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DingoDB

DingoDB 是一个分布式多模向量数据库，结合了数据湖和向量数据库的特点，可以存储任何类型和大小的数据（键值对、PDF、音频、视频等）。它具备实时低延迟的处理能力，能够实现快速洞察和响应，并能高效地进行即时分析和处理多模态数据。

您需要安装 langchain-community，命令为 pip install -qU langchain-community 才能使用此集成。

本笔记演示如何使用与 DingoDB 向量数据库相关的 funcionalidades。

要运行，您需要有一个 已启动并运行的 DingoDB 实例。

%pip install --upgrade --quiet  dingodb
# or install latest:
%pip install --upgrade --quiet  git+https://git@github.com/dingodb/pydingo.git

我们想使用 OpenAIEmbeddings，所以我们必须获取 OpenAI API 密钥。

import getpass
import os

if "OPENAI_API_KEY" not in os.environ:
    os.environ["OPENAI_API_KEY"] = getpass.getpass("OpenAI API Key:")
OpenAI API Key:········
from langchain_community.document_loaders import TextLoader
from langchain_community.vectorstores import Dingo
from langchain_openai import OpenAIEmbeddings
from langchain_text_splitters import CharacterTextSplitter
from langchain_community.document_loaders import TextLoader

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)

embeddings = OpenAIEmbeddings()
API Reference:TextLoader
from dingodb import DingoDB

index_name = "langchain_demo"

dingo_client = DingoDB(user="", password="", host=["127.0.0.1:13000"])
# First, check if our index already exists. If it doesn't, we create it
if (
    index_name not in dingo_client.get_index()
    and index_name.upper() not in dingo_client.get_index()
):
    # we create a new index, modify to your own
    dingo_client.create_index(
        index_name=index_name, dimension=1536, metric_type="cosine", auto_id=False
    )

# The OpenAI embedding model `text-embedding-ada-002 uses 1536 dimensions`
docsearch = Dingo.from_documents(
    docs, embeddings, client=dingo_client, index_name=index_name
)
from langchain_community.document_loaders import TextLoader
from langchain_community.vectorstores import Dingo
from langchain_openai import OpenAIEmbeddings
from langchain_text_splitters import CharacterTextSplitter
query = "What did the president say about Ketanji Brown Jackson"
docs = docsearch.similarity_search(query)
print(docs[0].page_content)

向现有索引添加更多文本​

您可以使用 add_texts 函数将更多文本嵌入并更新到现有的 Dingo 索引中。

vectorstore = Dingo(embeddings, "text", client=dingo_client, index_name=index_name)

vectorstore.add_texts(["More text!"])

最大边际相关性搜索​

除了在检索器对象中使用相似性搜索外，您还可以使用 mmr 作为检索器。

retriever = docsearch.as_retriever(search_type="mmr")
matched_docs = retriever.invoke(query)
for i, d in enumerate(matched_docs):
    print(f"\n## Document {i}\n")
    print(d.page_content)

或者直接使用 max_marginal_relevance_search：

found_docs = docsearch.max_marginal_relevance_search(query, k=2, fetch_k=10)
for i, doc in enumerate(found_docs):
    print(f"{i + 1}.", doc.page_content, "\n")