主题
LlamaIndex 的 Db2 集成（llama-index-vector-stores-db2）为 IBM 关系型数据库 Db2 v12.1.2 及以上版本提供向量存储和向量搜索能力，以 MIT 许可证分发。用户可以直接使用提供的实现，也可以根据特定需求进行自定义。主要特性包括：
- 带元数据的向量存储
- 向量相似度搜索和过滤选项
- 支持 EUCLIDEAN_DISTANCE、DOT_PRODUCT、COSINE、MANHATTAN_DISTANCE、HAMMING_DISTANCE 和 EUCLIDEAN_SQUARED 距离度量
- 通过创建索引和近似最近邻搜索进行性能优化。（即将推出）
将 LlamaIndex 与 Db2 向量存储和搜索配合使用的前提条件
安装包 llama-index-vector-stores-db2，这是 db2 LlamaIndex 向量存储和搜索的集成包。
# pip install llama-index-vector-stores-db2连接到 Db2 向量存储
以下示例代码将展示如何连接到 Db2 数据库。除上述依赖项外，你还需要一个运行中的 Db2 数据库实例（版本 v12.1.2+，支持向量数据类型）。
import ibm_db
import ibm_db_dbi
database = ""
username = ""
password = ""
try:
connection = ibm_db_dbi.connect(database, username, password)
print("Connection successful!")
except Exception as e:
print("Connection failed!", e)导入所需的依赖项
from llama_index.core.schema import NodeRelationship, RelatedNodeInfo, TextNode
from llama_index.core.vector_stores.types import (
ExactMatchFilter,
MetadataFilters,
VectorStoreQuery,
)
from llama_index.vector_stores.db2 import base as db2llamavs
from llama_index.vector_stores.db2 import DB2LlamaVS, DistanceStrategy加载文档
# Define a list of documents (These dummy examples are 4 random documents )
text_json_list = [
{
"text": "Db2 handles LOB data differently than other kinds of data. As a result, you sometimes need to take additional actions when you define LOB columns and insert the LOB data.",
"id_": "doc_1_2_P4",
"embedding": [1.0, 0.0],
"relationships": "test-0",
"metadata": {
"weight": 1.0,
"rank": "a",
"url": "https://www.ibm.com/docs/en/db2-for-zos/12?topic=programs-storing-lob-data-in-tables",
},
},
{
"text": "Introduced in Db2 13, SQL Data Insights brought artificial intelligence (AI) functionality to the Db2 for z/OS engine. It provided the capability to run SQL AI query to find valuable insights hidden in your Db2 data and help you make better business decisions.",
"id_": "doc_15.5.1_P1",
"embedding": [0.0, 1.0],
"relationships": "test-1",
"metadata": {
"weight": 2.0,
"rank": "c",
"url": "https://community.ibm.com/community/user/datamanagement/blogs/neena-cherian/2023/03/07/accelerating-db2-ai-queries-with-the-new-vector-pr",
},
},
{
"text": "Data structures are elements that are required to use DB2®. You can access and use these elements to organize your data. Examples of data structures include tables, table spaces, indexes, index spaces, keys, views, and databases.",
"id_": "id_22.3.4.3.1_P2",
"embedding": [1.0, 1.0],
"relationships": "test-2",
"metadata": {
"weight": 3.0,
"rank": "d",
"url": "https://www.ibm.com/docs/en/zos-basic-skills?topic=concepts-db2-data-structures",
},
},
{
"text": "DB2® maintains a set of tables that contain information about the data that DB2 controls. These tables are collectively known as the catalog. The catalog tables contain information about DB2 objects such as tables, views, and indexes. When you create, alter, or drop an object, DB2 inserts, updates, or deletes rows of the catalog that describe the object.",
"id_": "id_3.4.3.1_P3",
"embedding": [2.0, 1.0],
"relationships": "test-3",
"metadata": {
"weight": 4.0,
"rank": "e",
"url": "https://www.ibm.com/docs/en/zos-basic-skills?topic=objects-db2-catalog",
},
},
]# Create Llama Text Nodes
text_nodes = []
for text_json in text_json_list:
# Construct the relationships using RelatedNodeInfo
relationships = {
NodeRelationship.SOURCE: RelatedNodeInfo(
node_id=text_json["relationships"]
)
}
# Prepare the metadata dictionary; you might want to exclude certain metadata fields if necessary
metadata = {
"weight": text_json["metadata"]["weight"],
"rank": text_json["metadata"]["rank"],
}
# Create a TextNode instance
text_node = TextNode(
text=text_json["text"],
id_=text_json["id_"],
embedding=text_json["embedding"],
relationships=relationships,
metadata=metadata,
)
text_nodes.append(text_node)
print(text_nodes)使用 AI 向量搜索创建一组采用不同距离策略的向量存储
首先，我们将创建三个向量存储，每个使用不同的距离函数。
你可以手动连接到 Db2 数据库，并将看到三个表 Documents_DOT、Documents_COSINE 和 Documents_EUCLIDEAN。
# Ingest documents into Db2 Vector Store using different distance strategies
vector_store_dot = DB2LlamaVS.from_documents(
text_nodes,
table_name="Documents_DOT",
client=connection,
distance_strategy=DistanceStrategy.DOT_PRODUCT,
embed_dim=2,
)
vector_store_max = DB2LlamaVS.from_documents(
text_nodes,
table_name="Documents_COSINE",
client=connection,
distance_strategy=DistanceStrategy.COSINE,
embed_dim=2,
)
vector_store_euclidean = DB2LlamaVS.from_documents(
text_nodes,
table_name="Documents_EUCLIDEAN",
client=connection,
distance_strategy=DistanceStrategy.EUCLIDEAN_DISTANCE,
embed_dim=2,
)演示文本的添加、删除操作以及基本相似度搜索
def manage_texts(vector_stores):
for i, vs in enumerate(vector_stores, start=1):
# Adding texts
try:
vs.add_texts(text_nodes, metadata)
print(f"\n\n\nAdd texts complete for vector store {i}\n\n\n")
except Exception as ex:
print(
f"\n\n\nExpected error on duplicate add for vector store {i}\n\n\n"
)
# Deleting texts using the value of 'doc_id'
vs.delete("test-1")
print(f"\n\n\nDelete texts complete for vector store {i}\n\n\n")
# Similarity search
query = VectorStoreQuery(
query_embedding=[1.0, 1.0], similarity_top_k=3
)
results = vs.query(query=query)
print(
f"\n\n\nSimilarity search results for vector store {i}: {results}\n\n\n"
)
vector_store_list = [
vector_store_dot,
vector_store_max,
vector_store_euclidean,
]
manage_texts(vector_store_list)现在我们将对所有 3 个向量存储执行一系列高级搜索。
def conduct_advanced_searches(vector_stores):
for i, vs in enumerate(vector_stores, start=1):
def query_without_filters_returns_all_rows_sorted_by_similarity():
print(f"\n--- Vector Store {i} Advanced Searches ---")
# Similarity search without a filter
print("\nSimilarity search results without filter:")
query = VectorStoreQuery(
query_embedding=[1.0, 1.0], similarity_top_k=3
)
print(vs.query(query=query))
query_without_filters_returns_all_rows_sorted_by_similarity()
def query_with_filters_returns_multiple_matches():
print(f"\n--- Vector Store {i} Advanced Searches ---")
# Similarity search with filter
print("\nSimilarity search results with filter:")
filters = MetadataFilters(
filters=[ExactMatchFilter(key="rank", value="c")]
)
query = VectorStoreQuery(
query_embedding=[1.0, 1.0], filters=filters, similarity_top_k=3
)
result = vs.query(query)
print(result.ids)
query_with_filters_returns_multiple_matches()
def query_with_filter_applies_top_k():
print(f"\n--- Vector Store {i} Advanced Searches ---")
# Similarity search with a filter
print("\nSimilarity search results with top k filter:")
filters = MetadataFilters(
filters=[ExactMatchFilter(key="rank", value="c")]
)
query = VectorStoreQuery(
query_embedding=[1.0, 1.0], filters=filters, similarity_top_k=1
)
result = vs.query(query)
print(result.ids)
query_with_filter_applies_top_k()
def query_with_filter_applies_node_id_filter():
print(f"\n--- Vector Store {i} Advanced Searches ---")
# Similarity search with a filter
print("\nSimilarity search results with node_id filter:")
filters = MetadataFilters(
filters=[ExactMatchFilter(key="rank", value="c")]
)
query = VectorStoreQuery(
query_embedding=[1.0, 1.0],
filters=filters,
similarity_top_k=3,
node_ids=["452D24AB-F185-414C-A352-590B4B9EE51B"],
)
result = vs.query(query)
print(result.ids)
query_with_filter_applies_node_id_filter()
def query_with_exact_filters_returns_single_match():
print(f"\n--- Vector Store {i} Advanced Searches ---")
# Similarity search with a filter
print("\nSimilarity search results with filter:")
filters = MetadataFilters(
filters=[
ExactMatchFilter(key="rank", value="c"),
ExactMatchFilter(key="weight", value=2),
]
)
query = VectorStoreQuery(
query_embedding=[1.0, 1.0], filters=filters
)
result = vs.query(query)
print(result.ids)
query_with_exact_filters_returns_single_match()
def query_with_contradictive_filter_returns_no_matches():
filters = MetadataFilters(
filters=[
ExactMatchFilter(key="weight", value=2),
ExactMatchFilter(key="weight", value=3),
]
)
query = VectorStoreQuery(
query_embedding=[1.0, 1.0], filters=filters
)
result = vs.query(query)
print(result.ids)
query_with_contradictive_filter_returns_no_matches()
def query_with_filter_on_unknown_field_returns_no_matches():
print(f"\n--- Vector Store {i} Advanced Searches ---")
# Similarity search with a filter
print("\nSimilarity search results with filter:")
filters = MetadataFilters(
filters=[ExactMatchFilter(key="unknown_field", value="c")]
)
query = VectorStoreQuery(
query_embedding=[1.0, 1.0], filters=filters
)
result = vs.query(query)
print(result.ids)
query_with_filter_on_unknown_field_returns_no_matches()
def delete_removes_document_from_query_results():
vs.delete("test-1")
query = VectorStoreQuery(
query_embedding=[1.0, 1.0], similarity_top_k=2
)
result = vs.query(query)
print(result.ids)
delete_removes_document_from_query_results()
conduct_advanced_searches(vector_store_list)