AssertionError when using AutoGPT with Huggingface

[alienhd]

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..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?

[bbetters]

Having the same error

[AhmedSalem2]

I encountered a similar issue. I believe that the problem lies in the dimensions of Faiss. To resolve this (for me at least), you need to adjust the embedding_size to the appropriate value. If you're using HuggingFaceEmbeddings(), the correct value should be 768. Here's the updated code:

embedding_size = 768
index = faiss.IndexFlatL2(embedding_size)
vectorstore = FAISS(embeddings_model.embed_query, index, InMemoryDocstore({}), {})

I hope this helps you!

[abhishekbpandit]

I am facing this error when I am following the official langchain documentation: https://python.langchain.com/docs/use_cases/autonomous_agents/autogpt

As well as in the above case. Has anybody found a reliable solution for this?

[safimuhammad]

@AhmedSalem2 you're a lifesaver.