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Fix for OCI Generative AI tool_call_id not found because AIMessage.tool_calls are ignored #43

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Merged
merged 2 commits into from
Oct 14, 2025
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Merged

Fix for OCI Generative AI tool_call_id not found because AIMessage.tool_calls are ignored #43

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aa66265
Fix OCI tool calling with GenericProvider
jbrown9513 Oct 10, 2025
3c62fd7
Update unit test to verify tool call
jbrown9513 Oct 10, 2025
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6 changes: 3 additions & 3 deletions libs/oci/langchain_oci/chat_models/oci_generative_ai.py
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Original file line number Diff line number Diff line change
Expand Up @@ -712,9 +712,9 @@ def messages_to_oci_params(
)
else:
oci_message = self.oci_chat_message[role](content=tool_content)
elif isinstance(message, AIMessage) and message.additional_kwargs.get(
"tool_calls"
):
elif isinstance(message, AIMessage) and (
message.tool_calls or
message.additional_kwargs.get("tool_calls")):
# Process content and tool calls for assistant messages
content = self._process_message_content(message.content)
tool_calls = []
Expand Down
161 changes: 160 additions & 1 deletion libs/oci/tests/unit_tests/chat_models/test_oci_generative_ai.py
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Original file line number Diff line number Diff line change
Expand Up @@ -6,9 +6,9 @@
from unittest.mock import MagicMock

import pytest
from langchain_core.messages import HumanMessage
from pytest import MonkeyPatch

from langchain_core.messages import HumanMessage, AIMessage
from langchain_oci.chat_models.oci_generative_ai import ChatOCIGenAI


Expand Down Expand Up @@ -575,6 +575,165 @@ def mocked_response(*args, **kwargs): # type: ignore[no-untyped-def]
assert response["parsed"].conditions == "Sunny"


@pytest.mark.requires("oci")
def test_ai_message_tool_calls_direct_field(monkeypatch: MonkeyPatch) -> None:
"""Test AIMessage with tool_calls in the direct tool_calls field."""

oci_gen_ai_client = MagicMock()
llm = ChatOCIGenAI(model_id="meta.llama-3.3-70b-instruct", client=oci_gen_ai_client)

# Track if the tool_calls processing branch is executed
tool_calls_processed = False

def mocked_response(*args, **kwargs): # type: ignore[no-untyped-def]
nonlocal tool_calls_processed
# Check if the request contains tool_calls in the message
request = args[0]
if hasattr(request, 'chat_request') and hasattr(request.chat_request, 'messages'):
for msg in request.chat_request.messages:
if hasattr(msg, 'tool_calls') and msg.tool_calls:
tool_calls_processed = True
break
return MockResponseDict(
{
"status": 200,
"data": MockResponseDict(
{
"chat_response": MockResponseDict(
{
"api_format": "GENERIC",
"choices": [
MockResponseDict(
{
"message": MockResponseDict(
{
"role": "ASSISTANT",
"name": None,
"content": [
MockResponseDict(
{
"text": (
"I'll help you."
),
"type": "TEXT",
}
)
],
"tool_calls": [],
}
),
"finish_reason": "completed",
}
)
],
"time_created": "2025-08-14T10:00:01.100000+00:00",
}
),
"model_id": "meta.llama-3.3-70b-instruct",
"model_version": "1.0.0",
}
),
"request_id": "1234567890",
"headers": MockResponseDict({"content-length": "123"}),
}
)

monkeypatch.setattr(llm.client, "chat", mocked_response)

# Create AIMessage with tool_calls in the direct tool_calls field
ai_message = AIMessage(
content="I need to call a function",
tool_calls=[
{
"id": "call_123",
"name": "get_weather",
"args": {"location": "San Francisco"},
}
]
)

messages = [ai_message]

# This should not raise an error and should process the tool_calls correctly
response = llm.invoke(messages)
assert response.content == "I'll help you."


@pytest.mark.requires("oci")
def test_ai_message_tool_calls_additional_kwargs(monkeypatch: MonkeyPatch) -> None:
"""Test AIMessage with tool_calls in additional_kwargs field."""

oci_gen_ai_client = MagicMock()
llm = ChatOCIGenAI(model_id="meta.llama-3.3-70b-instruct", client=oci_gen_ai_client)

def mocked_response(*args, **kwargs): # type: ignore[no-untyped-def]
return MockResponseDict(
{
"status": 200,
"data": MockResponseDict(
{
"chat_response": MockResponseDict(
{
"api_format": "GENERIC",
"choices": [
MockResponseDict(
{
"message": MockResponseDict(
{
"role": "ASSISTANT",
"name": None,
"content": [
MockResponseDict(
{
"text": (
"I'll help you."
),
"type": "TEXT",
}
)
],
"tool_calls": [],
}
),
"finish_reason": "completed",
}
)
],
"time_created": "2025-08-14T10:00:01.100000+00:00",
}
),
"model_id": "meta.llama-3.3-70b-instruct",
"model_version": "1.0.0",
}
),
"request_id": "1234567890",
"headers": MockResponseDict({"content-length": "123"}),
}
)

monkeypatch.setattr(llm.client, "chat", mocked_response)

# Create AIMessage with tool_calls in additional_kwargs
ai_message = AIMessage(
content="I need to call a function",
additional_kwargs={
"tool_calls": [
{
"id": "call_456",
"name": "get_weather",
"args": {"location": "New York"},
}
]
}
)

messages = [ai_message]

# This should not raise an error and should process the tool_calls correctly
response = llm.invoke(messages)
assert response.content == "I'll help you."


def test_get_provider():
"""Test determining the provider based on the model_id."""
model_provider_map = {
Expand Down