- Support thinking blocks in core's `convert_to_openai_messages` (pass
through instead of error)
- Ignore thinking blocks in ChatOpenAI (instead of error)
- Support Anthropic-style image blocks in ChatOpenAI
---
Standard integration tests include a `supports_anthropic_inputs`
property which is currently enabled only for tests on `ChatAnthropic`.
This test enforces compatibility with message histories of the form:
```
- system message
- human message
- AI message with tool calls specified only through `tool_use` content blocks
- human message containing `tool_result` and an additional `text` block
```
It additionally checks support for Anthropic-style image inputs if
`supports_image_inputs` is enabled.
Here we change this test, such that if you enable
`supports_anthropic_inputs`:
- You support AI messages with text and `tool_use` content blocks
- You support Anthropic-style image inputs (if `supports_image_inputs`
is enabled)
- You support thinking content blocks.
That is, we add a test case for thinking content blocks, but we also
remove the requirement of handling tool results within HumanMessages
(motivated by existing agent abstractions, which should all return
ToolMessage). We move that requirement to a ChatAnthropic-specific test.
**Description:**
5 fix of example from function with_alisteners() in
libs/core/langchain_core/runnables/base.py
Replace incoherent example output with workable example's output.
1. SyntaxError: unterminated string literal
print(f"on start callback starts at {format_t(time.time())}
correct as
print(f"on start callback starts at {format_t(time.time())}")
2. SyntaxError: unterminated string literal
print(f"on end callback starts at {format_t(time.time())}
correct as
print(f"on end callback starts at {format_t(time.time())}")
3. NameError: name 'Runnable' is not defined
Fix as
from langchain_core.runnables import Runnable
4. NameError: name 'asyncio' is not defined
Fix as
import asyncio
5. NameError: name 'format_t' is not defined.
Implement format_t() as
from datetime import datetime, timezone
def format_t(timestamp: float) -> str:
return datetime.fromtimestamp(timestamp, tz=timezone.utc).isoformat()
- **Description:** Same changes as #26593 but for FileCallbackHandler
- **Issue:** Fixes#29941
- **Dependencies:** None
- **Twitter handle:** None
- [x] **Lint and test**: Run `make format`, `make lint` and `make test`
from the root of the package(s) you've modified. See contribution
guidelines for more: https://python.langchain.com/docs/contributing/
See https://docs.astral.sh/ruff/rules/#flake8-type-checking-tc
Some fixes done for TC001,TC002 and TC003 but these rules are excluded
since they don't play well with Pydantic.
---------
Co-authored-by: Chester Curme <chester.curme@gmail.com>
Resolves https://github.com/langchain-ai/langchain/issues/29951
Was able to reproduce the issue with Anthropic installing from pydantic
`main` and correct it with the fix recommended in the issue.
Thanks very much @Viicos for finding the bug and the detailed writeup!
Resolves https://github.com/langchain-ai/langchain/issues/29003,
https://github.com/langchain-ai/langchain/issues/27264
Related: https://github.com/langchain-ai/langchain-redis/issues/52
```python
from langchain.chat_models import init_chat_model
from langchain.globals import set_llm_cache
from langchain_community.cache import SQLiteCache
from pydantic import BaseModel
cache = SQLiteCache()
set_llm_cache(cache)
class Temperature(BaseModel):
value: int
city: str
llm = init_chat_model("openai:gpt-4o-mini")
structured_llm = llm.with_structured_output(Temperature)
```
```python
# 681 ms
response = structured_llm.invoke("What is the average temperature of Rome in May?")
```
```python
# 6.98 ms
response = structured_llm.invoke("What is the average temperature of Rome in May?")
```
See https://docs.astral.sh/ruff/rules/#flake8-annotations-ann
The interest compared to only mypy is that ruff is very fast at
detecting missing annotations.
ANN101 and ANN102 are deprecated so we ignore them
ANN401 (no Any type) ignored to be in sync with mypy config
---------
Co-authored-by: ccurme <chester.curme@gmail.com>
- **Description:** Add the new introduction about checking `store` in
in_memory.py, It’s necessary and useful for beginners.
```python
Check Documents:
.. code-block:: python
for doc in vector_store.store.values():
print(doc)
```
---------
Co-authored-by: Chester Curme <chester.curme@gmail.com>
- **Description:** Add tests for respecting max_concurrency and
implement it for abatch_as_completed so that test passes
- **Issue:** #29425
- **Dependencies:** none
- **Twitter handle:** keenanpepper
Description:
The change allows you to use the overloaded `+` operator correctly when
`+`ing two BaseMessageChunk subclasses. Without this you *must*
instantiate a subclass for it to work.
Which feels... wrong. Base classes should be decoupled from sub classes
and should have in no way a dependency on them.
Issue:
You can't `+` a BaseMessageChunk with a BaseMessageChunk
e.g. this will explode
```py
from langchain_core.outputs import (
ChatGenerationChunk,
)
from langchain_core.messages import BaseMessageChunk
chunk1 = ChatGenerationChunk(
message=BaseMessageChunk(
type="customChunk",
content="HI",
),
)
chunk2 = ChatGenerationChunk(
message=BaseMessageChunk(
type="customChunk",
content="HI",
),
)
# this will throw
new_chunk = chunk1 + chunk2
```
In case anyone ran into this issue themselves, it's probably best to use
the AIMessageChunk:
a la
```py
from langchain_core.outputs import (
ChatGenerationChunk,
)
from langchain_core.messages import AIMessageChunk
chunk1 = ChatGenerationChunk(
message=AIMessageChunk(
content="HI",
),
)
chunk2 = ChatGenerationChunk(
message=AIMessageChunk(
content="HI",
),
)
# No explosion!
new_chunk = chunk1 + chunk2
```
Dependencies:
None!
Twitter handle:
`aaron_vogler`
Keeping these for later if need be:
```
baskaryan
efriis
eyurtsev
ccurme
vbarda
hwchase17
baskaryan
efriis
```
Co-authored-by: Erick Friis <erick@langchain.dev>
**Description**
Currently, when parsing a partial JSON, if a string ends with the escape
character, the whole key/value is removed. For example:
```
>>> from langchain_core.utils.json import parse_partial_json
>>> my_str = '{"foo": "bar", "baz": "qux\\'
>>>
>>> parse_partial_json(my_str)
{'foo': 'bar'}
```
My expectation (and with this fix) would be for `parse_partial_json()`
to return:
```
>>> from langchain_core.utils.json import parse_partial_json
>>>
>>> my_str = '{"foo": "bar", "baz": "qux\\'
>>> parse_partial_json(my_str)
{'foo': 'bar', 'baz': 'qux'}
```
Notes:
1. It could be argued that current behavior is still desired.
2. I have experienced this issue when the streaming output from an LLM
and the chunk happens to end with `\\`
3. I haven't included tests. Will do if change is accepted.
4. This is specially troublesome when this function is used by
187131c55c/libs/core/langchain_core/output_parsers/transform.py (L111)
since what happens is that, for example, if the received sequence of
chunks are: `{"foo": "b` , `ar\\` :
Then, the result of calling `self.parse_result()` is:
```
{"foo": "b"}
```
and the second time:
```
{}
```
Co-authored-by: Erick Friis <erick@langchain.dev>
This pull request addresses an issue with import statements in the
langchain_core/retrievers.py file. The following changes have been made:
Corrected the import for Document from langchain_core.documents.base.
Corrected the import for BaseRetriever from langchain_core.retrievers.
These changes ensure that the SimpleRetriever class can correctly
reference the Document and BaseRetriever classes, improving code
reliability and maintainability.
---------
Co-authored-by: Matheus Torquato <mtorquat@jaguarlandrover.com>
Co-authored-by: Chester Curme <chester.curme@gmail.com>
TRY004 ("use TypeError rather than ValueError") existing errors are
marked as ignore to preserve backward compatibility.
LMK if you prefer to fix some of them.
Co-authored-by: Erick Friis <erick@langchain.dev>
`RunnableLambda`'s `__repr__` may do costly OS operation by calling
`get_lambda_source`.
So it's better to cache it.
See #29043
---------
Co-authored-by: Chester Curme <chester.curme@gmail.com>
Thank you for contributing to LangChain!
- [ ] **PR title**: "package: description"
- Where "package" is whichever of langchain, community, core, etc. is
being modified. Use "docs: ..." for purely docs changes, "infra: ..."
for CI changes.
- Example: "community: add foobar LLM"
- [ ] **PR message**: ***Delete this entire checklist*** and replace
with
- **Description:** a description of the change
- **Issue:** the issue # it fixes, if applicable
- **Dependencies:** any dependencies required for this change
- **Twitter handle:** if your PR gets announced, and you'd like a
mention, we'll gladly shout you out!
- [ ] **Add tests and docs**: If you're adding a new integration, please
include
1. a test for the integration, preferably unit tests that do not rely on
network access,
2. an example notebook showing its use. It lives in
`docs/docs/integrations` directory.
- [ ] **Lint and test**: Run `make format`, `make lint` and `make test`
from the root of the package(s) you've modified. See contribution
guidelines for more: https://python.langchain.com/docs/contributing/
Additional guidelines:
- Make sure optional dependencies are imported within a function.
- Please do not add dependencies to pyproject.toml files (even optional
ones) unless they are required for unit tests.
- Most PRs should not touch more than one package.
- Changes should be backwards compatible.
- If you are adding something to community, do not re-import it in
langchain.
If no one reviews your PR within a few days, please @-mention one of
baskaryan, efriis, eyurtsev, ccurme, vbarda, hwchase17.
Thank you for contributing to LangChain!
- [ ] **PR title**: "package: description"
- Where "package" is whichever of langchain, community, core, etc. is
being modified. Use "docs: ..." for purely docs changes, "infra: ..."
for CI changes.
- Example: "community: add foobar LLM"
- [ ] **PR message**: ***Delete this entire checklist*** and replace
with
- **Description:** a description of the change
- **Issue:** the issue # it fixes, if applicable
- **Dependencies:** any dependencies required for this change
- **Twitter handle:** if your PR gets announced, and you'd like a
mention, we'll gladly shout you out!
- [ ] **Add tests and docs**: If you're adding a new integration, please
include
1. a test for the integration, preferably unit tests that do not rely on
network access,
2. an example notebook showing its use. It lives in
`docs/docs/integrations` directory.
- [ ] **Lint and test**: Run `make format`, `make lint` and `make test`
from the root of the package(s) you've modified. See contribution
guidelines for more: https://python.langchain.com/docs/contributing/
Additional guidelines:
- Make sure optional dependencies are imported within a function.
- Please do not add dependencies to pyproject.toml files (even optional
ones) unless they are required for unit tests.
- Most PRs should not touch more than one package.
- Changes should be backwards compatible.
- If you are adding something to community, do not re-import it in
langchain.
If no one reviews your PR within a few days, please @-mention one of
baskaryan, efriis, eyurtsev, ccurme, vbarda, hwchase17.
Fixes#29010
This PR updates the example for FewShotChatMessagePromptTemplate by
modifying the human input prompt to include a more descriptive and
user-friendly question format ('What is {input}?') instead of just
'{input}'. This change enhances clarity and usability in the
documentation example.
Co-authored-by: Erick Friis <erick@langchain.dev>
Add option to return content and artifacts, to also be able to access
the full info of the retrieved documents.
They are returned as a list of dicts in the `artifacts` property if
parameter `response_format` is set to `"content_and_artifact"`.
Defaults to `"content"` to keep current behavior.
---------
Co-authored-by: Erick Friis <erick@langchain.dev>
