SAP HANA Cloud Vector Engine

SAP HANA Cloud Vector Engine is a vector store fully integrated into the SAP HANA Cloud database.

Setting up

Installation of the HANA database driver.

# Pip install necessary package
%pip install --upgrade --quiet  hdbcli

For OpenAIEmbeddings we use the OpenAI API key from the environment.

import os
# Use OPENAI_API_KEY env variable
# os.environ["OPENAI_API_KEY"] = "Your OpenAI API key"

Create a database connection to a HANA Cloud instance.

from hdbcli import dbapi

# Use connection settings from the environment
connection = dbapi.connect(
    address=os.environ.get("HANA_DB_ADDRESS"),
    port=os.environ.get("HANA_DB_PORT"),
    user=os.environ.get("HANA_DB_USER"),
    password=os.environ.get("HANA_DB_PASSWORD"),
    autocommit=True,
    sslValidateCertificate=False,
)

Example

Load the sample document "state_of_the_union.txt" and create chunks from it.

from langchain_community.docstore.document import Document
from langchain_community.document_loaders import TextLoader
from langchain_community.vectorstores.hanavector import HanaDB
from langchain_openai import OpenAIEmbeddings
from langchain_text_splitters import CharacterTextSplitter

text_documents = TextLoader("../../modules/state_of_the_union.txt").load()
text_splitter = CharacterTextSplitter(chunk_size=500, chunk_overlap=0)
text_chunks = text_splitter.split_documents(text_documents)
print(f"Number of document chunks: {len(text_chunks)}")

embeddings = OpenAIEmbeddings()

Create a LangChain VectorStore interface for the HANA database and specify the table (collection) to use for accessing the vector embeddings

db = HanaDB(
    embedding=embeddings, connection=connection, table_name="STATE_OF_THE_UNION"
)

Add the loaded document chunks to the table. For this example, we delete any previous content from the table which might exist from previous runs.

# Delete already existing documents from the table
db.delete(filter={})

# add the loaded document chunks
db.add_documents(text_chunks)

Perform a query to get the two best-matching document chunks from the ones that were added in the previous step. By default "Cosine Similarity" is used for the search.

query = "What did the president say about Ketanji Brown Jackson"
docs = db.similarity_search(query, k=2)

for doc in docs:
    print("-" * 80)
    print(doc.page_content)

Query the same content with "Euclidian Distance". The results shoud be the same as with "Cosine Similarity".

from langchain_community.vectorstores.utils import DistanceStrategy

db = HanaDB(
    embedding=embeddings,
    connection=connection,
    distance_strategy=DistanceStrategy.EUCLIDEAN_DISTANCE,
    table_name="STATE_OF_THE_UNION",
)

query = "What did the president say about Ketanji Brown Jackson"
docs = db.similarity_search(query, k=2)
for doc in docs:
    print("-" * 80)
    print(doc.page_content)

Maximal Marginal Relevance Search (MMR)

Maximal marginal relevance optimizes for similarity to query AND diversity among selected documents. The first 20 (fetch_k) items will be retrieved from the DB. The MMR algorithm will then find the best 2 (k) matches.

docs = db.max_marginal_relevance_search(query, k=2, fetch_k=20)
for doc in docs:
    print("-" * 80)
    print(doc.page_content)

Basic Vectorstore Operations

db = HanaDB(
    connection=connection, embedding=embeddings, table_name="LANGCHAIN_DEMO_BASIC"
)

# Delete already existing documents from the table
db.delete(filter={})

We can add simple text documents to the existing table.

docs = [Document(page_content="Some text"), Document(page_content="Other docs")]
db.add_documents(docs)

Add documents with metadata.

docs = [
    Document(
        page_content="foo",
        metadata={"start": 100, "end": 150, "doc_name": "foo.txt", "quality": "bad"},
    ),
    Document(
        page_content="bar",
        metadata={"start": 200, "end": 250, "doc_name": "bar.txt", "quality": "good"},
    ),
]
db.add_documents(docs)

Query documents with specific metadata.

docs = db.similarity_search("foobar", k=2, filter={"quality": "bad"})
# With filtering on "quality"=="bad", only one document should be returned
for doc in docs:
    print("-" * 80)
    print(doc.page_content)
    print(doc.metadata)

Delete documents with specific metadata.

db.delete(filter={"quality": "bad"})

# Now the similarity search with the same filter will return no results
docs = db.similarity_search("foobar", k=2, filter={"quality": "bad"})
print(len(docs))

Advanced filtering

In addition to the basic value-based filtering capabilities, it is possible to use more advanced filtering. The table below shows the available filter operators.

Operator	Semantic
$eq	Equality (==)
$ne	Inequality (!=)
$lt	Less than (<)
$lte	Less than or equal (<=)
$gt	Greater than (>)
$gte	Greater than or equal (>=)
$in	Contained in a set of given values (in)
$nin	Not contained in a set of given values (not in)
$between	Between the range of two boundary values
$like	Text equality based on the "LIKE" semantics in SQL (using "%" as wildcard)
$and	Logical "and", supporting 2 or more operands
$or	Logical "or", supporting 2 or more operands

# Prepare some test documents
docs = [
    Document(
        page_content="First",
        metadata={"name": "adam", "is_active": True, "id": 1, "height": 10.0},
    ),
    Document(
        page_content="Second",
        metadata={"name": "bob", "is_active": False, "id": 2, "height": 5.7},
    ),
    Document(
        page_content="Third",
        metadata={"name": "jane", "is_active": True, "id": 3, "height": 2.4},
    ),
]

db = HanaDB(
    connection=connection,
    embedding=embeddings,
    table_name="LANGCHAIN_DEMO_ADVANCED_FILTER",
)

# Delete already existing documents from the table
db.delete(filter={})
db.add_documents(docs)


# Helper function for printing filter results
def print_filter_result(result):
    if len(result) == 0:
        print("<empty result>")
    for doc in result:
        print(doc.metadata)

Filtering with $ne, $gt, $gte, $lt, $lte

advanced_filter = {"id": {"$ne": 1}}
print(f"Filter: {advanced_filter}")
print_filter_result(db.similarity_search("just testing", k=5, filter=advanced_filter))

advanced_filter = {"id": {"$gt": 1}}
print(f"Filter: {advanced_filter}")
print_filter_result(db.similarity_search("just testing", k=5, filter=advanced_filter))

advanced_filter = {"id": {"$gte": 1}}
print(f"Filter: {advanced_filter}")
print_filter_result(db.similarity_search("just testing", k=5, filter=advanced_filter))

advanced_filter = {"id": {"$lt": 1}}
print(f"Filter: {advanced_filter}")
print_filter_result(db.similarity_search("just testing", k=5, filter=advanced_filter))

advanced_filter = {"id": {"$lte": 1}}
print(f"Filter: {advanced_filter}")
print_filter_result(db.similarity_search("just testing", k=5, filter=advanced_filter))

Filtering with $between, $in, $nin

advanced_filter = {"id": {"$between": (1, 2)}}
print(f"Filter: {advanced_filter}")
print_filter_result(db.similarity_search("just testing", k=5, filter=advanced_filter))

advanced_filter = {"name": {"$in": ["adam", "bob"]}}
print(f"Filter: {advanced_filter}")
print_filter_result(db.similarity_search("just testing", k=5, filter=advanced_filter))

advanced_filter = {"name": {"$nin": ["adam", "bob"]}}
print(f"Filter: {advanced_filter}")
print_filter_result(db.similarity_search("just testing", k=5, filter=advanced_filter))

Text filtering with $like

advanced_filter = {"name": {"$like": "a%"}}
print(f"Filter: {advanced_filter}")
print_filter_result(db.similarity_search("just testing", k=5, filter=advanced_filter))

advanced_filter = {"name": {"$like": "%a%"}}
print(f"Filter: {advanced_filter}")
print_filter_result(db.similarity_search("just testing", k=5, filter=advanced_filter))

Combined filtering with $and, $or

advanced_filter = {"$or": [{"id": 1}, {"name": "bob"}]}
print(f"Filter: {advanced_filter}")
print_filter_result(db.similarity_search("just testing", k=5, filter=advanced_filter))

advanced_filter = {"$and": [{"id": 1}, {"id": 2}]}
print(f"Filter: {advanced_filter}")
print_filter_result(db.similarity_search("just testing", k=5, filter=advanced_filter))

advanced_filter = {"$or": [{"id": 1}, {"id": 2}, {"id": 3}]}
print(f"Filter: {advanced_filter}")
print_filter_result(db.similarity_search("just testing", k=5, filter=advanced_filter))

Using a VectorStore as a retriever in chains for retrieval augmented generation (RAG)

from langchain.memory import ConversationBufferMemory
from langchain_openai import ChatOpenAI

# Access the vector DB with a new table
db = HanaDB(
    connection=connection,
    embedding=embeddings,
    table_name="LANGCHAIN_DEMO_RETRIEVAL_CHAIN",
)

# Delete already existing entries from the table
db.delete(filter={})

# add the loaded document chunks from the "State Of The Union" file
db.add_documents(text_chunks)

# Create a retriever instance of the vector store
retriever = db.as_retriever()

Define the prompt.

from langchain_core.prompts import PromptTemplate

prompt_template = """
You are an expert in state of the union topics. You are provided multiple context items that are related to the prompt you have to answer.
Use the following pieces of context to answer the question at the end.

{context}

Question: {question}
"""

PROMPT = PromptTemplate(
    template=prompt_template, input_variables=["context", "question"]
)
chain_type_kwargs = {"prompt": PROMPT}

Create the ConversationalRetrievalChain, which handles the chat history and the retrieval of similar document chunks to be added to the prompt.

from langchain.chains import ConversationalRetrievalChain

llm = ChatOpenAI(model="gpt-3.5-turbo")
memory = ConversationBufferMemory(
    memory_key="chat_history", output_key="answer", return_messages=True
)
qa_chain = ConversationalRetrievalChain.from_llm(
    llm,
    db.as_retriever(search_kwargs={"k": 5}),
    return_source_documents=True,
    memory=memory,
    verbose=False,
    combine_docs_chain_kwargs={"prompt": PROMPT},
)

Ask the first question (and verify how many text chunks have been used).

question = "What about Mexico and Guatemala?"

result = qa_chain.invoke({"question": question})
print("Answer from LLM:")
print("================")
print(result["answer"])

source_docs = result["source_documents"]
print("================")
print(f"Number of used source document chunks: {len(source_docs)}")

Examine the used chunks of the chain in detail. Check if the best ranked chunk contains info about "Mexico and Guatemala" as mentioned in the question.

for doc in source_docs:
    print("-" * 80)
    print(doc.page_content)
    print(doc.metadata)

Ask another question on the same conversational chain. The answer should relate to the previous answer given.

question = "What about other countries?"

result = qa_chain.invoke({"question": question})
print("Answer from LLM:")
print("================")
print(result["answer"])

Standard tables vs. "custom" tables with vector data

As default behaviour, the table for the embeddings is created with 3 columns:

• A column VEC_TEXT, which contains the text of the Document
• A column VEC_META, which contains the metadata of the Document
• A column VEC_VECTOR, which contains the embeddings-vector of the Document's text

# Access the vector DB with a new table
db = HanaDB(
    connection=connection, embedding=embeddings, table_name="LANGCHAIN_DEMO_NEW_TABLE"
)

# Delete already existing entries from the table
db.delete(filter={})

# Add a simple document with some metadata
docs = [
    Document(
        page_content="A simple document",
        metadata={"start": 100, "end": 150, "doc_name": "simple.txt"},
    )
]
db.add_documents(docs)

Show the columns in table "LANGCHAIN_DEMO_NEW_TABLE"

cur = connection.cursor()
cur.execute(
    "SELECT COLUMN_NAME, DATA_TYPE_NAME FROM SYS.TABLE_COLUMNS WHERE SCHEMA_NAME = CURRENT_SCHEMA AND TABLE_NAME = 'LANGCHAIN_DEMO_NEW_TABLE'"
)
rows = cur.fetchall()
for row in rows:
    print(row)
cur.close()

Show the value of the inserted document in the three columns

cur = connection.cursor()
cur.execute(
    "SELECT VEC_TEXT, VEC_META, TO_NVARCHAR(VEC_VECTOR) FROM LANGCHAIN_DEMO_NEW_TABLE LIMIT 1"
)
rows = cur.fetchall()
print(rows[0][0])  # The text
print(rows[0][1])  # The metadata
print(rows[0][2])  # The vector
cur.close()

Custom tables must have at least three columns that match the semantics of a standard table

• A column with type NCLOB or NVARCHAR for the text/context of the embeddings
• A column with type NCLOB or NVARCHAR for the metadata
• A column with type REAL_VECTOR for the embedding vector

The table can contain additional columns. When new Documents are inserted into the table, these additional columns must allow NULL values.

# Create a new table "MY_OWN_TABLE" with three "standard" columns and one additional column
my_own_table_name = "MY_OWN_TABLE"
cur = connection.cursor()
cur.execute(
    (
        f"CREATE TABLE {my_own_table_name} ("
        "SOME_OTHER_COLUMN NVARCHAR(42), "
        "MY_TEXT NVARCHAR(2048), "
        "MY_METADATA NVARCHAR(1024), "
        "MY_VECTOR REAL_VECTOR )"
    )
)

# Create a HanaDB instance with the own table
db = HanaDB(
    connection=connection,
    embedding=embeddings,
    table_name=my_own_table_name,
    content_column="MY_TEXT",
    metadata_column="MY_METADATA",
    vector_column="MY_VECTOR",
)

# Add a simple document with some metadata
docs = [
    Document(
        page_content="Some other text",
        metadata={"start": 400, "end": 450, "doc_name": "other.txt"},
    )
]
db.add_documents(docs)

# Check if data has been inserted into our own table
cur.execute(f"SELECT * FROM {my_own_table_name} LIMIT 1")
rows = cur.fetchall()
print(rows[0][0])  # Value of column "SOME_OTHER_DATA". Should be NULL/None
print(rows[0][1])  # The text
print(rows[0][2])  # The metadata
print(rows[0][3])  # The vector

cur.close()

Add another document and perform a similarity search on the custom table.

docs = [
    Document(
        page_content="Some more text",
        metadata={"start": 800, "end": 950, "doc_name": "more.txt"},
    )
]
db.add_documents(docs)

query = "What's up?"
docs = db.similarity_search(query, k=2)
for doc in docs:
    print("-" * 80)
    print(doc.page_content)