docs: update content quickstart integration add mistral ollama & lm

docs/docs/quickstart/integration/lmstudio.mdx

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+---
+title: LM Studio
+sidebar_position: 8
+description: A step-by-step guide on how to integrate Jan with LM Studio.
+keywords:
+  [
+    Jan AI,
+    Jan,
+    ChatGPT alternative,
+    local AI,
+    private AI,
+    conversational AI,
+    no-subscription fee,
+    large language model,
+    LM Studio integration,
+  ]
+---
+import flow from './assets/lmstudio.png';
+
+## How to Integrate LM Studio with Jan
+
+[LM Studio](https://lmstudio.ai/) enables you to explore, download, and run local Large Language Models (LLMs). You can integrate Jan with LM Studio using two methods:
+1. Integrate the LM Studio server with Jan UI
+2. Migrate your downloaded model from LM Studio to Jan.
+
+<div class="text--center" > 
+    <img src={ flow } width = { 800} alt = "LM Studio" />
+</div>
+
+To integrate LM Studio with Jan follow the steps below:
+
+:::note
+
+In this guide, we're going to show you how to connect Jan to [LM Studio](https://lmstudio.ai/) using the second method. We'll use the [Phi 2 - GGUF](https://huggingface.co/TheBloke/phi-2-GGUF) model from Hugging Face as our example.
+:::
+### Step 1: Server Setup
+
+1. Access the `Local Inference Server` within LM Studio.
+2. Select your desired model.
+3. Start the server after configuring the port and options.
+
+4. Update the `openai.json` file in `~/jan/engines` to include the LM Studio server's full URL.
+
+```json title="~/jan/engines/openai.json"
+{
+  "full_url": "http://localhost:<port>/v1/chat/completions"
+}
+```
+
+:::tip
+
+Replace `(port)` with your chosen port number. The default is 1234.
+
+:::
+
+### Step 2: Model Configuration
+
+1. Navigate to `~/jan/models`.
+2. Create a folder named `lmstudio-(modelname)`, like `lmstudio-phi-2`.
+3. Inside, create a `model.json` file with these options:
+   - Set `format` to `api`.
+   - Specify `engine` as `openai`.
+   - Set `state` to `ready`.
+  
+```json title="~/jan/models/lmstudio-phi-2/model.json"
+{
+  "sources": [
+    {
+      "filename": "phi-2-GGUF",
+      "url": "https://huggingface.co/TheBloke/phi-2-GGUF"
+    }
+  ],
+  "id": "lmstudio-phi-2",
+  "object": "model",
+  "name": "LM Studio - Phi 2 - GGUF",
+  "version": "1.0",
+  "description": "TheBloke/phi-2-GGUF",
+  "format": "api",
+  "settings": {},
+  "parameters": {},
+  "metadata": {
+    "author": "Microsoft",
+    "tags": ["General", "Big Context Length"]
+  },
+  "engine": "openai"
+}
+```
+### Regarding `model.json`
+
+- In `settings`, two crucial values are:
+  - `ctx_len`: Defined based on the model's context size.
+  - `prompt_template`: Defined based on the model's trained template (e.g., ChatML, Alpaca).
+  - To set up the `prompt_template`:
+    1. Visit [Hugging Face](https://huggingface.co/), an open-source machine learning platform.
+    2. Find the current model that you're using (e.g., [Gemma 7b it](https://huggingface.co/google/gemma-7b-it)).
+    3. Review the text and identify the template.
+- In `parameters`, consider the following options. The fields in `parameters` are typically general and can be the same across models. An example is provided below:
+
+```json
+"parameters":{
+  "temperature": 0.7,
+  "top_p": 0.95,
+  "stream": true,
+  "max_tokens": 4096,
+  "frequency_penalty": 0,
+  "presence_penalty": 0
+}
+```
+
+
+### Step 3: Starting the Model
+
+1. Restart Jan and proceed to the **Hub**.
+2. Locate your model and click **Use** to activate it.
+
+## Migrating Models from LM Studio to Jan (version 0.4.6 and older)
+
+### Step 1: Model Migration
+
+1. In LM Studio, navigate to `My Models` and access your model folder.
+2. Copy the model folder to `~/jan/models`.
+3. Ensure the folder name matches the model name in the `.gguf` filename. Rename as necessary.
+
+### Step 2: Activating the Model
+
+1. Restart Jan and navigate to the **Hub**, where the model will be automatically detected.
+2. Click **Use** to use the model.
+
+## Direct Access to LM Studio Models from Jan (version 0.4.7+)
+
+Starting from version 0.4.7, Jan enables direct import of LM Studio models using absolute file paths.
+
+
+### Step 1: Locating the Model Path
+
+1. Access `My Models` in LM Studio and locate your model folder.
+2. Obtain the absolute path of your model.
+
+### Step 2: Model Configuration
+
+1. Go to `~/jan/models`.
+2. Create a folder named `(modelname)` (e.g., `phi-2.Q4_K_S`).
+3. Inside, craft a `model.json` file:
+   - Set `id` to match the folder name.
+   - Use the direct binary download link ending in `.gguf` as the `url`. You can now use the absolute filepath of the model file.
+   - Set `engine` as `nitro`.
+
+```json
+{
+  "object": "model",
+  "version": 1,
+  "format": "gguf",
+  "sources": [
+    {
+      "filename": "phi-2.Q4_K_S.gguf",
+      "url": "<absolute-path-of-model-file>"
+    }
+  ],
+  "id": "phi-2.Q4_K_S",
+  "name": "phi-2.Q4_K_S",
+  "created": 1708308111506,
+  "description": "phi-2.Q4_K_S - user self import model",
+  "settings": {
+    "ctx_len": 4096,
+    "embedding": false,
+    "prompt_template": "{system_message}\n### Instruction: {prompt}\n### Response:",
+    "llama_model_path": "phi-2.Q4_K_S.gguf"
+  },
+  "parameters": {
+    "temperature": 0.7,
+    "top_p": 0.95,
+    "stream": true,
+    "max_tokens": 2048,
+    "stop": ["<endofstring>"],
+    "frequency_penalty": 0,
+    "presence_penalty": 0
+  },
+  "metadata": {
+    "size": 1615568736,
+    "author": "User",
+    "tags": []
+  },
+  "engine": "nitro"
+}
+```
+
+:::warning
+
+For Windows users, ensure to include double backslashes in the URL property, such as `C:\\Users\\username\\filename.gguf`.
+
+:::
+
+### Step 3: Starting the Model
+
+1. Restart Jan and proceed to the **Hub**.
+2. Locate your model and click **Use** to activate it.

docs/docs/quickstart/integration/mistral.mdx

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+---
+title: Mistral AI
+sidebar_position: 7
+description: A step-by-step guide on how to integrate Jan with Mistral AI.
+keywords:
+  [
+    Jan AI,
+    Jan,
+    ChatGPT alternative,
+    local AI,
+    private AI,
+    conversational AI,
+    no-subscription fee,
+    large language model,
+    Mistral integration,
+  ]
+---
+
+import flow from './assets/mistral.png';
+
+## How to Integrate Mistral AI with Jan
+
+[Mistral AI](https://docs.mistral.ai/) provides two ways to use their Large Language Models (LLM): 
+1. API
+2. Open-source models on Hugging Face. 
+
+<div class="text--center" > 
+    <img src={ flow } width = { 800} alt = "Mistral" />
+</div>
+
+To integrate Jan with Mistral AI, follow the steps below:
+
+:::note
+This tutorial demonstrates integrating Mistral AI with Jan using the API.
+:::
+
+### Step 1: Configure Mistral API Key
+
+1. Obtain Mistral API keys from your [Mistral](https://console.mistral.ai/user/api-keys/) dashboard.
+2. Insert the Mistral AI API key into `~/jan/engines/openai.json`.
+
+```json title="~/jan/engines/openai.json"
+{
+  "full_url": "https://api.mistral.ai/v1/chat/completions",
+  "api_key": "<your-mistral-ai-api-key>"
+}
+```
+
+### Step 2: Model Configuration
+
+1. Navigate to `~/jan/models`.
+2. Create a folder named `mistral-(modelname)` (e.g., `mistral-tiny`).
+3. Inside, create a `model.json` file with these settings:
+    - Set `id` to the Mistral AI model ID.
+    - Set `format` to `api`.
+    - Set `engine` to `openai`.
+    - Set `state` to `ready`.
+
+```json title="~/jan/models/mistral-tiny/model.json"
+{
+  "sources": [
+    {
+      "filename": "mistral-tiny",
+      "url": "https://mistral.ai/"
+    }
+  ],
+  "id": "mistral-tiny",
+  "object": "model",
+  "name": "Mistral-7B-v0.2 (Tiny Endpoint)",
+  "version": "1.0",
+  "description": "Currently powered by Mistral-7B-v0.2, a better fine-tuning of the initial Mistral-7B released, inspired by the fantastic work of the community.",
+  "format": "api",
+  "settings": {},
+  "parameters": {},
+  "metadata": {
+    "author": "Mistral AI",
+    "tags": ["General", "Big Context Length"]
+  },
+  "engine": "openai"
+}
+
+```
+### Regarding `model.json`
+
+- In `settings`, two crucial values are:
+  - `ctx_len`: Defined based on the model's context size.
+  - `prompt_template`: Defined based on the model's trained template (e.g., ChatML, Alpaca).
+  - To set up the `prompt_template`:
+    1. Visit [Hugging Face](https://huggingface.co/), an open-source machine learning platform.
+    2. Find the current model that you're using (e.g., [Gemma 7b it](https://huggingface.co/google/gemma-7b-it)).
+    3. Review the text and identify the template.
+- In `parameters`, consider the following options. The fields in `parameters` are typically general and can be the same across models. An example is provided below:
+
+```json
+"parameters":{
+  "temperature": 0.7,
+  "top_p": 0.95,
+  "stream": true,
+  "max_tokens": 4096,
+  "frequency_penalty": 0,
+  "presence_penalty": 0
+}
+```
+
+:::note
+
+Mistral AI offers various endpoints. Refer to their [endpoint documentation](https://docs.mistral.ai/platform/endpoints/) to select the one that fits your requirements. Here, we use the `mistral-tiny` model as an example.
+
+:::
+
+### Step 3: Start the Model
+
+1. Restart Jan and navigate to the **Hub**. 
+2. Locate your model and click the **Use** button.

docs/docs/quickstart/integration/ollama.mdx

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+---
+title: Ollama
+sidebar_position: 9
+description: A step-by-step guide on how to integrate Jan with Ollama.
+keywords:
+  [
+    Jan AI,
+    Jan,
+    ChatGPT alternative,
+    local AI,
+    private AI,
+    conversational AI,
+    no-subscription fee,
+    large language model,
+    Ollama integration,
+  ]
+---
+
+import flow from './assets/ollama.png';
+
+## How to Integrate Ollama with Jan
+
+Ollama provides you with largen language that you can run locally. There are two methods to integrate Ollama with Jan:
+1. Integrate Ollama server with Jan.
+2. Migrate the downloaded model from Ollama to Jan.
+
+<div class="text--center" > 
+    <img src={ flow } width = { 800} alt = "Ollama" />
+</div>
+
+To integrate Ollama with Jan, follow the steps below:
+
+:::note
+In this tutorial, we'll show how to integrate Ollama with Jan using the first method. We will use the [llama2](https://ollama.com/library/llama2) model as an example.
+:::
+
+### Step 1: Start the Ollama Server
+
+1. Choose your model from the [Ollama library](https://ollama.com/library).
+2. Run your model with this command:
+
+```sh
+ollama run <model-name>
+```
+
+3. According to the [Ollama documentation on OpenAI compatibility](https://github.com/ollama/ollama/blob/main/docs/openai.md), you can connect to the Ollama server using the web address `http://localhost:11434/v1/chat/completions`. To do this, change the `openai.json` file in the `~/jan/engines` folder to add the Ollama server's full web address:
+
+
+```json title="~/jan/engines/openai.json"
+{
+  "full_url": "http://localhost:11434/v1/chat/completions"
+}
+```
+
+### Step 2: Model Configuration
+
+1. Navigate to the `~/jan/models` folder.
+2. Create a folder named `(ollam-modelname)`, for example, `lmstudio-phi-2`.
+3. Create a `model.json` file inside the folder including the following configurations:
+  - Set the `id` property to the model name as Ollama model name.
+  - Set the `format` property to `api`.
+  - Set the `engine` property to `openai`.
+  - Set the `state` property to `ready`.
+
+```json title="~/jan/models/llama2/model.json"
+{
+  "sources": [
+    {
+      "filename": "llama2",
+      "url": "https://ollama.com/library/llama2"
+    }
+  ],
+  "id": "llama2",
+  "object": "model",
+  "name": "Ollama - Llama2",
+  "version": "1.0",
+  "description": "Llama 2 is a collection of foundation language models ranging from 7B to 70B parameters.",
+  "format": "api",
+  "settings": {},
+  "parameters": {},
+  "metadata": {
+    "author": "Meta",
+    "tags": ["General", "Big Context Length"]
+  },
+  "engine": "openai"
+}
+```
+### Regarding `model.json`
+
+- In `settings`, two crucial values are:
+  - `ctx_len`: Defined based on the model's context size.
+  - `prompt_template`: Defined based on the model's trained template (e.g., ChatML, Alpaca).
+  - To set up the `prompt_template`:
+    1. Visit [Hugging Face](https://huggingface.co/), an open-source machine learning platform.
+    2. Find the current model that you're using (e.g., [Gemma 7b it](https://huggingface.co/google/gemma-7b-it)).
+    3. Review the text and identify the template.
+- In `parameters`, consider the following options. The fields in `parameters` are typically general and can be the same across models. An example is provided below:
+
+```json
+"parameters":{
+  "temperature": 0.7,
+  "top_p": 0.95,
+  "stream": true,
+  "max_tokens": 4096,
+  "frequency_penalty": 0,
+  "presence_penalty": 0
+}
+```
+
+### Step 3: Start the Model
+1. Restart Jan and navigate to the **Hub**. 
+2. Locate your model and click the **Use** button.