pandasai.exceptions.NoCodeFoundError: No code found in the response

To resolve the error "pandasai.exceptions.NoCodeFoundError: No code found in the response" while using Pandasai 2.2.12 with Python 3.10.14 and Amazon Bedrock - Anthropic Claude 2 as the LLM, you need to ensure that the response from the LLM contains valid Python code. The _extract_code method in the LLM class is responsible for extracting and validating the code from the response.

Here is the relevant part of the code:

def _extract_code(self, response: str, separator: str = "```") -> str:
    """
    Extract the code from the response.

    Args:
        response (str): Response
        separator (str, optional): Separator. Defaults to "```".

    Raises:
        NoCodeFoundError: No code found in the response

    Returns:
        str: Extracted code from the response

    """
    code = response
    # If separator is in the response then we want the code in between only
    if separator in response and len(code.split(separator)) > 1:
        code = code.split(separator)[1]
    code = self._polish_code(code)

    # Even if the separator is not in the response, the output might still be valid python code
    if not self._is_python_code(code):
        raise NoCodeFoundError("No code found in the response")

    return code

To resolve the error, you should:

Ensure the LLM response contains code: Make sure that the response from the LLM (Anthropic Claude 2) includes Python code enclosed within the specified separator (default is "```"). For example:
```
```python
print("Hello, World!")
```
Check the separator: If the code is not enclosed within the default separator, you might need to adjust the separator parameter when calling _extract_code.
Validate the code: Ensure that the extracted code is valid Python code. The _is_python_code method uses ast.parse to check for syntax errors.

If the response does not contain valid Python code or is not properly formatted, the _extract_code method will raise the NoCodeFoundError.

Here is an example of how you might call the generate_code method, which internally uses _extract_code:

instruction = BasePrompt("Generate a Python function to add two numbers.")
context = PipelineContext()  # Assuming you have a context object

try:
    code = llm.generate_code(instruction, context)
    print("Generated code:", code)
except NoCodeFoundError as e:
    print("Error:", e)

Ensure that the LLM is correctly configured and that the prompt provided to the LLM is likely to generate a response containing valid Python code. Additionally, verify that the BedrockClaude class is properly set up with the correct model and parameters. Here is a snippet from the BedrockClaude class for reference:

class BedrockClaude(LLM):
    """Bedrock Claude LLM
       Generates text using Anthropic Claude Messages API.

    Attributes:
        bedrock_runtime_client: The boto3 bedrock runtime client.
        max_tokens: Max number of tokens to generate.
        model: The Bedrock Claude model to use.
        temperature: (Optional) The amount of randomness injected into the response.
        top_p: (Optional) Use nucleus sampling. In nucleus sampling, Anthropic Claude computes the cumulative distribution over all the options for each subsequent token in decreasing probability order and cuts it off once it reaches a particular probability specified by top_p. You should alter either temperature or top_p, but not both.
        top_k: (Optional) Only sample from the top K options for each subsequent token.
        stop_sequences: (Optional) Custom text sequences that cause the model to stop generating. Anthropic Claude models normally stop when they have naturally completed their turn, in this case the value of the stop_reason response field is end_turn. If you want the model to stop generating when it encounters custom strings of text, you can use the stop_sequences parameter. If the model encounters one of the custom text strings, the value of the stop_reason response field is stop_sequence and the value of stop_sequence contains the matched stop sequence.
    """

    _supported__models = [
        "anthropic.claude-3-opus-20240229-v1:0",
        "anthropic.claude-3-5-sonnet-20240620-v1:0",
        "anthropic.claude-3-sonnet-20240229-v1:0",
        "anthropic.claude-3-haiku-20240307-v1:0",
    ]
    _valid_params = [
        "max_tokens",
        "model",
        "temperature",
        "top_p",
        "top_k",
        "stop_sequences",
    ]

    max_tokens: int = 1024
    model: str = "anthropic.claude-3-sonnet-20240229-v1:0"
    temperature: Optional[float] = None
    top_p: Optional[float] = None
    top_k: Optional[float] = None
    stop_sequences: Optional[str] = None
    client: Any

    def __init__(self, bedrock_runtime_client, **kwargs):
        for key, val in kwargs.items():
            if key in self._valid_params:
                setattr(self, key, val)

        self.client = bedrock_runtime_client

        if self.model not in self._supported__models:
            raise UnsupportedModelError(self.model)

        invoke_model = getattr(self.client, "invoke_model", None)
        if not callable(invoke_model):
            raise APIKeyNotFoundError

    @property
    def _default_params(self) -> Dict[str, Any]:
        """Get the default parameters for calling text generation inference API."""
        return {
            "max_tokens": self.max_tokens,
            "model": self.model,
            "temperature": self.temperature,
            "top_p": self.top_p,
            "top_k": self.top_k,
            "stop_sequences": self.stop_sequences,
        }

    def call(self, instruction: BasePrompt, context: PipelineContext = None) -> str:
        prompt = instruction.to_string()
        memory = context.memory if context else None

        messages = []
        system_prompt = ""
        if memory:
            if memory.agent_info:
                system_prompt = memory.get_system_prompt()
            for message in memory.all():
                if message["is_user"]:
                    if messages and messages[-1]["role"] == "user":
                        messages[-1]["content"].append(
                            {"type": "text", "text": message["message"]}
                        )
                    else:
                        messages.append(
                            {
                                "role": "user",
                                "content": [
                                    {"type": "text", "text": message["message"]}
                                ],
                            }
                        )
                else:
                    messages.append(
                        {
                            "role": "assistant",
                            "content": [{"type": "text", "text": message["message"]}],
                        }
                    )

        # adding current prompt as latest query message
        if messages and messages[-1]["role"] == "user":
            messages[-1]["content"].append({"type": "text", "text": prompt})
        else:
            messages.append(
                {"role": "user", "content": [{"type": "text", "text": prompt}]},
            )

        params = {
            "anthropic_version": "bedrock-2023-05-31",
            "system": system_prompt,
            "messages": messages,
        }
        for key, value in self._default_params.items():
            if key != "model" and value is not None:
                params[key] = value

        body = json.dumps(params)
        # print(body)

        response = self.client.invoke_model(modelId=self.model, body=body)

        response_body = json.loads(response.get("body").read())

        self.last_prompt = prompt
        # print(response_body["content"][0]["text"])
        return response_body["content"][0]["text"]

    @property
    def type(self) -> str:
        return "bedrock-claude"

Ensure that the BedrockClaude class is correctly instantiated and configured to use the appropriate model and parameters ^[1]^[2].

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Sinaptik-AI / pandas-ai

pandasai.exceptions.NoCodeFoundError: No code found in the response #1297

System Info

🐛 Describe the bug

Setup boto3 client to access bedrock in a shared AWS Account

---- ⚠️ Un-comment and edit the below lines as needed for your AWS setup ⚠️ ----

os.environ["AWS_PROFILE"] = ""

os.environ["BEDROCK_ASSUME_ROLE"] = "arn:aws:iam:::role/Crossaccountbedrock" # E.g. "arn:aws:..."

Import your dependencies

Initialize your connection

If no errors, print connected

Instantiate a LLM