text: embedding=GPT4AllEmbeddings())    Found model file at  /Users/rlm/.cache/gpt4all/ggml-all-MiniLM-L6-v2-f16.binTest similarity search is working with our local embeddings.question = "What are the approaches to Task Decomposition?"docs = vectorstore.similarity_search(question)len(docs)    4docs[0]    Document(page_content='Task decomposition can be done (1) by LLM with simple prompting like "Steps for XYZ.\\n1.", "What are the subgoals for achieving XYZ?", (2) by using task-specific instructions; e.g. "Write a story outline." for writing a novel, or (3) with human inputs.', metadata={'description': 'Building agents with LLM (large language model) as its core controller is a cool concept. Several proof-of-concepts demos, such as AutoGPT, GPT-Engineer and BabyAGI, serve as inspiring examples. The potentiality of LLM extends beyond generating well-written copies, stories, essays and programs; it can be framed as a powerful general problem solver.\nAgent System Overview In a LLM-powered autonomous agent system, LLM functions as the agentâ€™s brain, complemented by several key components:', 'language': 'en', 'source': 'https://lilianweng.github.io/posts/2023-06-23-agent/', 'title': "LLM Powered Autonomous Agents | Lil'Log"})Modelâ€‹Llama-v2â€‹Download a GGML converted model (e.g., here).pip install llama-cpp-pythonTo enable use of GPU on Apple Silicon, follow the steps here to use the Python binding with Metal support.In particular, ensure that conda is using the correct virtual enviorment that you created (miniforge3).E.g., for me:conda activate