mirror of
https://github.com/hwchase17/langchain.git
synced 2026-06-09 18:50:33 +00:00
70f5626579
## Description Fixes #36400 During streaming, `AIMessageChunk.__add__` list-concatenates `reasoning_details` in `additional_kwargs`, fragmenting a single entry into many. When `_convert_message_to_dict()` serializes conversation history back to the OpenRouter API for the next turn, these fragmented entries cause `BadRequestResponseError`. ### Changes - Add `_merge_reasoning_details()` helper that merges consecutive entries sharing the same `type` and `index` (streaming fragments) while preserving distinct entries (legitimate non-streaming data) - Metadata from later fragments (e.g. `signature`) is preserved in the merged result - Entries without `index` are never merged (safe for non-streaming responses) - Call `_merge_reasoning_details()` in `_convert_message_to_dict()` before serializing `reasoning_details` ### Why merge instead of drop? Non-streaming users (`invoke()`) rely on `reasoning_details` for structured metadata (`type`, `signature`, `format`, `index`). Dropping it entirely would be a regression. This approach fixes streaming while preserving non-streaming functionality, similar to `langchain-openai`'s `_implode_reasoning_blocks()`. ## Test plan - [x] Fragmented entries (same type + same index) are merged into one - [x] Distinct entries (different index) are preserved separately - [x] Entries without index are never merged - [x] Metadata from later fragments (e.g. signature) is preserved - [x] Single-entry lists pass through unchanged - [x] Round-trip (dict → message → dict) works correctly - [x] All 210 unit tests pass --------- Co-authored-by: Mason Daugherty <github@mdrxy.com> Co-authored-by: Mason Daugherty <mason@langchain.dev>
116 lines
3.9 KiB
Python
116 lines
3.9 KiB
Python
"""Integration tests for `ChatOpenRouter` chat model."""
|
|
|
|
from __future__ import annotations
|
|
|
|
import pytest
|
|
from langchain_core.messages import (
|
|
AIMessage,
|
|
AIMessageChunk,
|
|
BaseMessageChunk,
|
|
HumanMessage,
|
|
)
|
|
from pydantic import BaseModel, Field
|
|
|
|
from langchain_openrouter.chat_models import ChatOpenRouter
|
|
|
|
|
|
def test_basic_invoke() -> None:
|
|
"""Test basic invocation."""
|
|
model = ChatOpenRouter(model="openai/gpt-4o-mini", temperature=0)
|
|
response = model.invoke("Say 'hello' and nothing else.")
|
|
assert response.content
|
|
assert response.response_metadata.get("model_provider") == "openrouter"
|
|
|
|
|
|
def test_streaming() -> None:
|
|
"""Test streaming."""
|
|
model = ChatOpenRouter(model="openai/gpt-4o-mini", temperature=0)
|
|
full: BaseMessageChunk | None = None
|
|
for chunk in model.stream("Say 'hello' and nothing else."):
|
|
full = chunk if full is None else full + chunk
|
|
assert isinstance(full, AIMessageChunk)
|
|
assert full.content
|
|
|
|
|
|
def test_tool_calling() -> None:
|
|
"""Test tool calling via OpenRouter."""
|
|
|
|
class GetWeather(BaseModel):
|
|
"""Get the current weather in a given location."""
|
|
|
|
location: str = Field(description="The city and state")
|
|
|
|
model = ChatOpenRouter(model="openai/gpt-4o-mini", temperature=0)
|
|
model_with_tools = model.bind_tools([GetWeather])
|
|
response = model_with_tools.invoke("What's the weather in San Francisco?")
|
|
assert response.tool_calls
|
|
|
|
|
|
def test_structured_output() -> None:
|
|
"""Test structured output via OpenRouter."""
|
|
|
|
class Joke(BaseModel):
|
|
"""A joke."""
|
|
|
|
setup: str = Field(description="The setup of the joke")
|
|
punchline: str = Field(description="The punchline of the joke")
|
|
|
|
model = ChatOpenRouter(model="openai/gpt-4o-mini", temperature=0)
|
|
structured = model.with_structured_output(Joke)
|
|
result = structured.invoke("Tell me a joke about programming")
|
|
assert isinstance(result, Joke)
|
|
assert result.setup
|
|
assert result.punchline
|
|
|
|
|
|
@pytest.mark.xfail(reason="Depends on reasoning model availability on OpenRouter.")
|
|
def test_reasoning_content() -> None:
|
|
"""Test reasoning content from a reasoning model."""
|
|
model = ChatOpenRouter(
|
|
model="openai/o3-mini",
|
|
reasoning={"effort": "low"},
|
|
)
|
|
response = model.invoke("What is 2 + 2?")
|
|
assert response.content
|
|
|
|
|
|
def test_streaming_reasoning_multi_turn() -> None:
|
|
"""Multi-turn streaming with reasoning preserves the thinking signature.
|
|
|
|
Regression test for #36400. During streaming, `reasoning_details` is
|
|
fragmented into multiple list entries by `AIMessageChunk.__add__` (because
|
|
`index` is a float and `langchain_core.utils._merge.merge_lists` only
|
|
auto-merges int-indexed dicts). When sent back on the next turn, the
|
|
fragmented entries cause Anthropic via OpenRouter to reject the request
|
|
with `"Invalid signature in thinking block"`. The fix in
|
|
`_convert_message_to_dict` merges fragments before serialization.
|
|
"""
|
|
model = ChatOpenRouter(
|
|
model="anthropic/claude-haiku-4.5",
|
|
reasoning={"effort": "low"},
|
|
)
|
|
|
|
messages: list = [HumanMessage(content="What is 2+2? Think briefly.")]
|
|
|
|
full: BaseMessageChunk | None = None
|
|
for chunk in model.stream(messages):
|
|
full = chunk if full is None else full + chunk
|
|
assert isinstance(full, AIMessageChunk)
|
|
assert full.content
|
|
assert full.additional_kwargs.get("reasoning_details"), (
|
|
"expected reasoning_details on the streamed chunk"
|
|
)
|
|
|
|
# Hand-build the AIMessage from the accumulated chunk and continue the
|
|
# conversation. Pre-fix, this raises a 400 from the provider.
|
|
assistant_msg = AIMessage(
|
|
content=full.content,
|
|
additional_kwargs=full.additional_kwargs,
|
|
response_metadata=full.response_metadata,
|
|
)
|
|
messages.append(assistant_msg)
|
|
messages.append(HumanMessage(content="Now what is 3+3?"))
|
|
|
|
response = model.invoke(messages)
|
|
assert response.content
|