Add LLM-KG integration examples

Author: erblinaqeli

src/content/docs/guide/KG_llms.md

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+---
+title: Knowledge Graph Applications with LLMs
+description: Examples of integrating Knowledge Graphs with LLMs using Virtuoso, Apache Jena, and other tools.
+sidebar:
+  order: 3
+---
+
+Knowledge Graphs (KGs) combined with Large Language Models (LLMs) offer powerful solutions for data-driven applications. This guide showcases practical examples of how to integrate LLMs with Knowledge Graphs using tools like Virtuoso and Apache Jena.
+
+---
+
+## Example 1: Querying Knowledge Graphs with LLMs
+
+<details>
+
+### Overview
+
+In this example, we demonstrate how to query a Virtuoso Knowledge Graph using a Large Language Model (LLM) to retrieve meaningful insights from structured data. The core idea is to bridge the gap between natural language queries and structured data stored in RDF format within Virtuoso.The integration leverages `llama_index`, an interface that connects LLMs to structured data sources like SPARQL endpoints.
+
+---
+
+## Prerequisites
+
+### System Requirements:
+
+- **Python 3.x** installed.
+- **Virtuoso Server** running with SPARQL authentication enabled.
+
+### Required Installations:
+
+1. **Uninstall existing LlamaIndex (if any):**
+
+   ```bash
+   pip uninstall llama_index -y
+   ```
+
+2. **Install OpenLink's fork of LlamaIndex:**
+
+   ```bash
+   pip install git+https://github.com/OpenLinkSoftware/llama_index
+   ```
+
+3. **Set OpenAI API Key:**
+
+   ```bash
+   export OPENAI_API_KEY=your_openai_api_key_here
+   ```
+
+4. **Create a directory for graph data storage:**
+   ```bash
+   mkdir llama_storage_graph
+   ```
+
+---
+
+## Configuration
+
+### SPARQL Endpoint Details:
+
+Update the following connection details in your Python script:
+
+```python
+ENDPOINT = 'http://localhost:8890/sparql-auth/'
+GRAPH = 'http://purl.org/stuff/guardians'
+BASE_URI = 'http://purl.org/stuff/data'
+USER = 'dba'
+PASSWORD = 'dba'
+```
+
+### OpenAI API Configuration:
+
+```python
+import os
+import openai
+
+openai.api_key = os.environ["OPENAI_API_KEY"]
+```
+
+---
+
+## Full Python Code (`llama_test.py`)
+
+```python
+from llama_index import download_loader
+import os
+from llama_index import KnowledgeGraphIndex, ServiceContext
+from llama_index.storage.storage_context import StorageContext
+from llama_index.graph_stores import SparqlGraphStore
+from llama_index.llms import OpenAI
+from llama_index import load_index_from_storage
+import openai
+
+# OpenAI API Key
+openai.api_key = os.environ["OPENAI_API_KEY"]
+
+# Initialize LLM
+llm = OpenAI(temperature=0, model="text-davinci-002")
+service_context = ServiceContext.from_defaults(llm=llm, chunk_size=512)
+
+# Virtuoso SPARQL Endpoint Configuration
+ENDPOINT = 'http://localhost:8890/sparql-auth/'
+GRAPH = 'http://purl.org/stuff/guardians'
+BASE_URI = 'http://purl.org/stuff/data'
+USER = 'dba'
+PASSWORD = 'dba'
+
+# Connect to Virtuoso SPARQL Graph Store
+graph_store = SparqlGraphStore(
+    sparql_endpoint=ENDPOINT,
+    sparql_graph=GRAPH,
+    sparql_base_uri=BASE_URI,
+    create_graph=False,
+    user_name=USER,
+    user_password=PASSWORD,
+)
+
+# Load Index from Storage
+storage_context = StorageContext.from_defaults(persist_dir='./llama_storage_graph', graph_store=graph_store)
+kg_index = load_index_from_storage(
+    storage_context=storage_context,
+    service_context=service_context,
+    max_triplets_per_chunk=10,
+    sparql_endpoint=ENDPOINT,
+    sparql_graph=GRAPH,
+    sparql_base_uri=BASE_URI,
+    include_embeddings=True,
+    verbose=True,
+)
+
+# Query Engine Setup
+kg_rag_query_engine = kg_index.as_query_engine(
+    include_text=False,
+    retriever_mode="keyword",
+    response_mode="tree_summarize",
+)
+
+# Example Query
+response_graph_rag = kg_rag_query_engine.query("In the movie, what does Ken think about?")
+
+# Display the Response
+print(str(response_graph_rag))
+```
+
+---
+
+## Running the Code
+
+Execute the script in your terminal:
+
+```bash
+python llama_test.py
+```
+
+---
+
+## Expected Output
+
+When the code is executed, we expect the output to provide an insightful answer extracted from the Knowledge Graph:
+
+```bash
+Ken thinks about his identity, purpose, and the meaning of life, reflecting on his role beyond just being a supporting character.
+```
+
+This response is generated based on the RDF triples extracted from the Virtuoso knowledge graph.
+
+---
+
+## Key Concepts
+
+- **Virtuoso Integration:** The example connects to a Virtuoso SPARQL endpoint for querying RDF data.
+- **LLM Query Processing:** LLM enhances the query with natural language understanding, making it user-friendly.
+- **Knowledge Graph Indexing:** The Knowledge Graph Index improves retrieval efficiency by organizing data into meaningful chunks.
+
+---
+
+## Troubleshooting Tips
+
+- **Connection Errors:** Ensure Virtuoso is running and accessible via the specified SPARQL endpoint.
+- **Authentication Issues:** Verify that the provided `USER` and `PASSWORD` have the necessary SPARQL access rights.
+- **API Key Errors:** Confirm that the OpenAI API key is correctly set in the environment variables.
+
+---
+
+## Expanding the Dataset
+
+You can modify the SPARQL queries to explore more data points. For example:
+
+```python
+response_graph_rag = kg_rag_query_engine.query("Who is Barbie?")
+print(str(response_graph_rag))
+```
+
+### Expected Output:
+
+```bash
+Barbie is a character who thinks about becoming human and living in the real world. She also contemplates what it means to be human.
+```
+
+ </details>
+
+## Example 2: Extracting Triples from Text Using LLMs
+
+<details>
+
+### Overview
+
+In this example, we showcase how to automatically extract structured knowledge (in the form of triples: subject, relation, object) from unstructured text using a Large Language Model (LLM). The goal is to transform plain text into a format suitable for building knowledge graphs, which can later be queried using SPARQL or integrated with systems like Virtuoso or Apache Jena.
+
+### Full Python Code (`kg_generator.py`)
+
+```python
+from openai import OpenAI
+import csv
+
+# Set up OpenAI API key
+client = OpenAI(api_key="")
+
+# Sample text for extracting entities and relationships
+text = """
+Barack Obama was born in Hawaii. He was the 44th President of the United States.
+Michelle Obama is his wife. They have two daughters, Malia and Sasha.
+"""
+
+# Function to extract entities and relationships
+def extract_entities_relations(text):
+    prompt = f"""
+    Extract entities and relationships from the following text in the form of triples (subject, relation, object):
+
+    Text: {text}
+
+    Format:
+    (Subject, Relation, Object)
+    """
+
+    # Correct ChatCompletion API call using the instantiated client
+    response = client.chat.completions.create(
+        model="gpt-4",
+        messages=[
+            {"role": "system", "content": "You are an assistant that extracts entities and relationships from text."},
+            {"role": "user", "content": prompt}
+        ],
+        temperature=0.5,
+        max_tokens=200
+    )
+
+    return response.choices[0].message.content.strip()
+
+# Extract entities and relationships from the sample text
+extracted_triples = extract_entities_relations(text)
+
+# Display the extracted triples
+print("Extracted Triples:")
+print(extracted_triples)
+
+# Save triples to a CSV file
+csv_filename = "extracted_triples.csv"
+with open(csv_filename, mode='w', newline='', encoding='utf-8') as file:
+    writer = csv.writer(file)
+    writer.writerow(["Subject", "Predicate", "Object"])  # CSV Header
+    for triple in extracted_triples.split('\n'):
+        writer.writerow(triple.strip("()").split(", "))
+
+print(f"\nTriples successfully saved to {csv_filename}")
+```
+
+---
+
+### Running the Code
+
+Execute the script:
+
+```bash
+python kg_generator.py
+```
+
+---
+
+### Expected Output
+
+```bash
+Extracted Triples:
+(Barack Obama, was born in, Hawaii)
+(Barack Obama, was, 44th President of the United States)
+(Michelle Obama, is wife of, Barack Obama)
+(Barack Obama, has daughters, Malia)
+(Barack Obama, has daughters, Sasha)
+
+Triples successfully saved to extracted_triples.csv
+```
+
+---
+
+### CSV Output
+
+```csv
+        Subject,Predicate,Object
+        Barack Obama,was born in,Hawaii
+        Barack Obama,was,44th President of the United States
+        Michelle Obama,is wife of,Barack Obama
+        Barack Obama,has daughters,Malia
+        Barack Obama,has daughters,Sasha
+```
+
+This file can now be imported into Virtuoso or Apache Jena as part of a knowledge graph.
+
+---
+
+</details>
+
+## More Examples
+
+Using Virtuoso with LlamaIndex for RAG significantly improves the quality and reliability of LLM-generated content by grounding responses in factual data. This approach is particularly effective for minimizing hallucinations in knowledge-driven applications.