Question: I'm trying to build a RAG using the Chroma database, but when I try to create it I have the following error : AttributeError: 'SentenceTransformer' object has no attribute 'embed_documents'. I saw that you can somehow fix it by modifying the Chroma library directly, but I don't have the rights for it on my environment. If someone has a piece of an advice, be pleased. The ultimate goal is to use the index as a query engine for a chatbot. This is what I tried Code: #We load the chunks of texts and declare which column is to be embedded chunks = DataFrameLoader(final_df_for_chroma_injection, page_content_column='TEXT').load() #create the open-source embedding function embedding_model = SentenceTransformer('sentence-transformers/all-MiniLM-L12-v2') #-Load the persist directory on which are stored the previous embeddings #-And add the new ones from chunks/embeddings index = Chroma.from_documents(chunks, embedding_model, persist_directory="./chroma_db")  This is the error I get: --------------------------------------------------------------------------- AttributeError Traceback (most recent call last) Cell In[47], line 3 1 #-Load the persist directory on which are stored the previous embeddings 2 #-And add the new ones from chunks/embeddings ----> 3 index = Chroma.from_documents(chunks, 4 embedding_model, 5 persist_directory="./chroma_db") File /opt/anaconda3_envs/abeille_pytorch_p310/lib/python3.10/site-packages/langchain_community/vectorstores/chroma.py:778, in Chroma.from_documents(cls, documents, embedding, ids, collection_name, persist_directory, client_settings, client, collection_metadata, **kwargs) 776 texts = [doc.page_content for doc in documents] 777 metadatas = [doc.metadata for doc in documents] --> 778 return cls.from_texts( 779 texts=texts, 780 embedding=embedding, 781 metadatas=metadatas, 782 ids=ids, 783 collection_name=collection_name, 784 persist_directory=persist_directory, 785 client_settings=client_settings, 786 client=client, 787 collection_metadata=collection_metadata, 788 **kwargs, 789 ) File /opt/anaconda3_envs/abeille_pytorch_p310/lib/python3.10/site-packages/langchain_community/vectorstores/chroma.py:736, in Chroma.from_texts(cls, texts, embedding, metadatas, ids, collection_name, persist_directory, client_settings, client, collection_metadata, **kwargs) 728 from chromadb.utils.batch_utils import create_batches 730 for batch in create_batches( 731 api=chroma_collection._client, 732 ids=ids, 733 metadatas=metadatas, 734 documents=texts, 735 ): --> 736 chroma_collection.add_texts( 737 texts=batch[3] if batch[3] else [], 738 metadatas=batch[2] if batch[2] else None, 739 ids=batch[0], 740 ) 741 else: 742 chroma_collection.add_texts(texts=texts, metadatas=metadatas, ids=ids) File /opt/anaconda3_envs/abeille_pytorch_p310/lib/python3.10/site-packages/langchain_community/vectorstores/chroma.py:275, in Chroma.add_texts(self, texts, metadatas, ids, **kwargs) 273 texts = list(texts) 274 if self._embedding_function is not None: --> 275 embeddings = self._embedding_function.embed_documents(texts) 276 if metadatas: 277 # fill metadatas with empty dicts if somebody 278 # did not specify metadata for all texts 279 length_diff = len(texts) - len(metadatas) File /opt/anaconda3_envs/abeille_pytorch_p310/lib/python3.10/site-packages/torch/nn/modules/module.py:1688, in Module.__getattr__(self, name) 1686 if name in modules: 1687 return modules[name] -> 1688 raise AttributeError(f"'{type(self).__name__}' object has no attribute '{name}'") AttributeError: 'SentenceTransformer' object has no attribute 'embed_documents'``` 

Answer: Use SentenceTransformerEmbeddings instead of SentenceTransformer, or simply HuggingFaceEmbeddings Reference > https://python.langchain.com/docs/integrations/text_embedding/sentence_transformers