diff --git a/integrations/mcp-memgraph/pyproject.toml b/integrations/mcp-memgraph/pyproject.toml
index 3f454f9..bf80c8d 100644
--- a/integrations/mcp-memgraph/pyproject.toml
+++ b/integrations/mcp-memgraph/pyproject.toml
@@ -27,6 +27,7 @@ dependencies = [
   "memgraph-toolbox>=0.1.7",
   "pytest>=8.3.5",
   "fastmcp>=2.13.0.2",
+  "bs4>=0.0.2",
 ]
 
 [project.optional-dependencies]
diff --git a/integrations/mcp-memgraph/src/mcp_memgraph/config.py b/integrations/mcp-memgraph/src/mcp_memgraph/config.py
index 9e5cd16..c0203cb 100644
--- a/integrations/mcp-memgraph/src/mcp_memgraph/config.py
+++ b/integrations/mcp-memgraph/src/mcp_memgraph/config.py
@@ -15,6 +15,7 @@ class TransportType(str, Enum):
 
     STDIO = "stdio"
     STREAMABLE_HTTP = "streamable-http"
+    SSE = "sse"
 
     @classmethod
     def values(cls) -> list[str]:
diff --git a/integrations/mcp-memgraph/src/mcp_memgraph/main.py b/integrations/mcp-memgraph/src/mcp_memgraph/main.py
index c844cca..cb88960 100644
--- a/integrations/mcp-memgraph/src/mcp_memgraph/main.py
+++ b/integrations/mcp-memgraph/src/mcp_memgraph/main.py
@@ -35,16 +35,16 @@ def main():
 
         logger.warning("Failed to load server '%s', using default server", server_name)
 
-    logger.info("Server on %s with transport: %s", config.host, config.transport)
+    logger.info("Server on %s:%s with transport: %s", config.host, config.port, config.transport)
 
     # Run server with configuration
-    # Note: host parameter is only used for HTTP/SSE transports, not stdio
-    transport = cast(Literal["stdio", "streamable-http"], config.transport)
+    # Note: host and port parameters are only used for HTTP/SSE transports, not stdio
+    transport = cast(Literal["stdio", "streamable-http", "sse"], config.transport)
 
     if config.transport == "stdio":
         mcp.run(transport=transport)
     else:
-        mcp.run(host=config.host, transport=transport)
+        mcp.run(host=config.host, port=config.port, transport=transport)
 
 
 if __name__ == "__main__":
diff --git a/integrations/mcp-memgraph/src/mcp_memgraph/servers/__init__.py b/integrations/mcp-memgraph/src/mcp_memgraph/servers/__init__.py
index 002c6c8..89bf451 100644
--- a/integrations/mcp-memgraph/src/mcp_memgraph/servers/__init__.py
+++ b/integrations/mcp-memgraph/src/mcp_memgraph/servers/__init__.py
@@ -23,6 +23,13 @@
             "support for autonomous index management"
         ),
     },
+    "memgraph-docs": {
+        "module": "mcp_memgraph.servers.docs_server",
+        "emoji": "🚀",
+        "description": (
+            "Memgraph documentation server for searching Memgraph documentation information"
+        ),
+    },
     # Future servers can be added here:
     # "hygm": {
     #     "module": "mcp_memgraph.servers.hygm",
diff --git a/integrations/mcp-memgraph/src/mcp_memgraph/servers/docs_server.py b/integrations/mcp-memgraph/src/mcp_memgraph/servers/docs_server.py
new file mode 100644
index 0000000..bacaf90
--- /dev/null
+++ b/integrations/mcp-memgraph/src/mcp_memgraph/servers/docs_server.py
@@ -0,0 +1,448 @@
+from fastmcp import FastMCP
+from fastmcp.utilities.types import Image
+
+from memgraph_toolbox.api.memgraph import Memgraph
+from memgraph_toolbox.tools.cypher import CypherTool
+from memgraph_toolbox.utils.logger import logger_init
+
+from typing import Any, Dict, List, Union
+import re
+import httpx
+from bs4 import BeautifulSoup
+from urllib.parse import urljoin
+
+from mcp_memgraph.config import get_memgraph_config, get_mcp_config
+
+# Get configuration instances
+memgraph_config = get_memgraph_config()
+mcp_config = get_mcp_config()
+
+# Configure logging
+logger = logger_init("mcp-memgraph-docs")
+
+# Initialize FastMCP server
+mcp = FastMCP("mcp-memgraph-docs")
+
+# Read-only mode flag (from config)
+READ_ONLY_MODE = mcp_config.read_only
+
+# Patterns for write operations in Cypher
+WRITE_PATTERNS = [
+    r"\bCREATE\b",
+    r"\bMERGE\b",
+    r"\bDELETE\b",
+    r"\bREMOVE\b",
+    r"\bSET\b",
+    r"\bDROP\b",
+    r"\bCREATE\s+INDEX\b",
+    r"\bDROP\s+INDEX\b",
+    r"\bCREATE\s+CONSTRAINT\b",
+    r"\bDROP\s+CONSTRAINT\b",
+]
+
+
+def is_write_query(query: str) -> bool:
+    """Check if a Cypher query contains write operations"""
+    query_upper = query.upper()
+    for pattern in WRITE_PATTERNS:
+        if re.search(pattern, query_upper):
+            return True
+    return False
+
+
+# Initialize Memgraph client using configuration
+logger.info(
+    "Memgraph connection configured for db '%s' at %s with user '%s'",
+    memgraph_config.database,
+    memgraph_config.url,
+    memgraph_config.username,
+)
+logger.info("Read-only mode: %s", READ_ONLY_MODE)
+
+# Lazy initialization - connection will be established on first use
+db = None
+
+
+def get_db():
+    """Get or create Memgraph database connection."""
+    global db
+    if db is None:
+        try:
+            db = Memgraph(**memgraph_config.get_client_config())
+            logger.info("Successfully connected to Memgraph")
+        except Exception as e:
+            logger.error("Failed to connect to Memgraph: %s", str(e))
+            raise
+    return db
+
+
+@mcp.tool()
+def run_query(query: str) -> List[Dict[str, Any]]:
+    """Run a Cypher query on Memgraph. Write operations are blocked if
+    server is in read-only mode."""
+    logger.info("Running query: %s", query)
+
+    # Check if query is a write operation in read-only mode
+    if READ_ONLY_MODE and is_write_query(query):
+        logger.warning("Write operation blocked in read-only mode: %s", query)
+        return [
+            {
+                "error": "Write operations are not allowed in read-only mode",
+                "query": query,
+                "mode": "read-only",
+                "hint": "Set MCP_READ_ONLY=false to enable write operations",
+            }
+        ]
+
+    try:
+        result = CypherTool(db=get_db()).call({"query": query})
+        return result
+    except Exception as e:
+        return [{"error": f"Error running query: {str(e)}"}]
+
+
+@mcp.tool()
+def search_relevant_documents(question: str, limit: int = 10) -> List[Dict[str, Any]]:
+    """Search for relevant document chunks based on your question.
+
+    Use this tool when you need to find information in the documentation. It understands
+    the meaning of your question and returns the most relevant text passages that can
+    help answer it.
+
+    Args:
+        question: Your question or topic you want to find information about
+        limit: Maximum number of document chunks to return (default: 10, recommended to use 10 or more)
+
+    Returns:
+        List of relevant document chunks with similarity scores
+    """
+    logger.info("Performing vector search for: %s (limit: %d)", question, limit)
+
+    # Sanitize the question to prevent Cypher injection
+    sanitized_query = question.replace("'", "\\'").replace("\\", "\\\\")
+
+    # Construct the vector search query
+    cypher_query = f"""CALL embeddings.text(["{sanitized_query}"]) yield dimension, embeddings, success
+WITH embeddings[0] as embedding
+CALL vector_search.search("vs_name", {limit}, embedding) YIELD distance, node, similarity
+RETURN node, similarity"""
+
+    try:
+        result = CypherTool(db=get_db()).call({"query": cypher_query})
+
+        # Remove embedding fields to save tokens
+        def remove_embeddings(obj):
+            if isinstance(obj, dict):
+                return {
+                    k: remove_embeddings(v) for k, v in obj.items() if k != "embedding"
+                }
+            elif isinstance(obj, list):
+                return [remove_embeddings(item) for item in obj]
+            else:
+                return obj
+
+        cleaned_result = remove_embeddings(result)
+        return cleaned_result
+    except Exception as e:
+        logger.error("Error executing vector search: %s", str(e))
+        return [{"error": f"Error executing vector search: {str(e)}"}]
+
+
+@mcp.tool()
+def search_documentation_for_keyword(
+    search_term: str, limit: int = 10
+) -> List[Dict[str, Any]]:
+    """Search documentation by specific keywords or phrases.
+
+    Use this tool when you want to find exact matches for specific terms, names, or phrases
+    in the documentation. This is useful when you know the exact keyword you're looking for.
+
+    Args:
+        search_term: The specific keyword or phrase to find
+        limit: Maximum number of results to return (default: 10, recommended to use 10 or more)
+
+    Returns:
+        List of matching document chunks with relevance scores
+    """
+    logger.info("Keyword search for term '%s' (limit: %d)", search_term, limit)
+
+    # Sanitize inputs to prevent Cypher injection
+    sanitized_term = search_term.replace("'", "\\'").replace("\\", "\\\\")
+
+    # Construct the keyword search query with hardcoded "entity_id" property
+    cypher_query = f"""CALL text_search.search_all("entity_id", "{sanitized_term}") YIELD node, score
+RETURN node, score
+ORDER BY score DESC
+LIMIT {limit}"""
+
+    try:
+        result = CypherTool(db=get_db()).call({"query": cypher_query})
+        return result
+    except Exception as e:
+        logger.error("Error executing keyword search: %s", str(e))
+        return [{"error": f"Error executing keyword search: {str(e)}"}]
+
+
+@mcp.tool()
+def search_cypher_queries_for_keyword(
+    search_term: str, limit: int = 10
+) -> List[Dict[str, Any]]:
+    """Search for queries related to a specific concept or topic.
+
+    Use this tool when you want to find what questions or queries have been asked
+    about a particular concept. This helps you discover different ways people ask
+    about a topic or find related query patterns.
+
+    Args:
+        search_term: The concept or topic to find queries about
+        limit: Maximum number of queries to return (default: 10, recommended to use 10 or more)
+
+    Returns:
+        List of related queries with their relevance scores
+    """
+    logger.info("Searching queries for concept '%s' (limit: %d)", search_term, limit)
+
+    # Sanitize inputs to prevent Cypher injection
+    sanitized_term = search_term.replace("'", "\\'").replace("\\", "\\\\")
+
+    # Construct the query search
+    cypher_query = f"""CALL text_search.search_all("query", "{sanitized_term}") YIELD node, score
+RETURN node, score
+ORDER BY score DESC
+LIMIT {limit}"""
+
+    try:
+        result = CypherTool(db=get_db()).call({"query": cypher_query})
+        return result
+    except Exception as e:
+        logger.error("Error executing query search: %s", str(e))
+        return [{"error": f"Error executing query search: {str(e)}"}]
+
+
+@mcp.tool()
+def get_relevant_urls_for_search_term(
+    search_term: str, limit: int = 10
+) -> List[Dict[str, Any]]:
+    """Search for relevant URLs based on a search term.
+
+    Use this tool when you want to find URLs that are relevant to a specific topic or keyword.
+    This searches through URL descriptions to find the most relevant documentation pages.
+
+    Args:
+        search_term: The keyword or phrase to search for in URL descriptions
+        limit: Maximum number of URLs to return (default: 10)
+
+    Returns:
+        List of relevant URLs with their descriptions and relevance scores
+    """
+    logger.info("Searching URLs for term '%s' (limit: %d)", search_term, limit)
+
+    # Sanitize inputs to prevent Cypher injection
+    sanitized_term = search_term.replace("'", "\\'").replace("\\", "\\\\")
+
+    # Construct the URL search query
+    cypher_query = f"""CALL text_search.search_all("url_description", "{sanitized_term}") YIELD node, score
+RETURN node.name as url, node.description as description, score
+ORDER BY score DESC
+LIMIT {limit}"""
+
+    try:
+        result = CypherTool(db=get_db()).call({"query": cypher_query})
+        return result
+    except Exception as e:
+        logger.error("Error executing URL search: %s", str(e))
+        return [{"error": f"Error executing URL search: {str(e)}"}]
+
+
+@mcp.tool()
+def read_url(url: str) -> List[Dict[str, Any]]:
+    """Read and retrieve content from a URL on the internet.
+
+    Use this tool when you find a URL and want to read its actual content from the web.
+    This fetches the webpage, extracts the text content, and returns it along with all
+    URLs and image URLs found on the page for navigation. Use the get_image tool to
+    retrieve actual image data in base64 format. Useful for accessing external
+    documentation, references, or resources.
+
+    Args:
+        url: The URL to read content from
+
+    Returns:
+        The text content extracted from the URL, a list of URLs found on the page,
+        and a list of image URLs with alt text
+    """
+    logger.info("Fetching content from URL: %s", url)
+
+    try:
+        # Fetch the URL content
+        with httpx.Client(timeout=10.0) as client:
+            response = client.get(
+                url,
+                headers={
+                    "User-Agent": "Mozilla/5.0 (compatible; MCP-Memgraph-Docs/1.0)"
+                },
+            )
+            response.raise_for_status()
+
+            # Parse HTML content
+            soup = BeautifulSoup(response.content, "html.parser")
+
+            # Special handling for memgraph.com/docs - find main inside main
+            if "memgraph.com/docs" in url:
+                # Find outer <main> element
+                outer_main = soup.find("main")
+                if not outer_main:
+                    raise ValueError(
+                        "Could not find outer <main> element on memgraph.com/docs page"
+                    )
+
+                # Find inner <main> element within the outer one
+                inner_main = outer_main.find("main")
+                if not inner_main:
+                    raise ValueError(
+                        "Could not find inner <main> element within outer <main> on memgraph.com/docs page"
+                    )
+
+                content_element = inner_main
+            else:
+                content_element = soup
+
+            # Extract all URLs from the content element
+            page_urls = []
+            for link in content_element.find_all("a", href=True):
+                href = link["href"]
+                # Convert relative URLs to absolute
+                if href.startswith("/"):
+                    href = urljoin(url, href)
+                if href.startswith("http"):
+                    page_urls.append(
+                        {"url": href, "text": link.get_text(strip=True) or "No text"}
+                    )
+
+            # Extract image URLs from the content element only
+            images = []
+            for img in content_element.find_all("img", src=True):
+                img_url = img["src"]
+                # Convert relative URLs to absolute
+                if not img_url.startswith("http"):
+                    img_url = urljoin(url, img_url)
+
+                images.append({"url": img_url, "alt": img.get("alt", "")})
+
+            # Remove script and style elements
+            for script in content_element(
+                ["script", "style", "nav", "footer", "header"]
+            ):
+                script.decompose()
+
+            # Get text content
+            text = content_element.get_text(separator="\n", strip=True)
+
+            # Clean up whitespace
+            lines = (line.strip() for line in text.splitlines())
+            text = "\n".join(line for line in lines if line)
+
+            return [
+                {
+                    "url": url,
+                    "content": text[
+                        :50000
+                    ],  # Limit to 50k characters to avoid token overflow
+                    "page_urls": page_urls[
+                        :100
+                    ],  # Limit to 100 URLs to avoid token overflow
+                    "images": images[:50],  # Limit to 50 images to avoid excessive data
+                    "status": "success",
+                }
+            ]
+
+    except httpx.TimeoutException:
+        logger.error("Timeout while fetching URL: %s", url)
+        return [{"error": f"Timeout while fetching URL: {url}"}]
+    except httpx.HTTPStatusError as e:
+        logger.error("HTTP error fetching URL %s: %s", url, str(e))
+        return [{"error": f"HTTP error fetching URL: {str(e)}"}]
+    except httpx.RequestError as e:
+        logger.error("Error fetching URL %s: %s", url, str(e))
+        return [{"error": f"Error fetching URL: {str(e)}"}]
+    except Exception as e:
+        logger.error("Error processing URL %s: %s", url, str(e))
+        return [{"error": f"Error processing URL: {str(e)}"}]
+
+
+@mcp.tool()
+def get_image(image_url: str) ->  Union[Image, List[Dict[str, Any]]]:
+    """Retrieve an image from a URL and return a renderable format of it.
+
+    Use this tool when you have an image URL (e.g., from read_url results) and want
+    to retrieve the actual image data.
+    When you call the `read_url` tool, it will also display the urls of images on the page
+    you can use in order to augment the response with. It's always good to get the images in order
+    to have a more interactive response with the user.
+
+    Args:
+        image_url: The URL of the image to retrieve
+
+    Returns:
+        Image format that can be displayed to the user.
+    """
+    logger.info("Fetching image from URL: %s", image_url)
+    return f"![]({image_url})"
+
+    # try:
+    #     with httpx.Client(timeout=10.0) as client:
+    #         response = client.get(
+    #             image_url,
+    #             headers={
+    #                 "User-Agent": "Mozilla/5.0 (compatible; MCP-Memgraph-Docs/1.0)"
+    #             },
+    #         )
+    #         response.raise_for_status()
+
+    #         # Get content type from response headers
+    #         content_type = response.headers.get("content-type", "png").replace("image/", "")
+
+    #         return Image(data=response.content, format=content_type)
+
+    # except httpx.TimeoutException:
+    #     logger.error("Timeout while fetching image: %s", image_url)
+    #     return [{"error": f"Timeout while fetching image: {image_url}"}]
+    # except httpx.HTTPStatusError as e:
+    #     logger.error("HTTP error fetching image %s: %s", image_url, str(e))
+    #     return [{"error": f"HTTP error fetching image: {str(e)}"}]
+    # except httpx.RequestError as e:
+    #     logger.error("Error fetching image %s: %s", image_url, str(e))
+    #     return [{"error": f"Error fetching image: {str(e)}"}]
+    # except Exception as e:
+    #     logger.error("Error processing image %s: %s", image_url, str(e))
+    #     return [{"error": f"Error processing image: {str(e)}"}]
+
+@mcp.tool()
+def read_connected_information(node_id: int) -> List[Dict[str, Any]]:
+    """Read all information connected to a node you already found.
+
+    Use this tool when you have found something relevant (a document chunk, entity,
+    or any other node) and want to explore what's connected to it. This reveals
+    related information, surrounding context, and connected entities that can help
+    you build a more complete understanding.
+
+    Args:
+        node_id: The ID of the node you want to explore connections from
+
+    Returns:
+        The center node and all its connected neighbors with their relationships
+    """
+    logger.info("Reading connected information for node ID: %d", node_id)
+
+    # Construct the relevance expansion query
+    cypher_query = f"""MATCH (n)-[r]-(m)
+WHERE n.node_id = {node_id}
+RETURN n as center_node,
+       collect(DISTINCT {{relationship: r, neighbor: m}}) as connections"""
+
+    try:
+        result = CypherTool(db=get_db()).call({"query": cypher_query})
+        return result
+    except Exception as e:
+        logger.error("Error executing relevance expansion: %s", str(e))
+        return [{"error": f"Error executing relevance expansion: {str(e)}"}]
diff --git a/integrations/mcp-memgraph/uv.lock b/integrations/mcp-memgraph/uv.lock
index 8572a4c..29ee746 100644
--- a/integrations/mcp-memgraph/uv.lock
+++ b/integrations/mcp-memgraph/uv.lock
@@ -1,5 +1,5 @@
 version = 1
-revision = 1
+revision = 3
 requires-python = ">=3.10"
 resolution-markers = [
     "python_full_version >= '3.13'",
@@ -12,9 +12,9 @@ resolution-markers = [
 name = "aiohappyeyeballs"
 version = "2.6.1"
 source = { registry = "https://pypi.org/simple" }
-sdist = { url = "https://files.pythonhosted.org/packages/26/30/f84a107a9c4331c14b2b586036f40965c128aa4fee4dda5d3d51cb14ad54/aiohappyeyeballs-2.6.1.tar.gz", hash = "sha256:c3f9d0113123803ccadfdf3f0faa505bc78e6a72d1cc4806cbd719826e943558", size = 22760 }
+sdist = { url = "https://files.pythonhosted.org/packages/26/30/f84a107a9c4331c14b2b586036f40965c128aa4fee4dda5d3d51cb14ad54/aiohappyeyeballs-2.6.1.tar.gz", hash = "sha256:c3f9d0113123803ccadfdf3f0faa505bc78e6a72d1cc4806cbd719826e943558", size = 22760, upload-time = "2025-03-12T01:42:48.764Z" }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/0f/15/5bf3b99495fb160b63f95972b81750f18f7f4e02ad051373b669d17d44f2/aiohappyeyeballs-2.6.1-py3-none-any.whl", hash = "sha256:f349ba8f4b75cb25c99c5c2d84e997e485204d2902a9597802b0371f09331fb8", size = 15265 },
+    { url = "https://files.pythonhosted.org/packages/0f/15/5bf3b99495fb160b63f95972b81750f18f7f4e02ad051373b669d17d44f2/aiohappyeyeballs-2.6.1-py3-none-any.whl", hash = "sha256:f349ba8f4b75cb25c99c5c2d84e997e485204d2902a9597802b0371f09331fb8", size = 15265, upload-time = "2025-03-12T01:42:47.083Z" },
 ]
 
 [[package]]
@@ -31,110 +31,110 @@ dependencies = [
     { name = "propcache" },
     { name = "yarl" },
 ]
-sdist = { url = "https://files.pythonhosted.org/packages/1c/ce/3b83ebba6b3207a7135e5fcaba49706f8a4b6008153b4e30540c982fae26/aiohttp-3.13.2.tar.gz", hash = "sha256:40176a52c186aefef6eb3cad2cdd30cd06e3afbe88fe8ab2af9c0b90f228daca", size = 7837994 }
-wheels = [
-    { url = "https://files.pythonhosted.org/packages/6d/34/939730e66b716b76046dedfe0842995842fa906ccc4964bba414ff69e429/aiohttp-3.13.2-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:2372b15a5f62ed37789a6b383ff7344fc5b9f243999b0cd9b629d8bc5f5b4155", size = 736471 },
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 [[package]]
@@ -3122,71 +3174,71 @@ source = { registry = "https://pypi.org/simple" }
 dependencies = [
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 [[package]]
 name = "zipp"
 version = "3.23.0"
 source = { registry = "https://pypi.org/simple" }
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diff --git a/unstructured2graph/examples/documentation_graphrag.py b/unstructured2graph/examples/documentation_graphrag.py
new file mode 100644
index 0000000..2e14248
--- /dev/null
+++ b/unstructured2graph/examples/documentation_graphrag.py
@@ -0,0 +1,109 @@
+import os
+import logging
+import argparse
+import asyncio
+import shutil
+
+from dotenv import load_dotenv
+from lightrag_memgraph import MemgraphLightRAGWrapper
+from memgraph_toolbox.api.memgraph import Memgraph
+
+from unstructured2graph import from_unstructured, create_index
+
+load_dotenv()
+
+SCRIPT_DIR = os.path.dirname(os.path.realpath(__file__))
+LIGHTRAG_DIR = os.path.join(SCRIPT_DIR, "..", "lightrag_storage.out")
+
+# Hardcoded list of documentation URLs to process
+DOC_URLS = [
+    "https://memgraph.com/docs/deployment/workloads",
+    "https://memgraph.com/docs/deployment/workloads/memgraph-in-cybersecurity",
+    "https://memgraph.com/docs/deployment/workloads/memgraph-in-graphrag",
+    "https://memgraph.com/docs/deployment/workloads/memgraph-in-high-throughput-workloads",
+    "https://memgraph.com/docs/deployment/workloads/memgraph-in-mission-critical-workloads",
+    "https://memgraph.com/docs/deployment/workloads/memgraph-in-fraud-detection",
+    "https://memgraph.com/docs/deployment/workloads/memgraph-in-supply-chain",
+    "https://memgraph.com/docs/deployment/benchmarking-memgraph",
+]
+
+logger = logging.getLogger(__name__)
+
+
+async def ingest_docs(
+    only_chunks: bool = False,
+    link_chunks: bool = True,
+    cleanup: bool = True
+):
+    doc_urls = DOC_URLS
+    
+    if not doc_urls:
+        logger.warning("No documentation URLs provided. Exiting.")
+        return
+    
+    logger.info(f"Processing {len(doc_urls)} documentation pages. Starting ingestion...")
+    
+    lightrag_log_file = os.path.join(LIGHTRAG_DIR, "lightrag.log")
+    if cleanup:
+        if os.path.exists(lightrag_log_file):
+            os.remove(lightrag_log_file)
+        if os.path.exists(LIGHTRAG_DIR):
+            shutil.rmtree(LIGHTRAG_DIR)
+    if not os.path.exists(LIGHTRAG_DIR):
+        os.mkdir(LIGHTRAG_DIR)
+    
+    memgraph = Memgraph()
+    
+    if cleanup:
+        logger.info("Cleaning up existing data in Memgraph...")
+        memgraph.query("STORAGE MODE IN_MEMORY_ANALYTICAL")
+        memgraph.query("DROP GRAPH")
+        memgraph.query("STORAGE MODE IN_MEMORY_TRANSACTIONAL")
+
+    
+    create_index(memgraph, "Chunk", "hash")
+    
+    lightrag_wrapper = MemgraphLightRAGWrapper(log_level="WARNING", disable_embeddings=True)
+    await lightrag_wrapper.initialize(working_dir=LIGHTRAG_DIR)
+    
+    try:
+        await from_unstructured(
+            doc_urls,
+            memgraph,
+            lightrag_wrapper,
+            only_chunks=only_chunks,
+            link_chunks=link_chunks,
+        )
+        logger.info("Successfully ingested all documentation pages into Memgraph!")
+    finally:
+        await lightrag_wrapper.afinalize()
+
+
+async def main():    
+    # Verify OPENAI_API_KEY is set
+    # LightRAG Integration currently requires an OpenAI API key.
+    if not os.getenv("OPENAI_API_KEY"):
+        raise OSError("OPENAI_API_KEY environment variable is not set. Please set your OpenAI API key.")
+
+    parser = argparse.ArgumentParser(description="Ingest Memgraph documentation URLs into Memgraph")
+    parser.add_argument("--only-chunks", action="store_true", help="Only create chunk nodes without LightRAG")
+    parser.add_argument("--no-link-chunks", action="store_true", help="Don't link chunks with NEXT relationship")
+    parser.add_argument("--no-cleanup", action="store_true", help="Don't cleanup existing data")
+    parser.add_argument("--log-level", type=str, default="INFO", choices=["DEBUG", "INFO", "WARNING", "ERROR"], help="Logging level")
+    
+    args = parser.parse_args()
+    
+    logging.basicConfig(
+        level=getattr(logging, args.log_level),
+        format='%(asctime)s - %(name)s - %(levelname)s - %(message)s'
+    )
+    
+    await ingest_docs(
+        only_chunks=args.only_chunks,
+        link_chunks=not args.no_link_chunks,
+        cleanup=not args.no_cleanup
+    )
+
+
+if __name__ == "__main__":
+    asyncio.run(main())
diff --git a/uv.lock b/uv.lock
index 6dc2d0a..a6649f9 100644
--- a/uv.lock
+++ b/uv.lock
@@ -1,16 +1,16 @@
 version = 1
-revision = 1
+revision = 3
 requires-python = ">=3.10"
 resolution-markers = [
     "python_full_version >= '3.14' and platform_python_implementation == 'PyPy' and sys_platform == 'darwin'",
     "python_full_version >= '3.14' and platform_machine == 'aarch64' and platform_python_implementation == 'PyPy' and sys_platform == 'linux'",
     "(python_full_version >= '3.14' and platform_machine != 'aarch64' and platform_python_implementation == 'PyPy' and sys_platform == 'linux') or (python_full_version >= '3.14' and platform_python_implementation == 'PyPy' and sys_platform != 'darwin' and sys_platform != 'linux')",
-    "python_full_version == '3.13.*' and platform_python_implementation == 'PyPy' and sys_platform == 'darwin'",
-    "python_full_version == '3.13.*' and platform_machine == 'aarch64' and platform_python_implementation == 'PyPy' and sys_platform == 'linux'",
-    "(python_full_version == '3.13.*' and platform_machine != 'aarch64' and platform_python_implementation == 'PyPy' and sys_platform == 'linux') or (python_full_version == '3.13.*' and platform_python_implementation == 'PyPy' and sys_platform != 'darwin' and sys_platform != 'linux')",
     "python_full_version >= '3.14' and platform_python_implementation != 'PyPy' and sys_platform == 'darwin'",
     "python_full_version >= '3.14' and platform_machine == 'aarch64' and platform_python_implementation != 'PyPy' and sys_platform == 'linux'",
     "(python_full_version >= '3.14' and platform_machine != 'aarch64' and platform_python_implementation != 'PyPy' and sys_platform == 'linux') or (python_full_version >= '3.14' and platform_python_implementation != 'PyPy' and sys_platform != 'darwin' and sys_platform != 'linux')",
+    "python_full_version == '3.13.*' and platform_python_implementation == 'PyPy' and sys_platform == 'darwin'",
+    "python_full_version == '3.13.*' and platform_machine == 'aarch64' and platform_python_implementation == 'PyPy' and sys_platform == 'linux'",
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