diff --git a/dynamiq/nodes/llms/base.py b/dynamiq/nodes/llms/base.py index 058fc1b0..02b6dbe8 100644 --- a/dynamiq/nodes/llms/base.py +++ b/dynamiq/nodes/llms/base.py @@ -270,6 +270,14 @@ def _get_response_format_and_tools( return response_format, tools + def update_completion_params(self, params: dict[str, Any]) -> dict[str, Any]: + """ + This method can be overridden by subclasses to update or modify the + parameters passed to the completion method. + By default, it does not modify the params. + """ + return params + def execute( self, input_data: BaseLLMInputSchema, @@ -304,9 +312,9 @@ def execute( self.run_on_node_execute_run(callbacks=config.callbacks, prompt_messages=messages, **kwargs) # Use initialized client if it possible - params = self.connection.conn_params + params = self.connection.conn_params.copy() if self.client and not isinstance(self.connection, HttpApiKey): - params = {"client": self.client} + params.update({"client": self.client}) current_inference_mode = inference_mode or self.inference_mode current_schema = schema or self.schema_ @@ -315,23 +323,27 @@ def execute( ) tools = tools or base_tools - response = self._completion( - model=self.model, - messages=messages, - stream=self.streaming.enabled, - temperature=self.temperature, - max_tokens=self.max_tokens, - tools=tools, - tool_choice=self.tool_choice, - stop=self.stop, - top_p=self.top_p, - seed=self.seed, - presence_penalty=self.presence_penalty, - frequency_penalty=self.frequency_penalty, - response_format=response_format, - drop_params=True, + common_params: dict[str, Any] = { + "model": self.model, + "messages": messages, + "stream": self.streaming.enabled, + "temperature": self.temperature, + "max_tokens": self.max_tokens, + "tools": tools, + "tool_choice": self.tool_choice, + "stop": self.stop, + "top_p": self.top_p, + "seed": self.seed, + "presence_penalty": self.presence_penalty, + "frequency_penalty": self.frequency_penalty, + "response_format": response_format, + "drop_params": True, **params, - ) + } + + common_params = self.update_completion_params(common_params) + + response = self._completion(**common_params) handle_completion = ( self._handle_streaming_completion_response if self.streaming.enabled else self._handle_completion_response diff --git a/dynamiq/nodes/llms/openai.py b/dynamiq/nodes/llms/openai.py index 99bb7ebe..be8fb51e 100644 --- a/dynamiq/nodes/llms/openai.py +++ b/dynamiq/nodes/llms/openai.py @@ -1,3 +1,6 @@ +from functools import cached_property +from typing import Any, ClassVar + from dynamiq.connections import OpenAI as OpenAIConnection from dynamiq.nodes.llms.base import BaseLLM @@ -11,6 +14,7 @@ class OpenAI(BaseLLM): connection (OpenAIConnection | None): The connection to use for the OpenAI LLM. """ connection: OpenAIConnection | None = None + O_SERIES_MODEL_PREFIXES: ClassVar[tuple[str, ...]] = ("o1", "o3") def __init__(self, **kwargs): """Initialize the OpenAI LLM node. @@ -21,3 +25,23 @@ def __init__(self, **kwargs): if kwargs.get("client") is None and kwargs.get("connection") is None: kwargs["connection"] = OpenAIConnection() super().__init__(**kwargs) + + @cached_property + def is_o_series_model(self) -> bool: + """ + Determine if the model belongs to the O-series (e.g. o1 or o3) + by checking if the model starts with any of the O-series prefixes. + """ + model_lower = self.model.lower() + return any(model_lower.startswith(prefix) for prefix in self.O_SERIES_MODEL_PREFIXES) + + def update_completion_params(self, params: dict[str, Any]) -> dict[str, Any]: + """ + Override the base method to update the completion parameters for OpenAI. + For O-series models, use "max_completion_tokens" instead of "max_tokens". + """ + new_params = params.copy() + if self.is_o_series_model: + new_params["max_completion_tokens"] = self.max_tokens + new_params.pop("max_tokens", None) + return new_params diff --git a/examples/orchestrators/adaptive_article_o3.py b/examples/orchestrators/adaptive_article_o3.py new file mode 100644 index 00000000..aafa2a75 --- /dev/null +++ b/examples/orchestrators/adaptive_article_o3.py @@ -0,0 +1,84 @@ +from dynamiq.connections import E2B as E2BConnection +from dynamiq.connections import Exa, ZenRows +from dynamiq.nodes.agents.orchestrators.adaptive import AdaptiveOrchestrator +from dynamiq.nodes.agents.orchestrators.adaptive_manager import AdaptiveAgentManager +from dynamiq.nodes.agents.react import ReActAgent +from dynamiq.nodes.agents.simple import SimpleAgent +from dynamiq.nodes.tools.e2b_sandbox import E2BInterpreterTool +from dynamiq.nodes.tools.exa_search import ExaTool +from dynamiq.nodes.tools.zenrows import ZenRowsTool +from dynamiq.nodes.types import InferenceMode +from examples.llm_setup import setup_llm + +INPUT_TASK = ( + "Let's find data on optimizing " + "SEO campaigns in 2025, analyze it, " + "and provide predictions with calculations " + "on how to improve and implement these strategies." +) + + +if __name__ == "__main__": + python_tool = E2BInterpreterTool( + name="Code Executor", + connection=E2BConnection(), + ) + + zenrows_tool = ZenRowsTool( + connection=ZenRows(), + name="Web Scraper", + ) + + exa_tool = ExaTool( + connection=Exa(), + name="Search Engine", + ) + + llm = setup_llm(model_provider="gpt", model_name="o3-mini", max_tokens=100000) + + agent_coding = ReActAgent( + name="Coding Agent", + llm=llm, + tools=[python_tool], + max_loops=13, + inference_mode=InferenceMode.XML, + ) + + agent_web = ReActAgent( + name="Web Agent", + llm=llm, + tools=[zenrows_tool, exa_tool], + max_loops=13, + inference_mode=InferenceMode.XML, + ) + + agent_reflection = SimpleAgent( + name="Reflection Agent (Reviewer, Critic)", + llm=llm, + role=( + "Analyze and review the accuracy of any results, " + "including tasks, code, or data. 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index 33bce63e..25b2cb7f 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -28,7 +28,7 @@ boto3 = "~1.34.34" redis = "~5.0.0" google-generativeai = "~0.5.0" google-cloud-aiplatform = "~1.47.0" -litellm = "~1.54.0" +litellm = "1.59.12" requests = "~2.31.0" RestrictedPython = "~7.1" jsonpath-ng = "~1.6.1" diff --git a/tests/integration/flows/test_flow.py b/tests/integration/flows/test_flow.py index b2741d2d..850ebd9e 100644 --- a/tests/integration/flows/test_flow.py +++ b/tests/integration/flows/test_flow.py @@ -118,6 +118,7 @@ def test_workflow_with_depend_nodes_with_tracing( presence_penalty=None, frequency_penalty=None, top_p=None, + api_key=openai_node.connection.api_key, client=ANY, response_format=None, drop_params=True, @@ -260,6 +261,7 @@ def test_workflow_with_depend_nodes_and_depend_fail( messages=expected_openai_messages, stream=False, temperature=openai_node.temperature, + api_key=openai_node.connection.api_key, client=ANY, max_tokens=1000, stop=None, diff --git a/tests/integration/nodes/llms/test_ollama.py b/tests/integration/nodes/llms/test_ollama.py index ed98f76f..7e732a79 100644 --- a/tests/integration/nodes/llms/test_ollama.py +++ b/tests/integration/nodes/llms/test_ollama.py @@ -51,6 +51,7 @@ def test_workflow_with_ollama_llm(mock_llm_response_text, mock_llm_executor, mod model = model connection = connections.Ollama( id=str(uuid.uuid4()), + ) wf_ollama_ai = get_ollama_workflow(model=model, connection=connection) @@ -87,4 +88,5 @@ def test_workflow_with_ollama_llm(mock_llm_response_text, mock_llm_executor, mod response_format=None, drop_params=True, client=ANY, + api_base='http://localhost:11434', )