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from langchain_community.chat_models.oci_generative_ai import ChatOCIGenAI
from langchain_core.prompts import PromptTemplate
from langchain.schema.output_parser import StrOutputParser
from langchain_community.embeddings import OCIGenAIEmbeddings
from langchain_community.vectorstores import FAISS
from langchain.schema.runnable import RunnableMap
from langchain_community.document_loaders import UnstructuredPDFLoader, PyMuPDFLoader
from langchain_core.documents import Document
from langchain_core.runnables import RunnableLambda
from tqdm import tqdm
import os
import pickle
import re
import atexit
import oracledb
# =========================
# Oracle Autonomous Configuration
# =========================
WALLET_PATH = "Wallet_oradb23ai" # Your Wallet for Autonomous Database downloaded and unziped folder
DB_ALIAS = "oradb23ai_high" # Your database name plus _high as your TNS Definitions
USERNAME = "USERNAME" # Your Wallet username
PASSWORD = "PASSWORD" # Your Wallet password
os.environ["TNS_ADMIN"] = WALLET_PATH
GRAPH_NAME = "my_graph"
oracle_conn = oracledb.connect(
user=USERNAME,
password=PASSWORD,
dsn=DB_ALIAS,
config_dir=WALLET_PATH,
wallet_location=WALLET_PATH,
wallet_password=PASSWORD
)
atexit.register(lambda: oracle_conn.close())
# =========================
# Oracle Graph Client
# =========================
def create_tables_if_not_exist(conn):
cursor = conn.cursor()
try:
cursor.execute("""
BEGIN
EXECUTE IMMEDIATE '
CREATE TABLE ENTITIES (
ID NUMBER GENERATED BY DEFAULT ON NULL AS IDENTITY PRIMARY KEY,
NAME VARCHAR2(500)
)
';
EXCEPTION
WHEN OTHERS THEN
IF SQLCODE != -955 THEN
RAISE;
END IF;
END;
""")
cursor.execute("""
BEGIN
EXECUTE IMMEDIATE '
CREATE TABLE RELATIONS (
ID NUMBER GENERATED BY DEFAULT ON NULL AS IDENTITY PRIMARY KEY,
SOURCE_ID NUMBER,
TARGET_ID NUMBER,
RELATION_TYPE VARCHAR2(100),
SOURCE_TEXT VARCHAR2(4000)
)
';
EXCEPTION
WHEN OTHERS THEN
IF SQLCODE != -955 THEN
RAISE;
END IF;
END;
""")
conn.commit()
print("✅ ENTITIES and RELATIONS tables created or already exist.")
except Exception as e:
print(f"[ERROR] Failed to create tables: {e}")
finally:
cursor.close()
create_tables_if_not_exist(oracle_conn)
# =========================
# Global Configurations
# =========================
INDEX_PATH = "./faiss_index"
PROCESSED_DOCS_FILE = os.path.join(INDEX_PATH, "processed_docs.pkl")
chapter_separator_regex = r"^(#{1,6} .+|\*\*.+\*\*)$"
# =========================
# LLM Definitions
# =========================
llm = ChatOCIGenAI(
model_id="meta.llama-3.1-405b-instruct",
service_endpoint="https://inference.generativeai.us-chicago-1.oci.oraclecloud.com",
compartment_id="ocid1.compartment.oc1..aaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa",
auth_profile="DEFAULT",
model_kwargs={"temperature": 0.7, "top_p": 0.75, "max_tokens": 4000},
)
llm_for_rag = ChatOCIGenAI(
model_id="meta.llama-3.1-405b-instruct",
service_endpoint="https://inference.generativeai.us-chicago-1.oci.oraclecloud.com",
compartment_id="ocid1.compartment.oc1..aaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa",
auth_profile="DEFAULT",
)
embeddings = OCIGenAIEmbeddings(
model_id="cohere.embed-multilingual-v3.0",
service_endpoint="https://inference.generativeai.us-chicago-1.oci.oraclecloud.com",
compartment_id="ocid1.compartment.oc1..aaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa",
auth_profile="DEFAULT",
)
def create_knowledge_graph(chunks):
cursor = oracle_conn.cursor()
# Creates graph if it does not exist
try:
cursor.execute(f"""
BEGIN
EXECUTE IMMEDIATE '
CREATE PROPERTY GRAPH {GRAPH_NAME}
VERTEX TABLES (ENTITIES
KEY (ID)
LABEL ENTITIES
PROPERTIES (NAME))
EDGE TABLES (RELATIONS
KEY (ID)
SOURCE KEY (SOURCE_ID) REFERENCES ENTITIES(ID)
DESTINATION KEY (TARGET_ID) REFERENCES ENTITIES(ID)
LABEL RELATIONS
PROPERTIES (RELATION_TYPE, SOURCE_TEXT))
';
EXCEPTION
WHEN OTHERS THEN
IF SQLCODE != -55358 THEN -- ORA-55358: Graph already exists
RAISE;
END IF;
END;
""")
print(f"🧠 Graph '{GRAPH_NAME}' created or already exists.")
except Exception as e:
print(f"[GRAPH ERROR] Failed to create graph: {e}")
# Inserting vertices and edges into the tables
for doc in chunks:
text = doc.page_content
source = doc.metadata.get("source", "unknown")
if not text.strip():
continue
prompt = f"""
You are an expert in knowledge extraction.
Given the following technical text:
{text}
Extract key entities and relationships in the format:
- Entity1 -[RELATION]-> Entity2
Use UPPERCASE for RELATION types.
Return 'NONE' if nothing found.
"""
try:
response = llm_for_rag.invoke(prompt)
result = response.content.strip()
except Exception as e:
print(f"[ERROR] Gen AI call error: {e}")
continue
if result.upper() == "NONE":
continue
triples = result.splitlines()
for triple in triples:
parts = triple.split("-[")
if len(parts) != 2:
continue
right_part = parts[1].split("]->")
if len(right_part) != 2:
continue
raw_relation, entity2 = right_part
relation = re.sub(r'\W+', '_', raw_relation.strip().upper())
entity1 = parts[0].strip()
entity2 = entity2.strip()
try:
# Insertion of entities (with existence check)
cursor.execute("MERGE INTO ENTITIES e USING (SELECT :name AS NAME FROM dual) src ON (e.name = src.name) WHEN NOT MATCHED THEN INSERT (NAME) VALUES (:name)", [entity1, entity1])
cursor.execute("MERGE INTO ENTITIES e USING (SELECT :name AS NAME FROM dual) src ON (e.name = src.name) WHEN NOT MATCHED THEN INSERT (NAME) VALUES (:name)", [entity2, entity2])
# Retrieve the IDs
cursor.execute("SELECT ID FROM ENTITIES WHERE NAME = :name", [entity1])
source_id = cursor.fetchone()[0]
cursor.execute("SELECT ID FROM ENTITIES WHERE NAME = :name", [entity2])
target_id = cursor.fetchone()[0]
# Create relations
cursor.execute("""
INSERT INTO RELATIONS (SOURCE_ID, TARGET_ID, RELATION_TYPE, SOURCE_TEXT)
VALUES (:src, :tgt, :rel, :txt)
""", [source_id, target_id, relation, source])
print(f"✅ {entity1} -[{relation}]-> {entity2}")
except Exception as e:
print(f"[INSERT ERROR] {e}")
oracle_conn.commit()
cursor.close()
print("💾 Knowledge graph updated.")
def extract_graph_keywords(question: str) -> str:
prompt = f"""
Based on the question below, extract relevant keywords (1 to 2 words per term) that can be used to search for entities and relationships in a technical knowledge graph.
Question: "{question}"
Rules:
- Split compound terms (e.g., "API Gateway" → "API", "Gateway")
- Remove duplicates
- Do not include generic words such as: "what", "how", "the", "of", "in the document", etc.
- Return only the keywords, separated by commas. No explanations.
Result:
"""
try:
resp = llm_for_rag.invoke(prompt)
keywords_raw = resp.content.strip()
# Additional post-processing: remove duplicates, normalize
keywords = {kw.strip().lower() for kw in re.split(r'[,\n]+', keywords_raw)}
keywords = [kw for kw in keywords if kw] # remove empty strings
return ", ".join(sorted(keywords))
except Exception as e:
print(f"[KEYWORD EXTRACTION ERROR] {e}")
return ""
def query_knowledge_graph(query_text):
cursor = oracle_conn.cursor()
sanitized_text = query_text.lower()
pgql = f"""
SELECT from_entity,
relation_type,
to_entity
FROM GRAPH_TABLE(
{GRAPH_NAME}
MATCH (e1 is ENTITIES)-[r is RELATIONS]->(e2 is ENTITIES)
WHERE CONTAINS(e1.name, '{sanitized_text}') > 0
OR CONTAINS(e2.name, '{sanitized_text}') > 0
OR CONTAINS(r.RELATION_TYPE, '{sanitized_text}') > 0
COLUMNS (
e1.name AS from_entity,
r.RELATION_TYPE AS relation_type,
e2.name AS to_entity
)
)
FETCH FIRST 20 ROWS ONLY
"""
print(pgql)
try:
cursor.execute(pgql)
rows = cursor.fetchall()
if not rows:
return "⚠️ No relationships found in the graph."
return "\n".join(f"{r[0]} -[{r[1]}]-> {r[2]}" for r in rows)
except Exception as e:
return f"[PGQL ERROR] {e}"
finally:
cursor.close()
def split_llm_output_into_chapters(llm_text):
chapters = []
current_chapter = []
lines = llm_text.splitlines()
for line in lines:
if re.match(chapter_separator_regex, line):
if current_chapter:
chapters.append("\n".join(current_chapter).strip())
current_chapter = [line]
else:
current_chapter.append(line)
if current_chapter:
chapters.append("\n".join(current_chapter).strip())
return chapters
def semantic_chunking(text):
prompt = f"""
You received the following text extracted via OCR:
{text}
Your task:
1. Identify headings (short uppercase or bold lines, no period at the end)
2. Separate paragraphs by heading
3. Indicate columns with [COLUMN 1], [COLUMN 2] if present
4. Indicate tables with [TABLE] in markdown format
"""
get_out = False
while not get_out:
try:
response = llm_for_rag.invoke(prompt)
get_out = True
except:
print("[ERROR] Gen AI call error")
return response
def read_pdfs(pdf_path):
if "-ocr" in pdf_path:
doc_pages = PyMuPDFLoader(str(pdf_path)).load()
else:
doc_pages = UnstructuredPDFLoader(str(pdf_path)).load()
full_text = "\n".join([page.page_content for page in doc_pages])
return full_text
def smart_split_text(text, max_chunk_size=10_000):
chunks = []
start = 0
text_length = len(text)
while start < text_length:
end = min(start + max_chunk_size, text_length)
split_point = max(
text.rfind('.', start, end),
text.rfind('!', start, end),
text.rfind('?', start, end),
text.rfind('\n\n', start, end)
)
if split_point == -1 or split_point <= start:
split_point = end
else:
split_point += 1
chunk = text[start:split_point].strip()
if chunk:
chunks.append(chunk)
start = split_point
return chunks
def load_previously_indexed_docs():
if os.path.exists(PROCESSED_DOCS_FILE):
with open(PROCESSED_DOCS_FILE, "rb") as f:
return pickle.load(f)
return set()
def save_indexed_docs(docs):
with open(PROCESSED_DOCS_FILE, "wb") as f:
pickle.dump(docs, f)
# =========================
# Main Function
# =========================
def chat():
# pdf_paths = [
# './Manuals/SOASUITE.pdf',
# './Manuals/using-integrations-oracle-integration-3.pdf'
# ]
pdf_paths = ['AAAAAAAAAA.pdf'] # Your PDF Files as a Knowledge Base
already_indexed_docs = load_previously_indexed_docs()
updated_docs = set()
try:
vectorstore = FAISS.load_local(INDEX_PATH, embeddings, allow_dangerous_deserialization=True)
print("✔️ FAISS index loaded.")
except Exception:
print("⚠️ FAISS index not found, creating a new one.")
vectorstore = None
new_chunks = []
for pdf_path in tqdm(pdf_paths, desc=f"📄 Processing PDFs"):
print(f" {os.path.basename(pdf_path)}")
if pdf_path in already_indexed_docs:
print(f"✅ Document already indexed: {pdf_path}")
continue
full_text = read_pdfs(pdf_path=pdf_path)
text_chunks = smart_split_text(full_text, max_chunk_size=10_000)
overflow_buffer = ""
for chunk in tqdm(text_chunks, desc=f"📄 Processing text chunks", dynamic_ncols=True, leave=False):
current_text = overflow_buffer + chunk
treated_text = semantic_chunking(current_text)
if hasattr(treated_text, "content"):
chapters = split_llm_output_into_chapters(treated_text.content)
last_chapter = chapters[-1] if chapters else ""
if last_chapter and not last_chapter.strip().endswith((".", "!", "?", "\n\n")):
print("📌 Last chapter seems incomplete, saving for the next cycle")
overflow_buffer = last_chapter
chapters = chapters[:-1]
else:
overflow_buffer = ""
for chapter_text in chapters:
doc = Document(page_content=chapter_text, metadata={"source": pdf_path})
new_chunks.append(doc)
print(f"✅ New chapter indexed:\n{chapter_text}...\n")
else:
print(f"[ERROR] semantic_chunking returned unexpected type: {type(treated_text)}")
updated_docs.add(str(pdf_path))
if new_chunks:
if vectorstore:
vectorstore.add_documents(new_chunks)
else:
vectorstore = FAISS.from_documents(new_chunks, embedding=embeddings)
vectorstore.save_local(INDEX_PATH)
save_indexed_docs(already_indexed_docs.union(updated_docs))
print(f"💾 {len(new_chunks)} chunks added to FAISS index.")
print("🧠 Building knowledge graph...")
create_knowledge_graph(new_chunks)
else:
print("📁 No new documents to index.")
retriever = vectorstore.as_retriever(search_type="similarity", search_kwargs={"k": 50, "fetch_k": 100})
template = """
Document context:
{context}
Graph context:
{graph_context}
Question:
{input}
Interpretation rules:
- You can search for a step-by-step tutorial about a subject
- You can search a concept description about a subject
- You can search for a list of components about a subject
"""
prompt = PromptTemplate.from_template(template)
def get_context(x):
query = x.get("input") if isinstance(x, dict) else x
return retriever.invoke(query)
chain = (
RunnableMap({
"context": RunnableLambda(get_context),
"graph_context": RunnableLambda(lambda x: query_knowledge_graph(extract_graph_keywords(x.get("input") if isinstance(x, dict) else x))),
"input": lambda x: x.get("input") if isinstance(x, dict) else x
})
| prompt
| llm
| StrOutputParser()
)
print("✅ READY")
while True:
query = input("❓ Question (or 'quit' to exit): ")
if query.lower() == "quit":
break
response = chain.invoke(query)
print("\n📜 RESPONSE:\n")
print(response)
print("\n" + "=" * 80 + "\n")
# if __name__ == "__main__":
# print("Iniciando")
# print(query_knowledge_graph("gateway"))
# 🚀 Run
if __name__ == "__main__":
chat()

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## Understanding Graph Theory and Implementing a Knowledge Graph with Oracle Autonomous Database and PGQL
### Introduction
This document explores the concepts of graph theory, knowledge graphs, and how they are implemented using the Oracle Autonomous Database with PGQL (Property Graph Query Language). It also explains the Python implementation used to extract relationships from documents using LLMs and store them as graph structures in Oracle.
### What is Graph Theory?
Graph theory is a field of mathematics and computer science focused on modeling relationships between objects. A graph consists of:
• **Vertices** (nodes): Represent entities.
• **Edges** (links): Represent relationships between entities.
Graphs are widely used for representing data structures in social networks, semantic networks, knowledge graphs, and more.
### What is a Knowledge Graph?
A knowledge graph is a graph-based representation of real-world knowledge where:
• Nodes represent entities (people, places, products, etc.)
• Edges represent semantic relationships (e.g., “WORKS_AT”, “PART_OF”)
Knowledge graphs enhance semantic search, recommendation systems, and question-answering applications.
### Why Use Oracle Autonomous with PGQL?
Oracle provides a fully managed environment to store and query property graphs:
• PGQL (Property Graph Query Language) is SQL-like and designed for querying complex graph patterns.
• Oracle Autonomous Database allows running graph queries natively with property graph features, including creation, querying, and visualization.
• Integration with LLMs enables automatic extraction of graph structures from unstructured data (like PDFs).
### Comparison with Other Graph Query Languages
![img.png](./images/img.png)
### Advantages of Oracle Autonomous PGQL vs Traditional Graph DBs
![img_1.png](./images/img_1.png)
### Create Knowledge Graph
This is the processes executed on this service:
• Creating the graph schema
• Extracting entities and relationships using LLM
• Inserting data into Oracle
• Building the property graph
```python
def create_knowledge_graph(chunks):
cursor = oracle_conn.cursor()
# Creates graph if it does not exist
try:
cursor.execute(f"""
BEGIN
EXECUTE IMMEDIATE '
CREATE PROPERTY GRAPH {GRAPH_NAME}
VERTEX TABLES (ENTITIES
KEY (ID)
LABEL ENTITIES
PROPERTIES (NAME))
EDGE TABLES (RELATIONS
KEY (ID)
SOURCE KEY (SOURCE_ID) REFERENCES ENTITIES(ID)
DESTINATION KEY (TARGET_ID) REFERENCES ENTITIES(ID)
LABEL RELATIONS
PROPERTIES (RELATION_TYPE, SOURCE_TEXT))
';
EXCEPTION
WHEN OTHERS THEN
IF SQLCODE != -55358 THEN -- ORA-55358: Graph already exists
RAISE;
END IF;
END;
""")
print(f"🧠 Graph '{GRAPH_NAME}' created or already exists.")
except Exception as e:
print(f"[GRAPH ERROR] Failed to create graph: {e}")
# Inserting vertices and edges into the tables
for doc in chunks:
text = doc.page_content
source = doc.metadata.get("source", "unknown")
if not text.strip():
continue
prompt = f"""
You are an expert in knowledge extraction.
Given the following technical text:
{text}
Extract key entities and relationships in the format:
- Entity1 -[RELATION]-> Entity2
Use UPPERCASE for RELATION types.
Return 'NONE' if nothing found.
"""
try:
response = llm_for_rag.invoke(prompt)
result = response.content.strip()
except Exception as e:
print(f"[ERROR] Gen AI call error: {e}")
continue
if result.upper() == "NONE":
continue
triples = result.splitlines()
for triple in triples:
parts = triple.split("-[")
if len(parts) != 2:
continue
right_part = parts[1].split("]->")
if len(right_part) != 2:
continue
raw_relation, entity2 = right_part
relation = re.sub(r'\W+', '_', raw_relation.strip().upper())
entity1 = parts[0].strip()
entity2 = entity2.strip()
try:
# Insertion of entities (with existence check)
cursor.execute("MERGE INTO ENTITIES e USING (SELECT :name AS NAME FROM dual) src ON (e.name = src.name) WHEN NOT MATCHED THEN INSERT (NAME) VALUES (:name)", [entity1, entity1])
cursor.execute("MERGE INTO ENTITIES e USING (SELECT :name AS NAME FROM dual) src ON (e.name = src.name) WHEN NOT MATCHED THEN INSERT (NAME) VALUES (:name)", [entity2, entity2])
# Retrieve the IDs
cursor.execute("SELECT ID FROM ENTITIES WHERE NAME = :name", [entity1])
source_id = cursor.fetchone()[0]
cursor.execute("SELECT ID FROM ENTITIES WHERE NAME = :name", [entity2])
target_id = cursor.fetchone()[0]
# Create relations
cursor.execute("""
INSERT INTO RELATIONS (SOURCE_ID, TARGET_ID, RELATION_TYPE, SOURCE_TEXT)
VALUES (:src, :tgt, :rel, :txt)
""", [source_id, target_id, relation, source])
print(f"✅ {entity1} -[{relation}]-> {entity2}")
except Exception as e:
print(f"[INSERT ERROR] {e}")
oracle_conn.commit()
cursor.close()
print("💾 Knowledge graph updated.")
```
• The graph schema is created with CREATE PROPERTY GRAPH, linking ENTITIES (vertices) and RELATIONS (edges).
• Uses MERGE INTO to insert new entities only if they don’t exist (ensuring uniqueness).
• LLM (Oracle Generative AI) is used to extract triples of the form Entity1 -[RELATION]-> Entity2.
• All interactions with Oracle are done via oracledb and PL/SQL anonymous blocks.
#### Next Steps
• Use PGQL to explore and query graph relationships.
• Connect to Graph Studio for visualizations.
• Expose the graph through an API REST or LangChain Agent.
### 📌 Graph Query Support Functions
There are two essential functions that enable semantic search and reasoning over the knowledge graph: **extract_graph_keywords** and **query_knowledge_graph**. These components allow questions to be interpreted into meaningful graph queries using PGQL on Oracle Autonomous Database.
#### extract_graph_keywords
```python
def extract_graph_keywords(question: str) -> str:
prompt = f"""
Based on the question below, extract relevant keywords (1 to 2 words per term) that can be used to search for entities and relationships in a technical knowledge graph.
Question: "{question}"
Rules:
- Split compound terms (e.g., "API Gateway" → "API", "Gateway")
- Remove duplicates
- Do not include generic words such as: "what", "how", "the", "of", "in the document", etc.
- Return only the keywords, separated by commas. No explanations.
Result:
"""
try:
resp = llm_for_rag.invoke(prompt)
keywords_raw = resp.content.strip()
# Additional post-processing: remove duplicates, normalize
keywords = {kw.strip().lower() for kw in re.split(r'[,\n]+', keywords_raw)}
keywords = [kw for kw in keywords if kw] # remove empty strings
return ", ".join(sorted(keywords))
except Exception as e:
print(f"[KEYWORD EXTRACTION ERROR] {e}")
return ""
```
✅ What it does:
• Uses an LLM (llm_for_rag) to transform natural language questions into a list of graph-friendly keywords.
• The prompt is designed to cleanly extract entities and terms that are relevant for searching the graph.
💡 Why it’s important:
• It bridges the gap between unstructured questions and structured queries.
• Ensures that only specific, domain-relevant terms are used for matching in the PGQL query.
🧠 LLM-enhanced behavior:
• Breaks compound technical terms.
• Removes stop words (like “what”, “how”, etc.).
• Normalizes text by lowercasing and deduplicating terms.
📌 Example:
Input:
"What are the main components of an API Gateway architecture?"
Output keywords:
api, gateway, architecture, components
#### query_knowledge_graph
```python
def query_knowledge_graph(query_text):
cursor = oracle_conn.cursor()
sanitized_text = query_text.lower()
pgql = f"""
SELECT from_entity,
relation_type,
to_entity
FROM GRAPH_TABLE(
{GRAPH_NAME}
MATCH (e1 is ENTITIES)-[r is RELATIONS]->(e2 is ENTITIES)
WHERE CONTAINS(e1.name, '{sanitized_text}') > 0
OR CONTAINS(e2.name, '{sanitized_text}') > 0
OR CONTAINS(r.RELATION_TYPE, '{sanitized_text}') > 0
COLUMNS (
e1.name AS from_entity,
r.RELATION_TYPE AS relation_type,
e2.name AS to_entity
)
)
FETCH FIRST 20 ROWS ONLY
"""
print(pgql)
try:
cursor.execute(pgql)
rows = cursor.fetchall()
if not rows:
return "⚠️ No relationships found in the graph."
return "\n".join(f"{r[0]} -[{r[1]}]-> {r[2]}" for r in rows)
except Exception as e:
return f"[PGQL ERROR] {e}"
finally:
cursor.close()
```
✅ What it does:
• Accepts a keyword-based string (often produced by extract_graph_keywords) and constructs a PGQL query to retrieve relationships from the knowledge graph.
⚙️ Key mechanics:
• The GRAPH_TABLE clause uses MATCH to traverse the graph from source to target node.
• It uses CONTAINS() to allow partial and fuzzy search in node/edge attributes (e1.name, e2.name, r.RELATION_TYPE).
• Limits results to 20 to avoid flooding the output.
🆚 Why use Oracle PGQL:
• PGQL (Property Graph Query Language) is SQL-like but designed for graph traversal.
• Oracle Autonomous Database supports property graphs, which allows seamless integration between relational and graph worlds.
• Offers indexing, optimization, and native graph search capabilities that are enterprise-ready.
🧠 Oracle-Specific Notes:
• The GRAPH_TABLE() is unique to Oracle PGQL and allows queries over logical views of graphs defined via relational tables.
• Unlike Cypher (Neo4j), PGQL runs over structured data using SQL extensions, making it friendlier in RDBMS-heavy environments.
## Reference
- [Analyze PDF Documents in Natural Language with OCI Generative AI](https://docs.oracle.com/en/learn/oci-genai-pdf/)
- [Oracle Graph Learning Path](https://blogs.oracle.com/database/post/oracle-graph-learning-path)
- [Graph Developer's Guide for Property Graph](https://docs.oracle.com/en/database/oracle/property-graph/25.2/spgdg/oracle-graph-python-client.html#GUID-9800E556-0B6C-4EAF-A4FC-9AE9AB46023C)
- [Getting Started with Property Graphs in Oracle Database 23ai](https://blogs.oracle.com/ace/post/getting-started-with-property-graphs-in-oracle-database-23ai)
## Acknowledgments
- **Author** - Cristiano Hoshikawa (Oracle LAD A-Team Solution Engineer)