diff --git a/docs/api_reference/api_reference/storage/graph_stores/ladybug.md b/docs/api_reference/api_reference/storage/graph_stores/ladybug.md
new file mode 100644
index 0000000000..00097b406f
--- /dev/null
+++ b/docs/api_reference/api_reference/storage/graph_stores/ladybug.md
@@ -0,0 +1,3 @@
+::: llama_index.graph_stores.ladybug
+options:
+members: - LadybugGraphStore - LadybugPropertyGraphStore
diff --git a/docs/api_reference/mkdocs.yml b/docs/api_reference/mkdocs.yml
index 460b1889b6..4028331bdd 100644
--- a/docs/api_reference/mkdocs.yml
+++ b/docs/api_reference/mkdocs.yml
@@ -221,7 +221,7 @@ plugins:
- ../../llama-index-integrations/postprocessor/llama-index-postprocessor-flag-embedding-reranker
- ../../llama-index-integrations/graph_stores/llama-index-graph-stores-neo4j
- ../../llama-index-integrations/graph_stores/llama-index-graph-stores-falkordb
- - ../../llama-index-integrations/graph_stores/llama-index-graph-stores-kuzu
+ - ../../llama-index-integrations/graph_stores/llama-index-graph-stores-ladybug
- ../../llama-index-integrations/graph_stores/llama-index-graph-stores-nebula
- ../../llama-index-integrations/agent/llama-index-agent-openai
- ../../llama-index-integrations/agent/llama-index-agent-openai-legacy
diff --git a/docs/examples/index_structs/knowledge_graph/LadybugGraphDemo.ipynb b/docs/examples/index_structs/knowledge_graph/LadybugGraphDemo.ipynb
new file mode 100644
index 0000000000..8bc795bdec
--- /dev/null
+++ b/docs/examples/index_structs/knowledge_graph/LadybugGraphDemo.ipynb
@@ -0,0 +1,529 @@
+{
+ "cells": [
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "id": "82f90261",
+ "metadata": {},
+ "source": [
+ "# Ladybug Graph Store\n",
+ "\n",
+ "This notebook walks through configuring `Ladybug` to be the backend for graph storage in LlamaIndex."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "002c17ee",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "%pip install llama-index\n",
+ "%pip install llama-index-llms-openai\n",
+ "%pip install llama-index-graph-stores-ladybug\n",
+ "%pip install pyvis"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "3ec8eb6a",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# My OpenAI Key\n",
+ "import os\n",
+ "\n",
+ "os.environ[\"OPENAI_API_KEY\"] = \"API_KEY_HERE\""
+ ]
+ },
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "id": "02299c66",
+ "metadata": {},
+ "source": [
+ "## Prepare for Ladybug"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "d847733c",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Clean up all the directories used in this notebook\n",
+ "import shutil\n",
+ "\n",
+ "shutil.rmtree(\"./test1\", ignore_errors=True)\n",
+ "shutil.rmtree(\"./test2\", ignore_errors=True)\n",
+ "shutil.rmtree(\"./test3\", ignore_errors=True)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "22aa9152",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import ladybug\n",
+ "\n",
+ "db = ladybug.Database(\"test1\")"
+ ]
+ },
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "id": "be3f7baa-1c0a-430b-981b-83ddca9e71f2",
+ "metadata": {},
+ "source": [
+ "## Using Knowledge Graph with LadybugGraphStore"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "71c8a77a",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from llama_index.graph_stores.ladybug import LadybugGraphStore\n",
+ "\n",
+ "graph_store = LadybugGraphStore(db)"
+ ]
+ },
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "id": "75f1d565-04e8-41bc-9165-166dc89b6b47",
+ "metadata": {},
+ "source": [
+ "#### Building the Knowledge Graph"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "8d0b2364-4806-4656-81e7-3f6e4b910b5b",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from llama_index.core import SimpleDirectoryReader, KnowledgeGraphIndex\n",
+ "from llama_index.llms.openai import OpenAI\n",
+ "from llama_index.core import Settings\n",
+ "from IPython.display import Markdown, display\n",
+ "import ladybug"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "1c297fd3-3424-41d8-9d0d-25fe6310ab62",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "documents = SimpleDirectoryReader(\n",
+ " \"../../../examples/data/paul_graham\"\n",
+ ").load_data()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "61679142-7595-492b-8792-26cbc439caf8",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# define LLM\n",
+ "\n",
+ "llm = OpenAI(temperature=0, model=\"gpt-3.5-turbo\")\n",
+ "Settings.llm = llm\n",
+ "Settings.chunk_size = 512"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "370fd08f-56ff-4c24-b0c4-c93116a6d482",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from llama_index.core import StorageContext\n",
+ "\n",
+ "storage_context = StorageContext.from_defaults(graph_store=graph_store)\n",
+ "\n",
+ "# NOTE: can take a while!\n",
+ "index = KnowledgeGraphIndex.from_documents(\n",
+ " documents,\n",
+ " max_triplets_per_chunk=2,\n",
+ " storage_context=storage_context,\n",
+ ")\n",
+ "# # To reload from an existing graph store without recomputing each time, use:\n",
+ "# index = KnowledgeGraphIndex(nodes=[], storage_context=storage_context)"
+ ]
+ },
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "id": "c39a0eeb-ef16-4982-8ba8-b37c2c5f4437",
+ "metadata": {},
+ "source": [
+ "#### Querying the Knowledge Graph\n",
+ "\n",
+ "First, we can query and send only the triplets to the LLM."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "670300d8-d0a8-4201-bbcd-4a74b199fcdd",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "query_engine = index.as_query_engine(\n",
+ " include_text=False, response_mode=\"tree_summarize\"\n",
+ ")\n",
+ "response = query_engine.query(\n",
+ " \"Tell me more about Interleaf\",\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "eecf2d57-3efa-4b0d-941a-95438d42893c",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/markdown": [
+ "Interleaf was involved in making software, added a scripting language, was inspired by Emacs, taught what not to do, built impressive technology, and made software that became obsolete and was replaced by a service. Additionally, Interleaf made software that could launch as soon as it was done and was affected by rapid changes in the industry."
+ ],
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "display(Markdown(f\"{response}\"))"
+ ]
+ },
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "id": "9eeaba8a",
+ "metadata": {},
+ "source": [
+ "For more detailed answers, we can also send the text from where the retrieved tripets were extracted."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "bd14686d-1c53-4637-9340-3745f2121ae2",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "query_engine = index.as_query_engine(\n",
+ " include_text=True, response_mode=\"tree_summarize\"\n",
+ ")\n",
+ "response = query_engine.query(\n",
+ " \"Tell me more about Interleaf\",\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "b4c87d14-d2d8-4d80-89f6-1e5972973528",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/markdown": [
+ "Interleaf was a company that made software for creating documents. They added a scripting language inspired by Emacs, making it a dialect of Lisp. Despite having smart people and building impressive technology, Interleaf ultimately faced challenges due to the rapid advancements in technology, as they were affected by Moore's Law. The software they created could be launched as soon as it was done, and they made use of software that was considered slick in 1996. Additionally, Interleaf's experience taught valuable lessons about the importance of being run by product people rather than sales people in technology companies, the risks of editing code by too many people, the significance of office environment on productivity, and the impact of conventional office hours on optimal hacking times."
+ ],
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "display(Markdown(f\"{response}\"))"
+ ]
+ },
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "id": "ecc7342a",
+ "metadata": {},
+ "source": [
+ "#### Query with embeddings"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "b20f9da1",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# NOTE: can take a while!\n",
+ "db = ladybug.Database(\"test2\")\n",
+ "graph_store = LadybugGraphStore(db)\n",
+ "storage_context = StorageContext.from_defaults(graph_store=graph_store)\n",
+ "new_index = KnowledgeGraphIndex.from_documents(\n",
+ " documents,\n",
+ " max_triplets_per_chunk=2,\n",
+ " storage_context=storage_context,\n",
+ " include_embeddings=True,\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "01b74b2a",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# query using top 3 triplets plus keywords (duplicate triplets are removed)\n",
+ "query_engine = index.as_query_engine(\n",
+ " include_text=True,\n",
+ " response_mode=\"tree_summarize\",\n",
+ " embedding_mode=\"hybrid\",\n",
+ " similarity_top_k=5,\n",
+ ")\n",
+ "response = query_engine.query(\n",
+ " \"Tell me more about what the author worked on at Interleaf\",\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "02084f6d",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/markdown": [
+ "The author worked on software at Interleaf, a company that made software for creating documents. The software the author worked on was an online store builder, which required a private launch before a public launch to recruit an initial set of users. The author also learned valuable lessons at Interleaf, such as the importance of having technology companies run by product people, the pitfalls of editing code by too many people, the significance of office environment on productivity, and the impact of big bureaucratic customers. Additionally, the author discovered that low-end software tends to outperform high-end software, emphasizing the importance of being the \"entry level\" option in the market."
+ ],
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "display(Markdown(f\"{response}\"))"
+ ]
+ },
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "id": "cd582500-584c-409a-9963-921738f1beb8",
+ "metadata": {},
+ "source": [
+ "#### Visualizing the Graph"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "b9fe3d26-4f9a-4651-b83f-0018672a34e4",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "ladybuggraph_draw.html\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ " \n",
+ " "
+ ],
+ "text/plain": [
+ ""
+ ]
+ },
+ "execution_count": null,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "## create graph\n",
+ "from pyvis.network import Network\n",
+ "\n",
+ "g = index.get_networkx_graph()\n",
+ "net = Network(notebook=True, cdn_resources=\"in_line\", directed=True)\n",
+ "net.from_nx(g)\n",
+ "net.show(\"ladybuggraph_draw.html\")"
+ ]
+ },
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "id": "40b97044-d212-4151-bd72-6ea2cff35a29",
+ "metadata": {},
+ "source": [
+ "#### [Optional] Try building the graph and manually add triplets!"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "f9de2ddb-4e82-438b-ba3a-b7680efed944",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from llama_index.core.node_parser import SentenceSplitter"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "137176d9-1bc2-4203-8379-7b285cd41546",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "node_parser = SentenceSplitter()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "dc609c08-6fce-444c-84cd-a305fcad6bcd",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "nodes = node_parser.get_nodes_from_documents(documents)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "21c3ad61-6f2a-4176-96ba-6e9f52d6243d",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# initialize an empty database\n",
+ "db = ladybug.Database(\"test3\")\n",
+ "graph_store = LadybugGraphStore(db)\n",
+ "storage_context = StorageContext.from_defaults(graph_store=graph_store)\n",
+ "index = KnowledgeGraphIndex(\n",
+ " [],\n",
+ " storage_context=storage_context,\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "41e03f7e-bb98-4fe0-9fc0-369be2864a00",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# add keyword mappings and nodes manually\n",
+ "# add triplets (subject, relationship, object)\n",
+ "\n",
+ "# for node 0\n",
+ "node_0_tups = [\n",
+ " (\"author\", \"worked on\", \"writing\"),\n",
+ " (\"author\", \"worked on\", \"programming\"),\n",
+ "]\n",
+ "for tup in node_0_tups:\n",
+ " index.upsert_triplet_and_node(tup, nodes[0])\n",
+ "\n",
+ "# for node 1\n",
+ "node_1_tups = [\n",
+ " (\"Interleaf\", \"made software for\", \"creating documents\"),\n",
+ " (\"Interleaf\", \"added\", \"scripting language\"),\n",
+ " (\"software\", \"generate\", \"web sites\"),\n",
+ "]\n",
+ "for tup in node_1_tups:\n",
+ " index.upsert_triplet_and_node(tup, nodes[1])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "48b1a666-2f84-4524-851a-66efd2beb611",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "query_engine = index.as_query_engine(\n",
+ " include_text=False, response_mode=\"tree_summarize\"\n",
+ ")\n",
+ "response = query_engine.query(\n",
+ " \"Tell me more about Interleaf\",\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "fb4b99d7-452f-4594-94e9-da10a3a23fb8",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "'Interleaf was involved in creating documents and also added a scripting language to its software.'"
+ ]
+ },
+ "execution_count": null,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "str(response)"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3 (ipykernel)",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/docs/examples/index_structs/knowledge_graph/kuzugraph_draw.html b/docs/examples/index_structs/knowledge_graph/ladybuggraph_draw.html
similarity index 100%
rename from docs/examples/index_structs/knowledge_graph/kuzugraph_draw.html
rename to docs/examples/index_structs/knowledge_graph/ladybuggraph_draw.html
diff --git a/docs/examples/property_graph/property_graph_ladybug.ipynb b/docs/examples/property_graph/property_graph_ladybug.ipynb
new file mode 100644
index 0000000000..698a36e011
--- /dev/null
+++ b/docs/examples/property_graph/property_graph_ladybug.ipynb
@@ -0,0 +1,813 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# pip install llama-index llama-index-embeddings-openai llama-index-graph-stores-ladybug"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "[Ladybug](https://ladybugdb.com/) is an open source, embedded graph database that's designed for query speed and scalability. It implements the Cypher query language, and utilizes a structured property graph model (a variant of the labelled property graph model) with support for ACID transactions. Because Ladybug is embedded, there's no requirement for a server to set up and use the database.\n",
+ "\n",
+ "Let's begin by creating a graph from unstructured text to demonstrate how to use Ladybug as a graph and vector store to answer questions."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import nest_asyncio\n",
+ "\n",
+ "nest_asyncio.apply()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Environment Setup"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import os\n",
+ "\n",
+ "os.environ[\"OPENAI_API_KEY\"] = \"your-api-key-here\""
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We will be using OpenAI models for this example, so we'll specify the OpenAI API key."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "--2025-08-06 13:30:12-- https://raw.githubusercontent.com/run-llama/llama_index/main/docs/examples/data/paul_graham/paul_graham_essay.txt\n",
+ "Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.110.133, 185.199.108.133, 185.199.111.133, ...\n",
+ "Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.110.133|:443... connected.\n",
+ "HTTP request sent, awaiting response... 200 OK\n",
+ "Length: 75042 (73K) [text/plain]\n",
+ "Saving to: ‘data/paul_graham/paul_graham_essay.txt’\n",
+ "\n",
+ "data/paul_graham/pa 100%[===================>] 73.28K --.-KB/s in 0.004s \n",
+ "\n",
+ "2025-08-06 13:30:12 (19.3 MB/s) - ‘data/paul_graham/paul_graham_essay.txt’ saved [75042/75042]\n",
+ "\n"
+ ]
+ }
+ ],
+ "source": [
+ "!mkdir -p 'data/paul_graham/'\n",
+ "!wget 'https://raw.githubusercontent.com/run-llama/llama_index/main/docs/examples/data/paul_graham/paul_graham_essay.txt' -O 'data/paul_graham/paul_graham_essay.txt'"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from llama_index.core import SimpleDirectoryReader\n",
+ "\n",
+ "documents = SimpleDirectoryReader(\"./data/paul_graham/\").load_data()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Graph Construction\n",
+ "\n",
+ "We first need to create an empty Ladybug database directory by calling the `ladybug.Database` constructor. This step instantiates the database and creates the necessary directories and files within a local directory that stores the graph. This `Database` object is then passed to the `LadybugPropertyGraph` constructor."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from pathlib import Path\n",
+ "import ladybug\n",
+ "\n",
+ "DB_NAME = \"ex.ladybug\"\n",
+ "Path(DB_NAME).unlink(missing_ok=True)\n",
+ "db = ladybug.Database(DB_NAME)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Because Ladybug implements the structured graph property model, it imposes some level of structure on the schema of the graph. In the above case, because we did not specify a relationship schema that we want in our graph, it uses a generic schema, where the relationship types are not constrained, allowing the extracted triples from the LLM to be stored as relationships in the graph."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Define LLMs\n",
+ "\n",
+ "Below, we'll define the models used for embedding the text and the LLMs that are used to extract triples from the text and generate the response.\n",
+ "In this case, we specify different temperature settings for the same model - the extraction model has a temperature of 0."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from llama_index.embeddings.openai import OpenAIEmbedding\n",
+ "from llama_index.llms.openai import OpenAI\n",
+ "from llama_index.core import Settings\n",
+ "\n",
+ "embed_model = OpenAIEmbedding(model_name=\"text-embedding-3-small\")\n",
+ "extract_llm = OpenAI(model=\"gpt-4.1-mini\", temperature=0.0)\n",
+ "generate_llm = OpenAI(model=\"gpt-4.1-mini\", temperature=0.3)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Create property graph index with structure\n",
+ "\n",
+ "The recommended way to use Ladybug is to apply a structured schema to the graph. The schema is defined by specifying the relationship types (including direction) that we want in the graph. The imposition of structure helps with generating triples that are more meaningful for the types of questions we may want to answer from the graph.\n",
+ "\n",
+ "By specifying the below validation schema, we can enforce that the graph only contains relationships of the specified types."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from typing import Literal\n",
+ "\n",
+ "entities = Literal[\"PERSON\", \"PLACE\", \"ORGANIZATION\"]\n",
+ "relations = Literal[\"HAS\", \"PART_OF\", \"WORKED_ON\", \"WORKED_WITH\", \"WORKED_AT\"]\n",
+ "# Define the relationship schema that we will pass to our graph store\n",
+ "# This must be a list of valid triples in the form (head_entity, relation, tail_entity)\n",
+ "validation_schema = [\n",
+ " (\"ORGANIZATION\", \"HAS\", \"PERSON\"),\n",
+ " (\"PERSON\", \"WORKED_AT\", \"ORGANIZATION\"),\n",
+ " (\"PERSON\", \"WORKED_WITH\", \"PERSON\"),\n",
+ " (\"PERSON\", \"WORKED_ON\", \"ORGANIZATION\"),\n",
+ " (\"PERSON\", \"PART_OF\", \"ORGANIZATION\"),\n",
+ " (\"ORGANIZATION\", \"PART_OF\", \"ORGANIZATION\"),\n",
+ " (\"PERSON\", \"WORKED_AT\", \"PLACE\"),\n",
+ "]"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Create property graph store with a vector index\n",
+ "\n",
+ "To create a `LadybugPropertyGraphStore` with a vector index, we need to specify the `use_vector_index` parameter as `True`. This will create a vector index on the property graph, allowing us to perform vector-based queries on the graph."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Auto-detected embedding dimension: 1536\n"
+ ]
+ }
+ ],
+ "source": [
+ "from llama_index.graph_stores.ladybug import LadybugPropertyGraphStore\n",
+ "from llama_index.core import PropertyGraphIndex\n",
+ "from llama_index.core.indices.property_graph import SchemaLLMPathExtractor\n",
+ "\n",
+ "graph_store = LadybugPropertyGraphStore(\n",
+ " db,\n",
+ " has_structured_schema=True,\n",
+ " relationship_schema=validation_schema,\n",
+ " use_vector_index=True, # Enable vector index for similarity search\n",
+ " embed_model=embed_model, # Auto-detects embedding dimension from model\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "To construct a property graph with the desired schema, we'll use `SchemaLLMPathExtractor` with the following parameters."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/Users/prrao/code/llama_index/.venv/lib/python3.13/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
+ " from .autonotebook import tqdm as notebook_tqdm\n",
+ "Parsing nodes: 100%|██████████| 1/1 [00:00<00:00, 9.39it/s]\n",
+ "Extracting paths from text with schema: 100%|██████████| 22/22 [00:28<00:00, 1.28s/it]\n",
+ "Generating embeddings: 100%|██████████| 1/1 [00:00<00:00, 1.25it/s]\n",
+ "Generating embeddings: 100%|██████████| 4/4 [00:01<00:00, 3.59it/s]\n"
+ ]
+ }
+ ],
+ "source": [
+ "index = PropertyGraphIndex.from_documents(\n",
+ " documents,\n",
+ " embed_model=embed_model,\n",
+ " kg_extractors=[\n",
+ " SchemaLLMPathExtractor(\n",
+ " llm=extract_llm,\n",
+ " possible_entities=entities,\n",
+ " possible_relations=relations,\n",
+ " kg_validation_schema=validation_schema,\n",
+ " strict=True, # if false, will allow triples outside of the schema\n",
+ " )\n",
+ " ],\n",
+ " property_graph_store=graph_store,\n",
+ " show_progress=True,\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We can now apply the query engine on the index as before."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "The information provided mentions Viaweb and associates it with individuals named Trevor, Trevor Blackwell, Robert, and Paul Graham. However, there is no information given about Interleaf or further details about Viaweb beyond these associations.\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Switch to the generate LLM during retrieval\n",
+ "Settings.llm = generate_llm\n",
+ "\n",
+ "query_text = \"Tell me more about Interleaf and Viaweb?\"\n",
+ "query_engine = index.as_query_engine(include_text=False)\n",
+ "\n",
+ "response = query_engine.query(query_text)\n",
+ "print(str(response))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "'Viaweb -> HAS -> Trevor'"
+ ]
+ },
+ "execution_count": null,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "retriever = index.as_retriever(include_text=False)\n",
+ "nodes = retriever.retrieve(query_text)\n",
+ "nodes[0].text"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Query the vector index\n",
+ "\n",
+ "As an embedded graph database, Ladybug provides a fast and performance graph-based HNSW vector index (see the [docs](https://docs.ladybugdb.com/extensions/vector/)).\n",
+ "This allows you to also use Ladybug for similarity (vector-based) retrieval on chunk nodes.\n",
+ "The vector index is created after the embeddings are ingested into the chunk nodes, so you should be able to query them directly."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ " 1. Similarity: 0.421\n",
+ " Text: Large numbers of former students kept in touch with her, including me. After I moved to New York I became her de facto studio assistant.\n",
+ "\n",
+ "She liked to paint on big, square canvases, 4 to 5 feet on a side. One day in late 1994 as I was stretching one of these monsters there was something on the radio about a famous fund manager. He wasn't that much older than me, and was super rich. The thought suddenly occurred to me: why don't I become rich? Then I'll be able to work on whatever I want.\n",
+ "\n",
+ "Meanwhile I'd been hearing more and more about this new thing called the World Wide Web. Robert Morris showed it to me when I visited him in Cambridge, where he was now in grad school at Harvard. It seemed to me that the web would be a big deal. I'd seen what graphical user interfaces had done for the popularity of microcomputers. It seemed like the web would do the same for the internet.\n",
+ "\n",
+ "If I wanted to get rich, here was the next train leaving the station. I was right about that part. What I got wrong was the idea. I decided we should start a company to put art galleries online. I can't honestly say, after reading so many Y Combinator applications, that this was the worst startup idea ever, but it was up there. Art galleries didn't want to be online, and still don't, not the fancy ones. That's not how they sell. I wrote some software to generate web sites for galleries, and Robert wrote some to resize images and set up an http server to serve the pages. Then we tried to sign up galleries. To call this a difficult sale would be an understatement. It was difficult to give away. A few galleries let us make sites for them for free, but none paid us.\n",
+ "\n",
+ "Then some online stores started to appear, and I realized that except for the order buttons they were identical to the sites we'd been generating for galleries. This impressive-sounding thing called an \"internet storefront\" was something we already knew how to build.\n",
+ "\n",
+ "So in the summer of 1995, after I submitted the camera-ready copy of ANSI Common Lisp to the publishers, we started trying to write software to build online stores. At first this was going to be normal desktop software, which in those days meant Windows software. That was an alarming prospect, because neither of us knew how to write Windows software or wanted to learn. We lived in the Unix world. But we decided we'd at least try writing a prototype store builder on Unix. Robert wrote a shopping cart, and I wrote a new site generator for stores — in Lisp, of course.\n",
+ "\n",
+ "We were working out of Robert's apartment in Cambridge. His roommate was away for big chunks of time, during which I got to sleep in his room. For some reason there was no bed frame or sheets, just a mattress on the floor. One morning as I was lying on this mattress I had an idea that made me sit up like a capital L. What if we ran the software on the server, and let users control it by clicking on links? Then we'd never have to write anything to run on users' computers. We could generate the sites on the same server we'd serve them from. Users wouldn't need anything more than a browser.\n",
+ "\n",
+ "This kind of software, known as a web app, is common now, but at the time it wasn't clear that it was even possible. To find out, we decided to try making a version of our store builder that you could control through the browser. A couple days later, on August 12, we had one that worked. The UI was horrible, but it proved you could build a whole store through the browser, without any client software or typing anything into the command line on the server.\n",
+ "\n",
+ "Now we felt like we were really onto something. I had visions of a whole new generation of software working this way. You wouldn't need versions, or ports, or any of that crap. At Interleaf there had been a whole group called Release Engineering that seemed to be at least as big as the group that actually wrote the software. Now you could just update the software right on the server.\n",
+ "\n",
+ "We started a new company we called Viaweb, after the fact that our software worked via the web, and we got $10,000 in seed funding from Idelle's husband Julian. In return for that and doing the initial legal work and giving us business advice, we gave him 10% of the company. Ten years later this deal became the model for Y Combinator's....\n",
+ "\n",
+ " 2. Similarity: 0.390\n",
+ " Text: I'd compounded this problem by buying a house up in the Santa Cruz Mountains, with a beautiful view but miles from anywhere. I stuck it out for a few more months, then in desperation I went back to New York, where unless you understand about rent control you'll be surprised to hear I still had my apartment, sealed up like a tomb of my old life. Idelle was in New York at least, and there were other people trying to paint there, even though I didn't know any of them.\n",
+ "\n",
+ "When I got back to New York I resumed my old life, except now I was rich. It was as weird as it sounds. I resumed all my old patterns, except now there were doors where there hadn't been. Now when I was tired of walking, all I had to do was raise my hand, and (unless it was raining) a taxi would stop to pick me up. Now when I walked past charming little restaurants I could go in and order lunch. It was exciting for a while. Painting started to go better. I experimented with a new kind of still life where I'd paint one painting in the old way, then photograph it and print it, blown up, on canvas, and then use that as the underpainting for a second still life, painted from the same objects (which hopefully hadn't rotted yet).\n",
+ "\n",
+ "Meanwhile I looked for an apartment to buy. Now I could actually choose what neighborhood to live in. Where, I asked myself and various real estate agents, is the Cambridge of New York? Aided by occasional visits to actual Cambridge, I gradually realized there wasn't one. Huh.\n",
+ "\n",
+ "Around this time, in the spring of 2000, I had an idea. It was clear from our experience with Viaweb that web apps were the future. Why not build a web app for making web apps? Why not let people edit code on our server through the browser, and then host the resulting applications for them? [9] You could run all sorts of services on the servers that these applications could use just by making an API call: making and receiving phone calls, manipulating images, taking credit card payments, etc.\n",
+ "\n",
+ "I got so excited about this idea that I couldn't think about anything else. It seemed obvious that this was the future. I didn't particularly want to start another company, but it was clear that this idea would have to be embodied as one, so I decided to move to Cambridge and start it. I hoped to lure Robert into working on it with me, but there I ran into a hitch. Robert was now a postdoc at MIT, and though he'd made a lot of money the last time I'd lured him into working on one of my schemes, it had also been a huge time sink. So while he agreed that it sounded like a plausible idea, he firmly refused to work on it.\n",
+ "\n",
+ "Hmph. Well, I'd do it myself then. I recruited Dan Giffin, who had worked for Viaweb, and two undergrads who wanted summer jobs, and we got to work trying to build what it's now clear is about twenty companies and several open source projects worth of software. The language for defining applications would of course be a dialect of Lisp. But I wasn't so naive as to assume I could spring an overt Lisp on a general audience; we'd hide the parentheses, like Dylan did.\n",
+ "\n",
+ "By then there was a name for the kind of company Viaweb was, an \"application service provider,\" or ASP. This name didn't last long before it was replaced by \"software as a service,\" but it was current for long enough that I named this new company after it: it was going to be called Aspra.\n",
+ "\n",
+ "I started working on the application builder, Dan worked on network infrastructure, and the two undergrads worked on the first two services (images and phone calls). But about halfway through the summer I realized I really didn't want to run a company — especially not a big one, which it was looking like this would have to be. I'd only started Viaweb because I needed the money. Now that I didn't need money anymore, why was I doing this? If this vision had to be realized as a company, then screw the vision. I'd build a subset that could be done as an open source project.\n",
+ "\n",
+ "Much to my surprise, the time I spent working on this stuff was not wasted after all. After we started Y Combinator, I would often encounter startups working on parts of this new architecture, and it was very useful to have spent so much time thinking about it and even trying to write some of it....\n",
+ "\n",
+ " 3. Similarity: 0.385\n",
+ " Text: In its time, the editor was one of the best general-purpose site builders. I kept the code tight and didn't have to integrate with any other software except Robert's and Trevor's, so it was quite fun to work on. If all I'd had to do was work on this software, the next 3 years would have been the easiest of my life. Unfortunately I had to do a lot more, all of it stuff I was worse at than programming, and the next 3 years were instead the most stressful.\n",
+ "\n",
+ "There were a lot of startups making ecommerce software in the second half of the 90s. We were determined to be the Microsoft Word, not the Interleaf. Which meant being easy to use and inexpensive. It was lucky for us that we were poor, because that caused us to make Viaweb even more inexpensive than we realized. We charged $100 a month for a small store and $300 a month for a big one. This low price was a big attraction, and a constant thorn in the sides of competitors, but it wasn't because of some clever insight that we set the price low. We had no idea what businesses paid for things. $300 a month seemed like a lot of money to us.\n",
+ "\n",
+ "We did a lot of things right by accident like that. For example, we did what's now called \"doing things that don't scale,\" although at the time we would have described it as \"being so lame that we're driven to the most desperate measures to get users.\" The most common of which was building stores for them. This seemed particularly humiliating, since the whole raison d'etre of our software was that people could use it to make their own stores. But anything to get users.\n",
+ "\n",
+ "We learned a lot more about retail than we wanted to know. For example, that if you could only have a small image of a man's shirt (and all images were small then by present standards), it was better to have a closeup of the collar than a picture of the whole shirt. The reason I remember learning this was that it meant I had to rescan about 30 images of men's shirts. My first set of scans were so beautiful too.\n",
+ "\n",
+ "Though this felt wrong, it was exactly the right thing to be doing. Building stores for users taught us about retail, and about how it felt to use our software. I was initially both mystified and repelled by \"business\" and thought we needed a \"business person\" to be in charge of it, but once we started to get users, I was converted, in much the same way I was converted to fatherhood once I had kids. Whatever users wanted, I was all theirs. Maybe one day we'd have so many users that I couldn't scan their images for them, but in the meantime there was nothing more important to do.\n",
+ "\n",
+ "Another thing I didn't get at the time is that growth rate is the ultimate test of a startup. Our growth rate was fine. We had about 70 stores at the end of 1996 and about 500 at the end of 1997. I mistakenly thought the thing that mattered was the absolute number of users. And that is the thing that matters in the sense that that's how much money you're making, and if you're not making enough, you might go out of business. But in the long term the growth rate takes care of the absolute number. If we'd been a startup I was advising at Y Combinator, I would have said: Stop being so stressed out, because you're doing fine. You're growing 7x a year. Just don't hire too many more people and you'll soon be profitable, and then you'll control your own destiny.\n",
+ "\n",
+ "Alas I hired lots more people, partly because our investors wanted me to, and partly because that's what startups did during the Internet Bubble. A company with just a handful of employees would have seemed amateurish. So we didn't reach breakeven until about when Yahoo bought us in the summer of 1998. Which in turn meant we were at the mercy of investors for the entire life of the company. And since both we and our investors were noobs at startups, the result was a mess even by startup standards.\n",
+ "\n",
+ "It was a huge relief when Yahoo bought us. In principle our Viaweb stock was valuable. It was a share in a business that was profitable and growing rapidly. But it didn't feel very valuable to me; I had no idea how to value a business, but I was all too keenly aware of the near-death experiences we seemed to have every few months. Nor had I changed my grad student lifestyle significantly since we started....\n",
+ "\n",
+ " 4. Similarity: 0.318\n",
+ " Text: The UI was horrible, but it proved you could build a whole store through the browser, without any client software or typing anything into the command line on the server.\n",
+ "\n",
+ "Now we felt like we were really onto something. I had visions of a whole new generation of software working this way. You wouldn't need versions, or ports, or any of that crap. At Interleaf there had been a whole group called Release Engineering that seemed to be at least as big as the group that actually wrote the software. Now you could just update the software right on the server.\n",
+ "\n",
+ "We started a new company we called Viaweb, after the fact that our software worked via the web, and we got $10,000 in seed funding from Idelle's husband Julian. In return for that and doing the initial legal work and giving us business advice, we gave him 10% of the company. Ten years later this deal became the model for Y Combinator's. We knew founders needed something like this, because we'd needed it ourselves.\n",
+ "\n",
+ "At this stage I had a negative net worth, because the thousand dollars or so I had in the bank was more than counterbalanced by what I owed the government in taxes. (Had I diligently set aside the proper proportion of the money I'd made consulting for Interleaf? No, I had not.) So although Robert had his graduate student stipend, I needed that seed funding to live on.\n",
+ "\n",
+ "We originally hoped to launch in September, but we got more ambitious about the software as we worked on it. Eventually we managed to build a WYSIWYG site builder, in the sense that as you were creating pages, they looked exactly like the static ones that would be generated later, except that instead of leading to static pages, the links all referred to closures stored in a hash table on the server.\n",
+ "\n",
+ "It helped to have studied art, because the main goal of an online store builder is to make users look legit, and the key to looking legit is high production values. If you get page layouts and fonts and colors right, you can make a guy running a store out of his bedroom look more legit than a big company.\n",
+ "\n",
+ "(If you're curious why my site looks so old-fashioned, it's because it's still made with this software. It may look clunky today, but in 1996 it was the last word in slick.)\n",
+ "\n",
+ "In September, Robert rebelled. \"We've been working on this for a month,\" he said, \"and it's still not done.\" This is funny in retrospect, because he would still be working on it almost 3 years later. But I decided it might be prudent to recruit more programmers, and I asked Robert who else in grad school with him was really good. He recommended Trevor Blackwell, which surprised me at first, because at that point I knew Trevor mainly for his plan to reduce everything in his life to a stack of notecards, which he carried around with him. But Rtm was right, as usual. Trevor turned out to be a frighteningly effective hacker.\n",
+ "\n",
+ "It was a lot of fun working with Robert and Trevor. They're the two most independent-minded people I know, and in completely different ways. If you could see inside Rtm's brain it would look like a colonial New England church, and if you could see inside Trevor's it would look like the worst excesses of Austrian Rococo.\n",
+ "\n",
+ "We opened for business, with 6 stores, in January 1996. It was just as well we waited a few months, because although we worried we were late, we were actually almost fatally early. There was a lot of talk in the press then about ecommerce, but not many people actually wanted online stores. [8]\n",
+ "\n",
+ "There were three main parts to the software: the editor, which people used to build sites and which I wrote, the shopping cart, which Robert wrote, and the manager, which kept track of orders and statistics, and which Trevor wrote. In its time, the editor was one of the best general-purpose site builders. I kept the code tight and didn't have to integrate with any other software except Robert's and Trevor's, so it was quite fun to work on. If all I'd had to do was work on this software, the next 3 years would have been the easiest of my life. Unfortunately I had to do a lot more, all of it stuff I was worse at than programming, and the next 3 years were instead the most stressful.\n",
+ "\n",
+ "There were a lot of startups making ecommerce software in the second half of the 90s. We were determined to be the Microsoft Word, not the Interleaf. Which meant being easy to use and inexpensive....\n",
+ "\n",
+ " 5. Similarity: 0.297\n",
+ " Text: In the art world, money and coolness are tightly coupled. Anything expensive comes to be seen as cool, and anything seen as cool will soon become equally expensive.\n",
+ "\n",
+ "[7] Technically the apartment wasn't rent-controlled but rent-stabilized, but this is a refinement only New Yorkers would know or care about. The point is that it was really cheap, less than half market price.\n",
+ "\n",
+ "[8] Most software you can launch as soon as it's done. But when the software is an online store builder and you're hosting the stores, if you don't have any users yet, that fact will be painfully obvious. So before we could launch publicly we had to launch privately, in the sense of recruiting an initial set of users and making sure they had decent-looking stores.\n",
+ "\n",
+ "[9] We'd had a code editor in Viaweb for users to define their own page styles. They didn't know it, but they were editing Lisp expressions underneath. But this wasn't an app editor, because the code ran when the merchants' sites were generated, not when shoppers visited them.\n",
+ "\n",
+ "[10] This was the first instance of what is now a familiar experience, and so was what happened next, when I read the comments and found they were full of angry people. How could I claim that Lisp was better than other languages? Weren't they all Turing complete? People who see the responses to essays I write sometimes tell me how sorry they feel for me, but I'm not exaggerating when I reply that it has always been like this, since the very beginning. It comes with the territory. An essay must tell readers things they don't already know, and some people dislike being told such things.\n",
+ "\n",
+ "[11] People put plenty of stuff on the internet in the 90s of course, but putting something online is not the same as publishing it online. Publishing online means you treat the online version as the (or at least a) primary version.\n",
+ "\n",
+ "[12] There is a general lesson here that our experience with Y Combinator also teaches: Customs continue to constrain you long after the restrictions that caused them have disappeared. Customary VC practice had once, like the customs about publishing essays, been based on real constraints. Startups had once been much more expensive to start, and proportionally rare. Now they could be cheap and common, but the VCs' customs still reflected the old world, just as customs about writing essays still reflected the constraints of the print era.\n",
+ "\n",
+ "Which in turn implies that people who are independent-minded (i.e. less influenced by custom) will have an advantage in fields affected by rapid change (where customs are more likely to be obsolete).\n",
+ "\n",
+ "Here's an interesting point, though: you can't always predict which fields will be affected by rapid change. Obviously software and venture capital will be, but who would have predicted that essay writing would be?\n",
+ "\n",
+ "[13] Y Combinator was not the original name. At first we were called Cambridge Seed. But we didn't want a regional name, in case someone copied us in Silicon Valley, so we renamed ourselves after one of the coolest tricks in the lambda calculus, the Y combinator.\n",
+ "\n",
+ "I picked orange as our color partly because it's the warmest, and partly because no VC used it. In 2005 all the VCs used staid colors like maroon, navy blue, and forest green, because they were trying to appeal to LPs, not founders. The YC logo itself is an inside joke: the Viaweb logo had been a white V on a red circle, so I made the YC logo a white Y on an orange square.\n",
+ "\n",
+ "[14] YC did become a fund for a couple years starting in 2009, because it was getting so big I could no longer afford to fund it personally. But after Heroku got bought we had enough money to go back to being self-funded.\n",
+ "\n",
+ "[15] I've never liked the term \"deal flow,\" because it implies that the number of new startups at any given time is fixed. This is not only false, but it's the purpose of YC to falsify it, by causing startups to be founded that would not otherwise have existed.\n",
+ "\n",
+ "[16] She reports that they were all different shapes and sizes, because there was a run on air conditioners and she had to get whatever she could, but that they were all heavier than she could carry now.\n",
+ "\n",
+ "[17] Another problem with HN was a bizarre edge case that occurs when you both write essays and run a forum. When you run a forum, you're assumed to see if not every conversation, at least every conversation involving you. And when you write essays, people post highly imaginative misinterpretations of them on forums....\n",
+ "\n"
+ ]
+ }
+ ],
+ "source": [
+ "from llama_index.core.vector_stores.types import VectorStoreQuery\n",
+ "\n",
+ "query_text = \"How much funding did Idelle Weber provide to Viaweb?\"\n",
+ "query_embedding = embed_model.get_text_embedding(query_text)\n",
+ "# Perform direct vector search on the graph store\n",
+ "vector_query = VectorStoreQuery(\n",
+ " query_embedding=query_embedding, similarity_top_k=5\n",
+ ")\n",
+ "\n",
+ "nodes, similarities = graph_store.vector_query(vector_query)\n",
+ "\n",
+ "for i, (node, similarity) in enumerate(zip(nodes, similarities)):\n",
+ " print(f\" {i + 1}. Similarity: {similarity:.3f}\")\n",
+ " print(f\" Text: {node.text}...\")\n",
+ " print()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "1. Score: 0.371\n",
+ " Text: Large numbers of former students kept in touch with her, including me. After I moved to New York I b...\n",
+ " Node ID: 48bda642-e94d-4b79-96fc-4f92ab8813c3\n",
+ "\n",
+ "2. Score: 0.353\n",
+ " Text: I'd compounded this problem by buying a house up in the Santa Cruz Mountains, with a beautiful view ...\n",
+ " Node ID: 17e8d852-4c00-4eab-8258-641b074c8abb\n",
+ "\n",
+ "3. Score: 0.314\n",
+ " Text: In its time, the editor was one of the best general-purpose site builders. I kept the code tight and...\n",
+ " Node ID: 1c666a5d-9058-42cf-8369-b21749b401b4\n",
+ "\n",
+ "4. Score: 0.298\n",
+ " Text: I started working on the application builder, Dan worked on network infrastructure, and the two unde...\n",
+ " Node ID: 9e8a378c-015a-4184-a66b-b00939e11a31\n",
+ "\n",
+ "5. Score: 0.284\n",
+ " Text: The UI was horrible, but it proved you could build a whole store through the browser, without any cl...\n",
+ " Node ID: 9f4b9b54-7be7-4d18-8689-493729dd9078\n",
+ "\n",
+ "6. Score: 1.000\n",
+ " Text: Paul Graham -> WORKED_WITH -> McCarthy...\n",
+ " Node ID: b9d67e62-994c-416d-835b-3fa2c519612f\n",
+ "\n",
+ "7. Score: 1.000\n",
+ " Text: Paul Graham -> WORKED_WITH -> John McCarthy...\n",
+ " Node ID: 672336bd-f8b6-4902-b0e0-d5918c233efe\n",
+ "\n",
+ "8. Score: 1.000\n",
+ " Text: Paul Graham -> WORKED_WITH -> Jessica...\n",
+ " Node ID: 5c8d9d09-d018-44da-adc4-a3a1c0b40ce8\n",
+ "\n",
+ "9. Score: 1.000\n",
+ " Text: Paul Graham -> WORKED_WITH -> Dan...\n",
+ " Node ID: 5a2cd2a5-96f0-489b-a69e-531d9996c422\n",
+ "\n",
+ "10. Score: 1.000\n",
+ " Text: Paul Graham -> WORKED_WITH -> two undergrads...\n",
+ " Node ID: 7ffa6e08-72b5-46a3-9898-5c28c8ba10fe\n",
+ "\n",
+ "11. Score: 1.000\n",
+ " Text: Paul Graham -> WORKED_WITH -> Dan Giffin...\n",
+ " Node ID: 95bd8aaf-29a7-424e-ba83-0a90aeb87edf\n",
+ "\n",
+ "12. Score: 1.000\n",
+ " Text: Paul Graham -> WORKED_WITH -> Idelle...\n",
+ " Node ID: e9576435-ef91-4a2e-834c-790906cde85f\n",
+ "\n",
+ "13. Score: 1.000\n",
+ " Text: Paul Graham -> WORKED_WITH -> Trevor...\n",
+ " Node ID: 8c63d5e2-8abf-4b37-8f8f-5dbb03638ff3\n",
+ "\n",
+ "14. Score: 1.000\n",
+ " Text: Paul Graham -> WORKED_WITH -> Trevor Blackwell...\n",
+ " Node ID: 5fe159e0-010f-4dcd-9c1a-c660ccd98e6f\n",
+ "\n",
+ "15. Score: 1.000\n",
+ " Text: Paul Graham -> WORKED_WITH -> Idelle Weber...\n",
+ " Node ID: 8f722d33-f5d6-4fca-bc4a-03a9d60d7893\n",
+ "\n",
+ "16. Score: 1.000\n",
+ " Text: Paul Graham -> WORKED_WITH -> Robert...\n",
+ " Node ID: 7b82511d-b3ab-410d-b101-18cbe3d521f2\n",
+ "\n",
+ "17. Score: 1.000\n",
+ " Text: Paul Graham -> WORKED_WITH -> Robert Morris...\n",
+ " Node ID: d3d3aed7-c6d8-4a28-8c35-46377bee59fc\n",
+ "\n",
+ "18. Score: 1.000\n",
+ " Text: Paul Graham -> WORKED_WITH -> Idelle's husband Julian...\n",
+ " Node ID: 930a5bbb-bc2b-4f15-b3e5-af272a769660\n",
+ "\n",
+ "19. Score: 1.000\n",
+ " Text: Paul Graham -> WORKED_WITH -> Tom Cheatham...\n",
+ " Node ID: 157a81e5-f455-4831-825b-caa9a496e303\n",
+ "\n",
+ "20. Score: 1.000\n",
+ " Text: Paul Graham -> WORKED_WITH -> Sam Altman...\n",
+ " Node ID: ace9d483-7870-4fda-801c-ad6232b6a0a1\n",
+ "\n",
+ "21. Score: 1.000\n",
+ " Text: Paul Graham -> WORKED_WITH -> professor Cheatham...\n",
+ " Node ID: cf43c8dc-690e-4189-8547-8fe86701eea5\n",
+ "\n",
+ "22. Score: 1.000\n",
+ " Text: Paul Graham -> WORKED_WITH -> Maria Daniels...\n",
+ " Node ID: 4df8d654-22b4-492b-a089-7730444fc604\n",
+ "\n",
+ "23. Score: 1.000\n",
+ " Text: Paul Graham -> WORKED_WITH -> Bill Woods...\n",
+ " Node ID: a0353041-57f8-4252-9cd3-7600d8044bef\n",
+ "\n",
+ "24. Score: 1.000\n",
+ " Text: Paul Graham -> WORKED_WITH -> Roy Lichtenstein...\n",
+ " Node ID: 6cf4eb5b-3755-4d74-9a9d-d15e56a54051\n",
+ "\n",
+ "25. Score: 1.000\n",
+ " Text: Paul Graham -> WORKED_WITH -> Jessica Livingston...\n",
+ " Node ID: 1f29de49-92d5-4350-9e82-2101b72ed500\n",
+ "\n",
+ "26. Score: 1.000\n",
+ " Text: Paul Graham -> WORKED_WITH -> Rich Draves...\n",
+ " Node ID: 3aa36c90-6e87-48f9-937a-d8a71f4721bb\n",
+ "\n",
+ "27. Score: 1.000\n",
+ " Text: Paul Graham -> PART_OF -> Release Engineering...\n",
+ " Node ID: bc30a50d-7e13-4d59-b79f-327a19105ea3\n",
+ "\n",
+ "28. Score: 1.000\n",
+ " Text: Paul Graham -> PART_OF -> PhD program in computer science...\n",
+ " Node ID: 26ead48c-9ae6-42f0-9f20-0d241bb9202f\n",
+ "\n",
+ "29. Score: 1.000\n",
+ " Text: Paul Graham -> PART_OF -> Cornell University AI program...\n",
+ " Node ID: 80f3a0cc-0621-4d11-b73d-1854c132d3f1\n",
+ "\n",
+ "30. Score: 1.000\n",
+ " Text: Paul Graham -> WORKED_ON -> YC...\n",
+ " Node ID: fadc24a5-a3f1-4e6f-9de5-b4c6960c7bfa\n",
+ "\n",
+ "31. Score: 1.000\n",
+ " Text: Paul Graham -> WORKED_ON -> Hacker News...\n",
+ " Node ID: 394d6965-6751-4d47-bd64-7518ada93577\n",
+ "\n",
+ "32. Score: 1.000\n",
+ " Text: Paul Graham -> PART_OF -> Summer Founders Program...\n",
+ " Node ID: 57617b03-80a2-4297-8c36-7c88a3664c18\n",
+ "\n",
+ "33. Score: 1.000\n",
+ " Text: Paul Graham -> WORKED_ON -> Summer Founders Program...\n",
+ " Node ID: 21c16992-c734-4357-925a-72ca92467d16\n",
+ "\n",
+ "34. Score: 1.000\n",
+ " Text: Paul Graham -> WORKED_ON -> Arc...\n",
+ " Node ID: 07b07c4a-2e4d-4a4d-ba0d-e984820fb50a\n",
+ "\n",
+ "35. Score: 1.000\n",
+ " Text: Paul Graham -> WORKED_ON -> application builder...\n",
+ " Node ID: f75a93ac-42d3-4a74-b6bd-f202f7c780d4\n",
+ "\n"
+ ]
+ }
+ ],
+ "source": [
+ "from llama_index.core.retrievers import BaseRetriever\n",
+ "from llama_index.core.schema import QueryBundle, NodeWithScore, TextNode\n",
+ "from llama_index.core.vector_stores.types import VectorStoreQuery\n",
+ "from llama_index.core.indices.property_graph import LLMSynonymRetriever\n",
+ "from typing import List\n",
+ "\n",
+ "\n",
+ "class GraphVectorRetriever(BaseRetriever):\n",
+ " \"\"\"\n",
+ " A retriever that performs vector search on a property graph store.\n",
+ " \"\"\"\n",
+ "\n",
+ " def __init__(self, graph_store, embed_model, similarity_top_k: int = 5):\n",
+ " self.graph_store = graph_store\n",
+ " self.embed_model = embed_model\n",
+ " self.similarity_top_k = similarity_top_k\n",
+ " super().__init__()\n",
+ "\n",
+ " def _retrieve(self, query_bundle: QueryBundle) -> List[NodeWithScore]:\n",
+ " # Get query embedding\n",
+ " query_embedding = self.embed_model.get_text_embedding(\n",
+ " query_bundle.query_str\n",
+ " )\n",
+ "\n",
+ " # Perform vector search\n",
+ " vector_query = VectorStoreQuery(\n",
+ " query_embedding=query_embedding,\n",
+ " similarity_top_k=self.similarity_top_k,\n",
+ " )\n",
+ " nodes, similarities = self.graph_store.vector_query(vector_query)\n",
+ "\n",
+ " # Convert ChunkNodes to TextNodes\n",
+ " nodes_with_scores = []\n",
+ " for node, similarity in zip(nodes, similarities):\n",
+ " # Convert ChunkNode to TextNode\n",
+ " if hasattr(node, \"text\"):\n",
+ " text_node = TextNode(\n",
+ " text=node.text,\n",
+ " id_=node.id,\n",
+ " metadata=getattr(node, \"properties\", {}),\n",
+ " )\n",
+ " nodes_with_scores.append(\n",
+ " NodeWithScore(node=text_node, score=similarity)\n",
+ " )\n",
+ "\n",
+ " return nodes_with_scores\n",
+ "\n",
+ "\n",
+ "class CombinedGraphRetriever(BaseRetriever):\n",
+ " \"\"\"\n",
+ " A retriever that performs that combines graph and vector search on a property graph store.\n",
+ " \"\"\"\n",
+ "\n",
+ " def __init__(\n",
+ " self, graph_store, embed_model, llm, similarity_top_k: int = 5\n",
+ " ):\n",
+ " self.graph_store = graph_store\n",
+ " self.embed_model = embed_model\n",
+ " self.llm = llm\n",
+ " self.similarity_top_k = similarity_top_k\n",
+ " super().__init__()\n",
+ "\n",
+ " def _retrieve(self, query_bundle: QueryBundle) -> List[NodeWithScore]:\n",
+ " # 1. Vector retrieval\n",
+ " query_embedding = self.embed_model.get_text_embedding(\n",
+ " query_bundle.query_str\n",
+ " )\n",
+ " vector_query = VectorStoreQuery(\n",
+ " query_embedding=query_embedding,\n",
+ " similarity_top_k=self.similarity_top_k,\n",
+ " )\n",
+ " vector_nodes, similarities = self.graph_store.vector_query(\n",
+ " vector_query\n",
+ " )\n",
+ "\n",
+ " # Convert ChunkNodes to TextNodes for vector results\n",
+ " vector_results = []\n",
+ " for node, similarity in zip(vector_nodes, similarities):\n",
+ " if hasattr(node, \"text\"):\n",
+ " text_node = TextNode(\n",
+ " text=node.text,\n",
+ " id_=node.id,\n",
+ " metadata=getattr(node, \"properties\", {}),\n",
+ " )\n",
+ " vector_results.append(\n",
+ " NodeWithScore(node=text_node, score=similarity)\n",
+ " )\n",
+ "\n",
+ " # 2. Graph traversal retrieval\n",
+ " graph_retriever = LLMSynonymRetriever(\n",
+ " self.graph_store, llm=self.llm, include_text=True\n",
+ " )\n",
+ " graph_results = graph_retriever.retrieve(query_bundle)\n",
+ "\n",
+ " # 3. Combine and deduplicate\n",
+ " all_results = vector_results + graph_results\n",
+ " seen_nodes = set()\n",
+ " combined_results = []\n",
+ "\n",
+ " for node_with_score in all_results:\n",
+ " node_id = node_with_score.node.node_id\n",
+ " if node_id not in seen_nodes:\n",
+ " seen_nodes.add(node_id)\n",
+ " combined_results.append(node_with_score)\n",
+ "\n",
+ " return combined_results\n",
+ "\n",
+ "\n",
+ "# Use the combined retriever\n",
+ "combined_retriever = CombinedGraphRetriever(\n",
+ " graph_store=graph_store,\n",
+ " llm=generate_llm,\n",
+ " embed_model=embed_model,\n",
+ " similarity_top_k=5,\n",
+ ")\n",
+ "\n",
+ "# Test the combined retriever\n",
+ "query_text = \"What was the role of Idelle Weber in Viaweb?\"\n",
+ "query_bundle = QueryBundle(query_str=query_text)\n",
+ "results = combined_retriever.retrieve(query_bundle)\n",
+ "for i, node_with_score in enumerate(results):\n",
+ " print(f\"{i + 1}. Score: {node_with_score.score:.3f}\")\n",
+ " print(\n",
+ " f\" Text: {node_with_score.node.text[:100]}...\"\n",
+ " ) # Print first 100 chars\n",
+ " print(f\" Node ID: {node_with_score.node.node_id}\")\n",
+ " print()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Idelle Weber was connected to Viaweb through her husband Julian, who provided $10,000 in seed funding for the company. In return for this funding, legal work, and business advice, they gave him 10% of the company.\n"
+ ]
+ }
+ ],
+ "source": [
+ "from llama_index.core.query_engine import RetrieverQueryEngine\n",
+ "from llama_index.core.response_synthesizers import get_response_synthesizer\n",
+ "\n",
+ "# Create query engine with your combined retriever\n",
+ "query_engine = RetrieverQueryEngine.from_args(\n",
+ " retriever=combined_retriever,\n",
+ " llm=generate_llm,\n",
+ ")\n",
+ "\n",
+ "# Create response synthesizer\n",
+ "response_synthesizer = get_response_synthesizer(\n",
+ " llm=generate_llm, use_async=False\n",
+ ")\n",
+ "\n",
+ "# Create query engine\n",
+ "query_engine = RetrieverQueryEngine(\n",
+ " retriever=combined_retriever, response_synthesizer=response_synthesizer\n",
+ ")\n",
+ "\n",
+ "# Query and get answer\n",
+ "query_text = \"What was the role of Idelle Weber in Viaweb?\"\n",
+ "response = query_engine.query(query_text)\n",
+ "print(response)"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "llama-index (3.13.5)",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/docs/src/content/docs/framework/community/integrations/graph_stores.md b/docs/src/content/docs/framework/community/integrations/graph_stores.md
index b90a9918ad..5e9e338e8a 100644
--- a/docs/src/content/docs/framework/community/integrations/graph_stores.md
+++ b/docs/src/content/docs/framework/community/integrations/graph_stores.md
@@ -21,6 +21,17 @@ See the associated guides below:
- [Nebula Graph Store](/python/examples/index_structs/knowledge_graph/nebulagraphkgindexdemo)
- [Knowledge Graph Query Engine](/python/examples/query_engine/knowledge_graph_query_engine)
+## `LadybugGraphStore`
+
+We support a `LadybugGraphStore` integration, for persisting triples directly in [Ladybug](https://ladybugdb.com).
+Additionally, we support the `PropertyGraphIndex`, which allows you to store and query property graphs
+using a Ladybug backend.
+
+See the associated guides below:
+
+- [Ladybug Graph Store](/python/examples/index_structs/knowledge_graph/LadybugGraphDemo)
+- [Ladybug Graph Store](/python/examples/property_graph/property_graph_ladybug)
+
## `FalkorDBGraphStore`
We support a `FalkorDBGraphStore` integration, for persisting graphs directly in FalkorDB! Furthermore, you can generate cypher queries and return natural language responses for your FalkorDB graphs using the `KnowledgeGraphQueryEngine`.
diff --git a/llama-index-integrations/graph_stores/llama-index-graph-stores-ladybug/.gitignore b/llama-index-integrations/graph_stores/llama-index-graph-stores-ladybug/.gitignore
new file mode 100644
index 0000000000..990c18de22
--- /dev/null
+++ b/llama-index-integrations/graph_stores/llama-index-graph-stores-ladybug/.gitignore
@@ -0,0 +1,153 @@
+llama_index/_static
+.DS_Store
+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+bin/
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+etc/
+include/
+lib/
+lib64/
+parts/
+sdist/
+share/
+var/
+wheels/
+pip-wheel-metadata/
+share/python-wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+
+# PyInstaller
+# Usually these files are written by a python script from a template
+# before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.nox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+*.py,cover
+.hypothesis/
+.pytest_cache/
+.ruff_cache
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+local_settings.py
+db.sqlite3
+db.sqlite3-journal
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
+target/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+notebooks/
+
+# IPython
+profile_default/
+ipython_config.py
+
+# pyenv
+.python-version
+
+# pipenv
+# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
+# However, in case of collaboration, if having platform-specific dependencies or dependencies
+# having no cross-platform support, pipenv may install dependencies that don't work, or not
+# install all needed dependencies.
+#Pipfile.lock
+
+# PEP 582; used by e.g. github.com/David-OConnor/pyflow
+__pypackages__/
+
+# Celery stuff
+celerybeat-schedule
+celerybeat.pid
+
+# SageMath parsed files
+*.sage.py
+
+# Environments
+.env
+.venv
+env/
+venv/
+ENV/
+env.bak/
+venv.bak/
+pyvenv.cfg
+
+# Spyder project settings
+.spyderproject
+.spyproject
+
+# Rope project settings
+.ropeproject
+
+# mkdocs documentation
+/site
+
+# mypy
+.mypy_cache/
+.dmypy.json
+dmypy.json
+
+# Pyre type checker
+.pyre/
+
+# Jetbrains
+.idea
+modules/
+*.swp
+
+# VsCode
+.vscode
+
+# pipenv
+Pipfile
+Pipfile.lock
+
+# pyright
+pyrightconfig.json
diff --git a/llama-index-integrations/graph_stores/llama-index-graph-stores-ladybug/LICENSE b/llama-index-integrations/graph_stores/llama-index-graph-stores-ladybug/LICENSE
new file mode 100644
index 0000000000..e6074042d0
--- /dev/null
+++ b/llama-index-integrations/graph_stores/llama-index-graph-stores-ladybug/LICENSE
@@ -0,0 +1,21 @@
+The MIT License
+
+Copyright (c) Jerry Liu
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in
+all copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
+THE SOFTWARE.
diff --git a/llama-index-integrations/graph_stores/llama-index-graph-stores-ladybug/Makefile b/llama-index-integrations/graph_stores/llama-index-graph-stores-ladybug/Makefile
new file mode 100644
index 0000000000..b9eab05aa3
--- /dev/null
+++ b/llama-index-integrations/graph_stores/llama-index-graph-stores-ladybug/Makefile
@@ -0,0 +1,17 @@
+GIT_ROOT ?= $(shell git rev-parse --show-toplevel)
+
+help: ## Show all Makefile targets.
+ @grep -E '^[a-zA-Z_-]+:.*?## .*$$' $(MAKEFILE_LIST) | awk 'BEGIN {FS = ":.*?## "}; {printf "\033[33m%-30s\033[0m %s\n", $$1, $$2}'
+
+format: ## Run code autoformatters (black).
+ pre-commit install
+ git ls-files | xargs pre-commit run black --files
+
+lint: ## Run linters: pre-commit (black, ruff, codespell) and mypy
+ pre-commit install && git ls-files | xargs pre-commit run --show-diff-on-failure --files
+
+test: ## Run tests via pytest.
+ pytest tests
+
+watch-docs: ## Build and watch documentation.
+ sphinx-autobuild docs/ docs/_build/html --open-browser --watch $(GIT_ROOT)/llama_index/
diff --git a/llama-index-integrations/graph_stores/llama-index-graph-stores-ladybug/README.md b/llama-index-integrations/graph_stores/llama-index-graph-stores-ladybug/README.md
new file mode 100644
index 0000000000..c16c7ed149
--- /dev/null
+++ b/llama-index-integrations/graph_stores/llama-index-graph-stores-ladybug/README.md
@@ -0,0 +1 @@
+# LlamaIndex Graph Stores Integration: Ladybug
diff --git a/llama-index-integrations/graph_stores/llama-index-graph-stores-ladybug/llama_index/graph_stores/ladybug/__init__.py b/llama-index-integrations/graph_stores/llama-index-graph-stores-ladybug/llama_index/graph_stores/ladybug/__init__.py
new file mode 100644
index 0000000000..1e71fcd4a7
--- /dev/null
+++ b/llama-index-integrations/graph_stores/llama-index-graph-stores-ladybug/llama_index/graph_stores/ladybug/__init__.py
@@ -0,0 +1,6 @@
+from llama_index.graph_stores.ladybug.base import LadybugGraphStore
+from llama_index.graph_stores.ladybug.ladybug_property_graph import (
+ LadybugPropertyGraphStore,
+)
+
+__all__ = ["LadybugGraphStore", "LadybugPropertyGraphStore"]
diff --git a/llama-index-integrations/graph_stores/llama-index-graph-stores-ladybug/llama_index/graph_stores/ladybug/base.py b/llama-index-integrations/graph_stores/llama-index-graph-stores-ladybug/llama_index/graph_stores/ladybug/base.py
new file mode 100644
index 0000000000..fa5fd12859
--- /dev/null
+++ b/llama-index-integrations/graph_stores/llama-index-graph-stores-ladybug/llama_index/graph_stores/ladybug/base.py
@@ -0,0 +1,225 @@
+"""Ladybug graph store index."""
+
+from typing import Any, Dict, List, Optional
+
+from llama_index.core.graph_stores.types import GraphStore
+
+import real_ladybug as lb
+
+
+class LadybugGraphStore(GraphStore):
+ def __init__(
+ self,
+ database: Any,
+ node_table_name: str = "entity",
+ rel_table_name: str = "links",
+ **kwargs: Any,
+ ) -> None:
+ self.database = database
+ self.connection = lb.Connection(database)
+ self.node_table_name = node_table_name
+ self.rel_table_name = rel_table_name
+ self.init_schema()
+
+ def init_schema(self) -> None:
+ """Initialize schema if the tables do not exist."""
+ node_tables = self.connection._get_node_table_names()
+ if self.node_table_name not in node_tables:
+ self.connection.execute(
+ "CREATE NODE TABLE %s (ID STRING, PRIMARY KEY(ID))"
+ % self.node_table_name
+ )
+ rel_tables = self.connection._get_rel_table_names()
+ rel_tables = [rel_table["name"] for rel_table in rel_tables]
+ if self.rel_table_name not in rel_tables:
+ self.connection.execute(
+ "CREATE REL TABLE {} (FROM {} TO {}, predicate STRING)".format(
+ self.rel_table_name, self.node_table_name, self.node_table_name
+ )
+ )
+
+ @property
+ def client(self) -> Any:
+ return self.connection
+
+ def get(self, subj: str) -> List[List[str]]:
+ """Get triplets."""
+ query = """
+ MATCH (n1:%s)-[r:%s]->(n2:%s)
+ WHERE n1.ID = $subj
+ RETURN r.predicate, n2.ID;
+ """
+ prepared_statement = self.connection.prepare(
+ query % (self.node_table_name, self.rel_table_name, self.node_table_name)
+ )
+ query_result = self.connection.execute(prepared_statement, {"subj": subj})
+ retval = []
+ while query_result.has_next():
+ row = query_result.get_next()
+ retval.append([row[0], row[1]])
+ return retval
+
+ def get_rel_map(
+ self, subjs: Optional[List[str]] = None, depth: int = 2, limit: int = 30
+ ) -> Dict[str, List[List[str]]]:
+ """Get depth-aware rel map."""
+ rel_wildcard = "r:%s*1..%d" % (self.rel_table_name, depth)
+ match_clause = "MATCH (n1:{})-[{}]->(n2:{})".format(
+ self.node_table_name,
+ rel_wildcard,
+ self.node_table_name,
+ )
+ return_clause = "RETURN n1, r, n2 LIMIT %d" % limit
+ params = []
+ if subjs is not None:
+ for i, curr_subj in enumerate(subjs):
+ if i == 0:
+ where_clause = "WHERE n1.ID = $%d" % i
+ else:
+ where_clause += " OR n1.ID = $%d" % i
+ params.append((str(i), curr_subj))
+ else:
+ where_clause = ""
+ query = f"{match_clause} {where_clause} {return_clause}"
+ prepared_statement = self.connection.prepare(query)
+ if subjs is not None:
+ query_result = self.connection.execute(prepared_statement, dict(params))
+ else:
+ query_result = self.connection.execute(prepared_statement)
+ retval: Dict[str, List[List[str]]] = {}
+ while query_result.has_next():
+ row = query_result.get_next()
+ curr_path = []
+ subj = row[0]
+ recursive_rel = row[1]
+ obj = row[2]
+ nodes_map = {}
+ nodes_map[(subj["_id"]["table"], subj["_id"]["offset"])] = subj["ID"]
+ nodes_map[(obj["_id"]["table"], obj["_id"]["offset"])] = obj["ID"]
+ for node in recursive_rel["_nodes"]:
+ nodes_map[(node["_id"]["table"], node["_id"]["offset"])] = node["ID"]
+ for rel in recursive_rel["_rels"]:
+ predicate = rel["predicate"]
+ curr_subj_id = nodes_map[(rel["_src"]["table"], rel["_src"]["offset"])]
+ curr_path.append(curr_subj_id)
+ curr_path.append(predicate)
+ # Add the last node
+ curr_path.append(obj["ID"])
+ if subj["ID"] not in retval:
+ retval[subj["ID"]] = []
+ retval[subj["ID"]].append(curr_path)
+ return retval
+
+ def upsert_triplet(self, subj: str, rel: str, obj: str) -> None:
+ """Add triplet."""
+
+ def check_entity_exists(connection: Any, entity: str) -> bool:
+ is_exists_result = connection.execute(
+ "MATCH (n:%s) WHERE n.ID = $entity RETURN n.ID" % self.node_table_name,
+ {"entity": entity},
+ )
+ return is_exists_result.has_next()
+
+ def create_entity(connection: Any, entity: str) -> None:
+ connection.execute(
+ "CREATE (n:%s {ID: $entity})" % self.node_table_name,
+ {"entity": entity},
+ )
+
+ def check_rel_exists(connection: Any, subj: str, obj: str, rel: str) -> bool:
+ is_exists_result = connection.execute(
+ (
+ "MATCH (n1:{})-[r:{}]->(n2:{}) WHERE n1.ID = $subj AND n2.ID = "
+ "$obj AND r.predicate = $pred RETURN r.predicate"
+ ).format(
+ self.node_table_name, self.rel_table_name, self.node_table_name
+ ),
+ {"subj": subj, "obj": obj, "pred": rel},
+ )
+ return is_exists_result.has_next()
+
+ def create_rel(connection: Any, subj: str, obj: str, rel: str) -> None:
+ connection.execute(
+ (
+ "MATCH (n1:{}), (n2:{}) WHERE n1.ID = $subj AND n2.ID = $obj "
+ "CREATE (n1)-[r:{} {{predicate: $pred}}]->(n2)"
+ ).format(
+ self.node_table_name, self.node_table_name, self.rel_table_name
+ ),
+ {"subj": subj, "obj": obj, "pred": rel},
+ )
+
+ is_subj_exists = check_entity_exists(self.connection, subj)
+ is_obj_exists = check_entity_exists(self.connection, obj)
+
+ if not is_subj_exists:
+ create_entity(self.connection, subj)
+ if not is_obj_exists:
+ create_entity(self.connection, obj)
+
+ if is_subj_exists and is_obj_exists:
+ is_rel_exists = check_rel_exists(self.connection, subj, obj, rel)
+ if is_rel_exists:
+ return
+
+ create_rel(self.connection, subj, obj, rel)
+
+ def delete(self, subj: str, rel: str, obj: str) -> None:
+ """Delete triplet."""
+
+ def delete_rel(connection: Any, subj: str, obj: str, rel: str) -> None:
+ connection.execute(
+ (
+ "MATCH (n1:{})-[r:{}]->(n2:{}) WHERE n1.ID = $subj AND n2.ID"
+ " = $obj AND r.predicate = $pred DELETE r"
+ ).format(
+ self.node_table_name, self.rel_table_name, self.node_table_name
+ ),
+ {"subj": subj, "obj": obj, "pred": rel},
+ )
+
+ def delete_entity(connection: Any, entity: str) -> None:
+ connection.execute(
+ "MATCH (n:%s) WHERE n.ID = $entity DELETE n" % self.node_table_name,
+ {"entity": entity},
+ )
+
+ def check_edges(connection: Any, entity: str) -> bool:
+ is_exists_result = connection.execute(
+ "MATCH (n1:{})-[r:{}]-(n2:{}) WHERE n2.ID = $entity RETURN r.predicate".format(
+ self.node_table_name, self.rel_table_name, self.node_table_name
+ ),
+ {"entity": entity},
+ )
+ return is_exists_result.has_next()
+
+ delete_rel(self.connection, subj, obj, rel)
+ if not check_edges(self.connection, subj):
+ delete_entity(self.connection, subj)
+ if not check_edges(self.connection, obj):
+ delete_entity(self.connection, obj)
+
+ @classmethod
+ def from_persist_dir(
+ cls,
+ persist_dir: str,
+ node_table_name: str = "entity",
+ rel_table_name: str = "links",
+ ) -> "LadybugGraphStore":
+ """Load from persist dir."""
+ database = lb.Database(persist_dir)
+ return cls(database, node_table_name, rel_table_name)
+
+ @classmethod
+ def from_dict(cls, config_dict: Dict[str, Any]) -> "LadybugGraphStore":
+ """
+ Initialize graph store from configuration dictionary.
+
+ Args:
+ config_dict: Configuration dictionary.
+
+ Returns:
+ Graph store.
+
+ """
+ return cls(**config_dict)
diff --git a/llama-index-integrations/graph_stores/llama-index-graph-stores-ladybug/llama_index/graph_stores/ladybug/ladybug_property_graph.py b/llama-index-integrations/graph_stores/llama-index-graph-stores-ladybug/llama_index/graph_stores/ladybug/ladybug_property_graph.py
new file mode 100644
index 0000000000..ef6a36671a
--- /dev/null
+++ b/llama-index-integrations/graph_stores/llama-index-graph-stores-ladybug/llama_index/graph_stores/ladybug/ladybug_property_graph.py
@@ -0,0 +1,756 @@
+from typing import Any, Dict, List, Optional, Tuple
+
+import llama_index.graph_stores.ladybug.utils as utils
+from llama_index.core.graph_stores.types import (
+ ChunkNode,
+ EntityNode,
+ LabelledNode,
+ PropertyGraphStore,
+ Relation,
+ Triplet,
+)
+from llama_index.core.graph_stores.utils import value_sanitize
+from llama_index.core.vector_stores.types import VectorStoreQuery
+
+import real_ladybug as lb
+
+# Threshold for max number of returned triplets
+LIMIT = 100
+Triple = Tuple[str, str, str]
+
+
+class LadybugPropertyGraphStore(PropertyGraphStore):
+ """
+ Ladybug Property Graph Store.
+
+ This class implements a Ladybug property graph store.
+
+ Ladybug can be installed and used with this simple command:
+
+ ```
+ pip install real_ladybug
+ ```
+ """
+
+ def __init__(
+ self,
+ db: lb.Database,
+ relationship_schema: Optional[List[Tuple[str, str, str]]] = None,
+ has_structured_schema: Optional[bool] = False,
+ sanitize_query_output: Optional[bool] = True,
+ use_vector_index: bool = True,
+ embed_model: Optional[Any] = None,
+ embed_dimension: Optional[int] = None,
+ ) -> None:
+ self.db = db
+ self.connection = lb.Connection(self.db)
+ self.use_vector_index = use_vector_index
+
+ # Initialize embedding dimension with auto-detection and fallback logic
+ self.embed_dimension = self._initialize_embedding_dimension(
+ embed_model, embed_dimension
+ )
+
+ # Install and load vector extension if using vector indexes
+ if self.use_vector_index:
+ try:
+ self.connection.execute("INSTALL vector; LOAD vector;")
+ except RuntimeError:
+ # print("Warning: Skipping installing vector extension due to RuntimeError:", e)
+ # print("Check that you can install Ladybug's vector extension by running: " \
+ # "`INSTALL vector; LOAD vector;` in a Ladybug CLI session."
+ # )
+ pass
+
+ if has_structured_schema:
+ if relationship_schema is None:
+ raise ValueError(
+ "Please provide a relationship schema if structured_schema=True."
+ )
+ else:
+ self.validate_relationship_schema(relationship_schema)
+ else:
+ # Use a generic schema with node types of 'Entity' if no schema is required
+ relationship_schema = [("Entity", "LINKS", "Entity")]
+
+ self.relationship_schema = relationship_schema
+ self.entities = self.get_entities()
+ self.has_structured_schema = has_structured_schema
+ self.entities.extend(
+ ["Chunk"]
+ ) # Always include Chunk as an entity type, in all schemas
+ self.sanitize_query_output = sanitize_query_output
+ self.structured_schema = {}
+ self.init_schema()
+
+ def init_schema(self) -> None:
+ """Initialize schema if the required tables do not exist."""
+ utils.create_chunk_node_table(
+ self.connection, embedding_dimension=self.embed_dimension
+ )
+ utils.create_entity_node_tables(self.connection, entities=self.entities)
+ utils.create_relation_tables(
+ self.connection,
+ self.entities,
+ relationship_schema=self.relationship_schema,
+ )
+
+ def validate_relationship_schema(self, relationship_schema: List[Triple]) -> None:
+ # Check that validation schema is a list of tuples as required by Kùzu for relationships
+ if not all(isinstance(item, tuple) for item in relationship_schema):
+ raise ValueError(
+ "Please specify the relationship schema as "
+ "a list of tuples, for example: [('PERSON', 'IS_CEO_OF', 'ORGANIZATION')]"
+ )
+
+ @property
+ def client(self) -> lb.Connection:
+ return self.connection
+
+ def get_entities(self) -> List[str]:
+ return sorted(
+ set(
+ [rel[0] for rel in self.relationship_schema]
+ + [rel[2] for rel in self.relationship_schema]
+ )
+ )
+
+ def _initialize_embedding_dimension(
+ self, embed_model: Optional[Any], embedding_dimension: Optional[int]
+ ) -> Optional[int]:
+ """
+ Initialize embedding dimension using auto-detection and fallback logic.
+
+ Args:
+ embed_model: Optional embedding model for auto-detection
+ embedding_dimension: Optional manual dimension specification
+
+ Returns:
+ Detected or specified embedding dimension, or None if unavailable
+
+ """
+ if embed_model is not None:
+ # Try auto-detection first
+ detected_dim = self._detect_embedding_dimension(embed_model)
+ if detected_dim is not None:
+ print(f"Auto-detected embedding dimension: {detected_dim}")
+ return detected_dim
+ elif embedding_dimension is not None:
+ # Fall back to manual specification if auto-detection fails
+ print(
+ f"Using manually specified embedding dimension: {embedding_dimension}"
+ )
+ return embedding_dimension
+ else:
+ # Neither auto-detection nor manual specification available
+ print(
+ "Warning: Could not determine embedding dimension. Vector indexing may not work properly."
+ )
+ return None
+ else:
+ # No embed_model provided, use manual specification
+ if embedding_dimension is not None:
+ print(
+ f"Using manually specified embedding dimension: {embedding_dimension}"
+ )
+ return embedding_dimension
+
+ def _detect_embedding_dimension(self, embed_model: Any) -> Optional[int]:
+ """
+ Detect embedding dimension by creating a test embedding.
+
+ Args:
+ embed_model: The embedding model instance
+
+ Returns:
+ Detected dimension or None if cannot be determined
+
+ """
+ try:
+ test_embedding = embed_model.get_text_embedding("hello")
+ if isinstance(test_embedding, list) and len(test_embedding) > 0:
+ return len(test_embedding)
+ except Exception:
+ print(
+ "Error: Could not detect embedding dimension from model. Please specify it manually via the `embedding_dimension` parameter."
+ ) # noqa: E501
+
+ return None
+
+ def _create_vector_index(self, table_name: str) -> None:
+ """Create a vector index for the embedding column of a table."""
+ if not self.use_vector_index or table_name != "Chunk":
+ return
+
+ # Check if chunk_embedding_index already exists
+ existing_indexes_result = self.connection.execute(
+ "CALL SHOW_INDEXES() RETURN *"
+ )
+ for row in existing_indexes_result:
+ if len(row) > 1 and row[1] == "chunk_embedding_index":
+ return
+
+ # Check if table has any data - Ladybug requires data before creating vector index
+ count_result = self.connection.execute(
+ f"MATCH (n:{table_name}) RETURN COUNT(n)"
+ )
+ if not any(int(row[0]) > 0 for row in count_result):
+ return
+
+ # Create vector index for Chunk table
+ self.connection.execute("""
+ CALL CREATE_VECTOR_INDEX(
+ 'Chunk',
+ 'chunk_embedding_index',
+ 'embedding',
+ metric := 'cosine'
+ )
+ """)
+
+ def _ensure_vector_indexes(self) -> None:
+ """Ensure vector indexes are created for Chunk table only."""
+ if not self.use_vector_index:
+ return
+ # Only create index for Chunk table since these have larger blobs of text
+ # This makes the workflow easier to manage as a whole
+ self._create_vector_index("Chunk")
+
+ def refresh_vector_index(self) -> None:
+ """Drop and recreate the vector index for Chunk table."""
+ index_name = "chunk_embedding_index"
+ # Drop existing index if it exists
+ try:
+ self.connection.execute(f"DROP INDEX {index_name}")
+ print(f"Dropped vector index: {index_name}")
+ except Exception:
+ # Index may not exist, which is fine
+ pass
+
+ # Recreate the index
+ self._create_vector_index("Chunk")
+ print(f"Created vector index: {index_name}")
+
+ def upsert_nodes(self, nodes: List[LabelledNode]) -> None:
+ entity_list: List[EntityNode] = []
+ chunk_list: List[ChunkNode] = []
+ node_tables = self.connection._get_node_table_names()
+
+ for item in nodes:
+ if isinstance(item, EntityNode):
+ entity_list.append(item)
+ elif isinstance(item, ChunkNode):
+ chunk_list.append(item)
+
+ for chunk in chunk_list:
+ upsert_chunk_node_query = """
+ MERGE (c:Chunk {id: $id})
+ SET c.text = $text,
+ c.label = $label,
+ c.embedding = $embedding,
+ c.ref_doc_id = $ref_doc_id,
+ c.creation_date = date($creation_date),
+ c.last_modified_date = date($last_modified_date),
+ c.file_name = $file_name,
+ c.file_path = $file_path,
+ c.file_size = $file_size,
+ c.file_type = $file_type
+ """
+
+ self.connection.execute(
+ upsert_chunk_node_query,
+ parameters={
+ "id": chunk.id_,
+ "text": chunk.text.strip(),
+ "label": chunk.label,
+ "embedding": chunk.embedding,
+ "ref_doc_id": chunk.properties.get("ref_doc_id"),
+ "creation_date": chunk.properties.get("creation_date"),
+ "last_modified_date": chunk.properties.get("last_modified_date"),
+ "file_name": chunk.properties.get("file_name"),
+ "file_path": chunk.properties.get("file_path"),
+ "file_size": chunk.properties.get("file_size"),
+ "file_type": chunk.properties.get("file_type"),
+ },
+ )
+
+ for entity in entity_list:
+ entity_label = entity.label if entity.label in node_tables else "Entity"
+ upsert_entity_node_query = f"""
+ MERGE (e:{entity_label} {{id: $id}})
+ SET e.label = $label,
+ e.name = $name,
+ e.creation_date = date($creation_date),
+ e.last_modified_date = date($last_modified_date),
+ e.file_name = $file_name,
+ e.file_path = $file_path,
+ e.file_size = $file_size,
+ e.file_type = $file_type,
+ e.triplet_source_id = $triplet_source_id
+ """
+
+ self.connection.execute(
+ upsert_entity_node_query,
+ parameters={
+ "id": entity.name,
+ "label": entity.label,
+ "name": entity.name,
+ "creation_date": entity.properties.get("creation_date"),
+ "last_modified_date": entity.properties.get("last_modified_date"),
+ "file_name": entity.properties.get("file_name"),
+ "file_path": entity.properties.get("file_path"),
+ "file_size": entity.properties.get("file_size"),
+ "file_type": entity.properties.get("file_type"),
+ "triplet_source_id": entity.properties.get("triplet_source_id"),
+ },
+ )
+ # Create vector index for Chunk table if embeddings were inserted
+ if self.use_vector_index and any(
+ chunk.embedding is not None for chunk in chunk_list
+ ):
+ self._create_vector_index("Chunk")
+ # Manual checkpoint
+ self.connection.execute("CHECKPOINT;")
+
+ def upsert_relations(self, relations: List[Relation]) -> None:
+ for rel in relations:
+ if self.has_structured_schema:
+ src, rel_tbl_name, dst = utils.lookup_relation(
+ rel.label, self.relationship_schema
+ )
+ else:
+ src, rel_tbl_name, dst = "Entity", "LINKS", "Entity"
+
+ # Connect entities to each other
+ self.connection.execute(
+ f"""
+ MATCH (a:{src} {{id: $source_id}}),
+ (b:{dst} {{id: $target_id}})
+ MERGE (a)-[r:{rel_tbl_name} {{label: $label}}]->(b)
+ SET r.triplet_source_id = $triplet_source_id
+ """,
+ parameters={
+ "source_id": rel.source_id,
+ "target_id": rel.target_id,
+ "triplet_source_id": rel.properties.get("triplet_source_id"),
+ "label": rel.label,
+ },
+ )
+ # Connect chunks to entities
+ self.connection.execute(
+ f"""
+ MATCH (a:{src} {{id: $source_id}}),
+ (b:{dst} {{id: $target_id}}),
+ (c:Chunk {{id: $triplet_source_id}})
+ MERGE (c)-[:MENTIONS]->(a)
+ MERGE (c)-[:MENTIONS]->(b)
+ """,
+ parameters={
+ "source_id": rel.source_id,
+ "target_id": rel.target_id,
+ "triplet_source_id": rel.properties.get("triplet_source_id"),
+ },
+ )
+ # Manual checkpoint
+ self.connection.execute("CHECKPOINT;")
+
+ def structured_query(
+ self, query: str, param_map: Optional[Dict[str, Any]] = None
+ ) -> Any:
+ response = self.connection.execute(query, parameters=param_map)
+ column_names = response.get_column_names()
+ result = []
+ for row in response:
+ result.append(dict(zip(column_names, row)))
+
+ if self.sanitize_query_output:
+ return value_sanitize(result)
+ self.connection.execute("CHECKPOINT;")
+
+ return result
+
+ def vector_query(
+ self, query: VectorStoreQuery, **kwargs: Any
+ ) -> Tuple[List[LabelledNode], List[float]]:
+ """Perform vector similarity search on Chunk nodes."""
+ self._ensure_vector_indexes()
+
+ # Use Ladybug's vector index for similarity search
+ result = self.connection.execute(
+ """
+ CALL QUERY_VECTOR_INDEX(
+ 'Chunk',
+ 'chunk_embedding_index',
+ $query_embedding,
+ $top_k
+ )
+ RETURN node.id as id, distance
+ ORDER BY distance
+ """,
+ parameters={
+ "query_embedding": query.query_embedding,
+ "top_k": query.similarity_top_k,
+ },
+ )
+
+ # Get matching chunk nodes and convert distances to similarities
+ node_data = []
+
+ for row in result:
+ node_id, distance = row[0], row[1]
+
+ # Fetch the chunk node
+ chunk_result = self.structured_query(
+ "MATCH (n:Chunk {id: $node_id}) RETURN n.*",
+ param_map={"node_id": node_id},
+ )
+
+ if chunk_result:
+ record = chunk_result[0]
+ properties = {
+ k: v for k, v in record.items() if k not in ["n.id", "n.text"]
+ }
+ node = ChunkNode(
+ id_=record["n.id"],
+ text=record.get("n.text", ""),
+ properties=utils.remove_empty_values(properties),
+ )
+ # Convert distance to similarity (lower distance = higher similarity)
+ similarity = 1.0 - distance
+ node_data.append((node, similarity))
+
+ # Sort by similarity in descending order
+ node_data.sort(key=lambda x: x[1], reverse=True)
+
+ # Separate nodes and similarities
+ nodes = [item[0] for item in node_data]
+ similarities = [item[1] for item in node_data]
+
+ # Manual checkpoint
+ self.connection.execute("CHECKPOINT;")
+
+ return nodes, similarities
+
+ def get(
+ self,
+ properties: Optional[dict] = None,
+ ids: Optional[List[str]] = None,
+ ) -> List[LabelledNode]:
+ """Get nodes from the property graph store."""
+ cypher_statement = "MATCH (e) "
+
+ parameters = {}
+ if ids:
+ cypher_statement += "WHERE e.id in $ids "
+ parameters["ids"] = ids
+
+ return_statement = "RETURN e.*"
+ cypher_statement += return_statement
+ result = self.structured_query(cypher_statement, param_map=parameters)
+ result = result if result else []
+
+ nodes = []
+ for record in result:
+ # Text indicates a chunk node
+ # None on the label indicates an implicit node, likely a chunk node
+ if record.get("e.label") == "text_chunk":
+ properties = {
+ k: v for k, v in record.items() if k not in ["e.id", "e.text"]
+ }
+ text = record.get("e.text")
+ nodes.append(
+ ChunkNode(
+ id_=record["e.id"],
+ text=text,
+ properties=utils.remove_empty_values(properties),
+ )
+ )
+ else:
+ properties = {
+ k: v for k, v in record.items() if k not in ["e.id", "e.name"]
+ }
+ name = record["e.name"] if record.get("e.name") else record["e.id"]
+ label = record["e.label"] if record.get("e.label") else "Chunk"
+ nodes.append(
+ EntityNode(
+ name=name,
+ label=label,
+ properties=utils.remove_empty_values(properties),
+ )
+ )
+ return nodes
+
+ def get_triplets(
+ self,
+ entity_names: Optional[List[str]] = None,
+ relation_names: Optional[List[str]] = None,
+ ids: Optional[List[str]] = None,
+ ) -> List[Triplet]:
+ # Construct the Cypher query
+ cypher_statement = "MATCH (e)-[r]->(t) "
+
+ params = {}
+ if entity_names or relation_names or ids:
+ cypher_statement += "WHERE "
+
+ if entity_names:
+ cypher_statement += "e.name in $entity_names "
+ params["entity_names"] = entity_names
+
+ if relation_names and entity_names:
+ cypher_statement += f"AND "
+ if relation_names:
+ cypher_statement += "r.label in $relation_names "
+ params[f"relation_names"] = relation_names
+
+ if ids:
+ cypher_statement += "e.id in $ids "
+ params["ids"] = ids
+
+ # Avoid returning a massive list of triplets that represent a large portion of the graph
+ # This uses the LIMIT constant defined at the top of the file
+ if not (entity_names or relation_names or ids):
+ return_statement = f"WHERE e.label <> 'text_chunk' RETURN * LIMIT {LIMIT};"
+ else:
+ return_statement = f"AND e.label <> 'text_chunk' RETURN * LIMIT {LIMIT};"
+
+ cypher_statement += return_statement
+
+ result = self.structured_query(cypher_statement, param_map=params)
+ result = result if result else []
+
+ triples = []
+ for record in result:
+ if record["e"]["_label"] == "Chunk":
+ continue
+
+ src_table = record["e"]["_id"]["table"]
+ dst_table = record["t"]["_id"]["table"]
+ id_map = {src_table: record["e"]["id"], dst_table: record["t"]["id"]}
+ source = EntityNode(
+ name=record["e"]["id"],
+ label=record["e"]["_label"],
+ properties=utils.get_filtered_props(record["e"], ["_id", "_label"]),
+ )
+ target = EntityNode(
+ name=record["t"]["id"],
+ label=record["t"]["_label"],
+ properties=utils.get_filtered_props(record["t"], ["_id", "_label"]),
+ )
+ rel = Relation(
+ source_id=id_map.get(record["r"]["_src"]["table"], "unknown"),
+ target_id=id_map.get(record["r"]["_dst"]["table"], "unknown"),
+ label=record["r"]["label"],
+ )
+ triples.append([source, rel, target])
+ return triples
+
+ def get_rel_map(
+ self,
+ graph_nodes: List[LabelledNode],
+ depth: int = 2,
+ limit: int = 30,
+ ignore_rels: Optional[List[str]] = None,
+ ) -> List[Triplet]:
+ triples = []
+
+ ids = [node.id for node in graph_nodes]
+ if len(ids) > 0:
+ # Run recursive query
+ response = self.structured_query(
+ f"""
+ MATCH (e)
+ WHERE e.id IN $ids
+ MATCH (e)-[rel*1..{depth} (r, n | WHERE r.label <> "MENTIONS") ]->(other)
+ RETURN *
+ LIMIT {limit};
+ """,
+ param_map={"ids": ids},
+ )
+ else:
+ response = self.structured_query(
+ f"""
+ MATCH (e)
+ MATCH (e)-[rel*1..{depth} (r, n | WHERE r.label <> "MENTIONS") ]->(other)
+ RETURN *
+ LIMIT {limit};
+ """
+ )
+
+ ignore_rels = ignore_rels or []
+ for record in response:
+ for item in record["rel"]["_RELS"]:
+ if item["label"] in ignore_rels:
+ continue
+
+ src_table = item["_SRC"]["table"]
+ dst_table = item["_SRC"]["table"]
+ id_map = {
+ src_table: record["e"]["_ID"],
+ dst_table: record["other"]["id"],
+ }
+ source = EntityNode(
+ name=record["e"]["name"],
+ label=record["e"]["_LABEL"],
+ properties=utils.get_filtered_props(
+ record["e"], ["_ID", "name", "_LABEL"]
+ ),
+ )
+ target = EntityNode(
+ name=record["other"]["name"],
+ label=record["other"]["_LABEL"],
+ properties=utils.get_filtered_props(
+ record["e"], ["_ID", "name", "_LABEL"]
+ ),
+ )
+ rel = Relation(
+ source_id=id_map.get(item["_SRC"]["table"], "unknown"),
+ target_id=id_map.get(item["_DST"]["table"], "unknown"),
+ label=item["label"],
+ )
+ triples.append([source, rel, target])
+
+ return triples
+
+ def delete(
+ self,
+ entity_names: Optional[List[str]] = None,
+ relation_names: Optional[List[str]] = None,
+ properties: Optional[dict] = None,
+ ids: Optional[List[str]] = None,
+ ) -> None:
+ """Delete nodes and relationships from the property graph store."""
+ if entity_names:
+ self.structured_query(
+ "MATCH (n) WHERE n.name IN $entity_names DETACH DELETE n",
+ param_map={"entity_names": entity_names},
+ )
+
+ if ids:
+ self.structured_query(
+ "MATCH (n) WHERE n.id IN $ids DETACH DELETE n",
+ param_map={"ids": ids},
+ )
+
+ if relation_names:
+ for rel in relation_names:
+ src, _, dst = utils.lookup_relation(rel, self.relationship_schema)
+ self.structured_query(
+ f"""
+ MATCH (:{src})-[r {{label: $label}}]->(:{dst})
+ DELETE r
+ """,
+ param_map={"label": rel},
+ )
+
+ if properties:
+ assert isinstance(properties, dict), (
+ "`properties` should be a key-value mapping."
+ )
+ cypher = "MATCH (e) WHERE "
+ prop_list = []
+ params = {}
+ for i, prop in enumerate(properties):
+ prop_list.append(f"e.`{prop}` = $property_{i}")
+ params[f"property_{i}"] = properties[prop]
+ cypher += " AND ".join(prop_list)
+ self.structured_query(cypher + " DETACH DELETE e", param_map=params)
+
+ def get_schema(self) -> Any:
+ """
+ Returns a structured schema of the property graph store.
+
+ The schema contains `node_props`, `rel_props`, and `relationships` keys and
+ the associated metadata.
+ Example output:
+ {
+ 'node_props': {'Chunk': [{'property': 'id', 'type': 'STRING'},
+ {'property': 'text', 'type': 'STRING'},
+ {'property': 'label', 'type': 'STRING'},
+ {'property': 'embedding', 'type': 'DOUBLE'},
+ {'property': 'properties', 'type': 'STRING'},
+ {'property': 'ref_doc_id', 'type': 'STRING'}],
+ 'Entity': [{'property': 'id', 'type': 'STRING'},
+ {'property': 'name', 'type': 'STRING'},
+ {'property': 'label', 'type': 'STRING'},
+ {'property': 'embedding', 'type': 'DOUBLE'},
+ {'property': 'properties', 'type': 'STRING'}]},
+ 'rel_props': {'SOURCE': [{'property': 'label', 'type': 'STRING'}]},
+ 'relationships': [{'end': 'Chunk', 'start': 'Chunk', 'type': 'SOURCE'}]
+ }
+ """
+ current_table_schema = {"node_props": {}, "rel_props": {}, "relationships": []}
+ node_tables = self.connection._get_node_table_names()
+ for table_name in node_tables:
+ node_props = self.connection._get_node_property_names(table_name)
+ current_table_schema["node_props"][table_name] = []
+ for prop, attr in node_props.items():
+ schema = {}
+ schema["property"] = prop
+ schema["type"] = attr["type"]
+ current_table_schema["node_props"][table_name].append(schema)
+
+ rel_tables = self.connection._get_rel_table_names()
+ for i, table in enumerate(rel_tables):
+ table_name = table["name"]
+ prop_values = self.connection.execute(
+ f"MATCH ()-[r:{table_name}]->() RETURN distinct r.label AS label;"
+ )
+ for row in prop_values:
+ rel_label = row[0]
+ src, dst = rel_tables[i]["src"], rel_tables[i]["dst"]
+ current_table_schema["relationships"].append(
+ {"start": src, "type": rel_label, "end": dst}
+ )
+ current_table_schema["rel_props"][rel_label] = []
+ table_details = self.connection.execute(
+ f"CALL TABLE_INFO('{table_name}') RETURN *;"
+ )
+ for props in table_details:
+ rel_props = {}
+ rel_props["property"] = props[1]
+ rel_props["type"] = props[2]
+ current_table_schema["rel_props"][rel_label].append(rel_props)
+
+ self.structured_schema = current_table_schema
+
+ return self.structured_schema
+
+ def get_schema_str(self) -> str:
+ schema = self.get_schema()
+
+ formatted_node_props = []
+ formatted_rel_props = []
+
+ # Format node properties
+ for label, props in schema["node_props"].items():
+ props_str = ", ".join(
+ [f"{prop['property']}: {prop['type']}" for prop in props]
+ )
+ formatted_node_props.append(f"{label} {{{props_str}}}")
+
+ # Format relationship properties
+ for type, props in schema["rel_props"].items():
+ props_str = ", ".join(
+ [f"{prop['property']}: {prop['type']}" for prop in props]
+ )
+ formatted_rel_props.append(f"{type} {{{props_str}}}")
+
+ # Format relationships
+ formatted_rels = [
+ f"(:{rel['start']})-[:{rel['type']}]->(:{rel['end']})"
+ for rel in schema["relationships"]
+ ]
+
+ return "\n".join(
+ [
+ "Node properties:",
+ "\n".join(formatted_node_props),
+ "Relationship properties:",
+ "\n".join(formatted_rel_props),
+ "The relationships:",
+ "\n".join(formatted_rels),
+ ]
+ )
+
+
+LadybugPGStore = LadybugPropertyGraphStore
diff --git a/llama-index-integrations/graph_stores/llama-index-graph-stores-ladybug/llama_index/graph_stores/ladybug/utils.py b/llama-index-integrations/graph_stores/llama-index-graph-stores-ladybug/llama_index/graph_stores/ladybug/utils.py
new file mode 100644
index 0000000000..f0bbf556b0
--- /dev/null
+++ b/llama-index-integrations/graph_stores/llama-index-graph-stores-ladybug/llama_index/graph_stores/ladybug/utils.py
@@ -0,0 +1,154 @@
+from typing import List, Optional, Tuple, _LiteralGenericAlias, get_args
+
+import real_ladybug as lb
+
+Triple = Tuple[str, str, str]
+
+
+def create_fresh_database(db: str) -> None:
+ """
+ Create a new Ladybug database by removing existing database directory and its contents.
+ """
+ import shutil
+
+ shutil.rmtree(db, ignore_errors=True)
+
+
+def get_list_from_literal(literal: _LiteralGenericAlias) -> List[str]:
+ """
+ Get a list of strings from a Literal type.
+
+ Parameters
+ ----------
+ literal (_LiteralGenericAlias): The Literal type from which to extract the strings.
+
+ Returns
+ -------
+ List[str]: A list of strings extracted from the Literal type.
+
+ """
+ if not isinstance(literal, _LiteralGenericAlias):
+ raise TypeError(
+ f"{literal} must be a Literal type.\nTry using typing.Literal{literal}."
+ )
+ return list(get_args(literal))
+
+
+def remove_empty_values(input_dict):
+ """
+ Remove entries with empty values from the dictionary.
+
+ Parameters
+ ----------
+ input_dict (dict): The dictionary from which empty values need to be removed.
+
+ Returns
+ -------
+ dict: A new dictionary with all empty values removed.
+
+ """
+ # Create a new dictionary excluding empty values and remove the `e.` prefix from the keys
+ return {key.replace("e.", ""): value for key, value in input_dict.items() if value}
+
+
+def get_filtered_props(records: dict, filter_list: List[str]) -> dict:
+ return {k: v for k, v in records.items() if k not in filter_list}
+
+
+# Lookup entry by middle value of tuple
+def lookup_relation(relation: str, triples: List[Triple]) -> Triple:
+ """
+ Look up a triple in a list of triples by the middle value.
+ """
+ for triple in triples:
+ if triple[1] == relation:
+ return triple
+ return None
+
+
+def create_chunk_node_table(
+ connection: lb.Connection, embedding_dimension: Optional[int] = None
+) -> None:
+ # For now, the additional `properties` dict from LlamaIndex is stored as a string
+ # TODO: See if it makes sense to add better support for property metadata as columns
+
+ embedding_type = (
+ f"DOUBLE[{embedding_dimension}]" if embedding_dimension else "DOUBLE[]"
+ )
+
+ connection.execute(
+ f"""
+ CREATE NODE TABLE IF NOT EXISTS Chunk (
+ id STRING,
+ text STRING,
+ label STRING,
+ embedding {embedding_type},
+ creation_date DATE,
+ last_modified_date DATE,
+ file_name STRING,
+ file_path STRING,
+ file_size INT64,
+ file_type STRING,
+ ref_doc_id STRING,
+ PRIMARY KEY(id)
+ )
+ """
+ )
+
+
+def create_entity_node_tables(connection: lb.Connection, entities: List[str]) -> None:
+ for tbl_name in entities:
+ # Entity tables don't need embedding columns - only Chunk nodes have embeddings
+ # For now, the additional `properties` dict from LlamaIndex is stored as a string
+ # TODO: See if it makes sense to add better support for property metadata as columns
+ connection.execute(
+ f"""
+ CREATE NODE TABLE IF NOT EXISTS {tbl_name} (
+ id STRING,
+ name STRING,
+ label STRING,
+ creation_date DATE,
+ last_modified_date DATE,
+ file_name STRING,
+ file_path STRING,
+ file_size INT64,
+ file_type STRING,
+ triplet_source_id STRING,
+ PRIMARY KEY(id)
+ )
+ """
+ )
+
+
+def create_entity_relationship_table(
+ connection: lb.Connection, label: str, src_id: str, dst_id: str
+) -> None:
+ connection.execute(
+ f"""
+ CREATE REL TABLE IF NOT EXISTS {label} (
+ FROM {src_id} TO {dst_id},
+ label STRING,
+ triplet_source_id STRING
+ );
+ """
+ )
+
+
+def create_relation_tables(
+ connection: lb.Connection, entities: List[str], relationship_schema: List[Triple]
+) -> None:
+ # Create relationship tables for each entity
+ for src, rel_label, dst in relationship_schema:
+ create_entity_relationship_table(connection, rel_label, src, dst)
+
+ ddl = "CREATE REL TABLE IF NOT EXISTS MENTIONS ("
+ table_names = []
+ for entity in entities:
+ table_names.append(f"FROM Chunk TO {entity}")
+ table_names = list(set(table_names))
+ ddl += ", ".join(table_names)
+ # Add common properties for all the tables here
+ ddl += ", label STRING, triplet_source_id STRING)"
+
+ if ddl:
+ connection.execute(ddl)
diff --git a/llama-index-integrations/graph_stores/llama-index-graph-stores-ladybug/pyproject.toml b/llama-index-integrations/graph_stores/llama-index-graph-stores-ladybug/pyproject.toml
new file mode 100644
index 0000000000..8a6e996ed1
--- /dev/null
+++ b/llama-index-integrations/graph_stores/llama-index-graph-stores-ladybug/pyproject.toml
@@ -0,0 +1,65 @@
+[build-system]
+requires = ["hatchling"]
+build-backend = "hatchling.build"
+
+[dependency-groups]
+dev = [
+ "ipython==8.10.0",
+ "jupyter>=1.0.0,<2",
+ "mypy==0.991",
+ "pre-commit==3.2.0",
+ "pylint==2.15.10",
+ "pytest==7.2.1",
+ "pytest-mock==3.11.1",
+ "ruff==0.11.11",
+ "types-Deprecated>=0.1.0",
+ "types-PyYAML>=6.0.12.12,<7",
+ "types-protobuf>=4.24.0.4,<5",
+ "types-redis==4.5.5.0",
+ "types-requests==2.28.11.8",
+ "types-setuptools==67.1.0.0",
+ "black[jupyter]<=23.9.1,>=23.7.0",
+ "codespell[toml]>=v2.2.6",
+ "diff-cover>=9.2.0",
+ "pytest-cov>=6.1.1",
+]
+
+[project]
+name = "llama-index-graph-stores-ladybug"
+version = "0.10.0"
+description = "llama-index graph stores ladybug integration"
+authors = [{name = "Your Name", email = "you@example.com"}]
+requires-python = ">=3.9,<4.0"
+readme = "README.md"
+license = "MIT"
+dependencies = [
+ "real_ladybug>=0.12.0,<0.13",
+ "llama-index-core>=0.13.0,<0.15",
+]
+
+[tool.codespell]
+check-filenames = true
+check-hidden = true
+skip = "*.csv,*.html,*.json,*.jsonl,*.pdf,*.txt,*.ipynb"
+
+[tool.hatch.build.targets.sdist]
+include = ["llama_index/"]
+exclude = ["**/BUILD"]
+
+[tool.hatch.build.targets.wheel]
+include = ["llama_index/"]
+exclude = ["**/BUILD"]
+
+[tool.llamahub]
+contains_example = false
+import_path = "llama_index.graph_stores.ladybug"
+
+[tool.llamahub.class_authors]
+LadybugGraphStore = "llama-index"
+LadybugPropertyGraphStore = "llama-index"
+
+[tool.mypy]
+disallow_untyped_defs = true
+exclude = ["_static", "build", "examples", "notebooks", "venv"]
+ignore_missing_imports = true
+python_version = "3.8"
diff --git a/llama-index-integrations/graph_stores/llama-index-graph-stores-ladybug/tests/__init__.py b/llama-index-integrations/graph_stores/llama-index-graph-stores-ladybug/tests/__init__.py
new file mode 100644
index 0000000000..e69de29bb2
diff --git a/llama-index-integrations/graph_stores/llama-index-graph-stores-ladybug/tests/test_graph_stores_ladybug.py b/llama-index-integrations/graph_stores/llama-index-graph-stores-ladybug/tests/test_graph_stores_ladybug.py
new file mode 100644
index 0000000000..72d0d72144
--- /dev/null
+++ b/llama-index-integrations/graph_stores/llama-index-graph-stores-ladybug/tests/test_graph_stores_ladybug.py
@@ -0,0 +1,69 @@
+from pathlib import Path
+from typing import Generator
+
+import pytest
+from llama_index.core.graph_stores.types import GraphStore
+from llama_index.graph_stores.ladybug import LadybugGraphStore
+
+# Track all database files created during tests for cleanup
+_test_db_files = []
+
+
+def cleanup_test_databases():
+ """Clean up all test database files."""
+ for db_file in _test_db_files:
+ try:
+ Path(db_file).unlink(missing_ok=True)
+ except Exception:
+ pass # Ignore cleanup errors
+ _test_db_files.clear()
+
+
+@pytest.fixture(autouse=True)
+def setup_and_teardown():
+ """Setup and teardown fixture that runs for every test."""
+ # Setup: Clear any existing test databases
+ cleanup_test_databases()
+
+ yield
+
+ # Teardown: Clean up all databases created during tests
+ cleanup_test_databases()
+
+
+@pytest.fixture()
+def ladybug_graph_store() -> Generator[LadybugGraphStore, None, None]:
+ """Fixture for LadybugGraphStore with proper cleanup."""
+ import real_ladybug as lb
+
+ db_file = "test_ladybug_graph_store.ladybug"
+ Path(db_file).unlink(missing_ok=True)
+ _test_db_files.append(db_file)
+
+ db = lb.Database(db_file)
+ store = LadybugGraphStore(db)
+
+ yield store
+
+ # Close database connection
+ try:
+ db.close()
+ except Exception:
+ pass
+
+
+def test_ladybug_graph_store():
+ names_of_bases = [b.__name__ for b in LadybugGraphStore.__bases__]
+ assert GraphStore.__name__ in names_of_bases
+
+
+def test_ladybug_graph_store_basic_operations(ladybug_graph_store: LadybugGraphStore):
+ """Test basic graph store operations."""
+ # Test that the store can be instantiated and basic operations work
+ # This ensures database files are properly created and cleaned up
+ store = ladybug_graph_store
+
+ # Basic triplet operations should not crash
+ store.upsert_triplet("subject", "predicate", "object")
+ triplets = store.get("subject")
+ assert len(triplets) >= 0 # Should return some result without crashing
diff --git a/llama-index-integrations/graph_stores/llama-index-graph-stores-ladybug/tests/test_pg_stores_ladybug.py b/llama-index-integrations/graph_stores/llama-index-graph-stores-ladybug/tests/test_pg_stores_ladybug.py
new file mode 100644
index 0000000000..c945111c45
--- /dev/null
+++ b/llama-index-integrations/graph_stores/llama-index-graph-stores-ladybug/tests/test_pg_stores_ladybug.py
@@ -0,0 +1,381 @@
+from pathlib import Path
+from typing import Generator, List
+from unittest.mock import Mock, patch
+
+import pytest
+from llama_index.core.graph_stores.types import ChunkNode, EntityNode, Relation
+from llama_index.core.schema import TextNode
+from llama_index.core.vector_stores.types import VectorStoreQuery
+from llama_index.graph_stores.ladybug import LadybugPropertyGraphStore
+
+# Track all database files created during tests for cleanup
+_test_db_files = []
+
+
+def cleanup_test_databases():
+ """Clean up all test database files."""
+ for db_file in _test_db_files:
+ try:
+ Path(db_file).unlink(missing_ok=True)
+ except Exception:
+ pass # Ignore cleanup errors
+ _test_db_files.clear()
+
+
+@pytest.fixture(autouse=True)
+def setup_and_teardown():
+ """Setup and teardown fixture that runs for every test."""
+ # Setup: Clear any existing test databases
+ cleanup_test_databases()
+
+ yield
+
+ # Teardown: Clean up all databases created during tests
+ cleanup_test_databases()
+
+
+@pytest.fixture()
+def pg_store() -> Generator[LadybugPropertyGraphStore, None, None]:
+ import real_ladybug as lb
+
+ # Remove existing database file
+ db_file = "llama_test_db.ladybug"
+ Path(db_file).unlink(missing_ok=True)
+ _test_db_files.append(db_file)
+
+ db = lb.Database(db_file)
+ pg_store = LadybugPropertyGraphStore(db)
+ pg_store.structured_query("MATCH (n) DETACH DELETE n")
+ pg_store._test_db_file = db_file
+
+ yield pg_store
+
+ # Close database connection
+ try:
+ db.close()
+ except Exception:
+ pass
+
+
+@pytest.fixture()
+def pg_store_with_vectors() -> Generator[LadybugPropertyGraphStore, None, None]:
+ """Fixture for pg_store with vector indexing enabled."""
+ import uuid
+
+ import real_ladybug as lb
+
+ # Use unique database file name to avoid conflicts
+ db_file = f"llama_test_db_vector_{uuid.uuid4().hex[:8]}.ladybug"
+ Path(db_file).unlink(missing_ok=True)
+ _test_db_files.append(db_file)
+
+ db = lb.Database(db_file)
+
+ # Mock embedding model for testing
+ mock_embed_model = Mock()
+ mock_embed_model.get_text_embedding.return_value = [0.1] * 384
+
+ pg_store = LadybugPropertyGraphStore(
+ db=db, use_vector_index=True, embed_model=mock_embed_model, embed_dimension=384
+ )
+ pg_store.structured_query("MATCH (n) DETACH DELETE n")
+
+ # Store the db_file for cleanup
+ pg_store._test_db_file = db_file
+
+ yield pg_store
+
+ # Close database connection
+ try:
+ db.close()
+ except Exception:
+ pass
+
+
+@pytest.fixture()
+def sample_chunk_nodes() -> List[ChunkNode]:
+ """Sample chunk nodes with embeddings for testing."""
+ return [
+ ChunkNode(
+ id_="chunk1",
+ text="This is the first chunk of text",
+ embedding=[0.1, 0.2, 0.3, 0.4] * 96, # 384-dim embedding
+ properties={"file_name": "test1.txt"},
+ ),
+ ChunkNode(
+ id_="chunk2",
+ text="This is the second chunk of text",
+ embedding=[0.5, 0.6, 0.7, 0.8] * 96, # 384-dim embedding
+ properties={"file_name": "test2.txt"},
+ ),
+ ]
+
+
+def test_ladybugdb_pg_store(pg_store: LadybugPropertyGraphStore) -> None:
+ # Create a two entity nodes
+ entity1 = EntityNode(label="PERSON", name="Logan")
+ entity2 = EntityNode(label="ORGANIZATION", name="LlamaIndex")
+
+ # Create a relation
+ relation = Relation(
+ label="WORKS_FOR",
+ source_id=entity1.id,
+ target_id=entity2.id,
+ )
+
+ pg_store.upsert_nodes([entity1, entity2])
+ pg_store.upsert_relations([relation])
+
+ source_node = TextNode(text="Logan (age 28), works for LlamaIndex since 2023.")
+ relations = [
+ Relation(
+ label="MENTIONS",
+ target_id=entity1.id,
+ source_id=source_node.node_id,
+ ),
+ Relation(
+ label="MENTIONS",
+ target_id=entity2.id,
+ source_id=source_node.node_id,
+ ),
+ ]
+
+ pg_store.upsert_llama_nodes([source_node])
+ pg_store.upsert_relations(relations)
+
+ print(pg_store.get())
+
+ kg_nodes = pg_store.get(ids=[entity1.id])
+ assert len(kg_nodes) == 1
+ assert kg_nodes[0].label == "PERSON"
+ assert kg_nodes[0].name == "Logan"
+
+ # get paths from a node
+ paths = pg_store.get_rel_map(kg_nodes, depth=1)
+ for path in paths:
+ assert path[0].id == entity1.id
+ assert path[2].id == entity2.id
+ assert path[1].id == relation.id
+
+ query = "match (n:Entity) return n"
+ result = pg_store.structured_query(query)
+ assert len(result) == 2
+
+ # deleting
+ # delete our entities
+ pg_store.delete(ids=[entity1.id, entity2.id])
+
+ # delete our text nodes
+ pg_store.delete(ids=[source_node.node_id])
+
+ nodes = pg_store.get(ids=[entity1.id, entity2.id])
+ assert len(nodes) == 0
+
+ text_nodes = pg_store.get_llama_nodes([source_node.node_id])
+ assert len(text_nodes) == 0
+
+
+def test_create_vector_index_disabled(pg_store: LadybugPropertyGraphStore) -> None:
+ """Test _create_vector_index when use_vector_index is False."""
+ pg_store.use_vector_index = False
+
+ # Should return without doing anything
+ pg_store._create_vector_index("Chunk")
+
+ # Verify no indexes were created by checking database
+ indexes_result = pg_store.connection.execute("CALL SHOW_INDEXES() RETURN *")
+ index_names = [row[1] for row in indexes_result if len(row) > 1]
+ assert "chunk_embedding_index" not in index_names
+
+
+def test_create_vector_index_no_data(
+ pg_store_with_vectors: LadybugPropertyGraphStore,
+) -> None:
+ """Test _create_vector_index when table has no embedding data."""
+ # Try to create index on empty table - should not create index since Ladybug requires data first
+ pg_store_with_vectors._create_vector_index("Chunk")
+
+ # Should not create index since no data exists (Ladybug requirement)
+ indexes_result = pg_store_with_vectors.connection.execute(
+ "CALL SHOW_INDEXES() RETURN *"
+ )
+ index_names = [row[1] for row in indexes_result if len(row) > 1]
+ assert "chunk_embedding_index" not in index_names
+
+
+def test_create_vector_index_with_data(
+ pg_store_with_vectors: LadybugPropertyGraphStore, sample_chunk_nodes: List[ChunkNode]
+) -> None:
+ """Test _create_vector_index with actual embedding data."""
+ # First insert chunk nodes with embeddings
+ pg_store_with_vectors.upsert_nodes(sample_chunk_nodes)
+
+ # Now create vector index
+ pg_store_with_vectors._create_vector_index("Chunk")
+
+ # Verify index was created (if vector extension is available)
+ # Note: This may fail in test environment without vector extension
+ # In that case, the method should handle gracefully
+
+
+def test_create_vector_index_already_exists(
+ pg_store_with_vectors: LadybugPropertyGraphStore, sample_chunk_nodes: List[ChunkNode]
+) -> None:
+ """Test _create_vector_index when index already exists."""
+ # Insert data
+ pg_store_with_vectors.upsert_nodes(sample_chunk_nodes)
+
+ # Create index first time
+ pg_store_with_vectors._create_vector_index("Chunk")
+
+ # Verify index exists
+ indexes_result = pg_store_with_vectors.connection.execute(
+ "CALL SHOW_INDEXES() RETURN *"
+ )
+ index_names = [row[1] for row in indexes_result if len(row) > 1]
+ assert "chunk_embedding_index" in index_names
+ initial_count = len(index_names)
+
+ # Try to create again - should not duplicate
+ pg_store_with_vectors._create_vector_index("Chunk")
+
+ # Verify no duplicate was created
+ indexes_result = pg_store_with_vectors.connection.execute(
+ "CALL SHOW_INDEXES() RETURN *"
+ )
+ index_names = [row[1] for row in indexes_result if len(row) > 1]
+ final_count = len(index_names)
+ assert initial_count == final_count
+
+
+def test_ensure_vector_indexes_disabled(pg_store: LadybugPropertyGraphStore) -> None:
+ """Test _ensure_vector_indexes when use_vector_index is False."""
+ pg_store.use_vector_index = False
+
+ # Should return without doing anything
+ pg_store._ensure_vector_indexes()
+
+ # Verify no indexes were created
+ indexes_result = pg_store.connection.execute("CALL SHOW_INDEXES() RETURN *")
+ index_names = [row[1] for row in indexes_result if len(row) > 1]
+ assert "chunk_embedding_index" not in index_names
+
+
+def test_ensure_vector_indexes_enabled(
+ pg_store_with_vectors: LadybugPropertyGraphStore, sample_chunk_nodes: List[ChunkNode]
+) -> None:
+ """Test _ensure_vector_indexes with vector indexing enabled."""
+ # Insert chunk nodes with embeddings
+ pg_store_with_vectors.upsert_nodes(sample_chunk_nodes)
+
+ # Ensure vector indexes
+ pg_store_with_vectors._ensure_vector_indexes()
+
+ # This should attempt to create index for Chunk table
+ # Exact behavior depends on whether vector extension is available
+
+
+def test_refresh_vector_index(
+ pg_store_with_vectors: LadybugPropertyGraphStore, sample_chunk_nodes: List[ChunkNode]
+) -> None:
+ """Test refresh_vector_index method."""
+ # Insert chunk nodes with embeddings
+ pg_store_with_vectors.upsert_nodes(sample_chunk_nodes)
+
+ # Create initial index
+ pg_store_with_vectors._create_vector_index("Chunk")
+
+ # Refresh the index (should drop and recreate)
+ pg_store_with_vectors.refresh_vector_index()
+
+ # The method should handle both drop and recreate operations
+ # Even if they fail due to missing vector extension
+
+
+def test_vector_query_with_mock_results(
+ pg_store_with_vectors: LadybugPropertyGraphStore, sample_chunk_nodes: List[ChunkNode]
+) -> None:
+ """Test vector_query method with mocked vector search results."""
+ # Insert chunk nodes with embeddings
+ pg_store_with_vectors.upsert_nodes(sample_chunk_nodes)
+
+ # Create a vector query
+ query = VectorStoreQuery(
+ query_embedding=[0.2, 0.3, 0.4, 0.5] * 96, # 384-dim query embedding
+ similarity_top_k=2,
+ )
+
+ # Mock both vector index query and structured query calls
+ with (
+ patch.object(pg_store_with_vectors.connection, "execute") as mock_execute,
+ patch.object(
+ pg_store_with_vectors, "structured_query"
+ ) as mock_structured_query,
+ ):
+ # Mock different execute calls based on query type
+ def mock_execute_side_effect(query, **kwargs):
+ if "COUNT(n)" in query:
+ return [(2,)] # Return count of 2
+ elif "SHOW_INDEXES" in query:
+ return [("table", "chunk_embedding_index")] # Return existing index
+ else:
+ return [("chunk1", 0.1), ("chunk2", 0.3)] # Vector search results
+
+ mock_execute.side_effect = mock_execute_side_effect
+
+ # Mock structured query results for fetching chunk data
+ mock_structured_query.side_effect = [
+ # First call - fetch chunk1 data
+ [
+ {
+ "n.id": "chunk1",
+ "n.text": "This is the first chunk of text",
+ "n.file_name": "test1.txt",
+ }
+ ],
+ # Second call - fetch chunk2 data
+ [
+ {
+ "n.id": "chunk2",
+ "n.text": "This is the second chunk of text",
+ "n.file_name": "test2.txt",
+ }
+ ],
+ ]
+
+ # Execute vector query
+ nodes, similarities = pg_store_with_vectors.vector_query(query)
+
+ # Verify results
+ assert len(nodes) == 2
+ assert len(similarities) == 2
+
+ # Check similarity conversion (1.0 - distance)
+ assert similarities[0] == 1.0 - 0.1 # 0.9
+ assert similarities[1] == 1.0 - 0.3 # 0.7
+
+ # Verify nodes are sorted by similarity (descending)
+ assert similarities[0] > similarities[1]
+
+
+def test_vector_query_ensures_indexes(
+ pg_store_with_vectors: LadybugPropertyGraphStore, sample_chunk_nodes: List[ChunkNode]
+) -> None:
+ """Test that vector_query calls _ensure_vector_indexes."""
+ # Insert chunk nodes with embeddings
+ pg_store_with_vectors.upsert_nodes(sample_chunk_nodes)
+
+ # Create a vector query
+ query = VectorStoreQuery(
+ query_embedding=[0.2, 0.3, 0.4, 0.5] * 96, similarity_top_k=1
+ )
+
+ # Mock _ensure_vector_indexes to verify it's called
+ with patch.object(pg_store_with_vectors, "_ensure_vector_indexes") as mock_ensure:
+ with patch.object(pg_store_with_vectors.connection, "execute") as mock_execute:
+ mock_execute.return_value = [] # Empty results
+
+ pg_store_with_vectors.vector_query(query)
+
+ # Verify _ensure_vector_indexes was called
+ mock_ensure.assert_called_once()
diff --git a/llama-index-integrations/graph_stores/llama-index-graph-stores-ladybug/uv.lock b/llama-index-integrations/graph_stores/llama-index-graph-stores-ladybug/uv.lock
new file mode 100644
index 0000000000..3e00c34ee8
--- /dev/null
+++ b/llama-index-integrations/graph_stores/llama-index-graph-stores-ladybug/uv.lock
@@ -0,0 +1,5252 @@
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