diff --git a/docs/docs/concepts/index.mdx b/docs/docs/concepts/index.mdx
index 6a9d8d0db8c69..e7b86f41e4e0f 100644
--- a/docs/docs/concepts/index.mdx
+++ b/docs/docs/concepts/index.mdx
@@ -48,7 +48,7 @@ The conceptual guide does not cover step-by-step instructions or specific implem
 - **[AIMessage](/docs/concepts/messages#aimessage)**: Represents a complete response from an AI model.
 - **[astream_events](/docs/concepts/chat_models#key-methods)**: Stream granular information from [LCEL](/docs/concepts/lcel) chains.
 - **[BaseTool](/docs/concepts/tools/#tool-interface)**: The base class for all tools in LangChain.
-- **[batch](/docs/concepts/runnables)**: Use to execute a runnable with batch inputs a Runnable.
+- **[batch](/docs/concepts/runnables)**: Use to execute a runnable with batch inputs.
 - **[bind_tools](/docs/concepts/tool_calling/#tool-binding)**: Allows models to interact with tools.
 - **[Caching](/docs/concepts/chat_models#caching)**: Storing results to avoid redundant calls to a chat model.
 - **[Chat models](/docs/concepts/multimodality/#multimodality-in-chat-models)**: Chat models that handle multiple data modalities.
@@ -70,7 +70,7 @@ The conceptual guide does not cover step-by-step instructions or specific implem
 - **[langchain-core](/docs/concepts/architecture#langchain-core)**: Core langchain package. Includes base interfaces and in-memory implementations.
 - **[langchain](/docs/concepts/architecture#langchain)**: A package for higher level components (e.g., some pre-built chains).
 - **[langgraph](/docs/concepts/architecture#langgraph)**: Powerful orchestration layer for LangChain. Use to build complex pipelines and workflows.
-- **[langserve](/docs/concepts/architecture#langserve)**: Use to deploy LangChain Runnables as REST endpoints. Uses FastAPI. Works primarily for LangChain Runnables, does not currently integrate with LangGraph.
+- **[langserve](/docs/concepts/architecture#langserve)**: Used to deploy LangChain Runnables as REST endpoints. Uses FastAPI. Works primarily for LangChain Runnables, does not currently integrate with LangGraph.
 - **[LLMs (legacy)](/docs/concepts/text_llms)**: Older language models that take a string as input and return a string as output.
 - **[Managing chat history](/docs/concepts/chat_history#managing-chat-history)**: Techniques to maintain and manage the chat history.
 - **[OpenAI format](/docs/concepts/messages#openai-format)**: OpenAI's message format for chat models.
@@ -79,7 +79,7 @@ The conceptual guide does not cover step-by-step instructions or specific implem
 - **[RemoveMessage](/docs/concepts/messages/#removemessage)**: An abstraction used to remove a message from chat history, used primarily in LangGraph.
 - **[role](/docs/concepts/messages#role)**: Represents the role (e.g., user, assistant) of a chat message.
 - **[RunnableConfig](/docs/concepts/runnables/#runnableconfig)**: Use to pass run time information to Runnables (e.g., `run_name`, `run_id`, `tags`, `metadata`, `max_concurrency`, `recursion_limit`, `configurable`).
-- **[Standard parameters for chat models](/docs/concepts/chat_models#standard-parameters)**: Parameters such as API key, `temperature`, and `max_tokens`,
+- **[Standard parameters for chat models](/docs/concepts/chat_models#standard-parameters)**: Parameters such as API key, `temperature`, and `max_tokens`.
 - **[Standard tests](/docs/concepts/testing#standard-tests)**: A defined set of unit and integration tests that all integrations must pass.
 - **[stream](/docs/concepts/streaming)**: Use to stream output from a Runnable or a graph.
 - **[Tokenization](/docs/concepts/tokens)**: The process of converting data into tokens and vice versa.
diff --git a/docs/docs/integrations/llm_caching.ipynb b/docs/docs/integrations/llm_caching.ipynb
index 5356f90a7537c..3788ad5bfde39 100644
--- a/docs/docs/integrations/llm_caching.ipynb
+++ b/docs/docs/integrations/llm_caching.ipynb
@@ -15,12 +15,20 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "f938e881",
+   "id": "bedbf252-4ea5-4eea-a3dc-d18ccc84aca3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%pip install -qU langchain-openai langchain-community"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "88486f6f",
    "metadata": {},
    "outputs": [],
    "source": [
-    "%pip install -qU langchain-openai langchain-community\n",
-    "\n",
     "import os\n",
     "from getpass import getpass\n",
     "\n",
@@ -30,12 +38,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 3,
    "id": "10ad9224",
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2024-12-06T00:54:06.474593Z",
-     "start_time": "2024-12-06T00:53:58.727138Z"
+     "end_time": "2024-04-12T02:05:57.319706Z",
+     "start_time": "2024-04-12T02:05:57.303868Z"
     }
    },
    "outputs": [],
@@ -55,7 +63,7 @@
     "tags": []
    },
    "source": [
-    "## `In Memory` Cache"
+    "## `In Memory` cache"
    ]
   },
   {
@@ -139,7 +147,7 @@
     "tags": []
    },
    "source": [
-    "## `SQLite` Cache"
+    "## `SQLite` cache"
    ]
   },
   {
@@ -234,7 +242,7 @@
    "id": "e71273ab",
    "metadata": {},
    "source": [
-    "## `Upstash Redis` Cache"
+    "## `Upstash Redis` caches"
    ]
   },
   {
@@ -242,7 +250,7 @@
    "id": "f10dabef",
    "metadata": {},
    "source": [
-    "### Standard Cache\n",
+    "### Standard cache\n",
     "Use [Upstash Redis](https://upstash.com) to cache prompts and responses with a serverless HTTP API."
    ]
   },
@@ -340,7 +348,8 @@
    "id": "b29dd776",
    "metadata": {},
    "source": [
-    "### Semantic Cache\n",
+    "### Semantic cache\n",
+    "\n",
     "Use [Upstash Vector](https://upstash.com/docs/vector/overall/whatisvector) to do a semantic similarity search and cache the most similar response in the database. The vectorization is automatically done by the selected embedding model while creating Upstash Vector database. "
    ]
   },
@@ -454,11 +463,10 @@
    "cell_type": "markdown",
    "id": "278ad7ae",
    "metadata": {
-    "jp-MarkdownHeadingCollapsed": true,
     "tags": []
    },
    "source": [
-    "## `Redis` Cache\n",
+    "## `Redis` caches\n",
     "\n",
     "See the main [Redis cache docs](/docs/integrations/caches/redis_llm_caching/) for detail."
    ]
@@ -468,7 +476,7 @@
    "id": "c5c9a4d5",
    "metadata": {},
    "source": [
-    "### Standard Cache\n",
+    "### Standard cache\n",
     "Use [Redis](/docs/integrations/providers/redis) to cache prompts and responses."
    ]
   },
@@ -564,7 +572,7 @@
    "id": "82be23f6",
    "metadata": {},
    "source": [
-    "### Semantic Cache\n",
+    "### Semantic cache\n",
     "Use [Redis](/docs/integrations/providers/redis) to cache prompts and responses and evaluate hits based on semantic similarity."
    ]
   },
@@ -660,7 +668,6 @@
    "cell_type": "markdown",
    "id": "684eab55",
    "metadata": {
-    "jp-MarkdownHeadingCollapsed": true,
     "tags": []
    },
    "source": [
@@ -905,7 +912,7 @@
    "id": "9b2b2777",
    "metadata": {},
    "source": [
-    "## `MongoDB Atlas` Cache\n",
+    "## `MongoDB Atlas` caches\n",
     "\n",
     "[MongoDB Atlas](https://www.mongodb.com/docs/atlas/) is a fully-managed cloud database available in AWS, Azure, and GCP. It has native support for \n",
     "Vector Search on the MongoDB document data.\n",
@@ -917,8 +924,9 @@
    "id": "ecdc2a0a",
    "metadata": {},
    "source": [
-    "### `MongoDBCache`\n",
-    "An abstraction to store a simple cache in MongoDB. This does not use Semantic Caching, nor does it require an index to be made on the collection before generation.\n",
+    "### Standard cache\n",
+    "\n",
+    "Standard cache is a simple cache in MongoDB. It does not use Semantic Caching, nor does it require an index to be made on the collection before generation.\n",
     "\n",
     "To import this cache, first install the required dependency:\n",
     "\n",
@@ -950,8 +958,9 @@
     "```\n",
     "\n",
     "\n",
-    "### `MongoDBAtlasSemanticCache`\n",
-    "Semantic caching allows users to retrieve cached prompts based on semantic similarity between the user input and previously cached results. Under the hood it blends MongoDBAtlas as both a cache and a vectorstore.\n",
+    "### Semantic cache\n",
+    "\n",
+    "Semantic caching allows retrieval of cached prompts based on semantic similarity between the user input and previously cached results. Under the hood, it blends MongoDBAtlas as both a cache and a vectorstore.\n",
     "The MongoDBAtlasSemanticCache inherits from `MongoDBAtlasVectorSearch` and needs an Atlas Vector Search Index defined to work. Please look at the [usage example](/docs/integrations/vectorstores/mongodb_atlas) on how to set up the index.\n",
     "\n",
     "To import this cache:\n",
@@ -985,14 +994,13 @@
    "cell_type": "markdown",
    "id": "726fe754",
    "metadata": {
-    "jp-MarkdownHeadingCollapsed": true,
     "tags": []
    },
    "source": [
-    "## `Momento` Cache\n",
+    "## `Momento` cache\n",
     "Use [Momento](/docs/integrations/providers/momento) to cache prompts and responses.\n",
     "\n",
-    "Requires momento to use, uncomment below to install:"
+    "Requires installing the `momento` package:"
    ]
   },
   {
@@ -1096,13 +1104,14 @@
    "cell_type": "markdown",
    "id": "934943dc",
    "metadata": {
-    "jp-MarkdownHeadingCollapsed": true,
     "tags": []
    },
    "source": [
-    "## `SQLAlchemy` Cache\n",
+    "## `SQLAlchemy` cache\n",
+    "\n",
+    "You can use `SQLAlchemyCache` to cache with any SQL database supported by `SQLAlchemy`.\n",
     "\n",
-    "You can use `SQLAlchemyCache` to cache with any SQL database supported by `SQLAlchemy`."
+    "### Standard cache"
    ]
   },
   {
@@ -1112,11 +1121,11 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# from langchain.cache import SQLAlchemyCache\n",
-    "# from sqlalchemy import create_engine\n",
+    "from langchain.cache import SQLAlchemyCache\n",
+    "from sqlalchemy import create_engine\n",
     "\n",
-    "# engine = create_engine(\"postgresql://postgres:postgres@localhost:5432/postgres\")\n",
-    "# set_llm_cache(SQLAlchemyCache(engine))"
+    "engine = create_engine(\"postgresql://postgres:postgres@localhost:5432/postgres\")\n",
+    "set_llm_cache(SQLAlchemyCache(engine))"
    ]
   },
   {
@@ -1124,7 +1133,9 @@
    "id": "0959d640",
    "metadata": {},
    "source": [
-    "### Custom SQLAlchemy Schemas"
+    "### Custom SQLAlchemy schemas\n",
+    "\n",
+    "You can define your own declarative `SQLAlchemyCache` child class to customize the schema used for caching. For example, to support high-speed fulltext prompt indexing with `Postgres`, use:"
    ]
   },
   {
@@ -1134,8 +1145,6 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# You can define your own declarative SQLAlchemyCache child class to customize the schema used for caching. For example, to support high-speed fulltext prompt indexing with Postgres, use:\n",
-    "\n",
     "from langchain_community.cache import SQLAlchemyCache\n",
     "from sqlalchemy import Column, Computed, Index, Integer, Sequence, String, create_engine\n",
     "from sqlalchemy.ext.declarative import declarative_base\n",
@@ -1185,7 +1194,7 @@
    "id": "6cf6acb4-1bc4-4c4b-9325-2420c17e5e2b",
    "metadata": {},
    "source": [
-    "### Required dependency"
+    "Required dependency:"
    ]
   },
   {
@@ -1203,7 +1212,7 @@
    "id": "a4a6725d",
    "metadata": {},
    "source": [
-    "### Connect to the DB\n",
+    "### Connecting to the DB\n",
     "\n",
     "The Cassandra caches shown in this page can be used with Cassandra as well as other derived databases, such as Astra DB, which use the CQL (Cassandra Query Language) protocol.\n",
     "\n",
@@ -1217,7 +1226,7 @@
    "id": "15735abe-2567-43ce-aa91-f253b33b5a88",
    "metadata": {},
    "source": [
-    "#### Connecting to a Cassandra cluster\n",
+    "#### to a Cassandra cluster\n",
     "\n",
     "You first need to create a `cassandra.cluster.Session` object, as described in the [Cassandra driver documentation](https://docs.datastax.com/en/developer/python-driver/latest/api/cassandra/cluster/#module-cassandra.cluster). The details vary (e.g. with network settings and authentication), but this might be something like:"
    ]
@@ -1270,7 +1279,7 @@
    "id": "2cc7ba29-8f84-4fbf-aaf7-3daa1be7e7b0",
    "metadata": {},
    "source": [
-    "#### Connecting to Astra DB through CQL\n",
+    "#### to Astra DB through CQL\n",
     "\n",
     "In this case you initialize CassIO with the following connection parameters:\n",
     "\n",
@@ -1329,7 +1338,7 @@
    "id": "8665664a",
    "metadata": {},
    "source": [
-    "### Cassandra: Exact cache\n",
+    "### Standard cache\n",
     "\n",
     "This will avoid invoking the LLM when the supplied prompt is _exactly_ the same as one encountered already:"
    ]
@@ -1400,7 +1409,7 @@
    "id": "8fc4d017",
    "metadata": {},
    "source": [
-    "### Cassandra: Semantic cache\n",
+    "### Semantic cache\n",
     "\n",
     "This cache will do a semantic similarity search and return a hit if it finds a cached entry that is similar enough, For this, you need to provide an `Embeddings` instance of your choice."
    ]
@@ -1488,9 +1497,9 @@
    "id": "55dc84b3-37cb-4f19-b175-40e18e06f83f",
    "metadata": {},
    "source": [
-    "#### Attribution statement\n",
+    "**Attribution statement:**\n",
     "\n",
-    ">Apache Cassandra, Cassandra and Apache are either registered trademarks or trademarks of the [Apache Software Foundation](http://www.apache.org/) in the United States and/or other countries."
+    ">`Apache Cassandra`, `Cassandra` and `Apache` are either registered trademarks or trademarks of the [Apache Software Foundation](http://www.apache.org/) in the United States and/or other countries."
    ]
   },
   {
@@ -1498,7 +1507,7 @@
    "id": "8712f8fc-bb89-4164-beb9-c672778bbd91",
    "metadata": {},
    "source": [
-    "## `Astra DB` Caches"
+    "## `Astra DB` caches"
    ]
   },
   {
@@ -1543,7 +1552,7 @@
    "id": "ee6d587f-4b7c-43f4-9e90-5129c842a143",
    "metadata": {},
    "source": [
-    "### Astra DB exact LLM cache\n",
+    "### Standard cache\n",
     "\n",
     "This will avoid invoking the LLM when the supplied prompt is _exactly_ the same as one encountered already:"
    ]
@@ -1619,7 +1628,7 @@
    "id": "524b94fa-6162-4880-884d-d008749d14e2",
    "metadata": {},
    "source": [
-    "### Astra DB Semantic cache\n",
+    "### Semantic cache\n",
     "\n",
     "This cache will do a semantic similarity search and return a hit if it finds a cached entry that is similar enough, For this, you need to provide an `Embeddings` instance of your choice."
    ]
@@ -1713,7 +1722,7 @@
     }
    },
    "source": [
-    "## Azure Cosmos DB Semantic Cache\n",
+    "## `Azure Cosmos DB` semantic cache\n",
     "\n",
     "You can use this integrated [vector database](https://learn.microsoft.com/en-us/azure/cosmos-db/vector-database) for caching."
    ]
@@ -1820,7 +1829,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 83,
    "id": "bc1570a2a77b58c8",
    "metadata": {
     "ExecuteTime": {
@@ -1859,7 +1868,7 @@
    "id": "235ff73bf7143f13",
    "metadata": {},
    "source": [
-    "## Azure CosmosDB NoSql Semantic Cache\n",
+    "## `Azure Cosmos DB NoSql` semantic cache\n",
     "\n",
     "You can use this integrated [vector database](https://learn.microsoft.com/en-us/azure/cosmos-db/vector-database) for caching."
    ]
@@ -2002,7 +2011,8 @@
    "id": "306ff47b",
    "metadata": {},
    "source": [
-    "## `Elasticsearch` Cache\n",
+    "## `Elasticsearch` caches\n",
+    "\n",
     "A caching layer for LLMs that uses Elasticsearch.\n",
     "\n",
     "First install the LangChain integration with Elasticsearch."
@@ -2023,6 +2033,8 @@
    "id": "9e70b0a0",
    "metadata": {},
    "source": [
+    "### Standard cache\n",
+    "\n",
     "Use the class `ElasticsearchCache`.\n",
     "\n",
     "Simple example:"
@@ -2130,6 +2142,26 @@
     "please only make additive modifications, keeping the base mapping intact."
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "3dc15b3c-8793-432e-98c3-d2726d497a5a",
+   "metadata": {},
+   "source": [
+    "### Embedding cache\n",
+    "\n",
+    "An Elasticsearch store for caching embeddings."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b0ae5bd3-517d-470f-9b44-14d9359e6940",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain_elasticsearch import ElasticsearchEmbeddingsCache"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "0c69d84d",
@@ -2137,8 +2169,9 @@
     "tags": []
    },
    "source": [
-    "## Optional Caching\n",
-    "You can also turn off caching for specific LLMs should you choose. In the example below, even though global caching is enabled, we turn it off for a specific LLM"
+    "## LLM-specific optional caching\n",
+    "\n",
+    "You can also turn off caching for specific LLMs. In the example below, even though global caching is enabled, we turn it off for a specific LLM."
    ]
   },
   {
@@ -2218,7 +2251,8 @@
     "tags": []
    },
    "source": [
-    "## Optional Caching in Chains\n",
+    "## Optional caching in Chains\n",
+    "\n",
     "You can also turn off caching for particular nodes in chains. Note that because of certain interfaces, its often easier to construct the chain first, and then edit the LLM afterwards.\n",
     "\n",
     "As an example, we will load a summarizer map-reduce chain. We will cache results for the map-step, but then not freeze it for the combine step."
@@ -2385,7 +2419,7 @@
    "id": "9ecfa565038eff71",
    "metadata": {},
    "source": [
-    "## OpenSearch Semantic Cache\n",
+    "## `OpenSearch` semantic cache\n",
     "Use [OpenSearch](https://python.langchain.com/docs/integrations/vectorstores/opensearch/) as a semantic cache to cache prompts and responses and evaluate hits based on semantic similarity."
    ]
   },
@@ -2489,7 +2523,8 @@
    "id": "2ac1a8c7",
    "metadata": {},
    "source": [
-    "## SingleStoreDB Semantic Cache\n",
+    "## `SingleStoreDB` semantic cache\n",
+    "\n",
     "You can use [SingleStoreDB](https://python.langchain.com/docs/integrations/vectorstores/singlestoredb/) as a semantic cache to cache prompts and responses."
    ]
   },
@@ -2516,7 +2551,7 @@
    "id": "7e6b9b1a",
    "metadata": {},
    "source": [
-    "## `Memcached` Cache\n",
+    "## `Memcached` cache\n",
     "You can use [Memcached](https://www.memcached.org/) as a cache to cache prompts and responses through [pymemcache](https://github.com/pinterest/pymemcache).\n",
     "\n",
     "This cache requires the pymemcache dependency to be installed:"
@@ -2612,7 +2647,7 @@
    "id": "7019c991-0101-4f9c-b212-5729a5471293",
    "metadata": {},
    "source": [
-    "## Couchbase Caches\n",
+    "## `Couchbase` caches\n",
     "\n",
     "Use [Couchbase](https://couchbase.com/) as a cache for prompts and responses."
    ]
@@ -2622,7 +2657,7 @@
    "id": "d6aac680-ba32-4c19-8864-6471cf0e7d5a",
    "metadata": {},
    "source": [
-    "### Couchbase Cache\n",
+    "### Standard cache\n",
     "\n",
     "The standard cache that looks for an exact match of the user prompt."
    ]
@@ -2756,7 +2791,7 @@
    "id": "1dca39d8-233a-45ba-ad7d-0920dfbc4a50",
    "metadata": {},
    "source": [
-    "### Specifying a Time to Live (TTL) for the Cached entries\n",
+    "#### Time to Live (TTL) for the cached entries\n",
     "The Cached documents can be deleted after a specified time automatically by specifying a `ttl` parameter along with the initialization of the Cache."
    ]
   },
@@ -2785,7 +2820,7 @@
    "id": "43626f33-d184-4260-b641-c9341cef5842",
    "metadata": {},
    "source": [
-    "### Couchbase Semantic Cache\n",
+    "### Semantic cache\n",
     "Semantic caching allows users to retrieve cached prompts based on semantic similarity between the user input and previously cached inputs. Under the hood it uses Couchbase as both a cache and a vectorstore. This needs an appropriate Vector Search Index defined to work. Please look at the usage example on how to set up the index."
    ]
   },
@@ -2828,7 +2863,9 @@
     "- The search index for the semantic cache needs to be defined before using the semantic cache. \n",
     "- The optional parameter, `score_threshold` in the Semantic Cache that you can use to tune the results of the semantic search.\n",
     "\n",
-    "### How to Import an Index to the Full Text Search service?\n",
+    "#### Index to the Full Text Search service\n",
+    "\n",
+    "How to Import an Index to the Full Text Search service?\n",
     " - [Couchbase Server](https://docs.couchbase.com/server/current/search/import-search-index.html)\n",
     "     - Click on Search -> Add Index -> Import\n",
     "     - Copy the following Index definition in the Import screen\n",
@@ -2838,7 +2875,8 @@
     "     - Import the file in Capella using the instructions in the documentation.\n",
     "     - Click on Create Index to create the index.\n",
     "\n",
-    "#### Example index for the vector search. \n",
+    "**Example index for the vector search:**\n",
+    "\n",
     "  ```\n",
     "  {\n",
     "    \"type\": \"fulltext-index\",\n",
@@ -2998,7 +3036,8 @@
    "id": "f6f674fa-70b5-4cf9-a208-992aad2c3c89",
    "metadata": {},
    "source": [
-    "### Specifying a Time to Live (TTL) for the Cached entries\n",
+    "#### Time to Live (TTL) for the cached entries\n",
+    "\n",
     "The Cached documents can be deleted after a specified time automatically by specifying a `ttl` parameter along with the initialization of the Cache."
    ]
   },
@@ -3056,10 +3095,10 @@
    "source": [
     "**Cache** classes are implemented by inheriting the [BaseCache](https://python.langchain.com/api_reference/core/caches/langchain_core.caches.BaseCache.html) class.\n",
     "\n",
-    "This table lists all 21 derived classes with links to the API Reference.\n",
+    "This table lists all derived classes with links to the API Reference.\n",
     "\n",
     "\n",
-    "| Namespace 🔻 | Class |\n",
+    "| Namespace | Class 🔻 |\n",
     "|------------|---------|\n",
     "| langchain_astradb.cache | [AstraDBCache](https://python.langchain.com/api_reference/astradb/cache/langchain_astradb.cache.AstraDBCache.html) |\n",
     "| langchain_astradb.cache | [AstraDBSemanticCache](https://python.langchain.com/api_reference/astradb/cache/langchain_astradb.cache.AstraDBSemanticCache.html) |\n",
@@ -3068,22 +3107,31 @@
     "| langchain_community.cache | [AzureCosmosDBSemanticCache](https://python.langchain.com/api_reference/community/cache/langchain_community.cache.AzureCosmosDBSemanticCache.html) |\n",
     "| langchain_community.cache | [CassandraCache](https://python.langchain.com/api_reference/community/cache/langchain_community.cache.CassandraCache.html) |\n",
     "| langchain_community.cache | [CassandraSemanticCache](https://python.langchain.com/api_reference/community/cache/langchain_community.cache.CassandraSemanticCache.html) |\n",
+    "| langchain_couchbase.cache | [CouchbaseCache](https://python.langchain.com/api_reference/couchbase/cache/langchain_couchbase.cache.CouchbaseCache.html) |\n",
+    "| langchain_couchbase.cache | [CouchbaseSemanticCache](https://python.langchain.com/api_reference/couchbase/cache/langchain_couchbase.cache.CouchbaseSemanticCache.html) |\n",
+    "| langchain_elasticsearch.cache | [ElasticsearchCache](https://python.langchain.com/api_reference/elasticsearch/cache/langchain_elasticsearch.cache.AsyncElasticsearchCache.html) |\n",
+    "| langchain_elasticsearch.cache | [ElasticsearchEmbeddingsCache](https://python.langchain.com/api_reference/elasticsearch/cache/langchain_elasticsearch.cache.AsyncElasticsearchEmbeddingsCache.html) |\n",
     "| langchain_community.cache | [GPTCache](https://python.langchain.com/api_reference/community/cache/langchain_community.cache.GPTCache.html) |\n",
+    "| langchain_core.caches | [InMemoryCache](https://python.langchain.com/api_reference/core/caches/langchain_core.caches.InMemoryCache.html) |\n",
     "| langchain_community.cache | [InMemoryCache](https://python.langchain.com/api_reference/community/cache/langchain_community.cache.InMemoryCache.html) |\n",
     "| langchain_community.cache | [MomentoCache](https://python.langchain.com/api_reference/community/cache/langchain_community.cache.MomentoCache.html) |\n",
+    "| langchain_mongodb.cache | [MongoDBAtlasSemanticCache](https://python.langchain.com/api_reference/mongodb/cache/langchain_mongodb.cache.MongoDBAtlasSemanticCache.html) |\n",
+    "| langchain_mongodb.cache | [MongoDBCache](https://python.langchain.com/api_reference/mongodb/cache/langchain_mongodb.cache.MongoDBCache.html) |\n",
     "| langchain_community.cache | [OpenSearchSemanticCache](https://python.langchain.com/api_reference/community/cache/langchain_community.cache.OpenSearchSemanticCache.html) |\n",
     "| langchain_community.cache | [RedisSemanticCache](https://python.langchain.com/api_reference/community/cache/langchain_community.cache.RedisSemanticCache.html) |\n",
     "| langchain_community.cache | [SingleStoreDBSemanticCache](https://python.langchain.com/api_reference/community/cache/langchain_community.cache.SingleStoreDBSemanticCache.html) |\n",
     "| langchain_community.cache | [SQLAlchemyCache](https://python.langchain.com/api_reference/community/cache/langchain_community.cache.SQLAlchemyCache.html) |\n",
     "| langchain_community.cache | [SQLAlchemyMd5Cache](https://python.langchain.com/api_reference/community/cache/langchain_community.cache.SQLAlchemyMd5Cache.html) |\n",
-    "| langchain_community.cache | [UpstashRedisCache](https://python.langchain.com/api_reference/community/cache/langchain_community.cache.UpstashRedisCache.html) |\n",
-    "| langchain_core.caches | [InMemoryCache](https://python.langchain.com/api_reference/core/caches/langchain_core.caches.InMemoryCache.html) |\n",
-    "| langchain_elasticsearch.cache | [ElasticsearchCache](https://python.langchain.com/api_reference/elasticsearch/cache/langchain_elasticsearch.cache.ElasticsearchCache.html) |\n",
-    "| langchain_mongodb.cache | [MongoDBAtlasSemanticCache](https://python.langchain.com/api_reference/mongodb/cache/langchain_mongodb.cache.MongoDBAtlasSemanticCache.html) |\n",
-    "| langchain_mongodb.cache | [MongoDBCache](https://python.langchain.com/api_reference/mongodb/cache/langchain_mongodb.cache.MongoDBCache.html) |\n",
-    "| langchain_couchbase.cache | [CouchbaseCache](https://python.langchain.com/api_reference/couchbase/cache/langchain_couchbase.cache.CouchbaseCache.html) |\n",
-    "| langchain_couchbase.cache | [CouchbaseSemanticCache](https://python.langchain.com/api_reference/couchbase/cache/langchain_couchbase.cache.CouchbaseSemanticCache.html) |\n"
+    "| langchain_community.cache | [UpstashRedisCache](https://python.langchain.com/api_reference/community/cache/langchain_community.cache.UpstashRedisCache.html) |\n"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "1ef1a2d4-da2e-4fb1-aae4-ffc4aef6c3ad",
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {
@@ -3102,7 +3150,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.13"
+   "version": "3.10.12"
   }
  },
  "nbformat": 4,
diff --git a/docs/docs/integrations/providers/couchbase.mdx b/docs/docs/integrations/providers/couchbase.mdx
index 906fbda6b28b3..35ef674806967 100644
--- a/docs/docs/integrations/providers/couchbase.mdx
+++ b/docs/docs/integrations/providers/couchbase.mdx
@@ -33,7 +33,7 @@ from langchain_community.document_loaders.couchbase import CouchbaseLoader
 ### CouchbaseCache
 Use Couchbase as a cache for prompts and responses.
 
-See a [usage example](/docs/integrations/llm_caching/#couchbase-cache).
+See a [usage example](/docs/integrations/llm_caching/#couchbase-caches).
 
 To import this cache:
 ```python
@@ -61,7 +61,7 @@ set_llm_cache(
 Semantic caching allows users to retrieve cached prompts based on the semantic similarity between the user input and previously cached inputs. Under the hood it uses Couchbase as both a cache and a vectorstore.
 The CouchbaseSemanticCache needs a Search Index defined to work. Please look at the [usage example](/docs/integrations/vectorstores/couchbase) on how to set up the index.
 
-See a [usage example](/docs/integrations/llm_caching/#couchbase-semantic-cache).
+See a [usage example](/docs/integrations/llm_caching/#couchbase-caches).
 
 To import this cache:
 ```python
diff --git a/docs/docs/integrations/providers/dataherald.mdx b/docs/docs/integrations/providers/dataherald.mdx
index 9b589f6ae6cd3..2b1c276df526b 100644
--- a/docs/docs/integrations/providers/dataherald.mdx
+++ b/docs/docs/integrations/providers/dataherald.mdx
@@ -41,7 +41,7 @@ tool = DataheraldTextToSQL(api_wrapper=api_wrapper)
 llm = ChatOpenAI(model="gpt-3.5-turbo", temperature=0)
 prompt = hub.pull("hwchase17/react")
 agent = create_react_agent(llm, tools, prompt)
-agent_executor = AgentExecutor(agent=agent, tools=tools, verbose=True)
+agent_executor = AgentExecutor(agent=agent, tools=tools, verbose=True, handle_parsing_errors=True)
 agent_executor.invoke({"input":"Return the sql for this question: How many employees are in the company?"})
 ```
 
diff --git a/docs/docs/integrations/providers/elasticsearch.mdx b/docs/docs/integrations/providers/elasticsearch.mdx
index c3b123d47b80f..734ef9d46ce8e 100644
--- a/docs/docs/integrations/providers/elasticsearch.mdx
+++ b/docs/docs/integrations/providers/elasticsearch.mdx
@@ -84,7 +84,7 @@ from langchain_elasticsearch import ElasticsearchChatMessageHistory
 
 ## LLM cache
 
-See a [usage example](/docs/integrations/llm_caching/#elasticsearch-cache).
+See a [usage example](/docs/integrations/llm_caching/#elasticsearch-caches).
 
 ```python
 from langchain_elasticsearch import ElasticsearchCache
diff --git a/libs/community/langchain_community/graphs/age_graph.py b/libs/community/langchain_community/graphs/age_graph.py
index 4fba85731ad01..434491253c6ab 100644
--- a/libs/community/langchain_community/graphs/age_graph.py
+++ b/libs/community/langchain_community/graphs/age_graph.py
@@ -697,8 +697,9 @@ def add_graph_documents(
         # query for inserting nodes
         node_insert_query = (
             """
-            MERGE (n:`{label}` {properties})
-        """
+            MERGE (n:`{label}` {{`id`: "{id}"}})
+            SET n = {properties}
+            """
             if not include_source
             else """
             MERGE (n:`{label}` {properties})
@@ -735,6 +736,7 @@ def add_graph_documents(
                     query = node_insert_query.format(
                         label=AGEGraph.clean_graph_labels(node.type),
                         properties=self._format_properties(node.properties),
+                        id=node.id,
                     )
 
                 self.query(query)
diff --git a/libs/community/tests/integration_tests/graphs/test_age_graph.py b/libs/community/tests/integration_tests/graphs/test_age_graph.py
index 383ddb62f8df6..32c2fab33cb6c 100644
--- a/libs/community/tests/integration_tests/graphs/test_age_graph.py
+++ b/libs/community/tests/integration_tests/graphs/test_age_graph.py
@@ -10,7 +10,11 @@
 
 test_data = [
     GraphDocument(
-        nodes=[Node(id="foo", type="foo"), Node(id="bar", type="bar")],
+        nodes=[
+            Node(id="foo", type="foo"),
+            Node(id="bar", type="bar"),
+            Node(id="foo", type="foo", properties={"property_a": "a"}),
+        ],
         relationships=[
             Relationship(
                 source=Node(id="foo", type="foo"),
diff --git a/libs/partners/anthropic/Makefile b/libs/partners/anthropic/Makefile
index 256ab677b83de..0bab6dfe077fd 100644
--- a/libs/partners/anthropic/Makefile
+++ b/libs/partners/anthropic/Makefile
@@ -8,7 +8,7 @@ TEST_FILE ?= tests/unit_tests/
 integration_test integration_tests: TEST_FILE=tests/integration_tests/
 
 test tests integration_test integration_tests:
-	poetry run pytest -vvv --timeout 10 $(TEST_FILE)
+	poetry run pytest -vvv --timeout 30 $(TEST_FILE)
 
 test_watch:
 	poetry run ptw --snapshot-update --now . -- -vv $(TEST_FILE)
diff --git a/libs/partners/chroma/.gitignore b/libs/partners/chroma/.gitignore
index da0d250a6a8fd..aa64ea142f06f 100644
--- a/libs/partners/chroma/.gitignore
+++ b/libs/partners/chroma/.gitignore
@@ -1,2 +1,3 @@
 __pycache__
 */persist_dir
+chroma/
diff --git a/libs/partners/chroma/langchain_chroma/vectorstores.py b/libs/partners/chroma/langchain_chroma/vectorstores.py
index bc87f9e088f43..648ff439a3edb 100644
--- a/libs/partners/chroma/langchain_chroma/vectorstores.py
+++ b/libs/partners/chroma/langchain_chroma/vectorstores.py
@@ -318,6 +318,9 @@ def __init__(
                 client_settings.persist_directory = (
                     persist_directory or client_settings.persist_directory
                 )
+                client_settings.is_persistent = (
+                    client_settings.persist_directory is not None
+                )
 
                 _client_settings = client_settings
             elif persist_directory:
diff --git a/libs/partners/chroma/tests/integration_tests/test_vectorstores.py b/libs/partners/chroma/tests/integration_tests/test_vectorstores.py
index f7bed4cfa5588..7420a99ec5ef9 100644
--- a/libs/partners/chroma/tests/integration_tests/test_vectorstores.py
+++ b/libs/partners/chroma/tests/integration_tests/test_vectorstores.py
@@ -1,5 +1,7 @@
 """Test Chroma functionality."""
 
+import os.path
+import tempfile
 import uuid
 from typing import (
     Generator,
@@ -10,6 +12,7 @@
 import pytest  # type: ignore[import-not-found]
 import requests
 from chromadb.api.client import SharedSystemClient
+from chromadb.api.segment import SegmentAPI
 from chromadb.api.types import Embeddable
 from langchain_core.documents import Document
 from langchain_core.embeddings.fake import FakeEmbeddings as Fak
@@ -268,37 +271,96 @@ def test_chroma_search_filter_with_scores() -> None:
 
 def test_chroma_with_persistence() -> None:
     """Test end to end construction and search, with persistence."""
-    chroma_persist_dir = "./tests/persist_dir"
-    collection_name = "test_collection"
-    texts = ["foo", "bar", "baz"]
-    ids = [f"id_{i}" for i in range(len(texts))]
+    with tempfile.TemporaryDirectory() as chroma_persist_dir:
+        collection_name = "test_collection"
+        texts = ["foo", "bar", "baz"]
+        ids = [f"id_{i}" for i in range(len(texts))]
+
+        docsearch = Chroma.from_texts(
+            collection_name=collection_name,
+            texts=texts,
+            embedding=FakeEmbeddings(),
+            persist_directory=chroma_persist_dir,
+            ids=ids,
+        )
 
-    docsearch = Chroma.from_texts(
-        collection_name=collection_name,
-        texts=texts,
-        embedding=FakeEmbeddings(),
-        persist_directory=chroma_persist_dir,
-        ids=ids,
-    )
+        try:
+            output = docsearch.similarity_search("foo", k=1)
+            assert output == [Document(page_content="foo", id="id_0")]
 
-    output = docsearch.similarity_search("foo", k=1)
-    assert output == [Document(page_content="foo", id="id_0")]
+            assert os.path.exists(chroma_persist_dir)
 
-    # Get a new VectorStore from the persisted directory
-    docsearch = Chroma(
-        collection_name=collection_name,
-        embedding_function=FakeEmbeddings(),
-        persist_directory=chroma_persist_dir,
-    )
-    output = docsearch.similarity_search("foo", k=1)
-    assert output == [Document(page_content="foo", id="id_0")]
+            # Get a new VectorStore from the persisted directory
+            docsearch = Chroma(
+                collection_name=collection_name,
+                embedding_function=FakeEmbeddings(),
+                persist_directory=chroma_persist_dir,
+            )
+            output = docsearch.similarity_search("foo", k=1)
+            assert output == [Document(page_content="foo", id="id_0")]
 
-    # Clean up
-    docsearch.delete_collection()
+            # Clean up
+            docsearch.delete_collection()
+
+            # Persist doesn't need to be called again
+            # Data will be automatically persisted on object deletion
+            # Or on program exit
+
+        finally:
+            # Need to stop the chrom system database and segment manager
+            # to be able to delete the files after testing
+            client = docsearch._client
+            assert isinstance(client, chromadb.ClientCreator)
+            assert isinstance(client._server, SegmentAPI)
+            client._server._sysdb.stop()
+            client._server._manager.stop()
+
+
+def test_chroma_with_persistence_with_client_settings() -> None:
+    """Test end to end construction and search, with persistence."""
+    with tempfile.TemporaryDirectory() as chroma_persist_dir:
+        client_settings = chromadb.config.Settings()
+        collection_name = "test_collection"
+        texts = ["foo", "bar", "baz"]
+        ids = [f"id_{i}" for i in range(len(texts))]
+        docsearch = Chroma.from_texts(
+            collection_name=collection_name,
+            texts=texts,
+            embedding=FakeEmbeddings(),
+            persist_directory=chroma_persist_dir,
+            client_settings=client_settings,
+            ids=ids,
+        )
 
-    # Persist doesn't need to be called again
-    # Data will be automatically persisted on object deletion
-    # Or on program exit
+        try:
+            output = docsearch.similarity_search("foo", k=1)
+            assert output == [Document(page_content="foo", id="id_0")]
+
+            assert os.path.exists(chroma_persist_dir)
+
+            # Get a new VectorStore from the persisted directory
+            docsearch = Chroma(
+                collection_name=collection_name,
+                embedding_function=FakeEmbeddings(),
+                persist_directory=chroma_persist_dir,
+            )
+            output = docsearch.similarity_search("foo", k=1)
+
+            # Clean up
+            docsearch.delete_collection()
+
+            # Persist doesn't need to be called again
+            # Data will be automatically persisted on object deletion
+            # Or on program exit
+
+        finally:
+            # Need to stop the chrom system database and segment manager
+            # to be able to delete the files after testing
+            client = docsearch._client
+            assert isinstance(client, chromadb.ClientCreator)
+            assert isinstance(client._server, SegmentAPI)
+            client._server._sysdb.stop()
+            client._server._manager.stop()
 
 
 def test_chroma_mmr() -> None:
diff --git a/libs/partners/voyageai/langchain_voyageai/embeddings.py b/libs/partners/voyageai/langchain_voyageai/embeddings.py
index 725dc176ca1f7..c5d8efcc320da 100644
--- a/libs/partners/voyageai/langchain_voyageai/embeddings.py
+++ b/libs/partners/voyageai/langchain_voyageai/embeddings.py
@@ -1,5 +1,5 @@
 import logging
-from typing import Any, Iterable, List, Optional
+from typing import Any, Iterable, List, Literal, Optional, cast
 
 import voyageai  # type: ignore
 from langchain_core.embeddings import Embeddings
@@ -37,8 +37,10 @@ class VoyageAIEmbeddings(BaseModel, Embeddings):
     _aclient: voyageai.client_async.AsyncClient = PrivateAttr()
     model: str
     batch_size: int
+
+    output_dimension: Optional[Literal[256, 512, 1024, 2048]] = None
     show_progress_bar: bool = False
-    truncation: Optional[bool] = None
+    truncation: bool = True
     voyage_api_key: SecretStr = Field(
         alias="api_key",
         default_factory=secret_from_env(
@@ -105,22 +107,26 @@ def embed_documents(self, texts: List[str]) -> List[List[float]]:
 
         _iter = self._get_batch_iterator(texts)
         for i in _iter:
-            embeddings.extend(
-                self._client.embed(
-                    texts[i : i + self.batch_size],
-                    model=self.model,
-                    input_type="document",
-                    truncation=self.truncation,
-                ).embeddings
-            )
-
+            r = self._client.embed(
+                texts[i : i + self.batch_size],
+                model=self.model,
+                input_type="document",
+                truncation=self.truncation,
+                output_dimension=self.output_dimension,
+            ).embeddings
+            embeddings.extend(cast(Iterable[List[float]], r))
         return embeddings
 
     def embed_query(self, text: str) -> List[float]:
         """Embed query text."""
-        return self._client.embed(
-            [text], model=self.model, input_type="query", truncation=self.truncation
+        r = self._client.embed(
+            [text],
+            model=self.model,
+            input_type="query",
+            truncation=self.truncation,
+            output_dimension=self.output_dimension,
         ).embeddings[0]
+        return cast(List[float], r)
 
     async def aembed_documents(self, texts: List[str]) -> List[List[float]]:
         embeddings: List[List[float]] = []
@@ -132,8 +138,9 @@ async def aembed_documents(self, texts: List[str]) -> List[List[float]]:
                 model=self.model,
                 input_type="document",
                 truncation=self.truncation,
+                output_dimension=self.output_dimension,
             )
-            embeddings.extend(r.embeddings)
+            embeddings.extend(cast(Iterable[List[float]], r.embeddings))
 
         return embeddings
 
@@ -143,5 +150,6 @@ async def aembed_query(self, text: str) -> List[float]:
             model=self.model,
             input_type="query",
             truncation=self.truncation,
+            output_dimension=self.output_dimension,
         )
-        return r.embeddings[0]
+        return cast(List[float], r.embeddings[0])
diff --git a/libs/partners/voyageai/langchain_voyageai/rerank.py b/libs/partners/voyageai/langchain_voyageai/rerank.py
index d0ee0122fa5d6..4be3d8f7a2d08 100644
--- a/libs/partners/voyageai/langchain_voyageai/rerank.py
+++ b/libs/partners/voyageai/langchain_voyageai/rerank.py
@@ -16,8 +16,8 @@
 class VoyageAIRerank(BaseDocumentCompressor):
     """Document compressor that uses `VoyageAI Rerank API`."""
 
-    client: voyageai.Client = None
-    aclient: voyageai.AsyncClient = None
+    client: voyageai.Client = None  # type: ignore
+    aclient: voyageai.AsyncClient = None  # type: ignore
     """VoyageAI clients to use for compressing documents."""
     voyage_api_key: Optional[SecretStr] = None
     """VoyageAI API key. Must be specified directly or via environment variable 
diff --git a/libs/partners/voyageai/poetry.lock b/libs/partners/voyageai/poetry.lock
index 3f84762f35456..8acd96b3ed1b4 100644
--- a/libs/partners/voyageai/poetry.lock
+++ b/libs/partners/voyageai/poetry.lock
@@ -1,148 +1,134 @@
-# This file is automatically @generated by Poetry 1.8.2 and should not be changed by hand.
+# This file is automatically @generated by Poetry 1.8.4 and should not be changed by hand.
 
 [[package]]
 name = "aiohappyeyeballs"
-version = "2.4.0"
+version = "2.4.4"
 description = "Happy Eyeballs for asyncio"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "aiohappyeyeballs-2.4.0-py3-none-any.whl", hash = "sha256:7ce92076e249169a13c2f49320d1967425eaf1f407522d707d59cac7628d62bd"},
-    {file = "aiohappyeyeballs-2.4.0.tar.gz", hash = "sha256:55a1714f084e63d49639800f95716da97a1f173d46a16dfcfda0016abb93b6b2"},
+    {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 = "3.10.5"
+version = "3.11.10"
 description = "Async http client/server framework (asyncio)"
 optional = false
-python-versions = ">=3.8"
+python-versions = ">=3.9"
 files = [
-    {file = "aiohttp-3.10.5-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:18a01eba2574fb9edd5f6e5fb25f66e6ce061da5dab5db75e13fe1558142e0a3"},
-    {file = "aiohttp-3.10.5-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:94fac7c6e77ccb1ca91e9eb4cb0ac0270b9fb9b289738654120ba8cebb1189c6"},
-    {file = "aiohttp-3.10.5-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:2f1f1c75c395991ce9c94d3e4aa96e5c59c8356a15b1c9231e783865e2772699"},
-    {file = "aiohttp-3.10.5-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:4f7acae3cf1a2a2361ec4c8e787eaaa86a94171d2417aae53c0cca6ca3118ff6"},
-    {file = "aiohttp-3.10.5-cp310-cp310-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:94c4381ffba9cc508b37d2e536b418d5ea9cfdc2848b9a7fea6aebad4ec6aac1"},
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 aiosignal = ">=1.1.2"
-async-timeout = {version = ">=4.0,<5.0", markers = "python_version < \"3.11\""}
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 frozenlist = ">=1.1.1"
 multidict = ">=4.5,<7.0"
-yarl = ">=1.0,<2.0"
+propcache = ">=0.2.0"
+yarl = ">=1.17.0,<2.0"
 
 [package.extras]
 speedups = ["Brotli", "aiodns (>=3.2.0)", "brotlicffi"]
 
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 ]
 
 [[package]]
 name = "aiosignal"
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+version = "1.3.2"
 description = "aiosignal: a list of registered asynchronous callbacks"
 optional = false
-python-versions = ">=3.7"
+python-versions = ">=3.9"
 files = [
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 ]
 
 [package.dependencies]
@@ -161,164 +147,179 @@ files = [
 
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+version = "4.7.0"
 description = "High level compatibility layer for multiple asynchronous event loop implementations"
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 ]
 
 [package.dependencies]
 exceptiongroup = {version = ">=1.0.2", markers = "python_version < \"3.11\""}
 idna = ">=2.8"
 sniffio = ">=1.1"
-typing-extensions = {version = ">=4.1", markers = "python_version < \"3.11\""}
+typing_extensions = {version = ">=4.5", markers = "python_version < \"3.13\""}
 
 [package.extras]
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-test = ["anyio[trio]", "coverage[toml] (>=7)", "exceptiongroup (>=1.2.0)", "hypothesis (>=4.0)", "psutil (>=5.9)", "pytest (>=7.0)", "pytest-mock (>=3.6.1)", "trustme", "uvloop (>=0.17)"]
-trio = ["trio (>=0.23)"]
+doc = ["Sphinx (>=7.4,<8.0)", "packaging", "sphinx-autodoc-typehints (>=1.2.0)", "sphinx_rtd_theme"]
+test = ["anyio[trio]", "coverage[toml] (>=7)", "exceptiongroup (>=1.2.0)", "hypothesis (>=4.0)", "psutil (>=5.9)", "pytest (>=7.0)", "pytest-mock (>=3.6.1)", "trustme", "truststore (>=0.9.1)", "uvloop (>=0.21)"]
+trio = ["trio (>=0.26.1)"]
 
 [[package]]
 name = "async-timeout"
-version = "4.0.3"
+version = "5.0.1"
 description = "Timeout context manager for asyncio programs"
 optional = false
-python-versions = ">=3.7"
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@@ -363,6 +364,22 @@ files = [
 [package.extras]
 test = ["pytest (>=6)"]
 
+[[package]]
+name = "filelock"
+version = "3.16.1"
+description = "A platform independent file lock."
+optional = false
+python-versions = ">=3.8"
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+testing = ["covdefaults (>=2.3)", "coverage (>=7.6.1)", "diff-cover (>=9.2)", "pytest (>=8.3.3)", "pytest-asyncio (>=0.24)", "pytest-cov (>=5)", "pytest-mock (>=3.14)", "pytest-timeout (>=2.3.1)", "virtualenv (>=20.26.4)"]
+typing = ["typing-extensions (>=4.12.2)"]
+
 [[package]]
 name = "freezegun"
 version = "1.5.1"
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+[[package]]
+name = "fsspec"
+version = "2024.10.0"
+description = "File-system specification"
+optional = false
+python-versions = ">=3.8"
+files = [
+    {file = "fsspec-2024.10.0-py3-none-any.whl", hash = "sha256:03b9a6785766a4de40368b88906366755e2819e758b83705c88cd7cb5fe81871"},
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+]
+
+[package.extras]
+abfs = ["adlfs"]
+adl = ["adlfs"]
+arrow = ["pyarrow (>=1)"]
+dask = ["dask", "distributed"]
+dev = ["pre-commit", "ruff"]
+doc = ["numpydoc", "sphinx", "sphinx-design", "sphinx-rtd-theme", "yarl"]
+dropbox = ["dropbox", "dropboxdrivefs", "requests"]
+full = ["adlfs", "aiohttp (!=4.0.0a0,!=4.0.0a1)", "dask", "distributed", "dropbox", "dropboxdrivefs", "fusepy", "gcsfs", "libarchive-c", "ocifs", "panel", "paramiko", "pyarrow (>=1)", "pygit2", "requests", "s3fs", "smbprotocol", "tqdm"]
+fuse = ["fusepy"]
+gcs = ["gcsfs"]
+git = ["pygit2"]
+github = ["requests"]
+gs = ["gcsfs"]
+gui = ["panel"]
+hdfs = ["pyarrow (>=1)"]
+http = ["aiohttp (!=4.0.0a0,!=4.0.0a1)"]
+libarchive = ["libarchive-c"]
+oci = ["ocifs"]
+s3 = ["s3fs"]
+sftp = ["paramiko"]
+smb = ["smbprotocol"]
+ssh = ["paramiko"]
+test = ["aiohttp (!=4.0.0a0,!=4.0.0a1)", "numpy", "pytest", "pytest-asyncio (!=0.22.0)", "pytest-benchmark", "pytest-cov", "pytest-mock", "pytest-recording", "pytest-rerunfailures", "requests"]
+test-downstream = ["aiobotocore (>=2.5.4,<3.0.0)", "dask-expr", "dask[dataframe,test]", "moto[server] (>4,<5)", "pytest-timeout", "xarray"]
+test-full = ["adlfs", "aiohttp (!=4.0.0a0,!=4.0.0a1)", "cloudpickle", "dask", "distributed", "dropbox", "dropboxdrivefs", "fastparquet", "fusepy", "gcsfs", "jinja2", "kerchunk", "libarchive-c", "lz4", "notebook", "numpy", "ocifs", "pandas", "panel", "paramiko", "pyarrow", "pyarrow (>=1)", "pyftpdlib", "pygit2", "pytest", "pytest-asyncio (!=0.22.0)", "pytest-benchmark", "pytest-cov", "pytest-mock", "pytest-recording", "pytest-rerunfailures", "python-snappy", "requests", "smbprotocol", "tqdm", "urllib3", "zarr", "zstandard"]
+tqdm = ["tqdm"]
+
 [[package]]
 name = "h11"
 version = "0.14.0"
@@ -476,13 +547,13 @@ files = [
 
 [[package]]
 name = "httpcore"
-version = "1.0.5"
+version = "1.0.7"
 description = "A minimal low-level HTTP client."
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "httpcore-1.0.5-py3-none-any.whl", hash = "sha256:421f18bac248b25d310f3cacd198d55b8e6125c107797b609ff9b7a6ba7991b5"},
-    {file = "httpcore-1.0.5.tar.gz", hash = "sha256:34a38e2f9291467ee3b44e89dd52615370e152954ba21721378a87b2960f7a61"},
+    {file = "httpcore-1.0.7-py3-none-any.whl", hash = "sha256:a3fff8f43dc260d5bd363d9f9cf1830fa3a458b332856f34282de498ed420edd"},
+    {file = "httpcore-1.0.7.tar.gz", hash = "sha256:8551cb62a169ec7162ac7be8d4817d561f60e08eaa485234898414bb5a8a0b4c"},
 ]
 
 [package.dependencies]
@@ -493,17 +564,17 @@ h11 = ">=0.13,<0.15"
 asyncio = ["anyio (>=4.0,<5.0)"]
 http2 = ["h2 (>=3,<5)"]
 socks = ["socksio (==1.*)"]
-trio = ["trio (>=0.22.0,<0.26.0)"]
+trio = ["trio (>=0.22.0,<1.0)"]
 
 [[package]]
 name = "httpx"
-version = "0.27.2"
+version = "0.28.1"
 description = "The next generation HTTP client."
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "httpx-0.27.2-py3-none-any.whl", hash = "sha256:7bb2708e112d8fdd7829cd4243970f0c223274051cb35ee80c03301ee29a3df0"},
-    {file = "httpx-0.27.2.tar.gz", hash = "sha256:f7c2be1d2f3c3c3160d441802406b206c2b76f5947b11115e6df10c6c65e66c2"},
+    {file = "httpx-0.28.1-py3-none-any.whl", hash = "sha256:d909fcccc110f8c7faf814ca82a9a4d816bc5a6dbfea25d6591d6985b8ba59ad"},
+    {file = "httpx-0.28.1.tar.gz", hash = "sha256:75e98c5f16b0f35b567856f597f06ff2270a374470a5c2392242528e3e3e42fc"},
 ]
 
 [package.dependencies]
@@ -511,7 +582,6 @@ anyio = "*"
 certifi = "*"
 httpcore = "==1.*"
 idna = "*"
-sniffio = "*"
 
 [package.extras]
 brotli = ["brotli", "brotlicffi"]
@@ -520,6 +590,40 @@ http2 = ["h2 (>=3,<5)"]
 socks = ["socksio (==1.*)"]
 zstd = ["zstandard (>=0.18.0)"]
 
+[[package]]
+name = "huggingface-hub"
+version = "0.26.5"
+description = "Client library to download and publish models, datasets and other repos on the huggingface.co hub"
+optional = false
+python-versions = ">=3.8.0"
+files = [
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+    {file = "huggingface_hub-0.26.5.tar.gz", hash = "sha256:1008bd18f60bfb65e8dbc0a97249beeeaa8c99d3c2fa649354df9fa5a13ed83b"},
+]
+
+[package.dependencies]
+filelock = "*"
+fsspec = ">=2023.5.0"
+packaging = ">=20.9"
+pyyaml = ">=5.1"
+requests = "*"
+tqdm = ">=4.42.1"
+typing-extensions = ">=3.7.4.3"
+
+[package.extras]
+all = ["InquirerPy (==0.3.4)", "Jinja2", "Pillow", "aiohttp", "fastapi", "gradio (>=4.0.0)", "jedi", "libcst (==1.4.0)", "mypy (==1.5.1)", "numpy", "pytest (>=8.1.1,<8.2.2)", "pytest-asyncio", "pytest-cov", "pytest-env", "pytest-mock", "pytest-rerunfailures", "pytest-vcr", "pytest-xdist", "ruff (>=0.5.0)", "soundfile", "types-PyYAML", "types-requests", "types-simplejson", "types-toml", "types-tqdm", "types-urllib3", "typing-extensions (>=4.8.0)", "urllib3 (<2.0)"]
+cli = ["InquirerPy (==0.3.4)"]
+dev = ["InquirerPy (==0.3.4)", "Jinja2", "Pillow", "aiohttp", "fastapi", "gradio (>=4.0.0)", "jedi", "libcst (==1.4.0)", "mypy (==1.5.1)", "numpy", "pytest (>=8.1.1,<8.2.2)", "pytest-asyncio", "pytest-cov", "pytest-env", "pytest-mock", "pytest-rerunfailures", "pytest-vcr", "pytest-xdist", "ruff (>=0.5.0)", "soundfile", "types-PyYAML", "types-requests", "types-simplejson", "types-toml", "types-tqdm", "types-urllib3", "typing-extensions (>=4.8.0)", "urllib3 (<2.0)"]
+fastai = ["fastai (>=2.4)", "fastcore (>=1.3.27)", "toml"]
+hf-transfer = ["hf-transfer (>=0.1.4)"]
+inference = ["aiohttp"]
+quality = ["libcst (==1.4.0)", "mypy (==1.5.1)", "ruff (>=0.5.0)"]
+tensorflow = ["graphviz", "pydot", "tensorflow"]
+tensorflow-testing = ["keras (<3.0)", "tensorflow"]
+testing = ["InquirerPy (==0.3.4)", "Jinja2", "Pillow", "aiohttp", "fastapi", "gradio (>=4.0.0)", "jedi", "numpy", "pytest (>=8.1.1,<8.2.2)", "pytest-asyncio", "pytest-cov", "pytest-env", "pytest-mock", "pytest-rerunfailures", "pytest-vcr", "pytest-xdist", "soundfile", "urllib3 (<2.0)"]
+torch = ["safetensors[torch]", "torch"]
+typing = ["types-PyYAML", "types-requests", "types-simplejson", "types-toml", "types-tqdm", "types-urllib3", "typing-extensions (>=4.8.0)"]
+
 [[package]]
 name = "idna"
 version = "3.10"
@@ -572,7 +676,7 @@ files = [
 
 [[package]]
 name = "langchain-core"
-version = "0.3.15"
+version = "0.3.25"
 description = "Building applications with LLMs through composability"
 optional = false
 python-versions = ">=3.9,<4.0"
@@ -581,7 +685,7 @@ develop = true
 
 [package.dependencies]
 jsonpatch = "^1.33"
-langsmith = "^0.1.125"
+langsmith = ">=0.1.125,<0.3"
 packaging = ">=23.2,<25"
 pydantic = [
     {version = ">=2.5.2,<3.0.0", markers = "python_full_version < \"3.12.4\""},
@@ -597,18 +701,18 @@ url = "../../core"
 
 [[package]]
 name = "langsmith"
-version = "0.1.139"
+version = "0.2.3"
 description = "Client library to connect to the LangSmith LLM Tracing and Evaluation Platform."
 optional = false
-python-versions = "<4.0,>=3.8.1"
+python-versions = "<4.0,>=3.9"
 files = [
-    {file = "langsmith-0.1.139-py3-none-any.whl", hash = "sha256:2a4a541bfbd0a9727255df28a60048c85bc8c4c6a276975923785c3fd82dc879"},
-    {file = "langsmith-0.1.139.tar.gz", hash = "sha256:2f9e4d32fef3ad7ef42c8506448cce3a31ad6b78bb4f3310db04ddaa1e9d744d"},
+    {file = "langsmith-0.2.3-py3-none-any.whl", hash = "sha256:4958b6e918f57fedba6ddc55b8534d1e06478bb44c779aa73713ce898ca6ae87"},
+    {file = "langsmith-0.2.3.tar.gz", hash = "sha256:54c231b07fdeb0f8472925074a0ec0ed2cb654a0437d63c6ccf76a9da635900d"},
 ]
 
 [package.dependencies]
 httpx = ">=0.23.0,<1"
-orjson = ">=3.9.14,<4.0.0"
+orjson = {version = ">=3.9.14,<4.0.0", markers = "platform_python_implementation != \"PyPy\""}
 pydantic = [
     {version = ">=1,<3", markers = "python_full_version < \"3.12.4\""},
     {version = ">=2.7.4,<3.0.0", markers = "python_full_version >= \"3.12.4\""},
@@ -616,6 +720,9 @@ pydantic = [
 requests = ">=2,<3"
 requests-toolbelt = ">=1.0.0,<2.0.0"
 
+[package.extras]
+langsmith-pyo3 = ["langsmith-pyo3 (>=0.1.0rc2,<0.2.0)"]
+
 [[package]]
 name = "multidict"
 version = "6.1.0"
@@ -722,38 +829,43 @@ typing-extensions = {version = ">=4.1.0", markers = "python_version < \"3.11\""}
 
 [[package]]
 name = "mypy"
-version = "1.11.2"
+version = "1.13.0"
 description = "Optional static typing for Python"
 optional = false
 python-versions = ">=3.8"
 files = [
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 [package.dependencies]
@@ -763,6 +875,7 @@ typing-extensions = ">=4.6.0"
 
 [package.extras]
 dmypy = ["psutil (>=4.0)"]
+faster-cache = ["orjson"]
 install-types = ["pip"]
 mypyc = ["setuptools (>=50)"]
 reports = ["lxml"]
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 [[package]]
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 testing = ["pytest", "pytest-benchmark"]
 
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@@ -1286,30 +1608,113 @@ pytest = ">=7.0.0,<9.0.0"
 
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+version = "9.0.0"
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 [package.extras]
 doc = ["reno", "sphinx"]
 test = ["pytest", "tornado (>=4.5)", "typeguard"]
 
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+[package.dependencies]
+colorama = {version = "*", markers = "platform_system == \"Windows\""}
+
+[package.extras]
+dev = ["nbval", "pytest (>=6)", "pytest-asyncio (>=0.24)", "pytest-cov", "pytest-timeout"]
+discord = ["requests"]
+notebook = ["ipywidgets (>=6)"]
+slack = ["slack-sdk"]
+telegram = ["requests"]
+
 [[package]]
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@@ -1340,59 +1745,62 @@ zstd = ["zstandard (>=0.18.0)"]
 
 [[package]]
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 [package.extras]
@@ -1400,110 +1808,101 @@ watchmedo = ["PyYAML (>=3.10)"]
 
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+    {file = "yarl-1.18.3.tar.gz", hash = "sha256:ac1801c45cbf77b6c99242eeff4fffb5e4e73a800b5c4ad4fc0be5def634d2e1"},
 ]
 
 [package.dependencies]
 idna = ">=2.0"
 multidict = ">=4.0"
+propcache = ">=0.2.0"
 
 [metadata]
 lock-version = "2.0"
-python-versions = ">=3.9,<4.0"
-content-hash = "c037eee2ed1ce2ed484992fb08ee187a0db037bf8c84cf69d8715c7c62a59bfe"
+python-versions = ">=3.9,<3.13"
+content-hash = "e554f41a0c54d62654ba52624e92749d684ac6ef6483b5488c2c8810009e6fa9"
diff --git a/libs/partners/voyageai/pyproject.toml b/libs/partners/voyageai/pyproject.toml
index 0193cc36d5e26..f68f16826dd54 100644
--- a/libs/partners/voyageai/pyproject.toml
+++ b/libs/partners/voyageai/pyproject.toml
@@ -19,9 +19,9 @@ disallow_untyped_defs = "True"
 "Release Notes" = "https://github.com/langchain-ai/langchain/releases?q=tag%3A%22langchain-voyageai%3D%3D0%22&expanded=true"
 
 [tool.poetry.dependencies]
-python = ">=3.9,<4.0"
+python = ">=3.9,<3.13"
 langchain-core = "^0.3.15"
-voyageai = ">=0.2.1,<1"
+voyageai = ">=0.3.2,<1"
 pydantic = ">=2,<3"
 
 [tool.ruff.lint]
diff --git a/libs/partners/voyageai/tests/integration_tests/test_embeddings.py b/libs/partners/voyageai/tests/integration_tests/test_embeddings.py
index 532ded1756ce4..91ecc62a4573d 100644
--- a/libs/partners/voyageai/tests/integration_tests/test_embeddings.py
+++ b/libs/partners/voyageai/tests/integration_tests/test_embeddings.py
@@ -51,3 +51,12 @@ async def test_langchain_voyageai_async_embedding_query() -> None:
     embedding = VoyageAIEmbeddings(model=MODEL)  # type: ignore[call-arg]
     output = await embedding.aembed_query(document)
     assert len(output) == 1024
+
+
+def test_langchain_voyageai_embedding_documents_with_output_dimension() -> None:
+    """Test voyage embeddings."""
+    documents = ["foo bar"]
+    embedding = VoyageAIEmbeddings(model="voyage-3-large", output_dimension=256)  # type: ignore[call-arg]
+    output = embedding.embed_documents(documents)
+    assert len(output) == 1
+    assert len(output[0]) == 256
diff --git a/libs/partners/voyageai/tests/unit_tests/test_embeddings.py b/libs/partners/voyageai/tests/unit_tests/test_embeddings.py
index 354c2c6a32532..587376ab8b450 100644
--- a/libs/partners/voyageai/tests/unit_tests/test_embeddings.py
+++ b/libs/partners/voyageai/tests/unit_tests/test_embeddings.py
@@ -47,3 +47,15 @@ def test_initialization_voyage_1_batch_size() -> None:
     assert emb.batch_size == 15
     assert emb.model == "voyage-01"
     assert emb._client is not None
+
+
+def test_initialization_with_output_dimension() -> None:
+    emb = VoyageAIEmbeddings(
+        api_key="NOT_A_VALID_KEY",  # type: ignore
+        model="voyage-3-large",
+        output_dimension=256,
+        batch_size=10,
+    )
+    assert isinstance(emb, Embeddings)
+    assert emb.model == "voyage-3-large"
+    assert emb.output_dimension == 256