| Name | llama-index-graph-stores-memgraph JSON |
| Version |
0.3.0
JSON |
| download |
| home_page | None |
| Summary | llama-index graph-stores memgraph integration |
| upload_time | 2025-02-17 15:35:30 |
| maintainer | None |
| docs_url | None |
| author | Your Name |
| requires_python | <4.0,>=3.9 |
| license | MIT |
| keywords |
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| VCS |
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| bugtrack_url |
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| requirements |
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# LlamaIndex Graph-Stores Integration: Memgraph
Memgraph is an open source graph database built for real-time streaming and fast analysis.
In this project, we integrated Memgraph as a graph store to store the LlamaIndex graph data and query it.
- Property Graph Store: `MemgraphPropertyGraphStore`
- Knowledege Graph Store: `MemgraphGraphStore`
## Installation
```shell
pip install llama-index llama-index-graph-stores-memgraph
```
## Usage
### Property Graph Store
```python
import os
import urllib.request
import nest_asyncio
from llama_index.core import SimpleDirectoryReader, PropertyGraphIndex
from llama_index.graph_stores.memgraph import MemgraphPropertyGraphStore
from llama_index.embeddings.openai import OpenAIEmbedding
from llama_index.llms.openai import OpenAI
from llama_index.core.indices.property_graph import SchemaLLMPathExtractor
os.environ[
"OPENAI_API_KEY"
] = "<YOUR_API_KEY>" # Replace with your OpenAI API key
os.makedirs("data/paul_graham/", exist_ok=True)
url = "https://raw.githubusercontent.com/run-llama/llama_index/main/docs/docs/examples/data/paul_graham/paul_graham_essay.txt"
output_path = "data/paul_graham/paul_graham_essay.txt"
urllib.request.urlretrieve(url, output_path)
nest_asyncio.apply()
with open(output_path, "r", encoding="utf-8") as file:
content = file.read()
modified_content = content.replace("'", "\\'")
with open(output_path, "w", encoding="utf-8") as file:
file.write(modified_content)
documents = SimpleDirectoryReader("./data/paul_graham/").load_data()
# Setup Memgraph connection (ensure Memgraph is running)
username = "" # Enter your Memgraph username (default "")
password = "" # Enter your Memgraph password (default "")
url = "" # Specify the connection URL, e.g., 'bolt://localhost:7687'
graph_store = MemgraphPropertyGraphStore(
username=username,
password=password,
url=url,
)
index = PropertyGraphIndex.from_documents(
documents,
embed_model=OpenAIEmbedding(model_name="text-embedding-ada-002"),
kg_extractors=[
SchemaLLMPathExtractor(
llm=OpenAI(model="gpt-3.5-turbo", temperature=0.0),
)
],
property_graph_store=graph_store,
show_progress=True,
)
query_engine = index.as_query_engine(include_text=True)
response = query_engine.query("What happened at Interleaf and Viaweb?")
print("\nDetailed Query Response:")
print(str(response))
```
### Knowledge Graph Store
```python
import os
import logging
from llama_index.llms.openai import OpenAI
from llama_index.core import Settings
from llama_index.core import (
KnowledgeGraphIndex,
SimpleDirectoryReader,
StorageContext,
)
from llama_index.graph_stores.memgraph import MemgraphGraphStore
os.environ[
"OPENAI_API_KEY"
] = "<YOUR_API_KEY>" # Replace with your OpenAI API key
logging.basicConfig(level=logging.INFO)
llm = OpenAI(temperature=0, model="gpt-3.5-turbo")
Settings.llm = llm
Settings.chunk_size = 512
documents = {
"doc1.txt": "Python is a popular programming language known for its readability and simplicity. It was created by Guido van Rossum and first released in 1991. Python supports multiple programming paradigms, including procedural, object-oriented, and functional programming. It is widely used in web development, data science, artificial intelligence, and scientific computing.",
"doc2.txt": "JavaScript is a high-level programming language primarily used for web development. It was created by Brendan Eich and first appeared in 1995. JavaScript is a core technology of the World Wide Web, alongside HTML and CSS. It enables interactive web pages and is an essential part of web applications. JavaScript is also used in server-side development with environments like Node.js.",
"doc3.txt": "Java is a high-level, class-based, object-oriented programming language that is designed to have as few implementation dependencies as possible. It was developed by James Gosling and first released by Sun Microsystems in 1995. Java is widely used for building enterprise-scale applications, mobile applications, and large systems development.",
}
for filename, content in documents.items():
with open(filename, "w") as file:
file.write(content)
loaded_documents = SimpleDirectoryReader(".").load_data()
# Setup Memgraph connection (ensure Memgraph is running)
username = "" # Enter your Memgraph username (default "")
password = "" # Enter your Memgraph password (default "")
url = "" # Specify the connection URL, e.g., 'bolt://localhost:7687'
database = "memgraph" # Name of the database, default is 'memgraph'
graph_store = MemgraphGraphStore(
username=username,
password=password,
url=url,
database=database,
)
storage_context = StorageContext.from_defaults(graph_store=graph_store)
index = KnowledgeGraphIndex.from_documents(
loaded_documents,
storage_context=storage_context,
max_triplets_per_chunk=3,
)
query_engine = index.as_query_engine(
include_text=False, response_mode="tree_summarize"
)
response = query_engine.query("Tell me about Python and its uses")
print("Query Response:")
print(response)
```
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"description": "# LlamaIndex Graph-Stores Integration: Memgraph\n\nMemgraph is an open source graph database built for real-time streaming and fast analysis.\n\nIn this project, we integrated Memgraph as a graph store to store the LlamaIndex graph data and query it.\n\n- Property Graph Store: `MemgraphPropertyGraphStore`\n- Knowledege Graph Store: `MemgraphGraphStore`\n\n## Installation\n\n```shell\npip install llama-index llama-index-graph-stores-memgraph\n```\n\n## Usage\n\n### Property Graph Store\n\n```python\nimport os\nimport urllib.request\nimport nest_asyncio\nfrom llama_index.core import SimpleDirectoryReader, PropertyGraphIndex\nfrom llama_index.graph_stores.memgraph import MemgraphPropertyGraphStore\nfrom llama_index.embeddings.openai import OpenAIEmbedding\nfrom llama_index.llms.openai import OpenAI\nfrom llama_index.core.indices.property_graph import SchemaLLMPathExtractor\n\n\nos.environ[\n \"OPENAI_API_KEY\"\n] = \"<YOUR_API_KEY>\" # Replace with your OpenAI API key\n\nos.makedirs(\"data/paul_graham/\", exist_ok=True)\n\nurl = \"https://raw.githubusercontent.com/run-llama/llama_index/main/docs/docs/examples/data/paul_graham/paul_graham_essay.txt\"\noutput_path = \"data/paul_graham/paul_graham_essay.txt\"\nurllib.request.urlretrieve(url, output_path)\n\nnest_asyncio.apply()\n\nwith open(output_path, \"r\", encoding=\"utf-8\") as file:\n content = file.read()\n\nmodified_content = content.replace(\"'\", \"\\\\'\")\n\nwith open(output_path, \"w\", encoding=\"utf-8\") as file:\n file.write(modified_content)\n\ndocuments = SimpleDirectoryReader(\"./data/paul_graham/\").load_data()\n\n# Setup Memgraph connection (ensure Memgraph is running)\nusername = \"\" # Enter your Memgraph username (default \"\")\npassword = \"\" # Enter your Memgraph password (default \"\")\nurl = \"\" # Specify the connection URL, e.g., 'bolt://localhost:7687'\n\ngraph_store = MemgraphPropertyGraphStore(\n username=username,\n password=password,\n url=url,\n)\n\nindex = PropertyGraphIndex.from_documents(\n documents,\n embed_model=OpenAIEmbedding(model_name=\"text-embedding-ada-002\"),\n kg_extractors=[\n SchemaLLMPathExtractor(\n llm=OpenAI(model=\"gpt-3.5-turbo\", temperature=0.0),\n )\n ],\n property_graph_store=graph_store,\n show_progress=True,\n)\n\nquery_engine = index.as_query_engine(include_text=True)\n\nresponse = query_engine.query(\"What happened at Interleaf and Viaweb?\")\nprint(\"\\nDetailed Query Response:\")\nprint(str(response))\n```\n\n### Knowledge Graph Store\n\n```python\nimport os\nimport logging\nfrom llama_index.llms.openai import OpenAI\nfrom llama_index.core import Settings\nfrom llama_index.core import (\n KnowledgeGraphIndex,\n SimpleDirectoryReader,\n StorageContext,\n)\nfrom llama_index.graph_stores.memgraph import MemgraphGraphStore\n\nos.environ[\n \"OPENAI_API_KEY\"\n] = \"<YOUR_API_KEY>\" # Replace with your OpenAI API key\n\nlogging.basicConfig(level=logging.INFO)\n\nllm = OpenAI(temperature=0, model=\"gpt-3.5-turbo\")\nSettings.llm = llm\nSettings.chunk_size = 512\n\ndocuments = {\n \"doc1.txt\": \"Python is a popular programming language known for its readability and simplicity. It was created by Guido van Rossum and first released in 1991. Python supports multiple programming paradigms, including procedural, object-oriented, and functional programming. It is widely used in web development, data science, artificial intelligence, and scientific computing.\",\n \"doc2.txt\": \"JavaScript is a high-level programming language primarily used for web development. It was created by Brendan Eich and first appeared in 1995. JavaScript is a core technology of the World Wide Web, alongside HTML and CSS. It enables interactive web pages and is an essential part of web applications. JavaScript is also used in server-side development with environments like Node.js.\",\n \"doc3.txt\": \"Java is a high-level, class-based, object-oriented programming language that is designed to have as few implementation dependencies as possible. It was developed by James Gosling and first released by Sun Microsystems in 1995. Java is widely used for building enterprise-scale applications, mobile applications, and large systems development.\",\n}\n\nfor filename, content in documents.items():\n with open(filename, \"w\") as file:\n file.write(content)\n\nloaded_documents = SimpleDirectoryReader(\".\").load_data()\n\n# Setup Memgraph connection (ensure Memgraph is running)\nusername = \"\" # Enter your Memgraph username (default \"\")\npassword = \"\" # Enter your Memgraph password (default \"\")\nurl = \"\" # Specify the connection URL, e.g., 'bolt://localhost:7687'\ndatabase = \"memgraph\" # Name of the database, default is 'memgraph'\n\ngraph_store = MemgraphGraphStore(\n username=username,\n password=password,\n url=url,\n database=database,\n)\n\nstorage_context = StorageContext.from_defaults(graph_store=graph_store)\n\nindex = KnowledgeGraphIndex.from_documents(\n loaded_documents,\n storage_context=storage_context,\n max_triplets_per_chunk=3,\n)\n\nquery_engine = index.as_query_engine(\n include_text=False, response_mode=\"tree_summarize\"\n)\nresponse = query_engine.query(\"Tell me about Python and its uses\")\n\nprint(\"Query Response:\")\nprint(response)\n```\n",
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