# ElasticSearch Search

*author: Jael*

<br />

## Description

The search operator runs a search via ElasticSearch client given a query.
It takes a dictionary as input, which should be written in [Query DSL](https://www.elastic.co/guide/en/elasticsearch/reference/current/query-dsl.html).
To use this operator, you need to [set up ElasticSearch](https://www.elastic.co/guide/en/elasticsearch/reference/current/setup.html) in advance.

<br />

## Code Example

With an example ElasticSearch client 'localhost:9200',
search across the index 'test_index' using an example query `{'match_all': {}}` to get all documents.

```python
from towhee import pipe, ops, DataCollection


query = {'match_all': {}}
es_search = (
    pipe.input('index_name', 'query')
        .map(('index_name', 'query'), 'res', ops.elasticsearch.osschat_search(
            host='localhost', port=9200
        ))
        .output('query', 'res')
)

res = es_search('test_index', query)
DataCollection(res).show() # Optional: display output data
```

<br />

## Factory Constructor

Create the operator via the following factory method:

***elasticsearch.search(host='localhost', port=9200, user=None, password=None, ca_certs=None)***

**Parameters:**

***host***: *str*

The host to connect ElasticSearch client.

***port***: *int*

The port to connect ElasticSearch client.

***user***: *str*

The username to connect ElasticSearch client if needed, defaults to None.

***password***: *str*

The user password to connect ElasticSearch client if needed, defaults to None.

***ca_certs***: *str*

The path to CA certificates to connect ElasticSearch client if needed, defaults to None.


<br />

**Returns:**

Search results wrapped by `elastic_transport.ObjectApiResponse`.


# More Resources

- [Semantic Search with Milvus and OpenAI - Zilliz blog](https://zilliz.com/learn/semantic-search-with-milvus-and-openai): In this guide, we’ll explore semantic search capabilities through the integration of Milvus and OpenAI’s Embedding API, using a book title search as an example use case.
- [Accelerate Similarity Search on Big Data with Vector Indexing - II - Zilliz blog](https://zilliz.com/learn/index-overview-part-2): Discover how indexes dramatically accelerate vector similarity search, different types of indexes, and how to choose the right index for your next AI application.
- [What Is Semantic Search?](https://zilliz.com/glossary/semantic-search): Semantic search is a search technique that uses natural language processing (NLP) and machine learning (ML) to understand the context and meaning behind a user's search query.
- [ArXiv Papers Vector Similarity Search with Milvus 2.1 - Zilliz blog](https://zilliz.com/blog/Arxiv-scientific-papers-vector-similarity-search): Run semantic search queries on ~640K papers in <50ms using Dask, SBERT SPECTRE, and Milvus Vector database
- [Mastering BM25: A Deep Dive into the Algorithm and Its Application in Milvus - Zilliz blog](https://zilliz.com/learn/mastering-bm25-a-deep-dive-into-the-algorithm-and-application-in-milvus): We can easily implement the BM25 algorithm to turn a document and a query into a sparse vector with Milvus. Then, these sparse vectors can be used for vector search to find the most relevant documents according to a specific query.
- [The 2024 Playbook: Top Use Cases for Vector Search - Zilliz blog](https://zilliz.com/learn/top-use-cases-for-vector-search): An exploration of vector search technologies and their most popular use cases.
- [Simplifying Legal Research with RAG, Milvus, and Ollama - Zilliz blog](https://zilliz.com/blog/simplifying-legal-research-with-rag-milvus-ollama): In this blog post, we will see how we can apply RAG to Legal data. Legal research can be time-consuming. You usually need to review a large number of documents to find the answers you need. Retrieval-Augmented Generation (RAG) can help streamline your research process.
- [Evolution of Search: From Keyword Matching to Vector Search and GenAI - Zilliz blog](https://zilliz.com/learn/evolution-of-search-from-traditional-keyword-matching-to-vector-search-and-genai): Explores the evolution of search, the limitations of keyword-matching systems, and how vector search and GenAI are setting new standards for modern search.