Unable To Generate Schema: InvalidLLMOutputType (Response validation failed!)

[mayankagarwal19911]

System Info

pandasai 2.2.8 Python 3.9.6

🐛 Describe the bug

Sample code

df = pd.DataFrame(columns=["column1"], data=[["value1"]]) df.head() semantic_agent = SemanticAgent(dfs=df) print(semantic_agent._schema)

Error

in BaseAgent.call_llm_with_prompt(self, prompt) return result else: raise InvalidLLMOutputType("Response validation failed!") except Exception: if ( not self.context.config.use_error_correction_framework or retry_count >= self.context.config.max_retries - 1 ):

[dosubot[bot]]

Hello, @mayankagarwal19911! I'm here to assist you with any bugs, questions, or contributions you may have. Let's tackle this issue together!

To resolve the InvalidLLMOutputType: Response validation failed! error and successfully generate the schema using pandasai 2.2.8 and Python 3.9.6, follow these steps:

1. Ensure the Template Correctly Specifies the Expected Output Type: Modify the template in pandasai/prompts/templates/correct_output_type_error_prompt.tmpl to ensure it specifies the expected output type.

   {% for df in context.dfs %}{% set index = loop.index %}{% include 'shared/dataframe.tmpl' with context %}{% endfor %}
   
   The user asked the following question:
   {{context.memory.get_conversation()}}
   
   You generated this python code:
   {{code}}
   
   Fix the python code above and return the new python code but the result type should be: {{output_type}}

2. Set the Correct Output Type in Your Context: Ensure that the output_type variable is correctly set to the expected type in your context. For example, if you expect a DataFrame, output_type should be set to DataFrame.

   from jinja2 import Template
   
   template_str = """
   {% for df in context.dfs %}{% set index = loop.index %}{% include 'shared/dataframe.tmpl' with context %}{% endfor %}
   
   The user asked the following question:
   {{context.memory.get_conversation()}}
   
   You generated this python code:
   {{code}}
   
   Fix the python code above and return the new python code but the result type should be: {{output_type}}
   """
   
   context = {
      'dfs': [df],  # Your DataFrame
      'memory': memory,  # Your conversation memory
      'code': generated_code,  # The generated Python code
      'output_type': 'DataFrame'  # The expected output type
   }
   
   template = Template(template_str)
   corrected_code = template.render(context=context)
   
   print(corrected_code)

3. Handle Validation in call_llm_with_prompt Method: Ensure that the call_llm_with_prompt method in your BaseAgent class correctly handles the validation of the output type. The method should retry the prompt if the validation fails.

   def call_llm_with_prompt(self, prompt: BasePrompt):
      retry_count = 0
      while retry_count < self.context.config.max_retries:
          try:
              result: str = self.context.config.llm.call(prompt)
              if prompt.validate(result):
                  return result
              else:
                  raise InvalidLLMOutputType("Response validation failed!")
          except Exception:
              if (
                  not self.context.config.use_error_correction_framework
                  or retry_count >= self.context.config.max_retries - 1
              ):
                  raise
              retry_count += 1

4. Generate and Validate the Schema: Ensure that the schema generation and validation are correctly implemented in the SemanticAgent class.

   def _create_schema(self):
      if self._schema:
          self.logger.log(f"using user provided schema: {self._schema}")
          return
   
      key = self._get_schema_cache_key()
      if self.config.enable_cache:
          value = self._schema_cache.get(key)
          if value is not None:
              self._schema = json.loads(value)
              self.logger.log(f"using schema: {self._schema}")
              return
   
      prompt = GenerateDFSchemaPrompt(context=self.context)
      result = self.call_llm_with_prompt(prompt)
      self.logger.log(f"Initializing Schema:  {result}")
      self._schema = result.replace("# SAMPLE SCHEMA", "")
      schema_data = extract_json_from_json_str(result.replace("# SAMPLE SCHEMA", ""))
      if isinstance(schema_data, dict):
          schema_data = [schema_data]
   
      self._schema = schema_data
      if self.config.enable_cache:
          self._schema_cache.set(key, json.dumps(self._schema))
   
      self.logger.log(f"using schema: {self._schema}")

5. Ensure Correct Output Type Validation: Ensure that your generated code produces a result dictionary with the correct type and value fields that match the expected output type.

   class DataFrameOutputType(BaseOutputType):
      @property
      def template_hint(self):
          return """type (must be "dataframe"), value must be pd.DataFrame or pd.Series. Example: { "type": "dataframe", "value": pd.DataFrame({...}) }"""
   
      @property
      def name(self):
          return "dataframe"
   
      def _validate_value(self, actual_value: Any) -> bool:
          return bool(df_type(actual_value))

By following these steps, you should be able to resolve the InvalidLLMOutputType: Response validation failed! error and successfully generate the schema ^[1][2][3][4].

To continue talking to Dosu, mention @dosu.

[pratik9409]

1265 Will like to solve this bug

[gventuri]

@pratik9409 sure, thanks a lot for the availability, I've assigned it to you! :D

[pratik9409]

Testing with a simple DataFrame

df = pd.DataFrame(columns=["Empdata"], data=[[1], [2]])

df.head()

try:

Create an instance of the SemanticAgent with the provided dataframe

semantic_agent = SemanticAgent(dfs=df)
#  Print the generated schema
print(semantic_agent._schema)

except InvalidLLMOutputType as e:

If the LLM fails to generate a valid schema, catch the InvalidLLMOutputType exception

print(f"Error: {e}")  # Print the error message
print("Using fallback schema...")  # Inform the user that a fallback schema will be used