Add Support for OpenSearch Vector database (#1191)

### Description
This PR adds a wrapper which adds support for the OpenSearch vector
database. Using opensearch-py client we are ingesting the embeddings of
given text into opensearch cluster using Bulk API. We can perform the
`similarity_search` on the index using the 3 popular searching methods
of OpenSearch k-NN plugin:

- `Approximate k-NN Search` use approximate nearest neighbor (ANN)
algorithms from the [nmslib](https://github.com/nmslib/nmslib),
[faiss](https://github.com/facebookresearch/faiss), and
[Lucene](https://lucene.apache.org/) libraries to power k-NN search.
- `Script Scoring` extends OpenSearch’s script scoring functionality to
execute a brute force, exact k-NN search.
- `Painless Scripting` adds the distance functions as painless
extensions that can be used in more complex combinations. Also, supports
brute force, exact k-NN search like Script Scoring.

### Issues Resolved 
https://github.com/hwchase17/langchain/issues/1054

---------

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

tests/integration_tests/vectorstores/test_opensearch.py

@@ -0,0 +1,128 @@
+"""Test OpenSearch functionality."""
+
+import pytest
+
+from langchain.docstore.document import Document
+from langchain.vectorstores.opensearch_vector_search import (
+    PAINLESS_SCRIPTING_SEARCH,
+    SCRIPT_SCORING_SEARCH,
+    OpenSearchVectorSearch,
+)
+from tests.integration_tests.vectorstores.fake_embeddings import FakeEmbeddings
+
+DEFAULT_OPENSEARCH_URL = "http://localhost:9200"
+texts = ["foo", "bar", "baz"]
+
+
+def test_opensearch() -> None:
+    """Test end to end indexing and search using Approximate Search."""
+    docsearch = OpenSearchVectorSearch.from_texts(
+        texts, FakeEmbeddings(), opensearch_url=DEFAULT_OPENSEARCH_URL
+    )
+    output = docsearch.similarity_search("foo", k=1)
+    assert output == [Document(page_content="foo")]
+
+
+def test_opensearch_with_metadatas() -> None:
+    """Test end to end indexing and search with metadata."""
+    metadatas = [{"page": i} for i in range(len(texts))]
+    docsearch = OpenSearchVectorSearch.from_texts(
+        texts,
+        FakeEmbeddings(),
+        metadatas=metadatas,
+        opensearch_url=DEFAULT_OPENSEARCH_URL,
+    )
+    output = docsearch.similarity_search("foo", k=1)
+    assert output == [Document(page_content="foo", metadata={"page": 0})]
+
+
+def test_add_text() -> None:
+    """Test adding additional text elements to existing index."""
+    text_input = ["test", "add", "text", "method"]
+    metadatas = [{"page": i} for i in range(len(text_input))]
+    docsearch = OpenSearchVectorSearch.from_texts(
+        texts, FakeEmbeddings(), opensearch_url=DEFAULT_OPENSEARCH_URL
+    )
+    docids = OpenSearchVectorSearch.add_texts(docsearch, text_input, metadatas)
+    assert len(docids) == len(text_input)
+
+
+def test_opensearch_script_scoring() -> None:
+    """Test end to end indexing and search using Script Scoring Search."""
+    pre_filter_val = {"bool": {"filter": {"term": {"text": "bar"}}}}
+    docsearch = OpenSearchVectorSearch.from_texts(
+        texts,
+        FakeEmbeddings(),
+        opensearch_url=DEFAULT_OPENSEARCH_URL,
+        is_appx_search=False,
+    )
+    output = docsearch.similarity_search(
+        "foo", k=1, search_type=SCRIPT_SCORING_SEARCH, pre_filter=pre_filter_val
+    )
+    assert output == [Document(page_content="bar")]
+
+
+def test_add_text_script_scoring() -> None:
+    """Test adding additional text elements and validating using Script Scoring."""
+    text_input = ["test", "add", "text", "method"]
+    metadatas = [{"page": i} for i in range(len(text_input))]
+    docsearch = OpenSearchVectorSearch.from_texts(
+        text_input,
+        FakeEmbeddings(),
+        opensearch_url=DEFAULT_OPENSEARCH_URL,
+        is_appx_search=False,
+    )
+    OpenSearchVectorSearch.add_texts(docsearch, texts, metadatas)
+    output = docsearch.similarity_search(
+        "add", k=1, search_type=SCRIPT_SCORING_SEARCH, space_type="innerproduct"
+    )
+    assert output == [Document(page_content="test")]
+
+
+def test_opensearch_painless_scripting() -> None:
+    """Test end to end indexing and search using Painless Scripting Search."""
+    pre_filter_val = {"bool": {"filter": {"term": {"text": "baz"}}}}
+    docsearch = OpenSearchVectorSearch.from_texts(
+        texts,
+        FakeEmbeddings(),
+        opensearch_url=DEFAULT_OPENSEARCH_URL,
+        is_appx_search=False,
+    )
+    output = docsearch.similarity_search(
+        "foo", k=1, search_type=PAINLESS_SCRIPTING_SEARCH, pre_filter=pre_filter_val
+    )
+    assert output == [Document(page_content="baz")]
+
+
+def test_add_text_painless_scripting() -> None:
+    """Test adding additional text elements and validating using Painless Scripting."""
+    text_input = ["test", "add", "text", "method"]
+    metadatas = [{"page": i} for i in range(len(text_input))]
+    docsearch = OpenSearchVectorSearch.from_texts(
+        text_input,
+        FakeEmbeddings(),
+        opensearch_url=DEFAULT_OPENSEARCH_URL,
+        is_appx_search=False,
+    )
+    OpenSearchVectorSearch.add_texts(docsearch, texts, metadatas)
+    output = docsearch.similarity_search(
+        "add", k=1, search_type=PAINLESS_SCRIPTING_SEARCH, space_type="cosineSimilarity"
+    )
+    assert output == [Document(page_content="test")]
+
+
+def test_opensearch_invalid_search_type() -> None:
+    """Test to validate similarity_search by providing invalid search_type."""
+    docsearch = OpenSearchVectorSearch.from_texts(
+        texts, FakeEmbeddings(), opensearch_url=DEFAULT_OPENSEARCH_URL
+    )
+    with pytest.raises(ValueError):
+        docsearch.similarity_search("foo", k=1, search_type="invalid_search_type")
+
+
+def test_opensearch_embedding_size_zero() -> None:
+    """Test to validate indexing when embedding size is zero."""
+    with pytest.raises(RuntimeError):
+        OpenSearchVectorSearch.from_texts(
+            [], FakeEmbeddings(), opensearch_url=DEFAULT_OPENSEARCH_URL
+        )