forked from Archives/langchain
OpenSearch: Add Support for Boolean Filter with ANN search (#3038)
### Description Add Support for Boolean Filter with ANN search Documentation - https://opensearch.org/docs/latest/search-plugins/knn/filter-search-knn/#boolean-filter-with-ann-search ### Issues Resolved https://github.com/hwchase17/langchain/issues/2924 Signed-off-by: Naveen Tatikonda <navtat@amazon.com>
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@ -146,6 +146,28 @@ def _default_approximate_search_query(
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}
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def _approximate_search_query_with_boolean_filter(
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query_vector: List[float],
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boolean_filter: Dict,
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size: int = 4,
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k: int = 4,
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vector_field: str = "vector_field",
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subquery_clause: str = "must",
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) -> Dict:
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"""For Approximate k-NN Search, with Boolean Filter."""
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return {
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"size": size,
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"query": {
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"bool": {
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"filter": boolean_filter,
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subquery_clause: [
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{"knn": {vector_field: {"vector": query_vector, "k": k}}}
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],
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}
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},
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}
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def _default_script_query(
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query_vector: List[float],
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space_type: str = "l2",
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@ -317,6 +339,11 @@ class OpenSearchVectorSearch(VectorStore):
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size: number of results the query actually returns; default: 4
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boolean_filter: A Boolean filter consists of a Boolean query that
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contains a k-NN query and a filter
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subquery_clause: Query clause on the knn vector field; default: "must"
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Optional Args for Script Scoring Search:
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search_type: "script_scoring"; default: "approximate_search"
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@ -339,8 +366,16 @@ class OpenSearchVectorSearch(VectorStore):
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text_field = _get_kwargs_value(kwargs, "text_field", "text")
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metadata_field = _get_kwargs_value(kwargs, "metadata_field", "metadata")
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vector_field = _get_kwargs_value(kwargs, "vector_field", "vector_field")
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if search_type == "approximate_search":
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size = _get_kwargs_value(kwargs, "size", 4)
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boolean_filter = _get_kwargs_value(kwargs, "boolean_filter", {})
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subquery_clause = _get_kwargs_value(kwargs, "subquery_clause", "must")
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if boolean_filter != {}:
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search_query = _approximate_search_query_with_boolean_filter(
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embedding, boolean_filter, size, k, vector_field, subquery_clause
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)
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else:
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search_query = _default_approximate_search_query(
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embedding, size, k, vector_field
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)
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@ -150,3 +150,17 @@ def test_opensearch_embedding_size_zero() -> None:
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OpenSearchVectorSearch.from_texts(
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[], FakeEmbeddings(), opensearch_url=DEFAULT_OPENSEARCH_URL
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)
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def test_appx_search_with_boolean_filter() -> None:
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"""Test Approximate Search with Boolean Filter."""
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boolean_filter_val = {"bool": {"must": [{"term": {"text": "bar"}}]}}
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docsearch = OpenSearchVectorSearch.from_texts(
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texts,
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FakeEmbeddings(),
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opensearch_url=DEFAULT_OPENSEARCH_URL,
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)
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output = docsearch.similarity_search(
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"foo", k=3, boolean_filter=boolean_filter_val, subquery_clause="should"
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)
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assert output == [Document(page_content="bar")]
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