in ElasticKnnSearch added back create_index, add_texts, from_texts
Labels: name: auto:improvement

Fixes: 
https://github.com/hwchase17/langchain/issues/7117
https://github.com/hwchase17/langchain/issues/5760

Adding back `create_index` , `add_texts`, `from_texts` to ElasticKnnSearch

`from_texts` matches standard `from_texts` methods as quick start up method

`knn_search`  and `hybrid_result` return a list of [`Document()`, `score`,]

# Test `from_texts` for quick start
```
# create new index using from_text

from langchain.vectorstores.elastic_vector_search import ElasticKnnSearch
from langchain.embeddings import ElasticsearchEmbeddings

model_id = "sentence-transformers__all-distilroberta-v1" 
dims = 768
es_cloud_id = ""
es_user = ""
es_password = ""
test_index = "knn_test_index_305"

embeddings = ElasticsearchEmbeddings.from_credentials(
    model_id,
    #input_field=input_field,
    es_cloud_id=es_cloud_id,
    es_user=es_user,
    es_password=es_password,
)

# add texts and create class instance
texts = ["This is a test document", "This is another test document"]
knnvectorsearch = ElasticKnnSearch.from_texts(
    texts=texts,
    embedding=embeddings,
    index_name= test_index,
    vector_query_field='vector',
    query_field='text',
    model_id=model_id,
    dims=dims,
	es_cloud_id=es_cloud_id, 
	es_user=es_user, 
	es_password=es_password
)

# Test `add_texts` method
texts2 = ["Hello, world!", "Machine learning is fun.", "I love Python."]
knnvectorsearch.add_texts(texts2)

query = "Hello"
knn_result = knnvectorsearch.knn_search(query = query, model_id= model_id, k=2)

hybrid_result = knnvectorsearch.knn_hybrid_search(query = query, model_id= model_id, k=2)

```

The  mapping is as follows:
```
{
  "knn_test_index_012": {
    "mappings": {
      "properties": {
        "text": {
          "type": "text"
        },
        "vector": {
          "type": "dense_vector",
          "dims": 768,
          "index": true,
          "similarity": "dot_product"
        }
      }
    }
  }
}
```

# Check response type
```
>>> hybrid_result
[(Document(page_content='Hello, world!', metadata={}), 0.94232327), (Document(page_content='I love Python.', metadata={}), 0.5321523)]

>>> hybrid_result[0]
(Document(page_content='Hello, world!', metadata={}), 0.94232327)

>>> hybrid_result[0][0]
Document(page_content='Hello, world!', metadata={})

>>> type(hybrid_result[0][0])
<class 'langchain.schema.document.Document'>
```

# Test with existing Index
```
from langchain.vectorstores.elastic_vector_search import ElasticKnnSearch
from langchain.embeddings import ElasticsearchEmbeddings

## Initialize ElasticsearchEmbeddings
model_id = "sentence-transformers__all-distilroberta-v1" 
dims = 768
es_cloud_id = 
es_user = ""
es_password = ""
test_index = "knn_test_index_012"

embeddings = ElasticsearchEmbeddings.from_credentials(
    model_id,
    es_cloud_id=es_cloud_id,
    es_user=es_user,
    es_password=es_password,
)

## Initialize ElasticKnnSearch
knn_search = ElasticKnnSearch(
	es_cloud_id=es_cloud_id, 
	es_user=es_user, 
	es_password=es_password, 
	index_name= test_index, 
	embedding= embeddings
)


## Test adding vectors

### Test `add_texts` method when index created
texts = ["Hello, world!", "Machine learning is fun.", "I love Python."]
knn_search.add_texts(texts)

```