Bedrock llama2-13b-chat-v1 model throwing InvokeModel operation error

Question

I'm getting error while accessing bedrock chat model:

raise ValueError(f"Error raised by bedrock service: {e}")
ValueError: Error raised by bedrock service: An error occurred (ValidationException) when calling the InvokeModel operation: Malformed input request: 2 schema violations found, please reformat your input and try again.

I'm using below versions:

boto3 == 1.29.3
langchain == 0.0.338
 
below is the code:

def build_chain():
  region = os.environ["AWS_REGION"]
  kendra_index_id = os.environ["KENDRA_INDEX_ID"]
  credentials_profile_name = os.environ['AWS_PROFILE']

print(credentials_profile_name)
  
  model_id = "meta.llama2-13b-chat-v1"

llm = Bedrock(
      credentials_profile_name=credentials_profile_name,
      region_name = region,
      model_id=model_id
  )
      
  retriever = AmazonKendraRetriever(index_id=kendra_index_id,top_k=3,region_name=region)

prompt_template = """

Human: This is a friendly conversation between a human and an AI. 
  The AI is talkative and provides specific details from its context but limits it to 240 tokens.
  If the AI does not know the answer to a question, it truthfully says it 
  does not know.

Assistant: OK, got it, I'll be a talkative truthful AI assistant.

Human: Here are a few documents in  tags:
  
  {context}
  
  Based on the above documents, provide a detailed answer for, {question} 
  Answer "don't know" if not present in the document.

Assistant:
  """
  PROMPT = PromptTemplate(
      template=prompt_template, input_variables=["context", "question"]
  )

condense_qa_template = """
  Given the following conversation and a follow up question, rephrase the follow up question 
  to be a standalone question.

Chat History:
  {chat_history}
  Follow Up Input: {question}
  Standalone question:"""
  standalone_question_prompt = PromptTemplate.from_template(condense_qa_template)

qa = ConversationalRetrievalChain.from_llm(
        llm=llm, 
        retriever=retriever, 
        condense_question_prompt=standalone_question_prompt, 
        return_source_documents=True, 
        combine_docs_chain_kwargs={"prompt":PROMPT})

return qa

def run_chain(chain, prompt: str, history=[]):
  return chain({"question": prompt, "chat_history": history})

if __name__ == "__main__":

chat_history = []
  qa = build_chain()
  query = "what is sagemaker"
  result = run_chain(qa, query, chat_history)

what cloud be the solution for this?

Answer

Hi,

You may want to start from existing code samples like this one from https://lavaraja-padala.medium.com/how-to-access-llama2-models-on-aws-bedrock-using-boto3-f17033ec26b4 to have the right invocation structure in place

```
import json,boto3
region ="us-east-1"
client = boto3.client('bedrock-runtime',region)
prompt = "write a poem"
body = {
                "prompt": prompt,
                "temperature": 0.5,
                "top_p": 0.9,
                "max_gen_len": 512,
            }
response = client.invoke_model(
                modelId="meta.llama2-13b-chat-v1", body=json.dumps(body)
            )

response_body = json.loads(response["body"].read())
completion = response_body["generation"]
print(completion)
```

And then incrementally adapt it to your own needs in few iterations while keeping it working at all times.

Best,

Didier

Bedrock llama2-13b-chat-v1 model throwing InvokeModel operation error

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