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# OpenSearch

This notebook shows how to use functionality related to the OpenSearch database.

To run, you should have the opensearch instance up and running: here `similarity_search` by default performs the Approximate k-NN Search which uses one of the several algorithms like lucene, nmslib, faiss recommended for large datasets. To perform brute force search we have other search methods known as Script Scoring and Painless Scripting. Check this for more details.

    from langchain.embeddings.openai import OpenAIEmbeddings
    from langchain.text_splitter import CharacterTextSplitter
    from langchain.vectorstores import OpenSearchVectorSearch
    from langchain.document_loaders import TextLoader

    from langchain.document_loaders import TextLoader
    loader = TextLoader('../../../state_of_the_union.txt')
    documents = loader.load()
    text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
    docs = text_splitter.split_documents(documents)

    embeddings = OpenAIEmbeddings()

    docsearch = OpenSearchVectorSearch.from_documents(docs, embeddings, opensearch_url="http://localhost:9200")

    query = "What did the president say about Ketanji Brown Jackson"
    docs = docsearch.similarity_search(query)

    print(docs[0].page_content)

## similarity_search using Approximate k-NN Search with Custom Parameters

    docsearch = OpenSearchVectorSearch.from_documents(docs, embeddings, opensearch_url="http://localhost:9200", engine="faiss", space_type="innerproduct", ef_construction=256, m=48)

    query = "What did the president say about Ketanji Brown Jackson"
    docs = docsearch.similarity_search(query)

    print(docs[0].page_content)

## similarity_search using Script Scoring with Custom Parameters

    docsearch = OpenSearchVectorSearch.from_documents(docs, embeddings, opensearch_url="http://localhost:9200", is_appx_search=False)

    query = "What did the president say about Ketanji Brown Jackson"
    docs = docsearch.similarity_search("What did the president say about Ketanji Brown Jackson", k=1, search_type="script_scoring")

    print(docs[0].page_content)

## similarity_search using Painless Scripting with Custom Parameters

    docsearch = OpenSearchVectorSearch.from_documents(docs, embeddings, opensearch_url="http://localhost:9200", is_appx_search=False)
    filter = {"bool": {"filter": {"term": {"text": "smuggling"}}}}
    query = "What did the president say about Ketanji Brown Jackson"
    docs = docsearch.similarity_search("What did the president say about Ketanji Brown Jackson", search_type="painless_scripting", space_type="cosineSimilarity", pre_filter=filter)

    print(docs[0].page_content)

## Using a preexisting OpenSearch instance

It’s also possible to use a preexisting OpenSearch instance with documents that already have vectors present.

    # this is just an example, you would need to change these values to point to another opensearch instance
    docsearch = OpenSearchVectorSearch(index_name="index-*", embedding_function=embeddings, opensearch_url="http://localhost:9200")

    # you can specify custom field names to match the fields you're using to store your embedding, document text value, and metadata
    docs = docsearch.similarity_search("Who was asking about getting lunch today?", search_type="script_scoring", space_type="cosinesimil", vector_field="message_embedding", text_field="message", metadata_field="message_metadata")