AssertionError when using AutoGPT with Huggingface

# This code: 


`from langchain.experimental import AutoGPT
from langchain import HuggingFaceHub

repo_id = "google/flan-t5-xl" # See https://huggingface.co/models?pipeline_tag=text-generation&sort=downloads for some other options

agent = AutoGPT.from_llm_and_tools(
    ai_name="Tom",
    ai_role="Assistant",
    tools=tools,
    llm = HuggingFaceHub(repo_id=repo_id, model_kwargs={"temperature":0, "max_length":64}),
    memory=vectorstore.as_retriever()
)
agent.chain.verbose = True`
agent.run(["write a weather report for SF today"])


# outputs the error:


`AssertionError                            Traceback (most recent call last)
Cell In[21], line 1
----> 1 agent.run(["write a weather report for SF today"])

File ~\anaconda3\envs\langchain\Lib\site-packages\langchain\experimental\autonomous_agents\autogpt\agent.py:91, in AutoGPT.run(self, goals)
     88 loop_count += 1
     90 # Send message to AI, get response
---> 91 assistant_reply = self.chain.run(
     92     goals=goals,
     93     messages=self.full_message_history,
     94     memory=self.memory,
     95     user_input=user_input,
     96 )
     98 # Print Assistant thoughts
     99 print(assistant_reply)

File ~\anaconda3\envs\langchain\Lib\site-packages\langchain\chains\base.py:239, in Chain.run(self, callbacks, *args, **kwargs)
    236     return self(args[0], callbacks=callbacks)[self.output_keys[0]]
    238 if kwargs and not args:
--> 239     return self(kwargs, callbacks=callbacks)[self.output_keys[0]]
    241 if not kwargs and not args:
    242     raise ValueError(
    243         "`run` supported with either positional arguments or keyword arguments,"
    244         " but none were provided."
    245     )

File ~\anaconda3\envs\langchain\Lib\site-packages\langchain\chains\base.py:140, in Chain.__call__(self, inputs, return_only_outputs, callbacks)
    138 except (KeyboardInterrupt, Exception) as e:
    139     run_manager.on_chain_error(e)
--> 140     raise e
    141 run_manager.on_chain_end(outputs)
    142 return self.prep_outputs(inputs, outputs, return_only_outputs)

File ~\anaconda3\envs\langchain\Lib\site-packages\langchain\chains\base.py:134, in Chain.__call__(self, inputs, return_only_outputs, callbacks)
    128 run_manager = callback_manager.on_chain_start(
    129     {"name": self.__class__.__name__},
    130     inputs,
    131 )
    132 try:
    133     outputs = (
--> 134         self._call(inputs, run_manager=run_manager)
    135         if new_arg_supported
    136         else self._call(inputs)
    137     )
    138 except (KeyboardInterrupt, Exception) as e:
    139     run_manager.on_chain_error(e)

File ~\anaconda3\envs\langchain\Lib\site-packages\langchain\chains\llm.py:69, in LLMChain._call(self, inputs, run_manager)
     64 def _call(
     65     self,
     66     inputs: Dict[str, Any],
     67     run_manager: Optional[CallbackManagerForChainRun] = None,
     68 ) -> Dict[str, str]:
---> 69     response = self.generate([inputs], run_manager=run_manager)
     70     return self.create_outputs(response)[0]

File ~\anaconda3\envs\langchain\Lib\site-packages\langchain\chains\llm.py:78, in LLMChain.generate(self, input_list, run_manager)
     72 def generate(
     73     self,
     74     input_list: List[Dict[str, Any]],
     75     run_manager: Optional[CallbackManagerForChainRun] = None,
     76 ) -> LLMResult:
     77     """Generate LLM result from inputs."""
---> 78     prompts, stop = self.prep_prompts(input_list, run_manager=run_manager)
     79     return self.llm.generate_prompt(
     80         prompts, stop, callbacks=run_manager.get_child() if run_manager else None
     81     )

File ~\anaconda3\envs\langchain\Lib\site-packages\langchain\chains\llm.py:106, in LLMChain.prep_prompts(self, input_list, run_manager)
    104 for inputs in input_list:
    105     selected_inputs = {k: inputs[k] for k in self.prompt.input_variables}
--> 106     prompt = self.prompt.format_prompt(**selected_inputs)
    107     _colored_text = get_colored_text(prompt.to_string(), "green")
    108     _text = "Prompt after formatting:\n" + _colored_text

File ~\anaconda3\envs\langchain\Lib\site-packages\langchain\prompts\chat.py:144, in BaseChatPromptTemplate.format_prompt(self, **kwargs)
    143 def format_prompt(self, **kwargs: Any) -> PromptValue:
--> 144     messages = self.format_messages(**kwargs)
    145     return ChatPromptValue(messages=messages)

File ~\anaconda3\envs\langchain\Lib\site-packages\langchain\experimental\autonomous_agents\autogpt\prompt.py:51, in AutoGPTPrompt.format_messages(self, **kwargs)
     49 memory: VectorStoreRetriever = kwargs["memory"]
     50 previous_messages = kwargs["messages"]
---> 51 relevant_docs = memory.get_relevant_documents(str(previous_messages[-10:]))
     52 relevant_memory = [d.page_content for d in relevant_docs]
     53 relevant_memory_tokens = sum(
     54     [self.token_counter(doc) for doc in relevant_memory]
     55 )

File ~\anaconda3\envs\langchain\Lib\site-packages\langchain\vectorstores\base.py:377, in VectorStoreRetriever.get_relevant_documents(self, query)
    375 def get_relevant_documents(self, query: str) -> List[Document]:
    376     if self.search_type == "similarity":
--> 377         docs = self.vectorstore.similarity_search(query, **self.search_kwargs)
    378     elif self.search_type == "similarity_score_threshold":
    379         docs_and_similarities = (
    380             self.vectorstore.similarity_search_with_relevance_scores(
    381                 query, **self.search_kwargs
    382             )
    383         )

File ~\anaconda3\envs\langchain\Lib\site-packages\langchain\vectorstores\faiss.py:255, in FAISS.similarity_search(self, query, k, **kwargs)
    243 def similarity_search(
    244     self, query: str, k: int = 4, **kwargs: Any
    245 ) -> List[Document]:
    246     """Return docs most similar to query.
    247 
    248     Args:
   (...)
    253         List of Documents most similar to the query.
    254     """
--> 255     docs_and_scores = self.similarity_search_with_score(query, k)
    256     return [doc for doc, _ in docs_and_scores]

File ~\anaconda3\envs\langchain\Lib\site-packages\langchain\vectorstores\faiss.py:225, in FAISS.similarity_search_with_score(self, query, k)
    215 """Return docs most similar to query.
    216 
    217 Args:
   (...)
    222     List of Documents most similar to the query and score for each
    223 """
    224 embedding = self.embedding_function(query)
--> 225 docs = self.similarity_search_with_score_by_vector(embedding, k)
    226 return docs

File ~\anaconda3\envs\langchain\Lib\site-packages\langchain\vectorstores\faiss.py:199, in FAISS.similarity_search_with_score_by_vector(self, embedding, k)
    197 if self._normalize_L2:
    198     faiss.normalize_L2(vector)
--> 199 scores, indices = self.index.search(vector, k)
    200 docs = []
    201 for j, i in enumerate(indices[0]):

File ~\anaconda3\envs\langchain\Lib\site-packages\faiss\class_wrappers.py:329, in handle_Index.<locals>.replacement_search(self, x, k, params, D, I)
    327 n, d = x.shape
    328 x = np.ascontiguousarray(x, dtype='float32')
--> 329 assert d == self.d
    331 assert k > 0
    333 if D is None:

AssertionError: `

# How can I resolve this behaviour?