Add the usage of SSL certificates for Elasticsearch and user password authentication (langchain-ai#5058)

Enhance the code to support SSL authentication for Elasticsearch when
using the VectorStore module, as previous versions did not provide this
capability.
@dev2049

---------

Co-authored-by: caidong <zhucaidong1992@gmail.com>
Co-authored-by: Dev 2049 <dev.dev2049@gmail.com>

Author: 3 people

langchain/vectorstores/elastic_vector_search.py

@@ -7,7 +7,7 @@
 
 from langchain.docstore.document import Document
 from langchain.embeddings.base import Embeddings
-from langchain.utils import get_from_dict_or_env
+from langchain.utils import get_from_env
 from langchain.vectorstores.base import VectorStore
 
 
@@ -114,24 +114,31 @@ class ElasticVectorSearch(VectorStore, ABC):
         ValueError: If the elasticsearch python package is not installed.
     """
 
-    def __init__(self, elasticsearch_url: str, index_name: str, embedding: Embeddings):
+    def __init__(
+        self,
+        elasticsearch_url: str,
+        index_name: str,
+        embedding: Embeddings,
+        *,
+        ssl_verify: Optional[Dict[str, Any]] = None,
+    ):
         """Initialize with necessary components."""
         try:
             import elasticsearch
         except ImportError:
-            raise ValueError(
+            raise ImportError(
                 "Could not import elasticsearch python package. "
                 "Please install it with `pip install elasticsearch`."
             )
         self.embedding = embedding
         self.index_name = index_name
+        _ssl_verify = ssl_verify or {}
         try:
-            es_client = elasticsearch.Elasticsearch(elasticsearch_url)  # noqa
+            self.client = elasticsearch.Elasticsearch(elasticsearch_url, **_ssl_verify)
         except ValueError as e:
             raise ValueError(
-                f"Your elasticsearch client string is misformatted. Got error: {e} "
+                f"Your elasticsearch client string is mis-formatted. Got error: {e} "
             )
-        self.client = es_client
 
     def add_texts(
         self,
@@ -154,7 +161,7 @@ def add_texts(
             from elasticsearch.exceptions import NotFoundError
             from elasticsearch.helpers import bulk
         except ImportError:
-            raise ValueError(
+            raise ImportError(
                 "Could not import elasticsearch python package. "
                 "Please install it with `pip install elasticsearch`."
             )
@@ -239,6 +246,9 @@ def from_texts(
         texts: List[str],
         embedding: Embeddings,
         metadatas: Optional[List[dict]] = None,
+        elasticsearch_url: Optional[str] = None,
+        index_name: Optional[str] = None,
+        refresh_indices: bool = True,
         **kwargs: Any,
     ) -> ElasticVectorSearch:
         """Construct ElasticVectorSearch wrapper from raw documents.
@@ -262,48 +272,12 @@ def from_texts(
                     elasticsearch_url="http://localhost:9200"
                 )
         """
-        elasticsearch_url = get_from_dict_or_env(
-            kwargs, "elasticsearch_url", "ELASTICSEARCH_URL"
+        elasticsearch_url = elasticsearch_url or get_from_env(
+            "elasticsearch_url", "ELASTICSEARCH_URL"
         )
-        try:
-            import elasticsearch
-            from elasticsearch.exceptions import NotFoundError
-            from elasticsearch.helpers import bulk
-        except ImportError:
-            raise ValueError(
-                "Could not import elasticsearch python package. "
-                "Please install it with `pip install elasticsearch`."
-            )
-        try:
-            client = elasticsearch.Elasticsearch(elasticsearch_url)
-        except ValueError as e:
-            raise ValueError(
-                "Your elasticsearch client string is misformatted. " f"Got error: {e} "
-            )
-        index_name = kwargs.get("index_name", uuid.uuid4().hex)
-        embeddings = embedding.embed_documents(texts)
-        dim = len(embeddings[0])
-        mapping = _default_text_mapping(dim)
-
-        # check to see if the index already exists
-        try:
-            client.indices.get(index=index_name)
-        except NotFoundError:
-            # TODO would be nice to create index before embedding,
-            # just to save expensive steps for last
-            client.indices.create(index=index_name, mappings=mapping)
-
-        requests = []
-        for i, text in enumerate(texts):
-            metadata = metadatas[i] if metadatas else {}
-            request = {
-                "_op_type": "index",
-                "_index": index_name,
-                "vector": embeddings[i],
-                "text": text,
-                "metadata": metadata,
-            }
-            requests.append(request)
-        bulk(client, requests)
-        client.indices.refresh(index=index_name)
-        return cls(elasticsearch_url, index_name, embedding)
+        index_name = index_name or uuid.uuid4().hex
+        vectorsearch = cls(elasticsearch_url, index_name, embedding, **kwargs)
+        vectorsearch.add_texts(
+            texts, metadatas=metadatas, refresh_indices=refresh_indices
+        )
+        return vectorsearch

tests/integration_tests/vectorstores/test_elasticsearch.py

@@ -48,6 +48,23 @@ def test_similarity_search_without_metadata(self, elasticsearch_url: str) -> Non
         output = docsearch.similarity_search("foo", k=1)
         assert output == [Document(page_content="foo")]
 
+    def test_similarity_search_with_ssl_verify(self, elasticsearch_url: str) -> None:
+        """Test end to end construction and search with ssl verify."""
+        ssl_verify = {
+            "verify_certs": True,
+            "basic_auth": ("ES_USER", "ES_PASSWORD"),
+            "ca_certs": "ES_CA_CERTS_PATH",
+        }
+        texts = ["foo", "bar", "baz"]
+        docsearch = ElasticVectorSearch.from_texts(
+            texts,
+            FakeEmbeddings(),
+            elasticsearch_url=elasticsearch_url,
+            ssl_verify=ssl_verify,
+        )
+        output = docsearch.similarity_search("foo", k=1)
+        assert output == [Document(page_content="foo")]
+
     def test_similarity_search_with_metadata(self, elasticsearch_url: str) -> None:
         """Test end to end construction and search with metadata."""
         texts = ["foo", "bar", "baz"]