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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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| .. | ||
| agents | ||
| callbacks | ||
| chains | ||
| indexes | ||
| memory | ||
| models | ||
| prompts | ||
| utils/examples | ||
| agents.rst | ||
| chains.rst | ||
| indexes.rst | ||
| memory.rst | ||
| models.rst | ||
| paul_graham_essay.txt | ||
| prompts.rst | ||
| state_of_the_union.txt | ||