Es knn index search 5346 (langchain-ai#5569)

# Create elastic_vector_search.ElasticKnnSearch class

This extends `langchain/vectorstores/elastic_vector_search.py` by adding
a new class `ElasticKnnSearch`

Features:
- Allow creating an index with the `dense_vector` mapping compataible
with kNN search
- Store embeddings in index for use with kNN search (correct mapping
creates HNSW data structure)
- Perform approximate kNN search
- Perform hybrid BM25 (`query{}`) + kNN (`knn{}`) search
- perform knn search by either providing a `query_vector` or passing a
hosted `model_id` to use query_vector_builder to automatically generate
a query_vector at search time

Connection options
- Using `cloud_id` from Elastic Cloud
- Passing elasticsearch client object

search options
- query
- k
- query_vector
- model_id
- size
- source
- knn_boost (hybrid search)
- query_boost (hybrid search)
- fields


This also adds examples to
`docs/modules/indexes/vectorstores/examples/elasticsearch.ipynb`


Fixes # [5346](langchain-ai#5346)

cc: @dev2049

 -->

---------

Co-authored-by: Dev 2049 <[email protected]>

.gitignore

@@ -149,4 +149,7 @@ wandb/
 
 # integration test artifacts
 data_map*
-\[('_type', 'fake'), ('stop', None)]
+\[('_type', 'fake'), ('stop', None)]
+
+# Replit files
+*replit*