Ensures proper reStructuredText formatting by adding the required blank
line before closing docstring quotes, which resolves the "Block quote
ends without a blank line; unexpected unindent" warning.
This PR addresses the common issue where users struggle to pass custom
parameters to OpenAI-compatible APIs like LM Studio, vLLM, and others.
The problem occurs when users try to use `model_kwargs` for custom
parameters, which causes API errors.
## Problem
Users attempting to pass custom parameters (like LM Studio's `ttl`
parameter) were getting errors:
```python
# ❌ This approach fails
llm = ChatOpenAI(
base_url="http://localhost:1234/v1",
model="mlx-community/QwQ-32B-4bit",
model_kwargs={"ttl": 5} # Causes TypeError: unexpected keyword argument 'ttl'
)
```
## Solution
The `extra_body` parameter is the correct way to pass custom parameters
to OpenAI-compatible APIs:
```python
# ✅ This approach works correctly
llm = ChatOpenAI(
base_url="http://localhost:1234/v1",
model="mlx-community/QwQ-32B-4bit",
extra_body={"ttl": 5} # Custom parameters go in extra_body
)
```
## Changes Made
1. **Enhanced Documentation**: Updated the `extra_body` parameter
docstring with comprehensive examples for LM Studio, vLLM, and other
providers
2. **Added Documentation Section**: Created a new "OpenAI-compatible
APIs" section in the main class docstring with practical examples
3. **Unit Tests**: Added tests to verify `extra_body` functionality
works correctly:
- `test_extra_body_parameter()`: Verifies custom parameters are included
in request payload
- `test_extra_body_with_model_kwargs()`: Ensures `extra_body` and
`model_kwargs` work together
4. **Clear Guidance**: Documented when to use `extra_body` vs
`model_kwargs`
## Examples Added
**LM Studio with TTL (auto-eviction):**
```python
ChatOpenAI(
base_url="http://localhost:1234/v1",
api_key="lm-studio",
model="mlx-community/QwQ-32B-4bit",
extra_body={"ttl": 300} # Auto-evict after 5 minutes
)
```
**vLLM with custom sampling:**
```python
ChatOpenAI(
base_url="http://localhost:8000/v1",
api_key="EMPTY",
model="meta-llama/Llama-2-7b-chat-hf",
extra_body={
"use_beam_search": True,
"best_of": 4
}
)
```
## Why This Works
- `model_kwargs` parameters are passed directly to the OpenAI client's
`create()` method, causing errors for non-standard parameters
- `extra_body` parameters are included in the HTTP request body, which
is exactly what OpenAI-compatible APIs expect for custom parameters
Fixes#32115.
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---------
Co-authored-by: copilot-swe-agent[bot] <198982749+Copilot@users.noreply.github.com>
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Co-authored-by: Mason Daugherty <github@mdrxy.com>
Co-authored-by: Mason Daugherty <mason@langchain.dev>
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>
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.
