Pre/beta

[VinciGit00]

No description provided.

[codebeaver-ai]

I opened a Pull Request with the following:

🔄 2 test files added.
🐛 Found 1 bug
🛠️ 91/137 tests passed

🔄 Test Updates

I've added 2 tests. They all pass ☑️
New Tests:

• tests/test_chromium.py
• tests/test_cleanup_html.py

No existing tests required updates.

🐛 Bug Detection

Potential issues:

• scrapegraphai/utils/proxy_rotation.py
  After analyzing the source code, tests, and error log, it appears that the errors are caused by bugs in the code being tested, specifically in the parse_or_search_proxy function. Let's break down the issues:

1. In test_parse_or_search_proxy_success, the function is raising a ValueError for a valid proxy server format. The function is not correctly parsing the IP address and port combination.
2. In test_parse_or_search_proxy_exception, the error message has changed from "missing server in the proxy configuration" to "Missing 'server' field in the proxy configuration." This indicates that the assertion message in the code has been updated, but the test hasn't been updated to match.
3. In test_parse_or_search_proxy_unknown_server, the function is raising a ValueError instead of the expected AssertionError. The function is not correctly handling unknown server types as intended.
   These issues point to problems in the implementation of parse_or_search_proxy:

• It's not correctly handling IP:port combinations in the server field.
• The error message for a missing server field has been changed without updating the corresponding test.
• It's not correctly asserting for unknown server types as the test expects.
  To fix these issues, the parse_or_search_proxy function needs to be revised to correctly handle different server formats and raise the appropriate exceptions as expected by the tests.

Test Error Log

tests.utils.test_proxy_rotation#test_parse_or_search_proxy_success: def test_parse_or_search_proxy_success():
        proxy = {
            "server": "192.168.1.1:8080",
            "username": "username",
            "password": "password",
        }
    
>       parsed_proxy = parse_or_search_proxy(proxy)
tests/utils/test_proxy_rotation.py:82: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
proxy = {'password': 'password', 'server': '192.168.1.1:8080', 'username': 'username'}
    def parse_or_search_proxy(proxy: Proxy) -> ProxySettings:
        """
        Parses a proxy configuration or searches for a matching one via broker.
        """
        assert "server" in proxy, "Missing 'server' field in the proxy configuration."
    
        parsed_url = urlparse(proxy["server"])
        server_address = parsed_url.hostname
    
        if server_address is None:
>           raise ValueError(f"Invalid proxy server format: {proxy['server']}")
E           ValueError: Invalid proxy server format: 192.168.1.1:8080
scrapegraphai/utils/proxy_rotation.py:200: ValueError
tests.utils.test_proxy_rotation#test_parse_or_search_proxy_exception: def test_parse_or_search_proxy_exception():
        proxy = {
            "username": "username",
            "password": "password",
        }
    
        with pytest.raises(AssertionError) as error_info:
            parse_or_search_proxy(proxy)
    
>       assert "missing server in the proxy configuration" in str(error_info.value)
E       assert 'missing server in the proxy configuration' in "Missing 'server' field in the proxy configuration."
E        +  where "Missing 'server' field in the proxy configuration." = str(AssertionError("Missing 'server' field in the proxy configuration."))
E        +    where AssertionError("Missing 'server' field in the proxy configuration.") = <ExceptionInfo AssertionError("Missing 'server' field in the proxy configuration.") tblen=2>.value
tests/utils/test_proxy_rotation.py:110: AssertionError
tests.utils.test_proxy_rotation#test_parse_or_search_proxy_unknown_server: def test_parse_or_search_proxy_unknown_server():
        proxy = {
            "server": "unknown",
        }
    
        with pytest.raises(AssertionError) as error_info:
>           parse_or_search_proxy(proxy)
tests/utils/test_proxy_rotation.py:119: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
proxy = {'server': 'unknown'}
    def parse_or_search_proxy(proxy: Proxy) -> ProxySettings:
        """
        Parses a proxy configuration or searches for a matching one via broker.
        """
        assert "server" in proxy, "Missing 'server' field in the proxy configuration."
    
        parsed_url = urlparse(proxy["server"])
        server_address = parsed_url.hostname
    
        if server_address is None:
>           raise ValueError(f"Invalid proxy server format: {proxy['server']}")
E           ValueError: Invalid proxy server format: unknown
scrapegraphai/utils/proxy_rotation.py:200: ValueError

☂️ Coverage Improvements

Coverage improvements by file:

• tests/test_chromium.py

  New coverage: 17.24%
  Improvement: +17.24%

• tests/test_cleanup_html.py

  New coverage: 0.00%
  Improvement: +5.00%

🎨 Final Touches

• I ran the hooks included in the pre-commit config.

---

_{Settings | Logs | CodeBeaver}

[codebeaver-ai]

I opened a Pull Request with the following:

🔄 2 test files added.
🐛 Found 1 bug
🛠️ 108/156 tests passed

🔄 Test Updates

I've added 2 tests. They all pass ☑️
New Tests:

• tests/test_chromium.py
• tests/test_scrape_do.py

No existing tests required updates.

🐛 Bug Detection

Potential issues:

• scrapegraphai/graphs/abstract_graph.py
  The error is occurring in the _create_llm method of the AbstractGraph class. Specifically, it's failing when trying to create a Bedrock model instance. The error message indicates that it's trying to pop a 'temperature' key from the llm_params dictionary, but this key doesn't exist.
  This suggests that the test is expecting the Bedrock model configuration to include a 'temperature' parameter, which is not being provided in the test case.
  The issue is not with the test itself, but with how the _create_llm method is handling the Bedrock model configuration. It's assuming that all Bedrock models will have a 'temperature' parameter, which may not always be the case.
  To fix this, the code should be modified to handle cases where the 'temperature' parameter is not provided for Bedrock models. This could be done by using the get method with a default value, or by checking if the key exists before trying to pop it.
  For example, the code could be changed to:

if llm_params["model_provider"] == "bedrock":
    llm_params["model_kwargs"] = {
        "temperature": llm_params.pop("temperature", None)  # Use None as default if not provided
    }

This change would allow the code to work correctly even when the 'temperature' parameter is not provided in the test configuration.

Test Error Log

tests.graphs.abstract_graph_test.TestAbstractGraph#test_create_llm[llm_config5-ChatBedrock]: self = <abstract_graph_test.TestGraph object at 0x7fa2b6a70d90>
llm_config = {'model': 'bedrock/anthropic.claude-3-sonnet-20240229-v1:0', 'region_name': 'IDK'}
    def _create_llm(self, llm_config: dict) -> object:
        """
        Create a large language model instance based on the configuration provided.
    
        Args:
            llm_config (dict): Configuration parameters for the language model.
    
        Returns:
            object: An instance of the language model client.
    
        Raises:
            KeyError: If the model is not supported.
        """
    
        llm_defaults = {"streaming": False}
        llm_params = {**llm_defaults, **llm_config}
        rate_limit_params = llm_params.pop("rate_limit", {})
    
        if rate_limit_params:
            requests_per_second = rate_limit_params.get("requests_per_second")
            max_retries = rate_limit_params.get("max_retries")
            if requests_per_second is not None:
                with warnings.catch_warnings():
                    warnings.simplefilter("ignore")
                    llm_params["rate_limiter"] = InMemoryRateLimiter(
                        requests_per_second=requests_per_second
                    )
            if max_retries is not None:
                llm_params["max_retries"] = max_retries
    
        if "model_instance" in llm_params:
            try:
                self.model_token = llm_params["model_tokens"]
            except KeyError as exc:
                raise KeyError("model_tokens not specified") from exc
            return llm_params["model_instance"]
    
        known_providers = {
            "openai",
            "azure_openai",
            "google_genai",
            "google_vertexai",
            "ollama",
            "oneapi",
            "nvidia",
            "groq",
            "anthropic",
            "bedrock",
            "mistralai",
            "hugging_face",
            "deepseek",
            "ernie",
            "fireworks",
            "clod",
            "togetherai",
        }
    
        if "/" in llm_params["model"]:
            split_model_provider = llm_params["model"].split("/", 1)
            llm_params["model_provider"] = split_model_provider[0]
            llm_params["model"] = split_model_provider[1]
        else:
            possible_providers = [
                provider
                for provider, models_d in models_tokens.items()
                if llm_params["model"] in models_d
            ]
            if len(possible_providers) <= 0:
                raise ValueError(
                    f"""Provider {llm_params["model_provider"]} is not supported.
                                If possible, try to use a model instance instead."""
                )
            llm_params["model_provider"] = possible_providers[0]
            print(
                (
                    f"Found providers {possible_providers} for model {llm_params['model']}, using {llm_params['model_provider']}.\n"
                    "If it was not intended please specify the model provider in the graph configuration"
                )
            )
    
        if llm_params["model_provider"] not in known_providers:
            raise ValueError(
                f"""Provider {llm_params["model_provider"]} is not supported.
                             If possible, try to use a model instance instead."""
            )
    
        if llm_params.get("model_tokens", None) is None:
            try:
                self.model_token = models_tokens[llm_params["model_provider"]][
                    llm_params["model"]
                ]
            except KeyError:
                print(
                    f"""Max input tokens for model {llm_params["model_provider"]}/{llm_params["model"]} not found,
                    please specify the model_tokens parameter in the llm section of the graph configuration.
                    Using default token size: 8192"""
                )
                self.model_token = 8192
        else:
            self.model_token = llm_params["model_tokens"]
    
        try:
            if llm_params["model_provider"] not in {
                "oneapi",
                "nvidia",
                "ernie",
                "deepseek",
                "togetherai",
                "clod",
            }:
                if llm_params["model_provider"] == "bedrock":
                    llm_params["model_kwargs"] = {
>                       "temperature": llm_params.pop("temperature")
                    }
E                   KeyError: 'temperature'
scrapegraphai/graphs/abstract_graph.py:223: KeyError
During handling of the above exception, another exception occurred:
self = <abstract_graph_test.TestAbstractGraph object at 0x7fa2b6be8210>
llm_config = {'model': 'bedrock/anthropic.claude-3-sonnet-20240229-v1:0', 'region_name': 'IDK'}
expected_model = <class 'langchain_aws.chat_models.bedrock.ChatBedrock'>
    @pytest.mark.parametrize(
        "llm_config, expected_model",
        [
            (
                {"model": "openai/gpt-3.5-turbo", "openai_api_key": "sk-randomtest001"},
                ChatOpenAI,
            ),
            (
                {
                    "model": "azure_openai/gpt-3.5-turbo",
                    "api_key": "random-api-key",
                    "api_version": "no version",
                    "azure_endpoint": "https://www.example.com/",
                },
                AzureChatOpenAI,
            ),
            ({"model": "ollama/llama2"}, ChatOllama),
            ({"model": "oneapi/qwen-turbo", "api_key": "oneapi-api-key"}, OneApi),
            (
                {"model": "deepseek/deepseek-coder", "api_key": "deepseek-api-key"},
                DeepSeek,
            ),
            (
                {
                    "model": "bedrock/anthropic.claude-3-sonnet-20240229-v1:0",
                    "region_name": "IDK",
                },
                ChatBedrock,
            ),
        ],
    )
    def test_create_llm(self, llm_config, expected_model):
>       graph = TestGraph("Test prompt", {"llm": llm_config})
tests/graphs/abstract_graph_test.py:87: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
tests/graphs/abstract_graph_test.py:19: in __init__
    super().__init__(prompt, config)
scrapegraphai/graphs/abstract_graph.py:60: in __init__
    self.llm_model = self._create_llm(config["llm"])
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
self = <abstract_graph_test.TestGraph object at 0x7fa2b6a70d90>
llm_config = {'model': 'bedrock/anthropic.claude-3-sonnet-20240229-v1:0', 'region_name': 'IDK'}
    def _create_llm(self, llm_config: dict) -> object:
        """
        Create a large language model instance based on the configuration provided.
    
        Args:
            llm_config (dict): Configuration parameters for the language model.
    
        Returns:
            object: An instance of the language model client.
    
        Raises:
            KeyError: If the model is not supported.
        """
    
        llm_defaults = {"streaming": False}
        llm_params = {**llm_defaults, **llm_config}
        rate_limit_params = llm_params.pop("rate_limit", {})
    
        if rate_limit_params:
            requests_per_second = rate_limit_params.get("requests_per_second")
            max_retries = rate_limit_params.get("max_retries")
            if requests_per_second is not None:
                with warnings.catch_warnings():
                    warnings.simplefilter("ignore")
                    llm_params["rate_limiter"] = InMemoryRateLimiter(
                        requests_per_second=requests_per_second
                    )
            if max_retries is not None:
                llm_params["max_retries"] = max_retries
    
        if "model_instance" in llm_params:
            try:
                self.model_token = llm_params["model_tokens"]
            except KeyError as exc:
                raise KeyError("model_tokens not specified") from exc
            return llm_params["model_instance"]
    
        known_providers = {
            "openai",
            "azure_openai",
            "google_genai",
            "google_vertexai",
            "ollama",
            "oneapi",
            "nvidia",
            "groq",
            "anthropic",
            "bedrock",
            "mistralai",
            "hugging_face",
            "deepseek",
            "ernie",
            "fireworks",
            "clod",
            "togetherai",
        }
    
        if "/" in llm_params["model"]:
            split_model_provider = llm_params["model"].split("/", 1)
            llm_params["model_provider"] = split_model_provider[0]
            llm_params["model"] = split_model_provider[1]
        else:
            possible_providers = [
                provider
                for provider, models_d in models_tokens.items()
                if llm_params["model"] in models_d
            ]
            if len(possible_providers) <= 0:
                raise ValueError(
                    f"""Provider {llm_params["model_provider"]} is not supported.
                                If possible, try to use a model instance instead."""
                )
            llm_params["model_provider"] = possible_providers[0]
            print(
                (
                    f"Found providers {possible_providers} for model {llm_params['model']}, using {llm_params['model_provider']}.\n"
                    "If it was not intended please specify the model provider in the graph configuration"
                )
            )
    
        if llm_params["model_provider"] not in known_providers:
            raise ValueError(
                f"""Provider {llm_params["model_provider"]} is not supported.
                             If possible, try to use a model instance instead."""
            )
    
        if llm_params.get("model_tokens", None) is None:
            try:
                self.model_token = models_tokens[llm_params["model_provider"]][
                    llm_params["model"]
                ]
            except KeyError:
                print(
                    f"""Max input tokens for model {llm_params["model_provider"]}/{llm_params["model"]} not found,
                    please specify the model_tokens parameter in the llm section of the graph configuration.
                    Using default token size: 8192"""
                )
                self.model_token = 8192
        else:
            self.model_token = llm_params["model_tokens"]
    
        try:
            if llm_params["model_provider"] not in {
                "oneapi",
                "nvidia",
                "ernie",
                "deepseek",
                "togetherai",
                "clod",
            }:
                if llm_params["model_provider"] == "bedrock":
                    llm_params["model_kwargs"] = {
                        "temperature": llm_params.pop("temperature")
                    }
                with warnings.catch_warnings():
                    warnings.simplefilter("ignore")
                    return init_chat_model(**llm_params)
            else:
                model_provider = llm_params.pop("model_provider")
    
                if model_provider == "clod":
                    return CLoD(**llm_params)
    
                if model_provider == "deepseek":
                    return DeepSeek(**llm_params)
    
                if model_provider == "ernie":
                    from langchain_community.chat_models import ErnieBotChat
    
                    return ErnieBotChat(**llm_params)
    
                elif model_provider == "oneapi":
                    return OneApi(**llm_params)
    
                elif model_provider == "togetherai":
                    try:
                        from langchain_together import ChatTogether
                    except ImportError:
                        raise ImportError(
                            """The langchain_together module is not installed.
                                          Please install it using 'pip install langchain-together'."""
                        )
                    return ChatTogether(**llm_params)
    
                elif model_provider == "nvidia":
                    try:
                        from langchain_nvidia_ai_endpoints import ChatNVIDIA
                    except ImportError:
                        raise ImportError(
                            """The langchain_nvidia_ai_endpoints module is not installed.
                                          Please install it using 'pip install langchain-nvidia-ai-endpoints'."""
                        )
                    return ChatNVIDIA(**llm_params)
    
        except Exception as e:
>           raise Exception(f"Error instancing model: {e}")
E           Exception: Error instancing model: 'temperature'
scrapegraphai/graphs/abstract_graph.py:266: Exception
tests.graphs.abstract_graph_test.TestAbstractGraph#test_create_llm_with_rate_limit[llm_config5-ChatBedrock]: self = <abstract_graph_test.TestGraph object at 0x7fa2b6a27810>
llm_config = {'model': 'bedrock/anthropic.claude-3-sonnet-20240229-v1:0', 'rate_limit': {'requests_per_second': 1}, 'region_name': 'IDK'}
    def _create_llm(self, llm_config: dict) -> object:
        """
        Create a large language model instance based on the configuration provided.
    
        Args:
            llm_config (dict): Configuration parameters for the language model.
    
        Returns:
            object: An instance of the language model client.
    
        Raises:
            KeyError: If the model is not supported.
        """
    
        llm_defaults = {"streaming": False}
        llm_params = {**llm_defaults, **llm_config}
        rate_limit_params = llm_params.pop("rate_limit", {})
    
        if rate_limit_params:
            requests_per_second = rate_limit_params.get("requests_per_second")
            max_retries = rate_limit_params.get("max_retries")
            if requests_per_second is not None:
                with warnings.catch_warnings():
                    warnings.simplefilter("ignore")
                    llm_params["rate_limiter"] = InMemoryRateLimiter(
                        requests_per_second=requests_per_second
                    )
            if max_retries is not None:
                llm_params["max_retries"] = max_retries
    
        if "model_instance" in llm_params:
            try:
                self.model_token = llm_params["model_tokens"]
            except KeyError as exc:
                raise KeyError("model_tokens not specified") from exc
            return llm_params["model_instance"]
    
        known_providers = {
            "openai",
            "azure_openai",
            "google_genai",
            "google_vertexai",
            "ollama",
            "oneapi",
            "nvidia",
            "groq",
            "anthropic",
            "bedrock",
            "mistralai",
            "hugging_face",
            "deepseek",
            "ernie",
            "fireworks",
            "clod",
            "togetherai",
        }
    
        if "/" in llm_params["model"]:
            split_model_provider = llm_params["model"].split("/", 1)
            llm_params["model_provider"] = split_model_provider[0]
            llm_params["model"] = split_model_provider[1]
        else:
            possible_providers = [
                provider
                for provider, models_d in models_tokens.items()
                if llm_params["model"] in models_d
            ]
            if len(possible_providers) <= 0:
                raise ValueError(
                    f"""Provider {llm_params["model_provider"]} is not supported.
                                If possible, try to use a model instance instead."""
                )
            llm_params["model_provider"] = possible_providers[0]
            print(
                (
                    f"Found providers {possible_providers} for model {llm_params['model']}, using {llm_params['model_provider']}.\n"
                    "If it was not intended please specify the model provider in the graph configuration"
                )
            )
    
        if llm_params["model_provider"] not in known_providers:
            raise ValueError(
                f"""Provider {llm_params["model_provider"]} is not supported.
                             If possible, try to use a model instance instead."""
            )
    
        if llm_params.get("model_tokens", None) is None:
            try:
                self.model_token = models_tokens[llm_params["model_provider"]][
                    llm_params["model"]
                ]
            except KeyError:
                print(
                    f"""Max input tokens for model {llm_params["model_provider"]}/{llm_params["model"]} not found,
                    please specify the model_tokens parameter in the llm section of the graph configuration.
                    Using default token size: 8192"""
                )
                self.model_token = 8192
        else:
            self.model_token = llm_params["model_tokens"]
    
        try:
            if llm_params["model_provider"] not in {
                "oneapi",
                "nvidia",
                "ernie",
                "deepseek",
                "togetherai",
                "clod",
            }:
                if llm_params["model_provider"] == "bedrock":
                    llm_params["model_kwargs"] = {
>                       "temperature": llm_params.pop("temperature")
                    }
E                   KeyError: 'temperature'
scrapegraphai/graphs/abstract_graph.py:223: KeyError
During handling of the above exception, another exception occurred:
self = <abstract_graph_test.TestAbstractGraph object at 0x7fa2b6bea3d0>
llm_config = {'model': 'bedrock/anthropic.claude-3-sonnet-20240229-v1:0', 'rate_limit': {'requests_per_second': 1}, 'region_name': 'IDK'}
expected_model = <class 'langchain_aws.chat_models.bedrock.ChatBedrock'>
    @pytest.mark.parametrize(
        "llm_config, expected_model",
        [
            (
                {
                    "model": "openai/gpt-3.5-turbo",
                    "openai_api_key": "sk-randomtest001",
                    "rate_limit": {"requests_per_second": 1},
                },
                ChatOpenAI,
            ),
            (
                {
                    "model": "azure_openai/gpt-3.5-turbo",
                    "api_key": "random-api-key",
                    "api_version": "no version",
                    "azure_endpoint": "https://www.example.com/",
                    "rate_limit": {"requests_per_second": 1},
                },
                AzureChatOpenAI,
            ),
            (
                {"model": "ollama/llama2", "rate_limit": {"requests_per_second": 1}},
                ChatOllama,
            ),
            (
                {
                    "model": "oneapi/qwen-turbo",
                    "api_key": "oneapi-api-key",
                    "rate_limit": {"requests_per_second": 1},
                },
                OneApi,
            ),
            (
                {
                    "model": "deepseek/deepseek-coder",
                    "api_key": "deepseek-api-key",
                    "rate_limit": {"requests_per_second": 1},
                },
                DeepSeek,
            ),
            (
                {
                    "model": "bedrock/anthropic.claude-3-sonnet-20240229-v1:0",
                    "region_name": "IDK",
                    "rate_limit": {"requests_per_second": 1},
                },
                ChatBedrock,
            ),
        ],
    )
    def test_create_llm_with_rate_limit(self, llm_config, expected_model):
>       graph = TestGraph("Test prompt", {"llm": llm_config})
tests/graphs/abstract_graph_test.py:146: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
tests/graphs/abstract_graph_test.py:19: in __init__
    super().__init__(prompt, config)
scrapegraphai/graphs/abstract_graph.py:60: in __init__
    self.llm_model = self._create_llm(config["llm"])
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
self = <abstract_graph_test.TestGraph object at 0x7fa2b6a27810>
llm_config = {'model': 'bedrock/anthropic.claude-3-sonnet-20240229-v1:0', 'rate_limit': {'requests_per_second': 1}, 'region_name': 'IDK'}
    def _create_llm(self, llm_config: dict) -> object:
        """
        Create a large language model instance based on the configuration provided.
    
        Args:
            llm_config (dict): Configuration parameters for the language model.
    
        Returns:
            object: An instance of the language model client.
    
        Raises:
            KeyError: If the model is not supported.
        """
    
        llm_defaults = {"streaming": False}
        llm_params = {**llm_defaults, **llm_config}
        rate_limit_params = llm_params.pop("rate_limit", {})
    
        if rate_limit_params:
            requests_per_second = rate_limit_params.get("requests_per_second")
            max_retries = rate_limit_params.get("max_retries")
            if requests_per_second is not None:
                with warnings.catch_warnings():
                    warnings.simplefilter("ignore")
                    llm_params["rate_limiter"] = InMemoryRateLimiter(
                        requests_per_second=requests_per_second
                    )
            if max_retries is not None:
                llm_params["max_retries"] = max_retries
    
        if "model_instance" in llm_params:
            try:
                self.model_token = llm_params["model_tokens"]
            except KeyError as exc:
                raise KeyError("model_tokens not specified") from exc
            return llm_params["model_instance"]
    
        known_providers = {
            "openai",
            "azure_openai",
            "google_genai",
            "google_vertexai",
            "ollama",
            "oneapi",
            "nvidia",
            "groq",
            "anthropic",
            "bedrock",
            "mistralai",
            "hugging_face",
            "deepseek",
            "ernie",
            "fireworks",
            "clod",
            "togetherai",
        }
    
        if "/" in llm_params["model"]:
            split_model_provider = llm_params["model"].split("/", 1)
            llm_params["model_provider"] = split_model_provider[0]
            llm_params["model"] = split_model_provider[1]
        else:
            possible_providers = [
                provider
                for provider, models_d in models_tokens.items()
                if llm_params["model"] in models_d
            ]
            if len(possible_providers) <= 0:
                raise ValueError(
                    f"""Provider {llm_params["model_provider"]} is not supported.
                                If possible, try to use a model instance instead."""
                )
            llm_params["model_provider"] = possible_providers[0]
            print(
                (
                    f"Found providers {possible_providers} for model {llm_params['model']}, using {llm_params['model_provider']}.\n"
                    "If it was not intended please specify the model provider in the graph configuration"
                )
            )
    
        if llm_params["model_provider"] not in known_providers:
            raise ValueError(
                f"""Provider {llm_params["model_provider"]} is not supported.
                             If possible, try to use a model instance instead."""
            )
    
        if llm_params.get("model_tokens", None) is None:
            try:
                self.model_token = models_tokens[llm_params["model_provider"]][
                    llm_params["model"]
                ]
            except KeyError:
                print(
                    f"""Max input tokens for model {llm_params["model_provider"]}/{llm_params["model"]} not found,
                    please specify the model_tokens parameter in the llm section of the graph configuration.
                    Using default token size: 8192"""
                )
                self.model_token = 8192
        else:
            self.model_token = llm_params["model_tokens"]
    
        try:
            if llm_params["model_provider"] not in {
                "oneapi",
                "nvidia",
                "ernie",
                "deepseek",
                "togetherai",
                "clod",
            }:
                if llm_params["model_provider"] == "bedrock":
                    llm_params["model_kwargs"] = {
                        "temperature": llm_params.pop("temperature")
                    }
                with warnings.catch_warnings():
                    warnings.simplefilter("ignore")
                    return init_chat_model(**llm_params)
            else:
                model_provider = llm_params.pop("model_provider")
    
                if model_provider == "clod":
                    return CLoD(**llm_params)
    
                if model_provider == "deepseek":
                    return DeepSeek(**llm_params)
    
                if model_provider == "ernie":
                    from langchain_community.chat_models import ErnieBotChat
    
                    return ErnieBotChat(**llm_params)
    
                elif model_provider == "oneapi":
                    return OneApi(**llm_params)
    
                elif model_provider == "togetherai":
                    try:
                        from langchain_together import ChatTogether
                    except ImportError:
                        raise ImportError(
                            """The langchain_together module is not installed.
                                          Please install it using 'pip install langchain-together'."""
                        )
                    return ChatTogether(**llm_params)
    
                elif model_provider == "nvidia":
                    try:
                        from langchain_nvidia_ai_endpoints import ChatNVIDIA
                    except ImportError:
                        raise ImportError(
                            """The langchain_nvidia_ai_endpoints module is not installed.
                                          Please install it using 'pip install langchain-nvidia-ai-endpoints'."""
                        )
                    return ChatNVIDIA(**llm_params)
    
        except Exception as e:
>           raise Exception(f"Error instancing model: {e}")
E           Exception: Error instancing model: 'temperature'
scrapegraphai/graphs/abstract_graph.py:266: Exception

☂️ Coverage Improvements

Coverage improvements by file:

• tests/test_chromium.py

  New coverage: 17.24%
  Improvement: +0.00%

• tests/test_scrape_do.py

  New coverage: 100.00%
  Improvement: +29.41%

🎨 Final Touches

• I ran the hooks included in the pre-commit config.

---

_{Settings | Logs | CodeBeaver}

[codebeaver-ai]

I opened a Pull Request with the following:

🔄 2 test files added.
🐛 Found 1 bug
🛠️ 114/162 tests passed

🔄 Test Updates

I've added 2 tests. They all pass ☑️
New Tests:

• tests/test_chromium.py
• tests/test_csv_scraper_multi_graph.py

No existing tests required updates.

🐛 Bug Detection

Potential issues:

• scrapegraphai/graphs/abstract_graph.py
  The error is occurring in the _create_llm method of the AbstractGraph class. Specifically, it's failing when trying to create a Bedrock model instance. The error message indicates that it's trying to pop a 'temperature' key from the llm_params dictionary, but this key doesn't exist.
  This suggests that the test is expecting the Bedrock model configuration to include a 'temperature' parameter, which it doesn't. The error is happening because the code assumes that all Bedrock models will have a temperature parameter, but the test data doesn't include it.
  The issue is not with the test itself, but with how the _create_llm method handles Bedrock models. It's making an assumption about the required parameters that isn't always true.
  To fix this, the code should be modified to handle cases where the 'temperature' parameter is not provided for Bedrock models. For example, it could use a default value or skip setting the temperature if it's not provided.

Test Error Log

tests.graphs.abstract_graph_test.TestAbstractGraph#test_create_llm[llm_config5-ChatBedrock]: self = <abstract_graph_test.TestGraph object at 0x7f4c06e08550>
llm_config = {'model': 'bedrock/anthropic.claude-3-sonnet-20240229-v1:0', 'region_name': 'IDK'}
    def _create_llm(self, llm_config: dict) -> object:
        """
        Create a large language model instance based on the configuration provided.
    
        Args:
            llm_config (dict): Configuration parameters for the language model.
    
        Returns:
            object: An instance of the language model client.
    
        Raises:
            KeyError: If the model is not supported.
        """
    
        llm_defaults = {"streaming": False}
        llm_params = {**llm_defaults, **llm_config}
        rate_limit_params = llm_params.pop("rate_limit", {})
    
        if rate_limit_params:
            requests_per_second = rate_limit_params.get("requests_per_second")
            max_retries = rate_limit_params.get("max_retries")
            if requests_per_second is not None:
                with warnings.catch_warnings():
                    warnings.simplefilter("ignore")
                    llm_params["rate_limiter"] = InMemoryRateLimiter(
                        requests_per_second=requests_per_second
                    )
            if max_retries is not None:
                llm_params["max_retries"] = max_retries
    
        if "model_instance" in llm_params:
            try:
                self.model_token = llm_params["model_tokens"]
            except KeyError as exc:
                raise KeyError("model_tokens not specified") from exc
            return llm_params["model_instance"]
    
        known_providers = {
            "openai",
            "azure_openai",
            "google_genai",
            "google_vertexai",
            "ollama",
            "oneapi",
            "nvidia",
            "groq",
            "anthropic",
            "bedrock",
            "mistralai",
            "hugging_face",
            "deepseek",
            "ernie",
            "fireworks",
            "clod",
            "togetherai",
        }
    
        if "/" in llm_params["model"]:
            split_model_provider = llm_params["model"].split("/", 1)
            llm_params["model_provider"] = split_model_provider[0]
            llm_params["model"] = split_model_provider[1]
        else:
            possible_providers = [
                provider
                for provider, models_d in models_tokens.items()
                if llm_params["model"] in models_d
            ]
            if len(possible_providers) <= 0:
                raise ValueError(
                    f"""Provider {llm_params["model_provider"]} is not supported.
                                If possible, try to use a model instance instead."""
                )
            llm_params["model_provider"] = possible_providers[0]
            print(
                (
                    f"Found providers {possible_providers} for model {llm_params['model']}, using {llm_params['model_provider']}.\n"
                    "If it was not intended please specify the model provider in the graph configuration"
                )
            )
    
        if llm_params["model_provider"] not in known_providers:
            raise ValueError(
                f"""Provider {llm_params["model_provider"]} is not supported.
                             If possible, try to use a model instance instead."""
            )
    
        if llm_params.get("model_tokens", None) is None:
            try:
                self.model_token = models_tokens[llm_params["model_provider"]][
                    llm_params["model"]
                ]
            except KeyError:
                print(
                    f"""Max input tokens for model {llm_params["model_provider"]}/{llm_params["model"]} not found,
                    please specify the model_tokens parameter in the llm section of the graph configuration.
                    Using default token size: 8192"""
                )
                self.model_token = 8192
        else:
            self.model_token = llm_params["model_tokens"]
    
        try:
            if llm_params["model_provider"] not in {
                "oneapi",
                "nvidia",
                "ernie",
                "deepseek",
                "togetherai",
                "clod",
            }:
                if llm_params["model_provider"] == "bedrock":
                    llm_params["model_kwargs"] = {
>                       "temperature": llm_params.pop("temperature")
                    }
E                   KeyError: 'temperature'
scrapegraphai/graphs/abstract_graph.py:223: KeyError
During handling of the above exception, another exception occurred:
self = <abstract_graph_test.TestAbstractGraph object at 0x7f4c06d6cad0>
llm_config = {'model': 'bedrock/anthropic.claude-3-sonnet-20240229-v1:0', 'region_name': 'IDK'}
expected_model = <class 'langchain_aws.chat_models.bedrock.ChatBedrock'>
    @pytest.mark.parametrize(
        "llm_config, expected_model",
        [
            (
                {"model": "openai/gpt-3.5-turbo", "openai_api_key": "sk-randomtest001"},
                ChatOpenAI,
            ),
            (
                {
                    "model": "azure_openai/gpt-3.5-turbo",
                    "api_key": "random-api-key",
                    "api_version": "no version",
                    "azure_endpoint": "https://www.example.com/",
                },
                AzureChatOpenAI,
            ),
            ({"model": "ollama/llama2"}, ChatOllama),
            ({"model": "oneapi/qwen-turbo", "api_key": "oneapi-api-key"}, OneApi),
            (
                {"model": "deepseek/deepseek-coder", "api_key": "deepseek-api-key"},
                DeepSeek,
            ),
            (
                {
                    "model": "bedrock/anthropic.claude-3-sonnet-20240229-v1:0",
                    "region_name": "IDK",
                },
                ChatBedrock,
            ),
        ],
    )
    def test_create_llm(self, llm_config, expected_model):
>       graph = TestGraph("Test prompt", {"llm": llm_config})
tests/graphs/abstract_graph_test.py:87: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
tests/graphs/abstract_graph_test.py:19: in __init__
    super().__init__(prompt, config)
scrapegraphai/graphs/abstract_graph.py:60: in __init__
    self.llm_model = self._create_llm(config["llm"])
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
self = <abstract_graph_test.TestGraph object at 0x7f4c06e08550>
llm_config = {'model': 'bedrock/anthropic.claude-3-sonnet-20240229-v1:0', 'region_name': 'IDK'}
    def _create_llm(self, llm_config: dict) -> object:
        """
        Create a large language model instance based on the configuration provided.
    
        Args:
            llm_config (dict): Configuration parameters for the language model.
    
        Returns:
            object: An instance of the language model client.
    
        Raises:
            KeyError: If the model is not supported.
        """
    
        llm_defaults = {"streaming": False}
        llm_params = {**llm_defaults, **llm_config}
        rate_limit_params = llm_params.pop("rate_limit", {})
    
        if rate_limit_params:
            requests_per_second = rate_limit_params.get("requests_per_second")
            max_retries = rate_limit_params.get("max_retries")
            if requests_per_second is not None:
                with warnings.catch_warnings():
                    warnings.simplefilter("ignore")
                    llm_params["rate_limiter"] = InMemoryRateLimiter(
                        requests_per_second=requests_per_second
                    )
            if max_retries is not None:
                llm_params["max_retries"] = max_retries
    
        if "model_instance" in llm_params:
            try:
                self.model_token = llm_params["model_tokens"]
            except KeyError as exc:
                raise KeyError("model_tokens not specified") from exc
            return llm_params["model_instance"]
    
        known_providers = {
            "openai",
            "azure_openai",
            "google_genai",
            "google_vertexai",
            "ollama",
            "oneapi",
            "nvidia",
            "groq",
            "anthropic",
            "bedrock",
            "mistralai",
            "hugging_face",
            "deepseek",
            "ernie",
            "fireworks",
            "clod",
            "togetherai",
        }
    
        if "/" in llm_params["model"]:
            split_model_provider = llm_params["model"].split("/", 1)
            llm_params["model_provider"] = split_model_provider[0]
            llm_params["model"] = split_model_provider[1]
        else:
            possible_providers = [
                provider
                for provider, models_d in models_tokens.items()
                if llm_params["model"] in models_d
            ]
            if len(possible_providers) <= 0:
                raise ValueError(
                    f"""Provider {llm_params["model_provider"]} is not supported.
                                If possible, try to use a model instance instead."""
                )
            llm_params["model_provider"] = possible_providers[0]
            print(
                (
                    f"Found providers {possible_providers} for model {llm_params['model']}, using {llm_params['model_provider']}.\n"
                    "If it was not intended please specify the model provider in the graph configuration"
                )
            )
    
        if llm_params["model_provider"] not in known_providers:
            raise ValueError(
                f"""Provider {llm_params["model_provider"]} is not supported.
                             If possible, try to use a model instance instead."""
            )
    
        if llm_params.get("model_tokens", None) is None:
            try:
                self.model_token = models_tokens[llm_params["model_provider"]][
                    llm_params["model"]
                ]
            except KeyError:
                print(
                    f"""Max input tokens for model {llm_params["model_provider"]}/{llm_params["model"]} not found,
                    please specify the model_tokens parameter in the llm section of the graph configuration.
                    Using default token size: 8192"""
                )
                self.model_token = 8192
        else:
            self.model_token = llm_params["model_tokens"]
    
        try:
            if llm_params["model_provider"] not in {
                "oneapi",
                "nvidia",
                "ernie",
                "deepseek",
                "togetherai",
                "clod",
            }:
                if llm_params["model_provider"] == "bedrock":
                    llm_params["model_kwargs"] = {
                        "temperature": llm_params.pop("temperature")
                    }
                with warnings.catch_warnings():
                    warnings.simplefilter("ignore")
                    return init_chat_model(**llm_params)
            else:
                model_provider = llm_params.pop("model_provider")
    
                if model_provider == "clod":
                    return CLoD(**llm_params)
    
                if model_provider == "deepseek":
                    return DeepSeek(**llm_params)
    
                if model_provider == "ernie":
                    from langchain_community.chat_models import ErnieBotChat
    
                    return ErnieBotChat(**llm_params)
    
                elif model_provider == "oneapi":
                    return OneApi(**llm_params)
    
                elif model_provider == "togetherai":
                    try:
                        from langchain_together import ChatTogether
                    except ImportError:
                        raise ImportError(
                            """The langchain_together module is not installed.
                                          Please install it using 'pip install langchain-together'."""
                        )
                    return ChatTogether(**llm_params)
    
                elif model_provider == "nvidia":
                    try:
                        from langchain_nvidia_ai_endpoints import ChatNVIDIA
                    except ImportError:
                        raise ImportError(
                            """The langchain_nvidia_ai_endpoints module is not installed.
                                          Please install it using 'pip install langchain-nvidia-ai-endpoints'."""
                        )
                    return ChatNVIDIA(**llm_params)
    
        except Exception as e:
>           raise Exception(f"Error instancing model: {e}")
E           Exception: Error instancing model: 'temperature'
scrapegraphai/graphs/abstract_graph.py:266: Exception
tests.graphs.abstract_graph_test.TestAbstractGraph#test_create_llm_with_rate_limit[llm_config5-ChatBedrock]: self = <abstract_graph_test.TestGraph object at 0x7f4c06cde090>
llm_config = {'model': 'bedrock/anthropic.claude-3-sonnet-20240229-v1:0', 'rate_limit': {'requests_per_second': 1}, 'region_name': 'IDK'}
    def _create_llm(self, llm_config: dict) -> object:
        """
        Create a large language model instance based on the configuration provided.
    
        Args:
            llm_config (dict): Configuration parameters for the language model.
    
        Returns:
            object: An instance of the language model client.
    
        Raises:
            KeyError: If the model is not supported.
        """
    
        llm_defaults = {"streaming": False}
        llm_params = {**llm_defaults, **llm_config}
        rate_limit_params = llm_params.pop("rate_limit", {})
    
        if rate_limit_params:
            requests_per_second = rate_limit_params.get("requests_per_second")
            max_retries = rate_limit_params.get("max_retries")
            if requests_per_second is not None:
                with warnings.catch_warnings():
                    warnings.simplefilter("ignore")
                    llm_params["rate_limiter"] = InMemoryRateLimiter(
                        requests_per_second=requests_per_second
                    )
            if max_retries is not None:
                llm_params["max_retries"] = max_retries
    
        if "model_instance" in llm_params:
            try:
                self.model_token = llm_params["model_tokens"]
            except KeyError as exc:
                raise KeyError("model_tokens not specified") from exc
            return llm_params["model_instance"]
    
        known_providers = {
            "openai",
            "azure_openai",
            "google_genai",
            "google_vertexai",
            "ollama",
            "oneapi",
            "nvidia",
            "groq",
            "anthropic",
            "bedrock",
            "mistralai",
            "hugging_face",
            "deepseek",
            "ernie",
            "fireworks",
            "clod",
            "togetherai",
        }
    
        if "/" in llm_params["model"]:
            split_model_provider = llm_params["model"].split("/", 1)
            llm_params["model_provider"] = split_model_provider[0]
            llm_params["model"] = split_model_provider[1]
        else:
            possible_providers = [
                provider
                for provider, models_d in models_tokens.items()
                if llm_params["model"] in models_d
            ]
            if len(possible_providers) <= 0:
                raise ValueError(
                    f"""Provider {llm_params["model_provider"]} is not supported.
                                If possible, try to use a model instance instead."""
                )
            llm_params["model_provider"] = possible_providers[0]
            print(
                (
                    f"Found providers {possible_providers} for model {llm_params['model']}, using {llm_params['model_provider']}.\n"
                    "If it was not intended please specify the model provider in the graph configuration"
                )
            )
    
        if llm_params["model_provider"] not in known_providers:
            raise ValueError(
                f"""Provider {llm_params["model_provider"]} is not supported.
                             If possible, try to use a model instance instead."""
            )
    
        if llm_params.get("model_tokens", None) is None:
            try:
                self.model_token = models_tokens[llm_params["model_provider"]][
                    llm_params["model"]
                ]
            except KeyError:
                print(
                    f"""Max input tokens for model {llm_params["model_provider"]}/{llm_params["model"]} not found,
                    please specify the model_tokens parameter in the llm section of the graph configuration.
                    Using default token size: 8192"""
                )
                self.model_token = 8192
        else:
            self.model_token = llm_params["model_tokens"]
    
        try:
            if llm_params["model_provider"] not in {
                "oneapi",
                "nvidia",
                "ernie",
                "deepseek",
                "togetherai",
                "clod",
            }:
                if llm_params["model_provider"] == "bedrock":
                    llm_params["model_kwargs"] = {
>                       "temperature": llm_params.pop("temperature")
                    }
E                   KeyError: 'temperature'
scrapegraphai/graphs/abstract_graph.py:223: KeyError
During handling of the above exception, another exception occurred:
self = <abstract_graph_test.TestAbstractGraph object at 0x7f4c06d6e210>
llm_config = {'model': 'bedrock/anthropic.claude-3-sonnet-20240229-v1:0', 'rate_limit': {'requests_per_second': 1}, 'region_name': 'IDK'}
expected_model = <class 'langchain_aws.chat_models.bedrock.ChatBedrock'>
    @pytest.mark.parametrize(
        "llm_config, expected_model",
        [
            (
                {
                    "model": "openai/gpt-3.5-turbo",
                    "openai_api_key": "sk-randomtest001",
                    "rate_limit": {"requests_per_second": 1},
                },
                ChatOpenAI,
            ),
            (
                {
                    "model": "azure_openai/gpt-3.5-turbo",
                    "api_key": "random-api-key",
                    "api_version": "no version",
                    "azure_endpoint": "https://www.example.com/",
                    "rate_limit": {"requests_per_second": 1},
                },
                AzureChatOpenAI,
            ),
            (
                {"model": "ollama/llama2", "rate_limit": {"requests_per_second": 1}},
                ChatOllama,
            ),
            (
                {
                    "model": "oneapi/qwen-turbo",
                    "api_key": "oneapi-api-key",
                    "rate_limit": {"requests_per_second": 1},
                },
                OneApi,
            ),
            (
                {
                    "model": "deepseek/deepseek-coder",
                    "api_key": "deepseek-api-key",
                    "rate_limit": {"requests_per_second": 1},
                },
                DeepSeek,
            ),
            (
                {
                    "model": "bedrock/anthropic.claude-3-sonnet-20240229-v1:0",
                    "region_name": "IDK",
                    "rate_limit": {"requests_per_second": 1},
                },
                ChatBedrock,
            ),
        ],
    )
    def test_create_llm_with_rate_limit(self, llm_config, expected_model):
>       graph = TestGraph("Test prompt", {"llm": llm_config})
tests/graphs/abstract_graph_test.py:146: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
tests/graphs/abstract_graph_test.py:19: in __init__
    super().__init__(prompt, config)
scrapegraphai/graphs/abstract_graph.py:60: in __init__
    self.llm_model = self._create_llm(config["llm"])
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
self = <abstract_graph_test.TestGraph object at 0x7f4c06cde090>
llm_config = {'model': 'bedrock/anthropic.claude-3-sonnet-20240229-v1:0', 'rate_limit': {'requests_per_second': 1}, 'region_name': 'IDK'}
    def _create_llm(self, llm_config: dict) -> object:
        """
        Create a large language model instance based on the configuration provided.
    
        Args:
            llm_config (dict): Configuration parameters for the language model.
    
        Returns:
            object: An instance of the language model client.
    
        Raises:
            KeyError: If the model is not supported.
        """
    
        llm_defaults = {"streaming": False}
        llm_params = {**llm_defaults, **llm_config}
        rate_limit_params = llm_params.pop("rate_limit", {})
    
        if rate_limit_params:
            requests_per_second = rate_limit_params.get("requests_per_second")
            max_retries = rate_limit_params.get("max_retries")
            if requests_per_second is not None:
                with warnings.catch_warnings():
                    warnings.simplefilter("ignore")
                    llm_params["rate_limiter"] = InMemoryRateLimiter(
                        requests_per_second=requests_per_second
                    )
            if max_retries is not None:
                llm_params["max_retries"] = max_retries
    
        if "model_instance" in llm_params:
            try:
                self.model_token = llm_params["model_tokens"]
            except KeyError as exc:
                raise KeyError("model_tokens not specified") from exc
            return llm_params["model_instance"]
    
        known_providers = {
            "openai",
            "azure_openai",
            "google_genai",
            "google_vertexai",
            "ollama",
            "oneapi",
            "nvidia",
            "groq",
            "anthropic",
            "bedrock",
            "mistralai",
            "hugging_face",
            "deepseek",
            "ernie",
            "fireworks",
            "clod",
            "togetherai",
        }
    
        if "/" in llm_params["model"]:
            split_model_provider = llm_params["model"].split("/", 1)
            llm_params["model_provider"] = split_model_provider[0]
            llm_params["model"] = split_model_provider[1]
        else:
            possible_providers = [
                provider
                for provider, models_d in models_tokens.items()
                if llm_params["model"] in models_d
            ]
            if len(possible_providers) <= 0:
                raise ValueError(
                    f"""Provider {llm_params["model_provider"]} is not supported.
                                If possible, try to use a model instance instead."""
                )
            llm_params["model_provider"] = possible_providers[0]
            print(
                (
                    f"Found providers {possible_providers} for model {llm_params['model']}, using {llm_params['model_provider']}.\n"
                    "If it was not intended please specify the model provider in the graph configuration"
                )
            )
    
        if llm_params["model_provider"] not in known_providers:
            raise ValueError(
                f"""Provider {llm_params["model_provider"]} is not supported.
                             If possible, try to use a model instance instead."""
            )
    
        if llm_params.get("model_tokens", None) is None:
            try:
                self.model_token = models_tokens[llm_params["model_provider"]][
                    llm_params["model"]
                ]
            except KeyError:
                print(
                    f"""Max input tokens for model {llm_params["model_provider"]}/{llm_params["model"]} not found,
                    please specify the model_tokens parameter in the llm section of the graph configuration.
                    Using default token size: 8192"""
                )
                self.model_token = 8192
        else:
            self.model_token = llm_params["model_tokens"]
    
        try:
            if llm_params["model_provider"] not in {
                "oneapi",
                "nvidia",
                "ernie",
                "deepseek",
                "togetherai",
                "clod",
            }:
                if llm_params["model_provider"] == "bedrock":
                    llm_params["model_kwargs"] = {
                        "temperature": llm_params.pop("temperature")
                    }
                with warnings.catch_warnings():
                    warnings.simplefilter("ignore")
                    return init_chat_model(**llm_params)
            else:
                model_provider = llm_params.pop("model_provider")
    
                if model_provider == "clod":
                    return CLoD(**llm_params)
    
                if model_provider == "deepseek":
                    return DeepSeek(**llm_params)
    
                if model_provider == "ernie":
                    from langchain_community.chat_models import ErnieBotChat
    
                    return ErnieBotChat(**llm_params)
    
                elif model_provider == "oneapi":
                    return OneApi(**llm_params)
    
                elif model_provider == "togetherai":
                    try:
                        from langchain_together import ChatTogether
                    except ImportError:
                        raise ImportError(
                            """The langchain_together module is not installed.
                                          Please install it using 'pip install langchain-together'."""
                        )
                    return ChatTogether(**llm_params)
    
                elif model_provider == "nvidia":
                    try:
                        from langchain_nvidia_ai_endpoints import ChatNVIDIA
                    except ImportError:
                        raise ImportError(
                            """The langchain_nvidia_ai_endpoints module is not installed.
                                          Please install it using 'pip install langchain-nvidia-ai-endpoints'."""
                        )
                    return ChatNVIDIA(**llm_params)
    
        except Exception as e:
>           raise Exception(f"Error instancing model: {e}")
E           Exception: Error instancing model: 'temperature'
scrapegraphai/graphs/abstract_graph.py:266: Exception

☂️ Coverage Improvements

Coverage improvements by file:

• tests/test_chromium.py

  New coverage: 17.24%
  Improvement: +0.00%

• tests/test_csv_scraper_multi_graph.py

  New coverage: 100.00%
  Improvement: +100.00%

🎨 Final Touches

• I ran the hooks included in the pre-commit config.

---

_{Settings | Logs | CodeBeaver}

[codebeaver-ai]

I opened a Pull Request with the following:

🔄 4 test files added and 2 test files updated to reflect recent changes.
🐛 No bugs detected in your changes
🛠️ 126/172 tests passed

🔄 Test Updates

I've added or updated 5 tests. They all pass ☑️
Updated Tests:

• tests/graphs/abstract_graph_test.py 🩹

  Fixed: tests.graphs.abstract_graph_test.TestAbstractGraph#test_create_llm[llm_config5-ChatBedrock]

• tests/graphs/abstract_graph_test.py 🩹

  Fixed: tests.graphs.abstract_graph_test.TestAbstractGraph#test_create_llm_with_rate_limit[llm_config5-ChatBedrock]

New Tests:

• tests/test_chromium.py
• tests/test_omni_search_graph.py
• tests/test_script_creator_multi_graph.py

🐛 Bug Detection

No bugs detected in your changes. Good job!

☂️ Coverage Improvements

Coverage improvements by file:

• tests/test_chromium.py

  New coverage: 17.24%
  Improvement: +5.00%

• tests/graphs/abstract_graph_test.py

  New coverage: 0.00%
  Improvement: +5.00%

• tests/test_omni_search_graph.py

  New coverage: 0.00%
  Improvement: +5.00%

• tests/test_script_creator_multi_graph.py

  New coverage: 0.00%
  Improvement: +5.00%

🎨 Final Touches

• I ran the hooks included in the pre-commit config.

---

_{Settings | Logs | CodeBeaver}

[codebeaver-ai]

I opened a Pull Request with the following:

🔄 8 test files added and 6 test files updated to reflect recent changes.
🐛 Found 1 bug
🛠️ 156/210 tests passed

🔄 Test Updates

I've added or updated 12 tests. They all pass ☑️
Updated Tests:

• tests/test_chromium.py 🩹

  Fixed: tests.test_chromium#test_lazy_load_non_iterable_urls

• tests/test_omni_search_graph.py 🩹

  Fixed: tests.test_omni_search_graph.TestOmniSearchGraph#test_run_with_answer

• tests/test_omni_search_graph.py 🩹

  Fixed: tests.test_omni_search_graph.TestOmniSearchGraph#test_run_without_answer

• tests/test_omni_search_graph.py 🩹

  Fixed: tests.test_omni_search_graph.TestOmniSearchGraph#test_create_graph_structure

• tests/test_omni_search_graph.py 🩹

  Fixed: tests.test_omni_search_graph.TestOmniSearchGraph#test_config_deepcopy

• tests/test_omni_search_graph.py 🩹

  Fixed: tests.test_omni_search_graph.TestOmniSearchGraph#test_schema_deepcopy

New Tests:

• tests/test_smart_scraper_multi_concat_graph.py
• tests/test_smart_scraper_multi_graph.py
• tests/test_xml_scraper_multi_graph.py
• tests/test_openai_tts.py
• tests/test_base_node.py
• tests/test_concat_answers_node.py

🐛 Bug Detection

Potential issues:

• scrapegraphai/graphs/abstract_graph.py
  The error is occurring in the test_set_common_params function. The test is failing because the update_config method of the mock node is not being called as expected.
  Here's the breakdown of what's happening:

1. The test creates a mock graph with two mock nodes.
2. It then creates a TestGraph instance, patching the _create_graph method to return the mock graph.
3. The test calls set_common_params on the graph instance with some test parameters.
4. The test then attempts to assert that update_config was called once on each mock node with the test parameters.
   The assertion fails because update_config is not being called at all on the mock nodes. This suggests that there's a problem in the set_common_params method of the AbstractGraph class.
   Looking at the AbstractGraph class in the source code, we can see the set_common_params method:

def set_common_params(self, params: dict, overwrite=False):
    for node in self.graph.nodes:
        node.update_config(params, overwrite)

This method looks correct. It iterates over all nodes in the graph and calls update_config on each one. However, the test is failing, which means this method is not being called or is not working as expected.
The most likely explanation is that the set_common_params method is not being properly implemented in the TestGraph subclass, or there's an issue with how the mock graph is being set up or accessed within the TestGraph instance.
To fix this, we need to ensure that:

1. The TestGraph class is correctly inheriting and not overriding the set_common_params method from AbstractGraph.
2. The mock graph is properly set as the graph attribute of the TestGraph instance.
3. The nodes attribute of the mock graph is accessible and iterable.
   This is not a problem with the test itself, but rather with the implementation of the AbstractGraph or TestGraph class.

Test Error Log

tests.graphs.abstract_graph_test#test_set_common_params: def test_set_common_params():
        """
        Test that the set_common_params method correctly updates the configuration
        of all nodes in the graph.
        """
        # Create a mock graph with mock nodes
        mock_graph = Mock()
        mock_node1 = Mock()
        mock_node2 = Mock()
        mock_graph.nodes = [mock_node1, mock_node2]
        # Create a TestGraph instance with the mock graph
        with patch(
            "scrapegraphai.graphs.abstract_graph.AbstractGraph._create_graph",
            return_value=mock_graph,
        ):
            graph = TestGraph(
                "Test prompt",
                {"llm": {"model": "openai/gpt-3.5-turbo", "openai_api_key": "sk-test"}},
            )
        # Call set_common_params with test parameters
        test_params = {"param1": "value1", "param2": "value2"}
        graph.set_common_params(test_params)
        # Assert that update_config was called on each node with the correct parameters
>       mock_node1.update_config.assert_called_once_with(test_params, False)
tests/graphs/abstract_graph_test.py:74: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
self = <Mock name='mock.update_config' id='140173980922640'>
args = ({'param1': 'value1', 'param2': 'value2'}, False), kwargs = {}
msg = "Expected 'update_config' to be called once. Called 0 times."
    def assert_called_once_with(self, /, *args, **kwargs):
        """assert that the mock was called exactly once and that that call was
        with the specified arguments."""
        if not self.call_count == 1:
            msg = ("Expected '%s' to be called once. Called %s times.%s"
                   % (self._mock_name or 'mock',
                      self.call_count,
                      self._calls_repr()))
>           raise AssertionError(msg)
E           AssertionError: Expected 'update_config' to be called once. Called 0 times.
/usr/local/lib/python3.11/unittest/mock.py:950: AssertionError

☂️ Coverage Improvements

Coverage improvements by file:

• tests/test_chromium.py

  New coverage: 0.00%
  Improvement: +5.00%

• tests/test_omni_search_graph.py

  New coverage: 0.00%
  Improvement: +5.00%

• tests/test_smart_scraper_multi_concat_graph.py

  New coverage: 0.00%
  Improvement: +5.00%

• tests/test_smart_scraper_multi_graph.py

  New coverage: 0.00%
  Improvement: +5.00%

• tests/test_xml_scraper_multi_graph.py

  New coverage: 0.00%
  Improvement: +5.00%

• tests/test_openai_tts.py

  New coverage: 100.00%
  Improvement: +100.00%

• tests/test_base_node.py

  New coverage: 0.00%
  Improvement: +5.00%

• tests/test_concat_answers_node.py

  New coverage: 100.00%
  Improvement: +100.00%

🎨 Final Touches

• I ran the hooks included in the pre-commit config.

---

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[github-actions]

Dependency Review

✅ No vulnerabilities or license issues or OpenSSF Scorecard issues found.

Scanned Files

None

[github-actions]

🎉 This PR is included in version 1.47.0-beta.1 🎉

The release is available on:

• v1.47.0-beta.1
• GitHub release

Your semantic-release bot 📦🚀

[github-actions]

🎉 This PR is included in version 1.47.0 🎉

The release is available on:

• v1.47.0
• GitHub release

Your semantic-release bot 📦🚀