· ☕ 1 分钟

https://easyperf.net/blog/2019/02/09/Top-Down-performance-analysis-methodology

In this post I show more formal way to do performance analysis. It’s called Top-down Microarchitecture Analysis Method (TMAM) (Intel® 64 and IA-32 Architectures Optimization Reference Manual, Appendix B.1). In this metodology we try to detect what was stalling our execution starting from the high-level components (like Front End, Back End, Retiring, Branch predictor) and narrowing down the source of performance inefficiencies.

It’s an iterative process with 2 steps:

· ☕ 2 分钟

https://redis.io/topics/acl

ACL

The Redis ACL, short for Access Control List, is the feature that allows certain connections to be limited in terms of the commands that can be executed and the keys that can be accessed. The way it works is that, after connecting, a client is required to authenticate providing a username and a valid password: if the authentication stage succeeded, the connection is associated with a given user and the limits the user has. Redis can be configured so that new connections are already authenticated with a “default” user (this is the default configuration), so configuring the default user has, as a side effect, the ability to provide only a specific subset of functionalities to connections that are not explicitly authenticated.

· ☕ 10 分钟

https://redis.io/topics/cluster-spec

Implemented subset

Redis Cluster implements a concept called hash tags that can be used in order to force certain keys to be stored in the same hash slot. However during manual resharding, multi-key operations may become unavailable for some time while single key operations are always available.

Clients and Servers roles in the Redis Cluster protocol

In Redis Cluster nodes are responsible for holding the data, and taking the state of the cluster, including mapping keys to the right nodes. Cluster nodes are also able to auto-discover other nodes, detect non-working nodes, and promote replica nodes to master when needed in order to continue to operate when a failure occurs.

· ☕ 1 分钟

  • 微服务各种流控技术
    *

  • 分布式一致性技术

    • 锁
    • 数据一致性协议

  • Java

    • 网络 Netty
    • Thread Pool

  • DB
    *

· ☕ 0 分钟
· ☕ 6 分钟 
 1
 2
 3
 4
 5
 6
 7
 8
 9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30

from langchain.llms import Ollama
from langchain.embeddings import OllamaEmbeddings
from langchain.chains import RetrievalQA
from langchain.vectorstores import FAISS
from langchain.text_splitter import CharacterTextSplitter
from langchain.document_loaders import TextLoader

# 加载本地模型
llm = Ollama(model="llama2")
embeddings = OllamaEmbeddings(model="llama2")

# 加载文档
loader = TextLoader("your_knowledge_base.txt")
documents = loader.load()

# 切分文本
text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
texts = text_splitter.split_documents(documents)

# 创建向量数据库
db = FAISS.from_documents(texts, embeddings)

# 创建问答链
qa = RetrievalQA.from_chain_type(llm=llm, chain_type="stuff", retriever=db.as_retriever())

# 提问
query = "你的问题是什么？"
result = qa.run(query)

print(result)

FAISS.from_documents()

上面程序中，FAISS.from_documents 是什么作用？