feat: add file support for more LLM API Flavors

pyproject.toml

@@ -1,6 +1,6 @@
 [project]
 name = "uipath-langchain"
-version = "0.4.17"
+version = "0.4.18"
 description = "Python SDK that enables developers to build and deploy LangGraph agents to the UiPath Cloud Platform"
 readme = { file = "README.md", content-type = "text/markdown" }
 requires-python = ">=3.11"

src/uipath_langchain/agent/react/agent.py

@@ -8,6 +8,8 @@
 from pydantic import BaseModel
 from uipath.platform.guardrails import BaseGuardrail
 
+from uipath_langchain.chat.types import UiPathPassthroughChatModel
+
 from ..guardrails.actions import GuardrailAction
 from .guardrails.guardrails_subgraph import (
     create_agent_init_guardrails_subgraph,
@@ -33,6 +35,7 @@
     AgentGraphConfig,
     AgentGraphNode,
     AgentGraphState,
+    AgentSettings,
 )
 from .utils import create_state_with_input
 
@@ -62,6 +65,17 @@ def create_agent(
     """
     from ..tools import create_tool_node
 
+    if not isinstance(model, UiPathPassthroughChatModel):
+        raise TypeError(
+            f"Model {type(model).__name__} does not implement UiPathPassthroughChatModel. "
+            "The model must have llm_provider and api_flavor properties."
+        )
+
+    agent_settings = AgentSettings(
+        llm_provider=model.llm_provider,
+        api_flavor=model.api_flavor,
+    )
+
     if config is None:
         config = AgentGraphConfig()
 
@@ -71,7 +85,9 @@ def create_agent(
     )
     llm_tools: list[BaseTool] = [*agent_tools, *flow_control_tools]
 
-    init_node = create_init_node(messages, input_schema, config.is_conversational)
+    init_node = create_init_node(
+        messages, input_schema, config.is_conversational, agent_settings
+    )
 
     tool_nodes = create_tool_node(agent_tools)
     tool_nodes_with_guardrails = create_tools_guardrails_subgraph(

src/uipath_langchain/agent/react/init_node.py

@@ -9,13 +9,15 @@
 from .job_attachments import (
     get_job_attachments,
 )
+from .types import AgentSettings
 
 
 def create_init_node(
     messages: Sequence[SystemMessage | HumanMessage]
     | Callable[[Any], Sequence[SystemMessage | HumanMessage]],
     input_schema: type[BaseModel] | None,
     is_conversational: bool = False,
+    agent_settings: AgentSettings | None = None,
 ):
     def graph_state_init(state: Any) -> Any:
         resolved_messages: Sequence[SystemMessage | HumanMessage] | Overwrite
@@ -46,6 +48,7 @@ def graph_state_init(state: Any) -> Any:
             "messages": resolved_messages,
             "inner_state": {
                 "job_attachments": job_attachments_dict,
+                "agent_settings": agent_settings,
             },
         }
 

src/uipath_langchain/agent/react/llm_node.py

@@ -1,6 +1,6 @@
 """LLM node for ReAct Agent graph."""
 
-from typing import Literal, Sequence, TypeVar
+from typing import Sequence, TypeVar
 
 from langchain_core.language_models import BaseChatModel
 from langchain_core.messages import AIMessage, AnyMessage, ToolCall
@@ -14,6 +14,7 @@
 from uipath_langchain.agent.tools.structured_tool_with_argument_properties import (
     StructuredToolWithArgumentProperties,
 )
+from uipath_langchain.llm import get_payload_handler
 
 from ..exceptions import AgentTerminationException
 from .constants import (
@@ -23,28 +24,6 @@
 from .types import FLOW_CONTROL_TOOLS, AgentGraphState
 from .utils import count_consecutive_thinking_messages, extract_input_data_from_state
 
-OPENAI_COMPATIBLE_CHAT_MODELS = (
-    "UiPathChatOpenAI",
-    "AzureChatOpenAI",
-    "ChatOpenAI",
-    "UiPathChat",
-    "UiPathAzureChatOpenAI",
-)
-
-
-def _get_required_tool_choice_by_model(
-    model: BaseChatModel,
-) -> Literal["required", "any"]:
-    """Get the appropriate tool_choice value to enforce tool usage based on model type.
-
-    "required" - OpenAI compatible required tool_choice value
-    "any" - Vertex and Bedrock parameter for required tool_choice value
-    """
-    model_class_name = model.__class__.__name__
-    if model_class_name in OPENAI_COMPATIBLE_CHAT_MODELS:
-        return "required"
-    return "any"
-
 
 def _filter_control_flow_tool_calls(
     tool_calls: list[ToolCall],
@@ -82,7 +61,8 @@ def create_llm_node(
             before enforcing tool usage. 0 = force tools every time.
     """
     bindable_tools = list(tools) if tools else []
-    tool_choice_required_value = _get_required_tool_choice_by_model(model)
+    payload_handler = get_payload_handler(model)
+    tool_choice_required_value = payload_handler.get_required_tool_choice()
 
     async def llm_node(state: StateT):
         messages: list[AnyMessage] = state.messages