text: = OpenAI(temperature=0)Set up pluginsâ€‹Load and index plugins# Get all plugins from plugnplai.comurls = plugnplai.get_plugins()#  Get ChatGPT plugins - only ChatGPT verified pluginsurls = plugnplai.get_plugins(filter="ChatGPT")#  Get working plugins - only tested plugins (in progress)urls = plugnplai.get_plugins(filter="working")AI_PLUGINS = [AIPlugin.from_url(url + "/.well-known/ai-plugin.json") for url in urls]Tool Retrieverâ€‹We will use a vectorstore to create embeddings for each tool description. Then, for an incoming query we can create embeddings for that query and do a similarity search for relevant tools.from langchain.vectorstores import FAISSfrom langchain.embeddings import OpenAIEmbeddingsfrom langchain.schema import Documentembeddings = OpenAIEmbeddings()docs = [    Document(        page_content=plugin.description_for_model,        metadata={"plugin_name": plugin.name_for_model},    )    for plugin in AI_PLUGINS]vector_store = FAISS.from_documents(docs, embeddings)toolkits_dict = {    plugin.name_for_model: NLAToolkit.from_llm_and_ai_plugin(llm, plugin)    for plugin in AI_PLUGINS}    Attempting to load an OpenAPI 3.0.1 spec.  This may result in degraded performance. Convert your OpenAPI spec to 3.1.* spec for better support.    Attempting to load an OpenAPI 3.0.1 spec.  This may result in degraded performance. Convert your OpenAPI spec to 3.1.* spec for better support.    Attempting to load an OpenAPI 3.0.1 spec.  This may result