WiFddlmZddlZddlmZddlmZGddeZy))ListN) BaseEmbedder) EmbeddingscLeZdZdZdefdZd deedede jfdZ y) LangChainBackendaLangChain Embedding Model. This class uses the LangChain Embedding class to embed the documents. Argument: embedding_model: A LangChain Embedding Instance. Examples: ```python from langchain_community.embeddings import HuggingFaceInstructEmbeddings from bertopic.backend import LangChainBackend hf_embedding = HuggingFaceInstructEmbeddings() langchain_embedder = LangChainBackend(hf_embedding) ``` embedding_modelc||_y)N)r)selfrs g/home/sietch6/trending-topics-pipeline/venv/lib/python3.12/site-packages/bertopic/backend/_langchain.py__init__zLangChainBackend.__init__s . documentsverbosereturnc|Dcgc] }|dk(rdn| }}|jj|}tj|Scc}w)aEmbed a list of n documents/words into an n-dimensional matrix of embeddings. Arguments: documents: A list of documents or words to be embedded verbose: Controls the verbosity of the process Returns: Document/words embeddings with shape (n, m) with `n` documents/words that each have an embeddings size of `m`  )rembed_documentsnparray)r rrdocprepared_documentsresponses r embedzLangChainBackend.embedsQDMMCSBYcC7MM''778JKxx!!NsAN)F) __name__ __module__ __qualname____doc__rr rstrboolrndarrayrr r rrs5 / /"tCy"4"BJJ"r r) typingrnumpyrbertopic.backendrlangchain_core.embeddingsrrr"r r r's)0#"|#"r