This PR uses the [blockbuster](https://github.com/cbornet/blockbuster)
library in langchain-core to detect blocking calls made in the asyncio
event loop during unit tests.
Avoiding blocking calls is hard as these can be deeply buried in the
code or made in 3rd party libraries.
Blockbuster makes it easier to detect them by raising an exception when
a call is made to a known blocking function (eg: `time.sleep`).
Adding blockbuster allowed to find a blocking call in
`aconfig_with_context` (it ends up calling `get_function_nonlocals`
which loads function code).
**Dependencies:**
- blockbuster (test)
**Twitter handle:** cbornet_
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>
(Inspired by https://github.com/langchain-ai/langchain/issues/26918)
We rely on some deprecated public functions in the hot path for tool
binding (`convert_pydantic_to_openai_function`,
`convert_python_function_to_openai_function`, and
`format_tool_to_openai_function`). My understanding is that what is
deprecated is not the functionality they implement, but use of them in
the public API -- we expect to continue to rely on them.
Here we update these functions to be private and not deprecated. We keep
the public, deprecated functions as simple wrappers that can be safely
deleted.
The `@deprecated` wrapper adds considerable latency due to its use of
the `inspect` module. This update speeds up `bind_tools` by a factor of
~100x:
Before:
After:
---------
Co-authored-by: Erick Friis <erick@langchain.dev>
When using `create_xml_agent` or `create_json_chat_agent` to create a
agent, and the function corresponding to the tool is a parameterless
function, the `XMLAgentOutputParser` or `JSONAgentOutputParser` will
parse the tool input into an empty string, `BaseTool` will parse it into
a positional argument.
So, the program will crash finally because we invoke a parameterless
function but with a positional argument.Specially, below code will raise
StopIteration in
[_parse_input](https://github.com/langchain-ai/langchain/blob/master/libs/core/langchain_core/tools/base.py#L419)
```python
from langchain import hub
from langchain.agents import AgentExecutor, create_json_chat_agent, create_xml_agent
from langchain_openai import ChatOpenAI
prompt = hub.pull("hwchase17/react-chat-json")
llm = ChatOpenAI()
# agent = create_xml_agent(llm, tools, prompt)
agent = create_json_chat_agent(llm, tools, prompt)
agent_executor = AgentExecutor(agent=agent, tools=tools, verbose=True)
agent_executor.invoke(......)
```
---------
Co-authored-by: Erick Friis <erick@langchain.dev>
Co-authored-by: Bagatur <22008038+baskaryan@users.noreply.github.com>
Co-authored-by: Chester Curme <chester.curme@gmail.com>
- Convert developer openai messages to SystemMessage
- store additional_kwargs={"__openai_role__": "developer"} so that the
correct role can be reconstructed if needed
- update ChatOpenAI to read in openai_role
---------
Co-authored-by: Erick Friis <erick@langchain.dev>
Thank you for contributing to LangChain!
- [x] **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"
Description:
Improved the `_parse_google_docstring` function in `langchain/core` to
support parsing multi-paragraph descriptions before the `Args:` section
while maintaining compliance with Google-style docstring guidelines.
This change ensures better handling of docstrings with detailed function
descriptions.
Issue:
Fixes#28628
Dependencies:
None.
Twitter handle:
@isatyamks
---------
Co-authored-by: Erick Friis <erick@langchain.dev>
Co-authored-by: Chester Curme <chester.curme@gmail.com>
The delete methods in the VectorStore and DocumentIndex interfaces
return a status indicating the result. Therefore, we can assume that
their implementations don't throw exceptions but instead return a result
indicating whether the delete operations have failed. The current
implementation doesn't check the returned value, so I modified it to
throw an exception when the operation fails.
---------
Co-authored-by: Eugene Yurtsev <eyurtsev@gmail.com>
~Note that this PR is now Draft, so I didn't add change to `aindex`
function and didn't add test codes for my change.
After we have an agreement on the direction, I will add commits.~
`batch_size` is very difficult to decide because setting a large number
like >10000 will impact VectorDB and RecordManager, while setting a
small number will delete records unnecessarily, leading to redundant
work, as the `IMPORTANT` section says.
On the other hand, we can't use `full` because the loader returns just a
subset of the dataset in our use case.
I guess many people are in the same situation as us.
So, as one of the possible solutions for it, I would like to introduce a
new argument, `scoped_full_cleanup`.
This argument will be valid only when `claneup` is Full. If True, Full
cleanup deletes all documents that haven't been updated AND that are
associated with source ids that were seen during indexing. Default is
False.
This change keeps backward compatibility.
---------
Co-authored-by: Eugene Yurtsev <eugene@langchain.dev>
Co-authored-by: Eugene Yurtsev <eyurtsev@gmail.com>
I reported the bug 2 weeks ago here:
https://github.com/langchain-ai/langchain/issues/28447
I believe this is a critical bug for the indexer, so I submitted a PR to
revert the change and added unit tests to prevent similar bugs from
being introduced in the future.
@eyurtsev Could you check this?
Thank you for contributing to LangChain!
- [ ] **PR title**: "core: google docstring parsing fix"
- [x] **PR message**:
- **Description:** Added a solution for invalid parsing of google
docstring such as:
Args:
net_annual_income (float): The user's net annual income (in current year
dollars).
- **Issue:** Previous code would return arg = "net_annual_income
(float)" which would cause exception in
_validate_docstring_args_against_annotations
- **Dependencies:** None
If no one reviews your PR within a few days, please @-mention one of
baskaryan, efriis, eyurtsev, ccurme, vbarda, hwchase17.
Co-authored-by: Erick Friis <erick@langchain.dev>
- **Description:** `kwargs` are not being passed to `run` of the
`BaseTool` which has been fixed
- **Issue:** #28114
---------
Co-authored-by: Stevan Kapicic <kapicic.ste1@gmail.com>
Co-authored-by: Erick Friis <erick@langchain.dev>
- **Description:**
- Trim functions were incorrectly deleting nodes with more than 1
outgoing/incoming edge, so an extra condition was added to check for
this directly. A unit test "test_trim_multi_edge" was written to test
this test case specifically.
- **Issue:**
- Fixes#28411
- Fixes https://github.com/langchain-ai/langgraph/issues/1676
- **Dependencies:**
- No changes were made to the dependencies
- [x] Unit tests were added to verify the changes.
- [x] Updated documentation where necessary.
- [x] Ran make format, make lint, and make test to ensure compliance
with project standards.
---------
Co-authored-by: Tasif Hussain <tasif006@gmail.com>
We have a test
[test_structured_few_shot_examples](ad4333ca03/libs/standard-tests/langchain_tests/integration_tests/chat_models.py (L546))
in standard integration tests that implements a version of tool-calling
few shot examples that works with ~all tested providers. The formulation
supported by ~all providers is: `human message, tool call, tool message,
AI reponse`.
Here we update
`langchain_core.utils.function_calling.tool_example_to_messages` to
support this formulation.
The `tool_example_to_messages` util is undocumented outside of our API
reference. IMO, if we are testing that this function works across all
providers, it can be helpful to feature it in our guides. The structured
few-shot examples we document at the moment require users to implement
this function and can be simplified.
Last week Anthropic released version 0.39.0 of its python sdk, which
enabled support for Python 3.13. This release deleted a legacy
`client.count_tokens` method, which we currently access during init of
the `Anthropic` LLM. Anthropic has replaced this functionality with the
[client.beta.messages.count_tokens()
API](https://github.com/anthropics/anthropic-sdk-python/pull/726).
To enable support for `anthropic >= 0.39.0` and Python 3.13, here we
drop support for the legacy token counting method, and add support for
the new method via `ChatAnthropic.get_num_tokens_from_messages`.
To fully support the token counting API, we update the signature of
`get_num_tokens_from_message` to accept tools everywhere.
---------
Co-authored-by: Bagatur <22008038+baskaryan@users.noreply.github.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, "templates:
..." for template changes, "infra: ..." for CI changes.
- Example: "community: add foobar LLM"
- [ ] **description**
langchain_core.runnables.graph_mermaid.draw_mermaid_png calls this
function, but the Mermaid API returns JPEG by default. To be consistent,
add the option `file_type` with the default `png` type.
- [ ] **Add tests and docs**: If you're adding a new integration, please
include
With this small change, I didn't add tests and docs.
- [ ] **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:
One long sentence was divided into two.
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.
- **Description:**
Currently CommaSeparatedListOutputParser can't handle strings that may
contain commas within a column. It would parse any commas as the
delimiter.
Ex.
"foo, foo2", "bar", "baz"
It will create 4 columns: "foo", "foo2", "bar", "baz"
This should be 3 columns:
"foo, foo2", "bar", "baz"
- **Dependencies:**
Added 2 additional imports, but they are built in python packages.
import csv
from io import StringIO
- **Twitter handle:** @jkyamog
- [ ] **Add tests and docs**:
1. added simple unit test test_multiple_items_with_comma
---------
Co-authored-by: Erick Friis <erick@langchain.dev>
Co-authored-by: Bagatur <22008038+baskaryan@users.noreply.github.com>
Co-authored-by: Bagatur <baskaryan@gmail.com>
Thank you for contributing to LangChain!
- [x] **PR title**: "core: use friendlier names for duplicated nodes in
mermaid output"
- **Description:** When generating the Mermaid visualization of a chain,
if the chain had multiple nodes of the same type, the reid function
would replace their names with the UUID node_id. This made the generated
graph difficult to understand. This change deduplicates the nodes in a
chain by appending an index to their names.
- **Issue:** None
- **Discussion:**
https://github.com/langchain-ai/langchain/discussions/27714
- **Dependencies:** None
- [ ] **Add tests and docs**:
- Currently this functionality is not covered by unit tests, happy to
add tests if you'd like
- [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/
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.
# Example Code:
```python
from langchain_core.runnables import RunnablePassthrough
def fake_llm(prompt: str) -> str: # Fake LLM for the example
return "completion"
runnable = {
'llm1': fake_llm,
'llm2': fake_llm,
} | RunnablePassthrough.assign(
total_chars=lambda inputs: len(inputs['llm1'] + inputs['llm2'])
)
print(runnable.get_graph().draw_mermaid(with_styles=False))
```
# Before
```mermaid
graph TD;
Parallel_llm1_llm2_Input --> 0b01139db5ed4587ad37964e3a40c0ec;
0b01139db5ed4587ad37964e3a40c0ec --> Parallel_llm1_llm2_Output;
Parallel_llm1_llm2_Input --> a98d4b56bd294156a651230b9293347f;
a98d4b56bd294156a651230b9293347f --> Parallel_llm1_llm2_Output;
Parallel_total_chars_Input --> Lambda;
Lambda --> Parallel_total_chars_Output;
Parallel_total_chars_Input --> Passthrough;
Passthrough --> Parallel_total_chars_Output;
Parallel_llm1_llm2_Output --> Parallel_total_chars_Input;
```
# After
```mermaid
graph TD;
Parallel_llm1_llm2_Input --> fake_llm_1;
fake_llm_1 --> Parallel_llm1_llm2_Output;
Parallel_llm1_llm2_Input --> fake_llm_2;
fake_llm_2 --> Parallel_llm1_llm2_Output;
Parallel_total_chars_Input --> Lambda;
Lambda --> Parallel_total_chars_Output;
Parallel_total_chars_Input --> Passthrough;
Passthrough --> Parallel_total_chars_Output;
Parallel_llm1_llm2_Output --> Parallel_total_chars_Input;
```
**Description:**
This PR fixes an issue where non-ASCII characters in Pydantic field
descriptions were being escaped to their Unicode representations when
using `JsonOutputParser`. The change allows non-ASCII characters to be
preserved in the output, which is especially important for multilingual
support and when working with non-English languages.
**Issue:** Fixes#27256
**Example Code:**
```python
from pydantic import BaseModel, Field
from langchain_core.output_parsers import JsonOutputParser
class Article(BaseModel):
title: str = Field(description="科学文章的标题")
output_data_structure = Article
parser = JsonOutputParser(pydantic_object=output_data_structure)
print(parser.get_format_instructions())
```
**Previous Output**:
```... "title": {"description": "\\u79d1\\u5b66\\u6587\\u7ae0\\u7684\\u6807\\u9898", "title": "Title", "type": "string"}} ...```
**Current Output**:
```... "title": {"description": "科学文章的标题", "title": "Title", "type":
"string"}} ...```
**Changes made**:
- Modified `json.dumps()` call in
`langchain_core/output_parsers/json.py` to use `ensure_ascii=False`
- Added a unit test to verify Unicode handling
Co-authored-by: Harsimran-19 <harsimran1869@gmail.com>
**Description:**
When annotating a function with the @tool decorator, the symbol should
have type BaseTool. The previous type annotations did not convey that to
type checkers. This patch creates 4 overloads for the tool function for
the 4 different use cases.
1. @tool decorator with no arguments
2. @tool decorator with only keyword arguments
3. @tool decorator with a name argument (and possibly keyword arguments)
4. Invoking tool as function with a name and runnable positional
arguments
The main function is updated to match the overloads. The changes are
100% backwards compatible (all existing calls should continue to work,
just with better type annotations).
**Twitter handle:** @nvachhar
---------
Co-authored-by: Eugene Yurtsev <eyurtsev@gmail.com>
**Description:** We improve the performance of the InMemoryVectorStore.
**Isue:** Originally, similarity was computed document by document:
```
for doc in self.store.values():
vector = doc["vector"]
similarity = float(cosine_similarity([embedding], [vector]).item(0))
```
This is inefficient and does not make use of numpy vectorization.
This PR computes the similarity in one vectorized go:
```
docs = list(self.store.values())
similarity = cosine_similarity([embedding], [doc["vector"] for doc in docs])
```
**Dependencies:** None
**Twitter handle:** @b12_consulting, @Vincent_Min
---------
Co-authored-by: Eugene Yurtsev <eyurtsev@gmail.com>
