Fix/issue 2695 (langchain-ai#3608)

## Background
fixes langchain-ai#2695  

## Changes
The `add_text` method uses the internal embedding function if one was
passes to the `Weaviate` constructor.
NOTE: the latest merge on the `Weaviate` class made the specification of
a `weaviate_api_key` mandatory which might not be desirable for all
users and connection methods (for example weaviate also support Embedded
Weaviate which I am happy to add support to here if people think it's
desirable). I wrapped the fetching of the api key into a try catch in
order to allow the `weaviate_api_key` to be unspecified. Do let me know
if this is unsatisfactory.

## Test Plan
added test for `add_texts` method.

Author: cs0lar

langchain/vectorstores/weaviate.py

@@ -27,14 +27,18 @@ def _default_schema(index_name: str) -> Dict:
 
 def _create_weaviate_client(**kwargs: Any) -> Any:
     client = kwargs.get("client")
-
     if client is not None:
         return client
 
     weaviate_url = get_from_dict_or_env(kwargs, "weaviate_url", "WEAVIATE_URL")
-    weaviate_api_key = get_from_dict_or_env(
-        kwargs, "weaviate_api_key", "WEAVIATE_API_KEY", None
-    )
+
+    try:
+        # the weaviate api key param should not be mandatory
+        weaviate_api_key = get_from_dict_or_env(
+            kwargs, "weaviate_api_key", "WEAVIATE_API_KEY", None
+        )
+    except ValueError:
+        weaviate_api_key = None
 
     try:
         import weaviate
@@ -117,9 +121,21 @@ def add_texts(
                         data_properties[key] = metadatas[i][key]
 
                 _id = get_valid_uuid(uuid4())
-                batch.add_data_object(
-                    data_object=data_properties, class_name=self._index_name, uuid=_id
-                )
+
+                if self._embedding is not None:
+                    embeddings = self._embedding.embed_documents(list(doc))
+                    batch.add_data_object(
+                        data_object=data_properties,
+                        class_name=self._index_name,
+                        uuid=_id,
+                        vector=embeddings[0],
+                    )
+                else:
+                    batch.add_data_object(
+                        data_object=data_properties,
+                        class_name=self._index_name,
+                        uuid=_id,
+                    )
                 ids.append(_id)
         return ids
 

tests/integration_tests/vectorstores/cassettes/test_weaviate/TestWeaviate.test_max_marginal_relevance_search.yaml

@@ -9,6 +9,7 @@
 from langchain.docstore.document import Document
 from langchain.embeddings.openai import OpenAIEmbeddings
 from langchain.vectorstores.weaviate import Weaviate
+from tests.integration_tests.vectorstores.fake_embeddings import FakeEmbeddings
 
 logging.basicConfig(level=logging.DEBUG)
 
@@ -163,3 +164,20 @@ def test_max_marginal_relevance_search_with_filter(
         assert output == [
             Document(page_content="foo", metadata={"page": 0}),
         ]
+
+    def test_add_texts_with_given_embedding(self, weaviate_url: str) -> None:
+        texts = ["foo", "bar", "baz"]
+        embedding = FakeEmbeddings()
+
+        docsearch = Weaviate.from_texts(
+            texts, embedding=embedding, weaviate_url=weaviate_url
+        )
+
+        docsearch.add_texts(["foo"])
+        output = docsearch.similarity_search_by_vector(
+            embedding.embed_query("foo"), k=2
+        )
+        assert output == [
+            Document(page_content="foo"),
+            Document(page_content="foo"),
+        ]