Merge branch 'main' into qm-memory-improvements

Author: imilinovic

pages/ai-ecosystem/integrations.mdx

@@ -1,16 +1,102 @@
 import {CommunityLinks} from '/components/social-card/CommunityLinks'
 import { Cards } from 'nextra/components'
 import { Callout } from 'nextra/components'
-
+import GitHub from '/components/icons/GitHub'
 
 # Integrations
 
 Memgraph offers several integrations with popular AI frameworks to help you
-customize and build your own GenAI application from scratch. Below are some of
-the libraries integrated with Memgraph.
+customize and build your own GenAI application from scratch. Here is a list of
+Memgraph's officially supported integrations:
+- [Model Context Protocol](#model-context-protocol-mcp)
+- [LlamaIndex](#llamaindex)
+- [LangChain](#langchain)
+
+## Model Context Protocol (MCP)
+
+<Cards>
+  <Cards.Card
+    icon={<GitHub />}
+    title="Memgraph MCP Server"
+    href="https://github.com/memgraph/mcp-memgraph"
+  />
+</Cards>
+
+Memgraph offers Memgraph the [Memgraph MCP
+Server](https://github.com/memgraph/mcp-memgraph) - a lightweight server
+implementation of the Model Context Protocol (MCP) designed to connect Memgraph
+with LLMs.
+
+![mcp-server](/pages/ai-ecosystem/integrations/mcp-server.png)
+
+<h3 className="custom-header">Quick start</h3>
+
+<h4 className="custom-header">1. Run Memgraph MCP Server</h4>
+
+1. Install [`uv`](https://docs.astral.sh/uv/getting-started/installation/) and create `venv` with `uv venv`. Activate virtual environment with `.venv\Scripts\activate`. 
+2. Install dependencies: `uv add "mcp[cli]" httpx`
+3. Run Memgraph MCP server: `uv run server.py`.
+
+
+<h4 className="custom-header"> 2. Run MCP Client</h4>
+1. Install [Claude for Desktop](https://claude.ai/download).
+2. Add the Memgraph server to Claude config: 
+
+**MacOS/Linux**
+```
+code ~/Library/Application\ Support/Claude/claude_desktop_config.json
+```
+
+**Windows**
+
+```
+code $env:AppData\Claude\claude_desktop_config.json
+```
+
+Example config:
+```
+{
+    "mcpServers": {
+      "mpc-memgraph": {
+        "command": "/Users/katelatte/.local/bin/uv",
+        "args": [
+            "--directory",
+            "/Users/katelatte/projects/mcp-memgraph",
+            "run",
+            "server.py"
+        ]
+     }
+   }
+}
+```
+<Callout type="info">
+You may need to put the full path to the uv executable in the command field. You can get this by running `which uv` on MacOS/Linux or `where uv` on Windows. Make sure you pass in the absolute path to your server.
+</Callout>
+
+<h4 className="custom-header">3. Chat with the database</h4>
+1. Run Memgraph MAGE:
+   ```bash
+   docker run -p 7687:7687 memgraph/memgraph-mage --schema-info-enabled=True
+   ```
+
+   The `--schema-info-enabled` configuration setting is set to `True` to allow LLM to run `SHOW SCHEMA INFO` query.
+2. Open Claude Desktop and see the Memgraph tools and resources listed. Try it out! (You can load dummy data from [Memgraph Lab](https://memgraph.com/docs/data-visualization) Datasets)
+
+
+<h3 className="custom-header">Resources</h3>
+- [Memgraph MCP Server Quick start](https://www.youtube.com/watch?v=0Tjw5QWj_qY): A video showcasing how to run Memgraph MCP Server. 
+- [Introducing the Memgraph MCP Server](https://memgraph.com/blog/introducing-memgraph-mcp-server): A blog post on how to run Memgraph MCP Server and what are the future plans. 
 
 ## LlamaIndex
 
+<Cards>
+  <Cards.Card
+    icon={<GitHub />}
+    title="LlamaIndex integration"
+    href="https://github.com/run-llama/llama_index/tree/main/llama-index-integrations/graph_stores/llama-index-graph-stores-memgraph"
+  />
+</Cards>
+
 LlamaIndex is a simple, flexible data framework for connecting custom data
 sources to large language models. Currently, [Memgraph's
 integration](https://docs.llamaindex.ai/en/stable/api_reference/storage/graph_stores/memgraph/)
@@ -159,6 +245,14 @@ work together in real-world applications with these interactive demos:
 
 ## LangChain
 
+<Cards>
+  <Cards.Card
+    icon={<GitHub />}
+    title="LangChain integration"
+    href="https://github.com/memgraph/langchain-memgraph"
+  />
+</Cards>
+
 [LangChain](https://www.langchain.com/) is a framework for developing applications powered by large language
 models (LLMs). 
 

pages/clustering/high-availability.mdx

@@ -40,6 +40,7 @@ specifying only `--management-port` flag. This port is used for RPC network comm
 instances. When started by default, the data instance is MAIN. The coordinator will ensure that no data inconsistency can happen during and after the instance's
 restart. Once all instances are started, the user can start adding data instances to the cluster.
 
+
 <Callout type="info">
 
 The Raft consensus algorithm ensures that all nodes in a distributed system
@@ -51,6 +52,15 @@ since Raft, as a consensus algorithm, works by forming a majority in the decisio
 
 </Callout>
 
+<Callout type="info">
+
+When deploying coordinators to servers, you can use the instance of almost any size. Instances of 4GiB or 8GiB will suffice since coordinators'
+job mainly involves network communication and storing Raft metadata. Coordinators and data instances can be deployed on same servers (pairwise)
+but from the availability perspective, it is better to separate them physically.
+
+</Callout>
+
+
 
 ## Bolt+routing
 
@@ -874,4 +884,4 @@ that and automatically promote the first alive REPLICA to become the new MAIN. T
 
 </Steps>
 
-<CommunityLinks/>
+<CommunityLinks/>