* use `override` instead of directly patching things on `ModelRequest`
* rely on `ToolNode` for execution of tools related to said middleware,
using `wrap_model_call` to inject the relevant claude tool specs +
allowing tool node to forward them along to corresponding langchain tool
implementations
* making the same change for the native shell tool middleware
* allowing shell tool middleware to specify a name for the shell tool
(negative diff then for claude bash middleware)
long term I think the solution might be to attach metadata to a tool to
map the provider spec to a langchain implementation, which we could also
take some lessons from on the MCP front.
**Description:** This PR adds support for DeepSeek's beta strict mode
feature for structured
outputs and tool calling. It overrides `bind_tools()` and
`with_structured_output()` to automatically use
DeepSeek's beta endpoint (https://api.deepseek.com/beta) when
`strict=True`. Both methods need overriding because they're independent
entry points and user can call either directly. When DeepSeek's strict
mode graduates from beta, we can just remove both overriden methods. You
can read more about the beta feature here:
https://api-docs.deepseek.com/guides/function_calling#strict-mode-beta
**Issue:** Implements #32670
**Dependencies:** None
**Sample Code**
```python
from langchain_deepseek import ChatDeepSeek
from pydantic import BaseModel, Field
from typing import Optional
import os
# Enter your DeepSeek API Key here
API_KEY = "YOUR_API_KEY"
# location, temperature, condition are required fields
# humidity is optional field with default value
class WeatherInfo(BaseModel):
location: str = Field(description="City name")
temperature: int = Field(description="Temperature in Celsius")
condition: str = Field(description="Weather condition (sunny, cloudy, rainy)")
humidity: Optional[int] = Field(default=None, description="Humidity percentage")
llm = ChatDeepSeek(
model="deepseek-chat",
api_key=API_KEY,
)
# just to confirm that a new instance will use the default base url (instead of beta)
print(f"Default API base: {llm.api_base}")
# Test 1: bind_tools with strict=True shoud list all the tools calls
print("\nTest 1: bind_tools with strict=True")
llm_with_tools = llm.bind_tools([WeatherInfo], strict=True)
response = llm_with_tools.invoke("Tell me the weather in New York. It's 22 degrees, sunny.")
print(response.tool_calls)
# Test 2: with_structured_output with strict=True
print("\nTest 2: with_structured_output with strict=True")
structured_llm = llm.with_structured_output(WeatherInfo, strict=True)
result = structured_llm.invoke("Tell me the weather in New York.")
print(f" Result: {result}")
assert isinstance(result, WeatherInfo), "Result should be a WeatherInfo instance"
```
---------
Co-authored-by: Mason Daugherty <mason@langchain.dev>
Co-authored-by: Mason Daugherty <github@mdrxy.com>
**Description:**
Fixes the OpenCLIP × Chroma regression that caused nested embedding
errors when adding or searching image data.
The test case `test_openclip_chroma_embed_no_nesting_error` has been
restored and verified to work correctly with the current LangChain core
dependencies.
Functional validation confirms that `similarity_search_by_image` now
returns correct, metadata‑preserving results.
**Issue:**
Fixes #33851
**Dependencies:**
No new dependencies introduced.
**Testing:**
All tests under
```bash
uv run --group test pytest tests/unit_tests
```
result:
```
30 passed in 91.26s (0:01:31)
```
have passed successfully using Python 3.13.9 and uv‑managed environment.
This confirms that the regression has been fixed.
Running
```bash
make test
```
still produces cleanup‑time `AttributeError: 'ProactorEventLoop' object
has no attribute '_ssock'` on Windows (Python 3.13+).
This is a benign asyncio teardown message rather than a functional
failure.
`uv run pytest` closes event loops immediately after tests, while `make
test` invokes pytest through a secondary process layer that leaves a
background loop alive at interpreter shutdown.
This difference in teardown behavior explains the extra messages seen
only when using `make test`.
**Summary:**
- Verified the OpenCLIP + Chroma image pipeline works correctly.
- `uv run --group test pytest` fully passes; the fix is complete.
- The residual `_ssock` warnings occur only during
Windows asyncio cleanup and are not related to this code change.
This is my first time contributing code, please contact me with any
questions
---
---------
Co-authored-by: Mason Daugherty <mason@langchain.dev>
Co-authored-by: Mason Daugherty <github@mdrxy.com>
When Groq returns tool calls with no arguments, it sends arguments:
`'null'` (JSON null), but LangChain's core parsing expects either a dict
or converts null to Python None, which fails the `isinstance(args_,
dict)` check and incorrectly marks the tool call as invalid.
Related to #32017
**Description:**
Adds support for prompt caching usage metadata in ChatGroq. The
integration now captures cached token information from the Groq API
response and includes it in the `input_token_details` field of the
`usage_metadata`.
Changes:
- Created new `_create_usage_metadata()` helper function to centralize
usage metadata creation logic
- Extracts `cached_tokens` from `prompt_tokens_details` in API responses
and maps to `input_token_details.cache_read`
- Integrated the helper function in both streaming
(`_convert_chunk_to_message_chunk`) and non-streaming
(`_create_chat_result`) code paths
- Added comprehensive unit tests to verify caching metadata handling and
backward compatibility
This enables users to monitor prompt caching effectiveness when using
Groq models with prompt caching enabled.
**Issue:** N/A
**Dependencies:** None
---------
Co-authored-by: Mason Daugherty <github@mdrxy.com>
Co-authored-by: Mason Daugherty <mason@langchain.dev>
This PR fixes#32234 and improves HuggingFace chat model integration by:
Ensuring ChatHuggingFace inherits key parameters (temperature,
max_tokens, top_p, streaming, etc.) from the underlying LLM when not
explicitly set.
Adding and updating unit tests to verify property inheritance.
No breaking changes; these updates enhance reliability and
maintainability.
---------
Co-authored-by: Mason Daugherty <mason@langchain.dev>
Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com>
Co-authored-by: Mason Daugherty <github@mdrxy.com>
To fix nondeterministic results causing integration testing to sometimes
fail
Also speeds up from 10s to 0.5
---------
Co-authored-by: ccurme <chester.curme@gmail.com>
- use latest models in examples to highlight support
- standardize on using IDs in examples - no more aliases to improve
determinism in future tests
- bump lock
- in integration tests, fix stale casettes and use `MODEL_NAME`
uniformly where possible
- add case for default max tokens for sonnet-4-5 (was missing)
Moving all `ToolNode` related improvements back to LangGraph and
importing them in LC!
pairing w/ https://github.com/langchain-ai/langgraph/pull/6321
this fixes a couple of things:
1. `InjectedState`, store etc will continue to work as expected no
matter where the import is from
2. `ToolRuntime` is now usable w/in langgraph, woohoo!
if "url" in annotation: in Line 15 , already ensures "url" is key in
annotation , so no need to check again to set "url" key in out object
---------
Co-authored-by: Mason Daugherty <mason@langchain.dev>
