Return input query embedding in vector store retriever on end listener

### Checked other resources - [x] This is a feature request, not a bug report or usage question. - [x] I added a clear and descriptive title that summarizes the feature request. - [x] I used the GitHub search to find a similar feature request and didn't find it. - [x] I checked the LangChain documentation and API reference to see if this feature already exists. - [x] This is not related to the langchain-community package. ### Feature Description Assume we are building rag system with a custom cache runnable. In that case to improve performance we need to get the embedding generated by the input query. Otherwise we need to make another API call to the embedding. ``` def _get_rag_chain( self, generate_standalone_question: bool = False, answer_llm_config: Optional[RunnableConfig] = None, cache: Optional[RAGCacheRunnable] = None, retry: bool = False, gen_suggestion_cb: Callable[[Input], Output] = lambda _in: _in ) -> RunnableSerializable: if retry: logger.clog("\nRetrying RAG chain due to previous failure, attempting to get a valid answer...\n", Fore.BLUE) cache = cache or RAGCacheRunnable(self.model, self.model.client_id, self.model.ignore_cache, self.model.cache_id) vector_store = vector_store_manager.get_store(self.model.client_id, EnumCollectionType.RAG, embeddings=get_embeddings(self.model)) expression = vector_store.get_rag_expr_using_ids(**self.model.model_dump()) ranking_top_k = max(AppConfig.similarity_top_k_for_reranking, min(AppConfig.similarity_top_k_for_reranking * 2, 50)) if retry else AppConfig.similarity_top_k_for_reranking similarity_top_k = min(self.model.similarity_top_k * 2, MAX_RAG_SIMILARITY_TOP_K) if retry else self.model.similarity_top_k retriever = vector_store.as_retriever( search_type="similarity", search_kwargs={"k": ranking_top_k, vector_store.expr_arg_key: expression} ).with_…