feat: add integration test for files

testcases/multimodal-invoke/input.json

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+{
+    "prompt": "Describe the content of this file in one sentence."
+}

testcases/multimodal-invoke/langgraph.json

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+{
+    "dependencies": ["."],
+    "graphs": {
+        "agent": "./src/main.py:graph"
+    }
+}

testcases/multimodal-invoke/pyproject.toml

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+[project]
+name = "multimodal-invoke"
+version = "0.0.1"
+description = "Test multimodal LLM invoke with file attachments via get_chat_model"
+authors = [{ name = "John Doe", email = "john.doe@myemail.com" }]
+dependencies = [
+    "langgraph>=0.2.70",
+    "langchain-core>=0.3.34",
+    "langgraph-checkpoint-sqlite>=2.0.3",
+    "uipath-langchain[vertex,bedrock]",
+    "pydantic>=2.10.6",
+]
+requires-python = ">=3.11"
+
+[tool.uv.sources]
+uipath-langchain = { path = "../../", editable = true }

testcases/multimodal-invoke/run.sh

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+#!/bin/bash
+set -e
+
+echo "Syncing dependencies..."
+uv sync
+
+echo "Authenticating with UiPath..."
+uv run uipath auth --client-id="$CLIENT_ID" --client-secret="$CLIENT_SECRET" --base-url="$BASE_URL"
+
+echo "Initializing the project..."
+uv run uipath init
+
+echo "Packing agent..."
+uv run uipath pack
+
+echo "Running agent..."
+uv run uipath run agent --file input.json
+
+echo "Running agent again with empty UIPATH_JOB_KEY..."
+export UIPATH_JOB_KEY=""
+uv run uipath run agent --trace-file .uipath/traces.jsonl --file input.json >> local_run_output.log

testcases/multimodal-invoke/src/assert.py

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+import json
+
+with open("__uipath/output.json", "r", encoding="utf-8") as f:
+    output_data = json.load(f)
+
+output_content = output_data["output"]
+result_summary = output_content["result_summary"]
+
+print(f"Success: {output_content['success']}")
+print(f"Summary:\n{result_summary}")
+
+assert output_content["success"] is True, "Test did not succeed. See summary above."

testcases/multimodal-invoke/src/main.py

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+import logging
+
+from langchain_core.messages import AIMessage, HumanMessage
+from langgraph.checkpoint.memory import MemorySaver
+from langgraph.graph import END, START, StateGraph, MessagesState
+from pydantic import BaseModel, Field
+
+from uipath_langchain.agent.multimodal.invoke import llm_call_with_files
+from uipath_langchain.agent.multimodal.types import FileInfo
+from uipath_langchain.chat.chat_model_factory import get_chat_model
+
+logger = logging.getLogger(__name__)
+
+MODELS_TO_TEST = [
+    "gpt-4.1-2025-04-14",
+    "gemini-2.5-pro",
+    "anthropic.claude-sonnet-4-5-20250929-v1:0",
+]
+
+FILES_TO_TEST = [
+    FileInfo(
+        url="https://www.w3schools.com/css/img_5terre.jpg",
+        name="img_5terre.jpg",
+        mime_type="image/jpeg",
+    ),
+    FileInfo(
+        url="https://www.w3.org/WAI/ER/tests/xhtml/testfiles/resources/pdf/dummy.pdf",
+        name="dummy.pdf",
+        mime_type="application/pdf",
+    ),
+]
+
+
+class GraphInput(BaseModel):
+    prompt: str = Field(default="Describe the content of this file in one sentence.")
+
+
+class GraphOutput(BaseModel):
+    success: bool
+    result_summary: str
+
+
+class GraphState(MessagesState):
+    prompt: str
+    success: bool
+    result_summary: str
+    model_results: dict
+
+
+async def run_multimodal_invoke(state: GraphState) -> dict:
+    messages = [HumanMessage(content=state["prompt"])]
+    model_results = {}
+
+    for model_name in MODELS_TO_TEST:
+        logger.info(f"Testing {model_name}...")
+        model = get_chat_model(
+            model=model_name,
+            temperature=0.0,
+            max_tokens=200,
+            agenthub_config="agentsplayground",
+        )
+        test_results = {}
+        for file_info in FILES_TO_TEST:
+            label = file_info.name
+            logger.info(f"  {label}...")
+            try:
+                response: AIMessage = await llm_call_with_files(
+                    messages, [file_info], model
+                )
+                logger.info(f"    {label}: ✓")
+                test_results[label] = "✓"
+            except Exception as e:
+                logger.error(f"    {label}: ✗ {e}")
+                test_results[label] = f"✗ {str(e)[:60]}"
+        model_results[model_name] = test_results
+
+    summary_lines = []
+    for model_name, results in model_results.items():
+        summary_lines.append(f"{model_name}:")
+        for file_name, result in results.items():
+            summary_lines.append(f"  {file_name}: {result}")
+    has_failures = any(
+        "✗" in v for results in model_results.values() for v in results.values()
+    )
+
+    return {
+        "success": not has_failures,
+        "result_summary": "\n".join(summary_lines),
+        "model_results": model_results,
+    }
+
+
+async def return_results(state: GraphState) -> GraphOutput:
+    logger.info(f"Success: {state['success']}")
+    logger.info(f"Summary:\n{state['result_summary']}")
+    return GraphOutput(
+        success=state["success"],
+        result_summary=state["result_summary"],
+    )
+
+
+def build_graph() -> StateGraph:
+    builder = StateGraph(GraphState, input_schema=GraphInput, output_schema=GraphOutput)
+
+    builder.add_node("run_multimodal_invoke", run_multimodal_invoke)
+    builder.add_node("results", return_results)
+
+    builder.add_edge(START, "run_multimodal_invoke")
+    builder.add_edge("run_multimodal_invoke", "results")
+    builder.add_edge("results", END)
+
+    memory = MemorySaver()
+    return builder.compile(checkpointer=memory)
+
+
+graph = build_graph()