SAP HANA Cloud Vector Engine

SAP HANA Cloud Vector Engine 是一个完全集成到 SAP HANA Cloud 数据库中的向量数据库。

安装

安装 langchain-hana 外部集成包以及本文档中使用的其他包。

%pip install -qU langchain-hana

Credentials

请确保你的 SAP HANA 实例正在运行。从环境变量加载你的凭证并创建连接：

import os

from dotenv import load_dotenv
from hdbcli import dbapi

load_dotenv()
# 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,
)

在什么是 SAP HANA？中了解更多关于 SAP HANA 的信息。

初始化

要初始化 HanaDB 向量存储，你需要数据库连接和嵌入实例。SAP HANA Cloud Vector Engine 支持外部和内部嵌入。

使用外部嵌入

Select embeddings model:

pip install -qU langchain-openai

import getpass
import os

if not os.environ.get("OPENAI_API_KEY"):
  os.environ["OPENAI_API_KEY"] = getpass.getpass("Enter API key for OpenAI: ")

from langchain_openai import OpenAIEmbeddings

embeddings = OpenAIEmbeddings(model="text-embedding-3-large")

使用内部嵌入

您也可以使用其原生 VECTOR_EMBEDDING() 函数直接在 SAP HANA 中计算 embeddings。要启用此功能，请使用您的内部模型 ID 创建一个 HanaInternalEmbeddings 实例，并将其传递给 HanaDB。请注意，HanaInternalEmbeddings 实例专门用于 HanaDB，不适用于其他向量存储实现。有关内部 embeddings 的更多信息，请参阅 SAP HANA VECTOR_EMBEDDING Function。

注意： 请确保在您的 SAP HANA Cloud 实例中启用了 NLP。

from langchain_hana import HanaInternalEmbeddings

embeddings = HanaInternalEmbeddings(internal_embedding_model_id="SAP_NEB.20240715")

在拥有连接和嵌入实例后，通过将它们与用于存储向量的表名一起传递给 HanaDB 来创建向量存储：

from langchain_hana import HanaDB

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

示例

加载样本文档 "state_of_the_union.txt" 并从中创建块。

from langchain_community.document_loaders import TextLoader
from langchain_core.documents import Document
from langchain_openai import OpenAIEmbeddings
from langchain_text_splitters import CharacterTextSplitter

text_documents = TextLoader(
    "../../how_to/state_of_the_union.txt", encoding="UTF-8"
).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)}")

API Reference:TextLoader | Document | OpenAIEmbeddings | CharacterTextSplitter

Number of document chunks: 88

将已加载的文档块添加到表中。在此示例中，我们删除表中可能存在的任何先前内容，以防之前运行过。

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

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

[]

执行查询，从上一步添加的文档块中检索匹配度最高的两个文档块。默认情况下，搜索使用“余弦相似度”。

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)

--------------------------------------------------------------------------------
One of the most serious constitutional responsibilities a President has is nominating someone to serve on the United States Supreme Court. 

And I did that 4 days ago, when I nominated Circuit Court of Appeals Judge Ketanji Brown Jackson. One of our nation’s top legal minds, who will continue Justice Breyer’s legacy of excellence.
--------------------------------------------------------------------------------
As I said last year, especially to our younger transgender Americans, I will always have your back as your President, so you can be yourself and reach your God-given potential. 

While it often appears that we never agree, that isn’t true. I signed 80 bipartisan bills into law last year. From preventing government shutdowns to protecting Asian-Americans from still-too-common hate crimes to reforming military justice.

用“欧几里得距离”查询相同的内容。结果应该与“余弦相似度”相同。

from langchain_hana.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)

--------------------------------------------------------------------------------
One of the most serious constitutional responsibilities a President has is nominating someone to serve on the United States Supreme Court. 

And I did that 4 days ago, when I nominated Circuit Court of Appeals Judge Ketanji Brown Jackson. One of our nation’s top legal minds, who will continue Justice Breyer’s legacy of excellence.
--------------------------------------------------------------------------------
As I said last year, especially to our younger transgender Americans, I will always have your back as your President, so you can be yourself and reach your God-given potential. 

While it often appears that we never agree, that isn’t true. I signed 80 bipartisan bills into law last year. From preventing government shutdowns to protecting Asian-Americans from still-too-common hate crimes to reforming military justice.

最大化边际相关性搜索 (MMR)

最大化边际相关性 优化了与查询的相似性以及所选文档的多样性。最初将从数据库中检索 20 个 (fetch_k) 项目。然后 MMR 算法将找到最佳的 2 个 (k) 匹配项。

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

--------------------------------------------------------------------------------
One of the most serious constitutional responsibilities a President has is nominating someone to serve on the United States Supreme Court. 

And I did that 4 days ago, when I nominated Circuit Court of Appeals Judge Ketanji Brown Jackson. One of our nation’s top legal minds, who will continue Justice Breyer’s legacy of excellence.
--------------------------------------------------------------------------------
Groups of citizens blocking tanks with their bodies. Everyone from students to retirees teachers turned soldiers defending their homeland. 

In this struggle as President Zelenskyy said in his speech to the European Parliament “Light will win over darkness.” The Ukrainian Ambassador to the United States is here tonight. 

Let each of us here tonight in this Chamber send an unmistakable signal to Ukraine and to the world.

创建 HNSW 向量索引

向量索引可以显著加快向量的 top-k 最近邻查询。用户可以使用 create_hnsw_index 函数创建分层可导航小世界 (HNSW) 向量索引。

有关在数据库级别创建索引的更多信息，请参阅官方文档。

# HanaDB instance uses cosine similarity as default:
db_cosine = HanaDB(
    embedding=embeddings, connection=connection, table_name="STATE_OF_THE_UNION"
)

# Attempting to create the HNSW index with default parameters
db_cosine.create_hnsw_index()  # If no other parameters are specified, the default values will be used
# Default values: m=64, ef_construction=128, ef_search=200
# The default index name will be: STATE_OF_THE_UNION_COSINE_SIMILARITY_IDX (verify this naming pattern in HanaDB class)


# Creating a HanaDB instance with L2 distance as the similarity function and defined values
db_l2 = HanaDB(
    embedding=embeddings,
    connection=connection,
    table_name="STATE_OF_THE_UNION",
    distance_strategy=DistanceStrategy.EUCLIDEAN_DISTANCE,  # Specify L2 distance
)

# This will create an index based on L2 distance strategy.
db_l2.create_hnsw_index(
    index_name="STATE_OF_THE_UNION_L2_index",
    m=100,  # Max number of neighbors per graph node (valid range: 4 to 1000)
    ef_construction=200,  # Max number of candidates during graph construction (valid range: 1 to 100000)
    ef_search=500,  # Min number of candidates during the search (valid range: 1 to 100000)
)

# Use L2 index to perform MMR
docs = db_l2.max_marginal_relevance_search(query, k=2, fetch_k=20)
for doc in docs:
    print("-" * 80)
    print(doc.page_content)

--------------------------------------------------------------------------------
One of the most serious constitutional responsibilities a President has is nominating someone to serve on the United States Supreme Court. 

And I did that 4 days ago, when I nominated Circuit Court of Appeals Judge Ketanji Brown Jackson. One of our nation’s top legal minds, who will continue Justice Breyer’s legacy of excellence.
--------------------------------------------------------------------------------
Groups of citizens blocking tanks with their bodies. Everyone from students to retirees teachers turned soldiers defending their homeland. 

In this struggle as President Zelenskyy said in his speech to the European Parliament “Light will win over darkness.” The Ukrainian Ambassador to the United States is here tonight. 

Let each of us here tonight in this Chamber send an unmistakable signal to Ukraine and to the world.

关键点:

相似度函数: 索引的相似度函数默认为余弦相似度。如果您想使用其他相似度函数（例如 L2 距离），则在初始化 HanaDB 实例时需要指定。
默认参数: 在 create_hnsw_index 函数中，如果用户未提供 m、ef_construction 或 ef_search 等参数的自定义值，将自动使用默认值（例如 m=64、ef_construction=128、ef_search=200）。这些值可确保在无需用户干预的情况下，以合理的性能创建索引。

基础向量存储操作

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

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

True

我们可以向现有表中添加简单的文本文档。

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

[]

添加带有元数据的文档。

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)

[]

查询具有特定元数据的文档。

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)

--------------------------------------------------------------------------------
foo
{'start': 100, 'end': 150, 'doc_name': 'foo.txt', 'quality': 'bad'}

删除具有特定元数据的文档。

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))

高级筛选

除了基本的基于值的筛选功能外，还可以使用更高级的筛选。下表显示了可用的筛选运算符。

运算符	语义
`$eq`	等于 (==)
`$ne`	不等于 (!=)
`$lt`	小于 (<)
`$lte`	小于等于 (<=)
`$gt`	大于 (>)
`$gte`	大于等于 (>=)
`$in`	包含在给定值集合中 (in)
`$nin`	不包含在给定值集合中 (not in)
`$between`	在两个边界值范围内
`$like`	基于 SQL 中的 "LIKE" 语义进行文本相等性匹配 (使用 "%" 作为通配符)
`$contains`	筛选包含特定关键字的文档
`$and`	逻辑 "与"，支持 2 个或更多操作数
`$or`	逻辑 "或"，支持 2 个或更多操作数

# Prepare some test documents
docs = [
    Document(
        page_content="First",
        metadata={"name": "Adam Smith", "is_active": True, "id": 1, "height": 10.0},
    ),
    Document(
        page_content="Second",
        metadata={"name": "Bob Johnson", "is_active": False, "id": 2, "height": 5.7},
    ),
    Document(
        page_content="Third",
        metadata={"name": "Jane Doe", "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)

使用 $ne, $gt, $gte, $lt, $lte 进行过滤

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))

Filter: {'id': {'$ne': 1}}
{'name': 'Jane Doe', 'is_active': True, 'id': 3, 'height': 2.4}
{'name': 'Bob Johnson', 'is_active': False, 'id': 2, 'height': 5.7}
Filter: {'id': {'$gt': 1}}
{'name': 'Jane Doe', 'is_active': True, 'id': 3, 'height': 2.4}
{'name': 'Bob Johnson', 'is_active': False, 'id': 2, 'height': 5.7}
Filter: {'id': {'$gte': 1}}
{'name': 'Adam Smith', 'is_active': True, 'id': 1, 'height': 10.0}
{'name': 'Jane Doe', 'is_active': True, 'id': 3, 'height': 2.4}
{'name': 'Bob Johnson', 'is_active': False, 'id': 2, 'height': 5.7}
Filter: {'id': {'$lt': 1}}
<empty result>
Filter: {'id': {'$lte': 1}}
{'name': 'Adam Smith', 'is_active': True, 'id': 1, 'height': 10.0}

使用 $between、$in 和 $nin 进行筛选

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 Smith", "Bob Johnson"]}}
print(f"Filter: {advanced_filter}")
print_filter_result(db.similarity_search("just testing", k=5, filter=advanced_filter))

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

Filter: {'id': {'$between': (1, 2)}}
{'name': 'Adam Smith', 'is_active': True, 'id': 1, 'height': 10.0}
{'name': 'Bob Johnson', 'is_active': False, 'id': 2, 'height': 5.7}
Filter: {'name': {'$in': ['Adam Smith', 'Bob Johnson']}}
{'name': 'Adam Smith', 'is_active': True, 'id': 1, 'height': 10.0}
{'name': 'Bob Johnson', 'is_active': False, 'id': 2, 'height': 5.7}
Filter: {'name': {'$nin': ['Adam Smith', 'Bob Johnson']}}
{'name': 'Jane Doe', 'is_active': True, 'id': 3, 'height': 2.4}

使用 $like 进行文本过滤

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))

Filter: {'name': {'$like': 'a%'}}
<empty result>
Filter: {'name': {'$like': '%a%'}}
{'name': 'Adam Smith', 'is_active': True, 'id': 1, 'height': 10.0}
{'name': 'Jane Doe', 'is_active': True, 'id': 3, 'height': 2.4}

使用 $contains 进行文本过滤

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

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

advanced_filter = {"name": {"$contains": "Adam Johnson"}}
print(f"Filter: {advanced_filter}")
print_filter_result(db.similarity_search("just testing", k=5, filter=advanced_filter))

advanced_filter = {"name": {"$contains": "Adam Smith"}}
print(f"Filter: {advanced_filter}")
print_filter_result(db.similarity_search("just testing", k=5, filter=advanced_filter))

Filter: {'name': {'$contains': 'bob'}}
{'name': 'Bob Johnson', 'is_active': False, 'id': 2, 'height': 5.7}
Filter: {'name': {'$contains': 'bo'}}
<empty result>
Filter: {'name': {'$contains': 'Adam Johnson'}}
<empty result>
Filter: {'name': {'$contains': 'Adam Smith'}}
{'name': 'Adam Smith', 'is_active': True, 'id': 1, 'height': 10.0}

结合使用 $and 和 $or 进行筛选

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))

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

Filter: {'$or': [{'id': 1}, {'name': 'bob'}]}
{'name': 'Adam Smith', 'is_active': True, 'id': 1, 'height': 10.0}
Filter: {'$and': [{'id': 1}, {'id': 2}]}
<empty result>
Filter: {'$or': [{'id': 1}, {'id': 2}, {'id': 3}]}
{'name': 'Adam Smith', 'is_active': True, 'id': 1, 'height': 10.0}
{'name': 'Jane Doe', 'is_active': True, 'id': 3, 'height': 2.4}
{'name': 'Bob Johnson', 'is_active': False, 'id': 2, 'height': 5.7}
Filter: {'$and': [{'name': {'$contains': 'bob'}}, {'name': {'$contains': 'johnson'}}]}
{'name': 'Bob Johnson', 'is_active': False, 'id': 2, 'height': 5.7}

将 VectorStore 用作链中的检索器，实现检索增强生成 (RAG)

# 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()

定义提示。

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}

API Reference:PromptTemplate

创建 ConversationalRetrievalChain，它负责处理聊天记录以及检索相似文档块以添加到 prompt 中。

from langchain.chains import ConversationalRetrievalChain
from langchain.memory import ConversationBufferMemory
from langchain_openai import ChatOpenAI

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},
)

API Reference:ConversationalRetrievalChain | ConversationBufferMemory | ChatOpenAI

提第一个问题（并验证使用了多少文本块）。

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)}")

Answer from LLM:
================
The United States has set up joint patrols with Mexico and Guatemala to catch more human traffickers at the border. This collaborative effort aims to improve border security and combat illegal activities such as human trafficking.
================
Number of used source document chunks: 5

请提供需要翻译的英文 MDX 内容。

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

在同一会话链中提出另一个问题。答案应与之前给出的答案相关。

question = "How many casualties were reported after that?"

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

Answer from LLM:
================
Countries like Mexico and Guatemala are participating in joint patrols to catch human traffickers. The United States is also working with partners in South and Central America to host more refugees and secure their borders. Additionally, the U.S. is working with twenty-seven members of the European Union, as well as countries like France, Germany, Italy, the United Kingdom, Canada, Japan, Korea, Australia, New Zealand, and Switzerland.

标准表与包含向量数据的“自定义”表

作为默认行为，embeddings 表将创建 3 列：

VEC_TEXT 列，包含 Document 的文本
VEC_META 列，包含 Document 的元数据
VEC_VECTOR 列，包含 Document 文本的 embedding 向量

# 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)

[]

显示表格 "LANGCHAIN_DEMO_NEW_TABLE" 中的列

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()

('VEC_META', 'NCLOB')
('VEC_TEXT', 'NCLOB')
('VEC_VECTOR', 'REAL_VECTOR')

在三列中显示插入文档的值

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()

自定义表必须至少包含三个符合标准表语义的列：

一列类型为 NCLOB 或 NVARCHAR，用于存储嵌入的文本/上下文
一列类型为 NCLOB 或 NVARCHAR，用于存储元数据
一列类型为 REAL_VECTOR，用于存储嵌入向量

该表还可以包含额外的列。当新 Document 插入表中时，这些额外列必须允许 NULL 值。

# Create a new table "MY_OWN_TABLE_ADD" with three "standard" columns and one additional column
my_own_table_name = "MY_OWN_TABLE_ADD"
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()

None
Some other text
{"start": 400, "end": 450, "doc_name": "other.txt"}
<memory at 0x110f856c0>

添加另一个文档并对自定义表执行相似性搜索。

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)

--------------------------------------------------------------------------------
Some more text
--------------------------------------------------------------------------------
Some other text

自定义列的性能优化过滤

为了允许灵活的元数据值，默认情况下所有元数据都以 JSON 格式存储在 metadata 列中。如果已知一些使用的元数据键和值类型，可以通过将目标表的键名作为列名，并将它们通过 specific_metadata_columns 列表传递给 HanaDB 构造函数来将它们存储在其他列中。在插入期间，与这些值匹配的元数据键会被复制到特殊列中。过滤器将使用特殊列而不是 metadata JSON 列来过滤 specific_metadata_columns 列表中的键。

# Create a new table "PERFORMANT_CUSTOMTEXT_FILTER" with three "standard" columns and one additional column
my_own_table_name = "PERFORMANT_CUSTOMTEXT_FILTER"
cur = connection.cursor()
cur.execute(
    (
        f"CREATE TABLE {my_own_table_name} ("
        "CUSTOMTEXT NVARCHAR(500), "
        "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",
    specific_metadata_columns=["CUSTOMTEXT"],
)

# Add a simple document with some metadata
docs = [
    Document(
        page_content="Some other text",
        metadata={
            "start": 400,
            "end": 450,
            "doc_name": "other.txt",
            "CUSTOMTEXT": "Filters on this value are very performant",
        },
    )
]
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 "CUSTOMTEXT". Should be "Filters on this value are very performant"
print(rows[0][1])  # The text
print(
    rows[0][2]
)  # The metadata without the "CUSTOMTEXT" data, as this is extracted into a sperate column
print(rows[0][3])  # The vector

cur.close()

Filters on this value are very performant
Some other text
{"start": 400, "end": 450, "doc_name": "other.txt", "CUSTOMTEXT": "Filters on this value are very performant"}
<memory at 0x110f859c0>

特殊列对 langchain 接口的其余部分是完全透明的。所有功能正常，只是性能更佳。

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

advanced_filter = {"CUSTOMTEXT": {"$like": "%value%"}}
query = "What's up?"
docs = db.similarity_search(query, k=2, filter=advanced_filter)
for doc in docs:
    print("-" * 80)
    print(doc.page_content)

--------------------------------------------------------------------------------
Some more text
--------------------------------------------------------------------------------
Some other text

Vector store conceptual guide
Vector store how-to guides

安装​

Credentials​

初始化​

使用外部嵌入​

使用内部嵌入​

示例​

最大化边际相关性搜索 (MMR)​

创建 HNSW 向量索引​

基础向量存储操作​

高级筛选​

将 VectorStore 用作链中的检索器，实现检索增强生成 (RAG)​

标准表与包含向量数据的“自定义”表​

自定义列的性能优化过滤​

Related​

安装