腾讯云向量数据库
腾讯云向量数据库 是一款完全托管、自主研发、面向企业的分布式数据库服务，专为存储、检索和分析多维向量数据而设计。该数据库支持多种索引类型和相似度计算方法，单一索引可支持高达 10 亿的向量规模，并可支持百万级 QPS 和毫秒级查询延迟。腾讯云向量数据库不仅可以为大模型提供外部知识库，提升大模型响应的准确性，还可以广泛应用于推荐系统、NLP 服务、计算机视觉、智能客服等 AI 领域。
此笔记本展示了如何使用与腾讯向量数据库相关的功能。
要运行，您应该拥有一个数据库实例。
基本用法
!pip3 install tcvectordb langchain-community
from langchain_community.document_loaders import TextLoader
from langchain_community.embeddings.fake import FakeEmbeddings
from langchain_community.vectorstores import TencentVectorDB
from langchain_community.vectorstores.tencentvectordb import ConnectionParams
from langchain_text_splitters import CharacterTextSplitter
加载文档，并将它们拆分为块。
loader = TextLoader("../../how_to/state_of_the_union.txt")
documents = loader.load()
text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
docs = text_splitter.split_documents(documents)
我们支持两种嵌入文档的方式
- 使用任何与 Langchain Embeddings 兼容的嵌入模型。
- 指定腾讯向量存储数据库的嵌入模型名称，可选
bge-base-zh，维度：768m3e-base，维度：768text2vec-large-chinese，维度：1024e5-large-v2，维度：1024multilingual-e5-base，维度：768
以下代码展示了两种嵌入文档的方式，您可以通过注释其中一种来选择其中一种。
## you can use a Langchain Embeddings model, like OpenAIEmbeddings:
# from langchain_community.embeddings.openai import OpenAIEmbeddings
#
# embeddings = OpenAIEmbeddings()
# t_vdb_embedding = None
## Or you can use a Tencent Embedding model, like `bge-base-zh`:
t_vdb_embedding = "bge-base-zh" # bge-base-zh is the default model
embeddings = None
API 参考：OpenAIEmbeddings
现在我们可以创建一个 TencentVectorDB 实例，您必须至少提供 embeddings 或 t_vdb_embedding 参数之一。如果两者都提供，则将使用 embeddings 参数。
conn_params = ConnectionParams(
url="http://10.0.X.X",
key="eC4bLRy2va******************************",
username="root",
timeout=20,
)
vector_db = TencentVectorDB.from_documents(
docs, embeddings, connection_params=conn_params, t_vdb_embedding=t_vdb_embedding
)
query = "What did the president say about Ketanji Brown Jackson"
docs = vector_db.similarity_search(query)
docs[0].page_content
'Tonight. I call on the Senate to: Pass the Freedom to Vote Act. Pass the John Lewis Voting Rights Act. And while you’re at it, pass the Disclose Act so Americans can know who is funding our elections. \n\nTonight, I’d like to honor someone who has dedicated his life to serve this country: Justice Stephen Breyer—an Army veteran, Constitutional scholar, and retiring Justice of the United States Supreme Court. Justice Breyer, thank you for your service. \n\nOne of the most serious constitutional responsibilities a President has is nominating someone to serve on the United States Supreme Court. \n\nAnd 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.'
vector_db = TencentVectorDB(embeddings, conn_params)
vector_db.add_texts(["Ankush went to Princeton"])
query = "Where did Ankush go to college?"
docs = vector_db.max_marginal_relevance_search(query)
docs[0].page_content
'Ankush went to Princeton'
元数据和过滤
腾讯向量数据库支持元数据和过滤。您可以向文档添加元数据，并根据元数据过滤搜索结果。
现在我们将创建一个具有元数据的新 TencentVectorDB 集合，并演示如何根据元数据过滤搜索结果。
from langchain_community.vectorstores.tencentvectordb import (
META_FIELD_TYPE_STRING,
META_FIELD_TYPE_UINT64,
ConnectionParams,
MetaField,
TencentVectorDB,
)
from langchain_core.documents import Document
meta_fields = [
MetaField(name="year", data_type=META_FIELD_TYPE_UINT64, index=True),
MetaField(name="rating", data_type=META_FIELD_TYPE_STRING, index=False),
MetaField(name="genre", data_type=META_FIELD_TYPE_STRING, index=True),
MetaField(name="director", data_type=META_FIELD_TYPE_STRING, index=True),
]
docs = [
Document(
page_content="The Shawshank Redemption is a 1994 American drama film written and directed by Frank Darabont.",
metadata={
"year": 1994,
"rating": "9.3",
"genre": "drama",
"director": "Frank Darabont",
},
),
Document(
page_content="The Godfather is a 1972 American crime film directed by Francis Ford Coppola.",
metadata={
"year": 1972,
"rating": "9.2",
"genre": "crime",
"director": "Francis Ford Coppola",
},
),
Document(
page_content="The Dark Knight is a 2008 superhero film directed by Christopher Nolan.",
metadata={
"year": 2008,
"rating": "9.0",
"genre": "superhero",
"director": "Christopher Nolan",
},
),
Document(
page_content="Inception is a 2010 science fiction action film written and directed by Christopher Nolan.",
metadata={
"year": 2010,
"rating": "8.8",
"genre": "science fiction",
"director": "Christopher Nolan",
},
),
]
vector_db = TencentVectorDB.from_documents(
docs,
None,
connection_params=ConnectionParams(
url="http://10.0.X.X",
key="eC4bLRy2va******************************",
username="root",
timeout=20,
),
collection_name="movies",
meta_fields=meta_fields,
)
query = "film about dream by Christopher Nolan"
# you can use the tencentvectordb filtering syntax with the `expr` parameter:
result = vector_db.similarity_search(query, expr='director="Christopher Nolan"')
# you can either use the langchain filtering syntax with the `filter` parameter:
# result = vector_db.similarity_search(query, filter='eq("director", "Christopher Nolan")')
result
[Document(page_content='The Dark Knight is a 2008 superhero film directed by Christopher Nolan.', metadata={'year': 2008, 'rating': '9.0', 'genre': 'superhero', 'director': 'Christopher Nolan'}),
Document(page_content='The Dark Knight is a 2008 superhero film directed by Christopher Nolan.', metadata={'year': 2008, 'rating': '9.0', 'genre': 'superhero', 'director': 'Christopher Nolan'}),
Document(page_content='The Dark Knight is a 2008 superhero film directed by Christopher Nolan.', metadata={'year': 2008, 'rating': '9.0', 'genre': 'superhero', 'director': 'Christopher Nolan'}),
Document(page_content='Inception is a 2010 science fiction action film written and directed by Christopher Nolan.', metadata={'year': 2010, 'rating': '8.8', 'genre': 'science fiction', 'director': 'Christopher Nolan'})]