ElasticSearch BM25 #
This notebook goes over how to use a retriever that under the hood uses ElasticSearcha and BM25.
For more information on the details of BM25 see this blog post (opens in a new tab) .
from langchain.retrievers import ElasticSearchBM25Retriever
Create New Retriever #
elasticsearch_url="http://localhost:9200"
retriever = ElasticSearchBM25Retriever.create(elasticsearch_url, "langchain-index-4")
# Alternatively, you can load an existing index
# import elasticsearch
# elasticsearch_url="http://localhost:9200"
# retriever = ElasticSearchBM25Retriever(elasticsearch.Elasticsearch(elasticsearch_url), "langchain-index")
Add texts (if necessary) #
We can optionally add texts to the retriever (if they aren’t already in there)
retriever.add_texts(["foo", "bar", "world", "hello", "foo bar"])
['cbd4cb47-8d9f-4f34-b80e-ea871bc49856',
'f3bd2e24-76d1-4f9b-826b-ec4c0e8c7365',
'8631bfc8-7c12-48ee-ab56-8ad5f373676e',
'8be8374c-3253-4d87-928d-d73550a2ecf0',
'd79f457b-2842-4eab-ae10-77aa420b53d7']
Use Retriever #
We can now use the retriever!
result = retriever.get_relevant_documents("foo")
result
[Document(page_content='foo', metadata={}),
Document(page_content='foo bar', metadata={})]