Infinispan
Infinispan is an open-source key-value data grid, it can work as single node as well as distributed.
Vector search is supported since release 15.xFor more:Infinispan Home
# Ensure that all we need is installed
# You may want to skip this
%pip install sentence-transformers
%pip install langchain
%pip install langchain_core
%pip install langchain_community
Setup
To run this demo we need a running Infinispan instance without authentication and a data file.In the next three cells we're going to:
- download the data file
- create the configuration
- run Infinispan in docker
%%bash
#get an archive of news
wget https://raw.githubusercontent.com/rigazilla/infinispan-vector/main/bbc_news.csv.gz
%%bash
#create infinispan configuration file
echo 'infinispan:
cache-container:
name: default
transport:
cluster: cluster
stack: tcp
server:
interfaces:
interface:
name: public
inet-address:
value: 0.0.0.0
socket-bindings:
default-interface: public
port-offset: 0
socket-binding:
name: default
port: 11222
endpoints:
endpoint:
socket-binding: default
rest-connector:
' > infinispan-noauth.yaml
!docker rm--force infinispanvs-demo
!docker run-d--name infinispanvs-demo-v $(pwd):/user-config-p11222:11222 infinispan/server:15.0-c/user-config/infinispan-noauth.yaml
The Code
Pick up an embedding model
In this demo we're usinga HuggingFace embedding mode.
from langchain_core.embeddingsimport Embeddings
from langchain_huggingfaceimport HuggingFaceEmbeddings
model_name="sentence-transformers/all-MiniLM-L12-v2"
hf= HuggingFaceEmbeddings(model_name=model_name)
Setup Infinispan cache
Infinispan is a very flexible key-value store, it can store raw bits as well as complex data type.User has complete freedom in the datagrid configuration, but for simple data type everything is automaticallyconfigured by the python layer. We take advantage of this feature so we can focus on our application.
Prepare the data
In this demo we rely on the default configuration, thus texts, metadatas and vectors in the same cache, but other options are possible: i.e. content can be store somewhere else and vector store could contain only a reference to the actual content.
import csv
import gzip
import time
# Open the news file and process it as a csv
with gzip.open("bbc_news.csv.gz","rt", newline="")as csvfile:
spamreader= csv.reader(csvfile, delimiter=",", quotechar='"')
i=0
texts=[]
metas=[]
embeds=[]
for rowin spamreader:
# first and fifth values are joined to form the content
# to be processed
text= row[0]+"."+ row[4]
texts.append(text)
# Store text and title as metadata
meta={"text": row[4],"title": row[0]}
metas.append(meta)
i= i+1
# Change this to change the number of news you want to load
if i>=5000:
break
Populate the vector store
# add texts and fill vector db
from langchain_community.vectorstoresimport InfinispanVS
ispnvs= InfinispanVS.from_texts(texts, hf, metas)
An helper func that prints the result documents
By default InfinispanVS returns the protobufŧext field in theDocument.page_contentand all the remaining protobuf fields (except the vector) in themetadata. This behaviour isconfigurable via lambda functions at setup.
defprint_docs(docs):
for res, iinzip(docs,range(len(docs))):
print("----"+str(i+1)+"----")
print("TITLE: "+ res.metadata["title"])
print(res.page_content)
Try it!!!
Below some sample queries
docs= ispnvs.similarity_search("European nations",5)
print_docs(docs)
print_docs(ispnvs.similarity_search("Milan fashion week begins",2))
print_docs(ispnvs.similarity_search("Stock market is rising today",4))
print_docs(ispnvs.similarity_search("Why cats are so viral?",2))
print_docs(ispnvs.similarity_search("How to stay young",5))
!docker rm--force infinispanvs-demo
Related
- Vector storeconceptual guide
- Vector storehow-to guides