* `create_agent`'s `system_prompt` allows `str | SystemMessage`
* added `system_message: SystemMessage` on `ModelRequest`
* `ModelRequest.system_prompt` is a function of `system_message.text`,
now deprecated
* disallow setting `system_prompt` and `system_message`
* `ModelRequest.system_prompt` can still be set (w/ custom setattr) for
custom backwards compat, but the updates just get propogated to the
`ModelRequest.system_message`
---------
Co-authored-by: Chester Curme <chester.curme@gmail.com>
Fixed a bug where GPT-5 temperature validation was case-sensitive,
causing issues when users
specified Azure deployment names or model names in uppercase (e.g.,
`"GPT-5-2025-01-01"`, `"GPT-5-NANO"`). The validation now correctly
handles model names regardless of case.
Changes made:
- Updated `validate_temperature()` method in `BaseChatOpenAI` to perform
case-insensitive
model name comparisons
- Updated `_get_encoding_model()` method to use case-insensitive checks
for tiktoken encoder
selection
- Added comprehensive unit tests to verify case-insensitive behavior
with various case
combinations
**Issue:** Fixes#34003
**Dependencies:** None
**Test Coverage:**
- All existing tests pass
- New test `test_gpt_5_temperature_case_insensitive` covers uppercase,
lowercase, and
mixed-case model names
- Tests verify both non-chat GPT-5 models (temperature removed) and chat
models (temperature
preserved)
- Lint and format checks pass (`make lint`, `make format`)
---------
Co-authored-by: Mason Daugherty <github@mdrxy.com>
- Respect 300k token limit for embeddings API requests #33668
- fix create_agent / response_format for Responses API #33939
- fix response.incomplete event is not handled when using
stream_mode=['messages'] #33871
Now returns (`_iter`, `tokens`, `indices`, token_counts`). The
`token_counts` are calculated directly during tokenization, which is
more accurate and efficient than splitting strings later.
## Description
Fixes#31227 - Resolves the issue where `OpenAIEmbeddings` exceeds
OpenAI's 300,000 token per request limit, causing 400 BadRequest errors.
## Problem
When embedding large document sets, LangChain would send batches
containing more than 300,000 tokens in a single API request, causing
this error:
```
openai.BadRequestError: Error code: 400 - {'error': {'message': 'Requested 673477 tokens, max 300000 tokens per request'}}
```
The issue occurred because:
- The code chunks texts by `embedding_ctx_length` (8191 tokens per
chunk)
- Then batches chunks by `chunk_size` (default 1000 chunks per request)
- **But didn't check**: Total tokens per batch against OpenAI's 300k
limit
- Result: `1000 chunks × 8191 tokens = 8,191,000 tokens` → Exceeds
limit!
## Solution
This PR implements dynamic batching that respects the 300k token limit:
1. **Added constant**: `MAX_TOKENS_PER_REQUEST = 300000`
2. **Track token counts**: Calculate actual tokens for each chunk
3. **Dynamic batching**: Instead of fixed `chunk_size` batches,
accumulate chunks until approaching the 300k limit
4. **Applied to both sync and async**: Fixed both
`_get_len_safe_embeddings` and `_aget_len_safe_embeddings`
## Changes
- Modified `langchain_openai/embeddings/base.py`:
- Added `MAX_TOKENS_PER_REQUEST` constant
- Replaced fixed-size batching with token-aware dynamic batching
- Applied to both sync (line ~478) and async (line ~527) methods
- Added test in `tests/unit_tests/embeddings/test_base.py`:
- `test_embeddings_respects_token_limit()` - Verifies large document
sets are properly batched
## Testing
All existing tests pass (280 passed, 4 xfailed, 1 xpassed).
New test verifies:
- Large document sets (500 texts × 1000 tokens = 500k tokens) are split
into multiple API calls
- Each API call respects the 300k token limit
## Usage
After this fix, users can embed large document sets without errors:
```python
from langchain_openai import OpenAIEmbeddings
from langchain_chroma import Chroma
from langchain_text_splitters import CharacterTextSplitter
# This will now work without exceeding token limits
embeddings = OpenAIEmbeddings()
documents = CharacterTextSplitter().split_documents(large_documents)
Chroma.from_documents(documents, embeddings)
```
Resolves#31227
---------
Co-authored-by: Kaparthy Reddy <kaparthyreddy@Kaparthys-MacBook-Air.local>
Co-authored-by: Chester Curme <chester.curme@gmail.com>
Co-authored-by: Mason Daugherty <mason@langchain.dev>
Co-authored-by: Mason Daugherty <github@mdrxy.com>
* 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.
