Knowledge Graph Index with NebulaGraph and OpenAI LLM

This tutorial contains code to set up a Knowledge Graph Index using NebulaGraph as the storage backend and OpenAI's Language Model (LLM) for query processing.

Setup

Environment Configuration

1. Load Environment Variables: Ensure you have a .env file set up with required environment variables. Use dotenv to load them in your Python environment.

OPENAI_API_KEY=ak**

```python
import os
from dotenv import load_dotenv

load_dotenv()
```

1. Logging Configuration: Configure the logging settings.
   

   import logging
   import sys
   
   logging.basicConfig(
       stream=sys.stdout, level=logging.INFO
   )  # logging.DEBUG for more verbose output
   

2. Import Dependencies: Import necessary libraries and modules.
   

   # Import necessary modules and libraries
   from llama_index import (
       KnowledgeGraphIndex,
       LLMPredictor,
       ServiceContext,
       SimpleDirectoryReader,
   )
   from llama_index.storage.storage_context import StorageContext
   from llama_index.graph_stores import NebulaGraphStore
   from llama_index.llms import OpenAI
   from IPython.display import Markdown, display
   

NebulaGraph Setup

Ensure NebulaGraph (version 3.5.0 or newer) is set up with the following steps:

1. Cluster Creation: Create a NebulaGraph cluster using one of the following options:

• Docker Installation (Machines with Docker Installed):

  • Option 0: Run the command: curl -fsSL nebula-up.siwei.io/install.sh | bash

• Desktop Installation:

  • Option 1: Install NebulaGraph Docker Extension from Docker Hub.

• Manual Setup via NebulaGraph Console: If the above options are not applicable, manually create the NebulaGraph space, tags, edges, and indexes using the NebulaGraph console. Refer to the provided commands in the code comments.

1. Environment Configuration: Set NebulaGraph environment variables.
   

   os.environ["NEBULA_USER"] = "root"
   os.environ["NEBULA_PASSWORD"] = "nebula"  # default is "nebula"
   os.environ["NEBULA_ADDRESS"] = "127.0.0.1:9669"  # assumed NebulaGraph installed locally
   

Knowledge Graph Indexing

1. Define Graph Structure: Define the graph structure (space, tags, edges).
   

   space_name = "llamaindex"
   edge_types, rel_prop_names = ["relationship"], ["relationship"]
   tags = ["entity"]
   

2. Initialize NebulaGraph Store and Storage Context:
   

   graph_store = NebulaGraphStore(
       space_name=space_name,
       edge_types=edge_types,
       rel_prop_names=rel_prop_names,
       tags=tags,
   )
   storage_context = StorageContext.from_defaults(graph_store=graph_store)
   

3. Load Data: Load documents/data to be indexed.
   

   documents = SimpleDirectoryReader("data").load_data()
   

4. Create Knowledge Graph Index:
   

   kg_index = KnowledgeGraphIndex.from_documents(
       documents,
       storage_context=storage_context,
       max_triplets_per_chunk=10,
       service_context=service_context,
       space_name=space_name,
       edge_types=edge_types,
       rel_prop_names=rel_prop_names,
       tags=tags,
       include_embeddings=True,
   )
   

Querying the Knowledge Graph

1. Initialize Query Engine:
   

   from llama_index.query_engine import KnowledgeGraphQueryEngine
   
   query_engine = KnowledgeGraphQueryEngine(
       storage_context=storage_context,
       service_context=service_context,
       llm=llm,
       verbose=True,
   )
   

2. Querying:
   

   response = query_engine.query(
       "your question on the data?",
   )
   display(Markdown(f"<b>{response}</b>"))
   

Further Customization

• Adjust parameters and configurations in the code based on specific requirements or environment setup.

GITHUB REPO

• https://github.com/hasanmehmood/llamaindex-knowledge-graph