diff --git a/docs/docs/integrations/chat/ollama.ipynb b/docs/docs/integrations/chat/ollama.ipynb
index d8e3b6ca4aa..5a0b3850ebf 100644
--- a/docs/docs/integrations/chat/ollama.ipynb
+++ b/docs/docs/integrations/chat/ollama.ipynb
@@ -2,6 +2,7 @@
  "cells": [
   {
    "cell_type": "raw",
+   "id": "afaf8039",
    "metadata": {},
    "source": [
     "---\n",
@@ -11,6 +12,7 @@
   },
   {
    "cell_type": "markdown",
+   "id": "e49f1e0d",
    "metadata": {},
    "source": [
     "# ChatOllama\n",
@@ -23,6 +25,18 @@
     "\n",
     "For a complete list of supported models and model variants, see the [Ollama model library](https://github.com/jmorganca/ollama#model-library).\n",
     "\n",
+    "## Overview\n",
+    "### Integration details\n",
+    "\n",
+    "| Class | Package | Local | Serializable | [JS support](https://js.langchain.com/v0.2/docs/integrations/chat/ollama) | Package downloads | Package latest |\n",
+    "| :--- | :--- | :---: | :---: |  :---: | :---: | :---: |\n",
+    "| [ChatOllama](https://api.python.langchain.com/en/latest/chat_models/langchain_ollama.chat_models.ChatOllama.html) | [langchain-ollama](https://api.python.langchain.com/en/latest/ollama_api_reference.html) | ✅ | ❌ | ✅ | ![PyPI - Downloads](https://img.shields.io/pypi/dm/langchain-ollama?style=flat-square&label=%20) | ![PyPI - Version](https://img.shields.io/pypi/v/langchain-ollama?style=flat-square&label=%20) |\n",
+    "\n",
+    "### Model features\n",
+    "| [Tool calling](/docs/how_to/tool_calling/) | [Structured output](/docs/how_to/structured_output/) | JSON mode | [Image input](/docs/how_to/multimodal_inputs/) | Audio input | Video input | [Token-level streaming](/docs/how_to/chat_streaming/) | Native async | [Token usage](/docs/how_to/chat_token_usage_tracking/) | [Logprobs](/docs/how_to/logprobs/) |\n",
+    "| :---: | :---: | :---: | :---: |  :---: | :---: | :---: | :---: | :---: | :---: |\n",
+    "| ❌ | ❌ | ✅ | ❌ | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | \n",
+    "\n",
     "## Setup\n",
     "\n",
     "First, follow [these instructions](https://github.com/jmorganca/ollama) to set up and run a local Ollama instance:\n",
@@ -40,307 +54,202 @@
     "* Specify the exact version of the model of interest as such `ollama pull vicuna:13b-v1.5-16k-q4_0` (View the [various tags for the `Vicuna`](https://ollama.ai/library/vicuna/tags) model in this instance)\n",
     "* To view all pulled models, use `ollama list`\n",
     "* To chat directly with a model from the command line, use `ollama run <name-of-model>`\n",
-    "* View the [Ollama documentation](https://github.com/jmorganca/ollama) for more commands. Run `ollama help` in the terminal to see available commands too.\n",
-    "\n",
-    "## Usage\n",
-    "\n",
-    "You can see a full list of supported parameters on the [API reference page](https://api.python.langchain.com/en/latest/llms/langchain.llms.ollama.Ollama.html).\n",
-    "\n",
-    "If you are using a LLaMA `chat` model (e.g., `ollama pull llama3`) then you can use the `ChatOllama` interface.\n",
-    "\n",
-    "This includes [special tokens](https://huggingface.co/blog/llama2#how-to-prompt-llama-2) for system message and user input.\n",
-    "\n",
-    "## Interacting with Models \n",
-    "\n",
-    "Here are a few ways to interact with pulled local models\n",
-    "\n",
-    "#### In the terminal:\n",
-    "\n",
-    "* All of your local models are automatically served on `localhost:11434`\n",
-    "* Run `ollama run <name-of-model>` to start interacting via the command line directly\n",
-    "\n",
-    "#### Via an API\n",
-    "\n",
-    "Send an `application/json` request to the API endpoint of Ollama to interact.\n",
-    "\n",
-    "```bash\n",
-    "curl http://localhost:11434/api/generate -d '{\n",
-    "  \"model\": \"llama3\",\n",
-    "  \"prompt\":\"Why is the sky blue?\"\n",
-    "}'\n",
-    "```\n",
-    "\n",
-    "See the Ollama [API documentation](https://github.com/jmorganca/ollama/blob/main/docs/api.md) for all endpoints.\n",
-    "\n",
-    "#### Via LangChain\n",
-    "\n",
-    "See a typical basic example of using Ollama via the `ChatOllama` chat model in your LangChain application. \n",
-    "\n",
-    "View the [API Reference for ChatOllama](https://api.python.langchain.com/en/latest/chat_models/langchain_community.chat_models.ollama.ChatOllama.html#langchain_community.chat_models.ollama.ChatOllama) for more."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Why did the astronaut break up with his girlfriend?\n",
-      "\n",
-      "Because he needed space!\n"
-     ]
-    }
-   ],
-   "source": [
-    "# LangChain supports many other chat models. Here, we're using Ollama\n",
-    "from langchain_community.chat_models import ChatOllama\n",
-    "from langchain_core.output_parsers import StrOutputParser\n",
-    "from langchain_core.prompts import ChatPromptTemplate\n",
-    "\n",
-    "# supports many more optional parameters. Hover on your `ChatOllama(...)`\n",
-    "# class to view the latest available supported parameters\n",
-    "llm = ChatOllama(model=\"llama3\")\n",
-    "prompt = ChatPromptTemplate.from_template(\"Tell me a short joke about {topic}\")\n",
-    "\n",
-    "# using LangChain Expressive Language chain syntax\n",
-    "# learn more about the LCEL on\n",
-    "# /docs/concepts/#langchain-expression-language-lcel\n",
-    "chain = prompt | llm | StrOutputParser()\n",
-    "\n",
-    "# for brevity, response is printed in terminal\n",
-    "# You can use LangServe to deploy your application for\n",
-    "# production\n",
-    "print(chain.invoke({\"topic\": \"Space travel\"}))"
+    "* View the [Ollama documentation](https://github.com/jmorganca/ollama) for more commands. Run `ollama help` in the terminal to see available commands too.\n"
    ]
   },
   {
    "cell_type": "markdown",
+   "id": "72ee0c4b-9764-423a-9dbf-95129e185210",
    "metadata": {},
    "source": [
-    "LCEL chains, out of the box, provide extra functionalities, such as streaming of responses, and async support"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Why\n",
-      " did\n",
-      " the\n",
-      " astronaut\n",
-      " break\n",
-      " up\n",
-      " with\n",
-      " his\n",
-      " girlfriend\n",
-      " before\n",
-      " going\n",
-      " to\n",
-      " Mars\n",
-      "?\n",
-      "\n",
-      "\n",
-      "Because\n",
-      " he\n",
-      " needed\n",
-      " space\n",
-      "!\n",
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "topic = {\"topic\": \"Space travel\"}\n",
-    "\n",
-    "for chunks in chain.stream(topic):\n",
-    "    print(chunks)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "For streaming async support, here's an example - all possible via the single chain created above."
+    "If you want to get automated tracing of your model calls you can also set your [LangSmith](https://docs.smith.langchain.com/) API key by uncommenting below:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
+   "id": "a15d341e-3e26-4ca3-830b-5aab30ed66de",
    "metadata": {},
    "outputs": [],
    "source": [
-    "topic = {\"topic\": \"Space travel\"}\n",
-    "\n",
-    "async for chunks in chain.astream(topic):\n",
-    "    print(chunks)"
+    "# os.environ[\"LANGSMITH_API_KEY\"] = getpass.getpass(\"Enter your LangSmith API key: \")\n",
+    "# os.environ[\"LANGSMITH_TRACING\"] = \"true\""
    ]
   },
   {
    "cell_type": "markdown",
+   "id": "0730d6a1-c893-4840-9817-5e5251676d5d",
    "metadata": {},
    "source": [
-    "Take a look at the [LangChain Expressive Language (LCEL) Interface](/docs/concepts#interface) for the other available interfaces for use when a chain is created.\n",
+    "### Installation\n",
     "\n",
-    "## Building from source\n",
-    "\n",
-    "For up to date instructions on building from source, check the Ollama documentation on [Building from Source](https://github.com/ollama/ollama?tab=readme-ov-file#building)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Extraction\n",
-    " \n",
-    "Use the latest version of Ollama and supply the [`format`](https://github.com/jmorganca/ollama/blob/main/docs/api.md#json-mode) flag. The `format` flag will force the model to produce the response in JSON.\n",
-    "\n",
-    "> **Note:** You can also try out the experimental [OllamaFunctions](/docs/integrations/chat/ollama_functions) wrapper for convenience."
+    "The LangChain Ollama integration lives in the `langchain-ollama` package:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": null,
+   "id": "652d6238-1f87-422a-b135-f5abbb8652fc",
    "metadata": {},
    "outputs": [],
    "source": [
-    "from langchain_community.chat_models import ChatOllama\n",
+    "%pip install -qU langchain-ollama"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a38cde65-254d-4219-a441-068766c0d4b5",
+   "metadata": {},
+   "source": [
+    "## Instantiation\n",
     "\n",
-    "llm = ChatOllama(model=\"llama3\", format=\"json\", temperature=0)"
+    "Now we can instantiate our model object and generate chat completions:\n",
+    "\n",
+    "- TODO: Update model instantiation with relevant params."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "cb09c344-1836-4e0c-acf8-11d13ac1dbae",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain_ollama import ChatOllama\n",
+    "\n",
+    "llm = ChatOllama(\n",
+    "    model=\"llama3\",\n",
+    "    temperature=0,\n",
+    "    # other params...\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2b4f3e15",
+   "metadata": {},
+   "source": [
+    "## Invocation"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 7,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "content='{ \"morning\": \"blue\", \"noon\": \"clear blue\", \"afternoon\": \"hazy yellow\", \"evening\": \"orange-red\" }\\n\\n  \\n\\n\\n\\n\\n\\n  \\n\\n\\n\\n\\n\\n  \\n\\n\\n\\n\\n\\n  \\n\\n\\n\\n\\n\\n  \\n\\n\\n\\n\\n\\n  \\n\\n\\n\\n\\n\\n  \\n\\n\\n\\n\\n\\n  \\n\\n\\n\\n\\n\\n  \\n\\n\\n\\n\\n\\n  \\n\\n\\n\\n\\n\\n ' id='run-e893700f-e2d0-4df8-ad86-17525dcee318-0'\n"
-     ]
-    }
-   ],
-   "source": [
-    "from langchain_core.messages import HumanMessage\n",
-    "\n",
-    "messages = [\n",
-    "    HumanMessage(\n",
-    "        content=\"What color is the sky at different times of the day? Respond using JSON\"\n",
-    "    )\n",
-    "]\n",
-    "\n",
-    "chat_model_response = llm.invoke(messages)\n",
-    "print(chat_model_response)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "Name: John\n",
-      "Age: 35\n",
-      "Likes: Pizza\n"
-     ]
-    }
-   ],
-   "source": [
-    "import json\n",
-    "\n",
-    "from langchain_community.chat_models import ChatOllama\n",
-    "from langchain_core.messages import HumanMessage\n",
-    "from langchain_core.output_parsers import StrOutputParser\n",
-    "from langchain_core.prompts import ChatPromptTemplate\n",
-    "\n",
-    "json_schema = {\n",
-    "    \"title\": \"Person\",\n",
-    "    \"description\": \"Identifying information about a person.\",\n",
-    "    \"type\": \"object\",\n",
-    "    \"properties\": {\n",
-    "        \"name\": {\"title\": \"Name\", \"description\": \"The person's name\", \"type\": \"string\"},\n",
-    "        \"age\": {\"title\": \"Age\", \"description\": \"The person's age\", \"type\": \"integer\"},\n",
-    "        \"fav_food\": {\n",
-    "            \"title\": \"Fav Food\",\n",
-    "            \"description\": \"The person's favorite food\",\n",
-    "            \"type\": \"string\",\n",
-    "        },\n",
-    "    },\n",
-    "    \"required\": [\"name\", \"age\"],\n",
-    "}\n",
-    "\n",
-    "llm = ChatOllama(model=\"llama2\")\n",
-    "\n",
-    "messages = [\n",
-    "    HumanMessage(\n",
-    "        content=\"Please tell me about a person using the following JSON schema:\"\n",
-    "    ),\n",
-    "    HumanMessage(content=\"{dumps}\"),\n",
-    "    HumanMessage(\n",
-    "        content=\"Now, considering the schema, tell me about a person named John who is 35 years old and loves pizza.\"\n",
-    "    ),\n",
-    "]\n",
-    "\n",
-    "prompt = ChatPromptTemplate.from_messages(messages)\n",
-    "dumps = json.dumps(json_schema, indent=2)\n",
-    "\n",
-    "chain = prompt | llm | StrOutputParser()\n",
-    "\n",
-    "print(chain.invoke({\"dumps\": dumps}))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Multi-modal\n",
-    "\n",
-    "Ollama has support for multi-modal LLMs, such as [bakllava](https://ollama.ai/library/bakllava) and [llava](https://ollama.ai/library/llava).\n",
-    "\n",
-    "Browse the full set of versions for models with `tags`, such as [Llava](https://ollama.ai/library/llava/tags).\n",
-    "\n",
-    "Download the desired LLM via `ollama pull bakllava`\n",
-    "\n",
-    "Be sure to update Ollama so that you have the most recent version to support multi-modal.\n",
-    "\n",
-    "Check out the typical example of how to use ChatOllama multi-modal support below:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
+   "id": "62e0dbc3",
    "metadata": {
-    "scrolled": true
+    "tags": []
    },
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Note: you may need to restart the kernel to use updated packages.\n"
+      "AIMessage(content='Je adore le programmation.\\n\\n(Note: \"programmation\" is the feminine form of the noun in French, but if you want to use the masculine form, it would be \"le programme\" instead.)' response_metadata={'model': 'llama3', 'created_at': '2024-07-04T04:20:28.138164Z', 'message': {'role': 'assistant', 'content': ''}, 'done_reason': 'stop', 'done': True, 'total_duration': 1943337750, 'load_duration': 1128875, 'prompt_eval_count': 33, 'prompt_eval_duration': 322813000, 'eval_count': 43, 'eval_duration': 1618213000} id='run-ed8c17ab-7fc2-4c90-a88a-f6273b49bc78-0')\n"
      ]
     }
    ],
    "source": [
-    "!pip install --upgrade --quiet  pillow"
+    "from langchain_core.messages import AIMessage\n",
+    "\n",
+    "messages = [\n",
+    "    (\n",
+    "        \"system\",\n",
+    "        \"You are a helpful assistant that translates English to French. Translate the user sentence.\",\n",
+    "    ),\n",
+    "    (\"human\", \"I love programming.\"),\n",
+    "]\n",
+    "ai_msg = llm.invoke(messages)\n",
+    "ai_msg"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 8,
+   "id": "d86145b3-bfef-46e8-b227-4dda5c9c2705",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Je adore le programmation.\n",
+      "\n",
+      "(Note: \"programmation\" is the feminine form of the noun in French, but if you want to use the masculine form, it would be \"le programme\" instead.)\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(ai_msg.content)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "18e2bfc0-7e78-4528-a73f-499ac150dca8",
+   "metadata": {},
+   "source": [
+    "## Chaining\n",
+    "\n",
+    "We can [chain](/docs/how_to/sequence/) our model with a prompt template like so:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "e197d1d7-a070-4c96-9f8a-a0e86d046e0b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "AIMessage(content='Ich liebe Programmieren!\\n\\n(Note: \"Ich liebe\" means \"I love\", \"Programmieren\" is the verb for \"programming\")', response_metadata={'model': 'llama3', 'created_at': '2024-07-04T04:22:33.864132Z', 'message': {'role': 'assistant', 'content': ''}, 'done_reason': 'stop', 'done': True, 'total_duration': 1310800083, 'load_duration': 1782000, 'prompt_eval_count': 16, 'prompt_eval_duration': 250199000, 'eval_count': 29, 'eval_duration': 1057192000}, id='run-cbadbe59-2de2-4ec0-a18a-b3220226c3d2-0')"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from langchain_core.prompts import ChatPromptTemplate\n",
+    "\n",
+    "prompt = ChatPromptTemplate.from_messages(\n",
+    "    [\n",
+    "        (\n",
+    "            \"system\",\n",
+    "            \"You are a helpful assistant that translates {input_language} to {output_language}.\",\n",
+    "        ),\n",
+    "        (\"human\", \"{input}\"),\n",
+    "    ]\n",
+    ")\n",
+    "\n",
+    "chain = prompt | llm\n",
+    "chain.invoke(\n",
+    "    {\n",
+    "        \"input_language\": \"English\",\n",
+    "        \"output_language\": \"German\",\n",
+    "        \"input\": \"I love programming.\",\n",
+    "    }\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4c5e0197",
+   "metadata": {},
+   "source": [
+    "## Multi-modal\n",
+    "\n",
+    "Ollama has support for multi-modal LLMs, such as [bakllava](https://ollama.com/library/bakllava) and [llava](https://ollama.com/library/llava).\n",
+    "\n",
+    "    ollama pull bakllava\n",
+    "\n",
+    "Be sure to update Ollama so that you have the most recent version to support multi-modal."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "36c9b1c2",
    "metadata": {},
    "outputs": [
     {
@@ -399,7 +308,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 12,
+   "id": "32b3ba7b",
    "metadata": {},
    "outputs": [
     {
@@ -411,8 +321,8 @@
     }
    ],
    "source": [
-    "from langchain_community.chat_models import ChatOllama\n",
     "from langchain_core.messages import HumanMessage\n",
+    "from langchain_ollama import ChatOllama\n",
     "\n",
     "llm = ChatOllama(model=\"bakllava\", temperature=0)\n",
     "\n",
@@ -449,20 +359,12 @@
   },
   {
    "cell_type": "markdown",
+   "id": "3a5bb5ca-c3ae-4a58-be67-2cd18574b9a3",
    "metadata": {},
    "source": [
-    "## Concurrency Features\n",
+    "## API reference\n",
     "\n",
-    "Ollama supports concurrency inference for a single model, and or loading multiple models simulatenously (at least [version 0.1.33](https://github.com/ollama/ollama/releases)).\n",
-    "\n",
-    "Start the Ollama server with:\n",
-    "\n",
-    "* `OLLAMA_NUM_PARALLEL`: Handle multiple requests simultaneously for a single model\n",
-    "* `OLLAMA_MAX_LOADED_MODELS`: Load multiple models simultaneously\n",
-    "\n",
-    "Example: `OLLAMA_NUM_PARALLEL=4 OLLAMA_MAX_LOADED_MODELS=4 ollama serve`\n",
-    "\n",
-    "Learn more about configuring Ollama server in [the official guide](https://github.com/ollama/ollama/blob/main/docs/faq.md#how-do-i-configure-ollama-server)."
+    "For detailed documentation of all ChatOllama features and configurations head to the API reference: https://api.python.langchain.com/en/latest/chat_models/langchain_ollama.chat_models.ChatOllama.html"
    ]
   }
  ],
@@ -482,9 +384,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.8"
+   "version": "3.12.3"
   }
  },
  "nbformat": 4,
- "nbformat_minor": 4
+ "nbformat_minor": 5
 }
diff --git a/docs/docs/integrations/llms/ollama.ipynb b/docs/docs/integrations/llms/ollama.ipynb
index 68a0845376b..1ab33f6c018 100644
--- a/docs/docs/integrations/llms/ollama.ipynb
+++ b/docs/docs/integrations/llms/ollama.ipynb
@@ -1,10 +1,21 @@
 {
  "cells": [
   {
-   "cell_type": "markdown",
+   "cell_type": "raw",
+   "id": "67db2992",
    "metadata": {},
    "source": [
-    "# Ollama\n",
+    "---\n",
+    "sidebar_label: Ollama\n",
+    "---"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9597802c",
+   "metadata": {},
+   "source": [
+    "# OllamaLLM\n",
     "\n",
     ":::caution\n",
     "You are currently on a page documenting the use of Ollama models as [text completion models](/docs/concepts/#llms). Many popular Ollama models are [chat completion models](/docs/concepts/#chat-models).\n",
@@ -12,21 +23,35 @@
     "You may be looking for [this page instead](/docs/integrations/chat/ollama/).\n",
     ":::\n",
     "\n",
-    "[Ollama](https://ollama.ai/) allows you to run open-source large language models, such as Llama 2, locally.\n",
-    "\n",
-    "Ollama bundles model weights, configuration, and data into a single package, defined by a Modelfile. \n",
-    "\n",
-    "It optimizes setup and configuration details, including GPU usage.\n",
-    "\n",
-    "For a complete list of supported models and model variants, see the [Ollama model library](https://github.com/ollama/ollama#model-library).\n",
+    "This page goes over how to use LangChain to interact with `Ollama` models.\n",
     "\n",
+    "## Installation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "59c710c4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# install package\n",
+    "%pip install -U langchain-ollama"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0ee90032",
+   "metadata": {},
+   "source": [
     "## Setup\n",
     "\n",
-    "First, follow [these instructions](https://github.com/ollama/ollama) to set up and run a local Ollama instance:\n",
+    "First, follow [these instructions](https://github.com/jmorganca/ollama) to set up and run a local Ollama instance:\n",
     "\n",
     "* [Download](https://ollama.ai/download) and install Ollama onto the available supported platforms (including Windows Subsystem for Linux)\n",
     "* Fetch available LLM model via `ollama pull <name-of-model>`\n",
-    "    * View a list of available models via the [model library](https://ollama.ai/library) and pull to use locally with the command `ollama pull llama3`\n",
+    "    * View a list of available models via the [model library](https://ollama.ai/library)\n",
+    "    * e.g., `ollama pull llama3`\n",
     "* This will download the default tagged version of the model. Typically, the default points to the latest, smallest sized-parameter model.\n",
     "\n",
     "> On Mac, the models will be download to `~/.ollama/models`\n",
@@ -34,194 +59,67 @@
     "> On Linux (or WSL), the models will be stored at `/usr/share/ollama/.ollama/models`\n",
     "\n",
     "* Specify the exact version of the model of interest as such `ollama pull vicuna:13b-v1.5-16k-q4_0` (View the [various tags for the `Vicuna`](https://ollama.ai/library/vicuna/tags) model in this instance)\n",
-    "* To view all pulled models on your local instance, use `ollama list`\n",
+    "* To view all pulled models, use `ollama list`\n",
     "* To chat directly with a model from the command line, use `ollama run <name-of-model>`\n",
-    "* View the [Ollama documentation](https://github.com/ollama/ollama) for more commands. \n",
-    "* Run `ollama help` in the terminal to see available commands too.\n",
+    "* View the [Ollama documentation](https://github.com/jmorganca/ollama) for more commands. Run `ollama help` in the terminal to see available commands too.\n",
     "\n",
-    "## Usage\n",
-    "\n",
-    "You can see a full list of supported parameters on the [API reference page](https://api.python.langchain.com/en/latest/llms/langchain_community.llms.ollama.Ollama.html).\n",
-    "\n",
-    "If you are using a LLaMA `chat` model (e.g., `ollama pull llama3`) then you can use the `ChatOllama` [interface](https://python.langchain.com/v0.2/docs/integrations/chat/ollama/).\n",
-    "\n",
-    "This includes [special tokens](https://ollama.com/library/llama3) for system message and user input.\n",
-    "\n",
-    "## Interacting with Models \n",
-    "\n",
-    "Here are a few ways to interact with pulled local models\n",
-    "\n",
-    "#### In the terminal:\n",
-    "\n",
-    "* All of your local models are automatically served on `localhost:11434`\n",
-    "* Run `ollama run <name-of-model>` to start interacting via the command line directly\n",
-    "\n",
-    "#### Via the API\n",
-    "\n",
-    "Send an `application/json` request to the API endpoint of Ollama to interact.\n",
-    "\n",
-    "```bash\n",
-    "curl http://localhost:11434/api/generate -d '{\n",
-    "  \"model\": \"llama3\",\n",
-    "  \"prompt\":\"Why is the sky blue?\"\n",
-    "}'\n",
-    "```\n",
-    "\n",
-    "See the Ollama [API documentation](https://github.com/ollama/ollama/blob/main/docs/api.md) for all endpoints.\n",
-    "\n",
-    "#### via LangChain\n",
-    "\n",
-    "See a typical basic example of using [Ollama chat model](https://python.langchain.com/v0.2/docs/integrations/chat/ollama/) in your LangChain application."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install langchain-community"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "\"Here's one:\\n\\nWhy don't scientists trust atoms?\\n\\nBecause they make up everything!\\n\\nHope that made you smile! Do you want to hear another one?\""
-      ]
-     },
-     "execution_count": 1,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from langchain_community.llms import Ollama\n",
-    "\n",
-    "llm = Ollama(\n",
-    "    model=\"llama3\"\n",
-    ")  # assuming you have Ollama installed and have llama3 model pulled with `ollama pull llama3 `\n",
-    "\n",
-    "llm.invoke(\"Tell me a joke\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "To stream tokens, use the `.stream(...)` method:"
+    "## Usage"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 4,
-   "metadata": {},
+   "id": "035dea0f",
+   "metadata": {
+    "tags": []
+   },
    "outputs": [
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "\n",
-      "S\n",
-      "ure\n",
-      ",\n",
-      " here\n",
-      "'\n",
-      "s\n",
-      " one\n",
-      ":\n",
-      "\n",
-      "\n",
-      "\n",
-      "\n",
-      "Why\n",
-      " don\n",
-      "'\n",
-      "t\n",
-      " scient\n",
-      "ists\n",
-      " trust\n",
-      " atoms\n",
-      "?\n",
-      "\n",
-      "\n",
-      "B\n",
-      "ecause\n",
-      " they\n",
-      " make\n",
-      " up\n",
-      " everything\n",
-      "!\n",
-      "\n",
-      "\n",
-      "\n",
-      "\n",
-      "I\n",
-      " hope\n",
-      " you\n",
-      " found\n",
-      " that\n",
-      " am\n",
-      "using\n",
-      "!\n",
-      " Do\n",
-      " you\n",
-      " want\n",
-      " to\n",
-      " hear\n",
-      " another\n",
-      " one\n",
-      "?\n",
-      "\n"
-     ]
+     "data": {
+      "text/plain": [
+       "'A great start!\\n\\nLangChain is a type of AI model that uses language processing techniques to generate human-like text based on input prompts or chains of reasoning. In other words, it can have a conversation with humans, understanding the context and responding accordingly.\\n\\nHere\\'s a possible breakdown:\\n\\n* \"Lang\" likely refers to its focus on natural language processing (NLP) and linguistic analysis.\\n* \"Chain\" suggests that LangChain is designed to generate text in response to a series of connected ideas or prompts, rather than simply generating random text.\\n\\nSo, what do you think LangChain\\'s capabilities might be?'"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
     }
    ],
    "source": [
-    "query = \"Tell me a joke\"\n",
+    "from langchain_core.prompts import ChatPromptTemplate\n",
+    "from langchain_ollama.llms import OllamaLLM\n",
     "\n",
-    "for chunks in llm.stream(query):\n",
-    "    print(chunks)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "To learn more about the LangChain Expressive Language and the available methods on an LLM, see the [LCEL Interface](/docs/concepts#interface)"
+    "template = \"\"\"Question: {question}\n",
+    "\n",
+    "Answer: Let's think step by step.\"\"\"\n",
+    "\n",
+    "prompt = ChatPromptTemplate.from_template(template)\n",
+    "\n",
+    "model = OllamaLLM(model=\"llama3\")\n",
+    "\n",
+    "chain = prompt | model\n",
+    "\n",
+    "chain.invoke({\"question\": \"What is LangChain?\"})"
    ]
   },
   {
    "cell_type": "markdown",
+   "id": "e2d85456",
    "metadata": {},
    "source": [
     "## Multi-modal\n",
     "\n",
-    "Ollama has support for multi-modal LLMs, such as [bakllava](https://ollama.ai/library/bakllava) and [llava](https://ollama.ai/library/llava).\n",
+    "Ollama has support for multi-modal LLMs, such as [bakllava](https://ollama.com/library/bakllava) and [llava](https://ollama.com/library/llava).\n",
     "\n",
-    "`ollama pull bakllava`\n",
+    "    ollama pull bakllava\n",
     "\n",
     "Be sure to update Ollama so that you have the most recent version to support multi-modal."
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from langchain_community.llms import Ollama\n",
-    "\n",
-    "bakllava = Ollama(model=\"bakllava\")"
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": 2,
+   "id": "4043e202",
    "metadata": {},
    "outputs": [
     {
@@ -279,7 +177,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 4,
+   "id": "79aaf863",
    "metadata": {},
    "outputs": [
     {
@@ -288,38 +187,24 @@
        "'90%'"
       ]
      },
-     "execution_count": 8,
+     "execution_count": 4,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "llm_with_image_context = bakllava.bind(images=[image_b64])\n",
+    "from langchain_ollama import OllamaLLM\n",
+    "\n",
+    "llm = OllamaLLM(model=\"bakllava\")\n",
+    "\n",
+    "llm_with_image_context = llm.bind(images=[image_b64])\n",
     "llm_with_image_context.invoke(\"What is the dollar based gross retention rate:\")"
    ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Concurrency Features\n",
-    "\n",
-    "Ollama supports concurrency inference for a single model, and or loading multiple models simulatenously (at least [version 0.1.33](https://github.com/ollama/ollama/releases)).\n",
-    "\n",
-    "Start the Ollama server with:\n",
-    "\n",
-    "* `OLLAMA_NUM_PARALLEL`: Handle multiple requests simultaneously for a single model\n",
-    "* `OLLAMA_MAX_LOADED_MODELS`: Load multiple models simultaneously\n",
-    "\n",
-    "Example: `OLLAMA_NUM_PARALLEL=4 OLLAMA_MAX_LOADED_MODELS=4 ollama serve`\n",
-    "\n",
-    "Learn more about configuring Ollama server in [the official guide](https://github.com/ollama/ollama/blob/main/docs/faq.md#how-do-i-configure-ollama-server)."
-   ]
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": "Python 3.11.1 64-bit",
    "language": "python",
    "name": "python3"
   },
@@ -333,9 +218,14 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.8"
+   "version": "3.12.3"
+  },
+  "vscode": {
+   "interpreter": {
+    "hash": "e971737741ff4ec9aff7dc6155a1060a59a8a6d52c757dbbe66bf8ee389494b1"
+   }
   }
  },
  "nbformat": 4,
- "nbformat_minor": 4
+ "nbformat_minor": 5
 }
diff --git a/docs/docs/integrations/text_embedding/ollama.ipynb b/docs/docs/integrations/text_embedding/ollama.ipynb
index c7af848287d..8937902c6f7 100644
--- a/docs/docs/integrations/text_embedding/ollama.ipynb
+++ b/docs/docs/integrations/text_embedding/ollama.ipynb
@@ -1,99 +1,3114 @@
 {
  "cells": [
   {
-   "cell_type": "markdown",
-   "id": "278b6c63",
+   "cell_type": "raw",
+   "id": "afaf8039",
    "metadata": {},
    "source": [
-    "# Ollama\n",
-    "\n",
-    "\"Ollama supports embedding models, making it possible to build retrieval augmented generation (RAG) applications that combine text prompts with existing documents or other data.\" Learn more about the introduction to [Ollama Embeddings](https://ollama.com/blog/embedding-models) in the blog post.\n",
-    "\n",
-    "To use Ollama Embeddings, first, install [LangChain Community](https://pypi.org/project/langchain-community/) package:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "854d6a2e",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install langchain-community"
+    "---\n",
+    "sidebar_label: Ollama\n",
+    "---"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "54fbb4cd",
+   "id": "e49f1e0d",
    "metadata": {},
    "source": [
-    "Load the Ollama Embeddings class:"
+    "# OllamaEmbeddings\n",
+    "\n",
+    "This notebook covers how to get started with Ollama embedding models.\n",
+    "\n",
+    "## Installation"
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "0be1af71",
-   "metadata": {},
-   "outputs": [],
+   "cell_type": "raw",
+   "id": "57f50aa5",
+   "metadata": {
+    "vscode": {
+     "languageId": "raw"
+    }
+   },
    "source": [
-    "from langchain_community.embeddings import OllamaEmbeddings\n",
-    "\n",
-    "embeddings = (\n",
-    "    OllamaEmbeddings()\n",
-    ")  # by default, uses llama2. Run `ollama pull llama2` to pull down the model\n",
-    "\n",
-    "text = \"This is a test document.\""
+    "# install package\n",
+    "%pip install langchain_ollama"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "a42e4035",
+   "id": "2b4f3e15",
    "metadata": {},
    "source": [
-    "To generate embeddings, you can either query an invidivual text, or you can query a list of texts."
+    "## Setup\n",
+    "\n",
+    "First, follow [these instructions](https://github.com/jmorganca/ollama) to set up and run a local Ollama instance:\n",
+    "\n",
+    "* [Download](https://ollama.ai/download) and install Ollama onto the available supported platforms (including Windows Subsystem for Linux)\n",
+    "* Fetch available LLM model via `ollama pull <name-of-model>`\n",
+    "    * View a list of available models via the [model library](https://ollama.ai/library)\n",
+    "    * e.g., `ollama pull llama3`\n",
+    "* This will download the default tagged version of the model. Typically, the default points to the latest, smallest sized-parameter model.\n",
+    "\n",
+    "> On Mac, the models will be download to `~/.ollama/models`\n",
+    "> \n",
+    "> On Linux (or WSL), the models will be stored at `/usr/share/ollama/.ollama/models`\n",
+    "\n",
+    "* Specify the exact version of the model of interest as such `ollama pull vicuna:13b-v1.5-16k-q4_0` (View the [various tags for the `Vicuna`](https://ollama.ai/library/vicuna/tags) model in this instance)\n",
+    "* To view all pulled models, use `ollama list`\n",
+    "* To chat directly with a model from the command line, use `ollama run <name-of-model>`\n",
+    "* View the [Ollama documentation](https://github.com/jmorganca/ollama) for more commands. Run `ollama help` in the terminal to see available commands too.\n",
+    "\n",
+    "\n",
+    "## Usage"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 4,
-   "id": "91bc875d-829b-4c3d-8e6f-fc2dda30a3bd",
+   "id": "62e0dbc3",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "from langchain_ollama import OllamaEmbeddings\n",
+    "\n",
+    "embeddings = OllamaEmbeddings(model=\"llama3\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "12fcfb4b",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "[-0.09996652603149414,\n",
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-       " 0.17670190334320068,\n",
-       " 0.16521021723747253,\n",
-       " 0.21193109452724457]"
+       "[1.1588108539581299,\n",
+       " -3.3943021297454834,\n",
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+       " 2.3018438816070557,\n",
+       " -0.20140409469604492,\n",
+       " -2.3490500450134277,\n",
+       " 1.472577691078186,\n",
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+       " 0.378415584564209,\n",
+       " ...]"
       ]
      },
-     "execution_count": 4,
+     "execution_count": 5,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "query_result = embeddings.embed_query(text)\n",
-    "query_result[:5]"
+    "embeddings.embed_query(\"My query to look up\")"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 6,
-   "id": "a4b0d49e-0c73-44b6-aed5-5b426564e085",
+   "id": "1f2e6104",
    "metadata": {},
    "outputs": [
     {
      "data": {
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-       " 0.2079043835401535]"
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+       "  ...]]"
       ]
      },
      "execution_count": 6,
@@ -102,94 +3117,3056 @@
     }
    ],
    "source": [
-    "doc_result = embeddings.embed_documents([text])\n",
-    "doc_result[0][:5]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "bb61bbeb",
-   "metadata": {},
-   "source": [
-    "### Embedding Models\n",
-    "\n",
-    "Ollama has embedding models, that are lightweight enough for use in embeddings, with the smallest about the size of 25Mb. See some of the available [embedding models from Ollama](https://ollama.com/blog/embedding-models).\n",
-    "\n",
-    "Let's load the Ollama Embeddings class with smaller model (e.g. `mxbai-embed-large`). \n",
-    "\n",
-    "> Note: See other supported models [https://ollama.ai/library](https://ollama.ai/library)"
+    "embeddings.embed_documents(\n",
+    "    [\"This is a content of the document\", \"This is another document\"]\n",
+    ")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
-   "id": "a56b70f5",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "embeddings = OllamaEmbeddings(model=\"mxbai-embed-large\")\n",
-    "text = \"This is a test document.\"\n",
-    "query_result = embeddings.embed_query(text)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "id": "2ee7ce9f-d506-4810-8897-e44334412714",
+   "execution_count": 7,
+   "id": "46739f68",
    "metadata": {},
    "outputs": [
     {
      "data": {
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+       " -1.1743320226669312,\n",
+       " -1.6903272867202759,\n",
+       " 1.4239429235458374,\n",
+       " 1.0980380773544312,\n",
+       " 5.632511615753174,\n",
+       " -1.5414550304412842,\n",
+       " 1.132023811340332,\n",
+       " -0.28032270073890686,\n",
+       " 0.020960716530680656,\n",
+       " -2.2207391262054443,\n",
+       " -0.07656984776258469,\n",
+       " 1.0084593296051025,\n",
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+       " -0.013524290174245834,\n",
+       " 0.378415584564209,\n",
+       " ...]"
       ]
      },
-     "execution_count": 17,
+     "execution_count": 7,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "query_result[:5]"
+    "# async embed query\n",
+    "await embeddings.aembed_query(\"My query to look up\")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
-   "id": "e3221db6",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "doc_result = embeddings.embed_documents([text])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "id": "a0865409-3a6d-468f-939f-abde17c7cac3",
+   "execution_count": 8,
+   "id": "e48632ea",
    "metadata": {},
    "outputs": [
     {
      "data": {
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-       " 0.20790338516235352]"
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+       "  -8.875129699707031,\n",
+       "  -0.887072741985321,\n",
+       "  -2.702098846435547,\n",
+       "  1.2441999912261963,\n",
+       "  -0.060320161283016205,\n",
+       "  3.2127902507781982,\n",
+       "  -4.787941932678223,\n",
+       "  -1.7735847234725952,\n",
+       "  2.3401880264282227,\n",
+       "  0.5994641780853271,\n",
+       "  -3.2655715942382812,\n",
+       "  0.7493075728416443,\n",
+       "  0.20031413435935974,\n",
+       "  0.269188791513443,\n",
+       "  -0.6489375233650208,\n",
+       "  -1.1827962398529053,\n",
+       "  0.6488084197044373,\n",
+       "  3.799541473388672,\n",
+       "  0.08789312839508057,\n",
+       "  0.4126721918582916,\n",
+       "  2.273970365524292,\n",
+       "  -1.4167795181274414,\n",
+       "  3.361281156539917,\n",
+       "  5.478500843048096,\n",
+       "  1.255025029182434,\n",
+       "  0.6729123592376709,\n",
+       "  -2.490095615386963,\n",
+       "  0.7573592066764832,\n",
+       "  1.5050171613693237,\n",
+       "  1.3889999389648438,\n",
+       "  6.226860046386719,\n",
+       "  2.4419143199920654,\n",
+       "  1.325605869293213,\n",
+       "  -2.8164639472961426,\n",
+       "  0.0338548943400383,\n",
+       "  -1.7200915813446045,\n",
+       "  0.08039979636669159,\n",
+       "  4.257935047149658,\n",
+       "  0.769836962223053,\n",
+       "  -0.56553715467453,\n",
+       "  0.8128640055656433,\n",
+       "  0.5728873014450073,\n",
+       "  3.774625301361084,\n",
+       "  -0.36815983057022095,\n",
+       "  0.5384926199913025,\n",
+       "  -1.7822424173355103,\n",
+       "  2.6060216426849365,\n",
+       "  4.28328275680542,\n",
+       "  0.27838122844696045,\n",
+       "  0.5124579668045044,\n",
+       "  -3.385490894317627,\n",
+       "  2.756040334701538,\n",
+       "  0.21497005224227905,\n",
+       "  -1.1206204891204834,\n",
+       "  -0.687329113483429,\n",
+       "  0.37097248435020447,\n",
+       "  1.7511025667190552,\n",
+       "  -3.067614793777466,\n",
+       "  -0.7502833008766174,\n",
+       "  -0.30752646923065186,\n",
+       "  0.6274518370628357,\n",
+       "  -1.3394132852554321,\n",
+       "  0.38683128356933594,\n",
+       "  2.7706401348114014,\n",
+       "  2.2502565383911133,\n",
+       "  -2.318115711212158,\n",
+       "  -0.014325552619993687,\n",
+       "  -2.630859136581421,\n",
+       "  1.4868338108062744,\n",
+       "  2.3034861087799072,\n",
+       "  -1.639756202697754,\n",
+       "  0.5782852172851562,\n",
+       "  1.0485166311264038,\n",
+       "  0.23609744012355804,\n",
+       "  -0.7751088738441467,\n",
+       "  1.0902869701385498,\n",
+       "  -4.068610191345215,\n",
+       "  -0.9249467849731445,\n",
+       "  1.5832273960113525,\n",
+       "  1.1743921041488647,\n",
+       "  -0.2737315595149994,\n",
+       "  -5.202696323394775,\n",
+       "  2.8652448654174805,\n",
+       "  0.3447275161743164,\n",
+       "  3.166260004043579,\n",
+       "  1.63128662109375,\n",
+       "  -2.5201687812805176,\n",
+       "  0.5847077965736389,\n",
+       "  -1.5145337581634521,\n",
+       "  -0.3376529812812805,\n",
+       "  ...]]"
       ]
      },
-     "execution_count": 19,
+     "execution_count": 8,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "doc_result[0][:5]"
+    "# async embed documents\n",
+    "await embeddings.aembed_documents(\n",
+    "    [\"This is a content of the document\", \"This is another document\"]\n",
+    ")"
    ]
   }
  ],
@@ -209,12 +6186,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.12"
-  },
-  "vscode": {
-   "interpreter": {
-    "hash": "e971737741ff4ec9aff7dc6155a1060a59a8a6d52c757dbbe66bf8ee389494b1"
-   }
+   "version": "3.12.3"
   }
  },
  "nbformat": 4,
diff --git a/libs/partners/ollama/.gitignore b/libs/partners/ollama/.gitignore
new file mode 100644
index 00000000000..bee8a64b79a
--- /dev/null
+++ b/libs/partners/ollama/.gitignore
@@ -0,0 +1 @@
+__pycache__
diff --git a/libs/partners/ollama/LICENSE b/libs/partners/ollama/LICENSE
new file mode 100644
index 00000000000..fc0602feecd
--- /dev/null
+++ b/libs/partners/ollama/LICENSE
@@ -0,0 +1,21 @@
+MIT License
+
+Copyright (c) 2024 LangChain, Inc.
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.
diff --git a/libs/partners/ollama/Makefile b/libs/partners/ollama/Makefile
new file mode 100644
index 00000000000..85e987175c5
--- /dev/null
+++ b/libs/partners/ollama/Makefile
@@ -0,0 +1,64 @@
+.PHONY: all format lint test tests integration_tests docker_tests help extended_tests
+
+# Default target executed when no arguments are given to make.
+all: help
+
+# Define a variable for the test file path.
+TEST_FILE ?= tests/unit_tests/
+integration_test: TEST_FILE = tests/integration_tests/
+# note: leaving out integration_tests (with s) command to skip release testing for now
+# TODO(erick) configure ollama server to run in CI, in separate repo
+
+
+# unit tests are run with the --disable-socket flag to prevent network calls
+test tests:
+	poetry run pytest --disable-socket --allow-unix-socket $(TEST_FILE)
+
+# integration tests are run without the --disable-socket flag to allow network calls
+integration_test integration_tests:
+	poetry run pytest $(TEST_FILE)
+
+######################
+# LINTING AND FORMATTING
+######################
+
+# Define a variable for Python and notebook files.
+PYTHON_FILES=.
+MYPY_CACHE=.mypy_cache
+lint format: PYTHON_FILES=.
+lint_diff format_diff: PYTHON_FILES=$(shell git diff --relative=libs/partners/ollama --name-only --diff-filter=d master | grep -E '\.py$$|\.ipynb$$')
+lint_package: PYTHON_FILES=langchain_ollama
+lint_tests: PYTHON_FILES=tests
+lint_tests: MYPY_CACHE=.mypy_cache_test
+
+lint lint_diff lint_package lint_tests:
+	poetry run ruff .
+	poetry run ruff format $(PYTHON_FILES) --diff
+	poetry run ruff --select I $(PYTHON_FILES)
+	mkdir -p $(MYPY_CACHE); poetry run mypy $(PYTHON_FILES) --cache-dir $(MYPY_CACHE)
+
+format format_diff:
+	poetry run ruff format $(PYTHON_FILES)
+	poetry run ruff --select I --fix $(PYTHON_FILES)
+
+spell_check:
+	poetry run codespell --toml pyproject.toml
+
+spell_fix:
+	poetry run codespell --toml pyproject.toml -w
+
+check_imports: $(shell find langchain_ollama -name '*.py')
+	poetry run python ./scripts/check_imports.py $^
+
+######################
+# HELP
+######################
+
+help:
+	@echo '----'
+	@echo 'check_imports				- check imports'
+	@echo 'format                       - run code formatters'
+	@echo 'lint                         - run linters'
+	@echo 'test                         - run unit tests'
+	@echo 'tests                        - run unit tests'
+	@echo 'test TEST_FILE=<test_file>   - run all tests in file'
diff --git a/libs/partners/ollama/README.md b/libs/partners/ollama/README.md
new file mode 100644
index 00000000000..640264f5a74
--- /dev/null
+++ b/libs/partners/ollama/README.md
@@ -0,0 +1,44 @@
+# langchain-ollama
+
+This package contains the LangChain integration with Ollama
+
+## Installation
+
+```bash
+pip install -U langchain-ollama
+```
+
+You will also need to run the Ollama server locally. 
+You can download it [here](https://ollama.com/download).
+
+## Chat Models
+
+`ChatOllama` class exposes chat models from Ollama.
+
+```python
+from langchain_ollama import ChatOllama
+
+llm = ChatOllama(model="llama3-groq-tool-use")
+llm.invoke("Sing a ballad of LangChain.")
+```
+
+## Embeddings
+
+`OllamaEmbeddings` class exposes embeddings from Ollama.
+
+```python
+from langchain_ollama import OllamaEmbeddings
+
+embeddings = OllamaEmbeddings(model="llama3")
+embeddings.embed_query("What is the meaning of life?")
+```
+
+## LLMs
+`OllamaLLM` class exposes LLMs from Ollama.
+
+```python
+from langchain_ollama import OllamaLLM
+
+llm = OllamaLLM(model="llama3")
+llm.invoke("The meaning of life is")
+```
diff --git a/libs/partners/ollama/langchain_ollama/__init__.py b/libs/partners/ollama/langchain_ollama/__init__.py
new file mode 100644
index 00000000000..4921503fa6e
--- /dev/null
+++ b/libs/partners/ollama/langchain_ollama/__init__.py
@@ -0,0 +1,19 @@
+from importlib import metadata
+
+from langchain_ollama.chat_models import ChatOllama
+from langchain_ollama.embeddings import OllamaEmbeddings
+from langchain_ollama.llms import OllamaLLM
+
+try:
+    __version__ = metadata.version(__package__)
+except metadata.PackageNotFoundError:
+    # Case where package metadata is not available.
+    __version__ = ""
+del metadata  # optional, avoids polluting the results of dir(__package__)
+
+__all__ = [
+    "ChatOllama",
+    "OllamaLLM",
+    "OllamaEmbeddings",
+    "__version__",
+]
diff --git a/libs/partners/ollama/langchain_ollama/chat_models.py b/libs/partners/ollama/langchain_ollama/chat_models.py
new file mode 100644
index 00000000000..339c2b2711a
--- /dev/null
+++ b/libs/partners/ollama/langchain_ollama/chat_models.py
@@ -0,0 +1,719 @@
+"""Ollama chat models."""
+
+from typing import (
+    Any,
+    AsyncIterator,
+    Callable,
+    Dict,
+    Iterator,
+    List,
+    Literal,
+    Mapping,
+    Optional,
+    Sequence,
+    Type,
+    Union,
+    cast,
+)
+from uuid import uuid4
+
+import ollama
+from langchain_core.callbacks import (
+    CallbackManagerForLLMRun,
+)
+from langchain_core.callbacks.manager import AsyncCallbackManagerForLLMRun
+from langchain_core.language_models import LanguageModelInput
+from langchain_core.language_models.chat_models import BaseChatModel, LangSmithParams
+from langchain_core.messages import (
+    AIMessage,
+    AIMessageChunk,
+    BaseMessage,
+    HumanMessage,
+    SystemMessage,
+    ToolCall,
+    ToolMessage,
+)
+from langchain_core.messages.ai import UsageMetadata
+from langchain_core.messages.tool import tool_call
+from langchain_core.outputs import ChatGeneration, ChatGenerationChunk, ChatResult
+from langchain_core.pydantic_v1 import BaseModel
+from langchain_core.runnables import Runnable
+from langchain_core.tools import BaseTool
+from langchain_core.utils.function_calling import convert_to_openai_tool
+from ollama import AsyncClient, Message, Options
+
+
+def _get_usage_metadata_from_generation_info(
+    generation_info: Optional[Mapping[str, Any]],
+) -> Optional[UsageMetadata]:
+    """Get usage metadata from ollama generation info mapping."""
+    if generation_info is None:
+        return None
+    input_tokens: Optional[int] = generation_info.get("prompt_eval_count")
+    output_tokens: Optional[int] = generation_info.get("eval_count")
+    if input_tokens is not None and output_tokens is not None:
+        return UsageMetadata(
+            input_tokens=input_tokens,
+            output_tokens=output_tokens,
+            total_tokens=input_tokens + output_tokens,
+        )
+    return None
+
+
+def _get_tool_calls_from_response(
+    response: Mapping[str, Any],
+) -> List[ToolCall]:
+    """Get tool calls from ollama response."""
+    tool_calls = []
+    if "message" in response:
+        if "tool_calls" in response["message"]:
+            for tc in response["message"]["tool_calls"]:
+                tool_calls.append(
+                    tool_call(
+                        id=str(uuid4()),
+                        name=tc["function"]["name"],
+                        args=tc["function"]["arguments"],
+                    )
+                )
+    return tool_calls
+
+
+def _lc_tool_call_to_openai_tool_call(tool_call: ToolCall) -> dict:
+    return {
+        "type": "function",
+        "id": tool_call["id"],
+        "function": {
+            "name": tool_call["name"],
+            "arguments": tool_call["args"],
+        },
+    }
+
+
+class ChatOllama(BaseChatModel):
+    """Ollama chat model integration.
+
+    Setup:
+        Install ``langchain-ollama`` and download any models you want to use from ollama.
+
+        .. code-block:: bash
+
+            ollama pull mistral:v0.3
+            pip install -U langchain-ollama
+
+    Key init args — completion params:
+        model: str
+            Name of Ollama model to use.
+        temperature: float
+            Sampling temperature. Ranges from 0.0 to 1.0.
+        num_predict: Optional[int]
+            Max number of tokens to generate.
+
+    See full list of supported init args and their descriptions in the params section.
+
+    Instantiate:
+        .. code-block:: python
+
+            from langchain_ollama import ChatOllama
+
+            llm = ChatOllama(
+                model = "llama3",
+                temperature = 0.8,
+                num_predict = 256,
+                # other params ...
+            )
+
+    Invoke:
+        .. code-block:: python
+
+            messages = [
+                ("system", "You are a helpful translator. Translate the user sentence to French."),
+                ("human", "I love programming."),
+            ]
+            llm.invoke(messages)
+
+        .. code-block:: python
+
+            AIMessage(content='J'adore le programmation. (Note: "programming" can also refer to the act of writing code, so if you meant that, I could translate it as "J'adore programmer". But since you didn\'t specify, I assumed you were talking about the activity itself, which is what "le programmation" usually refers to.)', response_metadata={'model': 'llama3', 'created_at': '2024-07-04T03:37:50.182604Z', 'message': {'role': 'assistant', 'content': ''}, 'done_reason': 'stop', 'done': True, 'total_duration': 3576619666, 'load_duration': 788524916, 'prompt_eval_count': 32, 'prompt_eval_duration': 128125000, 'eval_count': 71, 'eval_duration': 2656556000}, id='run-ba48f958-6402-41a5-b461-5e250a4ebd36-0')
+
+    Stream:
+        .. code-block:: python
+
+            messages = [
+                ("human", "Return the words Hello World!"),
+            ]
+            for chunk in llm.stream(messages):
+                print(chunk)
+
+
+        .. code-block:: python
+
+            content='Hello' id='run-327ff5ad-45c8-49fe-965c-0a93982e9be1'
+            content=' World' id='run-327ff5ad-45c8-49fe-965c-0a93982e9be1'
+            content='!' id='run-327ff5ad-45c8-49fe-965c-0a93982e9be1'
+            content='' response_metadata={'model': 'llama3', 'created_at': '2024-07-04T03:39:42.274449Z', 'message': {'role': 'assistant', 'content': ''}, 'done_reason': 'stop', 'done': True, 'total_duration': 411875125, 'load_duration': 1898166, 'prompt_eval_count': 14, 'prompt_eval_duration': 297320000, 'eval_count': 4, 'eval_duration': 111099000} id='run-327ff5ad-45c8-49fe-965c-0a93982e9be1'
+
+
+        .. code-block:: python
+
+            stream = llm.stream(messages)
+            full = next(stream)
+            for chunk in stream:
+                full += chunk
+            full
+
+        .. code-block:: python
+
+            AIMessageChunk(content='Je adore le programmation.(Note: "programmation" is the formal way to say "programming" in French, but informally, people might use the phrase "le développement logiciel" or simply "le code")', response_metadata={'model': 'llama3', 'created_at': '2024-07-04T03:38:54.933154Z', 'message': {'role': 'assistant', 'content': ''}, 'done_reason': 'stop', 'done': True, 'total_duration': 1977300042, 'load_duration': 1345709, 'prompt_eval_duration': 159343000, 'eval_count': 47, 'eval_duration': 1815123000}, id='run-3c81a3ed-3e79-4dd3-a796-04064d804890')
+
+    Async:
+        .. code-block:: python
+
+            messages = [
+                ("human", "Hello how are you!"),
+            ]
+            await llm.ainvoke(messages)
+
+        .. code-block:: python
+
+            AIMessage(content="Hi there! I'm just an AI, so I don't have feelings or emotions like humans do. But I'm functioning properly and ready to help with any questions or tasks you may have! How can I assist you today?", response_metadata={'model': 'llama3', 'created_at': '2024-07-04T03:52:08.165478Z', 'message': {'role': 'assistant', 'content': ''}, 'done_reason': 'stop', 'done': True, 'total_duration': 2138492875, 'load_duration': 1364000, 'prompt_eval_count': 10, 'prompt_eval_duration': 297081000, 'eval_count': 47, 'eval_duration': 1838524000}, id='run-29c510ae-49a4-4cdd-8f23-b972bfab1c49-0')
+
+        .. code-block:: python
+
+            messages = [
+                ("human", "Say hello world!"),
+            ]
+            async for chunk in llm.astream(messages):
+                print(chunk.content)
+
+        .. code-block:: python
+
+            HEL
+            LO
+            WORLD
+            !
+
+        .. code-block:: python
+
+            messages = [
+                ("human", "Say hello world!"),
+                ("human","Say goodbye world!")
+            ]
+            await llm.abatch(messages)
+
+        .. code-block:: python
+
+            [AIMessage(content='HELLO, WORLD!', response_metadata={'model': 'llama3', 'created_at': '2024-07-04T03:55:07.315396Z', 'message': {'role': 'assistant', 'content': ''}, 'done_reason': 'stop', 'done': True, 'total_duration': 1696745458, 'load_duration': 1505000, 'prompt_eval_count': 8, 'prompt_eval_duration': 111627000, 'eval_count': 6, 'eval_duration': 185181000}, id='run-da6c7562-e25a-4a44-987a-2c83cd8c2686-0'),
+            AIMessage(content="It's been a blast chatting with you! Say goodbye to the world for me, and don't forget to come back and visit us again soon!", response_metadata={'model': 'llama3', 'created_at': '2024-07-04T03:55:07.018076Z', 'message': {'role': 'assistant', 'content': ''}, 'done_reason': 'stop', 'done': True, 'total_duration': 1399391083, 'load_duration': 1187417, 'prompt_eval_count': 20, 'prompt_eval_duration': 230349000, 'eval_count': 31, 'eval_duration': 1166047000}, id='run-96cad530-6f3e-4cf9-86b4-e0f8abba4cdb-0')]
+
+    JSON mode:
+        .. code-block:: python
+
+
+            json_llm = ChatOllama(format="json")
+            messages = [
+                ("human", "Return a query for the weather in a random location and time of day with two keys: location and time_of_day. Respond using JSON only."),
+            ]
+            llm.invoke(messages).content
+
+        .. code-block:: python
+
+            '{"location": "Pune, India", "time_of_day": "morning"}'
+
+    Tool Calling:
+        .. warning::
+            Ollama currently does not support streaming for tools
+
+        .. code-block:: python
+
+            from langchain_ollama import ChatOllama
+            from langchain_core.pydantic_v1 import BaseModel, Field
+
+            class Multiply(BaseModel):
+                a: int = Field(..., description="First integer")
+                b: int = Field(..., description="Second integer")
+
+            ans = await chat.invoke("What is 45*67")
+            ans.tool_calls
+
+        .. code-block:: python
+
+            [{'name': 'Multiply',
+            'args': {'a': 45, 'b': 67},
+            'id': '420c3f3b-df10-4188-945f-eb3abdb40622',
+            'type': 'tool_call'}]
+    """  # noqa: E501
+
+    model: str
+    """Model name to use."""
+
+    mirostat: Optional[int] = None
+    """Enable Mirostat sampling for controlling perplexity.
+    (default: 0, 0 = disabled, 1 = Mirostat, 2 = Mirostat 2.0)"""
+
+    mirostat_eta: Optional[float] = None
+    """Influences how quickly the algorithm responds to feedback
+    from the generated text. A lower learning rate will result in
+    slower adjustments, while a higher learning rate will make
+    the algorithm more responsive. (Default: 0.1)"""
+
+    mirostat_tau: Optional[float] = None
+    """Controls the balance between coherence and diversity
+    of the output. A lower value will result in more focused and
+    coherent text. (Default: 5.0)"""
+
+    num_ctx: Optional[int] = None
+    """Sets the size of the context window used to generate the
+    next token. (Default: 2048)	"""
+
+    num_gpu: Optional[int] = None
+    """The number of GPUs to use. On macOS it defaults to 1 to
+    enable metal support, 0 to disable."""
+
+    num_thread: Optional[int] = None
+    """Sets the number of threads to use during computation.
+    By default, Ollama will detect this for optimal performance.
+    It is recommended to set this value to the number of physical
+    CPU cores your system has (as opposed to the logical number of cores)."""
+
+    num_predict: Optional[int] = None
+    """Maximum number of tokens to predict when generating text.
+    (Default: 128, -1 = infinite generation, -2 = fill context)"""
+
+    repeat_last_n: Optional[int] = None
+    """Sets how far back for the model to look back to prevent
+    repetition. (Default: 64, 0 = disabled, -1 = num_ctx)"""
+
+    repeat_penalty: Optional[float] = None
+    """Sets how strongly to penalize repetitions. A higher value (e.g., 1.5)
+    will penalize repetitions more strongly, while a lower value (e.g., 0.9)
+    will be more lenient. (Default: 1.1)"""
+
+    temperature: Optional[float] = None
+    """The temperature of the model. Increasing the temperature will
+    make the model answer more creatively. (Default: 0.8)"""
+
+    stop: Optional[List[str]] = None
+    """Sets the stop tokens to use."""
+
+    tfs_z: Optional[float] = None
+    """Tail free sampling is used to reduce the impact of less probable
+    tokens from the output. A higher value (e.g., 2.0) will reduce the
+    impact more, while a value of 1.0 disables this setting. (default: 1)"""
+
+    top_k: Optional[int] = None
+    """Reduces the probability of generating nonsense. A higher value (e.g. 100)
+    will give more diverse answers, while a lower value (e.g. 10)
+    will be more conservative. (Default: 40)"""
+
+    top_p: Optional[float] = None
+    """Works together with top-k. A higher value (e.g., 0.95) will lead
+    to more diverse text, while a lower value (e.g., 0.5) will
+    generate more focused and conservative text. (Default: 0.9)"""
+
+    format: Literal["", "json"] = ""
+    """Specify the format of the output (options: json)"""
+
+    keep_alive: Optional[Union[int, str]] = None
+    """How long the model will stay loaded into memory."""
+
+    @property
+    def _default_params(self) -> Dict[str, Any]:
+        """Get the default parameters for calling Ollama."""
+        return {
+            "model": self.model,
+            "format": self.format,
+            "options": {
+                "mirostat": self.mirostat,
+                "mirostat_eta": self.mirostat_eta,
+                "mirostat_tau": self.mirostat_tau,
+                "num_ctx": self.num_ctx,
+                "num_gpu": self.num_gpu,
+                "num_thread": self.num_thread,
+                "num_predict": self.num_predict,
+                "repeat_last_n": self.repeat_last_n,
+                "repeat_penalty": self.repeat_penalty,
+                "temperature": self.temperature,
+                "stop": self.stop,
+                "tfs_z": self.tfs_z,
+                "top_k": self.top_k,
+                "top_p": self.top_p,
+            },
+            "keep_alive": self.keep_alive,
+        }
+
+    def _convert_messages_to_ollama_messages(
+        self, messages: List[BaseMessage]
+    ) -> Sequence[Message]:
+        ollama_messages: List = []
+        for message in messages:
+            role = ""
+            tool_call_id: Optional[str] = None
+            tool_calls: Optional[List[Dict[str, Any]]] = None
+            if isinstance(message, HumanMessage):
+                role = "user"
+            elif isinstance(message, AIMessage):
+                role = "assistant"
+                tool_calls = (
+                    [
+                        _lc_tool_call_to_openai_tool_call(tool_call)
+                        for tool_call in message.tool_calls
+                    ]
+                    if message.tool_calls
+                    else None
+                )
+            elif isinstance(message, SystemMessage):
+                role = "system"
+            elif isinstance(message, ToolMessage):
+                role = "tool"
+                tool_call_id = message.tool_call_id
+            else:
+                raise ValueError("Received unsupported message type for Ollama.")
+
+            content = ""
+            images = []
+            if isinstance(message.content, str):
+                content = message.content
+            else:
+                for content_part in cast(List[Dict], message.content):
+                    if content_part.get("type") == "text":
+                        content += f"\n{content_part['text']}"
+                    elif content_part.get("type") == "tool_use":
+                        continue
+                    elif content_part.get("type") == "image_url":
+                        image_url = None
+                        temp_image_url = content_part.get("image_url")
+                        if isinstance(temp_image_url, str):
+                            image_url = content_part["image_url"]
+                        elif (
+                            isinstance(temp_image_url, dict) and "url" in temp_image_url
+                        ):
+                            image_url = temp_image_url
+                        else:
+                            raise ValueError(
+                                "Only string image_url or dict with string 'url' "
+                                "inside content parts are supported."
+                            )
+
+                        image_url_components = image_url.split(",")
+                        # Support data:image/jpeg;base64,<image> format
+                        # and base64 strings
+                        if len(image_url_components) > 1:
+                            images.append(image_url_components[1])
+                        else:
+                            images.append(image_url_components[0])
+
+                    else:
+                        raise ValueError(
+                            "Unsupported message content type. "
+                            "Must either have type 'text' or type 'image_url' "
+                            "with a string 'image_url' field."
+                        )
+            msg = {
+                "role": role,
+                "content": content,
+                "images": images,
+            }
+            if tool_call_id:
+                msg["tool_call_id"] = tool_call_id
+            if tool_calls:
+                msg["tool_calls"] = tool_calls
+            ollama_messages.append(msg)
+
+        return ollama_messages
+
+    async def _acreate_chat_stream(
+        self,
+        messages: List[BaseMessage],
+        stop: Optional[List[str]] = None,
+        **kwargs: Any,
+    ) -> AsyncIterator[Union[Mapping[str, Any], str]]:
+        ollama_messages = self._convert_messages_to_ollama_messages(messages)
+
+        stop = stop if stop is not None else self.stop
+
+        params = self._default_params
+
+        for key in self._default_params:
+            if key in kwargs:
+                params[key] = kwargs[key]
+
+        params["options"]["stop"] = stop
+        if "tools" in kwargs:
+            yield await AsyncClient().chat(
+                model=params["model"],
+                messages=ollama_messages,
+                stream=False,
+                options=Options(**params["options"]),
+                keep_alive=params["keep_alive"],
+                format=params["format"],
+                tools=kwargs["tools"],
+            )  # type:ignore
+        else:
+            async for part in await AsyncClient().chat(
+                model=params["model"],
+                messages=ollama_messages,
+                stream=True,
+                options=Options(**params["options"]),
+                keep_alive=params["keep_alive"],
+                format=params["format"],
+            ):  # type:ignore
+                yield part
+
+    def _create_chat_stream(
+        self,
+        messages: List[BaseMessage],
+        stop: Optional[List[str]] = None,
+        **kwargs: Any,
+    ) -> Iterator[Union[Mapping[str, Any], str]]:
+        ollama_messages = self._convert_messages_to_ollama_messages(messages)
+
+        stop = stop if stop is not None else self.stop
+
+        params = self._default_params
+
+        for key in self._default_params:
+            if key in kwargs:
+                params[key] = kwargs[key]
+
+        params["options"]["stop"] = stop
+        if "tools" in kwargs:
+            yield ollama.chat(
+                model=params["model"],
+                messages=ollama_messages,
+                stream=False,
+                options=Options(**params["options"]),
+                keep_alive=params["keep_alive"],
+                format=params["format"],
+                tools=kwargs["tools"],
+            )
+        else:
+            yield from ollama.chat(
+                model=params["model"],
+                messages=ollama_messages,
+                stream=True,
+                options=Options(**params["options"]),
+                keep_alive=params["keep_alive"],
+                format=params["format"],
+            )
+
+    def _chat_stream_with_aggregation(
+        self,
+        messages: List[BaseMessage],
+        stop: Optional[List[str]] = None,
+        run_manager: Optional[CallbackManagerForLLMRun] = None,
+        verbose: bool = False,
+        **kwargs: Any,
+    ) -> ChatGenerationChunk:
+        final_chunk = None
+        for stream_resp in self._create_chat_stream(messages, stop, **kwargs):
+            if not isinstance(stream_resp, str):
+                chunk = ChatGenerationChunk(
+                    message=AIMessageChunk(
+                        content=(
+                            stream_resp["message"]["content"]
+                            if "message" in stream_resp
+                            and "content" in stream_resp["message"]
+                            else ""
+                        ),
+                        usage_metadata=_get_usage_metadata_from_generation_info(
+                            stream_resp
+                        ),
+                        tool_calls=_get_tool_calls_from_response(stream_resp),
+                    ),
+                    generation_info=(
+                        dict(stream_resp) if stream_resp.get("done") is True else None
+                    ),
+                )
+                if final_chunk is None:
+                    final_chunk = chunk
+                else:
+                    final_chunk += chunk
+                if run_manager:
+                    run_manager.on_llm_new_token(
+                        chunk.text,
+                        chunk=chunk,
+                        verbose=verbose,
+                    )
+        if final_chunk is None:
+            raise ValueError("No data received from Ollama stream.")
+
+        return final_chunk
+
+    async def _achat_stream_with_aggregation(
+        self,
+        messages: List[BaseMessage],
+        stop: Optional[List[str]] = None,
+        run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
+        verbose: bool = False,
+        **kwargs: Any,
+    ) -> ChatGenerationChunk:
+        final_chunk = None
+        async for stream_resp in self._acreate_chat_stream(messages, stop, **kwargs):
+            if not isinstance(stream_resp, str):
+                chunk = ChatGenerationChunk(
+                    message=AIMessageChunk(
+                        content=(
+                            stream_resp["message"]["content"]
+                            if "message" in stream_resp
+                            and "content" in stream_resp["message"]
+                            else ""
+                        ),
+                        usage_metadata=_get_usage_metadata_from_generation_info(
+                            stream_resp
+                        ),
+                        tool_calls=_get_tool_calls_from_response(stream_resp),
+                    ),
+                    generation_info=(
+                        dict(stream_resp) if stream_resp.get("done") is True else None
+                    ),
+                )
+                if final_chunk is None:
+                    final_chunk = chunk
+                else:
+                    final_chunk += chunk
+                if run_manager:
+                    await run_manager.on_llm_new_token(
+                        chunk.text,
+                        chunk=chunk,
+                        verbose=verbose,
+                    )
+        if final_chunk is None:
+            raise ValueError("No data received from Ollama stream.")
+
+        return final_chunk
+
+    def _get_ls_params(
+        self, stop: Optional[List[str]] = None, **kwargs: Any
+    ) -> LangSmithParams:
+        """Get standard params for tracing."""
+        params = self._get_invocation_params(stop=stop, **kwargs)
+        ls_params = LangSmithParams(
+            ls_provider="ollama",
+            ls_model_name=self.model,
+            ls_model_type="chat",
+            ls_temperature=params.get("temperature", self.temperature),
+        )
+        if ls_stop := stop or params.get("stop", None) or self.stop:
+            ls_params["ls_stop"] = ls_stop
+        return ls_params
+
+    def _generate(
+        self,
+        messages: List[BaseMessage],
+        stop: Optional[List[str]] = None,
+        run_manager: Optional[CallbackManagerForLLMRun] = None,
+        **kwargs: Any,
+    ) -> ChatResult:
+        final_chunk = self._chat_stream_with_aggregation(
+            messages, stop, run_manager, verbose=self.verbose, **kwargs
+        )
+        generation_info = final_chunk.generation_info
+        chat_generation = ChatGeneration(
+            message=AIMessage(
+                content=final_chunk.text,
+                usage_metadata=cast(AIMessageChunk, final_chunk.message).usage_metadata,
+                tool_calls=cast(AIMessageChunk, final_chunk.message).tool_calls,
+            ),
+            generation_info=generation_info,
+        )
+        return ChatResult(generations=[chat_generation])
+
+    def _stream(
+        self,
+        messages: List[BaseMessage],
+        stop: Optional[List[str]] = None,
+        run_manager: Optional[CallbackManagerForLLMRun] = None,
+        **kwargs: Any,
+    ) -> Iterator[ChatGenerationChunk]:
+        for stream_resp in self._create_chat_stream(messages, stop, **kwargs):
+            if not isinstance(stream_resp, str):
+                chunk = ChatGenerationChunk(
+                    message=AIMessageChunk(
+                        content=(
+                            stream_resp["message"]["content"]
+                            if "message" in stream_resp
+                            and "content" in stream_resp["message"]
+                            else ""
+                        ),
+                        usage_metadata=_get_usage_metadata_from_generation_info(
+                            stream_resp
+                        ),
+                        tool_calls=_get_tool_calls_from_response(stream_resp),
+                    ),
+                    generation_info=(
+                        dict(stream_resp) if stream_resp.get("done") is True else None
+                    ),
+                )
+                if run_manager:
+                    run_manager.on_llm_new_token(
+                        chunk.text,
+                        verbose=self.verbose,
+                    )
+                yield chunk
+
+    async def _astream(
+        self,
+        messages: List[BaseMessage],
+        stop: Optional[List[str]] = None,
+        run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
+        **kwargs: Any,
+    ) -> AsyncIterator[ChatGenerationChunk]:
+        async for stream_resp in self._acreate_chat_stream(messages, stop, **kwargs):
+            if not isinstance(stream_resp, str):
+                chunk = ChatGenerationChunk(
+                    message=AIMessageChunk(
+                        content=(
+                            stream_resp["message"]["content"]
+                            if "message" in stream_resp
+                            and "content" in stream_resp["message"]
+                            else ""
+                        ),
+                        usage_metadata=_get_usage_metadata_from_generation_info(
+                            stream_resp
+                        ),
+                        tool_calls=_get_tool_calls_from_response(stream_resp),
+                    ),
+                    generation_info=(
+                        dict(stream_resp) if stream_resp.get("done") is True else None
+                    ),
+                )
+                if run_manager:
+                    await run_manager.on_llm_new_token(
+                        chunk.text,
+                        verbose=self.verbose,
+                    )
+                yield chunk
+
+    async def _agenerate(
+        self,
+        messages: List[BaseMessage],
+        stop: Optional[List[str]] = None,
+        run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
+        **kwargs: Any,
+    ) -> ChatResult:
+        final_chunk = await self._achat_stream_with_aggregation(
+            messages, stop, run_manager, verbose=self.verbose, **kwargs
+        )
+        generation_info = final_chunk.generation_info
+        chat_generation = ChatGeneration(
+            message=AIMessage(
+                content=final_chunk.text,
+                usage_metadata=cast(AIMessageChunk, final_chunk.message).usage_metadata,
+                tool_calls=cast(AIMessageChunk, final_chunk.message).tool_calls,
+            ),
+            generation_info=generation_info,
+        )
+        return ChatResult(generations=[chat_generation])
+
+    @property
+    def _llm_type(self) -> str:
+        """Return type of chat model."""
+        return "chat-ollama"
+
+    def bind_tools(
+        self,
+        tools: Sequence[Union[Dict[str, Any], Type[BaseModel], Callable, BaseTool]],
+        **kwargs: Any,
+    ) -> Runnable[LanguageModelInput, BaseMessage]:
+        formatted_tools = [convert_to_openai_tool(tool) for tool in tools]
+        return super().bind(tools=formatted_tools, **kwargs)
diff --git a/libs/partners/ollama/langchain_ollama/embeddings.py b/libs/partners/ollama/langchain_ollama/embeddings.py
new file mode 100644
index 00000000000..5233f68f48d
--- /dev/null
+++ b/libs/partners/ollama/langchain_ollama/embeddings.py
@@ -0,0 +1,51 @@
+from typing import List
+
+import ollama
+from langchain_core.embeddings import Embeddings
+from langchain_core.pydantic_v1 import BaseModel, Extra
+from ollama import AsyncClient
+
+
+class OllamaEmbeddings(BaseModel, Embeddings):
+    """OllamaEmbeddings embedding model.
+
+    Example:
+        .. code-block:: python
+
+            from langchain_ollama import OllamaEmbeddings
+
+            model = OllamaEmbeddings(model="llama3")
+            embedder.embed_query("what is the place that jonathan worked at?")
+    """
+
+    model: str
+    """Model name to use."""
+
+    class Config:
+        """Configuration for this pydantic object."""
+
+        extra = Extra.forbid
+
+    def embed_documents(self, texts: List[str]) -> List[List[float]]:
+        """Embed search docs."""
+        embedded_docs = []
+        for doc in texts:
+            embedded_docs.append(list(ollama.embeddings(self.model, doc)["embedding"]))
+        return embedded_docs
+
+    def embed_query(self, text: str) -> List[float]:
+        """Embed query text."""
+        return self.embed_documents([text])[0]
+
+    async def aembed_documents(self, texts: List[str]) -> List[List[float]]:
+        """Embed search docs."""
+        embedded_docs = []
+        for doc in texts:
+            embedded_docs.append(
+                list((await AsyncClient().embeddings(self.model, doc))["embedding"])
+            )
+        return embedded_docs
+
+    async def aembed_query(self, text: str) -> List[float]:
+        """Embed query text."""
+        return (await self.aembed_documents([text]))[0]
diff --git a/libs/partners/ollama/langchain_ollama/llms.py b/libs/partners/ollama/langchain_ollama/llms.py
new file mode 100644
index 00000000000..e2bf73cfabd
--- /dev/null
+++ b/libs/partners/ollama/langchain_ollama/llms.py
@@ -0,0 +1,347 @@
+"""Ollama large language models."""
+
+from typing import (
+    Any,
+    AsyncIterator,
+    Dict,
+    Iterator,
+    List,
+    Literal,
+    Mapping,
+    Optional,
+    Union,
+)
+
+import ollama
+from langchain_core.callbacks import (
+    AsyncCallbackManagerForLLMRun,
+    CallbackManagerForLLMRun,
+)
+from langchain_core.language_models import BaseLLM
+from langchain_core.outputs import GenerationChunk, LLMResult
+from ollama import AsyncClient, Options
+
+
+class OllamaLLM(BaseLLM):
+    """OllamaLLM large language models.
+
+    Example:
+        .. code-block:: python
+
+            from langchain_ollama import OllamaLLM
+
+            model = OllamaLLM(model="llama3")
+            model.invoke("Come up with 10 names for a song about parrots")
+    """
+
+    model: str
+    """Model name to use."""
+
+    mirostat: Optional[int] = None
+    """Enable Mirostat sampling for controlling perplexity.
+    (default: 0, 0 = disabled, 1 = Mirostat, 2 = Mirostat 2.0)"""
+
+    mirostat_eta: Optional[float] = None
+    """Influences how quickly the algorithm responds to feedback
+    from the generated text. A lower learning rate will result in
+    slower adjustments, while a higher learning rate will make
+    the algorithm more responsive. (Default: 0.1)"""
+
+    mirostat_tau: Optional[float] = None
+    """Controls the balance between coherence and diversity
+    of the output. A lower value will result in more focused and
+    coherent text. (Default: 5.0)"""
+
+    num_ctx: Optional[int] = None
+    """Sets the size of the context window used to generate the
+    next token. (Default: 2048)	"""
+
+    num_gpu: Optional[int] = None
+    """The number of GPUs to use. On macOS it defaults to 1 to
+    enable metal support, 0 to disable."""
+
+    num_thread: Optional[int] = None
+    """Sets the number of threads to use during computation.
+    By default, Ollama will detect this for optimal performance.
+    It is recommended to set this value to the number of physical
+    CPU cores your system has (as opposed to the logical number of cores)."""
+
+    num_predict: Optional[int] = None
+    """Maximum number of tokens to predict when generating text.
+    (Default: 128, -1 = infinite generation, -2 = fill context)"""
+
+    repeat_last_n: Optional[int] = None
+    """Sets how far back for the model to look back to prevent
+    repetition. (Default: 64, 0 = disabled, -1 = num_ctx)"""
+
+    repeat_penalty: Optional[float] = None
+    """Sets how strongly to penalize repetitions. A higher value (e.g., 1.5)
+    will penalize repetitions more strongly, while a lower value (e.g., 0.9)
+    will be more lenient. (Default: 1.1)"""
+
+    temperature: Optional[float] = None
+    """The temperature of the model. Increasing the temperature will
+    make the model answer more creatively. (Default: 0.8)"""
+
+    stop: Optional[List[str]] = None
+    """Sets the stop tokens to use."""
+
+    tfs_z: Optional[float] = None
+    """Tail free sampling is used to reduce the impact of less probable
+    tokens from the output. A higher value (e.g., 2.0) will reduce the
+    impact more, while a value of 1.0 disables this setting. (default: 1)"""
+
+    top_k: Optional[int] = None
+    """Reduces the probability of generating nonsense. A higher value (e.g. 100)
+    will give more diverse answers, while a lower value (e.g. 10)
+    will be more conservative. (Default: 40)"""
+
+    top_p: Optional[float] = None
+    """Works together with top-k. A higher value (e.g., 0.95) will lead
+    to more diverse text, while a lower value (e.g., 0.5) will
+    generate more focused and conservative text. (Default: 0.9)"""
+
+    format: Literal["", "json"] = ""
+    """Specify the format of the output (options: json)"""
+
+    keep_alive: Optional[Union[int, str]] = None
+    """How long the model will stay loaded into memory."""
+
+    @property
+    def _default_params(self) -> Dict[str, Any]:
+        """Get the default parameters for calling Ollama."""
+        return {
+            "model": self.model,
+            "format": self.format,
+            "options": {
+                "mirostat": self.mirostat,
+                "mirostat_eta": self.mirostat_eta,
+                "mirostat_tau": self.mirostat_tau,
+                "num_ctx": self.num_ctx,
+                "num_gpu": self.num_gpu,
+                "num_thread": self.num_thread,
+                "num_predict": self.num_predict,
+                "repeat_last_n": self.repeat_last_n,
+                "repeat_penalty": self.repeat_penalty,
+                "temperature": self.temperature,
+                "stop": self.stop,
+                "tfs_z": self.tfs_z,
+                "top_k": self.top_k,
+                "top_p": self.top_p,
+            },
+            "keep_alive": self.keep_alive,
+        }
+
+    @property
+    def _llm_type(self) -> str:
+        """Return type of LLM."""
+        return "ollama-llm"
+
+    async def _acreate_generate_stream(
+        self,
+        prompt: str,
+        stop: Optional[List[str]] = None,
+        **kwargs: Any,
+    ) -> AsyncIterator[Union[Mapping[str, Any], str]]:
+        if self.stop is not None and stop is not None:
+            raise ValueError("`stop` found in both the input and default params.")
+        elif self.stop is not None:
+            stop = self.stop
+
+        params = self._default_params
+
+        for key in self._default_params:
+            if key in kwargs:
+                params[key] = kwargs[key]
+
+        params["options"]["stop"] = stop
+        async for part in await AsyncClient().generate(
+            model=params["model"],
+            prompt=prompt,
+            stream=True,
+            options=Options(**params["options"]),
+            keep_alive=params["keep_alive"],
+            format=params["format"],
+        ):  # type: ignore
+            yield part
+
+    def _create_generate_stream(
+        self,
+        prompt: str,
+        stop: Optional[List[str]] = None,
+        **kwargs: Any,
+    ) -> Iterator[Union[Mapping[str, Any], str]]:
+        if self.stop is not None and stop is not None:
+            raise ValueError("`stop` found in both the input and default params.")
+        elif self.stop is not None:
+            stop = self.stop
+
+        params = self._default_params
+
+        for key in self._default_params:
+            if key in kwargs:
+                params[key] = kwargs[key]
+
+        params["options"]["stop"] = stop
+        yield from ollama.generate(
+            model=params["model"],
+            prompt=prompt,
+            stream=True,
+            options=Options(**params["options"]),
+            keep_alive=params["keep_alive"],
+            format=params["format"],
+        )
+
+    async def _astream_with_aggregation(
+        self,
+        prompt: str,
+        stop: Optional[List[str]] = None,
+        run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
+        verbose: bool = False,
+        **kwargs: Any,
+    ) -> GenerationChunk:
+        final_chunk = None
+        async for stream_resp in self._acreate_generate_stream(prompt, stop, **kwargs):
+            if not isinstance(stream_resp, str):
+                chunk = GenerationChunk(
+                    text=stream_resp["response"] if "response" in stream_resp else "",
+                    generation_info=(
+                        dict(stream_resp) if stream_resp.get("done") is True else None
+                    ),
+                )
+                if final_chunk is None:
+                    final_chunk = chunk
+                else:
+                    final_chunk += chunk
+                if run_manager:
+                    await run_manager.on_llm_new_token(
+                        chunk.text,
+                        chunk=chunk,
+                        verbose=verbose,
+                    )
+        if final_chunk is None:
+            raise ValueError("No data received from Ollama stream.")
+
+        return final_chunk
+
+    def _stream_with_aggregation(
+        self,
+        prompt: str,
+        stop: Optional[List[str]] = None,
+        run_manager: Optional[CallbackManagerForLLMRun] = None,
+        verbose: bool = False,
+        **kwargs: Any,
+    ) -> GenerationChunk:
+        final_chunk = None
+        for stream_resp in self._create_generate_stream(prompt, stop, **kwargs):
+            if not isinstance(stream_resp, str):
+                chunk = GenerationChunk(
+                    text=stream_resp["response"] if "response" in stream_resp else "",
+                    generation_info=(
+                        dict(stream_resp) if stream_resp.get("done") is True else None
+                    ),
+                )
+                if final_chunk is None:
+                    final_chunk = chunk
+                else:
+                    final_chunk += chunk
+                if run_manager:
+                    run_manager.on_llm_new_token(
+                        chunk.text,
+                        chunk=chunk,
+                        verbose=verbose,
+                    )
+        if final_chunk is None:
+            raise ValueError("No data received from Ollama stream.")
+
+        return final_chunk
+
+    def _generate(
+        self,
+        prompts: List[str],
+        stop: Optional[List[str]] = None,
+        run_manager: Optional[CallbackManagerForLLMRun] = None,
+        **kwargs: Any,
+    ) -> LLMResult:
+        generations = []
+        for prompt in prompts:
+            final_chunk = self._stream_with_aggregation(
+                prompt,
+                stop=stop,
+                run_manager=run_manager,
+                verbose=self.verbose,
+                **kwargs,
+            )
+            generations.append([final_chunk])
+        return LLMResult(generations=generations)  # type: ignore[arg-type]
+
+    async def _agenerate(
+        self,
+        prompts: List[str],
+        stop: Optional[List[str]] = None,
+        run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
+        **kwargs: Any,
+    ) -> LLMResult:
+        generations = []
+        for prompt in prompts:
+            final_chunk = await self._astream_with_aggregation(
+                prompt,
+                stop=stop,
+                run_manager=run_manager,
+                verbose=self.verbose,
+                **kwargs,
+            )
+            generations.append([final_chunk])
+        return LLMResult(generations=generations)  # type: ignore[arg-type]
+
+    def _stream(
+        self,
+        prompt: str,
+        stop: Optional[List[str]] = None,
+        run_manager: Optional[CallbackManagerForLLMRun] = None,
+        **kwargs: Any,
+    ) -> Iterator[GenerationChunk]:
+        for stream_resp in self._create_generate_stream(prompt, stop, **kwargs):
+            if not isinstance(stream_resp, str):
+                chunk = GenerationChunk(
+                    text=(
+                        stream_resp["message"]["content"]
+                        if "message" in stream_resp
+                        else ""
+                    ),
+                    generation_info=(
+                        dict(stream_resp) if stream_resp.get("done") is True else None
+                    ),
+                )
+                if run_manager:
+                    run_manager.on_llm_new_token(
+                        chunk.text,
+                        verbose=self.verbose,
+                    )
+                yield chunk
+
+    async def _astream(
+        self,
+        prompt: str,
+        stop: Optional[List[str]] = None,
+        run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
+        **kwargs: Any,
+    ) -> AsyncIterator[GenerationChunk]:
+        async for stream_resp in self._acreate_generate_stream(prompt, stop, **kwargs):
+            if not isinstance(stream_resp, str):
+                chunk = GenerationChunk(
+                    text=(
+                        stream_resp["message"]["content"]
+                        if "message" in stream_resp
+                        else ""
+                    ),
+                    generation_info=(
+                        dict(stream_resp) if stream_resp.get("done") is True else None
+                    ),
+                )
+                if run_manager:
+                    await run_manager.on_llm_new_token(
+                        chunk.text,
+                        verbose=self.verbose,
+                    )
+                yield chunk
diff --git a/libs/partners/ollama/langchain_ollama/py.typed b/libs/partners/ollama/langchain_ollama/py.typed
new file mode 100644
index 00000000000..e69de29bb2d
diff --git a/libs/partners/ollama/poetry.lock b/libs/partners/ollama/poetry.lock
new file mode 100644
index 00000000000..679c8dd0a76
--- /dev/null
+++ b/libs/partners/ollama/poetry.lock
@@ -0,0 +1,879 @@
+# This file is automatically @generated by Poetry 1.8.3 and should not be changed by hand.
+
+[[package]]
+name = "annotated-types"
+version = "0.7.0"
+description = "Reusable constraint types to use with typing.Annotated"
+optional = false
+python-versions = ">=3.8"
+files = [
+    {file = "annotated_types-0.7.0-py3-none-any.whl", hash = "sha256:1f02e8b43a8fbbc3f3e0d4f0f4bfc8131bcb4eebe8849b8e5c773f3a1c582a53"},
+    {file = "annotated_types-0.7.0.tar.gz", hash = "sha256:aff07c09a53a08bc8cfccb9c85b05f1aa9a2a6f23728d790723543408344ce89"},
+]
+
+[package.dependencies]
+typing-extensions = {version = ">=4.0.0", markers = "python_version < \"3.9\""}
+
+[[package]]
+name = "anyio"
+version = "4.4.0"
+description = "High level compatibility layer for multiple asynchronous event loop implementations"
+optional = false
+python-versions = ">=3.8"
+files = [
+    {file = "anyio-4.4.0-py3-none-any.whl", hash = "sha256:c1b2d8f46a8a812513012e1107cb0e68c17159a7a594208005a57dc776e1bdc7"},
+    {file = "anyio-4.4.0.tar.gz", hash = "sha256:5aadc6a1bbb7cdb0bede386cac5e2940f5e2ff3aa20277e991cf028e0585ce94"},
+]
+
+[package.dependencies]
+exceptiongroup = {version = ">=1.0.2", markers = "python_version < \"3.11\""}
+idna = ">=2.8"
+sniffio = ">=1.1"
+typing-extensions = {version = ">=4.1", markers = "python_version < \"3.11\""}
+
+[package.extras]
+doc = ["Sphinx (>=7)", "packaging", "sphinx-autodoc-typehints (>=1.2.0)", "sphinx-rtd-theme"]
+test = ["anyio[trio]", "coverage[toml] (>=7)", "exceptiongroup (>=1.2.0)", "hypothesis (>=4.0)", "psutil (>=5.9)", "pytest (>=7.0)", "pytest-mock (>=3.6.1)", "trustme", "uvloop (>=0.17)"]
+trio = ["trio (>=0.23)"]
+
+[[package]]
+name = "certifi"
+version = "2024.7.4"
+description = "Python package for providing Mozilla's CA Bundle."
+optional = false
+python-versions = ">=3.6"
+files = [
+    {file = "certifi-2024.7.4-py3-none-any.whl", hash = "sha256:c198e21b1289c2ab85ee4e67bb4b4ef3ead0892059901a8d5b622f24a1101e90"},
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+]
+
+[[package]]
+name = "charset-normalizer"
+version = "3.3.2"
+description = "The Real First Universal Charset Detector. Open, modern and actively maintained alternative to Chardet."
+optional = false
+python-versions = ">=3.7.0"
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diff --git a/libs/partners/ollama/pyproject.toml b/libs/partners/ollama/pyproject.toml
new file mode 100644
index 00000000000..159bb232c3a
--- /dev/null
+++ b/libs/partners/ollama/pyproject.toml
@@ -0,0 +1,90 @@
+[tool.poetry]
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+authors = []
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+
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+[tool.poetry.group.test]
+optional = true
+
+[tool.poetry.group.test.dependencies]
+pytest = "^7.4.3"
+pytest-asyncio = "^0.23.2"
+syrupy = "^4.0.2"
+pytest-socket = "^0.7.0"
+langchain-core = { path = "../../core", develop = true }
+langchain-standard-tests = { path = "../../standard-tests", develop = true }
+
+[tool.poetry.group.codespell]
+optional = true
+
+[tool.poetry.group.codespell.dependencies]
+codespell = "^2.2.6"
+
+[tool.poetry.group.test_integration]
+optional = true
+
+[tool.poetry.group.test_integration.dependencies]
+
+[tool.poetry.group.lint]
+optional = true
+
+[tool.poetry.group.lint.dependencies]
+ruff = "^0.1.8"
+
+[tool.poetry.group.typing.dependencies]
+mypy = "^1.7.1"
+langchain-core = { path = "../../core", develop = true }
+
+[tool.poetry.group.dev]
+optional = true
+
+[tool.poetry.group.dev.dependencies]
+langchain-core = { path = "../../core", develop = true }
+
+[tool.ruff.lint]
+select = [
+  "E",    # pycodestyle
+  "F",    # pyflakes
+  "I",    # isort
+  "T201", # print
+]
+
+[tool.mypy]
+disallow_untyped_defs = "True"
+
+[tool.coverage.run]
+omit = ["tests/*"]
+
+[build-system]
+requires = ["poetry-core>=1.0.0"]
+build-backend = "poetry.core.masonry.api"
+
+[tool.pytest.ini_options]
+# --strict-markers will raise errors on unknown marks.
+# https://docs.pytest.org/en/7.1.x/how-to/mark.html#raising-errors-on-unknown-marks
+#
+# https://docs.pytest.org/en/7.1.x/reference/reference.html
+# --strict-config       any warnings encountered while parsing the `pytest`
+#                       section of the configuration file raise errors.
+#
+# https://github.com/tophat/syrupy
+# --snapshot-warn-unused    Prints a warning on unused snapshots rather than fail the test suite.
+addopts = "--snapshot-warn-unused --strict-markers --strict-config --durations=5"
+# Registering custom markers.
+# https://docs.pytest.org/en/7.1.x/example/markers.html#registering-markers
+markers = [
+  "compile: mark placeholder test used to compile integration tests without running them",
+]
+asyncio_mode = "auto"
diff --git a/libs/partners/ollama/scripts/check_imports.py b/libs/partners/ollama/scripts/check_imports.py
new file mode 100644
index 00000000000..365f5fa118d
--- /dev/null
+++ b/libs/partners/ollama/scripts/check_imports.py
@@ -0,0 +1,17 @@
+import sys
+import traceback
+from importlib.machinery import SourceFileLoader
+
+if __name__ == "__main__":
+    files = sys.argv[1:]
+    has_failure = False
+    for file in files:
+        try:
+            SourceFileLoader("x", file).load_module()
+        except Exception:
+            has_faillure = True
+            print(file)  # noqa: T201
+            traceback.print_exc()
+            print()  # noqa: T201
+
+    sys.exit(1 if has_failure else 0)
diff --git a/libs/partners/ollama/scripts/check_pydantic.sh b/libs/partners/ollama/scripts/check_pydantic.sh
new file mode 100755
index 00000000000..06b5bb81ae2
--- /dev/null
+++ b/libs/partners/ollama/scripts/check_pydantic.sh
@@ -0,0 +1,27 @@
+#!/bin/bash
+#
+# This script searches for lines starting with "import pydantic" or "from pydantic"
+# in tracked files within a Git repository.
+#
+# Usage: ./scripts/check_pydantic.sh /path/to/repository
+
+# Check if a path argument is provided
+if [ $# -ne 1 ]; then
+  echo "Usage: $0 /path/to/repository"
+  exit 1
+fi
+
+repository_path="$1"
+
+# Search for lines matching the pattern within the specified repository
+result=$(git -C "$repository_path" grep -E '^import pydantic|^from pydantic')
+
+# Check if any matching lines were found
+if [ -n "$result" ]; then
+  echo "ERROR: The following lines need to be updated:"
+  echo "$result"
+  echo "Please replace the code with an import from langchain_core.pydantic_v1."
+  echo "For example, replace 'from pydantic import BaseModel'"
+  echo "with 'from langchain_core.pydantic_v1 import BaseModel'"
+  exit 1
+fi
diff --git a/libs/partners/ollama/scripts/lint_imports.sh b/libs/partners/ollama/scripts/lint_imports.sh
new file mode 100755
index 00000000000..19ccec1480c
--- /dev/null
+++ b/libs/partners/ollama/scripts/lint_imports.sh
@@ -0,0 +1,18 @@
+#!/bin/bash
+
+set -eu
+
+# Initialize a variable to keep track of errors
+errors=0
+
+# make sure not importing from langchain, langchain_experimental, or langchain_community
+git --no-pager grep '^from langchain\.' . && errors=$((errors+1))
+git --no-pager grep '^from langchain_experimental\.' . && errors=$((errors+1))
+git --no-pager grep '^from langchain_community\.' . && errors=$((errors+1))
+
+# Decide on an exit status based on the errors
+if [ "$errors" -gt 0 ]; then
+    exit 1
+else
+    exit 0
+fi
diff --git a/libs/partners/ollama/tests/__init__.py b/libs/partners/ollama/tests/__init__.py
new file mode 100644
index 00000000000..e69de29bb2d
diff --git a/libs/partners/ollama/tests/integration_tests/__init__.py b/libs/partners/ollama/tests/integration_tests/__init__.py
new file mode 100644
index 00000000000..e69de29bb2d
diff --git a/libs/partners/ollama/tests/integration_tests/test_chat_models.py b/libs/partners/ollama/tests/integration_tests/test_chat_models.py
new file mode 100644
index 00000000000..0d54b2c738b
--- /dev/null
+++ b/libs/partners/ollama/tests/integration_tests/test_chat_models.py
@@ -0,0 +1,17 @@
+"""Test chat model integration."""
+
+from typing import Type
+
+from langchain_standard_tests.integration_tests import ChatModelIntegrationTests
+
+from langchain_ollama.chat_models import ChatOllama
+
+
+class TestChatOllama(ChatModelIntegrationTests):
+    @property
+    def chat_model_class(self) -> Type[ChatOllama]:
+        return ChatOllama
+
+    @property
+    def chat_model_params(self) -> dict:
+        return {"model": "llama3-groq-tool-use"}
diff --git a/libs/partners/ollama/tests/integration_tests/test_compile.py b/libs/partners/ollama/tests/integration_tests/test_compile.py
new file mode 100644
index 00000000000..33ecccdfa0f
--- /dev/null
+++ b/libs/partners/ollama/tests/integration_tests/test_compile.py
@@ -0,0 +1,7 @@
+import pytest
+
+
+@pytest.mark.compile
+def test_placeholder() -> None:
+    """Used for compiling integration tests without running any real tests."""
+    pass
diff --git a/libs/partners/ollama/tests/integration_tests/test_embeddings.py b/libs/partners/ollama/tests/integration_tests/test_embeddings.py
new file mode 100644
index 00000000000..e0310bf3b0f
--- /dev/null
+++ b/libs/partners/ollama/tests/integration_tests/test_embeddings.py
@@ -0,0 +1,20 @@
+"""Test Ollama embeddings."""
+
+from langchain_ollama.embeddings import OllamaEmbeddings
+
+
+def test_langchain_ollama_embedding_documents() -> None:
+    """Test cohere embeddings."""
+    documents = ["foo bar"]
+    embedding = OllamaEmbeddings(model="llama3")
+    output = embedding.embed_documents(documents)
+    assert len(output) == 1
+    assert len(output[0]) > 0
+
+
+def test_langchain_ollama_embedding_query() -> None:
+    """Test cohere embeddings."""
+    document = "foo bar"
+    embedding = OllamaEmbeddings(model="llama3")
+    output = embedding.embed_query(document)
+    assert len(output) > 0
diff --git a/libs/partners/ollama/tests/integration_tests/test_llms.py b/libs/partners/ollama/tests/integration_tests/test_llms.py
new file mode 100644
index 00000000000..9d731d24e9b
--- /dev/null
+++ b/libs/partners/ollama/tests/integration_tests/test_llms.py
@@ -0,0 +1,66 @@
+"""Test OllamaLLM llm."""
+
+from langchain_ollama.llms import OllamaLLM
+
+MODEL_NAME = "llama3"
+
+
+def test_stream() -> None:
+    """Test streaming tokens from OpenAI."""
+    llm = OllamaLLM(model=MODEL_NAME)
+
+    for token in llm.stream("I'm Pickle Rick"):
+        assert isinstance(token, str)
+
+
+async def test_astream() -> None:
+    """Test streaming tokens from OpenAI."""
+    llm = OllamaLLM(model=MODEL_NAME)
+
+    async for token in llm.astream("I'm Pickle Rick"):
+        assert isinstance(token, str)
+
+
+async def test_abatch() -> None:
+    """Test streaming tokens from OllamaLLM."""
+    llm = OllamaLLM(model=MODEL_NAME)
+
+    result = await llm.abatch(["I'm Pickle Rick", "I'm not Pickle Rick"])
+    for token in result:
+        assert isinstance(token, str)
+
+
+async def test_abatch_tags() -> None:
+    """Test batch tokens from OllamaLLM."""
+    llm = OllamaLLM(model=MODEL_NAME)
+
+    result = await llm.abatch(
+        ["I'm Pickle Rick", "I'm not Pickle Rick"], config={"tags": ["foo"]}
+    )
+    for token in result:
+        assert isinstance(token, str)
+
+
+def test_batch() -> None:
+    """Test batch tokens from OllamaLLM."""
+    llm = OllamaLLM(model=MODEL_NAME)
+
+    result = llm.batch(["I'm Pickle Rick", "I'm not Pickle Rick"])
+    for token in result:
+        assert isinstance(token, str)
+
+
+async def test_ainvoke() -> None:
+    """Test invoke tokens from OllamaLLM."""
+    llm = OllamaLLM(model=MODEL_NAME)
+
+    result = await llm.ainvoke("I'm Pickle Rick", config={"tags": ["foo"]})
+    assert isinstance(result, str)
+
+
+def test_invoke() -> None:
+    """Test invoke tokens from OllamaLLM."""
+    llm = OllamaLLM(model=MODEL_NAME)
+
+    result = llm.invoke("I'm Pickle Rick", config=dict(tags=["foo"]))
+    assert isinstance(result, str)
diff --git a/libs/partners/ollama/tests/unit_tests/__init__.py b/libs/partners/ollama/tests/unit_tests/__init__.py
new file mode 100644
index 00000000000..e69de29bb2d
diff --git a/libs/partners/ollama/tests/unit_tests/test_chat_models.py b/libs/partners/ollama/tests/unit_tests/test_chat_models.py
new file mode 100644
index 00000000000..d0869143104
--- /dev/null
+++ b/libs/partners/ollama/tests/unit_tests/test_chat_models.py
@@ -0,0 +1,17 @@
+"""Test chat model integration."""
+
+from typing import Dict, Type
+
+from langchain_standard_tests.unit_tests import ChatModelUnitTests
+
+from langchain_ollama.chat_models import ChatOllama
+
+
+class TestChatOllama(ChatModelUnitTests):
+    @property
+    def chat_model_class(self) -> Type[ChatOllama]:
+        return ChatOllama
+
+    @property
+    def chat_model_params(self) -> Dict:
+        return {"model": "llama3-groq-tool-use"}
diff --git a/libs/partners/ollama/tests/unit_tests/test_embeddings.py b/libs/partners/ollama/tests/unit_tests/test_embeddings.py
new file mode 100644
index 00000000000..b1db475ce2a
--- /dev/null
+++ b/libs/partners/ollama/tests/unit_tests/test_embeddings.py
@@ -0,0 +1,8 @@
+"""Test embedding model integration."""
+
+from langchain_ollama.embeddings import OllamaEmbeddings
+
+
+def test_initialization() -> None:
+    """Test embedding model initialization."""
+    OllamaEmbeddings(model="llama3")
diff --git a/libs/partners/ollama/tests/unit_tests/test_imports.py b/libs/partners/ollama/tests/unit_tests/test_imports.py
new file mode 100644
index 00000000000..a5afb37b048
--- /dev/null
+++ b/libs/partners/ollama/tests/unit_tests/test_imports.py
@@ -0,0 +1,12 @@
+from langchain_ollama import __all__
+
+EXPECTED_ALL = [
+    "OllamaLLM",
+    "ChatOllama",
+    "OllamaEmbeddings",
+    "__version__",
+]
+
+
+def test_all_imports() -> None:
+    assert sorted(EXPECTED_ALL) == sorted(__all__)
diff --git a/libs/partners/ollama/tests/unit_tests/test_llms.py b/libs/partners/ollama/tests/unit_tests/test_llms.py
new file mode 100644
index 00000000000..56287792a41
--- /dev/null
+++ b/libs/partners/ollama/tests/unit_tests/test_llms.py
@@ -0,0 +1,8 @@
+"""Test Ollama Chat API wrapper."""
+
+from langchain_ollama import OllamaLLM
+
+
+def test_initialization() -> None:
+    """Test integration initialization."""
+    OllamaLLM(model="llama3")