## 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.x For more: [Infinispan Home](https://infinispan.org/)

```
# 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 newswget https://raw.githubusercontent.com/rigazilla/infinispan-vector/main/bbc_news.csv.gz
```

```
%%bash#create infinispan configuration fileecho '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  -p 11222:11222 infinispan/server:15.0 -c /user-config/infinispan-noauth.yaml
```

## The Code

## Pick up an embedding model[​](#pick-up-an-embedding-model "Direct link to Pick up an embedding model")

In this demo we’re using a HuggingFace embedding mode.

```
from langchain.embeddings import HuggingFaceEmbeddingsfrom langchain_core.embeddings import Embeddingsmodel_name = "sentence-transformers/all-MiniLM-L12-v2"hf = HuggingFaceEmbeddings(model_name=model_name)
```

## Setup Infinispan cache[​](#setup-infinispan-cache "Direct link to 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 automatically configured by the python layer. We take advantage of this feature so we can focus on our application.

## Prepare the data[​](#prepare-the-data "Direct link to 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 csvimport gzipimport time# Open the news file and process it as a csvwith gzip.open("bbc_news.csv.gz", "rt", newline="") as csvfile:    spamreader = csv.reader(csvfile, delimiter=",", quotechar='"')    i = 0    texts = []    metas = []    embeds = []    for row in 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 dbfrom langchain_community.vectorstores import InfinispanVSispnvs = InfinispanVS.from_texts(texts, hf, metas)
```

## An helper func that prints the result documents

By default InfinispanVS returns the protobuf `ŧext` field in the `Document.page_content` and all the remaining protobuf fields (except the vector) in the `metadata`. This behaviour is configurable via lambda functions at setup.

```
def print_docs(docs):    for res, i in zip(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
```