Add LightRAG to integrations page (#1338)

* add lightRAG to integrations page

* minor fixes

* Apply suggestions from code review

* list lightrag in main page

---------

Co-authored-by: Matea Pesic <80577904+matea16@users.noreply.github.com>
Co-authored-by: matea16 <mateapesic@hotmail.com>

Author: DavIvek

pages/ai-ecosystem.mdx

@@ -24,7 +24,7 @@ This section of Memgraph’s documentation is your guide to using Memgraph for A
   integrations with popular AI frameworks to help you customize and build your
   own GenAI application from scratch. Some of the libraries that support
   Memgraph include **Model Context Protocol (MCP)**, **LangChain**,
-  **LlamaIndex**, **Cognee** and **Mem0**.
+  **LlamaIndex**, **Cognee**, **Mem0** and **LightRAG**.
 - [Machine learning with Memgraph](/ai-ecosystem/machine-learning): Learn how
   Memgraph powers ML workflows with graph-powered insights.
 - [GraphChat in Memgraph Lab](/memgraph-lab/features/graphchat): Explore how

pages/ai-ecosystem/integrations.mdx

@@ -16,6 +16,7 @@ Memgraph's officially supported integrations:
 - [LangChain](#langchain)
 - [Cognee](#cognee) 
 - [Mem0](#mem0)
+- [LightRAG](#lightrag)
 
 
 ## Model Context Protocol (MCP)
@@ -910,6 +911,142 @@ the official [Mem0
 docs](https://docs.mem0.ai/open-source/graph_memory/overview#initialize-memgraph).
 
 
+## LightRAG
+
+<SocialCards>
+  <SocialCard 
+    icon={<GitHub />}
+    title="LightRAG integration"
+    href="https://github.com/HKUDS/LightRAG"
+    is_external={true}
+  />
+</SocialCards>
+
+LightRAG is a simple and fast retrieval-augmented generation framework that
+combines the power of graph databases with large language models. It provides
+efficient graph-based knowledge representation and retrieval capabilities,
+making it ideal for building RAG applications that require sophisticated
+understanding of entity relationships and context.
+
+LightRAG supports [Memgraph as a graph storage
+backend](https://github.com/HKUDS/LightRAG/blob/main/lightrag/kg/memgraph_impl.py),
+offering high-performance in-memory graph operations with full compatibility
+with the Neo4j Bolt protocol.
+
+{<h3 className="custom-header">Installation</h3>}
+
+To use LightRAG with Memgraph, you need to install LightRAG from source since
+Memgraph support is not yet available in the PyPI release:
+
+```shell
+# Clone the LightRAG repository
+git clone https://github.com/HKUDS/LightRAG.git
+cd LightRAG
+
+# Install from source
+pip install -e .
+```
+
+{<h3 className="custom-header">Environment setup</h3>}
+
+Before getting started, ensure you have [Memgraph](/getting-started) running.
+You can quickly start Memgraph using Docker:
+
+```bash
+docker run -p 7687:7687 memgraph/memgraph:latest
+```
+
+Configure your environment variables for Memgraph connection:
+
+```bash
+export MEMGRAPH_URI="bolt://localhost:7687"
+export MEMGRAPH_USERNAME=""  # Default is empty
+export MEMGRAPH_PASSWORD=""  # Default is empty
+export MEMGRAPH_DATABASE="memgraph"  # Default database name
+```
+
+{<h3 className="custom-header">Basic usage</h3>}
+
+Here's a simple example of using LightRAG with Memgraph:
+
+Export your `OPENAI_API_KEY` environment variable before running the script.
+
+```python
+import os
+import asyncio
+from lightrag import LightRAG, QueryParam
+from lightrag.llm.openai import gpt_4o_mini_complete, gpt_4o_complete, openai_embed
+from lightrag.kg.shared_storage import initialize_pipeline_status
+from lightrag.utils import setup_logger
+
+setup_logger("lightrag", level="INFO")
+
+WORKING_DIR = "./rag_storage"
+if not os.path.exists(WORKING_DIR):
+    os.mkdir(WORKING_DIR)
+
+async def initialize_rag():
+    rag = LightRAG(
+        working_dir=WORKING_DIR,
+        embedding_func=openai_embed,
+        llm_model_func=gpt_4o_mini_complete,
+        graph_storage="MemgraphStorage", #<-----------override KG default
+    )
+    # IMPORTANT: Both initialization calls are required!
+    await rag.initialize_storages()  # Initialize storage backends
+    await initialize_pipeline_status()  # Initialize processing pipeline
+    return rag
+
+async def main():
+    try:
+        # Initialize RAG instance
+        rag = await initialize_rag()
+        rag.insert("Your text")
+
+        # Perform hybrid search
+        mode = "hybrid"
+        print(
+          await rag.query(
+              "What are the top themes in this story?",
+              param=QueryParam(mode=mode)
+          )
+        )
+
+    except Exception as e:
+        print(f"An error occurred: {e}")
+    finally:
+        if rag:
+            await rag.finalize_storages()
+
+if __name__ == "__main__":
+    asyncio.run(main())
+```
+
+{<h3 className="custom-header">Advanced configuration</h3>}
+
+LightRAG provides several configuration options for working with Memgraph:
+
+**Workspace isolation**: Use the `MEMGRAPH_WORKSPACE` environment variable to
+isolate data between different LightRAG instances:
+
+```bash
+export MEMGRAPH_WORKSPACE="project_a"
+```
+
+**Custom connection parameters**: You can also configure connection parameters
+through a `config.ini` file:
+
+```ini
+[memgraph]
+uri = bolt://localhost:7687
+username = 
+password = 
+database = memgraph
+```
+
+For more information about LightRAG's advanced configurations, features, and
+usage examples with Memgraph, visit the [LightRAG GitHub
+repository](https://github.com/HKUDS/LightRAG).
 
 
 <CommunityLinks/>