diff --git a/docs/docs/how_to/agent_executor.ipynb b/docs/docs/how_to/agent_executor.ipynb
index e6a357e3ca9..9c59f569e43 100644
--- a/docs/docs/how_to/agent_executor.ipynb
+++ b/docs/docs/how_to/agent_executor.ipynb
@@ -461,7 +461,7 @@
    "id": "f8014c9d",
    "metadata": {},
    "source": [
-    "Now, we can initalize the agent with the LLM, the prompt, and the tools. The agent is responsible for taking in input and deciding what actions to take. Crucially, the Agent does not execute those actions - that is done by the AgentExecutor (next step). For more information about how to think about these components, see our [conceptual guide](/docs/concepts/#agents).\n",
+    "Now, we can initialize the agent with the LLM, the prompt, and the tools. The agent is responsible for taking in input and deciding what actions to take. Crucially, the Agent does not execute those actions - that is done by the AgentExecutor (next step). For more information about how to think about these components, see our [conceptual guide](/docs/concepts/#agents).\n",
     "\n",
     "Note that we are passing in the `model`, not `model_with_tools`. That is because `create_tool_calling_agent` will call `.bind_tools` for us under the hood."
    ]
diff --git a/docs/docs/how_to/graph_constructing.ipynb b/docs/docs/how_to/graph_constructing.ipynb
index 52481975a6f..e9b6a014177 100644
--- a/docs/docs/how_to/graph_constructing.ipynb
+++ b/docs/docs/how_to/graph_constructing.ipynb
@@ -24,7 +24,7 @@
     "\n",
     "## Architecture\n",
     "\n",
-    "At a high-level, the steps of constructing a knowledge are from text are:\n",
+    "At a high-level, the steps of constructing a knowledge graph from text are:\n",
     "\n",
     "1. **Extracting structured information from text**: Model is used to extract structured graph information from text.\n",
     "2. **Storing into graph database**: Storing the extracted structured graph information into a graph database enables downstream RAG applications\n",
diff --git a/docs/docs/how_to/tools_human.ipynb b/docs/docs/how_to/tools_human.ipynb
index c5bcbf3c01a..5e5312debde 100644
--- a/docs/docs/how_to/tools_human.ipynb
+++ b/docs/docs/how_to/tools_human.ipynb
@@ -129,13 +129,13 @@
     "\n",
     "@tool\n",
     "def count_emails(last_n_days: int) -> int:\n",
-    "    \"\"\"Multiply two integers together.\"\"\"\n",
+    "    \"\"\"Dummy function to count number of e-mails. Returns 2 * last_n_days.\"\"\"\n",
     "    return last_n_days * 2\n",
     "\n",
     "\n",
     "@tool\n",
     "def send_email(message: str, recipient: str) -> str:\n",
-    "    \"Add two integers.\"\n",
+    "    \"\"\"Dummy function for sending an e-mail.\"\"\"\n",
     "    return f\"Successfully sent email to {recipient}.\"\n",
     "\n",
     "\n",
diff --git a/docs/docs/integrations/chat/ai21.ipynb b/docs/docs/integrations/chat/ai21.ipynb
index efdb43cab42..38429aa9153 100644
--- a/docs/docs/integrations/chat/ai21.ipynb
+++ b/docs/docs/integrations/chat/ai21.ipynb
@@ -50,18 +50,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
    "id": "62e0dbc3",
    "metadata": {
     "tags": []
    },
-   "outputs": [],
    "source": [
     "import os\n",
     "from getpass import getpass\n",
     "\n",
     "os.environ[\"AI21_API_KEY\"] = getpass()"
-   ]
+   ],
+   "outputs": [],
+   "execution_count": null
   },
   {
    "cell_type": "markdown",
@@ -73,14 +73,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
    "id": "7c2e19d3-7c58-4470-9e1a-718b27a32056",
    "metadata": {},
-   "outputs": [],
    "source": [
     "# os.environ[\"LANGCHAIN_TRACING_V2\"] = \"true\"\n",
     "# os.environ[\"LANGCHAIN_API_KEY\"] = getpass.getpass(\"Enter your LangSmith API key: \")"
-   ]
+   ],
+   "outputs": [],
+   "execution_count": null
   },
   {
    "cell_type": "markdown",
@@ -115,15 +115,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
    "id": "c40756fb-cbf8-4d44-a293-3989d707237e",
    "metadata": {},
-   "outputs": [],
    "source": [
     "from langchain_ai21 import ChatAI21\n",
     "\n",
     "llm = ChatAI21(model=\"jamba-instruct\", temperature=0)"
-   ]
+   ],
+   "outputs": [],
+   "execution_count": null
   },
   {
    "cell_type": "markdown",
@@ -135,21 +135,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
    "id": "46b982dc-5d8a-46da-a711-81c03ccd6adc",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "AIMessage(content=\"J'adore programmer.\", id='run-2e8d16d6-a06e-45cb-8d0c-1c8208645033-0')"
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
    "source": [
     "messages = [\n",
     "    (\n",
@@ -160,7 +147,9 @@
     "]\n",
     "ai_msg = llm.invoke(messages)\n",
     "ai_msg"
-   ]
+   ],
+   "outputs": [],
+   "execution_count": null
   },
   {
    "cell_type": "markdown",
@@ -174,7 +163,6 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
    "id": "39353473fce5dd2e",
    "metadata": {
     "collapsed": false,
@@ -182,18 +170,6 @@
      "outputs_hidden": false
     }
    },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "AIMessage(content='Ich liebe das Programmieren.', id='run-e1bd82dc-1a7e-4b2e-bde9-ac995929ac0f-0')"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
    "source": [
     "from langchain_core.prompts import ChatPromptTemplate\n",
     "\n",
@@ -215,7 +191,95 @@
     "        \"input\": \"I love programming.\",\n",
     "    }\n",
     ")"
-   ]
+   ],
+   "outputs": [],
+   "execution_count": null
+  },
+  {
+   "metadata": {},
+   "cell_type": "markdown",
+   "source": "# Tool Calls / Function Calling",
+   "id": "39c0ccd229927eab"
+  },
+  {
+   "metadata": {},
+   "cell_type": "markdown",
+   "source": "This example shows how to use tool calling with AI21 models:",
+   "id": "2bf6b40be07fe2d4"
+  },
+  {
+   "metadata": {},
+   "cell_type": "code",
+   "source": [
+    "import os\n",
+    "from getpass import getpass\n",
+    "\n",
+    "from langchain_ai21.chat_models import ChatAI21\n",
+    "from langchain_core.messages import HumanMessage, SystemMessage, ToolMessage\n",
+    "from langchain_core.tools import tool\n",
+    "from langchain_core.utils.function_calling import convert_to_openai_tool\n",
+    "\n",
+    "os.environ[\"AI21_API_KEY\"] = getpass()\n",
+    "\n",
+    "\n",
+    "@tool\n",
+    "def get_weather(location: str, date: str) -> str:\n",
+    "    \"\"\"“Provide the weather for the specified location on the given date.”\"\"\"\n",
+    "    if location == \"New York\" and date == \"2024-12-05\":\n",
+    "        return \"25 celsius\"\n",
+    "    elif location == \"New York\" and date == \"2024-12-06\":\n",
+    "        return \"27 celsius\"\n",
+    "    elif location == \"London\" and date == \"2024-12-05\":\n",
+    "        return \"22 celsius\"\n",
+    "    return \"32 celsius\"\n",
+    "\n",
+    "\n",
+    "llm = ChatAI21(model=\"jamba-1.5-mini\")\n",
+    "\n",
+    "llm_with_tools = llm.bind_tools([convert_to_openai_tool(get_weather)])\n",
+    "\n",
+    "chat_messages = [\n",
+    "    SystemMessage(\n",
+    "        content=\"You are a helpful assistant. You can use the provided tools \"\n",
+    "        \"to assist with various tasks and provide accurate information\"\n",
+    "    )\n",
+    "]\n",
+    "\n",
+    "human_messages = [\n",
+    "    HumanMessage(\n",
+    "        content=\"What is the forecast for the weather in New York on December 5, 2024?\"\n",
+    "    ),\n",
+    "    HumanMessage(content=\"And what about the 2024-12-06?\"),\n",
+    "    HumanMessage(content=\"OK, thank you.\"),\n",
+    "    HumanMessage(content=\"What is the expected weather in London on December 5, 2024?\"),\n",
+    "]\n",
+    "\n",
+    "\n",
+    "for human_message in human_messages:\n",
+    "    print(f\"User: {human_message.content}\")\n",
+    "    chat_messages.append(human_message)\n",
+    "    response = llm_with_tools.invoke(chat_messages)\n",
+    "    chat_messages.append(response)\n",
+    "    if response.tool_calls:\n",
+    "        tool_call = response.tool_calls[0]\n",
+    "        if tool_call[\"name\"] == \"get_weather\":\n",
+    "            weather = get_weather.invoke(\n",
+    "                {\n",
+    "                    \"location\": tool_call[\"args\"][\"location\"],\n",
+    "                    \"date\": tool_call[\"args\"][\"date\"],\n",
+    "                }\n",
+    "            )\n",
+    "            chat_messages.append(\n",
+    "                ToolMessage(content=weather, tool_call_id=tool_call[\"id\"])\n",
+    "            )\n",
+    "            llm_answer = llm_with_tools.invoke(chat_messages)\n",
+    "            print(f\"Assistant: {llm_answer.content}\")\n",
+    "    else:\n",
+    "        print(f\"Assistant: {response.content}\")"
+   ],
+   "id": "a181a28df77120fb",
+   "outputs": [],
+   "execution_count": null
   },
   {
    "cell_type": "markdown",
diff --git a/docs/docs/integrations/document_loaders/scrapfly.ipynb b/docs/docs/integrations/document_loaders/scrapfly.ipynb
index 0f37335925d..64ed86ffd4c 100644
--- a/docs/docs/integrations/document_loaders/scrapfly.ipynb
+++ b/docs/docs/integrations/document_loaders/scrapfly.ipynb
@@ -49,7 +49,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The ScrapflyLoader also allows passigng ScrapeConfig object for customizing the scrape request. See the documentation for the full feature details and their API params: https://scrapfly.io/docs/scrape-api/getting-started"
+    "The ScrapflyLoader also allows passing ScrapeConfig object for customizing the scrape request. See the documentation for the full feature details and their API params: https://scrapfly.io/docs/scrape-api/getting-started"
    ]
   },
   {
diff --git a/docs/docs/integrations/platforms/huggingface.mdx b/docs/docs/integrations/platforms/huggingface.mdx
index b558f8947fc..da7d39c1c7a 100644
--- a/docs/docs/integrations/platforms/huggingface.mdx
+++ b/docs/docs/integrations/platforms/huggingface.mdx
@@ -12,7 +12,7 @@ pip install langchain-huggingface
 
 ## Chat models
 
-### Models from Hugging Face
+### ChatHuggingFace
 
 We can use the `Hugging Face` LLM classes or directly use the `ChatHuggingFace` class.
 
@@ -24,7 +24,16 @@ from langchain_huggingface import ChatHuggingFace
 
 ## LLMs
 
-### Hugging Face Local Pipelines
+### HuggingFaceEndpoint
+
+
+See a [usage example](/docs/integrations/llms/huggingface_endpoint).
+
+```python
+from langchain_huggingface import HuggingFaceEndpoint
+```
+
+### HuggingFacePipeline
 
 Hugging Face models can be run locally through the `HuggingFacePipeline` class.
 
@@ -44,6 +53,22 @@ See a [usage example](/docs/integrations/text_embedding/huggingfacehub).
 from langchain_huggingface import HuggingFaceEmbeddings
 ```
 
+### HuggingFaceEndpointEmbeddings
+
+See a [usage example](/docs/integrations/text_embedding/huggingfacehub).
+
+```python
+from langchain_huggingface import HuggingFaceEndpointEmbeddings
+```
+
+### HuggingFaceInferenceAPIEmbeddings
+
+See a [usage example](/docs/integrations/text_embedding/huggingfacehub).
+
+```python
+from langchain_community.embeddings import HuggingFaceInferenceAPIEmbeddings
+```
+
 ### HuggingFaceInstructEmbeddings
 
 See a [usage example](/docs/integrations/text_embedding/instruct_embeddings).
@@ -63,25 +88,6 @@ See a [usage example](/docs/integrations/text_embedding/bge_huggingface).
 from langchain_community.embeddings import HuggingFaceBgeEmbeddings
 ```
 
-### Hugging Face Text Embeddings Inference (TEI)
-
->[Hugging Face Text Embeddings Inference (TEI)](https://huggingface.co/docs/text-generation-inference/index) is a toolkit for deploying and serving open-source
-> text embeddings and sequence classification models. `TEI` enables high-performance extraction for the most popular models,
->including `FlagEmbedding`, `Ember`, `GTE` and `E5`.
-
-We need to install `huggingface-hub` python package.
-
-```bash
-pip install huggingface-hub
-```
-
-See a [usage example](/docs/integrations/text_embedding/text_embeddings_inference).
-
-```python
-from langchain_community.embeddings import HuggingFaceHubEmbeddings
-```
-
-
 ## Document Loaders
 
 ### Hugging Face dataset
@@ -104,7 +110,34 @@ See a [usage example](/docs/integrations/document_loaders/hugging_face_dataset).
 from langchain_community.document_loaders.hugging_face_dataset import HuggingFaceDatasetLoader
 ```
 
+### Hugging Face model loader
 
+>Load model information from `Hugging Face Hub`, including README content.
+>
+>This loader interfaces with the `Hugging Face Models API` to fetch 
+> and load model metadata and README files. 
+> The API allows you to search and filter models based on 
+> specific criteria such as model tags, authors, and more.
+
+```python
+from langchain_community.document_loaders import HuggingFaceModelLoader
+```
+
+### Image captions
+
+It uses the Hugging Face models to generate image captions.
+
+We need to install several python packages.
+
+```bash
+pip install transformers pillow
+```
+
+See a [usage example](/docs/integrations/document_loaders/image_captions).
+
+```python
+from langchain_community.document_loaders import ImageCaptionLoader
+```
 
 ## Tools
 
@@ -124,3 +157,12 @@ See a [usage example](/docs/integrations/tools/huggingface_tools).
 ```python
 from langchain_community.agent_toolkits.load_tools import load_huggingface_tool
 ```
+
+### Hugging Face Text-to-Speech Model Inference.
+
+> It is a wrapper around `OpenAI Text-to-Speech API`.
+
+```python
+from langchain_community.tools.audio import HuggingFaceTextToSpeechModelInference
+```
+
diff --git a/docs/docs/integrations/platforms/microsoft.mdx b/docs/docs/integrations/platforms/microsoft.mdx
index fb0b4efbbb4..f6adefb1e49 100644
--- a/docs/docs/integrations/platforms/microsoft.mdx
+++ b/docs/docs/integrations/platforms/microsoft.mdx
@@ -436,6 +436,8 @@ See a [usage example](/docs/integrations/tools/azure_ai_services).
 from langchain_community.agent_toolkits import azure_ai_services
 ```
 
+#### Azure AI Services individual tools
+
 The `azure_ai_services` toolkit includes the following tools:
 
 - Image Analysis: [AzureAiServicesImageAnalysisTool](https://python.langchain.com/v0.2/api_reference/community/tools/langchain_community.tools.azure_ai_services.image_analysis.AzureAiServicesImageAnalysisTool.html)
@@ -460,6 +462,23 @@ See a [usage example](/docs/integrations/tools/office365).
 from langchain_community.agent_toolkits import O365Toolkit
 ```
 
+#### Office 365 individual tools
+
+You can use individual tools from the Office 365 Toolkit:
+- `O365CreateDraftMessage`: tool for creating a draft email in Office 365
+- `O365SearchEmails`: tool for searching email messages in Office 365
+- `O365SearchEvents`: tool for searching calendar events in Office 365
+- `O365SendEvent`: tool for sending calendar events in Office 365
+- `O365SendMessage`: tool for sending an email in Office 365
+
+```python
+from langchain_community.tools.office365 import O365CreateDraftMessage
+from langchain_community.tools.office365 import O365SearchEmails
+from langchain_community.tools.office365 import O365SearchEvents
+from langchain_community.tools.office365 import O365SendEvent
+from langchain_community.tools.office365 import O365SendMessage
+```
+
 ### Microsoft Azure PowerBI
 
 We need to install `azure-identity` python package.
@@ -475,6 +494,20 @@ from langchain_community.agent_toolkits import PowerBIToolkit
 from langchain_community.utilities.powerbi import PowerBIDataset
 ```
 
+#### PowerBI individual tools
+
+You can use individual tools from the Azure PowerBI Toolkit:
+- `InfoPowerBITool`: tool for getting metadata about a PowerBI Dataset
+- `ListPowerBITool`: tool for getting tables names
+- `QueryPowerBITool`: tool for querying a PowerBI Dataset
+
+```python
+from langchain_community.tools.powerbi.tool import InfoPowerBITool
+from langchain_community.tools.powerbi.tool import ListPowerBITool
+from langchain_community.tools.powerbi.tool import QueryPowerBITool
+```
+
+
 ### PlayWright Browser Toolkit
 
 >[Playwright](https://github.com/microsoft/playwright) is an open-source automation tool 
diff --git a/docs/docs/integrations/providers/apache.mdx b/docs/docs/integrations/providers/apache.mdx
new file mode 100644
index 00000000000..6acb7156111
--- /dev/null
+++ b/docs/docs/integrations/providers/apache.mdx
@@ -0,0 +1,63 @@
+# Apache Software Foundation
+
+>[The Apache Software Foundation (Wikipedia)](https://en.wikipedia.org/wiki/The_Apache_Software_Foundation) 
+> is a decentralized open source community of developers. The software they 
+> produce is distributed under the terms of the Apache License, a permissive 
+> open-source license for free and open-source software (FOSS). The Apache projects 
+> are characterized by a collaborative, consensus-based development process 
+> and an open and pragmatic software license, which is to say that it 
+> allows developers, who receive the software freely, to redistribute 
+> it under non-free terms. Each project is managed by a self-selected 
+> team of technical experts who are active contributors to the project.
+
+## Apache AGE
+
+>[Apache AGE](https://age.apache.org/) is a `PostgreSQL` extension that provides 
+> graph database functionality. `AGE` is an acronym for `A Graph Extension`, and 
+> is inspired by Bitnine’s fork of `PostgreSQL 10`, `AgensGraph`, which is 
+> a multimodal database. The goal of the project is to create single 
+> storage that can handle both relational and graph model data so that users 
+> can use standard ANSI SQL along with `openCypher`, the Graph query language. 
+> The data elements `Apache AGE` stores are nodes, edges connecting them, and 
+> attributes of nodes and edges.
+ 
+See more about [integrating with Apache AGE](/docs/integrations/graphs/apache_age).
+
+## Apache Cassandra
+
+>[Apache Cassandra](https://cassandra.apache.org/) is a NoSQL, row-oriented, 
+> highly scalable and highly available database. Starting with version 5.0, 
+> the database ships with vector search capabilities.
+ 
+See more about [integrating with Apache Cassandra](/docs/integrations/providers/cassandra/).
+
+## Apache Doris
+
+>[Apache Doris](https://doris.apache.org/) is a modern data warehouse for 
+> real-time analytics. It delivers lightning-fast analytics on real-time data at scale.
+>
+>Usually `Apache Doris` is categorized into OLAP, and it has showed excellent 
+> performance in ClickBench — a Benchmark For Analytical DBMS. Since it has 
+> a super-fast vectorized execution engine, it could also be used as a fast vectordb.
+ 
+See more about [integrating with Apache Doris](/docs/integrations/providers/apache_doris/).
+
+## Apache Kafka
+
+>[Apache Kafka](https://github.com/apache/kafka) is a distributed messaging system 
+> that is used to publish and subscribe to streams of records.
+ 
+See more about [integrating with Apache Kafka](/docs/integrations/memory/kafka_chat_message_history).
+
+
+## Apache Spark
+
+>[Apache Spark](https://spark.apache.org/) is a unified analytics engine for 
+> large-scale data processing. It provides high-level APIs in Scala, Java, 
+> Python, and R, and an optimized engine that supports general computation 
+> graphs for data analysis. It also supports a rich set of higher-level 
+> tools including `Spark SQL` for SQL and DataFrames, `pandas API on Spark` 
+> for pandas workloads, `MLlib` for machine learning, 
+> `GraphX` for graph processing, and `Structured Streaming` for stream processing.
+
+See more about [integrating with Apache Spark](/docs/integrations/providers/spark).
diff --git a/docs/docs/integrations/providers/apple.mdx b/docs/docs/integrations/providers/apple.mdx
new file mode 100644
index 00000000000..5a87afeb6c5
--- /dev/null
+++ b/docs/docs/integrations/providers/apple.mdx
@@ -0,0 +1,22 @@
+# Apple
+
+>[Apple Inc. (Wikipedia)](https://en.wikipedia.org/wiki/Apple_Inc.) is an American 
+> multinational corporation and technology company.
+>
+> [iMessage (Wikipedia)](https://en.wikipedia.org/wiki/IMessage) is an instant 
+> messaging service developed by Apple Inc. and launched in 2011. 
+> `iMessage` functions exclusively on Apple platforms.
+
+## Installation and Setup
+
+See [setup instructions](/docs/integrations/chat_loaders/imessage).
+
+## Chat loader
+
+It loads chat sessions from the `iMessage` `chat.db` `SQLite` file.
+
+See a [usage example](/docs/integrations/chat_loaders/imessage).
+
+```python
+from langchain_community.chat_loaders.imessage import IMessageChatLoader
+```
diff --git a/docs/docs/integrations/providers/nomic.ipynb b/docs/docs/integrations/providers/nomic.ipynb
deleted file mode 100644
index b1f4861612a..00000000000
--- a/docs/docs/integrations/providers/nomic.ipynb
+++ /dev/null
@@ -1,69 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Nomic\n",
-    "\n",
-    "Nomic currently offers two products:\n",
-    "\n",
-    "- Atlas: their Visual Data Engine\n",
-    "- GPT4All: their Open Source Edge Language Model Ecosystem\n",
-    "\n",
-    "The Nomic integration exists in its own [partner package](https://pypi.org/project/langchain-nomic/). You can install it with:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "%pip install -qU langchain-nomic"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Currently, you can import their hosted [embedding model](/docs/integrations/text_embedding/nomic) as follows:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {
-    "id": "y8ku6X96sebl"
-   },
-   "outputs": [],
-   "source": [
-    "from langchain_nomic import NomicEmbeddings"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "provenance": []
-  },
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.10.11"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 1
-}
diff --git a/docs/docs/integrations/providers/nomic.mdx b/docs/docs/integrations/providers/nomic.mdx
new file mode 100644
index 00000000000..f825e3c74e3
--- /dev/null
+++ b/docs/docs/integrations/providers/nomic.mdx
@@ -0,0 +1,58 @@
+# Nomic
+
+>[Nomic](https://www.nomic.ai/) builds tools that enable everyone to interact with AI scale datasets and run AI models on consumer computers.
+>
+>`Nomic` currently offers two products:
+>
+>- `Atlas`: the Visual Data Engine
+>- `GPT4All`: the Open Source Edge Language Model Ecosystem
+
+The Nomic integration exists in two partner packages: [langchain-nomic](https://pypi.org/project/langchain-nomic/)
+and in [langchain-community](https://pypi.org/project/langchain-community/). 
+
+## Installation
+
+You can install them with:
+
+```bash
+pip install -U langchain-nomic
+pip install -U langchain-community
+```
+
+## LLMs
+
+### GPT4All
+
+See [a usage example](/docs/integrations/llms/gpt4all).
+
+```python
+from langchain_community.llms import GPT4All
+```
+
+## Embedding models
+
+### NomicEmbeddings
+
+See [a usage example](/docs/integrations/text_embedding/nomic).
+
+```python
+from langchain_nomic import NomicEmbeddings
+```
+
+### GPT4All
+
+See [a usage example](/docs/integrations/text_embedding/gpt4all).
+
+```python
+from langchain_community.embeddings import GPT4AllEmbeddings
+```
+
+## Vector store
+
+### Atlas
+
+See [a usage example and installation instructions](/docs/integrations/vectorstores/atlas).
+
+```python
+from langchain_community.vectorstores import AtlasDB
+```
diff --git a/docs/docs/integrations/providers/spark.mdx b/docs/docs/integrations/providers/spark.mdx
new file mode 100644
index 00000000000..a7721415af8
--- /dev/null
+++ b/docs/docs/integrations/providers/spark.mdx
@@ -0,0 +1,49 @@
+# Spark
+
+>[Apache Spark](https://spark.apache.org/) is a unified analytics engine for 
+> large-scale data processing. It provides high-level APIs in Scala, Java, 
+> Python, and R, and an optimized engine that supports general computation 
+> graphs for data analysis. It also supports a rich set of higher-level 
+> tools including `Spark SQL` for SQL and DataFrames, `pandas API on Spark` 
+> for pandas workloads, `MLlib` for machine learning, 
+> `GraphX` for graph processing, and `Structured Streaming` for stream processing.
+
+## Document loaders
+
+### PySpark
+
+It loads data from a `PySpark` DataFrame.
+
+See a [usage example](/docs/integrations/document_loaders/pyspark_dataframe).
+
+```python
+from langchain_community.document_loaders import PySparkDataFrameLoader
+```
+
+## Tools/Toolkits
+
+### Spark SQL toolkit
+
+Toolkit for interacting with `Spark SQL`.
+
+See a [usage example](/docs/integrations/tools/spark_sql).
+
+```python
+from langchain_community.agent_toolkits import SparkSQLToolkit, create_spark_sql_agent
+from langchain_community.utilities.spark_sql import SparkSQL
+```
+
+#### Spark SQL individual tools
+
+You can use individual tools from the Spark SQL Toolkit:
+- `InfoSparkSQLTool`: tool for getting metadata about a Spark SQL
+- `ListSparkSQLTool`: tool for getting tables names
+- `QueryCheckerTool`: tool uses an LLM to check if a query is correct
+- `QuerySparkSQLTool`: tool for querying a Spark SQL
+
+```python
+from langchain_community.tools.spark_sql.tool import InfoSparkSQLTool
+from langchain_community.tools.spark_sql.tool import ListSparkSQLTool
+from langchain_community.tools.spark_sql.tool import QueryCheckerTool
+from langchain_community.tools.spark_sql.tool import QuerySparkSQLTool
+```
diff --git a/docs/docs/integrations/providers/sparkllm.mdx b/docs/docs/integrations/providers/sparkllm.mdx
index e9d7f94b186..c4a661f1e33 100644
--- a/docs/docs/integrations/providers/sparkllm.mdx
+++ b/docs/docs/integrations/providers/sparkllm.mdx
@@ -4,11 +4,26 @@
 It has cross-domain knowledge and language understanding ability by learning a large amount of texts, codes and images.
 It can understand and perform tasks based on natural dialogue.
 
-## SparkLLM LLM Model
-An example is available at [example](/docs/integrations/llms/sparkllm).
+## Chat models
 
-## SparkLLM Chat Model
-An example is available at [example](/docs/integrations/chat/sparkllm).
+See a [usage example](/docs/integrations/chat/sparkllm).
 
-## SparkLLM Text Embedding Model
-An example is available at [example](/docs/integrations/text_embedding/sparkllm)
+```python
+from langchain_community.chat_models import ChatSparkLLM
+```
+
+## LLMs
+
+See a [usage example](/docs/integrations/llms/sparkllm).
+
+```python
+from langchain_community.llms import SparkLLM
+```
+
+## Embedding models
+
+See a [usage example](/docs/integrations/text_embedding/sparkllm)
+
+```python
+from langchain_community.embeddings import SparkLLMTextEmbeddings
+```
diff --git a/docs/docs/integrations/providers/transwarp.mdx b/docs/docs/integrations/providers/transwarp.mdx
new file mode 100644
index 00000000000..4406f885bab
--- /dev/null
+++ b/docs/docs/integrations/providers/transwarp.mdx
@@ -0,0 +1,34 @@
+# Transwarp
+
+>[Transwarp](https://www.transwarp.cn/en/introduction) aims to build 
+> enterprise-level big data and AI infrastructure software, 
+> to shape the future of data world. It provides enterprises with 
+> infrastructure software and services around the whole data lifecycle, 
+> including integration, storage, governance, modeling, analysis, 
+> mining and circulation. 
+> 
+> `Transwarp` focuses on technology research and 
+> development and has accumulated core technologies in these aspects: 
+> distributed computing, SQL compilations, database technology, 
+> unification for multi-model data management, container-based cloud computing, 
+> and big data analytics and intelligence.
+
+## Installation
+
+You have to install several python packages:
+
+```bash
+pip install -U tiktoken hippo-api
+```
+
+and get the connection configuration.
+
+## Vector stores
+
+### Hippo
+
+See [a usage example and installation instructions](/docs/integrations/vectorstores/hippo).
+
+```python
+from langchain_community.vectorstores.hippo import Hippo
+```
diff --git a/docs/docs/integrations/providers/upstage.ipynb b/docs/docs/integrations/providers/upstage.ipynb
index b43bfe163d2..9dfce63e351 100644
--- a/docs/docs/integrations/providers/upstage.ipynb
+++ b/docs/docs/integrations/providers/upstage.ipynb
@@ -6,45 +6,18 @@
    "source": [
     "# Upstage\n",
     "\n",
-    "[Upstage](https://upstage.ai) is a leading artificial intelligence (AI) company specializing in delivering above-human-grade performance LLM components. \n"
+    ">[Upstage](https://upstage.ai) is a leading artificial intelligence (AI) company specializing in delivering above-human-grade performance LLM components.\n",
+    ">\n",
+    ">**Solar Mini Chat** is a fast yet powerful advanced large language model focusing on English and Korean. It has been specifically fine-tuned for multi-turn chat purposes, showing enhanced performance across a wide range of natural language processing tasks, like multi-turn conversation or tasks that require an understanding of long contexts, such as RAG (Retrieval-Augmented Generation), compared to other models of a similar size. This fine-tuning equips it with the ability to handle longer conversations more effectively, making it particularly adept for interactive applications.\n",
+    "\n",
+    ">Other than Solar, Upstage also offers features for real-world RAG (retrieval-augmented generation), such as **Groundedness Check** and **Layout Analysis**. \n"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Solar LLM\n",
-    "\n",
-    "**Solar Mini Chat** is a fast yet powerful advanced large language model focusing on English and Korean. It has been specifically fine-tuned for multi-turn chat purposes, showing enhanced performance across a wide range of natural language processing tasks, like multi-turn conversation or tasks that require an understanding of long contexts, such as RAG (Retrieval-Augmented Generation), compared to other models of a similar size. This fine-tuning equips it with the ability to handle longer conversations more effectively, making it particularly adept for interactive applications.\n",
-    "\n",
-    "Other than Solar, Upstage also offers features for real-world RAG (retrieval-augmented generation), such as **Groundedness Check** and **Layout Analysis**. "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Installation and Setup\n",
-    "\n",
-    "Install `langchain-upstage` package:\n",
-    "\n",
-    "```bash\n",
-    "pip install -qU langchain-core langchain-upstage\n",
-    "```"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Get [API Keys](https://console.upstage.ai) and set environment variable `UPSTAGE_API_KEY`."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Upstage LangChain integrations\n",
+    "### Upstage LangChain integrations\n",
     "\n",
     "| API | Description | Import | Example usage |\n",
     "| --- | --- | --- | --- |\n",
@@ -60,9 +33,20 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Quick Examples\n",
+    "## Installation and Setup\n",
     "\n",
-    "### Environment Setup"
+    "Install `langchain-upstage` package:\n",
+    "\n",
+    "```bash\n",
+    "pip install -qU langchain-core langchain-upstage\n",
+    "```\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Get [API Keys](https://console.upstage.ai) and set environment variable `UPSTAGE_API_KEY`."
    ]
   },
   {
@@ -80,8 +64,11 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
+    "## Chat models\n",
     "\n",
-    "### Chat\n"
+    "### Solar LLM\n",
+    "\n",
+    "See [a usage example](/docs/integrations/chat/upstage)."
    ]
   },
   {
@@ -101,10 +88,9 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
+    "## Embedding models\n",
     "\n",
-    "\n",
-    "### Text embedding\n",
-    "\n"
+    "See [a usage example](/docs/integrations/text_embedding/upstage)."
    ]
   },
   {
@@ -134,7 +120,45 @@
     }
    },
    "source": [
-    "### Groundedness Check"
+    "## Document loader\n",
+    "\n",
+    "### Layout Analysis\n",
+    "\n",
+    "See [a usage example](/docs/integrations/document_loaders/upstage)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain_upstage import UpstageLayoutAnalysisLoader\n",
+    "\n",
+    "file_path = \"/PATH/TO/YOUR/FILE.pdf\"\n",
+    "layzer = UpstageLayoutAnalysisLoader(file_path, split=\"page\")\n",
+    "\n",
+    "# For improved memory efficiency, consider using the lazy_load method to load documents page by page.\n",
+    "docs = layzer.load()  # or layzer.lazy_load()\n",
+    "\n",
+    "for doc in docs[:3]:\n",
+    "    print(doc)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
+   },
+   "source": [
+    "## Tools\n",
+    "\n",
+    "### Groundedness Check\n",
+    "\n",
+    "See [a usage example](/docs/integrations/tools/upstage_groundedness_check)."
    ]
   },
   {
@@ -159,36 +183,6 @@
     "response = groundedness_check.invoke(request_input)\n",
     "print(response)"
    ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "collapsed": false,
-    "jupyter": {
-     "outputs_hidden": false
-    }
-   },
-   "source": [
-    "### Layout Analysis"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from langchain_upstage import UpstageLayoutAnalysisLoader\n",
-    "\n",
-    "file_path = \"/PATH/TO/YOUR/FILE.pdf\"\n",
-    "layzer = UpstageLayoutAnalysisLoader(file_path, split=\"page\")\n",
-    "\n",
-    "# For improved memory efficiency, consider using the lazy_load method to load documents page by page.\n",
-    "docs = layzer.load()  # or layzer.lazy_load()\n",
-    "\n",
-    "for doc in docs[:3]:\n",
-    "    print(doc)"
-   ]
   }
  ],
  "metadata": {
@@ -210,7 +204,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.13"
+   "version": "3.10.12"
   }
  },
  "nbformat": 4,
diff --git a/docs/docs/integrations/vectorstores/google_bigquery_vector_search.ipynb b/docs/docs/integrations/vectorstores/google_bigquery_vector_search.ipynb
index 8287bb04ca4..f5cc0e92c4e 100644
--- a/docs/docs/integrations/vectorstores/google_bigquery_vector_search.ipynb
+++ b/docs/docs/integrations/vectorstores/google_bigquery_vector_search.ipynb
@@ -325,7 +325,13 @@
     "id": "20cf6074081b"
    },
    "source": [
-    "### Search for documents with metadata filter"
+    "### Searching Documents with Metadata Filters\n",
+    "The vectorstore supports two methods for applying filters to metadata fields when performing document searches:\n",
+    "\n",
+    "-   Dictionary-based Filters\n",
+    "    -   You can pass a dictionary (dict) where the keys represent metadata fields and the values specify the filter condition. This method applies an equality filter between the key and the corresponding value. When multiple key-value pairs are provided, they are combined using a logical AND operation.\n",
+    "-   SQL-based Filters\n",
+    "    -   Alternatively, you can provide a string representing an SQL WHERE clause to define more complex filtering conditions. This allows for greater flexibility, supporting SQL expressions such as comparison operators and logical operators."
    ]
   },
   {
@@ -336,11 +342,24 @@
    },
    "outputs": [],
    "source": [
+    "# Dictionary-based Filters\n",
     "# This should only return \"Banana\" document.\n",
     "docs = store.similarity_search_by_vector(query_vector, filter={\"len\": 6})\n",
     "print(docs)"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# SQL-based Filters\n",
+    "# This should return \"Banana\", \"Apples and oranges\" and \"Cars and airplanes\" documents.\n",
+    "docs = store.similarity_search_by_vector(query_vector, filter={\"len = 6 AND len > 17\"})\n",
+    "print(docs)"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {
diff --git a/docs/docs/tutorials/chatbot.ipynb b/docs/docs/tutorials/chatbot.ipynb
index 05f73ef9525..996892c9bc1 100644
--- a/docs/docs/tutorials/chatbot.ipynb
+++ b/docs/docs/tutorials/chatbot.ipynb
@@ -105,7 +105,7 @@
     "\n",
     "## Quickstart\n",
     "\n",
-    "First up, let's learn how to use a language model by itself. LangChain supports many different language models that you can use interchangably - select the one you want to use below!\n",
+    "First up, let's learn how to use a language model by itself. LangChain supports many different language models that you can use interchangeably - select the one you want to use below!\n",
     "\n",
     "```{=mdx}\n",
     "import ChatModelTabs from \"@theme/ChatModelTabs\";\n",
@@ -254,7 +254,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# ! pip install langchain_community"
+    "%pip install langchain_community"
    ]
   },
   {
@@ -952,7 +952,7 @@
    "source": [
     "## Streaming\n",
     "\n",
-    "Now we've got a function chatbot. However, one *really* important UX consideration for chatbot application is streaming. LLMs can sometimes take a while to respond, and so in order to improve the user experience one thing that most application do is stream back each token as it is generated. This allows the user to see progress.\n",
+    "Now we've got a functioning chatbot. However, one *really* important UX consideration for chatbot applications is streaming. LLMs can sometimes take a while to respond, and so in order to improve the user experience one thing that most applications do is stream back each token as it is generated. This allows the user to see progress.\n",
     "\n",
     "It's actually super easy to do this!\n",
     "\n",
diff --git a/docs/docs/tutorials/llm_chain.ipynb b/docs/docs/tutorials/llm_chain.ipynb
index 3236d2c5811..ef6c92e6561 100644
--- a/docs/docs/tutorials/llm_chain.ipynb
+++ b/docs/docs/tutorials/llm_chain.ipynb
@@ -95,7 +95,7 @@
    "source": [
     "## Using Language Models\n",
     "\n",
-    "First up, let's learn how to use a language model by itself. LangChain supports many different language models that you can use interchangably - select the one you want to use below!\n",
+    "First up, let's learn how to use a language model by itself. LangChain supports many different language models that you can use interchangeably - select the one you want to use below!\n",
     "\n",
     "```{=mdx}\n",
     "import ChatModelTabs from \"@theme/ChatModelTabs\";\n",
@@ -159,9 +159,7 @@
    "cell_type": "markdown",
    "id": "f83373db",
    "metadata": {},
-   "source": [
-    "If we've enable LangSmith, we can see that this run is logged to LangSmith, and can see the [LangSmith trace](https://smith.langchain.com/public/88baa0b2-7c1a-4d09-ba30-a47985dde2ea/r)"
-   ]
+   "source": "If we've enabled LangSmith, we can see that this run is logged to LangSmith, and can see the [LangSmith trace](https://smith.langchain.com/public/88baa0b2-7c1a-4d09-ba30-a47985dde2ea/r)"
   },
   {
    "cell_type": "markdown",
diff --git a/docs/docusaurus.config.js b/docs/docusaurus.config.js
index 869abd69035..714e217cfad 100644
--- a/docs/docusaurus.config.js
+++ b/docs/docusaurus.config.js
@@ -125,8 +125,11 @@ const config = {
     /** @type {import('@docusaurus/preset-classic').ThemeConfig} */
     ({
       announcementBar: {
-        content: 'LangChain 0.2 is out! Leave feedback on the v0.2 docs <a href="https://github.com/langchain-ai/langchain/discussions/21716">here</a>. You can view the v0.1 docs <a href="/v0.1/docs/get_started/introduction/">here</a>.',
+        content:
+          'Share your thoughts on AI agents. <a target="_blank" href="https://langchain.typeform.com/state-of-agents">Take the 3-min survey</a>.',
         isCloseable: true,
+        backgroundColor: "rgba(53, 151, 147, 0.1)",
+        textColor: "rgb(53, 151, 147)",
       },
       docs: {
         sidebar: {
diff --git a/libs/community/langchain_community/chat_models/octoai.py b/libs/community/langchain_community/chat_models/octoai.py
index 77d7572d48d..cbe1ee4ca88 100644
--- a/libs/community/langchain_community/chat_models/octoai.py
+++ b/libs/community/langchain_community/chat_models/octoai.py
@@ -1,8 +1,22 @@
 """OctoAI Endpoints chat wrapper. Relies heavily on ChatOpenAI."""
 
-from typing import Dict
+from typing import (
+    Any,
+    Callable,
+    Dict,
+    Literal,
+    Optional,
+    Sequence,
+    Type,
+    Union,
+)
 
+from langchain_core.language_models import LanguageModelInput
+from langchain_core.messages import BaseMessage
+from langchain_core.runnables import Runnable
+from langchain_core.tools import BaseTool
 from langchain_core.utils import convert_to_secret_str, get_from_dict_or_env, pre_init
+from langchain_core.utils.function_calling import convert_to_openai_tool
 from pydantic import Field, SecretStr
 
 from langchain_community.chat_models.openai import ChatOpenAI
@@ -92,3 +106,53 @@ class ChatOctoAI(ChatOpenAI):
             )
 
         return values
+
+    def bind_tools(
+        self,
+        tools: Sequence[Union[Dict[str, Any], Type, Callable, BaseTool]],
+        *,
+        tool_choice: Optional[
+            Union[dict, str, Literal["auto", "none", "required", "any"], bool]
+        ] = None,
+        strict: Optional[bool] = None,
+        **kwargs: Any,
+    ) -> Runnable[LanguageModelInput, BaseMessage]:
+        """Imitating bind_tool method from langchain_openai.ChatOpenAI"""
+
+        formatted_tools = [
+            convert_to_openai_tool(tool, strict=strict) for tool in tools
+        ]
+        if tool_choice:
+            if isinstance(tool_choice, str):
+                # tool_choice is a tool/function name
+                if tool_choice not in ("auto", "none", "any", "required"):
+                    tool_choice = {
+                        "type": "function",
+                        "function": {"name": tool_choice},
+                    }
+                # 'any' is not natively supported by OpenAI API.
+                # We support 'any' since other models use this instead of 'required'.
+                if tool_choice == "any":
+                    tool_choice = "required"
+            elif isinstance(tool_choice, bool):
+                tool_choice = "required"
+            elif isinstance(tool_choice, dict):
+                tool_names = [
+                    formatted_tool["function"]["name"]
+                    for formatted_tool in formatted_tools
+                ]
+                if not any(
+                    tool_name == tool_choice["function"]["name"]
+                    for tool_name in tool_names
+                ):
+                    raise ValueError(
+                        f"Tool choice {tool_choice} was specified, but the only "
+                        f"provided tools were {tool_names}."
+                    )
+            else:
+                raise ValueError(
+                    f"Unrecognized tool_choice type. Expected str, bool or dict. "
+                    f"Received: {tool_choice}"
+                )
+            kwargs["tool_choice"] = tool_choice
+        return super().bind(tools=formatted_tools, **kwargs)
diff --git a/libs/community/langchain_community/graph_vectorstores/extractors/gliner_link_extractor.py b/libs/community/langchain_community/graph_vectorstores/extractors/gliner_link_extractor.py
index a485f849c7a..9d567a1f8d4 100644
--- a/libs/community/langchain_community/graph_vectorstores/extractors/gliner_link_extractor.py
+++ b/libs/community/langchain_community/graph_vectorstores/extractors/gliner_link_extractor.py
@@ -14,7 +14,117 @@ GLiNERInput = Union[str, Document]
 
 @beta()
 class GLiNERLinkExtractor(LinkExtractor[GLiNERInput]):
-    """Link documents with common named entities using GLiNER <https://github.com/urchade/GLiNER>."""
+    """Link documents with common named entities using `GLiNER`_.
+
+    `GLiNER`_ is a Named Entity Recognition (NER) model capable of identifying any
+    entity type using a bidirectional transformer encoder (BERT-like).
+
+    The ``GLiNERLinkExtractor`` uses GLiNER to create links between documents that
+    have named entities in common.
+
+    Example::
+
+        extractor = GLiNERLinkExtractor(
+            labels=["Person", "Award", "Date", "Competitions", "Teams"]
+        )
+        results = extractor.extract_one("some long text...")
+
+    .. _GLiNER: https://github.com/urchade/GLiNER
+
+    .. seealso::
+
+            - :mod:`How to use a graph vector store <langchain_community.graph_vectorstores>`
+            - :class:`How to create links between documents <langchain_core.graph_vectorstores.links.Link>`
+
+    How to link Documents on common named entities
+    ==============================================
+
+    Preliminaries
+    -------------
+
+    Install the ``gliner`` package:
+
+    .. code-block:: bash
+
+        pip install -q langchain_community gliner
+
+    Usage
+    -----
+
+    We load the ``state_of_the_union.txt`` file, chunk it, then for each chunk we
+    extract named entity links and add them to the chunk.
+
+    Using extract_one()
+    ^^^^^^^^^^^^^^^^^^^
+
+    We can use :meth:`extract_one` on a document to get the links and add the links
+    to the document metadata with
+    :meth:`~langchain_core.graph_vectorstores.links.add_links`::
+
+        from langchain_community.document_loaders import TextLoader
+        from langchain_community.graph_vectorstores import CassandraGraphVectorStore
+        from langchain_community.graph_vectorstores.extractors import GLiNERLinkExtractor
+        from langchain_core.graph_vectorstores.links import add_links
+        from langchain_text_splitters import CharacterTextSplitter
+
+        loader = TextLoader("state_of_the_union.txt")
+        raw_documents = loader.load()
+
+        text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
+        documents = text_splitter.split_documents(raw_documents)
+
+        ner_extractor = GLiNERLinkExtractor(["Person", "Topic"])
+        for document in documents:
+            links = ner_extractor.extract_one(document)
+            add_links(document, links)
+
+        print(documents[0].metadata)
+
+    .. code-block:: output
+
+        {'source': 'state_of_the_union.txt', 'links': [Link(kind='entity:Person', direction='bidir', tag='President Zelenskyy'), Link(kind='entity:Person', direction='bidir', tag='Vladimir Putin')]}
+
+    Using LinkExtractorTransformer
+    ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+    Using the :class:`~langchain_community.graph_vectorstores.extractors.keybert_link_extractor.LinkExtractorTransformer`,
+    we can simplify the link extraction::
+
+        from langchain_community.document_loaders import TextLoader
+        from langchain_community.graph_vectorstores.extractors import (
+            GLiNERLinkExtractor,
+            LinkExtractorTransformer,
+        )
+        from langchain_text_splitters import CharacterTextSplitter
+
+        loader = TextLoader("state_of_the_union.txt")
+        raw_documents = loader.load()
+
+        text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
+        documents = text_splitter.split_documents(raw_documents)
+
+        ner_extractor = GLiNERLinkExtractor(["Person", "Topic"])
+        transformer = LinkExtractorTransformer([ner_extractor])
+        documents = transformer.transform_documents(documents)
+
+        print(documents[0].metadata)
+
+    .. code-block:: output
+
+        {'source': 'state_of_the_union.txt', 'links': [Link(kind='entity:Person', direction='bidir', tag='President Zelenskyy'), Link(kind='entity:Person', direction='bidir', tag='Vladimir Putin')]}
+
+    The documents with named entity links can then be added to a :class:`~langchain_core.graph_vectorstores.base.GraphVectorStore`::
+
+        from langchain_community.graph_vectorstores import CassandraGraphVectorStore
+
+        store = CassandraGraphVectorStore.from_documents(documents=documents, embedding=...)
+
+    Args:
+        labels: List of kinds of entities to extract.
+        kind: Kind of links to produce with this extractor.
+        model: GLiNER model to use.
+        extract_kwargs: Keyword arguments to pass to GLiNER.
+    """  # noqa: E501
 
     def __init__(
         self,
@@ -24,23 +134,6 @@ class GLiNERLinkExtractor(LinkExtractor[GLiNERInput]):
         model: str = "urchade/gliner_mediumv2.1",
         extract_kwargs: Optional[Dict[str, Any]] = None,
     ):
-        """Extract keywords using GLiNER.
-
-        Example:
-
-            .. code-block:: python
-
-                extractor = GLiNERLinkExtractor(
-                    labels=["Person", "Award", "Date", "Competitions", "Teams"]
-                )
-                results = extractor.extract_one("some long text...")
-
-        Args:
-            labels: List of kinds of entities to extract.
-            kind: Kind of links to produce with this extractor.
-            model: GLiNER model to use.
-            extract_kwargs: Keyword arguments to pass to GLiNER.
-        """
         try:
             from gliner import GLiNER
 
diff --git a/libs/community/langchain_community/graph_vectorstores/extractors/html_link_extractor.py b/libs/community/langchain_community/graph_vectorstores/extractors/html_link_extractor.py
index bc820958473..270de351e6e 100644
--- a/libs/community/langchain_community/graph_vectorstores/extractors/html_link_extractor.py
+++ b/libs/community/langchain_community/graph_vectorstores/extractors/html_link_extractor.py
@@ -69,11 +69,175 @@ class HtmlLinkExtractor(LinkExtractor[HtmlInput]):
 
         Expects the input to be an HTML string or a `BeautifulSoup` object.
 
+        Example::
+
+            extractor = HtmlLinkExtractor()
+            results = extractor.extract_one(HtmlInput(html, url))
+
+        .. seealso::
+
+            - :mod:`How to use a graph vector store <langchain_community.graph_vectorstores>`
+            - :class:`How to create links between documents <langchain_core.graph_vectorstores.links.Link>`
+
+        How to link Documents on hyperlinks in HTML
+        ===========================================
+
+        Preliminaries
+        -------------
+
+        Install the ``beautifulsoup4`` package:
+
+        .. code-block:: bash
+
+            pip install -q langchain_community beautifulsoup4
+
+        Usage
+        -----
+
+        For this example, we'll scrape 2 HTML pages that have an hyperlink from one
+        page to the other using an ``AsyncHtmlLoader``.
+        Then we use the ``HtmlLinkExtractor`` to create the links in the documents.
+
+        Using extract_one()
+        ^^^^^^^^^^^^^^^^^^^
+
+        We can use :meth:`extract_one` on a document to get the links and add the links
+        to the document metadata with
+        :meth:`~langchain_core.graph_vectorstores.links.add_links`::
+
+            from langchain_community.document_loaders import AsyncHtmlLoader
+            from langchain_community.graph_vectorstores.extractors import (
+                HtmlInput,
+                HtmlLinkExtractor,
+            )
+            from langchain_community.graph_vectorstores.links import add_links
+            from langchain_core.documents import Document
+
+            loader = AsyncHtmlLoader(
+                [
+                    "https://python.langchain.com/v0.2/docs/integrations/providers/astradb/",
+                    "https://docs.datastax.com/en/astra/home/astra.html",
+                ]
+            )
+
+            documents = loader.load()
+
+            html_extractor = HtmlLinkExtractor()
+
+            for doc in documents:
+                links = html_extractor.extract_one(HtmlInput(doc.page_content, url))
+                add_links(doc, links)
+
+            documents[0].metadata["links"][:5]
+
+        .. code-block:: output
+
+            [Link(kind='hyperlink', direction='out', tag='https://python.langchain.com/v0.2/docs/integrations/providers/spreedly/'),
+             Link(kind='hyperlink', direction='out', tag='https://python.langchain.com/v0.2/docs/integrations/providers/nvidia/'),
+             Link(kind='hyperlink', direction='out', tag='https://python.langchain.com/v0.2/docs/integrations/providers/ray_serve/'),
+             Link(kind='hyperlink', direction='out', tag='https://python.langchain.com/v0.2/docs/integrations/providers/bageldb/'),
+             Link(kind='hyperlink', direction='out', tag='https://python.langchain.com/v0.2/docs/introduction/')]
+
+        Using as_document_extractor()
+        ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+        If you use a document loader that returns the raw HTML and that sets the source
+        key in the document metadata such as ``AsyncHtmlLoader``,
+        you can simplify by using :meth:`as_document_extractor` that takes directly a
+        ``Document`` as input::
+
+            from langchain_community.document_loaders import AsyncHtmlLoader
+            from langchain_community.graph_vectorstores.extractors import HtmlLinkExtractor
+            from langchain_core.graph_vectorstores.links import add_links
+
+            loader = AsyncHtmlLoader(
+                [
+                    "https://python.langchain.com/v0.2/docs/integrations/providers/astradb/",
+                    "https://docs.datastax.com/en/astra/home/astra.html",
+                ]
+            )
+            documents = loader.load()
+            html_extractor = HtmlLinkExtractor().as_document_extractor()
+
+            for document in documents:
+                links = html_extractor.extract_one(document)
+                add_links(document, links)
+
+            documents[0].metadata["links"][:5]
+
+        .. code-block:: output
+
+            [Link(kind='hyperlink', direction='out', tag='https://python.langchain.com/v0.2/docs/integrations/providers/spreedly/'),
+             Link(kind='hyperlink', direction='out', tag='https://python.langchain.com/v0.2/docs/integrations/providers/nvidia/'),
+             Link(kind='hyperlink', direction='out', tag='https://python.langchain.com/v0.2/docs/integrations/providers/ray_serve/'),
+             Link(kind='hyperlink', direction='out', tag='https://python.langchain.com/v0.2/docs/integrations/providers/bageldb/'),
+             Link(kind='hyperlink', direction='out', tag='https://python.langchain.com/v0.2/docs/introduction/')]
+
+        Using LinkExtractorTransformer
+        ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+        Using the :class:`~langchain_community.graph_vectorstores.extractors.keybert_link_extractor.LinkExtractorTransformer`,
+        we can simplify the link extraction::
+
+            from langchain_community.document_loaders import AsyncHtmlLoader
+            from langchain_community.graph_vectorstores.extractors import (
+                HtmlLinkExtractor,
+                LinkExtractorTransformer,
+            )
+            from langchain_community.graph_vectorstores.links import add_links
+
+            loader = AsyncHtmlLoader(
+                [
+                    "https://python.langchain.com/v0.2/docs/integrations/providers/astradb/",
+                    "https://docs.datastax.com/en/astra/home/astra.html",
+                ]
+            )
+
+            documents = loader.load()
+            transformer = LinkExtractorTransformer([HtmlLinkExtractor().as_document_extractor()])
+            documents = transformer.transform_documents(documents)
+
+            documents[0].metadata["links"][:5]
+
+        .. code-block:: output
+
+            [Link(kind='hyperlink', direction='out', tag='https://python.langchain.com/v0.2/docs/integrations/providers/spreedly/'),
+             Link(kind='hyperlink', direction='out', tag='https://python.langchain.com/v0.2/docs/integrations/providers/nvidia/'),
+             Link(kind='hyperlink', direction='out', tag='https://python.langchain.com/v0.2/docs/integrations/providers/ray_serve/'),
+             Link(kind='hyperlink', direction='out', tag='https://python.langchain.com/v0.2/docs/integrations/providers/bageldb/'),
+             Link(kind='hyperlink', direction='out', tag='https://python.langchain.com/v0.2/docs/introduction/')]
+
+        We can check that there is a link from the first document to the second::
+
+            for doc_to in documents:
+                for link_to in doc_to.metadata["links"]:
+                    if link_to.direction == "in":
+                        for doc_from in documents:
+                            for link_from in doc_from.metadata["links"]:
+                                if (
+                                    link_to.direction == "in"
+                                    and link_from.direction == "out"
+                                    and link_to.tag == link_from.tag
+                                ):
+                                    print(
+                                        f"Found link from {doc_from.metadata['source']} to {doc_to.metadata['source']}."
+                                    )
+
+        .. code-block:: output
+
+            Found link from https://python.langchain.com/v0.2/docs/integrations/providers/astradb/ to https://docs.datastax.com/en/astra/home/astra.html.
+
+        The documents with URL links can then be added to a :class:`~langchain_core.graph_vectorstores.base.GraphVectorStore`::
+
+            from langchain_community.graph_vectorstores import CassandraGraphVectorStore
+
+            store = CassandraGraphVectorStore.from_documents(documents=documents, embedding=...)
+
         Args:
-            kind: The kind of edge to extract. Defaults to "hyperlink".
+            kind: The kind of edge to extract. Defaults to ``hyperlink``.
             drop_fragments: Whether fragments in URLs and links should be
-                dropped. Defaults to `True`.
-        """
+                dropped. Defaults to ``True``.
+        """  # noqa: E501
         try:
             import bs4  # noqa:F401
         except ImportError as e:
@@ -90,9 +254,10 @@ class HtmlLinkExtractor(LinkExtractor[HtmlInput]):
     ) -> LinkExtractor[Document]:
         """Return a LinkExtractor that applies to documents.
 
-        NOTE: Since the HtmlLinkExtractor parses HTML, if you use with other similar
-        link extractors it may be more efficient to call the link extractors directly
-        on the parsed BeautifulSoup object.
+        Note:
+            Since the HtmlLinkExtractor parses HTML, if you use with other similar
+            link extractors it may be more efficient to call the link extractors
+            directly on the parsed BeautifulSoup object.
 
         Args:
             url_metadata_key: The name of the filed in document metadata with the URL of
diff --git a/libs/community/langchain_community/graph_vectorstores/extractors/keybert_link_extractor.py b/libs/community/langchain_community/graph_vectorstores/extractors/keybert_link_extractor.py
index 54be34d4631..90a208b9878 100644
--- a/libs/community/langchain_community/graph_vectorstores/extractors/keybert_link_extractor.py
+++ b/libs/community/langchain_community/graph_vectorstores/extractors/keybert_link_extractor.py
@@ -20,22 +20,114 @@ class KeybertLinkExtractor(LinkExtractor[KeybertInput]):
         embedding_model: str = "all-MiniLM-L6-v2",
         extract_keywords_kwargs: Optional[Dict[str, Any]] = None,
     ):
-        """Extract keywords using KeyBERT <https://maartengr.github.io/KeyBERT/>.
+        """Extract keywords using `KeyBERT <https://maartengr.github.io/KeyBERT/>`_.
 
-        Example:
+        KeyBERT is a minimal and easy-to-use keyword extraction technique that
+        leverages BERT embeddings to create keywords and keyphrases that are most
+        similar to a document.
 
-            .. code-block:: python
+        The KeybertLinkExtractor uses KeyBERT to create links between documents that
+        have keywords in common.
 
-                extractor = KeybertLinkExtractor()
+        Example::
 
-                results = extractor.extract_one(PAGE_1)
+            extractor = KeybertLinkExtractor()
+            results = extractor.extract_one("lorem ipsum...")
+
+        .. seealso::
+
+            - :mod:`How to use a graph vector store <langchain_community.graph_vectorstores>`
+            - :class:`How to create links between documents <langchain_core.graph_vectorstores.links.Link>`
+
+        How to link Documents on common keywords using Keybert
+        ======================================================
+
+        Preliminaries
+        -------------
+
+        Install the keybert package:
+
+        .. code-block:: bash
+
+            pip install -q langchain_community keybert
+
+        Usage
+        -----
+
+        We load the ``state_of_the_union.txt`` file, chunk it, then for each chunk we
+        extract keyword links and add them to the chunk.
+
+        Using extract_one()
+        ^^^^^^^^^^^^^^^^^^^
+
+        We can use :meth:`extract_one` on a document to get the links and add the links
+        to the document metadata with
+        :meth:`~langchain_core.graph_vectorstores.links.add_links`::
+
+            from langchain_community.document_loaders import TextLoader
+            from langchain_community.graph_vectorstores import CassandraGraphVectorStore
+            from langchain_community.graph_vectorstores.extractors import KeybertLinkExtractor
+            from langchain_core.graph_vectorstores.links import add_links
+            from langchain_text_splitters import CharacterTextSplitter
+
+            loader = TextLoader("state_of_the_union.txt")
+
+            raw_documents = loader.load()
+            text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
+
+            documents = text_splitter.split_documents(raw_documents)
+            keyword_extractor = KeybertLinkExtractor()
+
+            for document in documents:
+                links = keyword_extractor.extract_one(document)
+                add_links(document, links)
+
+            print(documents[0].metadata)
+
+        .. code-block:: output
+
+            {'source': 'state_of_the_union.txt', 'links': [Link(kind='kw', direction='bidir', tag='ukraine'), Link(kind='kw', direction='bidir', tag='ukrainian'), Link(kind='kw', direction='bidir', tag='putin'), Link(kind='kw', direction='bidir', tag='vladimir'), Link(kind='kw', direction='bidir', tag='russia')]}
+
+        Using LinkExtractorTransformer
+        ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+        Using the :class:`~langchain_community.graph_vectorstores.extractors.keybert_link_extractor.LinkExtractorTransformer`,
+        we can simplify the link extraction::
+
+            from langchain_community.document_loaders import TextLoader
+            from langchain_community.graph_vectorstores.extractors import (
+                KeybertLinkExtractor,
+                LinkExtractorTransformer,
+            )
+            from langchain_text_splitters import CharacterTextSplitter
+
+            loader = TextLoader("state_of_the_union.txt")
+            raw_documents = loader.load()
+
+            text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
+            documents = text_splitter.split_documents(raw_documents)
+
+            transformer = LinkExtractorTransformer([KeybertLinkExtractor()])
+            documents = transformer.transform_documents(documents)
+
+            print(documents[0].metadata)
+
+        .. code-block:: output
+
+            {'source': 'state_of_the_union.txt', 'links': [Link(kind='kw', direction='bidir', tag='ukraine'), Link(kind='kw', direction='bidir', tag='ukrainian'), Link(kind='kw', direction='bidir', tag='putin'), Link(kind='kw', direction='bidir', tag='vladimir'), Link(kind='kw', direction='bidir', tag='russia')]}
+
+        The documents with keyword links can then be added to a :class:`~langchain_core.graph_vectorstores.base.GraphVectorStore`::
+
+            from langchain_community.graph_vectorstores import CassandraGraphVectorStore
+
+            store = CassandraGraphVectorStore.from_documents(documents=documents, embedding=...)
 
         Args:
             kind: Kind of links to produce with this extractor.
             embedding_model: Name of the embedding model to use with KeyBERT.
             extract_keywords_kwargs: Keyword arguments to pass to KeyBERT's
-                `extract_keywords` method.
-        """
+                ``extract_keywords`` method.
+        """  # noqa: E501
         try:
             import keybert
 
diff --git a/libs/community/langchain_community/graphs/neo4j_graph.py b/libs/community/langchain_community/graphs/neo4j_graph.py
index 309f5301250..6c93dc253e8 100644
--- a/libs/community/langchain_community/graphs/neo4j_graph.py
+++ b/libs/community/langchain_community/graphs/neo4j_graph.py
@@ -410,7 +410,9 @@ class Neo4jGraph(GraphStore):
         """Returns the structured schema of the Graph"""
         return self.structured_schema
 
-    def query(self, query: str, params: dict = {}) -> List[Dict[str, Any]]:
+    def query(
+        self, query: str, params: dict = {}, retry_on_session_expired: bool = True
+    ) -> List[Dict[str, Any]]:
         """Query Neo4j database.
 
         Args:
@@ -421,7 +423,7 @@ class Neo4jGraph(GraphStore):
             List[Dict[str, Any]]: The list of dictionaries containing the query results.
         """
         from neo4j import Query
-        from neo4j.exceptions import CypherSyntaxError
+        from neo4j.exceptions import CypherSyntaxError, SessionExpired
 
         with self._driver.session(database=self._database) as session:
             try:
@@ -432,6 +434,15 @@ class Neo4jGraph(GraphStore):
                 return json_data
             except CypherSyntaxError as e:
                 raise ValueError(f"Generated Cypher Statement is not valid\n{e}")
+            except (
+                SessionExpired
+            ) as e:  # Session expired is a transient error that can be retried
+                if retry_on_session_expired:
+                    return self.query(
+                        query, params=params, retry_on_session_expired=False
+                    )
+                else:
+                    raise e
 
     def refresh_schema(self) -> None:
         """
diff --git a/libs/community/poetry.lock b/libs/community/poetry.lock
index d44a825ac2f..20c2bda8827 100644
--- a/libs/community/poetry.lock
+++ b/libs/community/poetry.lock
@@ -1084,69 +1084,77 @@ files = [
 
 [[package]]
 name = "greenlet"
-version = "3.0.3"
+version = "3.1.0"
 description = "Lightweight in-process concurrent programming"
 optional = false
 python-versions = ">=3.7"
 files = [
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-    {file = "greenlet-3.0.3-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:d353cadd6083fdb056bb46ed07e4340b0869c305c8ca54ef9da3421acbdf6881"},
-    {file = "greenlet-3.0.3-cp310-cp310-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:dca1e2f3ca00b84a396bc1bce13dd21f680f035314d2379c4160c98153b2059b"},
-    {file = "greenlet-3.0.3-cp310-cp310-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:3ed7fb269f15dc662787f4119ec300ad0702fa1b19d2135a37c2c4de6fadfd4a"},
-    {file = "greenlet-3.0.3-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:dd4f49ae60e10adbc94b45c0b5e6a179acc1736cf7a90160b404076ee283cf83"},
-    {file = "greenlet-3.0.3-cp310-cp310-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:73a411ef564e0e097dbe7e866bb2dda0f027e072b04da387282b02c308807405"},
-    {file = "greenlet-3.0.3-cp310-cp310-musllinux_1_1_aarch64.whl", hash = "sha256:7f362975f2d179f9e26928c5b517524e89dd48530a0202570d55ad6ca5d8a56f"},
-    {file = "greenlet-3.0.3-cp310-cp310-musllinux_1_1_x86_64.whl", hash = "sha256:649dde7de1a5eceb258f9cb00bdf50e978c9db1b996964cd80703614c86495eb"},
-    {file = "greenlet-3.0.3-cp310-cp310-win_amd64.whl", hash = "sha256:68834da854554926fbedd38c76e60c4a2e3198c6fbed520b106a8986445caaf9"},
-    {file = "greenlet-3.0.3-cp311-cp311-macosx_11_0_universal2.whl", hash = "sha256:b1b5667cced97081bf57b8fa1d6bfca67814b0afd38208d52538316e9422fc61"},
-    {file = "greenlet-3.0.3-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:52f59dd9c96ad2fc0d5724107444f76eb20aaccb675bf825df6435acb7703559"},
-    {file = "greenlet-3.0.3-cp311-cp311-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:afaff6cf5200befd5cec055b07d1c0a5a06c040fe5ad148abcd11ba6ab9b114e"},
-    {file = "greenlet-3.0.3-cp311-cp311-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:fe754d231288e1e64323cfad462fcee8f0288654c10bdf4f603a39ed923bef33"},
-    {file = "greenlet-3.0.3-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:2797aa5aedac23af156bbb5a6aa2cd3427ada2972c828244eb7d1b9255846379"},
-    {file = "greenlet-3.0.3-cp311-cp311-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:b7f009caad047246ed379e1c4dbcb8b020f0a390667ea74d2387be2998f58a22"},
-    {file = "greenlet-3.0.3-cp311-cp311-musllinux_1_1_aarch64.whl", hash = "sha256:c5e1536de2aad7bf62e27baf79225d0d64360d4168cf2e6becb91baf1ed074f3"},
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+    {file = "yarl-1.11.1-py3-none-any.whl", hash = "sha256:72bf26f66456baa0584eff63e44545c9f0eaed9b73cb6601b647c91f14c11f38"},
+    {file = "yarl-1.11.1.tar.gz", hash = "sha256:1bb2d9e212fb7449b8fb73bc461b51eaa17cc8430b4a87d87be7b25052d92f53"},
 ]
 
 [package.dependencies]
diff --git a/libs/community/pyproject.toml b/libs/community/pyproject.toml
index 6d4ef5d828c..07b19a10a1b 100644
--- a/libs/community/pyproject.toml
+++ b/libs/community/pyproject.toml
@@ -43,6 +43,7 @@ tenacity = "^8.1.0,!=8.4.0"
 dataclasses-json = ">= 0.5.7, < 0.7"
 pydantic-settings = "^2.4.0"
 langsmith = "^0.1.112"
+
 [[tool.poetry.dependencies.numpy]]
 version = "^1"
 python = "<3.12"
diff --git a/libs/core/langchain_core/runnables/graph_mermaid.py b/libs/core/langchain_core/runnables/graph_mermaid.py
index a471ffe26f6..1e05a5af610 100644
--- a/libs/core/langchain_core/runnables/graph_mermaid.py
+++ b/libs/core/langchain_core/runnables/graph_mermaid.py
@@ -11,6 +11,8 @@ from langchain_core.runnables.graph import (
     NodeStyles,
 )
 
+MARKDOWN_SPECIAL_CHARS = "*_`"
+
 
 def draw_mermaid(
     nodes: Dict[str, Node],
@@ -58,13 +60,19 @@ def draw_mermaid(
         default_class_label = "default"
         format_dict = {default_class_label: "{0}({1})"}
         if first_node is not None:
-            format_dict[first_node] = "{0}([{0}]):::first"
+            format_dict[first_node] = "{0}([{1}]):::first"
         if last_node is not None:
-            format_dict[last_node] = "{0}([{0}]):::last"
+            format_dict[last_node] = "{0}([{1}]):::last"
 
         # Add nodes to the graph
         for key, node in nodes.items():
-            label = node.name.split(":")[-1]
+            node_name = node.name.split(":")[-1]
+            label = (
+                f"<p>{node_name}</p>"
+                if node_name.startswith(tuple(MARKDOWN_SPECIAL_CHARS))
+                and node_name.endswith(tuple(MARKDOWN_SPECIAL_CHARS))
+                else node_name
+            )
             if node.metadata:
                 label = (
                     f"{label}<hr/><small><em>"
@@ -119,9 +127,9 @@ def draw_mermaid(
                         for i in range(0, len(words), wrap_label_n_words)
                     )
                 if edge.conditional:
-                    edge_label = f" -. &nbsp{edge_data}&nbsp .-> "
+                    edge_label = f" -. &nbsp;{edge_data}&nbsp; .-> "
                 else:
-                    edge_label = f" -- &nbsp{edge_data}&nbsp --> "
+                    edge_label = f" -- &nbsp;{edge_data}&nbsp; --> "
             else:
                 if edge.conditional:
                     edge_label = " -.-> "
diff --git a/libs/core/poetry.lock b/libs/core/poetry.lock
index c31d1a76e3f..b89b0561daa 100644
--- a/libs/core/poetry.lock
+++ b/libs/core/poetry.lock
@@ -1177,13 +1177,13 @@ url = "../text-splitters"
 
 [[package]]
 name = "langsmith"
-version = "0.1.116"
+version = "0.1.117"
 description = "Client library to connect to the LangSmith LLM Tracing and Evaluation Platform."
 optional = false
 python-versions = "<4.0,>=3.8.1"
 files = [
-    {file = "langsmith-0.1.116-py3-none-any.whl", hash = "sha256:4b5ea64c81ba5ca309695c85dc3fb4617429a985129ed5d9eca00d1c9d6483f4"},
-    {file = "langsmith-0.1.116.tar.gz", hash = "sha256:5ccd7f5c1840f7c507ab3ee56334a1391de28c8bf72669782e2d82cafeefffa7"},
+    {file = "langsmith-0.1.117-py3-none-any.whl", hash = "sha256:e936ee9bcf8293b0496df7ba462a3702179fbe51f9dc28744b0fbec0dbf206ae"},
+    {file = "langsmith-0.1.117.tar.gz", hash = "sha256:a1b532f49968b9339bcaff9118d141846d52ed3d803f342902e7448edf1d662b"},
 ]
 
 [package.dependencies]
@@ -1654,19 +1654,19 @@ ptyprocess = ">=0.5"
 
 [[package]]
 name = "platformdirs"
-version = "4.2.2"
+version = "4.3.2"
 description = "A small Python package for determining appropriate platform-specific dirs, e.g. a `user data dir`."
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "platformdirs-4.2.2-py3-none-any.whl", hash = "sha256:2d7a1657e36a80ea911db832a8a6ece5ee53d8de21edd5cc5879af6530b1bfee"},
-    {file = "platformdirs-4.2.2.tar.gz", hash = "sha256:38b7b51f512eed9e84a22788b4bce1de17c0adb134d6becb09836e37d8654cd3"},
+    {file = "platformdirs-4.3.2-py3-none-any.whl", hash = "sha256:eb1c8582560b34ed4ba105009a4badf7f6f85768b30126f351328507b2beb617"},
+    {file = "platformdirs-4.3.2.tar.gz", hash = "sha256:9e5e27a08aa095dd127b9f2e764d74254f482fef22b0970773bfba79d091ab8c"},
 ]
 
 [package.extras]
-docs = ["furo (>=2023.9.10)", "proselint (>=0.13)", "sphinx (>=7.2.6)", "sphinx-autodoc-typehints (>=1.25.2)"]
-test = ["appdirs (==1.4.4)", "covdefaults (>=2.3)", "pytest (>=7.4.3)", "pytest-cov (>=4.1)", "pytest-mock (>=3.12)"]
-type = ["mypy (>=1.8)"]
+docs = ["furo (>=2024.8.6)", "proselint (>=0.14)", "sphinx (>=8.0.2)", "sphinx-autodoc-typehints (>=2.4)"]
+test = ["appdirs (==1.4.4)", "covdefaults (>=2.3)", "pytest (>=8.3.2)", "pytest-cov (>=5)", "pytest-mock (>=3.14)"]
+type = ["mypy (>=1.11.2)"]
 
 [[package]]
 name = "pluggy"
@@ -1778,123 +1778,123 @@ files = [
 
 [[package]]
 name = "pydantic"
-version = "2.9.0"
+version = "2.9.1"
 description = "Data validation using Python type hints"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "pydantic-2.9.0-py3-none-any.whl", hash = "sha256:f66a7073abd93214a20c5f7b32d56843137a7a2e70d02111f3be287035c45370"},
-    {file = "pydantic-2.9.0.tar.gz", hash = "sha256:c7a8a9fdf7d100afa49647eae340e2d23efa382466a8d177efcd1381e9be5598"},
+    {file = "pydantic-2.9.1-py3-none-any.whl", hash = "sha256:7aff4db5fdf3cf573d4b3c30926a510a10e19a0774d38fc4967f78beb6deb612"},
+    {file = "pydantic-2.9.1.tar.gz", hash = "sha256:1363c7d975c7036df0db2b4a61f2e062fbc0aa5ab5f2772e0ffc7191a4f4bce2"},
 ]
 
 [package.dependencies]
-annotated-types = ">=0.4.0"
-pydantic-core = "2.23.2"
+annotated-types = ">=0.6.0"
+pydantic-core = "2.23.3"
 typing-extensions = [
     {version = ">=4.6.1", markers = "python_version < \"3.13\""},
     {version = ">=4.12.2", markers = "python_version >= \"3.13\""},
 ]
-tzdata = {version = "*", markers = "python_version >= \"3.9\""}
 
 [package.extras]
 email = ["email-validator (>=2.0.0)"]
+timezone = ["tzdata"]
 
 [[package]]
 name = "pydantic-core"
-version = "2.23.2"
+version = "2.23.3"
 description = "Core functionality for Pydantic validation and serialization"
 optional = false
 python-versions = ">=3.8"
 files = [
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 [package.dependencies]
@@ -2518,117 +2518,104 @@ testing-integration = ["build[virtualenv]", "filelock (>=3.4.0)", "jaraco.envs (
 
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+    {file = "simsimd-5.1.2-cp39-cp39-win_arm64.whl", hash = "sha256:0ec21cde673571bc5831601253e51c930c1fd0a6e43c32c5a9077d60bb094dc4"},
+    {file = "simsimd-5.1.2.tar.gz", hash = "sha256:358f6a6fe44b9ace4cf6ebae5d388edcaeea33f1982719ba8dca2562c95b716a"},
 ]
 
 [[package]]
@@ -2846,13 +2833,13 @@ files = [
 
 [[package]]
 name = "types-requests"
-version = "2.32.0.20240905"
+version = "2.32.0.20240907"
 description = "Typing stubs for requests"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "types-requests-2.32.0.20240905.tar.gz", hash = "sha256:e97fd015a5ed982c9ddcd14cc4afba9d111e0e06b797c8f776d14602735e9bd6"},
-    {file = "types_requests-2.32.0.20240905-py3-none-any.whl", hash = "sha256:f46ecb55f5e1a37a58be684cf3f013f166da27552732ef2469a0cc8e62a72881"},
+    {file = "types-requests-2.32.0.20240907.tar.gz", hash = "sha256:ff33935f061b5e81ec87997e91050f7b4af4f82027a7a7a9d9aaea04a963fdf8"},
+    {file = "types_requests-2.32.0.20240907-py3-none-any.whl", hash = "sha256:1d1e79faeaf9d42def77f3c304893dea17a97cae98168ac69f3cb465516ee8da"},
 ]
 
 [package.dependencies]
@@ -2869,17 +2856,6 @@ files = [
     {file = "typing_extensions-4.12.2.tar.gz", hash = "sha256:1a7ead55c7e559dd4dee8856e3a88b41225abfe1ce8df57b7c13915fe121ffb8"},
 ]
 
-[[package]]
-name = "tzdata"
-version = "2024.1"
-description = "Provider of IANA time zone data"
-optional = false
-python-versions = ">=2"
-files = [
-    {file = "tzdata-2024.1-py2.py3-none-any.whl", hash = "sha256:9068bc196136463f5245e51efda838afa15aaeca9903f49050dfa2679db4d252"},
-    {file = "tzdata-2024.1.tar.gz", hash = "sha256:2674120f8d891909751c38abcdfd386ac0a5a1127954fbc332af6b5ceae07efd"},
-]
-
 [[package]]
 name = "uri-template"
 version = "1.3.0"
diff --git a/libs/core/tests/unit_tests/runnables/__snapshots__/test_graph.ambr b/libs/core/tests/unit_tests/runnables/__snapshots__/test_graph.ambr
index bf14546547a..a9fd8ebac4e 100644
--- a/libs/core/tests/unit_tests/runnables/__snapshots__/test_graph.ambr
+++ b/libs/core/tests/unit_tests/runnables/__snapshots__/test_graph.ambr
@@ -3,13 +3,13 @@
   '''
   %%{init: {'flowchart': {'curve': 'linear'}}}%%
   graph TD;
-  	__start__([__start__]):::first
+  	__start__([<p>__start__</p>]):::first
   	parent_1(parent_1)
   	child_child_1_grandchild_1(grandchild_1)
   	child_child_1_grandchild_2(grandchild_2<hr/><small><em>__interrupt = before</em></small>)
   	child_child_2(child_2)
   	parent_2(parent_2)
-  	__end__([__end__]):::last
+  	__end__([<p>__end__</p>]):::last
   	__start__ --> parent_1;
   	child_child_2 --> parent_2;
   	parent_1 --> child_child_1_grandchild_1;
@@ -1861,7 +1861,7 @@
   	PromptTemplate(PromptTemplate)
   	FakeListLLM(FakeListLLM)
   	Parallel_as_list_as_str_Input(Parallel<as_list,as_str>Input)
-  	Parallel_as_list_as_str_Output([Parallel_as_list_as_str_Output]):::last
+  	Parallel_as_list_as_str_Output([Parallel<as_list,as_str>Output]):::last
   	CommaSeparatedListOutputParser(CommaSeparatedListOutputParser)
   	conditional_str_parser_input(conditional_str_parser_input)
   	conditional_str_parser_output(conditional_str_parser_output)
@@ -1922,14 +1922,14 @@
   '''
   %%{init: {'flowchart': {'curve': 'linear'}}}%%
   graph TD;
-  	__start__([__start__]):::first
+  	__start__([<p>__start__</p>]):::first
   	outer_1(outer_1)
   	inner_1_inner_1(inner_1)
   	inner_1_inner_2(inner_2<hr/><small><em>__interrupt = before</em></small>)
   	inner_2_inner_1(inner_1)
   	inner_2_inner_2(inner_2)
   	outer_2(outer_2)
-  	__end__([__end__]):::last
+  	__end__([<p>__end__</p>]):::last
   	__start__ --> outer_1;
   	inner_1_inner_2 --> outer_2;
   	inner_2_inner_2 --> outer_2;
diff --git a/libs/langchain/poetry.lock b/libs/langchain/poetry.lock
index dffedb484e2..a5ab398861f 100644
--- a/libs/langchain/poetry.lock
+++ b/libs/langchain/poetry.lock
@@ -374,42 +374,42 @@ css = ["tinycss2 (>=1.1.0,<1.3)"]
 
 [[package]]
 name = "cassandra-driver"
-version = "3.29.1"
+version = "3.29.2"
 description = "DataStax Driver for Apache Cassandra"
 optional = false
 python-versions = "*"
 files = [
-    {file = "cassandra-driver-3.29.1.tar.gz", hash = "sha256:38e9c2a2f2a9664bb03f1f852d5fccaeff2163942b5db35dffcf8bf32a51cfe5"},
-    {file = "cassandra_driver-3.29.1-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:a8f175c7616a63ca48cb8bd4acc443e2a3d889964d5157cead761f23cc8db7bd"},
-    {file = "cassandra_driver-3.29.1-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:7d66398952b9cd21c40edff56e22b6d3bce765edc94b207ddb5896e7bc9aa088"},
-    {file = "cassandra_driver-3.29.1-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:5bbc6f575ef109ce5d4abfa2033bf36c394032abd83e32ab671159ce68e7e17b"},
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 ]
 
 [package.dependencies]
diff --git a/libs/partners/anthropic/poetry.lock b/libs/partners/anthropic/poetry.lock
index 883464bba23..a0c76fc6071 100644
--- a/libs/partners/anthropic/poetry.lock
+++ b/libs/partners/anthropic/poetry.lock
@@ -552,13 +552,13 @@ url = "../../standard-tests"
 
 [[package]]
 name = "langsmith"
-version = "0.1.116"
+version = "0.1.117"
 description = "Client library to connect to the LangSmith LLM Tracing and Evaluation Platform."
 optional = false
 python-versions = "<4.0,>=3.8.1"
 files = [
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+    {file = "langsmith-0.1.117-py3-none-any.whl", hash = "sha256:e936ee9bcf8293b0496df7ba462a3702179fbe51f9dc28744b0fbec0dbf206ae"},
+    {file = "langsmith-0.1.117.tar.gz", hash = "sha256:a1b532f49968b9339bcaff9118d141846d52ed3d803f342902e7448edf1d662b"},
 ]
 
 [package.dependencies]
@@ -722,123 +722,123 @@ testing = ["pytest", "pytest-benchmark"]
 
 [[package]]
 name = "pydantic"
-version = "2.9.0"
+version = "2.9.1"
 description = "Data validation using Python type hints"
 optional = false
 python-versions = ">=3.8"
 files = [
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+    {file = "pydantic-2.9.1-py3-none-any.whl", hash = "sha256:7aff4db5fdf3cf573d4b3c30926a510a10e19a0774d38fc4967f78beb6deb612"},
+    {file = "pydantic-2.9.1.tar.gz", hash = "sha256:1363c7d975c7036df0db2b4a61f2e062fbc0aa5ab5f2772e0ffc7191a4f4bce2"},
 ]
 
 [package.dependencies]
-annotated-types = ">=0.4.0"
-pydantic-core = "2.23.2"
+annotated-types = ">=0.6.0"
+pydantic-core = "2.23.3"
 typing-extensions = [
     {version = ">=4.12.2", markers = "python_version >= \"3.13\""},
     {version = ">=4.6.1", markers = "python_version < \"3.13\""},
 ]
-tzdata = {version = "*", markers = "python_version >= \"3.9\""}
 
 [package.extras]
 email = ["email-validator (>=2.0.0)"]
+timezone = ["tzdata"]
 
 [[package]]
 name = "pydantic-core"
-version = "2.23.2"
+version = "2.23.3"
 description = "Core functionality for Pydantic validation and serialization"
 optional = false
 python-versions = ">=3.8"
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+    {file = "pydantic_core-2.23.3.tar.gz", hash = "sha256:3cb0f65d8b4121c1b015c60104a685feb929a29d7cf204387c7f2688c7974690"},
 ]
 
 [package.dependencies]
@@ -1250,17 +1250,6 @@ files = [
     {file = "typing_extensions-4.12.2.tar.gz", hash = "sha256:1a7ead55c7e559dd4dee8856e3a88b41225abfe1ce8df57b7c13915fe121ffb8"},
 ]
 
-[[package]]
-name = "tzdata"
-version = "2024.1"
-description = "Provider of IANA time zone data"
-optional = false
-python-versions = ">=2"
-files = [
-    {file = "tzdata-2024.1-py2.py3-none-any.whl", hash = "sha256:9068bc196136463f5245e51efda838afa15aaeca9903f49050dfa2679db4d252"},
-    {file = "tzdata-2024.1.tar.gz", hash = "sha256:2674120f8d891909751c38abcdfd386ac0a5a1127954fbc332af6b5ceae07efd"},
-]
-
 [[package]]
 name = "urllib3"
 version = "2.2.2"
diff --git a/libs/partners/fireworks/poetry.lock b/libs/partners/fireworks/poetry.lock
index 61ff8eb97a4..0e9b625ed61 100644
--- a/libs/partners/fireworks/poetry.lock
+++ b/libs/partners/fireworks/poetry.lock
@@ -1,4 +1,4 @@
-# This file is automatically @generated by Poetry 1.8.3 and should not be changed by hand.
+# This file is automatically @generated by Poetry 1.8.2 and should not be changed by hand.
 
 [[package]]
 name = "aiohappyeyeballs"
@@ -667,7 +667,7 @@ files = [
 
 [[package]]
 name = "langchain-core"
-version = "0.3.0.dev2"
+version = "0.3.0.dev4"
 description = "Building applications with LLMs through composability"
 optional = false
 python-versions = ">=3.9,<4.0"
@@ -676,7 +676,7 @@ develop = true
 
 [package.dependencies]
 jsonpatch = "^1.33"
-langsmith = "^0.1.75"
+langsmith = "^0.1.112"
 packaging = ">=23.2,<25"
 pydantic = "^2.7.4"
 PyYAML = ">=5.3"
@@ -708,123 +708,128 @@ url = "../../standard-tests"
 
 [[package]]
 name = "langsmith"
-version = "0.1.115"
+version = "0.1.117"
 description = "Client library to connect to the LangSmith LLM Tracing and Evaluation Platform."
 optional = false
 python-versions = "<4.0,>=3.8.1"
 files = [
-    {file = "langsmith-0.1.115-py3-none-any.whl", hash = "sha256:04e35cfd4c2d4ff1ea10bb577ff43957b05ebb3d9eb4e06e200701f4a2b4ac9f"},
-    {file = "langsmith-0.1.115.tar.gz", hash = "sha256:3b775377d858d32354f3ee0dd1ed637068cfe9a1f13e7b3bfa82db1615cdffc9"},
+    {file = "langsmith-0.1.117-py3-none-any.whl", hash = "sha256:e936ee9bcf8293b0496df7ba462a3702179fbe51f9dc28744b0fbec0dbf206ae"},
+    {file = "langsmith-0.1.117.tar.gz", hash = "sha256:a1b532f49968b9339bcaff9118d141846d52ed3d803f342902e7448edf1d662b"},
 ]
 
 [package.dependencies]
 httpx = ">=0.23.0,<1"
 orjson = ">=3.9.14,<4.0.0"
 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\""},
+    {version = ">=1,<3", markers = "python_full_version < \"3.12.4\""},
 ]
 requests = ">=2,<3"
 
 [[package]]
 name = "multidict"
-version = "6.0.5"
+version = "6.1.0"
 description = "multidict implementation"
 optional = false
-python-versions = ">=3.7"
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 ]
 
 [package.dependencies]
diff --git a/libs/partners/groq/poetry.lock b/libs/partners/groq/poetry.lock
index 8e3738b5fa7..c1f7b56030b 100644
--- a/libs/partners/groq/poetry.lock
+++ b/libs/partners/groq/poetry.lock
@@ -362,13 +362,13 @@ url = "../../standard-tests"
 
 [[package]]
 name = "langsmith"
-version = "0.1.115"
+version = "0.1.117"
 description = "Client library to connect to the LangSmith LLM Tracing and Evaluation Platform."
 optional = false
 python-versions = "<4.0,>=3.8.1"
 files = [
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+    {file = "langsmith-0.1.117.tar.gz", hash = "sha256:a1b532f49968b9339bcaff9118d141846d52ed3d803f342902e7448edf1d662b"},
 ]
 
 [package.dependencies]
@@ -532,123 +532,123 @@ testing = ["pytest", "pytest-benchmark"]
 
 [[package]]
 name = "pydantic"
-version = "2.9.0"
+version = "2.9.1"
 description = "Data validation using Python type hints"
 optional = false
 python-versions = ">=3.8"
 files = [
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+    {file = "pydantic-2.9.1.tar.gz", hash = "sha256:1363c7d975c7036df0db2b4a61f2e062fbc0aa5ab5f2772e0ffc7191a4f4bce2"},
 ]
 
 [package.dependencies]
-annotated-types = ">=0.4.0"
-pydantic-core = "2.23.2"
+annotated-types = ">=0.6.0"
+pydantic-core = "2.23.3"
 typing-extensions = [
     {version = ">=4.12.2", markers = "python_version >= \"3.13\""},
     {version = ">=4.6.1", markers = "python_version < \"3.13\""},
 ]
-tzdata = {version = "*", markers = "python_version >= \"3.9\""}
 
 [package.extras]
 email = ["email-validator (>=2.0.0)"]
+timezone = ["tzdata"]
 
 [[package]]
 name = "pydantic-core"
-version = "2.23.2"
+version = "2.23.3"
 description = "Core functionality for Pydantic validation and serialization"
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 python-versions = ">=3.8"
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 ]
 
 [package.dependencies]
@@ -898,17 +898,6 @@ files = [
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-[[package]]
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-description = "Provider of IANA time zone data"
-optional = false
-python-versions = ">=2"
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 [[package]]
 name = "urllib3"
 version = "2.2.2"
diff --git a/libs/partners/huggingface/poetry.lock b/libs/partners/huggingface/poetry.lock
index 1c24b01db7f..275584be9ca 100644
--- a/libs/partners/huggingface/poetry.lock
+++ b/libs/partners/huggingface/poetry.lock
@@ -1,4 +1,4 @@
-# This file is automatically @generated by Poetry 1.8.3 and should not be changed by hand.
+# This file is automatically @generated by Poetry 1.8.2 and should not be changed by hand.
 
 [[package]]
 name = "aiohappyeyeballs"
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 [package.dependencies]
@@ -550,19 +519,19 @@ tests = ["asttokens (>=2.1.0)", "coverage", "coverage-enable-subprocess", "ipyth
 
 [[package]]
 name = "filelock"
-version = "3.15.4"
+version = "3.16.0"
 description = "A platform independent file lock."
 optional = false
 python-versions = ">=3.8"
 files = [
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-typing = ["typing-extensions (>=4.8)"]
+docs = ["furo (>=2024.8.6)", "sphinx (>=8.0.2)", "sphinx-autodoc-typehints (>=2.4)"]
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+typing = ["typing-extensions (>=4.12.2)"]
 
 [[package]]
 name = "frozenlist"
@@ -691,69 +660,77 @@ tqdm = ["tqdm"]
 
 [[package]]
 name = "greenlet"
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+version = "3.1.0"
 description = "Lightweight in-process concurrent programming"
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 python-versions = ">=3.7"
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 [package.extras]
@@ -1118,7 +1095,7 @@ aiohttp = "^3.8.3"
 dataclasses-json = ">= 0.5.7, < 0.7"
 langchain = "^0.3.0.dev1"
 langchain-core = "^0.3.0.dev2"
-langsmith = "^0.1.0"
+langsmith = "^0.1.112"
 numpy = [
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     {version = ">=1.26.0,<2.0.0", markers = "python_version >= \"3.12\""},
@@ -1135,7 +1112,7 @@ url = "../../community"
 
 [[package]]
 name = "langchain-core"
-version = "0.3.0.dev2"
+version = "0.3.0.dev4"
 description = "Building applications with LLMs through composability"
 optional = false
 python-versions = ">=3.9,<4.0"
@@ -1144,7 +1121,7 @@ develop = true
 
 [package.dependencies]
 jsonpatch = "^1.33"
-langsmith = "^0.1.75"
+langsmith = "^0.1.112"
 packaging = ">=23.2,<25"
 pydantic = "^2.7.4"
 PyYAML = ">=5.3"
@@ -1190,13 +1167,13 @@ langchain-core = ">=0.3.0.dev1,<0.4.0"
 
 [[package]]
 name = "langsmith"
-version = "0.1.115"
+version = "0.1.117"
 description = "Client library to connect to the LangSmith LLM Tracing and Evaluation Platform."
 optional = false
 python-versions = "<4.0,>=3.8.1"
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 ]
 
 [package.dependencies]
@@ -1329,103 +1306,108 @@ tests = ["pytest (>=4.6)"]
 
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+
 [[package]]
 name = "mypy"
 version = "1.11.2"
@@ -1907,19 +1889,19 @@ xmp = ["defusedxml"]
 
 [[package]]
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 [[package]]
 name = "pluggy"
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 [package.extras]
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 name = "yarl"
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 optional = false
 python-versions = ">=3.8"
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 ]
 
 [package.dependencies]
diff --git a/libs/partners/milvus/poetry.lock b/libs/partners/milvus/poetry.lock
index dcbf6d87e00..e253804f992 100644
--- a/libs/partners/milvus/poetry.lock
+++ b/libs/partners/milvus/poetry.lock
@@ -1,4 +1,4 @@
-# This file is automatically @generated by Poetry 1.8.3 and should not be changed by hand.
+# This file is automatically @generated by Poetry 1.8.2 and should not be changed by hand.
 
 [[package]]
 name = "annotated-types"
@@ -225,19 +225,19 @@ test = ["pytest (>=6)"]
 
 [[package]]
 name = "filelock"
-version = "3.15.4"
+version = "3.16.0"
 description = "A platform independent file lock."
 optional = false
 python-versions = ">=3.8"
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 [[package]]
 name = "flatbuffers"
@@ -515,7 +515,7 @@ files = [
 
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+version = "0.3.0.dev4"
 description = "Building applications with LLMs through composability"
 optional = false
 python-versions = ">=3.9,<4.0"
@@ -524,7 +524,7 @@ develop = true
 
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 pydantic = "^2.7.4"
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@@ -537,13 +537,13 @@ url = "../../core"
 
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-tzdata = {version = "*", markers = "python_version >= \"3.9\""}
 
 [package.extras]
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+timezone = ["tzdata"]
 
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+    {file = "simsimd-5.1.2-cp39-cp39-manylinux_2_28_x86_64.whl", hash = "sha256:d242375faa35c959e48af4de27a733793195f66898a852d353169870cd2bf8ec"},
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+    {file = "simsimd-5.1.2-cp39-cp39-musllinux_1_2_s390x.whl", hash = "sha256:6ae4fa625870834b618d01fbe8c6b7a6690b34ab60915385910f8f485177dec8"},
+    {file = "simsimd-5.1.2-cp39-cp39-musllinux_1_2_x86_64.whl", hash = "sha256:c5628471ac7f02b49a664f3fd505b2a10ab9c06d078c56bb6a5c73b006149e2f"},
+    {file = "simsimd-5.1.2-cp39-cp39-win32.whl", hash = "sha256:bb5cfb6bb195dc68025dffeb14fb276b3c7c6a6fe7a14694d38562c3f4392aed"},
+    {file = "simsimd-5.1.2-cp39-cp39-win_amd64.whl", hash = "sha256:3a1d11818ff6ad495c40116832b04626d48531d153db2e0d8d5e5bd28ee477d4"},
+    {file = "simsimd-5.1.2-cp39-cp39-win_arm64.whl", hash = "sha256:0ec21cde673571bc5831601253e51c930c1fd0a6e43c32c5a9077d60bb094dc4"},
+    {file = "simsimd-5.1.2.tar.gz", hash = "sha256:358f6a6fe44b9ace4cf6ebae5d388edcaeea33f1982719ba8dca2562c95b716a"},
 ]
 
 [[package]]
@@ -2079,13 +2067,13 @@ vision = ["Pillow (>=10.0.1,<=15.0)"]
 
 [[package]]
 name = "types-requests"
-version = "2.32.0.20240905"
+version = "2.32.0.20240907"
 description = "Typing stubs for requests"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "types-requests-2.32.0.20240905.tar.gz", hash = "sha256:e97fd015a5ed982c9ddcd14cc4afba9d111e0e06b797c8f776d14602735e9bd6"},
-    {file = "types_requests-2.32.0.20240905-py3-none-any.whl", hash = "sha256:f46ecb55f5e1a37a58be684cf3f013f166da27552732ef2469a0cc8e62a72881"},
+    {file = "types-requests-2.32.0.20240907.tar.gz", hash = "sha256:ff33935f061b5e81ec87997e91050f7b4af4f82027a7a7a9d9aaea04a963fdf8"},
+    {file = "types_requests-2.32.0.20240907-py3-none-any.whl", hash = "sha256:1d1e79faeaf9d42def77f3c304893dea17a97cae98168ac69f3cb465516ee8da"},
 ]
 
 [package.dependencies]
diff --git a/libs/partners/mistralai/poetry.lock b/libs/partners/mistralai/poetry.lock
index 0de9e7ee94d..d62abe9e745 100644
--- a/libs/partners/mistralai/poetry.lock
+++ b/libs/partners/mistralai/poetry.lock
@@ -432,13 +432,22 @@ url = "../../standard-tests"
 
 [[package]]
 name = "langsmith"
+<<<<<<< HEAD
 version = "0.1.115"
+=======
+version = "0.1.111"
+>>>>>>> master
 description = "Client library to connect to the LangSmith LLM Tracing and Evaluation Platform."
 optional = false
 python-versions = "<4.0,>=3.8.1"
 files = [
+<<<<<<< HEAD
     {file = "langsmith-0.1.115-py3-none-any.whl", hash = "sha256:04e35cfd4c2d4ff1ea10bb577ff43957b05ebb3d9eb4e06e200701f4a2b4ac9f"},
     {file = "langsmith-0.1.115.tar.gz", hash = "sha256:3b775377d858d32354f3ee0dd1ed637068cfe9a1f13e7b3bfa82db1615cdffc9"},
+=======
+    {file = "langsmith-0.1.111-py3-none-any.whl", hash = "sha256:e5c702764911193c9812fe55136ae01cd0b9ddf5dff0b068ce6fd60eeddbcb40"},
+    {file = "langsmith-0.1.111.tar.gz", hash = "sha256:bab24fd6125685f588d682693c4a3253e163804242829b1ff902e1a3e984a94c"},
+>>>>>>> master
 ]
 
 [package.dependencies]
@@ -1118,6 +1127,7 @@ zstd = ["zstandard (>=0.18.0)"]
 
 [[package]]
 name = "watchdog"
+<<<<<<< HEAD
 version = "5.0.2"
 description = "Filesystem events monitoring"
 optional = false
@@ -1153,6 +1163,48 @@ files = [
     {file = "watchdog-5.0.2-py3-none-win_amd64.whl", hash = "sha256:d010be060c996db725fbce7e3ef14687cdcc76f4ca0e4339a68cc4532c382a73"},
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     {file = "watchdog-5.0.2.tar.gz", hash = "sha256:dcebf7e475001d2cdeb020be630dc5b687e9acdd60d16fea6bb4508e7b94cf76"},
+=======
+version = "4.0.2"
+description = "Filesystem events monitoring"
+optional = false
+python-versions = ">=3.8"
+files = [
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+    {file = "watchdog-4.0.2-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:c50f148b31b03fbadd6d0b5980e38b558046b127dc483e5e4505fcef250f9503"},
+    {file = "watchdog-4.0.2-cp311-cp311-macosx_10_9_universal2.whl", hash = "sha256:7c7d4bf585ad501c5f6c980e7be9c4f15604c7cc150e942d82083b31a7548930"},
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+    {file = "watchdog-4.0.2-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:132937547a716027bd5714383dfc40dc66c26769f1ce8a72a859d6a48f371f3a"},
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+    {file = "watchdog-4.0.2-cp39-cp39-macosx_10_9_x86_64.whl", hash = "sha256:eeea812f38536a0aa859972d50c76e37f4456474b02bd93674d1947cf1e39578"},
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+    {file = "watchdog-4.0.2-pp38-pypy38_pp73-macosx_11_0_arm64.whl", hash = "sha256:f5315a8c8dd6dd9425b974515081fc0aadca1d1d61e078d2246509fd756141ee"},
+    {file = "watchdog-4.0.2-pp39-pypy39_pp73-macosx_10_15_x86_64.whl", hash = "sha256:2d468028a77b42cc685ed694a7a550a8d1771bb05193ba7b24006b8241a571a1"},
+    {file = "watchdog-4.0.2-pp39-pypy39_pp73-macosx_11_0_arm64.whl", hash = "sha256:f15edcae3830ff20e55d1f4e743e92970c847bcddc8b7509bcd172aa04de506e"},
+    {file = "watchdog-4.0.2-py3-none-manylinux2014_aarch64.whl", hash = "sha256:936acba76d636f70db8f3c66e76aa6cb5136a936fc2a5088b9ce1c7a3508fc83"},
+    {file = "watchdog-4.0.2-py3-none-manylinux2014_armv7l.whl", hash = "sha256:e252f8ca942a870f38cf785aef420285431311652d871409a64e2a0a52a2174c"},
+    {file = "watchdog-4.0.2-py3-none-manylinux2014_i686.whl", hash = "sha256:0e83619a2d5d436a7e58a1aea957a3c1ccbf9782c43c0b4fed80580e5e4acd1a"},
+    {file = "watchdog-4.0.2-py3-none-manylinux2014_ppc64.whl", hash = "sha256:88456d65f207b39f1981bf772e473799fcdc10801062c36fd5ad9f9d1d463a73"},
+    {file = "watchdog-4.0.2-py3-none-manylinux2014_ppc64le.whl", hash = "sha256:32be97f3b75693a93c683787a87a0dc8db98bb84701539954eef991fb35f5fbc"},
+    {file = "watchdog-4.0.2-py3-none-manylinux2014_s390x.whl", hash = "sha256:c82253cfc9be68e3e49282831afad2c1f6593af80c0daf1287f6a92657986757"},
+    {file = "watchdog-4.0.2-py3-none-manylinux2014_x86_64.whl", hash = "sha256:c0b14488bd336c5b1845cee83d3e631a1f8b4e9c5091ec539406e4a324f882d8"},
+    {file = "watchdog-4.0.2-py3-none-win32.whl", hash = "sha256:0d8a7e523ef03757a5aa29f591437d64d0d894635f8a50f370fe37f913ce4e19"},
+    {file = "watchdog-4.0.2-py3-none-win_amd64.whl", hash = "sha256:c344453ef3bf875a535b0488e3ad28e341adbd5a9ffb0f7d62cefacc8824ef2b"},
+    {file = "watchdog-4.0.2-py3-none-win_ia64.whl", hash = "sha256:baececaa8edff42cd16558a639a9b0ddf425f93d892e8392a56bf904f5eff22c"},
+    {file = "watchdog-4.0.2.tar.gz", hash = "sha256:b4dfbb6c49221be4535623ea4474a4d6ee0a9cef4a80b20c28db4d858b64e270"},
+>>>>>>> master
 ]
 
 [package.extras]
@@ -1160,5 +1212,10 @@ watchmedo = ["PyYAML (>=3.10)"]
 
 [metadata]
 lock-version = "2.0"
+<<<<<<< HEAD
 python-versions = ">=3.9,<4.0"
 content-hash = "504fdae5c6e17ac01afda63594261596ea1c380e76d659cf093252c83552253c"
+=======
+python-versions = ">=3.8.1,<4.0"
+content-hash = "644c2bff44971ab41eecdc6cc46649addcbf155136cd918733f928fea4c47228"
+>>>>>>> master
diff --git a/libs/partners/mongodb/poetry.lock b/libs/partners/mongodb/poetry.lock
index 90aa1690d70..d82fef34fa6 100644
--- a/libs/partners/mongodb/poetry.lock
+++ b/libs/partners/mongodb/poetry.lock
@@ -1,4 +1,4 @@
-# This file is automatically @generated by Poetry 1.8.3 and should not be changed by hand.
+# This file is automatically @generated by Poetry 1.8.2 and should not be changed by hand.
 
 [[package]]
 name = "aiohappyeyeballs"
@@ -485,69 +485,77 @@ files = [
 
 [[package]]
 name = "greenlet"
-version = "3.0.3"
+version = "3.1.0"
 description = "Lightweight in-process concurrent programming"
 optional = false
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 ]
 
 [package.extras]
@@ -740,14 +748,14 @@ develop = true
 [package.dependencies]
 aiohttp = "^3.8.3"
 async-timeout = {version = "^4.0.0", markers = "python_version < \"3.11\""}
-langchain-core = "^0.3.0.dev"
-langchain-text-splitters = "^0.2.0"
+langchain-core = "^0.3.0.dev2"
+langchain-text-splitters = "^0.3.0.dev1"
 langsmith = "^0.1.17"
 numpy = [
     {version = ">=1,<2", markers = "python_version < \"3.12\""},
     {version = ">=1.26.0,<2.0.0", markers = "python_version >= \"3.12\""},
 ]
-pydantic = ">=1,<3"
+pydantic = "^2.7.4"
 PyYAML = ">=5.3"
 requests = "^2"
 SQLAlchemy = ">=1.4,<3"
@@ -759,7 +767,7 @@ url = "../../langchain"
 
 [[package]]
 name = "langchain-core"
-version = "0.3.0.dev1"
+version = "0.3.0.dev4"
 description = "Building applications with LLMs through composability"
 optional = false
 python-versions = ">=3.9,<4.0"
@@ -768,7 +776,7 @@ develop = true
 
 [package.dependencies]
 jsonpatch = "^1.33"
-langsmith = "^0.1.75"
+langsmith = "^0.1.112"
 packaging = ">=23.2,<25"
 pydantic = "^2.7.4"
 PyYAML = ">=5.3"
@@ -781,7 +789,7 @@ url = "../../core"
 
 [[package]]
 name = "langchain-openai"
-version = "0.1.23"
+version = "0.2.0.dev2"
 description = "An integration package connecting OpenAI and LangChain"
 optional = false
 python-versions = ">=3.9,<4.0"
@@ -789,7 +797,7 @@ files = []
 develop = true
 
 [package.dependencies]
-langchain-core = "^0.3.0.dev"
+langchain-core = "^0.3.0.dev1"
 openai = "^1.40.0"
 tiktoken = ">=0.7,<1"
 
@@ -799,7 +807,7 @@ url = "../openai"
 
 [[package]]
 name = "langchain-text-splitters"
-version = "0.2.4"
+version = "0.3.0.dev1"
 description = "LangChain text splitting utilities"
 optional = false
 python-versions = ">=3.9,<4.0"
@@ -815,13 +823,13 @@ url = "../../text-splitters"
 
 [[package]]
 name = "langsmith"
-version = "0.1.115"
+version = "0.1.117"
 description = "Client library to connect to the LangSmith LLM Tracing and Evaluation Platform."
 optional = false
 python-versions = "<4.0,>=3.8.1"
 files = [
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 ]
 
 [package.dependencies]
@@ -835,103 +843,108 @@ requests = ">=2,<3"
 
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-version = "6.0.5"
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 description = "multidict implementation"
 optional = false
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+    {file = "yarl-1.11.1-py3-none-any.whl", hash = "sha256:72bf26f66456baa0584eff63e44545c9f0eaed9b73cb6601b647c91f14c11f38"},
+    {file = "yarl-1.11.1.tar.gz", hash = "sha256:1bb2d9e212fb7449b8fb73bc461b51eaa17cc8430b4a87d87be7b25052d92f53"},
 ]
 
 [package.dependencies]
diff --git a/libs/partners/mongodb/pyproject.toml b/libs/partners/mongodb/pyproject.toml
index c4ac2be36d0..f55b714b77e 100644
--- a/libs/partners/mongodb/pyproject.toml
+++ b/libs/partners/mongodb/pyproject.toml
@@ -4,7 +4,7 @@ build-backend = "poetry.core.masonry.api"
 
 [tool.poetry]
 name = "langchain-mongodb"
-version = "0.1.8"
+version = "0.1.9"
 description = "An integration package connecting MongoDB and LangChain"
 authors = []
 readme = "README.md"
@@ -23,6 +23,7 @@ python = ">=3.9,<4.0"
 pymongo = ">=4.6.1,<5.0"
 langchain-core = "^0.3.0.dev"
 pydantic = ">=2,<3"
+
 [[tool.poetry.dependencies.numpy]]
 version = "^1"
 python = "<3.12"
diff --git a/libs/partners/ollama/langchain_ollama/chat_models.py b/libs/partners/ollama/langchain_ollama/chat_models.py
index df32e73c1a5..f0db82b835f 100644
--- a/libs/partners/ollama/langchain_ollama/chat_models.py
+++ b/libs/partners/ollama/langchain_ollama/chat_models.py
@@ -92,7 +92,9 @@ def _lc_tool_call_to_openai_tool_call(tool_call: ToolCall) -> dict:
 class ChatOllama(BaseChatModel):
     """Ollama chat model integration.
 
-    Setup:
+    .. dropdown:: Setup
+        :open:
+
         Install ``langchain-ollama`` and download any models you want to use from ollama.
 
         .. code-block:: bash
diff --git a/libs/partners/ollama/poetry.lock b/libs/partners/ollama/poetry.lock
index 16d46a244cb..e159c090ddf 100644
--- a/libs/partners/ollama/poetry.lock
+++ b/libs/partners/ollama/poetry.lock
@@ -1,4 +1,4 @@
-# This file is automatically @generated by Poetry 1.8.3 and should not be changed by hand.
+# This file is automatically @generated by Poetry 1.8.2 and should not be changed by hand.
 
 [[package]]
 name = "annotated-types"
@@ -332,13 +332,13 @@ url = "../../standard-tests"
 
 [[package]]
 name = "langsmith"
-version = "0.1.115"
+version = "0.1.117"
 description = "Client library to connect to the LangSmith LLM Tracing and Evaluation Platform."
 optional = false
 python-versions = "<4.0,>=3.8.1"
 files = [
-    {file = "langsmith-0.1.115-py3-none-any.whl", hash = "sha256:04e35cfd4c2d4ff1ea10bb577ff43957b05ebb3d9eb4e06e200701f4a2b4ac9f"},
-    {file = "langsmith-0.1.115.tar.gz", hash = "sha256:3b775377d858d32354f3ee0dd1ed637068cfe9a1f13e7b3bfa82db1615cdffc9"},
+    {file = "langsmith-0.1.117-py3-none-any.whl", hash = "sha256:e936ee9bcf8293b0496df7ba462a3702179fbe51f9dc28744b0fbec0dbf206ae"},
+    {file = "langsmith-0.1.117.tar.gz", hash = "sha256:a1b532f49968b9339bcaff9118d141846d52ed3d803f342902e7448edf1d662b"},
 ]
 
 [package.dependencies]
@@ -410,13 +410,13 @@ files = [
 
 [[package]]
 name = "ollama"
-version = "0.3.2"
+version = "0.3.3"
 description = "The official Python client for Ollama."
 optional = false
 python-versions = "<4.0,>=3.8"
 files = [
-    {file = "ollama-0.3.2-py3-none-any.whl", hash = "sha256:ed2a6f752bd91c49b477d84a259c5657785d7777689d4a27ffe0a4d5b5dd3cae"},
-    {file = "ollama-0.3.2.tar.gz", hash = "sha256:7deb3287cdefa1c39cc046163096f8597b83f59ca31a1f8ae78e71eccb7af95f"},
+    {file = "ollama-0.3.3-py3-none-any.whl", hash = "sha256:ca6242ce78ab34758082b7392df3f9f6c2cb1d070a9dede1a4c545c929e16dba"},
+    {file = "ollama-0.3.3.tar.gz", hash = "sha256:f90a6d61803117f40b0e8ff17465cab5e1eb24758a473cfe8101aff38bc13b51"},
 ]
 
 [package.dependencies]
@@ -516,123 +516,123 @@ testing = ["pytest", "pytest-benchmark"]
 
 [[package]]
 name = "pydantic"
-version = "2.9.0"
+version = "2.9.1"
 description = "Data validation using Python type hints"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "pydantic-2.9.0-py3-none-any.whl", hash = "sha256:f66a7073abd93214a20c5f7b32d56843137a7a2e70d02111f3be287035c45370"},
-    {file = "pydantic-2.9.0.tar.gz", hash = "sha256:c7a8a9fdf7d100afa49647eae340e2d23efa382466a8d177efcd1381e9be5598"},
+    {file = "pydantic-2.9.1-py3-none-any.whl", hash = "sha256:7aff4db5fdf3cf573d4b3c30926a510a10e19a0774d38fc4967f78beb6deb612"},
+    {file = "pydantic-2.9.1.tar.gz", hash = "sha256:1363c7d975c7036df0db2b4a61f2e062fbc0aa5ab5f2772e0ffc7191a4f4bce2"},
 ]
 
 [package.dependencies]
-annotated-types = ">=0.4.0"
-pydantic-core = "2.23.2"
+annotated-types = ">=0.6.0"
+pydantic-core = "2.23.3"
 typing-extensions = [
     {version = ">=4.12.2", markers = "python_version >= \"3.13\""},
     {version = ">=4.6.1", markers = "python_version < \"3.13\""},
 ]
-tzdata = {version = "*", markers = "python_version >= \"3.9\""}
 
 [package.extras]
 email = ["email-validator (>=2.0.0)"]
+timezone = ["tzdata"]
 
 [[package]]
 name = "pydantic-core"
-version = "2.23.2"
+version = "2.23.3"
 description = "Core functionality for Pydantic validation and serialization"
 optional = false
 python-versions = ">=3.8"
 files = [
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 ]
 
 [package.dependencies]
@@ -878,17 +878,6 @@ files = [
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-[[package]]
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-version = "2024.1"
-description = "Provider of IANA time zone data"
-optional = false
-python-versions = ">=2"
-files = [
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-
 [[package]]
 name = "urllib3"
 version = "2.2.2"
diff --git a/libs/partners/unstructured/poetry.lock b/libs/partners/unstructured/poetry.lock
index 4e5c6754f2b..25ba4d56a49 100644
--- a/libs/partners/unstructured/poetry.lock
+++ b/libs/partners/unstructured/poetry.lock
@@ -1,4 +1,4 @@
-# This file is automatically @generated by Poetry 1.8.3 and should not be changed by hand.
+# This file is automatically @generated by Poetry 1.8.2 and should not be changed by hand.
 
 [[package]]
 name = "annotated-types"
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 ]
 
 [package.dependencies]
@@ -605,19 +574,19 @@ test = ["pytest (>=6)"]
 
 [[package]]
 name = "filelock"
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+version = "3.16.0"
 description = "A platform independent file lock."
 optional = false
 python-versions = ">=3.8"
 files = [
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-typing = ["typing-extensions (>=4.8)"]
+docs = ["furo (>=2024.8.6)", "sphinx (>=8.0.2)", "sphinx-autodoc-typehints (>=2.4)"]
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+typing = ["typing-extensions (>=4.12.2)"]
 
 [[package]]
 name = "filetype"
@@ -1044,13 +1013,13 @@ test = ["flufl.flake8", "importlib-resources (>=1.3)", "jaraco.test (>=5.4)", "p
 
 [[package]]
 name = "importlib-resources"
-version = "6.4.4"
+version = "6.4.5"
 description = "Read resources from Python packages"
 optional = false
 python-versions = ">=3.8"
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 [package.dependencies]
@@ -1282,7 +1251,7 @@ files = [
 
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-version = "0.3.0.dev2"
+version = "0.3.0.dev4"
 description = "Building applications with LLMs through composability"
 optional = false
 python-versions = ">=3.9,<4.0"
@@ -1291,7 +1260,7 @@ develop = true
 
 [package.dependencies]
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-langsmith = "^0.1.75"
+langsmith = "^0.1.112"
 packaging = ">=23.2,<25"
 pydantic = "^2.7.4"
 PyYAML = ">=5.3"
@@ -1318,13 +1287,13 @@ six = "*"
 
 [[package]]
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-version = "0.1.115"
+version = "0.1.117"
 description = "Client library to connect to the LangSmith LLM Tracing and Evaluation Platform."
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 python-versions = "<4.0,>=3.8.1"
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@@ -2783,123 +2752,123 @@ files = [
 
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+version = "2.9.1"
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-tzdata = {version = "*", markers = "python_version >= \"3.9\""}
 
 [package.extras]
 email = ["email-validator (>=2.0.0)"]
+timezone = ["tzdata"]
 
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+    {file = "pydantic_core-2.23.3.tar.gz", hash = "sha256:3cb0f65d8b4121c1b015c60104a685feb929a29d7cf204387c7f2688c7974690"},
 ]
 
 [package.dependencies]
@@ -4184,13 +4153,13 @@ files = [
 
 [[package]]
 name = "unstructured"
-version = "0.15.9"
+version = "0.15.10"
 description = "A library that prepares raw documents for downstream ML tasks."
 optional = false
 python-versions = "<3.13,>=3.9.0"
 files = [
-    {file = "unstructured-0.15.9-py3-none-any.whl", hash = "sha256:ddbb043461cfb9efa1d48a18e62e3b43ff4e0cec25fbf0f28bf345589c1af4d2"},
-    {file = "unstructured-0.15.9.tar.gz", hash = "sha256:de26d0e38bac4aa3ae2950f175d0c53a5ccae5c45806b67f55a4af8dea4c407a"},
+    {file = "unstructured-0.15.10-py3-none-any.whl", hash = "sha256:7bc9a300f6ec7c9f0cc61305c0765f17dd8a6fff77460fcb0ada94a0a0358406"},
+    {file = "unstructured-0.15.10.tar.gz", hash = "sha256:d77abe8bd12ae645b23ae04c5e6089acdd7fa5bc4005da1d0f2f88809cca5008"},
 ]
 
 [package.dependencies]
@@ -4248,7 +4217,7 @@ clarifai = ["clarifai"]
 confluence = ["atlassian-python-api"]
 csv = ["pandas"]
 databricks-volumes = ["databricks-sdk"]
-delta-table = ["deltalake", "fsspec"]
+delta-table = ["deltalake (<=0.19.1)", "fsspec"]
 discord = ["discord-py"]
 doc = ["python-docx (>=1.1.2)"]
 docx = ["python-docx (>=1.1.2)"]
@@ -4302,13 +4271,13 @@ xlsx = ["networkx", "openpyxl", "pandas", "xlrd"]
 
 [[package]]
 name = "unstructured-client"
-version = "0.25.7"
+version = "0.25.8"
 description = "Python Client SDK for Unstructured API"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "unstructured-client-0.25.7.tar.gz", hash = "sha256:6dffe9439469b37e0f38efe9d85551cfec93e42c10d3b42256b0a6d8fb2c68ca"},
-    {file = "unstructured_client-0.25.7-py3-none-any.whl", hash = "sha256:eedd90a562fe59bcd39f1cc917d6b25130bcdb2841299f7d70c6a00a1f6bc5bc"},
+    {file = "unstructured-client-0.25.8.tar.gz", hash = "sha256:f2023c6110c1fcf84d0086fb0ba2b5a60e93a4d8d6518dd5213bcfe60449493c"},
+    {file = "unstructured_client-0.25.8-py3-none-any.whl", hash = "sha256:79b23bce01842ea22d4efd832b015877bac39597f90f1cc8d393d4dc0f811279"},
 ]
 
 [package.dependencies]
diff --git a/libs/standard-tests/poetry.lock b/libs/standard-tests/poetry.lock
index 63b5814d85c..9980511fa68 100644
--- a/libs/standard-tests/poetry.lock
+++ b/libs/standard-tests/poetry.lock
@@ -1,4 +1,4 @@
-# This file is automatically @generated by Poetry 1.8.3 and should not be changed by hand.
+# This file is automatically @generated by Poetry 1.8.2 and should not be changed by hand.
 
 [[package]]
 name = "annotated-types"
@@ -291,7 +291,7 @@ files = [
 
 [[package]]
 name = "langchain-core"
-version = "0.3.0.dev2"
+version = "0.3.0.dev4"
 description = "Building applications with LLMs through composability"
 optional = false
 python-versions = ">=3.9,<4.0"
@@ -300,7 +300,7 @@ develop = true
 
 [package.dependencies]
 jsonpatch = "^1.33"
-langsmith = "^0.1.75"
+langsmith = "^0.1.112"
 packaging = ">=23.2,<25"
 pydantic = "^2.7.4"
 PyYAML = ">=5.3"
@@ -313,21 +313,21 @@ url = "../core"
 
 [[package]]
 name = "langsmith"
-version = "0.1.115"
+version = "0.1.117"
 description = "Client library to connect to the LangSmith LLM Tracing and Evaluation Platform."
 optional = false
 python-versions = "<4.0,>=3.8.1"
 files = [
-    {file = "langsmith-0.1.115-py3-none-any.whl", hash = "sha256:04e35cfd4c2d4ff1ea10bb577ff43957b05ebb3d9eb4e06e200701f4a2b4ac9f"},
-    {file = "langsmith-0.1.115.tar.gz", hash = "sha256:3b775377d858d32354f3ee0dd1ed637068cfe9a1f13e7b3bfa82db1615cdffc9"},
+    {file = "langsmith-0.1.117-py3-none-any.whl", hash = "sha256:e936ee9bcf8293b0496df7ba462a3702179fbe51f9dc28744b0fbec0dbf206ae"},
+    {file = "langsmith-0.1.117.tar.gz", hash = "sha256:a1b532f49968b9339bcaff9118d141846d52ed3d803f342902e7448edf1d662b"},
 ]
 
 [package.dependencies]
 httpx = ">=0.23.0,<1"
 orjson = ">=3.9.14,<4.0.0"
 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\""},
+    {version = ">=1,<3", markers = "python_full_version < \"3.12.4\""},
 ]
 requests = ">=2,<3"
 
@@ -486,123 +486,123 @@ testing = ["pytest", "pytest-benchmark"]
 
 [[package]]
 name = "pydantic"
-version = "2.9.0"
+version = "2.9.1"
 description = "Data validation using Python type hints"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "pydantic-2.9.0-py3-none-any.whl", hash = "sha256:f66a7073abd93214a20c5f7b32d56843137a7a2e70d02111f3be287035c45370"},
-    {file = "pydantic-2.9.0.tar.gz", hash = "sha256:c7a8a9fdf7d100afa49647eae340e2d23efa382466a8d177efcd1381e9be5598"},
+    {file = "pydantic-2.9.1-py3-none-any.whl", hash = "sha256:7aff4db5fdf3cf573d4b3c30926a510a10e19a0774d38fc4967f78beb6deb612"},
+    {file = "pydantic-2.9.1.tar.gz", hash = "sha256:1363c7d975c7036df0db2b4a61f2e062fbc0aa5ab5f2772e0ffc7191a4f4bce2"},
 ]
 
 [package.dependencies]
-annotated-types = ">=0.4.0"
-pydantic-core = "2.23.2"
+annotated-types = ">=0.6.0"
+pydantic-core = "2.23.3"
 typing-extensions = [
-    {version = ">=4.6.1", markers = "python_version < \"3.13\""},
     {version = ">=4.12.2", markers = "python_version >= \"3.13\""},
+    {version = ">=4.6.1", markers = "python_version < \"3.13\""},
 ]
-tzdata = {version = "*", markers = "python_version >= \"3.9\""}
 
 [package.extras]
 email = ["email-validator (>=2.0.0)"]
+timezone = ["tzdata"]
 
 [[package]]
 name = "pydantic-core"
-version = "2.23.2"
+version = "2.23.3"
 description = "Core functionality for Pydantic validation and serialization"
 optional = false
 python-versions = ">=3.8"
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 [package.dependencies]
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 [[package]]
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 python-versions = ">=3.8"
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 ]
 
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-description = "Provider of IANA time zone data"
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-python-versions = ">=2"
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-
 [[package]]
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 version = "2.2.2"