Question: I found this code: https://github.com/pixegami/langchain-rag-tutorial/blob/main/create_database.py It takes the document, splits it into chunks, creates vector embeddings for each chunk, and saves those into Chroma Database. However, the source code uses OpenAI key to create embeddings. Since I don't have access to OpenAI I tried to use free alternative, - SentenceTransformers. But I wasn't able to rewrite the code properly. Here is my curent attempt: from langchain_community.document_loaders import DirectoryLoader from langchain.text_splitter import RecursiveCharacterTextSplitter from langchain.schema import Document from sentence_transformers import SentenceTransformer from langchain.vectorstores.chroma import Chroma import os import shutil CHROMA_PATH = "chroma" DATA_PATH = "data/books" embedder = SentenceTransformer("all-MiniLM-L6-v2") def main(): generate_data_store() def generate_data_store(): documents = load_documents() chunks = split_text(documents) save_to_chroma(chunks) def load_documents(): loader = DirectoryLoader(DATA_PATH, glob="*.md") documents = loader.load() return documents def split_text(documents: list[Document]): text_splitter = RecursiveCharacterTextSplitter( chunk_size=300, chunk_overlap=100, length_function=len, add_start_index=True, ) chunks = text_splitter.split_documents(documents) print(f"Split {len(documents)} documents into {len(chunks)} chunks.") document = chunks[10] print(document.page_content) print(document.metadata) return chunks def save_to_chroma(chunks: list[Document]): # Clear out the database first. if os.path.exists(CHROMA_PATH): shutil.rmtree(CHROMA_PATH) # Create a new DB from the documents. db = Chroma.from_documents( chunks, embedder.encode, persist_directory=CHROMA_PATH ) db.persist() print(f"Saved {len(chunks)} chunks to {CHROMA_PATH}.") if __name__ == "__main__": main()  This is the error that I get: Traceback (most recent call last): File "/media/andrew/Simple Tom/Robotics/Crew_AI/langchain-rag-tutorial/create_database.py", line 56, in <module> main() File "/media/andrew/Simple Tom/Robotics/Crew_AI/langchain-rag-tutorial/create_database.py", line 15, in main generate_data_store() File "/media/andrew/Simple Tom/Robotics/Crew_AI/langchain-rag-tutorial/create_database.py", line 20, in generate_data_store save_to_chroma(chunks) File "/media/andrew/Simple Tom/Robotics/Crew_AI/langchain-rag-tutorial/create_database.py", line 49, in save_to_chroma db = Chroma.from_documents( File "/home/andrew/.local/lib/python3.10/site-packages/langchain_community/vectorstores/chroma.py", line 778, in from_documents return cls.from_texts( File "/home/andrew/.local/lib/python3.10/site-packages/langchain_community/vectorstores/chroma.py", line 736, in from_texts chroma_collection.add_texts( File "/home/andrew/.local/lib/python3.10/site-packages/langchain_community/vectorstores/chroma.py", line 275, in add_texts embeddings = self._embedding_function.embed_documents(texts) AttributeError: 'function' object has no attribute 'embed_documents'  I'm not a programmer. If someone could point out if it's at all possible to achive this without OpenAI key and if yes, then where my mistake is, that'd be great.

Answer:  Define your embedding model, with HuggingFaceEmbeddings  from langchain_community.embeddings import HuggingFaceEmbeddings embedder = HuggingFaceEmbeddings( model_name = "sentence-transformers/all-MiniLM-L6-v2" )   Then embed the chunks into vectorDB   db = Chroma.from_documents( documents=chunks, embedding=embedder, persist_directory=CHROMA_PATH ) db.persist()