[ OpenSearch ] : Add AOSS Support to OpenSearch (#8256)

### Description

This PR includes the following changes:

- Adds AOSS (Amazon OpenSearch Service Serverless) support to
OpenSearch. Please refer to the documentation on how to use it.
- While creating an index, AOSS only supports Approximate Search with
`nmslib` and `faiss` engines. During Search, only Approximate Search and
Script Scoring (on doc values) are supported.
- This PR also adds support to `efficient_filter` which can be used with
`faiss` and `lucene` engines.
- The `lucene_filter` is deprecated. Instead please use the
`efficient_filter` for the lucene engine.


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

docs/extras/integrations/vectorstores/opensearch.ipynb

@@ -315,6 +315,101 @@
     "    metadata_field=\"message_metadata\",\n",
     ")"
    ]
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "## Using AOSS (Amazon OpenSearch Service Serverless)"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "outputs": [],
+   "source": [
+    "# This is just an example to show how to use AOSS with faiss engine and efficient_filter, you need to set proper values.\n",
+    "\n",
+    "service = 'aoss' # must set the service as 'aoss'\n",
+    "region = 'us-east-2'\n",
+    "credentials = boto3.Session(aws_access_key_id='xxxxxx',aws_secret_access_key='xxxxx').get_credentials()\n",
+    "awsauth = AWS4Auth('xxxxx', 'xxxxxx', region,service, session_token=credentials.token)\n",
+    "\n",
+    "docsearch = OpenSearchVectorSearch.from_documents(\n",
+    "    docs,\n",
+    "    embeddings,\n",
+    "    opensearch_url=\"host url\",\n",
+    "    http_auth=awsauth,\n",
+    "    timeout = 300,\n",
+    "    use_ssl = True,\n",
+    "    verify_certs = True,\n",
+    "    connection_class = RequestsHttpConnection,\n",
+    "    index_name=\"test-index-using-aoss\",\n",
+    "    engine=\"faiss\",\n",
+    ")\n",
+    "\n",
+    "docs = docsearch.similarity_search(\n",
+    "    \"What is feature selection\",\n",
+    "     efficient_filter=filter,\n",
+    "     k=200,\n",
+    ")"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "## Using AOS (Amazon OpenSearch Service)"
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "outputs": [],
+   "source": [
+    "# This is just an example to show how to use AOS , you need to set proper values.\n",
+    "\n",
+    "service = 'es' # must set the service as 'es'\n",
+    "region = 'us-east-2'\n",
+    "credentials = boto3.Session(aws_access_key_id='xxxxxx',aws_secret_access_key='xxxxx').get_credentials()\n",
+    "awsauth = AWS4Auth('xxxxx', 'xxxxxx', region,service, session_token=credentials.token)\n",
+    "\n",
+    "docsearch = OpenSearchVectorSearch.from_documents(\n",
+    "    docs,\n",
+    "    embeddings,\n",
+    "    opensearch_url=\"host url\",\n",
+    "    http_auth=awsauth,\n",
+    "    timeout = 300,\n",
+    "    use_ssl = True,\n",
+    "    verify_certs = True,\n",
+    "    connection_class = RequestsHttpConnection,\n",
+    "    index_name=\"test-index\",\n",
+    ")\n",
+    "\n",
+    "docs = docsearch.similarity_search(\n",
+    "    \"What is feature selection\",\n",
+    "     k=200,\n",
+    ")"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
   }
  ],
  "metadata": {

libs/langchain/langchain/vectorstores/opensearch_vector_search.py

@@ -2,6 +2,7 @@
 from __future__ import annotations
 
 import uuid
+import warnings
 from typing import Any, Dict, Iterable, List, Optional, Tuple
 
 import numpy as np
@@ -71,6 +72,26 @@ def _validate_embeddings_and_bulk_size(embeddings_length: int, bulk_size: int) -
         )
 
 
+def _validate_aoss_with_engines(is_aoss: bool, engine: str) -> None:
+    """Validate AOSS with the engine."""
+    if is_aoss and engine != "nmslib" and engine != "faiss":
+        raise ValueError(
+            "Amazon OpenSearch Service Serverless only "
+            "supports `nmslib` or `faiss` engines"
+        )
+
+
+def _is_aoss_enabled(http_auth: Any) -> bool:
+    """Check if the service is http_auth is set as `aoss`."""
+    if (
+        http_auth is not None
+        and http_auth.service is not None
+        and http_auth.service == "aoss"
+    ):
+        return True
+    return False
+
+
 def _bulk_ingest_embeddings(
     client: Any,
     index_name: str,
@@ -82,6 +103,7 @@ def _bulk_ingest_embeddings(
     text_field: str = "text",
     mapping: Optional[Dict] = None,
     max_chunk_bytes: Optional[int] = 1 * 1024 * 1024,
+    is_aoss: bool = False,
 ) -> List[str]:
     """Bulk Ingest Embeddings into given index."""
     if not mapping:
@@ -107,12 +129,16 @@ def _bulk_ingest_embeddings(
             vector_field: embeddings[i],
             text_field: text,
             "metadata": metadata,
-            "_id": _id,
         }
+        if is_aoss:
+            request["id"] = _id
+        else:
+            request["_id"] = _id
         requests.append(request)
         return_ids.append(_id)
     bulk(client, requests, max_chunk_bytes=max_chunk_bytes)
-    client.indices.refresh(index=index_name)
+    if not is_aoss:
+        client.indices.refresh(index=index_name)
     return return_ids
 
 
@@ -192,17 +218,18 @@ def _approximate_search_query_with_boolean_filter(
     }
 
 
-def _approximate_search_query_with_lucene_filter(
+def _approximate_search_query_with_efficient_filter(
     query_vector: List[float],
-    lucene_filter: Dict,
+    efficient_filter: Dict,
     k: int = 4,
     vector_field: str = "vector_field",
 ) -> Dict:
-    """For Approximate k-NN Search, with Lucene Filter."""
+    """For Approximate k-NN Search, with Efficient Filter for Lucene and
+    Faiss Engines."""
     search_query = _default_approximate_search_query(
         query_vector, k=k, vector_field=vector_field
     )
-    search_query["query"]["knn"][vector_field]["filter"] = lucene_filter
+    search_query["query"]["knn"][vector_field]["filter"] = efficient_filter
     return search_query
 
 
@@ -309,11 +336,13 @@ class OpenSearchVectorSearch(VectorStore):
         opensearch_url: str,
         index_name: str,
         embedding_function: Embeddings,
+        is_aoss: bool,
         **kwargs: Any,
     ):
         """Initialize with necessary components."""
         self.embedding_function = embedding_function
         self.index_name = index_name
+        self.is_aoss = is_aoss
         self.client = _get_opensearch_client(opensearch_url, **kwargs)
 
     @property
@@ -358,6 +387,8 @@ class OpenSearchVectorSearch(VectorStore):
         vector_field = _get_kwargs_value(kwargs, "vector_field", "vector_field")
         max_chunk_bytes = _get_kwargs_value(kwargs, "max_chunk_bytes", 1 * 1024 * 1024)
 
+        _validate_aoss_with_engines(self.is_aoss, engine)
+
         mapping = _default_text_mapping(
             dim, engine, space_type, ef_search, ef_construction, m, vector_field
         )
@@ -373,6 +404,7 @@ class OpenSearchVectorSearch(VectorStore):
             text_field=text_field,
             mapping=mapping,
             max_chunk_bytes=max_chunk_bytes,
+            is_aoss=self.is_aoss,
         )
 
     def similarity_search(
@@ -404,14 +436,18 @@ class OpenSearchVectorSearch(VectorStore):
         Optional Args for Approximate Search:
             search_type: "approximate_search"; default: "approximate_search"
 
-            boolean_filter: A Boolean filter consists of a Boolean query that
+            boolean_filter: A Boolean filter is a post filter consists of a Boolean
-            contains a k-NN query and a filter.
+            query that contains a k-NN query and a filter.
 
             subquery_clause: Query clause on the knn vector field; default: "must"
 
             lucene_filter: the Lucene algorithm decides whether to perform an exact
             k-NN search with pre-filtering or an approximate search with modified
-            post-filtering.
+            post-filtering. (deprecated, use `efficient_filter`)
+
+            efficient_filter: the Lucene Engine or Faiss Engine decides whether to
+            perform an exact k-NN search with pre-filtering or an approximate search
+            with modified post-filtering.
 
         Optional Args for Script Scoring Search:
             search_type: "script_scoring"; default: "approximate_search"
@@ -494,15 +530,41 @@ class OpenSearchVectorSearch(VectorStore):
         search_type = _get_kwargs_value(kwargs, "search_type", "approximate_search")
         vector_field = _get_kwargs_value(kwargs, "vector_field", "vector_field")
 
+        if (
+            self.is_aoss
+            and search_type != "approximate_search"
+            and search_type != SCRIPT_SCORING_SEARCH
+        ):
+            raise ValueError(
+                "Amazon OpenSearch Service Serverless only "
+                "supports `approximate_search` and `script_scoring`"
+            )
+
         if search_type == "approximate_search":
             boolean_filter = _get_kwargs_value(kwargs, "boolean_filter", {})
             subquery_clause = _get_kwargs_value(kwargs, "subquery_clause", "must")
+            efficient_filter = _get_kwargs_value(kwargs, "efficient_filter", {})
+            # `lucene_filter` is deprecated, added for Backwards Compatibility
             lucene_filter = _get_kwargs_value(kwargs, "lucene_filter", {})
-            if boolean_filter != {} and lucene_filter != {}:
+
+            if boolean_filter != {} and efficient_filter != {}:
                 raise ValueError(
-                    "Both `boolean_filter` and `lucene_filter` are provided which "
+                    "Both `boolean_filter` and `efficient_filter` are provided which "
                     "is invalid"
                 )
+
+            if lucene_filter != {} and efficient_filter != {}:
+                raise ValueError(
+                    "Both `lucene_filter` and `efficient_filter` are provided which "
+                    "is invalid. `lucene_filter` is deprecated"
+                )
+
+            if lucene_filter != {} and boolean_filter != {}:
+                raise ValueError(
+                    "Both `lucene_filter` and `boolean_filter` are provided which "
+                    "is invalid. `lucene_filter` is deprecated"
+                )
+
             if boolean_filter != {}:
                 search_query = _approximate_search_query_with_boolean_filter(
                     embedding,
@@ -511,8 +573,16 @@ class OpenSearchVectorSearch(VectorStore):
                     vector_field=vector_field,
                     subquery_clause=subquery_clause,
                 )
+            elif efficient_filter != {}:
+                search_query = _approximate_search_query_with_efficient_filter(
+                    embedding, efficient_filter, k=k, vector_field=vector_field
+                )
             elif lucene_filter != {}:
-                search_query = _approximate_search_query_with_lucene_filter(
+                warnings.warn(
+                    "`lucene_filter` is deprecated. Please use the keyword argument"
+                    " `efficient_filter`"
+                )
+                search_query = _approximate_search_query_with_efficient_filter(
                     embedding, lucene_filter, k=k, vector_field=vector_field
                 )
             else:
@@ -659,6 +729,7 @@ class OpenSearchVectorSearch(VectorStore):
             "ef_construction",
             "m",
             "max_chunk_bytes",
+            "is_aoss",
         ]
         embeddings = embedding.embed_documents(texts)
         _validate_embeddings_and_bulk_size(len(embeddings), bulk_size)
@@ -672,6 +743,15 @@ class OpenSearchVectorSearch(VectorStore):
         vector_field = _get_kwargs_value(kwargs, "vector_field", "vector_field")
         text_field = _get_kwargs_value(kwargs, "text_field", "text")
         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)
+
+        if is_aoss and not is_appx_search:
+            raise ValueError(
+                "Amazon OpenSearch Service Serverless only "
+                "supports `approximate_search`"
+            )
+
         if is_appx_search:
             engine = _get_kwargs_value(kwargs, "engine", "nmslib")
             space_type = _get_kwargs_value(kwargs, "space_type", "l2")
@@ -679,6 +759,8 @@ class OpenSearchVectorSearch(VectorStore):
             ef_construction = _get_kwargs_value(kwargs, "ef_construction", 512)
             m = _get_kwargs_value(kwargs, "m", 16)
 
+            _validate_aoss_with_engines(is_aoss, engine)
+
             mapping = _default_text_mapping(
                 dim, engine, space_type, ef_search, ef_construction, m, vector_field
             )
@@ -697,5 +779,6 @@ class OpenSearchVectorSearch(VectorStore):
             text_field=text_field,
             mapping=mapping,
             max_chunk_bytes=max_chunk_bytes,
+            is_aoss=is_aoss,
         )
-        return cls(opensearch_url, index_name, embedding, **kwargs)
+        return cls(opensearch_url, index_name, embedding, is_aoss, **kwargs)

libs/langchain/tests/integration_tests/vectorstores/test_opensearch.py

@@ -1,6 +1,8 @@
 """Test OpenSearch functionality."""
 
+import boto3
 import pytest
+from opensearchpy import AWSV4SignerAuth
 
 from langchain.docstore.document import Document
 from langchain.vectorstores.opensearch_vector_search import (
@@ -213,3 +215,95 @@ def test_opensearch_with_custom_field_name_appx_false() -> None:
     )
     output = docsearch.similarity_search("add", k=1)
     assert output == [Document(page_content="add")]
+
+
+def test_opensearch_serverless_with_scripting_search_indexing_throws_error() -> None:
+    """Test to validate indexing using Serverless without Approximate Search."""
+    region = "test-region"
+    service = "aoss"
+    credentials = boto3.Session().get_credentials()
+    auth = AWSV4SignerAuth(credentials, region, service)
+    with pytest.raises(ValueError):
+        OpenSearchVectorSearch.from_texts(
+            texts,
+            FakeEmbeddings(),
+            opensearch_url=DEFAULT_OPENSEARCH_URL,
+            is_appx_search=False,
+            http_auth=auth,
+        )
+
+
+def test_opensearch_serverless_with_lucene_engine_throws_error() -> None:
+    """Test to validate indexing using lucene engine with Serverless."""
+    region = "test-region"
+    service = "aoss"
+    credentials = boto3.Session().get_credentials()
+    auth = AWSV4SignerAuth(credentials, region, service)
+    with pytest.raises(ValueError):
+        OpenSearchVectorSearch.from_texts(
+            texts,
+            FakeEmbeddings(),
+            opensearch_url=DEFAULT_OPENSEARCH_URL,
+            engine="lucene",
+            http_auth=auth,
+        )
+
+
+def test_appx_search_with_efficient_and_bool_filter_throws_error() -> None:
+    """Test Approximate Search with Efficient and Bool Filter throws Error."""
+    efficient_filter_val = {"bool": {"must": [{"term": {"text": "baz"}}]}}
+    boolean_filter_val = {"bool": {"must": [{"term": {"text": "bar"}}]}}
+    docsearch = OpenSearchVectorSearch.from_texts(
+        texts, FakeEmbeddings(), opensearch_url=DEFAULT_OPENSEARCH_URL, engine="lucene"
+    )
+    with pytest.raises(ValueError):
+        docsearch.similarity_search(
+            "foo",
+            k=3,
+            efficient_filter=efficient_filter_val,
+            boolean_filter=boolean_filter_val,
+        )
+
+
+def test_appx_search_with_efficient_and_lucene_filter_throws_error() -> None:
+    """Test Approximate Search with Efficient and Lucene Filter throws Error."""
+    efficient_filter_val = {"bool": {"must": [{"term": {"text": "baz"}}]}}
+    lucene_filter_val = {"bool": {"must": [{"term": {"text": "bar"}}]}}
+    docsearch = OpenSearchVectorSearch.from_texts(
+        texts, FakeEmbeddings(), opensearch_url=DEFAULT_OPENSEARCH_URL, engine="lucene"
+    )
+    with pytest.raises(ValueError):
+        docsearch.similarity_search(
+            "foo",
+            k=3,
+            efficient_filter=efficient_filter_val,
+            lucene_filter=lucene_filter_val,
+        )
+
+
+def test_appx_search_with_boolean_and_lucene_filter_throws_error() -> None:
+    """Test Approximate Search with Boolean and Lucene Filter throws Error."""
+    boolean_filter_val = {"bool": {"must": [{"term": {"text": "baz"}}]}}
+    lucene_filter_val = {"bool": {"must": [{"term": {"text": "bar"}}]}}
+    docsearch = OpenSearchVectorSearch.from_texts(
+        texts, FakeEmbeddings(), opensearch_url=DEFAULT_OPENSEARCH_URL, engine="lucene"
+    )
+    with pytest.raises(ValueError):
+        docsearch.similarity_search(
+            "foo",
+            k=3,
+            boolean_filter=boolean_filter_val,
+            lucene_filter=lucene_filter_val,
+        )
+
+
+def test_appx_search_with_faiss_efficient_filter() -> None:
+    """Test Approximate Search with Faiss Efficient Filter."""
+    efficient_filter_val = {"bool": {"must": [{"term": {"text": "bar"}}]}}
+    docsearch = OpenSearchVectorSearch.from_texts(
+        texts, FakeEmbeddings(), opensearch_url=DEFAULT_OPENSEARCH_URL, engine="faiss"
+    )
+    output = docsearch.similarity_search(
+        "foo", k=3, efficient_filter=efficient_filter_val
+    )
+    assert output == [Document(page_content="bar")]