add LibreChat MCP guide

Author: Blargian

docs/use-cases/AI_ML/MCP/librechat.md

@@ -0,0 +1,244 @@
+---
+slug: /use-cases/AI/MCP/librechat
+sidebar_label: 'LibreChat and ClickHouse MCP'
+title: 'Set Up ClickHouse MCP Server with LibreChat and ClickHouse Cloud'
+pagination_prev: null
+pagination_next: null
+description: 'This guide explains how to set up LibreChat with a ClickHouse MCP server using Docker.'
+keywords: ['AI', 'Librechat', 'MCP']
+show_related_blogs: true
+---
+
+import {CardHorizontal} from '@clickhouse/click-ui/bundled'
+import Link from '@docusaurus/Link';
+import Image from '@theme/IdealImage';
+import LibreInterface from '@site/static/images/use-cases/AI_ML/MCP/librechat.png';
+
+# Using ClickHouse MCP server with LibreChat
+
+> This guide explains how to set up LibreChat with a ClickHouse MCP server using Docker
+> and connect it to the ClickHouse example datasets.
+
+<VerticalStepper headerLevel="h2">
+
+## Install docker {#install-docker}
+
+You will need Docker to run LibreChat and the MCP server. To get Docker:
+1. Visit [docker.com](https://www.docker.com/products/docker-desktop)
+2. Download Docker desktop for your operating system
+3. Install Docker by following the instructions for your operating system
+4. Open Docker Desktop and ensure it is running
+<br/>
+For more information, see the [Docker documentation](https://docs.docker.com/get-docker/).
+
+## Clone LibreChat repository {#clone-librechat-repo}
+
+Open a terminal (command prompt, terminal or PowerShell) and clone the 
+LibreChat repository using the following command:
+
+```bash
+git clone https://github.com/danny-avila/LibreChat.git
+cd LibreChat
+```
+
+## Create and edit the .env file {#create-and-edit-env-file}
+
+Copy the example configuration file from `.env.example` to `.env`:
+
+```bash
+cp .env.example .env
+```
+
+Open the `.env` file in your favorite text editor. You will see sections for 
+many popular LLM providers, including OpenAI, Anthropic, AWS bedrock etc, for 
+example:
+
+```text title=".venv"
+#============#
+# Anthropic  #
+#============#
+#highlight-next-line
+ANTHROPIC_API_KEY=user_provided
+# ANTHROPIC_MODELS=claude-opus-4-20250514,claude-sonnet-4-20250514,claude-3-7-sonnet-20250219,claude-3-5-sonnet-20241022,claude-3-5-haiku-20241022,claude-3-opus-20240229,claude-3-sonnet-20240229,claude-3-haiku-20240307
+# ANTHROPIC_REVERSE_PROXY=
+```
+
+Replace `user_provided` with your API key for the LLM provider you want to use,
+making sure it is quoted, e.g. `"A2bC3d4e5f6g7h8i9j0k1l2m3n4o5p6q7r8s9t0u1v2w3x4y5z"`.
+
+:::note Using a local LLM
+If you don't have an API key you can use a local LLM like Ollama. You'll see how 
+to do this later in step ["Install Ollama"](#add-local-llm-using-ollama). For now
+don't modify the .env file and continue with the next steps.
+:::
+
+## Create a librechat.yaml file {#create-librechat-yaml-file}
+
+Run the following command to create a new `librechat.yaml` file:
+
+```bash
+cp librechat.example.yaml librechat.yaml
+```
+
+This creates the main [configuration file](https://www.librechat.ai/docs/configuration/librechat_yaml) for LibreChat.
+
+## Add ClickHouse MCP server to Docker compose {#add-clickhouse-mcp-server-to-docker-compose}
+
+Next we'll add the ClickHouse MCP server to the LibreChat Docker compose file 
+called `docker-compose.yaml` so that the LLM can interact with the 
+[ClickHouse SQL playground](https://sql.clickhouse.com/).
+
+Find the `services` section in the `docker-compose.yaml` file and add 
+`mcp-clickhouse` as a new service with the following configuration:
+
+```yml title="docker-compose.yaml"
+mcp-clickhouse:
+  image: mcp/clickhouse
+  container_name: mcp-clickhouse
+  ports:
+    - 8001:8000
+  extra_hosts:
+    - "host.docker.internal:host-gateway"
+  environment:
+    - CLICKHOUSE_HOST=sql-clickhouse.clickhouse.com
+    - CLICKHOUSE_USER=demo
+    - CLICKHOUSE_PASSWORD=
+    - CLICKHOUSE_MCP_SERVER_TRANSPORT=sse
+    - CLICKHOUSE_MCP_BIND_HOST=0.0.0.0
+```
+
+If you want to explore your own data, you can do so by
+using the [host, username and password](https://clickhouse.com/docs/getting-started/quick-start/cloud#connect-with-your-app) 
+of your own ClickHouse Cloud service.
+
+<Link to="https://cloud.clickhouse.com/">
+<CardHorizontal
+badgeIcon="cloud"
+badgeIconDir=""
+badgeState="default"
+badgeText=""
+description="
+If you don't have a Cloud account yet, get started with ClickHouse Cloud today and
+receive $300 in credits. At the end of your 30-day free trial, continue with a 
+pay-as-you-go plan, or contact us to learn more about our volume-based discounts.
+Visit our pricing page for details.
+"
+icon="cloud"
+infoText=""
+infoUrl=""
+title="Get started with ClickHouse Cloud"
+isSelected={true}
+/>
+</Link>
+
+## Mount the librechat.yaml file {#mount-librechat-yaml-file}
+
+In `docker-compose.yml`, find the API service:
+
+```yml title="docker-compose.yaml"
+services:
+  #highlight-next-line
+  api:
+    container_name: LibreChat
+    ports:
+      - "${PORT}:${PORT}"
+    ⋮
+    #highlight-next-line
+    volumes:
+      - type: bind
+        source: ./.env
+        target: /app/.env
+      - ./images:/app/client/public/images
+      - ./uploads:/app/uploads
+      - ./logs:/app/api/logs
+```
+
+Under the volumes section, add the following line:
+
+```yml
+- ./librechat.yaml:/librechat/librechat.yaml
+```
+
+This will make the configuration file available to the backend service.
+
+## Configure MCP server in librechat.yaml {#configure-mcp-server-in-librechat-yaml}
+
+Open `librechat.yaml` and place the following configuration at the end of the file:
+
+```yml
+mcpServers:
+  clickhouse-playground:
+    type: sse
+    url: http://host.docker.internal:8001/sse
+```
+
+This configures LibreChat to connect to the MCP server running on Docker.
+
+## Add a local LLM using Ollama (optional) {#add-local-llm-using-ollama}
+
+### Install Ollama {#install-ollama}
+
+Go to the [Ollama website](https://ollama.com/download) and install Ollama for your system.
+
+Once installed, you can run a model like this:
+
+```bash
+ollama run qwen3:32b
+```
+
+This will pull the model to your local machine if it is not present.
+
+For a list of models see the [Ollama library](https://ollama.com/library)
+
+### Configure Ollama in librechat.yaml {#configure-ollama-in-librechat-yaml}
+
+Once the model has downloaded, configure it in `librechat.yaml`:
+
+```text title="librechat.yaml"
+custom:
+  - name: "Ollama"
+    apiKey: "ollama"
+    baseURL: "http://host.docker.internal:11434/v1/"
+    models:
+      default:
+        [
+          "qwen3:32b"
+        ]
+      fetch: false
+    titleConvo: true
+    titleModel: "current_model"
+    summarize: false
+    summaryModel: "current_model"
+    forcePrompt: false
+    modelDisplayLabel: "Ollama"
+```
+
+## Start all services {#start-all-services}
+
+From the root of the LibreChat project folder, run the following command to start the services:
+
+```bash
+docker compose -f docker-compose.yml up
+```
+
+Wait until all services are fully running.
+
+## Open LibreChat in your browser {#open-librechat-in-browser}
+
+Once all services are up and running, open your browser and go to `http://localhost:3080/`
+
+Create a free LibreChat account if you don't yet have one, and sign in. You should 
+now see the LibreChat interface connected to the ClickHouse MCP server, and optionally,
+your local LLM.
+
+From the chat interface, select `clickhouse-playground` as your MCP server:
+
+<Image img={LibreInterface} alt="Select your MCP server" size="md"/>
+
+You can now prompt the LLM to explore the ClickHouse example datasets. Give it a go:
+
+```text title="Prompt"
+What datasets do you have access to?
+```
+
+</VerticalStepper>

docs/use-cases/AI_ML/index.md

@@ -169,7 +169,20 @@ const sidebars = {
             "use-cases/data_lake/glue_catalog",
             "use-cases/data_lake/unity_catalog" 
           ]
-        }
+        },
+        {
+          type: "category",
+          label: "AI/ML",
+          collapsed: true,
+          collapsible: true,
+          link: { type: "doc", id: "use-cases/AI_ML/index" },
+          items: [
+            {
+              type: "autogenerated",
+              dirName: "use-cases/AI_ML",
+            }
+          ]
+        },
       ]
     },
     {