(ValidationException) when calling the InvokeModel operation: Malformed input request: expected minLength: 1, actual: 0, please reformat your input and try again

[ajaylamba-provar]

Getting below error sometimes in continued conversations.

"message":"All records failed processing. 1 individual errors logged separately below.\n\nTraceback (most recent call last):\n File \"/opt/python/aws_lambda_powertools/utilities/batch/base.py\", line 500, in _process_record\n result = self.handler(record=data)\n ^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/opt/python/aws_lambda_powertools/tracing/tracer.py\", line 678, in decorate\n response = method(*args, **kwargs)\n ^^^^^^^^^^^^^^^^^^^^^^^\n File \"/var/task/index.py\", line 122, in record_handler\n handle_run(detail)\n File \"/var/task/index.py\", line 95, in handle_run\n response = model.run(\n ^^^^^^^^^^\n File \"/var/task/adapters/base/base.py\", line 170, in run\n return self.run_with_chain(prompt, workspace_id)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/var/task/adapters/base/base.py\", line 107, in run_with_chain\n result = conversation({\"question\": user_prompt})\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/opt/python/langchain/chains/base.py\", line 310, in __call__\n raise e\n File \"/opt/python/langchain/chains/base.py\", line 304, in __call__\n self._call(inputs, run_manager=run_manager)\n File \"/opt/python/langchain/chains/conversational_retrieval/base.py\", line 148, in _call\n docs = self._get_docs(new_question, inputs, run_manager=_run_manager)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/opt/python/langchain/chains/conversational_retrieval/base.py\", line 305, in _get_docs\n docs = self.retriever.get_relevant_documents(\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/opt/python/langchain/schema/retriever.py\", line 211, in get_relevant_documents\n raise e\n File \"/opt/python/langchain/schema/retriever.py\", line 204, in get_relevant_documents\n result = self._get_relevant_documents(\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/opt/python/genai_core/langchain/workspace_retriever.py\", line 13, in _get_relevant_documents\n result = genai_core.semantic_search.semantic_search(\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/opt/python/genai_core/semantic_search.py\", line 25, in semantic_search\n return query_workspace_open_search(\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/opt/python/genai_core/opensearch/query.py\", line 48, in query_workspace_open_search\n query_embeddings = genai_core.embeddings.generate_embeddings(\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/opt/python/genai_core/embeddings.py\", line 28, in generate_embeddings\n ret_value.extend(_generate_embeddings_bedrock(model, batch))\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/opt/python/genai_core/embeddings.py\", line 88, in _generate_embeddings_bedrock\n response = bedrock.invoke_model(\n ^^^^^^^^^^^^^^^^^^^^^\n File \"/opt/python/botocore/client.py\", line 535, in _api_call\n return self._make_api_call(operation_name, kwargs)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/opt/python/aws_xray_sdk/ext/botocore/patch.py\", line 38, in _xray_traced_botocore\n return xray_recorder.record_subsegment(\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/opt/python/aws_xray_sdk/core/recorder.py\", line 456, in record_subsegment\n return_value = wrapped(*args, **kwargs)\n ^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/opt/python/botocore/client.py\", line 983, in _make_api_call\n raise error_class(parsed_response, operation_name)\nbotocore.errorfactory.ValidationException: An error occurred (ValidationException) when calling the InvokeModel operation: Malformed input request: expected minLength: 1, actual: 0, please reformat your input and try again.\n",

Could be related to get_condense_question_prompt where questions are rephrased.

`def get_condense_question_prompt(self):
    template = """
        Human: Given the following conversation and a follow up input, rephrase the follow up question to be a standalone question. The question should be rephrased such that the rephrased question structure remains as if Human has asked it.
        If the followup input is just a feedback and not actually a question, just take the input and rephrase it as a feedback not a question.

        Chat History:
        {chat_history}
        Follow Up Input: {question}
        Standalone question:"""

    return PromptTemplate(
        input_variables=["chat_history", "question"],
        chat_history="{chat_history}",
        template=template,
    )`

I am using Claude 2.1 for responses and Amazon Titan for embeddings and OpenSearch for storage.

[ajaylamba-provar]

Following is the example of conversation:

[massi-ang]

Can you provide the prompts? You'll find them by enabling metadata, using the settings gear in the UI bottom right

[ajaylamba-provar]

The metadata is empty when the error is thrown:

Also, see the following conversation, for one input, it throws error but when the input is slightly changed, it gives response.

[ajaylamba-provar]

I have noticed that the issue occurs when the followup question is very short e.g. thanks, bye, ok bye etc.

[ajaylamba-provar]

I am able to reduce the occurrence of this by using following code for rephrasing the question

def get_condense_question_prompt(self): template = """ Your task is to rephrase the follow up input, keeping in mind the current conversation. The question should be rephrased such that the rephrased question structure remains as if Human has asked it. If the followup input is just a feedback and not actually a question, just take the input and rephrase it as a feedback. The rephrased standalone question/feedback MUST NOT be empty.

        Human: Here is the current conversation so far

        {chat_history}

        Given the above conversation and a follow up input, rephrase the follow up input to be a standalone question/feedback. 

        Follow Up Input: {question}

        Standalone question:"""

    return PromptTemplate(
        input_variables=["chat_history", "question"],
        chat_history="{chat_history}",
        template=template,
    )

[ajaylamba-provar]

I have started getting this issue again with increased frequency now. Any suggestions regarding the fix please? @massi-ang @bigadsoleiman

[roselle11111]

@ajaylamba-provar i am getting the same issue using this condensed prompt do you have any workaround?

[ajaylamba-provar]

@roselle11111 The workaround I applied was to fine tune the condensation prompt to make sure that the rephrased question is never blank.