diff --git a/langchain/prompts/optimized.py b/langchain/prompts/optimized.py
index 56b5dd4e8d..c8240671bb 100644
--- a/langchain/prompts/optimized.py
+++ b/langchain/prompts/optimized.py
@@ -18,7 +18,6 @@ class OptimizedPrompt(BaseModel):
             from langchain import DynamicPrompt
             vectorstore = FAISS.from_texts(examples, OpenAIEmbeddings()
             optimized_prompt = OptimizedPrompt(
-                examples=["Say hi. Hi", "Say ho. Ho"],
                 example_separator="\n\n",
                 prefix="",
                 suffix="\n\nSay {foo}"
@@ -29,9 +28,6 @@ class OptimizedPrompt(BaseModel):
             )
     """
 
-    examples: List[str]
-    """A list of the examples that the prompt template expects."""
-
     example_separator: str = "\n\n"
     """Example separator, e.g. \n\n, for the dynamic prompt creation."""
 
@@ -162,7 +158,6 @@ class OptimizedPrompt(BaseModel):
             examples, embeddings, **vectorstore_cls_kwargs
         )
         return cls(
-            examples=examples,
             suffix=suffix,
             input_variables=input_variables,
             example_separator=example_separator,
diff --git a/langchain/vectorstores/elastic_vector_search.py b/langchain/vectorstores/elastic_vector_search.py
index 13114cce8a..b96faa46d8 100644
--- a/langchain/vectorstores/elastic_vector_search.py
+++ b/langchain/vectorstores/elastic_vector_search.py
@@ -39,7 +39,6 @@ class ElasticVectorSearch(VectorStore):
             elastic_vector_search = ElasticVectorSearch(
                 "http://localhost:9200",
                 "embeddings",
-                mapping,
                 embedding_function
             )
 
@@ -49,7 +48,6 @@ class ElasticVectorSearch(VectorStore):
         self,
         elasticsearch_url: str,
         index_name: str,
-        mapping: Dict,
         embedding_function: Callable,
     ):
         """Initialize with necessary components."""
@@ -69,7 +67,6 @@ class ElasticVectorSearch(VectorStore):
                 "Your elasticsearch client string is misformatted. " f"Got error: {e} "
             )
         self.client = es_client
-        self.mapping = mapping
 
     def similarity_search(self, query: str, k: int = 4) -> List[Document]:
         """Return docs most similar to query.
@@ -155,4 +152,4 @@ class ElasticVectorSearch(VectorStore):
             requests.append(request)
         bulk(client, requests)
         client.indices.refresh(index=index_name)
-        return cls(elasticsearch_url, index_name, mapping, embedding.embed_query)
+        return cls(elasticsearch_url, index_name, embedding.embed_query)