[OpenSearch] Add Self Query Retriever Support to OpenSearch (#11184)

### Description
Add Self Query Retriever Support to OpenSearch

### Maintainers
@rlancemartin, @eyurtsev, @navneet1v

### Twitter Handle
@OpenSearchProj

Signed-off-by: Naveen Tatikonda <navtat@amazon.com>

libs/langchain/langchain/retrievers/self_query/base.py

@@ -14,6 +14,7 @@ from langchain.retrievers.self_query.deeplake import DeepLakeTranslator
 from langchain.retrievers.self_query.elasticsearch import ElasticsearchTranslator
 from langchain.retrievers.self_query.milvus import MilvusTranslator
 from langchain.retrievers.self_query.myscale import MyScaleTranslator
+from langchain.retrievers.self_query.opensearch import OpenSearchTranslator
 from langchain.retrievers.self_query.pinecone import PineconeTranslator
 from langchain.retrievers.self_query.qdrant import QdrantTranslator
 from langchain.retrievers.self_query.redis import RedisTranslator
@@ -30,6 +31,7 @@ from langchain.vectorstores import (
     ElasticsearchStore,
     Milvus,
     MyScale,
+    OpenSearchVectorSearch,
     Pinecone,
     Qdrant,
     Redis,
@@ -56,6 +58,7 @@ def _get_builtin_translator(vectorstore: VectorStore) -> Visitor:
         Milvus: MilvusTranslator,
         SupabaseVectorStore: SupabaseVectorTranslator,
         TimescaleVector: TimescaleVectorTranslator,
+        OpenSearchVectorSearch: OpenSearchTranslator,
     }
     if isinstance(vectorstore, Qdrant):
         return QdrantTranslator(metadata_key=vectorstore.metadata_payload_key)

libs/langchain/langchain/retrievers/self_query/opensearch.py

@@ -0,0 +1,84 @@
+from typing import Dict, Tuple, Union
+
+from langchain.chains.query_constructor.ir import (
+    Comparator,
+    Comparison,
+    Operation,
+    Operator,
+    StructuredQuery,
+    Visitor,
+)
+
+
+class OpenSearchTranslator(Visitor):
+    """Translate `OpenSearch` internal query domain-specific
+    language elements to valid filters."""
+
+    allowed_comparators = [
+        Comparator.EQ,
+        Comparator.LT,
+        Comparator.LTE,
+        Comparator.GT,
+        Comparator.GTE,
+        Comparator.CONTAIN,
+        Comparator.LIKE,
+    ]
+    """Subset of allowed logical comparators."""
+
+    allowed_operators = [Operator.AND, Operator.OR, Operator.NOT]
+    """Subset of allowed logical operators."""
+
+    def _format_func(self, func: Union[Operator, Comparator]) -> str:
+        self._validate_func(func)
+        comp_operator_map = {
+            Comparator.EQ: "term",
+            Comparator.LT: "lt",
+            Comparator.LTE: "lte",
+            Comparator.GT: "gt",
+            Comparator.GTE: "gte",
+            Comparator.CONTAIN: "match",
+            Comparator.LIKE: "fuzzy",
+            Operator.AND: "must",
+            Operator.OR: "should",
+            Operator.NOT: "must_not",
+        }
+        return comp_operator_map[func]
+
+    def visit_operation(self, operation: Operation) -> Dict:
+        args = [arg.accept(self) for arg in operation.arguments]
+
+        return {"bool": {self._format_func(operation.operator): args}}
+
+    def visit_comparison(self, comparison: Comparison) -> Dict:
+        field = f"metadata.{comparison.attribute}"
+
+        if comparison.comparator in [
+            Comparator.LT,
+            Comparator.LTE,
+            Comparator.GT,
+            Comparator.GTE,
+        ]:
+            return {
+                "range": {
+                    field: {self._format_func(comparison.comparator): comparison.value}
+                }
+            }
+
+        if comparison.comparator == Comparator.LIKE:
+            return {
+                self._format_func(comparison.comparator): {
+                    field: {"value": comparison.value}
+                }
+            }
+        field = f"{field}.keyword" if isinstance(comparison.value, str) else field
+
+        return {self._format_func(comparison.comparator): {field: comparison.value}}
+
+    def visit_structured_query(
+        self, structured_query: StructuredQuery
+    ) -> Tuple[str, dict]:
+        if structured_query.filter is None:
+            kwargs = {}
+        else:
+            kwargs = {"filter": structured_query.filter.accept(self)}
+        return structured_query.query, kwargs

libs/langchain/langchain/vectorstores/opensearch_vector_search.py

@@ -341,6 +341,7 @@ class OpenSearchVectorSearch(VectorStore):
         http_auth = _get_kwargs_value(kwargs, "http_auth", None)
         self.is_aoss = _is_aoss_enabled(http_auth=http_auth)
         self.client = _get_opensearch_client(opensearch_url, **kwargs)
+        self.engine = _get_kwargs_value(kwargs, "engine", None)
 
     @property
     def embeddings(self) -> Embeddings:
@@ -528,6 +529,7 @@ class OpenSearchVectorSearch(VectorStore):
         search_type = _get_kwargs_value(kwargs, "search_type", "approximate_search")
         vector_field = _get_kwargs_value(kwargs, "vector_field", "vector_field")
         index_name = _get_kwargs_value(kwargs, "index_name", self.index_name)
+        filter = _get_kwargs_value(kwargs, "filter", {})
 
         if (
             self.is_aoss
@@ -564,6 +566,17 @@ class OpenSearchVectorSearch(VectorStore):
                     "is invalid. `lucene_filter` is deprecated"
                 )
 
+            if (
+                efficient_filter == {}
+                and boolean_filter == {}
+                and lucene_filter == {}
+                and filter != {}
+            ):
+                if self.engine in ["faiss", "lucene"]:
+                    efficient_filter = filter
+                else:
+                    boolean_filter = filter
+
             if boolean_filter != {}:
                 search_query = _approximate_search_query_with_boolean_filter(
                     embedding,
@@ -745,6 +758,7 @@ class OpenSearchVectorSearch(VectorStore):
         max_chunk_bytes = _get_kwargs_value(kwargs, "max_chunk_bytes", 1 * 1024 * 1024)
         http_auth = _get_kwargs_value(kwargs, "http_auth", None)
         is_aoss = _is_aoss_enabled(http_auth=http_auth)
+        engine = None
 
         if is_aoss and not is_appx_search:
             raise ValueError(
@@ -782,4 +796,5 @@ class OpenSearchVectorSearch(VectorStore):
             max_chunk_bytes=max_chunk_bytes,
             is_aoss=is_aoss,
         )
+        kwargs["engine"] = engine
         return cls(opensearch_url, index_name, embedding, **kwargs)

libs/langchain/tests/unit_tests/retrievers/self_query/test_opensearch.py

@@ -0,0 +1,87 @@
+from langchain.chains.query_constructor.ir import (
+    Comparator,
+    Comparison,
+    Operation,
+    Operator,
+    StructuredQuery,
+)
+from langchain.retrievers.self_query.opensearch import OpenSearchTranslator
+
+DEFAULT_TRANSLATOR = OpenSearchTranslator()
+
+
+def test_visit_comparison() -> None:
+    comp = Comparison(comparator=Comparator.EQ, attribute="foo", value="10")
+    expected = {"term": {"metadata.foo.keyword": "10"}}
+    actual = DEFAULT_TRANSLATOR.visit_comparison(comp)
+    assert expected == actual
+
+
+def test_visit_operation() -> None:
+    op = Operation(
+        operator=Operator.AND,
+        arguments=[
+            Comparison(comparator=Comparator.GTE, attribute="bar", value=5),
+            Comparison(comparator=Comparator.LT, attribute="bar", value=10),
+            Comparison(comparator=Comparator.EQ, attribute="baz", value="abcd"),
+        ],
+    )
+    expected = {
+        "bool": {
+            "must": [
+                {"range": {"metadata.bar": {"gte": 5}}},
+                {"range": {"metadata.bar": {"lt": 10}}},
+                {"term": {"metadata.baz.keyword": "abcd"}},
+            ]
+        }
+    }
+    actual = DEFAULT_TRANSLATOR.visit_operation(op)
+    assert expected == actual
+
+
+def test_visit_structured_query() -> None:
+    query = "What is the capital of France?"
+    operation = Operation(
+        operator=Operator.AND,
+        arguments=[
+            Comparison(comparator=Comparator.EQ, attribute="foo", value="20"),
+            Operation(
+                operator=Operator.OR,
+                arguments=[
+                    Comparison(comparator=Comparator.LTE, attribute="bar", value=7),
+                    Comparison(
+                        comparator=Comparator.LIKE, attribute="baz", value="abc"
+                    ),
+                ],
+            ),
+        ],
+    )
+    structured_query = StructuredQuery(query=query, filter=operation, limit=None)
+    expected = (
+        query,
+        {
+            "filter": {
+                "bool": {
+                    "must": [
+                        {"term": {"metadata.foo.keyword": "20"}},
+                        {
+                            "bool": {
+                                "should": [
+                                    {"range": {"metadata.bar": {"lte": 7}}},
+                                    {
+                                        "fuzzy": {
+                                            "metadata.baz": {
+                                                "value": "abc",
+                                            }
+                                        }
+                                    },
+                                ]
+                            }
+                        },
+                    ]
+                }
+            }
+        },
+    )
+    actual = DEFAULT_TRANSLATOR.visit_structured_query(structured_query)
+    assert expected == actual