Oracle AI Vector Search ट्यूटोरियल: Python के साथ एम्बेडिंग स्टोर करें और क्वेरी चलाएँ

मैट्रिक

यह क्या तुलना करता है

उपयुक्त उपयोग

ध्यान रखने योग्य बातें

Cosine

वेक्टर की दिशा

टेक्स्ट एम्बेडिंग और सेमांटिक समानता

परिणाम एक ही एम्बेडिंग मॉडल और सुसंगत प्रीप्रोसेसिंग पर निर्भर करते हैं

Euclidean

सीधी रेखा की दूरी

छोटे उदाहरण, ज्यामिति अंतर्दृष्टि, कुछ न्यूमेरिक फीचर वेक्टर

मैग्निट्यूड दूरी को प्रभावित करता है, इसलिए स्केलिंग मायने रख सकती है

Dot product

दिशा और मैग्निट्यूड साथ में

normalized वेक्टर या inner-product स्कोरिंग पर आधारित वर्कफ़्लोज़

स्कोर दिशा और normalization नियम समझने के बाद ही परिणामों की तुलना करें

इंडेक्स फैमिली

मूल विचार

ताकत

ट्रेडऑफ

कब उपयोग करें

IVF / neighbor partitions

वेक्टर स्पेस को पार्टिशन करें, फिर संभावित पार्टिशन खोजें

सिंपल मेंटल मॉडल, कई बैच-लोडेड डेटासेट के लिए व्यावहारिक

रिकॉल और स्पीड पार्टिशनिंग और सर्च सेटिंग्स पर निर्भर

हम बढ़ते डेटासेट के लिए एक सीधा approximate-search पथ चाहते हैं

HNSW / neighbor graph

तेज़ traversal के लिए पास-पास वेक्टरों को लिंक करने वाला ग्राफ बनाएँ

कई nearest-neighbor वर्कलोड्स के लिए मजबूत रिकॉल और लेटेंसी प्रोफ़ाइल

अधिक मेमोरी-ओरिएंटेड, और कॉन्फ़िगरेशन अधिक मायने रख सकता है

हमें कम-लेटेंसी सर्च चाहिए और हम मेमोरी व ट्यूनिंग प्रयास का बजट कर सकते हैं

mkdir oracle-ai-vector-search-python
 cd oracle-ai-vector-search-pythonसंचालित द्वारा

docker run --name oracle-free-26ai-vector \
   --detach \
   --publish 1521:1521 \
   --publish 5500:5500 \
   --shm-size=1g \
   --env ORACLE_PWD="replace-with-a-strong-password" \
   container-registry.oracle.com/database/free:23.26.1.0-lite-amd64संचालित द्वारा

docker logs oracle-free-26ai-vectorसंचालित द्वारा

# Create the application user and grant the privileges needed for the demo.
 docker exec -i oracle-free-26ai-vector \
   sqlplus -s 'sys/"replace-with-oracle-pwd"@FREEPDB1 as sysdba' <<'SQL'
 WHENEVER SQLERROR EXIT SQL.SQLCODE

 CREATE USER vector_demo IDENTIFIED BY "replace-with-vector-demo-password";

 GRANT CREATE SESSION, CREATE TABLE, CREATE SEQUENCE, UNLIMITED TABLESPACE TO vector_demo;

 EXIT
 SQLसंचालित द्वारा

python -m venv .venv
 source .venv/bin/activate
 python -m pip install --upgrade pipसंचालित द्वारा

python -m pip install oracledb openaiसंचालित द्वारा

export DB_USER="vector_demo"
 export DB_PASSWORD="replace-with-vector-demo-password"
 export DB_DSN="localhost:1521/FREEPDB1"
 export OPENAI_API_KEY="replace-with-your-api-key"
 export EMBEDDING_MODEL="text-embedding-3-small"
 export EMBEDDING_DIM="1536"संचालित द्वारा

import os

 import oracledb

 ADMIN_USERS = {"SYS", "SYSTEM", "PDBADMIN"}


 def required_env(name):
 	value = os.getenv(name)
 	if not value:
         raise SystemExit(f"Set {name} before running this script.")
 	return value


 def main():
 	# Load required database settings from environment variables.
 	user = required_env("DB_USER")
     password = required_env("DB_PASSWORD")
 	dsn = required_env("DB_DSN")

 	if user.upper() in ADMIN_USERS:
         raise SystemExit(
             "Use the vector_demo application user, not an admin user."
     	)

 	# Open a Thin-mode connection with the application user.
 	with oracledb.connect(user=user, password=password, dsn=dsn) as connection:
         mode = "thin" if oracledb.is_thin_mode() else "thick"
         print("Connected to Oracle Database.")
         print(f"Driver mode: {mode}")
         print(f"Database version: {connection.version}")


 if __name__ == "__main__":
 	main()संचालित द्वारा

python 01_check_connection.pyसंचालित द्वारा

Connected to Oracle Database.
 Driver mode: thin
 Database version: 23.26.1.0.0संचालित द्वारा

import array
 import os

 import oracledb

 ADMIN_USERS = {"SYS", "SYSTEM", "PDBADMIN"}


 def required_env(name):
 	value = os.getenv(name)
 	if not value:
         raise SystemExit(f"Set {name} before running this script.")
 	return value


 def main():
 	user = required_env("DB_USER")
 	if user.upper() in ADMIN_USERS:
         raise SystemExit(
             "Use the vector_demo application user, not an admin user."
     	)

 	# Define three small vectors so the distance calculation is easy to inspect.
 	rows = [
         ("apple", array.array("f", [1.0, 0.0, 0.0])),
         ("banana", array.array("f", [0.9, 0.1, 0.0])),
         ("car", array.array("f", [0.0, 1.0, 0.0])),
 	]

 	with oracledb.connect(
         user=user,
         password=required_env("DB_PASSWORD"),
         dsn=required_env("DB_DSN"),
 	) as connection:
         with connection.cursor() as cursor:
             # Recreate the table so the script can be run repeatedly.
             cursor.execute(
                 """
                 BEGIN
                 	EXECUTE IMMEDIATE 'DROP TABLE manual_vectors PURGE';
                 EXCEPTION
                 	WHEN OTHERS THEN
                     	IF SQLCODE <> -942 THEN
                         	RAISE;
                     	END IF;
                 END;
                 """
             )
             cursor.execute(
                 """
                 CREATE TABLE manual_vectors (
                 	id NUMBER GENERATED BY DEFAULT AS IDENTITY PRIMARY KEY,
                 	label VARCHAR2(100),
                 	embedding VECTOR(3, FLOAT32)
                 )
                 """
             )
             # Bind each vector as a 32-bit floating-point array.
             cursor.executemany(
                 """
                 INSERT INTO manual_vectors (label, embedding)
                 VALUES (:1, :2)
                 """,
                 rows,
             )
             connection.commit()

             # Rank rows by Euclidean distance from the query vector.
             cursor.execute(
                 """
                 SELECT label,
                    	VECTOR_DISTANCE(
                        	embedding,
                        	:query_vector,
                        	EUCLIDEAN
                    	) AS distance
                 FROM manual_vectors
                 ORDER BY distance
                 FETCH EXACT FIRST 2 ROWS ONLY
                 """,
                 query_vector=array.array("f", [1.0, 0.0, 0.0]),
             )

             print("Created manual_vectors.")
             print("Inserted 3 manual vectors.")
             print("\nNearest manual vectors:")
             for rank, (label, distance) in enumerate(cursor, start=1):
                 print(f"{rank}. {label:<6} distance={float(distance):.4f}")


 if __name__ == "__main__":
 	main()संचालित द्वारा

python 02_manual_vectors.pyसंचालित द्वारा

Created manual_vectors.
 Inserted 3 manual vectors.

 Nearest manual vectors:
 1. apple  distance=0.0000
 2. banana distance=0.1414संचालित द्वारा

import os

 import oracledb

 ADMIN_USERS = {"SYS", "SYSTEM", "PDBADMIN"}


 def required_env(name):
 	value = os.getenv(name)
 	if not value:
         raise SystemExit(f"Set {name} before running this script.")
 	return value


 def configured_dimension():
 	try:
         dimension = int(required_env("EMBEDDING_DIM"))
 	except ValueError as exc:
         raise SystemExit("EMBEDDING_DIM must be an integer.") from exc

 	if dimension <= 0:
         raise SystemExit("EMBEDDING_DIM must be a positive integer.")

 	return dimension


 def main():
 	user = required_env("DB_USER")
 	if user.upper() in ADMIN_USERS:
         raise SystemExit(
             "Use the vector_demo application user, not an admin user."
     	)

 	# Validate the configured model name and vector dimension.
     model_name = required_env("EMBEDDING_MODEL")
     dimension = configured_dimension()

 	with oracledb.connect(
         user=user,
         password=required_env("DB_PASSWORD"),
         dsn=required_env("DB_DSN"),
 	) as connection:
         with connection.cursor() as cursor:
             # Recreate the documents table so the script is repeatable.
             cursor.execute(
                 """
                 BEGIN
                 	EXECUTE IMMEDIATE 'DROP TABLE documents PURGE';
                 EXCEPTION
                 	WHEN OTHERS THEN
                     	IF SQLCODE <> -942 THEN
                         	RAISE;
                     	END IF;
                 END;
                 """
             )
             cursor.execute(
                 f"""
                 CREATE TABLE documents (
                 	id NUMBER GENERATED BY DEFAULT AS IDENTITY PRIMARY KEY,
                 	title VARCHAR2(200) NOT NULL,
                 	content VARCHAR2(1000) NOT NULL,
                 	embedding VECTOR({dimension}, FLOAT32)
                 )
                 """
             )

     print(f"Embedding model: {model_name}")
     print(f"Embedding dimension: {dimension}")
     print("Recreated documents table.")


 if __name__ == "__main__":
 	main()संचालित द्वारा

python 03_create_documents_table.pyसंचालित द्वारा

Embedding model: text-embedding-3-small
 Embedding dimension: 1536
 Recreated documents table.संचालित द्वारा

import os

 from openai import OpenAI


 def required_env(name):
 	value = os.getenv(name)
 	if not value:
         raise SystemExit(f"Set {name} before running this script.")
 	return value


 def configured_dimension():
 	try:
         return int(required_env("EMBEDDING_DIM"))
 	except ValueError as exc:
         raise SystemExit("EMBEDDING_DIM must be an integer.") from exc


 def main():
     required_env("OPENAI_API_KEY")

     model_name = required_env("EMBEDDING_MODEL")
     expected_dimension = configured_dimension()

 	# Request one embedding to confirm the model output dimension.
 	client = OpenAI()
     response = client.embeddings.create(
         model=model_name,
         input="Oracle AI Vector Search dimension check",
 	)

     returned_dimension = len(response.data[0].embedding)

     print(f"Embedding model: {model_name}")
     print(f"Returned embedding length: {returned_dimension}")
     print(f"Configured EMBEDDING_DIM: {expected_dimension}")

 	# Stop before loading data if the table dimension is incompatible.
 	if returned_dimension != expected_dimension:
         raise SystemExit(
             "Dimension check failed. Update EMBEDDING_DIM, rerun "
             "03_create_documents_table.py, and try again."
     	)

     print("Dimension check passed.")


 if __name__ == "__main__":
 	main()संचालित द्वारा

python 04_check_embedding_dimension.pyसंचालित द्वारा

Embedding model: text-embedding-3-small
 Returned embedding length: 1536
 Configured EMBEDDING_DIM: 1536
 Dimension check passed.संचालित द्वारा

import array
 import os

 import oracledb
 from openai import OpenAI

 DOCUMENTS = [
 	(
         "Oracle AI Vector Search",
         "Oracle Database can store vector embeddings in native VECTOR "
         "columns and query them with SQL similarity search.",
 	),
 	(
         "Semantic Search for Applications",
         "Semantic search ranks results by meaning, helping applications "
         "find related content even when exact words differ.",
 	),
 	(
         "Vector Index Workflow",
         "Vector indexes help approximate nearest-neighbor search scale "
         "when datasets grow beyond small examples.",
 	),
 	(
         "Local Docker Development",
         "Docker containers make it practical to run a local database for "
         "development and repeatable tutorials.",
 	),
 	(
         "Python Database Code",
         "Python applications can connect to Oracle Database with the "
         "oracledb driver and use environment variables for credentials.",
 	),
 	(
         "Acoustic Guitar Practice",
         "Daily guitar practice improves timing, chord transitions, and "
         "confidence when learning new songs.",
 	),
 	(
         "Weeknight Vegetable Soup",
         "A simple soup can combine onions, carrots, beans, herbs, and "
         "broth for an easy weeknight meal.",
 	),
 ]

 ADMIN_USERS = {"SYS", "SYSTEM", "PDBADMIN"}


 def required_env(name):
 	value = os.getenv(name)
 	if not value:
         raise SystemExit(f"Set {name} before running this script.")
 	return value


 def configured_dimension():
 	try:
         return int(required_env("EMBEDDING_DIM"))
 	except ValueError as exc:
         raise SystemExit("EMBEDDING_DIM must be an integer.") from exc


 def main():
 	user = required_env("DB_USER")
 	if user.upper() in ADMIN_USERS:
         raise SystemExit(
             "Use the vector_demo application user, not an admin user."
     	)

     required_env("OPENAI_API_KEY")
     model_name = required_env("EMBEDDING_MODEL")
     expected_dimension = configured_dimension()

 	# Generate embeddings for all sample documents in one API request.
 	client = OpenAI()
     response = client.embeddings.create(
         model=model_name,
         input=[content for _, content in DOCUMENTS],
 	)

 	# Restore the response order using each item's index.
     embeddings = [
         item.embedding
     	for item in sorted(
             response.data,
             key=lambda item: item.index,
     	)
 	]

 	if any(len(embedding) != expected_dimension for embedding in embeddings):
         raise SystemExit(
             "An embedding length did not match EMBEDDING_DIM. "
             "Rerun 04_check_embedding_dimension.py."
     	)

 	# Bind each embedding as a 32-bit floating-point array.
 	rows = [
         (title, content, array.array("f", embedding))
     	for (title, content), embedding in zip(DOCUMENTS, embeddings)
 	]

 	# Replace the sample rows and commit the transaction.
 	with oracledb.connect(
         user=user,
         password=required_env("DB_PASSWORD"),
         dsn=required_env("DB_DSN"),
 	) as connection:
         with connection.cursor() as cursor:
             cursor.execute("DELETE FROM documents")
             cursor.executemany(
                 """
                 INSERT INTO documents (title, content, embedding)
                 VALUES (:1, :2, :3)
                 """,
                 rows,
             )
             cursor.execute("SELECT COUNT(*) FROM documents")
             row_count = cursor.fetchone()[0]
             connection.commit()

     print(f"Embedding model: {model_name}")
     print(f"Generated embeddings for {len(DOCUMENTS)} documents.")
     print(f"Inserted {len(DOCUMENTS)} documents.")
     print(f"documents row count: {row_count}")


 if __name__ == "__main__":
 	main()संचालित द्वारा

python 05_insert_embeddings.pyसंचालित द्वारा

Embedding model: text-embedding-3-small
 Generated embeddings for 7 documents.
 Inserted 7 documents.
 documents row count: 7संचालित द्वारा

import array
 import os

 import oracledb
 from openai import OpenAI

 QUERY_TEXT = "Where should an AI app keep meaning-based search data?"
 ADMIN_USERS = {"SYS", "SYSTEM", "PDBADMIN"}


 def required_env(name):
 	value = os.getenv(name)
 	if not value:
         raise SystemExit(f"Set {name} before running this script.")
 	return value


 def main():
 	user = required_env("DB_USER")
 	if user.upper() in ADMIN_USERS:
         raise SystemExit(
             "Use the vector_demo application user, not an admin user."
     	)

     required_env("OPENAI_API_KEY")

 	# Generate an embedding for the natural-language query.
 	client = OpenAI()
     response = client.embeddings.create(
         model=required_env("EMBEDDING_MODEL"),
         input=QUERY_TEXT,
 	)
     query_vector = array.array("f", response.data[0].embedding)

 	with oracledb.connect(
         user=user,
         password=required_env("DB_PASSWORD"),
         dsn=required_env("DB_DSN"),
 	) as connection:
         with connection.cursor() as cursor:
             # Rank stored documents by cosine distance from the query vector.
             cursor.execute(
                 """
                 SELECT title,
                    	content,
                    	VECTOR_DISTANCE(
                        	embedding,
                        	:query_vector,
                        	COSINE
                    	) AS distance
                 FROM documents
                 ORDER BY distance
                 FETCH EXACT FIRST 5 ROWS ONLY
                 """,
                 query_vector=query_vector,
             )
             rows = cursor.fetchall()

 	if not rows:
         raise SystemExit("No documents found. Run 05_insert_embeddings.py first.")

     print(f"Query: {QUERY_TEXT}")
     print("\nTop 5 results:")

 	for rank, (title, content, distance) in enumerate(rows, start=1):
         print(f"{rank}. {title}")
         print(f"   distance: {float(distance):.6f}")
         print(f"   {content[:90]}...")


 if __name__ == "__main__":
 	main()संचालित द्वारा

python 06_semantic_search.pyसंचालित द्वारा

Query: Where should an AI app keep meaning-based search data?

 Top 5 results:
 1. Oracle AI Vector Search
    distance: <cosine distance from your run>
    Oracle Database can store vector embeddings in native VECTOR columns and query them with SQL...

 2. Semantic Search for Applications
    distance: <cosine distance from your run>
    Semantic search ranks results by meaning, helping applications find related content even when...

 3. Python Database Code
    distance: <cosine distance from your run>
    Python applications can connect to Oracle Database with the oracledb driver and use environment...

 4. Vector Index Workflow
    distance: <cosine distance from your run>
    Vector indexes help approximate nearest-neighbor search scale when datasets grow beyond small...

 5. Local Docker Development
    distance: <cosine distance from your run>
    Docker containers make it practical to run a local database for development and repeatable...संचालित द्वारा

import array
 import os

 import oracledb
 from openai import OpenAI

 SEMANTIC_QUERY = "Where should an AI app keep meaning-based search data?"
 KEYWORD_PHRASE = "meaning-based search data"
 ADMIN_USERS = {"SYS", "SYSTEM", "PDBADMIN"}


 def required_env(name):
 	value = os.getenv(name)
 	if not value:
         raise SystemExit(f"Set {name} before running this script.")
 	return value


 def main():
 	user = required_env("DB_USER")
 	if user.upper() in ADMIN_USERS:
         raise SystemExit(
             "Use the vector_demo application user, not an admin user."
     	)

     required_env("OPENAI_API_KEY")

 	# Generate an embedding for the semantic query.
 	client = OpenAI()
     response = client.embeddings.create(
         model=required_env("EMBEDDING_MODEL"),
         input=SEMANTIC_QUERY,
 	)
     query_vector = array.array("f", response.data[0].embedding)

 	with oracledb.connect(
         user=user,
         password=required_env("DB_PASSWORD"),
         dsn=required_env("DB_DSN"),
 	) as connection:
         with connection.cursor() as cursor:
             # Rank documents by semantic similarity.
             cursor.execute(
                 """
                 SELECT title,
                        VECTOR_DISTANCE(embedding, :query_vector, COSINE)
                 FROM documents
                 ORDER BY VECTOR_DISTANCE(embedding, :query_vector, COSINE)
                 FETCH EXACT FIRST 3 ROWS ONLY
                 """,
                 query_vector=query_vector,
             )
             semantic_rows = cursor.fetchall()

             # Compare semantic results with an exact phrase predicate.
             cursor.execute(
                 """
                 SELECT title
                 FROM documents
                 WHERE LOWER(title || ' ' || content)
                   	LIKE '%' || :phrase || '%'
                 ORDER BY title
                 FETCH FIRST 3 ROWS ONLY
                 """,
                 phrase=KEYWORD_PHRASE.lower(),
             )
             keyword_rows = cursor.fetchall()

     print(f"Semantic query: {SEMANTIC_QUERY}")
     print(f"Keyword phrase: {KEYWORD_PHRASE}")

     print("\nSemantic results:")
 	for rank, (title, distance) in enumerate(semantic_rows, start=1):
         print(f"{rank}. {title}")
         print(f"   distance: {float(distance):.6f}")

     print("\nKeyword results:")
 	if keyword_rows:
     	for rank, (title,) in enumerate(keyword_rows, start=1):
             print(f"{rank}. {title}")
 	else:
         print("0 exact phrase matches")


 if __name__ == "__main__":
 	main()संचालित द्वारा

python 07_semantic_vs_keyword.pyसंचालित द्वारा

Semantic query: Where should an AI app keep meaning-based search data?
 Keyword phrase: meaning-based search data

 Semantic results:
 1. Oracle AI Vector Search
    distance: <cosine distance from your run>
 2. Semantic Search for Applications
    distance: <cosine distance from your run>
 3. Python Database Code
    distance: <cosine distance from your run>

 Keyword results:
 0 exact phrase matchesसंचालित द्वारा

import array
 import os

 import oracledb
 from openai import OpenAI

 INDEX_NAME = "DOCUMENT_EMBEDDING_IDX"
 QUERY_TEXT = "Where should an AI app keep meaning-based search data?"
 ADMIN_USERS = {"SYS", "SYSTEM", "PDBADMIN"}


 def required_env(name):
 	value = os.getenv(name)
 	if not value:
         raise SystemExit(f"Set {name} before running this script.")
 	return value


 def main():
 	user = required_env("DB_USER")
 	if user.upper() in ADMIN_USERS:
         raise SystemExit(
             "Use the vector_demo application user, not an admin user."
     	)

     required_env("OPENAI_API_KEY")

 	# Generate the query vector used for the validation search.
 	client = OpenAI()
     response = client.embeddings.create(
         model=required_env("EMBEDDING_MODEL"),
         input=QUERY_TEXT,
 	)
     query_vector = array.array("f", response.data[0].embedding)

 	with oracledb.connect(
         user=user,
         password=required_env("DB_PASSWORD"),
         dsn=required_env("DB_DSN"),
 	) as connection:
         with connection.cursor() as cursor:
             # Remove a prior index so the script can be rerun safely.
             try:
                 cursor.execute(f"DROP INDEX {INDEX_NAME}")
             except oracledb.DatabaseError as exc:
                 error = exc.args[0]
                 if error.code != 1418:
                 	raise

             # Create an approximate neighbor-partitions vector index.
             cursor.execute(
                 """
                 CREATE VECTOR INDEX document_embedding_idx
                 ON documents (embedding)
                 ORGANIZATION NEIGHBOR PARTITIONS
                 DISTANCE COSINE
                 WITH TARGET ACCURACY 95
                 """
             )

             # Confirm that Oracle reports the vector index as valid.
             cursor.execute(
                 """
                 SELECT index_name, index_type, status
                 FROM user_indexes
                 WHERE index_name = :index_name
                 """,
                 index_name=INDEX_NAME,
             )
             index_row = cursor.fetchone()

             # Confirm that semantic search still returns rows after index creation.
             cursor.execute(
                 """
                 SELECT title
                 FROM documents
                 ORDER BY VECTOR_DISTANCE(embedding, :query_vector, COSINE)
                 FETCH EXACT FIRST 3 ROWS ONLY
                 """,
                 query_vector=query_vector,
             )
             rows = cursor.fetchall()

 	if not index_row:
         raise SystemExit("Vector index was not found in USER_INDEXES.")

     print("Created vector index DOCUMENT_EMBEDDING_IDX.")
     print("\nIndex validation:")
     print(f"Index name: {index_row[0]}")
     print(f"Index type: {index_row[1]}")
     print(f"Status: {index_row[2]}")
     result_count = len(rows)

 	print(
         "\nSemantic search still returns "
         f"{result_count} results after index creation."
 	)


 if __name__ == "__main__":
 	main()संचालित द्वारा

python 08_create_vector_index.pyसंचालित द्वारा

Created vector index DOCUMENT_EMBEDDING_IDX.

 Index validation:
 Index name: DOCUMENT_EMBEDDING_IDX
 Index type: VECTOR
 Status: VALID

 Semantic search still returns 3 results after index creation.संचालित द्वारा

docker stop oracle-free-26ai-vector
docker rm oracle-free-26ai-vectorसंचालित द्वारा