=15.0)", "accelerate (>=0.26.0)", "beautifulsoup4", "codecarbon (>=2.8.1)", "cookiecutter (==1.7.3)", "datasets (!=2.5.0)", "dill (<0.3.5)", "evaluate (>=0.2.0)", "faiss-cpu", "fugashi (>=1.0)", "ipadic (>=1.0.0,<2.0)", "isort (>=5.5.4)", "kenlm", "libcst", "librosa", "nltk (<=3.8.1)", "onnxruntime (>=1.4.0)", "onnxruntime-tools (>=1.4.2)", "optuna", "parameterized", "phonemizer", "protobuf", "psutil", "pyctcdecode (>=0.4.0)", "pydantic", "pytest (>=7.2.0,<8.0.0)", "pytest-asyncio", "pytest-rich", "pytest-timeout", "pytest-xdist", "ray[tune] (>=2.7.0)", "rhoknp (>=1.1.0,<1.3.1)", "rich", "rjieba", "rouge-score (!=0.0.7,!=0.0.8,!=0.1,!=0.1.1)", "ruff (==0.5.1)", "sacrebleu (>=1.4.12,<2.0.0)", "sacremoses", "scikit-learn", "sentencepiece (>=0.1.91,!=0.1.92)", "sigopt", "sudachidict-core (>=20220729)", "sudachipy (>=0.6.6)", "tensorboard", "timeout-decorator", "timm (<=1.0.11)", "tokenizers (>=0.21,<0.22)", "torch (>=2.0)", "torchaudio", "torchvision", "unidic (>=1.0.2)", "unidic-lite (>=1.0.7)", "urllib3 (<2.0.0)"]
 flax = ["flax (>=0.4.1,<=0.7.0)", "jax (>=0.4.1,<=0.4.13)", "jaxlib (>=0.4.1,<=0.4.13)", "optax (>=0.0.8,<=0.1.4)", "scipy (<1.13.0)"]
 flax-speech = ["kenlm", "librosa", "phonemizer", "pyctcdecode (>=0.4.0)"]
 ftfy = ["ftfy"]
@@ -5321,17 +5752,17 @@ serving = ["fastapi", "pydantic", "starlette", "uvicorn"]
 sigopt = ["sigopt"]
 sklearn = ["scikit-learn"]
 speech = ["kenlm", "librosa", "phonemizer", "pyctcdecode (>=0.4.0)", "torchaudio"]
-testing = ["GitPython (<3.1.19)", "beautifulsoup4", "cookiecutter (==1.7.3)", "datasets (!=2.5.0)", "dill (<0.3.5)", "evaluate (>=0.2.0)", "faiss-cpu", "nltk (<=3.8.1)", "parameterized", "psutil", "pydantic", "pytest (>=7.2.0,<8.0.0)", "pytest-rich", "pytest-timeout", "pytest-xdist", "rjieba", "rouge-score (!=0.0.7,!=0.0.8,!=0.1,!=0.1.1)", "ruff (==0.5.1)", "sacrebleu (>=1.4.12,<2.0.0)", "sacremoses", "sentencepiece (>=0.1.91,!=0.1.92)", "tensorboard", "timeout-decorator"]
+testing = ["GitPython (<3.1.19)", "beautifulsoup4", "cookiecutter (==1.7.3)", "datasets (!=2.5.0)", "dill (<0.3.5)", "evaluate (>=0.2.0)", "faiss-cpu", "nltk (<=3.8.1)", "parameterized", "psutil", "pydantic", "pytest (>=7.2.0,<8.0.0)", "pytest-asyncio", "pytest-rich", "pytest-timeout", "pytest-xdist", "rjieba", "rouge-score (!=0.0.7,!=0.0.8,!=0.1,!=0.1.1)", "ruff (==0.5.1)", "sacrebleu (>=1.4.12,<2.0.0)", "sacremoses", "sentencepiece (>=0.1.91,!=0.1.92)", "tensorboard", "timeout-decorator"]
 tf = ["keras-nlp (>=0.3.1,<0.14.0)", "onnxconverter-common", "tensorflow (>2.9,<2.16)", "tensorflow-text (<2.16)", "tf2onnx"]
 tf-cpu = ["keras (>2.9,<2.16)", "keras-nlp (>=0.3.1,<0.14.0)", "onnxconverter-common", "tensorflow-cpu (>2.9,<2.16)", "tensorflow-probability (<0.24)", "tensorflow-text (<2.16)", "tf2onnx"]
 tf-speech = ["kenlm", "librosa", "phonemizer", "pyctcdecode (>=0.4.0)"]
 tiktoken = ["blobfile", "tiktoken"]
 timm = ["timm (<=1.0.11)"]
 tokenizers = ["tokenizers (>=0.21,<0.22)"]
-torch = ["accelerate (>=0.26.0)", "torch"]
+torch = ["accelerate (>=0.26.0)", "torch (>=2.0)"]
 torch-speech = ["kenlm", "librosa", "phonemizer", "pyctcdecode (>=0.4.0)", "torchaudio"]
 torch-vision = ["Pillow (>=10.0.1,<=15.0)", "torchvision"]
-torchhub = ["filelock", "huggingface-hub (>=0.24.0,<1.0)", "importlib-metadata", "numpy (>=1.17)", "packaging (>=20.0)", "protobuf", "regex (!=2019.12.17)", "requests", "sentencepiece (>=0.1.91,!=0.1.92)", "tokenizers (>=0.21,<0.22)", "torch", "tqdm (>=4.27)"]
+torchhub = ["filelock", "huggingface-hub (>=0.24.0,<1.0)", "importlib-metadata", "numpy (>=1.17)", "packaging (>=20.0)", "protobuf", "regex (!=2019.12.17)", "requests", "sentencepiece (>=0.1.91,!=0.1.92)", "tokenizers (>=0.21,<0.22)", "torch (>=2.0)", "tqdm (>=4.27)"]
 video = ["av (==9.2.0)"]
 vision = ["Pillow (>=10.0.1,<=15.0)"]
 
@@ -5470,13 +5901,13 @@ urllib3 = ">=2"
 
 [[package]]
 name = "types-setuptools"
-version = "75.6.0.20241223"
+version = "75.8.0.20250110"
 description = "Typing stubs for setuptools"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "types_setuptools-75.6.0.20241223-py3-none-any.whl", hash = "sha256:7cbfd3bf2944f88bbcdd321b86ddd878232a277be95d44c78a53585d78ebc2f6"},
-    {file = "types_setuptools-75.6.0.20241223.tar.gz", hash = "sha256:d9478a985057ed48a994c707f548e55aababa85fe1c9b212f43ab5a1fffd3211"},
+    {file = "types_setuptools-75.8.0.20250110-py3-none-any.whl", hash = "sha256:a9f12980bbf9bcdc23ecd80755789085bad6bfce4060c2275bc2b4ca9f2bc480"},
+    {file = "types_setuptools-75.8.0.20250110.tar.gz", hash = "sha256:96f7ec8bbd6e0a54ea180d66ad68ad7a1d7954e7281a710ea2de75e355545271"},
 ]
 
 [[package]]
@@ -5574,6 +6005,27 @@ h2 = ["h2 (>=4,<5)"]
 socks = ["pysocks (>=1.5.6,!=1.5.7,<2.0)"]
 zstd = ["zstandard (>=0.18.0)"]
 
+[[package]]
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+    {file = "yarl-1.18.3-cp313-cp313-musllinux_1_2_aarch64.whl", hash = "sha256:82123d0c954dc58db301f5021a01854a85bf1f3bb7d12ae0c01afc414a882ca2"},
+    {file = "yarl-1.18.3-cp313-cp313-musllinux_1_2_armv7l.whl", hash = "sha256:2ec9bbba33b2d00999af4631a3397d1fd78290c48e2a3e52d8dd72db3a067ac8"},
+    {file = "yarl-1.18.3-cp313-cp313-musllinux_1_2_i686.whl", hash = "sha256:fbd6748e8ab9b41171bb95c6142faf068f5ef1511935a0aa07025438dd9a9bc1"},
+    {file = "yarl-1.18.3-cp313-cp313-musllinux_1_2_ppc64le.whl", hash = "sha256:877d209b6aebeb5b16c42cbb377f5f94d9e556626b1bfff66d7b0d115be88d0a"},
+    {file = "yarl-1.18.3-cp313-cp313-musllinux_1_2_s390x.whl", hash = "sha256:b464c4ab4bfcb41e3bfd3f1c26600d038376c2de3297760dfe064d2cb7ea8e10"},
+    {file = "yarl-1.18.3-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:8d39d351e7faf01483cc7ff7c0213c412e38e5a340238826be7e0e4da450fdc8"},
+    {file = "yarl-1.18.3-cp313-cp313-win32.whl", hash = "sha256:61ee62ead9b68b9123ec24bc866cbef297dd266175d53296e2db5e7f797f902d"},
+    {file = "yarl-1.18.3-cp313-cp313-win_amd64.whl", hash = "sha256:578e281c393af575879990861823ef19d66e2b1d0098414855dd367e234f5b3c"},
+    {file = "yarl-1.18.3-cp39-cp39-macosx_10_9_universal2.whl", hash = "sha256:61e5e68cb65ac8f547f6b5ef933f510134a6bf31bb178be428994b0cb46c2a04"},
+    {file = "yarl-1.18.3-cp39-cp39-macosx_10_9_x86_64.whl", hash = "sha256:fe57328fbc1bfd0bd0514470ac692630f3901c0ee39052ae47acd1d90a436719"},
+    {file = "yarl-1.18.3-cp39-cp39-macosx_11_0_arm64.whl", hash = "sha256:a440a2a624683108a1b454705ecd7afc1c3438a08e890a1513d468671d90a04e"},
+    {file = "yarl-1.18.3-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:09c7907c8548bcd6ab860e5f513e727c53b4a714f459b084f6580b49fa1b9cee"},
+    {file = "yarl-1.18.3-cp39-cp39-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:b4f6450109834af88cb4cc5ecddfc5380ebb9c228695afc11915a0bf82116789"},
+    {file = "yarl-1.18.3-cp39-cp39-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:a9ca04806f3be0ac6d558fffc2fdf8fcef767e0489d2684a21912cc4ed0cd1b8"},
+    {file = "yarl-1.18.3-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:77a6e85b90a7641d2e07184df5557132a337f136250caafc9ccaa4a2a998ca2c"},
+    {file = "yarl-1.18.3-cp39-cp39-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:6333c5a377c8e2f5fae35e7b8f145c617b02c939d04110c76f29ee3676b5f9a5"},
+    {file = "yarl-1.18.3-cp39-cp39-musllinux_1_2_aarch64.whl", hash = "sha256:0b3c92fa08759dbf12b3a59579a4096ba9af8dd344d9a813fc7f5070d86bbab1"},
+    {file = "yarl-1.18.3-cp39-cp39-musllinux_1_2_armv7l.whl", hash = "sha256:4ac515b860c36becb81bb84b667466885096b5fc85596948548b667da3bf9f24"},
+    {file = "yarl-1.18.3-cp39-cp39-musllinux_1_2_i686.whl", hash = "sha256:045b8482ce9483ada4f3f23b3774f4e1bf4f23a2d5c912ed5170f68efb053318"},
+    {file = "yarl-1.18.3-cp39-cp39-musllinux_1_2_ppc64le.whl", hash = "sha256:a4bb030cf46a434ec0225bddbebd4b89e6471814ca851abb8696170adb163985"},
+    {file = "yarl-1.18.3-cp39-cp39-musllinux_1_2_s390x.whl", hash = "sha256:54d6921f07555713b9300bee9c50fb46e57e2e639027089b1d795ecd9f7fa910"},
+    {file = "yarl-1.18.3-cp39-cp39-musllinux_1_2_x86_64.whl", hash = "sha256:1d407181cfa6e70077df3377938c08012d18893f9f20e92f7d2f314a437c30b1"},
+    {file = "yarl-1.18.3-cp39-cp39-win32.whl", hash = "sha256:ac36703a585e0929b032fbaab0707b75dc12703766d0b53486eabd5139ebadd5"},
+    {file = "yarl-1.18.3-cp39-cp39-win_amd64.whl", hash = "sha256:ba87babd629f8af77f557b61e49e7c7cac36f22f871156b91e10a6e9d4f829e9"},
+    {file = "yarl-1.18.3-py3-none-any.whl", hash = "sha256:b57f4f58099328dfb26c6a771d09fb20dbbae81d20cfb66141251ea063bd101b"},
+    {file = "yarl-1.18.3.tar.gz", hash = "sha256:ac1801c45cbf77b6c99242eeff4fffb5e4e73a800b5c4ad4fc0be5def634d2e1"},
 ]
 
+[package.dependencies]
+idna = ">=2.0"
+multidict = ">=4.0"
+propcache = ">=0.2.0"
+
 [[package]]
 name = "zipp"
 version = "3.21.0"
@@ -5734,8 +6289,9 @@ mistralai = ["mistralai"]
 openai = ["openai"]
 sentence-transformers = ["sentence-transformers"]
 vertexai = ["google-cloud-aiplatform", "protobuf"]
+voyageai = ["voyageai"]
 
 [metadata]
 lock-version = "2.0"
 python-versions = ">=3.9,<3.13"
-content-hash = "da2883e4b839be0a25e4dd5bb2861b27881f4dca9ea8ed159999d015b07410e5"
+content-hash = "b20421e470da84100bb94c6c2e6f1bfe32ac5b94da78e47623ae6b8060ce00fc"
diff --git a/pyproject.toml b/pyproject.toml
index 908170d8..93550693 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -34,6 +34,7 @@ protobuf = { version = ">=5.29.1,<6.0.0.dev0", optional = true }
 cohere = { version = ">=4.44", optional = true }
 mistralai = { version = ">=1.0.0", optional = true }
 boto3 = { version = ">=1.34.0", optional = true }
+voyageai = { version = ">=0.2.2", optional = true }
 
 [tool.poetry.extras]
 openai = ["openai"]
@@ -42,6 +43,7 @@ vertexai = ["google_cloud_aiplatform", "protobuf"]
 cohere = ["cohere"]
 mistralai = ["mistralai"]
 bedrock = ["boto3"]
+voyageai = ["voyageai"]
 
 [tool.poetry.group.dev.dependencies]
 black = ">=20.8b1"
diff --git a/redisvl/utils/rerank/__init__.py b/redisvl/utils/rerank/__init__.py
index faafd809..3a2d28ba 100644
--- a/redisvl/utils/rerank/__init__.py
+++ b/redisvl/utils/rerank/__init__.py
@@ -1,5 +1,11 @@
 from redisvl.utils.rerank.base import BaseReranker
 from redisvl.utils.rerank.cohere import CohereReranker
 from redisvl.utils.rerank.hf_cross_encoder import HFCrossEncoderReranker
+from redisvl.utils.rerank.voyageai import VoyageAIReranker
 
-__all__ = ["BaseReranker", "CohereReranker", "HFCrossEncoderReranker"]
+__all__ = [
+    "BaseReranker",
+    "CohereReranker",
+    "HFCrossEncoderReranker",
+    "VoyageAIReranker",
+]
diff --git a/redisvl/utils/rerank/hf_cross_encoder.py b/redisvl/utils/rerank/hf_cross_encoder.py
index 65e323a8..ff8a1300 100644
--- a/redisvl/utils/rerank/hf_cross_encoder.py
+++ b/redisvl/utils/rerank/hf_cross_encoder.py
@@ -124,7 +124,7 @@ def rank(
         scores = scores[:limit]
 
         if return_score:
-            return reranked_docs, scores
+            return reranked_docs, scores  # type: ignore
         return reranked_docs
 
     async def arank(
diff --git a/redisvl/utils/rerank/voyageai.py b/redisvl/utils/rerank/voyageai.py
new file mode 100644
index 00000000..2c3741ce
--- /dev/null
+++ b/redisvl/utils/rerank/voyageai.py
@@ -0,0 +1,198 @@
+import os
+from typing import Any, Dict, List, Optional, Tuple, Union
+
+from pydantic.v1 import PrivateAttr
+
+from redisvl.utils.rerank.base import BaseReranker
+
+
+class VoyageAIReranker(BaseReranker):
+    """
+    The VoyageAIReranker class uses VoyageAI's API to rerank documents based on an
+    input query.
+
+    This reranker is designed to interact with VoyageAI's /rerank API,
+    requiring an API key for authentication. The key can be provided
+    directly in the `api_config` dictionary or through the `VOYAGE_API_KEY`
+    environment variable. User must obtain an API key from VoyageAI's website
+    (https://dash.voyageai.com/). Additionally, the `voyageai` python
+    client must be installed with `pip install voyageai`.
+
+    .. code-block:: python
+
+        from redisvl.utils.rerank import VoyageAIReranker
+
+        # set up the VoyageAI reranker with some configuration
+        reranker = VoyageAIReranker(rank_by=["content"], limit=2)
+        # rerank raw search results based on user input/query
+        results = reranker.rank(
+            query="your input query text here",
+            docs=[
+                {"content": "document 1"},
+                {"content": "document 2"},
+                {"content": "document 3"}
+            ]
+        )
+    """
+
+    _client: Any = PrivateAttr()
+    _aclient: Any = PrivateAttr()
+
+    def __init__(
+        self,
+        model: str,
+        rank_by: Optional[List[str]] = None,
+        limit: int = 5,
+        return_score: bool = True,
+        api_config: Optional[Dict] = None,
+    ) -> None:
+        """
+        Initialize the VoyageAIReranker with specified model, ranking criteria,
+        and API configuration.
+
+        Parameters:
+            model (str): The identifier for the VoyageAI model used for reranking.
+            rank_by (Optional[List[str]]): Optional list of keys specifying the
+                attributes in the documents that should be considered for
+                ranking. None means ranking will rely on the model's default
+                behavior.
+            limit (int): The maximum number of results to return after
+                reranking. Must be a positive integer.
+            return_score (bool): Whether to return scores alongside the
+                reranked results.
+            api_config (Optional[Dict], optional): Dictionary containing the API key.
+                Defaults to None.
+
+        Raises:
+            ImportError: If the voyageai library is not installed.
+            ValueError: If the API key is not provided.
+        """
+        super().__init__(
+            model=model, rank_by=rank_by, limit=limit, return_score=return_score
+        )
+        self._initialize_clients(api_config)
+
+    def _initialize_clients(self, api_config: Optional[Dict]):
+        """
+        Setup the VoyageAI clients using the provided API key or an
+        environment variable.
+        """
+        # Dynamic import of the voyageai module
+        try:
+            from voyageai import AsyncClient, Client
+        except ImportError:
+            raise ImportError(
+                "VoyageAI reranker requires the voyageai library. \
+                    Please install with `pip install voyageai`"
+            )
+
+        # Fetch the API key from api_config or environment variable
+        api_key = (
+            api_config.get("api_key") if api_config else os.getenv("VOYAGE_API_KEY")
+        )
+        if not api_key:
+            raise ValueError(
+                "VoyageAI API key is required. "
+                "Provide it in api_config or set the VOYAGE_API_KEY environment variable."
+            )
+        self._client = Client(api_key=api_key)
+        self._aclient = AsyncClient(api_key=api_key)
+
+    def _preprocess(
+        self, query: str, docs: Union[List[Dict[str, Any]], List[str]], **kwargs
+    ):
+        """
+        Prepare and validate reranking config based on provided input and
+        optional overrides.
+        """
+        limit = kwargs.get("limit", self.limit)
+        return_score = kwargs.get("return_score", self.return_score)
+        truncation = kwargs.get("truncation")
+
+        if not isinstance(docs, list):
+            raise ValueError("Docs to rerank should either be a list of str or dict.")
+
+        if all(isinstance(doc, dict) and "content" in doc for doc in docs):
+            texts = [
+                str(doc["content"])
+                for doc in docs
+                if isinstance(doc, dict) and "content" in doc
+            ]
+        else:
+            texts = [str(doc) for doc in docs]
+
+        reranker_kwargs = {
+            "query": query,
+            "documents": texts,
+            "model": self.model,
+            "top_k": limit,
+        }
+        if truncation is not None:
+            reranker_kwargs["truncation"] = truncation
+
+        return reranker_kwargs, return_score
+
+    @staticmethod
+    def _postprocess(
+        docs: Union[List[Dict[str, Any]], List[str]],
+        rankings: List[Any],
+    ) -> Tuple[List[Any], List[float]]:
+        """
+        Post-process the initial list of documents to include ranking scores,
+        if specified.
+        """
+        reranked_docs, scores = [], []
+        for item in rankings.results:  # type: ignore
+            scores.append(item.relevance_score)
+            reranked_docs.append(docs[item.index])
+        return reranked_docs, scores
+
+    def rank(
+        self, query: str, docs: Union[List[Dict[str, Any]], List[str]], **kwargs
+    ) -> Union[Tuple[List[Dict[str, Any]], List[float]], List[Dict[str, Any]]]:
+        """
+        Rerank documents based on the provided query using the VoyageAI rerank API.
+
+        This method processes the user's query and the provided documents to
+        rerank them in a manner that is potentially more relevant to the
+        query's context.
+
+        Parameters:
+            query (str): The user's search query.
+            docs (Union[List[Dict[str, Any]], List[str]]): The list of documents
+                to be ranked, either as dictionaries or strings.
+
+        Returns:
+            Union[Tuple[Union[List[Dict[str, Any]], List[str]], float], List[Dict[str, Any]]]: The reranked list of documents and optionally associated scores.
+        """
+        reranker_kwargs, return_score = self._preprocess(query, docs, **kwargs)
+        rankings = self._client.rerank(**reranker_kwargs)
+        reranked_docs, scores = self._postprocess(docs, rankings)
+        if return_score:
+            return reranked_docs, scores
+        return reranked_docs
+
+    async def arank(
+        self, query: str, docs: Union[List[Dict[str, Any]], List[str]], **kwargs
+    ) -> Union[Tuple[List[Dict[str, Any]], List[float]], List[Dict[str, Any]]]:
+        """
+        Rerank documents based on the provided query using the VoyageAI rerank API.
+
+        This method processes the user's query and the provided documents to
+        rerank them in a manner that is potentially more relevant to the
+        query's context.
+
+        Parameters:
+            query (str): The user's search query.
+            docs (Union[List[Dict[str, Any]], List[str]]): The list of documents
+                to be ranked, either as dictionaries or strings.
+
+        Returns:
+            Union[Tuple[Union[List[Dict[str, Any]], List[str]], float], List[Dict[str, Any]]]: The reranked list of documents and optionally associated scores.
+        """
+        reranker_kwargs, return_score = self._preprocess(query, docs, **kwargs)
+        rankings = await self._aclient.rerank(**reranker_kwargs)
+        reranked_docs, scores = self._postprocess(docs, rankings)
+        if return_score:
+            return reranked_docs, scores
+        return reranked_docs
diff --git a/redisvl/utils/vectorize/__init__.py b/redisvl/utils/vectorize/__init__.py
index 3675ae56..d305306d 100644
--- a/redisvl/utils/vectorize/__init__.py
+++ b/redisvl/utils/vectorize/__init__.py
@@ -7,6 +7,7 @@
 from redisvl.utils.vectorize.text.mistral import MistralAITextVectorizer
 from redisvl.utils.vectorize.text.openai import OpenAITextVectorizer
 from redisvl.utils.vectorize.text.vertexai import VertexAITextVectorizer
+from redisvl.utils.vectorize.text.voyageai import VoyageAITextVectorizer
 
 __all__ = [
     "BaseVectorizer",
@@ -18,6 +19,7 @@
     "MistralAITextVectorizer",
     "CustomTextVectorizer",
     "BedrockTextVectorizer",
+    "VoyageAITextVectorizer",
 ]
 
 
@@ -36,3 +38,5 @@ def vectorizer_from_dict(vectorizer: dict) -> BaseVectorizer:
         return MistralAITextVectorizer(model)
     elif vectorizer_type == Vectorizers.vertexai:
         return VertexAITextVectorizer(model)
+    elif vectorizer_type == Vectorizers.voyageai:
+        return VoyageAITextVectorizer(model)
diff --git a/redisvl/utils/vectorize/base.py b/redisvl/utils/vectorize/base.py
index d27553be..864b4150 100644
--- a/redisvl/utils/vectorize/base.py
+++ b/redisvl/utils/vectorize/base.py
@@ -15,6 +15,7 @@ class Vectorizers(Enum):
     mistral = "mistral"
     vertexai = "vertexai"
     hf = "hf"
+    voyageai = "voyageai"
 
 
 class BaseVectorizer(BaseModel, ABC):
diff --git a/redisvl/utils/vectorize/text/voyageai.py b/redisvl/utils/vectorize/text/voyageai.py
new file mode 100644
index 00000000..e4de9817
--- /dev/null
+++ b/redisvl/utils/vectorize/text/voyageai.py
@@ -0,0 +1,359 @@
+import os
+from typing import Any, Callable, Dict, List, Optional
+
+from pydantic.v1 import PrivateAttr
+from tenacity import retry, stop_after_attempt, wait_random_exponential
+from tenacity.retry import retry_if_not_exception_type
+
+from redisvl.utils.vectorize.base import BaseVectorizer
+
+# ignore that voyageai isn't imported
+# mypy: disable-error-code="name-defined"
+
+
+class VoyageAITextVectorizer(BaseVectorizer):
+    """The VoyageAITextVectorizer class utilizes VoyageAI's API to generate
+    embeddings for text data.
+
+    This vectorizer is designed to interact with VoyageAI's /embed API,
+    requiring an API key for authentication. The key can be provided
+    directly in the `api_config` dictionary or through the `VOYAGE_API_KEY`
+    environment variable. User must obtain an API key from VoyageAI's website
+    (https://dash.voyageai.com/). Additionally, the `voyageai` python
+    client must be installed with `pip install voyageai`.
+
+    The vectorizer supports both synchronous and asynchronous operations, allows for batch
+    processing of texts and flexibility in handling preprocessing tasks.
+
+    .. code-block:: python
+
+        from redisvl.utils.vectorize import VoyageAITextVectorizer
+
+        vectorizer = VoyageAITextVectorizer(
+            model="voyage-large-2",
+            api_config={"api_key": "your-voyageai-api-key"} # OR set VOYAGE_API_KEY in your env
+        )
+        query_embedding = vectorizer.embed(
+            text="your input query text here",
+            input_type="query"
+        )
+        doc_embeddings = vectorizer.embed_many(
+            texts=["your document text", "more document text"],
+            input_type="document"
+        )
+
+    """
+
+    _client: Any = PrivateAttr()
+    _aclient: Any = PrivateAttr()
+
+    def __init__(
+        self,
+        model: str = "voyage-large-2",
+        api_config: Optional[Dict] = None,
+        dtype: str = "float32",
+    ):
+        """Initialize the VoyageAI vectorizer.
+
+        Visit https://docs.voyageai.com/docs/embeddings to learn about embeddings and check the available models.
+
+        Args:
+            model (str): Model to use for embedding. Defaults to "voyage-large-2".
+            api_config (Optional[Dict], optional): Dictionary containing the API key.
+                Defaults to None.
+            dtype (str): the default datatype to use when embedding text as byte arrays.
+                Used when setting `as_buffer=True` in calls to embed() and embed_many().
+                Defaults to 'float32'.
+
+        Raises:
+            ImportError: If the voyageai library is not installed.
+            ValueError: If the API key is not provided.
+
+        """
+        self._initialize_client(api_config)
+        super().__init__(model=model, dims=self._set_model_dims(model), dtype=dtype)
+
+    def _initialize_client(self, api_config: Optional[Dict]):
+        """
+        Setup the VoyageAI clients using the provided API key or an
+        environment variable.
+        """
+        # Dynamic import of the voyageai module
+        try:
+            from voyageai import AsyncClient, Client
+        except ImportError:
+            raise ImportError(
+                "VoyageAI vectorizer requires the voyageai library. \
+                    Please install with `pip install voyageai`"
+            )
+
+        # Fetch the API key from api_config or environment variable
+        api_key = (
+            api_config.get("api_key") if api_config else os.getenv("VOYAGE_API_KEY")
+        )
+        if not api_key:
+            raise ValueError(
+                "VoyageAI API key is required. "
+                "Provide it in api_config or set the VOYAGE_API_KEY environment variable."
+            )
+        self._client = Client(api_key=api_key)
+        self._aclient = AsyncClient(api_key=api_key)
+
+    def _set_model_dims(self, model) -> int:
+        try:
+            embedding = self._client.embed(
+                texts=["dimension test"],
+                model=model,
+                input_type="document",
+            ).embeddings[0]
+        except (KeyError, IndexError) as ke:
+            raise ValueError(f"Unexpected response from the VoyageAI API: {str(ke)}")
+        except Exception as e:  # pylint: disable=broad-except
+            # fall back (TODO get more specific)
+            raise ValueError(f"Error setting embedding model dimensions: {str(e)}")
+        return len(embedding)
+
+    def embed(
+        self,
+        text: str,
+        preprocess: Optional[Callable] = None,
+        as_buffer: bool = False,
+        **kwargs,
+    ) -> List[float]:
+        """Embed a chunk of text using the VoyageAI Embeddings API.
+
+        Can provide the embedding `input_type` as a `kwarg` to this method
+        that specifies the type of input you're giving to the model. For retrieval/search use cases,
+        we recommend specifying this argument when encoding queries or documents to enhance retrieval quality.
+        Embeddings generated with and without the input_type argument are compatible.
+
+        Supported input types are ``document`` and ``query``
+
+        When hydrating your Redis DB, the documents you want to search over
+        should be embedded with input_type="document" and when you are
+        querying the database, you should set the input_type="query".
+
+        Args:
+            text (str): Chunk of text to embed.
+            preprocess (Optional[Callable], optional): Optional preprocessing callable to
+                perform before vectorization. Defaults to None.
+            as_buffer (bool, optional): Whether to convert the raw embedding
+                to a byte string. Defaults to False.
+            input_type (str): Specifies the type of input passed to the model.
+            truncation (bool): Whether to truncate the input texts to fit within the context length.
+                Check https://docs.voyageai.com/docs/embeddings
+
+        Returns:
+            List[float]: Embedding.
+
+        Raises:
+            TypeError: If an invalid input_type is provided.
+        """
+        return self.embed_many(
+            texts=[text], preprocess=preprocess, as_buffer=as_buffer, **kwargs
+        )[0]
+
+    @retry(
+        wait=wait_random_exponential(min=1, max=60),
+        stop=stop_after_attempt(6),
+        retry=retry_if_not_exception_type(TypeError),
+    )
+    def embed_many(
+        self,
+        texts: List[str],
+        preprocess: Optional[Callable] = None,
+        batch_size: Optional[int] = None,
+        as_buffer: bool = False,
+        **kwargs,
+    ) -> List[List[float]]:
+        """Embed many chunks of text using the VoyageAI Embeddings API.
+
+        Can provide the embedding `input_type` as a `kwarg` to this method
+        that specifies the type of input you're giving to the model. For retrieval/search use cases,
+        we recommend specifying this argument when encoding queries or documents to enhance retrieval quality.
+        Embeddings generated with and without the input_type argument are compatible.
+
+        Supported input types are ``document`` and ``query``
+
+        When hydrating your Redis DB, the documents you want to search over
+        should be embedded with input_type="document" and when you are
+        querying the database, you should set the input_type="query".
+
+        Args:
+            texts (List[str]): List of text chunks to embed.
+            preprocess (Optional[Callable], optional): Optional preprocessing callable to
+                perform before vectorization. Defaults to None.
+            batch_size (int, optional): Batch size of texts to use when creating
+                embeddings. .
+            as_buffer (bool, optional): Whether to convert the raw embedding
+                to a byte string. Defaults to False.
+            input_type (str): Specifies the type of input passed to the model.
+            truncation (bool): Whether to truncate the input texts to fit within the context length.
+                Check https://docs.voyageai.com/docs/embeddings
+
+        Returns:
+            List[List[float]]: List of embeddings.
+
+        Raises:
+            TypeError: If an invalid input_type is provided.
+
+        """
+        input_type = kwargs.get("input_type")
+        truncation = kwargs.get("truncation")
+        dtype = kwargs.pop("dtype", self.dtype)
+
+        if not isinstance(texts, list):
+            raise TypeError("Must pass in a list of str values to embed.")
+        if len(texts) > 0 and not isinstance(texts[0], str):
+            raise TypeError("Must pass in a list of str values to embed.")
+        if input_type is not None and input_type not in ["document", "query"]:
+            raise TypeError(
+                "Must pass in a allowed value for voyageai embedding input_type. \
+                    See https://docs.voyageai.com/docs/embeddings."
+            )
+
+        if truncation is not None and not isinstance(truncation, bool):
+            raise TypeError("Truncation (optional) parameter is a bool.")
+
+        if batch_size is None:
+            batch_size = (
+                72
+                if self.model in ["voyage-2", "voyage-02"]
+                else (
+                    30
+                    if self.model == "voyage-3-lite"
+                    else (10 if self.model == "voyage-3" else 7)
+                )
+            )
+
+        embeddings: List = []
+        for batch in self.batchify(texts, batch_size, preprocess):
+            response = self._client.embed(
+                texts=batch, model=self.model, input_type=input_type
+            )
+            embeddings += [
+                self._process_embedding(embedding, as_buffer, dtype)
+                for embedding in response.embeddings
+            ]
+        return embeddings
+
+    async def aembed_many(
+        self,
+        texts: List[str],
+        preprocess: Optional[Callable] = None,
+        batch_size: Optional[int] = None,
+        as_buffer: bool = False,
+        **kwargs,
+    ) -> List[List[float]]:
+        """Embed many chunks of text using the VoyageAI Embeddings API.
+
+        Can provide the embedding `input_type` as a `kwarg` to this method
+        that specifies the type of input you're giving to the model. For retrieval/search use cases,
+        we recommend specifying this argument when encoding queries or documents to enhance retrieval quality.
+        Embeddings generated with and without the input_type argument are compatible.
+
+        Supported input types are ``document`` and ``query``
+
+        When hydrating your Redis DB, the documents you want to search over
+        should be embedded with input_type="document" and when you are
+        querying the database, you should set the input_type="query".
+
+        Args:
+            texts (List[str]): List of text chunks to embed.
+            preprocess (Optional[Callable], optional): Optional preprocessing callable to
+                perform before vectorization. Defaults to None.
+            batch_size (int, optional): Batch size of texts to use when creating
+                embeddings. .
+            as_buffer (bool, optional): Whether to convert the raw embedding
+                to a byte string. Defaults to False.
+            input_type (str): Specifies the type of input passed to the model.
+            truncation (bool): Whether to truncate the input texts to fit within the context length.
+                Check https://docs.voyageai.com/docs/embeddings
+
+        Returns:
+            List[List[float]]: List of embeddings.
+
+        Raises:
+            TypeError: In an invalid input_type is provided.
+
+        """
+        input_type = kwargs.get("input_type")
+        truncation = kwargs.get("truncation")
+        dtype = kwargs.pop("dtype", self.dtype)
+
+        if not isinstance(texts, list):
+            raise TypeError("Must pass in a list of str values to embed.")
+        if len(texts) > 0 and not isinstance(texts[0], str):
+            raise TypeError("Must pass in a list of str values to embed.")
+        if input_type is not None and input_type not in ["document", "query"]:
+            raise TypeError(
+                "Must pass in a allowed value for voyageai embedding input_type. \
+                    See https://docs.voyageai.com/docs/embeddings."
+            )
+
+        if truncation is not None and not isinstance(truncation, bool):
+            raise TypeError("Truncation (optional) parameter is a bool.")
+
+        if batch_size is None:
+            batch_size = (
+                72
+                if self.model in ["voyage-2", "voyage-02"]
+                else (
+                    30
+                    if self.model == "voyage-3-lite"
+                    else (10 if self.model == "voyage-3" else 7)
+                )
+            )
+
+        embeddings: List = []
+        for batch in self.batchify(texts, batch_size, preprocess):
+            response = await self._aclient.embed(
+                texts=batch, model=self.model, input_type=input_type
+            )
+            embeddings += [
+                self._process_embedding(embedding, as_buffer, dtype)
+                for embedding in response.embeddings
+            ]
+        return embeddings
+
+    async def aembed(
+        self,
+        text: str,
+        preprocess: Optional[Callable] = None,
+        as_buffer: bool = False,
+        **kwargs,
+    ) -> List[float]:
+        """Embed a chunk of text using the VoyageAI Embeddings API.
+
+        Can provide the embedding `input_type` as a `kwarg` to this method
+        that specifies the type of input you're giving to the model. For retrieval/search use cases,
+        we recommend specifying this argument when encoding queries or documents to enhance retrieval quality.
+        Embeddings generated with and without the input_type argument are compatible.
+
+        Supported input types are ``document`` and ``query``
+
+        When hydrating your Redis DB, the documents you want to search over
+        should be embedded with input_type="document" and when you are
+        querying the database, you should set the input_type="query".
+
+        Args:
+            text (str): Chunk of text to embed.
+            preprocess (Optional[Callable], optional): Optional preprocessing callable to
+                perform before vectorization. Defaults to None.
+            as_buffer (bool, optional): Whether to convert the raw embedding
+                to a byte string. Defaults to False.
+            input_type (str): Specifies the type of input passed to the model.
+            truncation (bool): Whether to truncate the input texts to fit within the context length.
+                Check https://docs.voyageai.com/docs/embeddings
+
+        Returns:
+            List[float]: Embedding.
+
+        Raises:
+            TypeError: In an invalid input_type is provided.
+        """
+
+        result = await self.aembed_many(
+            texts=[text], preprocess=preprocess, as_buffer=as_buffer, **kwargs
+        )
+        return result[0]
diff --git a/tests/integration/test_rerankers.py b/tests/integration/test_rerankers.py
index e7da8fa3..65aad333 100644
--- a/tests/integration/test_rerankers.py
+++ b/tests/integration/test_rerankers.py
@@ -2,16 +2,35 @@
 
 import pytest
 
-from redisvl.utils.rerank import CohereReranker, HFCrossEncoderReranker
+from redisvl.utils.rerank import (
+    CohereReranker,
+    HFCrossEncoderReranker,
+    VoyageAIReranker,
+)
 
 
-# Fixture for the reranker instance
 @pytest.fixture
-def cohereReranker():
-    skip_reranker = os.getenv("SKIP_RERANKERS", "False").lower() == "true"
+def skip_reranker() -> bool:
+    # os.getenv returns a string
+    v = os.getenv("SKIP_RERANKERS", "False").lower() == "true"
+    return v
+
+
+# Fixture for the reranker instance
+@pytest.fixture(
+    params=[
+        CohereReranker,
+        VoyageAIReranker,
+    ]
+)
+def reranker(request, skip_reranker):
     if skip_reranker:
         pytest.skip("Skipping reranker instantiation...")
-    return CohereReranker()
+
+    if request.param == CohereReranker:
+        return request.param()
+    elif request.param == VoyageAIReranker:
+        return request.param(model="rerank-lite-1")
 
 
 @pytest.fixture
@@ -25,11 +44,11 @@ def hfCrossEncoderRerankerWithCustomModel():
 
 
 # Test for basic ranking functionality
-def test_rank_documents_cohere(cohereReranker):
+def test_rank_documents(reranker):
     docs = ["document one", "document two", "document three"]
     query = "search query"
 
-    reranked_docs, scores = cohereReranker.rank(query, docs)
+    reranked_docs, scores = reranker.rank(query, docs)
 
     assert isinstance(reranked_docs, list)
     assert len(reranked_docs) == len(docs)  # Ensure we get back as many docs as we sent
@@ -38,11 +57,11 @@ def test_rank_documents_cohere(cohereReranker):
 
 # Test for asynchronous ranking functionality
 @pytest.mark.asyncio
-async def test_async_rank_documents_cohere(cohereReranker):
+async def test_async_rank_documents(reranker):
     docs = ["document one", "document two", "document three"]
     query = "search query"
 
-    reranked_docs, scores = await cohereReranker.arank(query, docs)
+    reranked_docs, scores = await reranker.arank(query, docs)
 
     assert isinstance(reranked_docs, list)
     assert len(reranked_docs) == len(docs)  # Ensure we get back as many docs as we sent
@@ -50,20 +69,21 @@ async def test_async_rank_documents_cohere(cohereReranker):
 
 
 # Test handling of bad input
-def test_bad_input_cohere(cohereReranker):
+def test_bad_input(reranker):
     with pytest.raises(Exception):
-        cohereReranker.rank("", [])  # Empty query or documents
+        reranker.rank("", [])  # Empty query or documents
 
     with pytest.raises(Exception):
-        cohereReranker.rank(123, ["valid document"])  # Invalid type for query
+        reranker.rank(123, ["valid document"])  # Invalid type for query
 
     with pytest.raises(Exception):
-        cohereReranker.rank("valid query", "not a list")  # Invalid type for documents
+        reranker.rank("valid query", "not a list")  # Invalid type for documents
 
-    with pytest.raises(Exception):
-        cohereReranker.rank(
-            "valid query", [{"field": "valid document"}], rank_by=["invalid_field"]
-        )  # Invalid rank_by field
+    if isinstance(reranker, CohereReranker):
+        with pytest.raises(Exception):
+            reranker.rank(
+                "valid query", [{"field": "valid document"}], rank_by=["invalid_field"]
+            )  # Invalid rank_by field
 
 
 def test_rank_documents_cross_encoder(hfCrossEncoderReranker):
diff --git a/tests/integration/test_vectorizers.py b/tests/integration/test_vectorizers.py
index dd3112fa..a0d954b5 100644
--- a/tests/integration/test_vectorizers.py
+++ b/tests/integration/test_vectorizers.py
@@ -11,6 +11,7 @@
     MistralAITextVectorizer,
     OpenAITextVectorizer,
     VertexAITextVectorizer,
+    VoyageAITextVectorizer,
 )
 
 
@@ -30,6 +31,7 @@ def skip_vectorizer() -> bool:
         BedrockTextVectorizer,
         MistralAITextVectorizer,
         CustomTextVectorizer,
+        VoyageAITextVectorizer,
     ]
 )
 def vectorizer(request, skip_vectorizer):
@@ -46,6 +48,8 @@ def vectorizer(request, skip_vectorizer):
         return request.param()
     elif request.param == MistralAITextVectorizer:
         return request.param()
+    elif request.param == VoyageAITextVectorizer:
+        return request.param(model="voyage-large-2")
     elif request.param == AzureOpenAITextVectorizer:
         return request.param(
             model=os.getenv("AZURE_OPENAI_DEPLOYMENT_NAME", "text-embedding-ada-002")
@@ -108,6 +112,8 @@ def test_vectorizer_embed(vectorizer):
     text = "This is a test sentence."
     if isinstance(vectorizer, CohereTextVectorizer):
         embedding = vectorizer.embed(text, input_type="search_document")
+    elif isinstance(vectorizer, VoyageAITextVectorizer):
+        embedding = vectorizer.embed(text, input_type="document")
     else:
         embedding = vectorizer.embed(text)
 
@@ -119,6 +125,8 @@ def test_vectorizer_embed_many(vectorizer):
     texts = ["This is the first test sentence.", "This is the second test sentence."]
     if isinstance(vectorizer, CohereTextVectorizer):
         embeddings = vectorizer.embed_many(texts, input_type="search_document")
+    elif isinstance(vectorizer, VoyageAITextVectorizer):
+        embeddings = vectorizer.embed_many(texts, input_type="document")
     else:
         embeddings = vectorizer.embed_many(texts)
 
@@ -246,9 +254,10 @@ def bad_return_type(text: str) -> str:
         CohereTextVectorizer,
         CustomTextVectorizer,
         HFTextVectorizer,
-        # MistralAITextVectorizer,
+        MistralAITextVectorizer,
         OpenAITextVectorizer,
         VertexAITextVectorizer,
+        VoyageAITextVectorizer,
     ],
 )
 def test_dtypes(vector_class, skip_vectorizer):
@@ -264,6 +273,7 @@ def test_dtypes(vector_class, skip_vectorizer):
         )
     else:
         vectorizer = vector_class()
+
     assert vectorizer.dtype == "float32"
 
     # test initializing dtype in constructor
@@ -279,6 +289,7 @@ def test_dtypes(vector_class, skip_vectorizer):
             )
         else:
             vectorizer = vector_class(dtype=dtype)
+
         assert vectorizer.dtype == dtype
 
     # test validation of dtype on init
@@ -301,19 +312,14 @@ def test_dtypes(vector_class, skip_vectorizer):
         BedrockTextVectorizer,
         MistralAITextVectorizer,
         CustomTextVectorizer,
+        VoyageAITextVectorizer,
     ]
 )
 def avectorizer(request, skip_vectorizer):
     if skip_vectorizer:
         pytest.skip("Skipping vectorizer instantiation...")
 
-    if request.param == OpenAITextVectorizer:
-        return request.param()
-    elif request.param == BedrockTextVectorizer:
-        return request.param()
-    elif request.param == MistralAITextVectorizer:
-        return request.param()
-    elif request.param == CustomTextVectorizer:
+    if request.param == CustomTextVectorizer:
 
         def embed_func(text):
             return [1.1, 2.2, 3.3, 4.4]
@@ -327,6 +333,8 @@ async def aembed_many_func(texts):
         return request.param(
             embed=embed_func, aembed=aembed_func, aembed_many=aembed_many_func
         )
+    else:
+        return request.param()
 
 
 @pytest.mark.asyncio