Indexer¶

from semantic_ai.indexer import ElasticsearchIndexer
from semantic_ai.embeddings.huggingface import HFEmbeddings

embeddings = await HFEmbeddings().embed()
elastic_search = await ElasticsearchIndexer(
        url="http://localhost:9200",
        index_name="test_index",
        embedding=embeddings
).create()

Index:

Before index, we need to extract the content from the documents and constructed as json format. Here we can use the DF Extraction. Once we extracted the data using df-extraction and converted as json format we can start to index.

await elastic_search.index("/home/test/sample.json")

If we lots of files, stored into directory and we can use directory path in index

await elastic_search.index("/home/test/")

You can check your elasticsearch vector database its indexing or not.