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# 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](https://github.com/hwchase17/langchain/issues/5346)
cc: @dev2049
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Co-authored-by: Dev 2049 <dev.dev2049@gmail.com>
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| .. | ||
| analyticdb.ipynb | ||
| annoy.ipynb | ||
| atlas.ipynb | ||
| chroma.ipynb | ||
| deeplake.ipynb | ||
| docarray_hnsw.ipynb | ||
| docarray_in_memory.ipynb | ||
| elasticsearch.ipynb | ||
| faiss.ipynb | ||
| lancedb.ipynb | ||
| matchingengine.ipynb | ||
| milvus.ipynb | ||
| mongodb_atlas_vector_search.ipynb | ||
| myscale.ipynb | ||
| opensearch.ipynb | ||
| pgvector.ipynb | ||
| pinecone.ipynb | ||
| qdrant.ipynb | ||
| redis.ipynb | ||
| sklearn.ipynb | ||
| supabase.ipynb | ||
| tair.ipynb | ||
| typesense.ipynb | ||
| vectara.ipynb | ||
| weaviate.ipynb | ||
| zilliz.ipynb | ||