OpenAI changed their API to require the `partial_images` parameter when
using image generation + streaming.
As described in https://github.com/langchain-ai/langchain/pull/31424, we
are ignoring partial images. Here, we accept the `partial_images`
parameter (as required by OpenAI), but emit a warning and continue to
ignore partial images.
Does not support partial images during generation at the moment. Before
doing that I'd like to figure out how to specify the aggregation logic
without requiring changes in core.
---------
Co-authored-by: Chester Curme <chester.curme@gmail.com>
Scheduled testing started failing today because the Responses API
stopped raising `BadRequestError` for a schema that was previously
invalid when `strict=True`.
Although docs still say that [some type-specific keywords are not yet
supported](https://platform.openai.com/docs/guides/structured-outputs#some-type-specific-keywords-are-not-yet-supported)
(including `minimum` and `maximum` for numbers), the below appears to
run and correctly respect the constraints:
```python
import json
import openai
maximums = list(range(1, 11))
arg_values = []
for maximum in maximums:
tool = {
"type": "function",
"name": "magic_function",
"description": "Applies a magic function to an input.",
"parameters": {
"properties": {
"input": {"maximum": maximum, "minimum": 0, "type": "integer"}
},
"required": ["input"],
"type": "object",
"additionalProperties": False
},
"strict": True
}
client = openai.OpenAI()
response = client.responses.create(
model="gpt-4.1",
input=[{"role": "user", "content": "What is the value of magic_function(3)? Use the tool."}],
tools=[tool],
)
function_call = next(item for item in response.output if item.type == "function_call")
args = json.loads(function_call.arguments)
arg_values.append(args["input"])
print(maximums)
print(arg_values)
# [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
# [1, 2, 3, 3, 3, 3, 3, 3, 3, 3]
```
Until yesterday this raised BadRequestError.
The same is not true of Chat Completions, which appears to still raise
BadRequestError
```python
tool = {
"type": "function",
"function": {
"name": "magic_function",
"description": "Applies a magic function to an input.",
"parameters": {
"properties": {
"input": {"maximum": 5, "minimum": 0, "type": "integer"}
},
"required": ["input"],
"type": "object",
"additionalProperties": False
},
"strict": True
}
}
response = client.chat.completions.create(
model="gpt-4.1",
messages=[{"role": "user", "content": "What is the value of magic_function(3)? Use the tool."}],
tools=[tool],
)
response # raises BadRequestError
```
Here we update tests accordingly.
Some providers include (legacy) function calls in `additional_kwargs` in
addition to tool calls. We currently unpack both function calls and tool
calls if present, but OpenAI will raise 400 in this case.
This can come up if providers are mixed in a tool-calling loop. Example:
```python
from langchain.chat_models import init_chat_model
from langchain_core.messages import HumanMessage
from langchain_core.tools import tool
@tool
def get_weather(location: str) -> str:
"""Get weather at a location."""
return "It's sunny."
gemini = init_chat_model("google_genai:gemini-2.0-flash-001").bind_tools([get_weather])
openai = init_chat_model("openai:gpt-4.1-mini").bind_tools([get_weather])
input_message = HumanMessage("What's the weather in Boston?")
tool_call_message = gemini.invoke([input_message])
assert len(tool_call_message.tool_calls) == 1
tool_call = tool_call_message.tool_calls[0]
tool_message = get_weather.invoke(tool_call)
response = openai.invoke( # currently raises 400 / BadRequestError
[input_message, tool_call_message, tool_message]
)
```
Here we ignore function calls if tool calls are present.
partners: (langchain-openai) total_tokens should not add 'Nonetype' t…
# PR Description
## Description
Fixed an issue in `langchain-openai` where `total_tokens` was
incorrectly adding `None` to an integer, causing a TypeError. The fix
ensures proper type checking before adding token counts.
## Issue
Fixes the TypeError traceback shown in the image where `'NoneType'`
cannot be added to an integer.
## Dependencies
None
## Twitter handle
None
Co-authored-by: qiulijie <qiulijie@yuaiweiwu.com>
**What does this PR do?**
This PR replaces deprecated usages of ```.dict()``` with
```.model_dump()``` to ensure compatibility with Pydantic v2 and prepare
for v3, addressing the deprecation warning
```PydanticDeprecatedSince20``` as required in [Issue#
31103](https://github.com/langchain-ai/langchain/issues/31103).
**Changes made:**
* Replaced ```.dict()``` with ```.model_dump()``` in multiple locations
* Ensured consistency with Pydantic v2 migration guidelines
* Verified compatibility across affected modules
**Notes**
* This is a code maintenance and compatibility update
* Tested locally with Pydantic v2.11
* No functional logic changes; only internal method replacements to
prevent deprecation issues
When aggregating AIMessageChunks in a stream, core prefers the leftmost
non-null ID. This is problematic because:
- Core assigns IDs when they are null to `f"run-{run_manager.run_id}"`
- The desired meaningful ID might not be available until midway through
the stream, as is the case for the OpenAI Responses API.
For the OpenAI Responses API, we assign message IDs to the top-level
`AIMessage.id`. This works in `.(a)invoke`, but during `.(a)stream` the
IDs get overwritten by the defaults assigned in langchain-core. These
IDs
[must](https://community.openai.com/t/how-to-solve-badrequesterror-400-item-rs-of-type-reasoning-was-provided-without-its-required-following-item-error-in-responses-api/1151686/9)
be available on the AIMessage object to support passing reasoning items
back to the API (e.g., if not using OpenAI's `previous_response_id`
feature). We could add them elsewhere, but seeing as we've already made
the decision to store them in `.id` during `.(a)invoke`, addressing the
issue in core lets us fix the problem with no interface changes.
Follow up to https://github.com/langchain-ai/langsmith-sdk/pull/1696,
I've bumped the `langsmith` version where applicable in `uv.lock`.
Type checking problems here because deps have been updated in
`pyproject.toml` and `uv lock` hasn't been run - we should enforce that
in the future - goes with the other dependabot todos :).
The `_chunk_size` has not changed by method `self._tokenize`, So i think
these is duplicate code.
Signed-off-by: zhanluxianshen <zhanluxianshen@163.com>
Following https://github.com/langchain-ai/langchain/pull/30909: need to
retain "empty" reasoning output when streaming, e.g.,
```python
{'id': 'rs_...', 'summary': [], 'type': 'reasoning'}
```
Tested by existing integration tests, which are currently failing.
