### Description
Add keep_separator arg to HTMLSemanticPreservingSplitter and pass value
to instance of RecursiveCharacterTextSplitter used under the hood.
### Issue
Documents returned by `HTMLSemanticPreservingSplitter.split_text(text)`
are defaulted to use separators at beginning of page_content. [See third
and fourth document in example output from how-to
guide](https://python.langchain.com/docs/how_to/split_html/#using-htmlsemanticpreservingsplitter):
```
[Document(metadata={'Header 1': 'Main Title'}, page_content='This is an introductory paragraph with some basic content.'),
Document(metadata={'Header 2': 'Section 1: Introduction'}, page_content='This section introduces the topic'),
Document(metadata={'Header 2': 'Section 1: Introduction'}, page_content='. Below is a list: First item Second item Third item with bold text and a link Subsection 1.1: Details This subsection provides additional details'),
Document(metadata={'Header 2': 'Section 1: Introduction'}, page_content=". Here's a table: Header 1 Header 2 Header 3 Row 1, Cell 1 Row 1, Cell 2 Row 1, Cell 3 Row 2, Cell 1 Row 2, Cell 2 Row 2, Cell 3"),
Document(metadata={'Header 2': 'Section 2: Media Content'}, page_content='This section contains an image and a video:  '),
Document(metadata={'Header 2': 'Section 3: Code Example'}, page_content='This section contains a code block: <code:html> <div> <p>This is a paragraph inside a div.</p> </div> </code>'),
Document(metadata={'Header 2': 'Conclusion'}, page_content='This is the conclusion of the document.')]
```
### Dependencies
None
@ttrumper3
docs: update multimodal PDF and image usage for gpt-4.1
**Description:**
This update revises the LangChain documentation to support the new
GPT-4.1 multimodal API format. It fixes the previous broken example for
PDF uploads (which returned a 400 error: "Missing required parameter:
'messages[0].content[1].file'") and adds clear instructions on how to
include base64-encoded images for OpenAI models.
**Issue:**
error appointed in foruns for pdf load into api ->
'''
@[Albaeld](https://github.com/Albaeld)
Albaeld
[8 days
ago](https://github.com/langchain-ai/langchain/discussions/27702#discussioncomment-13369460)
This simply does not work with openai:gpt-4.1. I get:
Error code: 400 - {'error': {'message': "Missing required parameter:
'messages[0].content[1].file'.", 'type': 'invalid_request_error',
'param': 'messages[0].content[1].file', 'code':
'missing_required_parameter'}}
'''
**Dependencies:**
None
**Twitter handle:**
N/A
---------
Co-authored-by: Chester Curme <chester.curme@gmail.com>
Description:
This pull request corrects minor typographical errors in the
documentation notebooks for vectorstore integrations. Specifically, it
fixes the spelling of "datastore" in `llm_rails.ipynb` and
"pre-existent" in `redis.ipynb`. These changes improve the clarity and
professionalism of the documentation. No functional code changes are
included.
Thank you for contributing to LangChain!
[x] PR title: langchain_ollama: support custom headers for Ollama
partner APIs
Where "package" is whichever of langchain, core, etc. is being modified.
Use "docs: ..." for purely docs changes, "infra: ..." for CI changes.
Example: "core: add foobar LLM"
[x] PR message:
**Description: This PR adds support for passing custom HTTP headers to
Ollama models when used as a LangChain integration. This is especially
useful for enterprise users or partners who need to send authentication
tokens, API keys, or custom tracking headers when querying secured
Ollama servers.
Issue: N/A (new enhancement)
**Dependencies: No external dependencies introduced.
Twitter handle: @arunkumar_offl
[x] Add tests and docs: If you're adding a new integration, please
include
1.Added a unit test in test_chat_models.py to validate headers are
passed correctly.
2. Added an example notebook:
docs/docs/integrations/llms/ollama_custom_headers.ipynb showing how to
use custom headers.
[x] Lint and test: Ran make format, make lint, and make test to ensure
the code is clean and passing all checks.
Additional guidelines:
Make sure optional dependencies are imported within a function.
Please do not add dependencies to pyproject.toml files (even optional
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Most PRs should not touch more than one package.
Changes should be backwards compatible.
If no one reviews your PR within a few days, please @-mention one of
baskaryan, eyurtsev, ccurme, vbarda, hwchase17.
This MR is only for the docs. Added integration with Nebius AI Studio to
docs. The integration package is available at
[https://github.com/nebius/langchain-nebius](https://github.com/nebius/langchain-nebius).
---------
Co-authored-by: Akim Tsvigun <aktsvigun@nebius.com>
Co-authored-by: Chester Curme <chester.curme@gmail.com>
Thank you for contributing to LangChain!
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- **Description:** Remove the outdated Gemini models and replace those
with the latest models.
- **Issue:** Earlier the code was not running, now the code runs.
- **Dependencies:** No
- **Twitter handle:** [soumendrak_](https://x.com/soumendrak_)
## Description
Updating Exa integration documentation to showcase the latest features
and best practices.
## Changes
- Added examples for `ExaSearchResults` tool with advanced search
options
- Added examples for `ExaFindSimilarResults` tool
- Updated agent example to use LangGraph
- Demonstrated text content options, summaries, and highlights
- Included examples of search type control and live crawling
## Additional Context
I'm from the Exa team updating our integration documentation to reflect
current capabilities and best practices.
Remove proxy imports to langchain_experimental.
Previously, these imports would work if a user manually installed
langchain_experimental. However, we want to drop support even for that
as langchain_experimental is generally not recommended to be run in
production.
---------
Co-authored-by: ccurme <chester.curme@gmail.com>
**Description:**
Fixed a small grammatical error in the `retrievers.mdx` documentation.
Replaced "we can be built retrievers on top of search APIs..." with
"we can build retrievers on top of search APIs..." for clarity and
correctness.
**Issue:**
N/A
**Dependencies:**
None
**Twitter handle:**
@hassan_zameel
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.
**Description:**
`langchain_huggingface` has a very large installation size of around 600
MB (on a Mac with Python 3.11). This is due to its dependency on
`sentence-transformers`, which in turn depends on `torch`, which is 320
MB all by itself. Similarly, the depedency on `transformers` adds
another set of heavy dependencies. With those dependencies removed, the
installation of `langchain_huggingface` only takes up ~26 MB. This is
only 5 % of the full installation!
These libraries are not necessary to use `langchain_huggingface`'s API
wrapper classes, only for local inferences/embeddings. All import
statements for those two libraries already have import guards in place
(try/catch with a helpful "please install x" message).
This PR therefore moves those two libraries to an optional dependency
group `full`. So a `pip install langchain_huggingface` will only install
the lightweight version, and a `pip install
"langchain_huggingface[full]"` will install all dependencies.
I know this may break existing code, because `sentence-transformers` and
`transformers` are now no longer installed by default. Given that users
will see helpful error messages when that happens, and the major impact
of this small change, I hope that you will still consider this PR.
**Dependencies:** No new dependencies, but new optional grouping.
- **Description:**
- In _infer_arg_descriptions, the annotations dictionary contains string
representations of types instead of actual typing objects. This causes
_is_annotated_type to fail, preventing the correct description from
being generated.
- This is a simple fix using the get_type_hints method, which resolves
the annotations properly and is supported across all Python versions.
- **Issue:** #31051
---------
Co-authored-by: Eugene Yurtsev <eyurtsev@gmail.com>
https://github.com/langchain-ai/langchain/pull/31286 included an update
to the return type for `BaseChatModel.(a)stream`, from
`Iterator[BaseMessageChunk]` to `Iterator[BaseMessage]`.
This change is correct, because when streaming is disabled, the stream
methods return an iterator of `BaseMessage`, and the inheritance is such
that an `BaseMessage` is not a `BaseMessageChunk` (but the reverse is
true).
However, LangChain includes a pattern throughout its docs of [summing
BaseMessageChunks](https://python.langchain.com/docs/how_to/streaming/#llms-and-chat-models)
to accumulate a chat model stream. This pattern is implemented in tests
for most integration packages and appears in application code. So
https://github.com/langchain-ai/langchain/pull/31286 introduces mypy
errors throughout the ecosystem (or maybe more accurately, it reveals
that this pattern does not account for use of the `.stream` method when
streaming is disabled).
Here we revert just the change to the stream return type to unblock
things. A fix for this should address docs + integration packages (or if
we elect to just force people to update code, be explicit about that).
**Description:**
This PR updates approximately 4' occurences of the deprecated
`initialize_agent` function in LangChain documentation and examples,
replacing it with the recommended `create_react_agent` and pattern. It
also refactors related examples to align with current best practices.
**Issue:**
Partially Fixes#29277
**Dependencies:**
None
**X handle:**
@TK1475
---------
Co-authored-by: Eugene Yurtsev <eyurtsev@gmail.com>
This PR adds documentation to our Microsft Provider page for LangChain
Azure AI. This PR does not add any extra dependencies or require any
tests besides passing CI.
Thank you for contributing to LangChain!
- [x] **PR title**: "package: description"
- Where "package" is whichever of langchain, core, etc. is being
modified. Use "docs: ..." for purely docs changes, "infra: ..." for CI
changes.
- Example: "core: add foobar LLM"
- [x] **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!
- [x] **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
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`docs/docs/integrations` directory.
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from the root of the package(s) you've modified. See contribution
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Additional guidelines:
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If no one reviews your PR within a few days, please @-mention one of
baskaryan, eyurtsev, ccurme, vbarda, hwchase17.
## PR Title
```
docs: enhance Salesforce Toolkit documentation
```
## PR Description
**Description:** Enhanced the Salesforce Toolkit documentation to
provide a more comprehensive overview of the `langchain-salesforce`
package. The updates include improved descriptions of the toolkit's
capabilities, detailed setup instructions for authentication using
environment variables, updated code snippets with consistent parameter
naming and improved readability, and additional resources with API
references for better user guidance.
**Issue:** N/A (documentation improvement)
**Dependencies:** None
**Twitter handle:** @colesmcintosh
---
### Changes Made:
- Improved description of the Salesforce Toolkit's capabilities and
features
- Added detailed setup instructions for authentication using environment
variables
- Updated code snippets to use consistent parameter naming and improved
readability
- Included additional resources and API references for better user
guidance
- Enhanced overall documentation structure and clarity
### Files Modified:
- `docs/docs/integrations/tools/salesforce.ipynb` (83 insertions, 36
deletions)
This is a documentation-only change that improves the user experience
for developers working with the Salesforce Toolkit. The changes are
backwards compatible and follow LangChain's documentation standards.
---------
Co-authored-by: Eugene Yurtsev <eyurtsev@gmail.com>
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>
change `--no-cache-dirclear` -> `--no-cache-dir`.
pip throws `no such option: --no-cache-dirclear` since its invalid.
`--no-cache-dir` is the correct one.
Thank you for contributing to LangChain!
- [ ] **PR title**: "package: description"
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If no one reviews your PR within a few days, please @-mention one of
baskaryan, eyurtsev, ccurme, vbarda, hwchase17.
…cs/integrations/tools/ All tools section
Thank you for contributing to LangChain!
- [x] **PR title**: "package: description"
- Where "package" is whichever of langchain, core, etc. is being
modified. Use "docs: ..." for purely docs changes, "infra: ..." for CI
changes.
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- [x] **Lint and test**: Run `make format`, `make lint` and `make test`
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…tegrations/tools/ All tools section
Thank you for contributing to LangChain!
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- [x] **Lint and test**: Run `make format`, `make lint` and `make test`
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Llama-3.1 started failing consistently with
> groq.BadRequestError: Error code: 400 - ***'error': ***'message':
"Failed to call a function. Please adjust your prompt. See
'failed_generation' for more details.", 'type': 'invalid_request_error',
'code': 'tool_use_failed', 'failed_generation':
'<function=brave_search>***"query": "Hello!"***</function>'***
Thank you for contributing to LangChain!
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Co-authored-by: ccurme <chester.curme@gmail.com>
Release core 0.3.63
Small update just to expand the list of well known tools. This is
necessary while the logic lives in langchain-core.
---------
Co-authored-by: Chester Curme <chester.curme@gmail.com>
Add image generation tool to the list of well known tools. This is needed for changes in the ChatOpenAI client.
TODO: Some of this logic needs to be moved from core directly into the client as changes in core should not be required to add a new tool to the openai chat client.
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Deleted two outdated phrases that were reflecting the current versions
of packages at the time i.e.: 1-"langchain-community is currently on
version 0.2.x." 2-langchain-"experimental is currently on version 0.0.x"
docs: update Valyu integration notebooks to reflect current
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…integrations/retrievers/ All retrievers section
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---------
Co-authored-by: Chester Curme <chester.curme@gmail.com>
…integrations/document_loaders/ All document loaders section
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…tionguard.ipynb
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### Description
Added a note above the retriever overview table to clarify that the
descriptions are truncated for readability and how to view the full
version (via hover or click).
### Issue
Fixes#31311 — Users were confused by incomplete retriever descriptions
in the integration docs.
### Dependencies
None
---------
Co-authored-by: Chester Curme <chester.curme@gmail.com>
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- Most PRs should not touch more than one package.
- Changes should be backwards compatible.
If no one reviews your PR within a few days, please @-mention one of
baskaryan, eyurtsev, ccurme, vbarda, hwchase17.
Thank you for contributing to LangChain!
- [x] **PR title**: "package: description"
- Where "package" is whichever of langchain, core, etc. is being
modified. Use "docs: ..." for purely docs changes, "infra: ..." for CI
changes.
- Example: "core: add foobar LLM"
- [x] **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!
- [x] **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.
- [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 no one reviews your PR within a few days, please @-mention one of
baskaryan, eyurtsev, ccurme, vbarda, hwchase17.
As part of core releases we run tests on the last released version of
some packages (including langchain-openai) using the new version of
langchain-core. We run langchain-openai's test suite as it was when it
was last released.
OpenAI has since updated their API— relaxing constraints on what schemas
are supported when `strict=True`— causing these tests to break. They
have since been fixed. But the old tests will continue to fail.
Will revert this change after we release OpenAI today.
Added support for new Exa API features. Updated Exa docs and python
package (langchain-exa).
Description
Added support for new Exa API features in the langchain-exa package:
- Added max_characters option for text content
- Added support for summary and custom summary prompts
- Added livecrawl option with "always", "fallback", "never" settings
- Added "auto" option for search type
- Updated documentation and tests
Dependencies
- No new dependencies required. Using existing features from exa-py.
twitter: @theishangoswami
---------
Co-authored-by: Chester Curme <chester.curme@gmail.com>
**Description:** ConversationChain has been deprecated, and the
documentation says to use RunnableWithMessageHistory in its place, but
the link at the top of the page to RunnableWithMessageHistory is broken
(it's rendering as "html()"). See here at the top of the page:
https://python.langchain.com/api_reference/langchain/chains/langchain.chains.conversation.base.ConversationChain.html.
This PR fixes the link.
**Issue**: N/A
**Dependencies**: N/A
**Twitter handle:**: If you're on Bluesky, I'm @vikramsaraph.com
Scheduled testing started failing today because the Responses API
stopped raising `BadRequestError` for a schema that was previously
invalid when `strict=True`.
Although docs still say that [some type-specific keywords are not yet
supported](https://platform.openai.com/docs/guides/structured-outputs#some-type-specific-keywords-are-not-yet-supported)
(including `minimum` and `maximum` for numbers), the below appears to
run and correctly respect the constraints:
```python
import json
import openai
maximums = list(range(1, 11))
arg_values = []
for maximum in maximums:
tool = {
"type": "function",
"name": "magic_function",
"description": "Applies a magic function to an input.",
"parameters": {
"properties": {
"input": {"maximum": maximum, "minimum": 0, "type": "integer"}
},
"required": ["input"],
"type": "object",
"additionalProperties": False
},
"strict": True
}
client = openai.OpenAI()
response = client.responses.create(
model="gpt-4.1",
input=[{"role": "user", "content": "What is the value of magic_function(3)? Use the tool."}],
tools=[tool],
)
function_call = next(item for item in response.output if item.type == "function_call")
args = json.loads(function_call.arguments)
arg_values.append(args["input"])
print(maximums)
print(arg_values)
# [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
# [1, 2, 3, 3, 3, 3, 3, 3, 3, 3]
```
Until yesterday this raised BadRequestError.
The same is not true of Chat Completions, which appears to still raise
BadRequestError
```python
tool = {
"type": "function",
"function": {
"name": "magic_function",
"description": "Applies a magic function to an input.",
"parameters": {
"properties": {
"input": {"maximum": 5, "minimum": 0, "type": "integer"}
},
"required": ["input"],
"type": "object",
"additionalProperties": False
},
"strict": True
}
}
response = client.chat.completions.create(
model="gpt-4.1",
messages=[{"role": "user", "content": "What is the value of magic_function(3)? Use the tool."}],
tools=[tool],
)
response # raises BadRequestError
```
Here we update tests accordingly.
Thank you for contributing to LangChain!
- [x] **PR title**: "package: description"
- Where "package" is whichever of langchain, core, etc. is being
modified. Use "docs: ..." for purely docs changes, "infra: ..." for CI
changes.
- Example: "core: add foobar LLM"
- [x] **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!
- [x] **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.
- [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 no one reviews your PR within a few days, please @-mention one of
baskaryan, eyurtsev, ccurme, vbarda, hwchase17.
**Description**:
This PR updates the documentation to address a potential issue when
using `hub.pull(...)` with non-US LangSmith endpoints (e.g.,
`https://eu.api.smith.langchain.com`).
By default, the `hub.pull` function assumes the non US-based API URL.
When the `LANGSMITH_ENDPOINT` environment variable is set to a non-US
region, this can lead to `LangSmithNotFoundError 404 not found` errors
when pulling public assets from the LangChain Hub.
Issue: #31191
---------
Co-authored-by: Chester Curme <chester.curme@gmail.com>
Thank you for contributing to LangChain!
- [x] **PR title**: "package: description"
- Where "package" is whichever of langchain, core, etc. is being
modified. Use "docs: ..." for purely docs changes, "infra: ..." for CI
changes.
- Example: "core: add foobar LLM"
- [x] **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!
- [x] **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.
- [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 no one reviews your PR within a few days, please @-mention one of
baskaryan, eyurtsev, ccurme, vbarda, hwchase17.
Update docs to add Featherless.ai Provider & Chat Model
- **Description:** Adding Featherless.ai as provider in teh
documentations giving access to over 4300+ open-source models
- **Twitter handle:** https://x.com/FeatherlessAI
DSPy removed their LangChain integration in version 2.6.6.
Here we remove the page and add a redirect to the LangChain v0.2 docs
for posterity.
We add an admonition to the v0.2 docs in
https://github.com/langchain-ai/langchain/pull/31277.
* It is possible to chain a `Runnable` with an `AsyncIterator` as seen
in `test_runnable.py`.
* Iterator and AsyncIterator Input/Output of Callables must be put
before `Callable[[Other], Any]` otherwise the pattern matching picks the
latter.
**PR message**: Not sure if I put the check at the right spot, but I
thought throwing the error before the loop made sense to me.
**Description:** Checks if there are only system messages using
AnthropicChat model and throws an error if it's the case. Check Issue
for more details
**Issue:** #30764
---------
Co-authored-by: Chester Curme <chester.curme@gmail.com>
**Issue:**[
#309070](https://github.com/langchain-ai/langchain/issues/30970)
**Cause**
Arg type in python code
```
arg: Union[SubSchema1, SubSchema2]
```
is translated to `anyOf` in **json schema**
```
"anyOf" : [{sub schema 1 ...}, {sub schema 1 ...}]
```
The value of anyOf is a list sub schemas.
The bug is caused since the sub schemas inside `anyOf` list is not taken
care of.
The location where the issue happens is `convert_to_openai_function`
function -> `_recursive_set_additional_properties_false` function, that
recursively adds `"additionalProperties": false` to json schema which is
[required by OpenAI's strict function
calling](https://platform.openai.com/docs/guides/structured-outputs?api-mode=responses#additionalproperties-false-must-always-be-set-in-objects)
**Solution:**
This PR fixes this issue by iterating each sub schema inside `anyOf`
list.
A unit test is added.
**Twitter handle:** shengboma
If no one reviews your PR within a few days, please @-mention one of
baskaryan, eyurtsev, ccurme, vbarda, hwchase17.
---------
Co-authored-by: ccurme <chester.curme@gmail.com>
`aindex` function should check not only `adelete` method, but `delete`
method too
**PR title**: "core: fix async indexing issue with adelete/delete
checking"
**PR message**: Currently `langchain.indexes.aindex` checks if vector
store has overrided adelete method. But due to `adelete` default
implementation store can have just `delete` overrided to make `adelete`
working.
---------
Co-authored-by: Eugene Yurtsev <eyurtsev@gmail.com>
Description: This document change concerns the document-loader
integration, specifically `Confluence`.
I am trying to use the ConfluenceLoader and came across deprecations
when I followed the instructions in the documentation. So I updated the
code blocks with the latest changes made to langchain, and also updated
the documentation for better readability
---------
Co-authored-by: ccurme <chester.curme@gmail.com>
Thank you for contributing to LangChain!
- [ ] **PR title**: "package: description"
- Where "package" is whichever of langchain, core, etc. is being
modified. Use "docs: ..." for purely docs changes, "infra: ..." for CI
changes.
- Example: "core: 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 no one reviews your PR within a few days, please @-mention one of
baskaryan, eyurtsev, ccurme, vbarda, hwchase17.
---------
Co-authored-by: Chester Curme <chester.curme@gmail.com>
- **Description:** The file ```docs/api_reference/create_api_rst.py```
uses a pyproject.toml check to remove partners which don't have a valid
pyproject.toml. This PR extends that check to ```/langchain/libs/*```
sub-directories as well. Without this the ```make api_docs_build```
command fails (see error).
- **Issue:** #31109
- **Dependencies:** none
- **Error Traceback:**
uv run --no-group test python docs/api_reference/create_api_rst.py
Starting to build API reference files.
Building package: community
pyproject.toml not found in /langchain/libs/community.
You are either attempting to build a directory which is not a package or
the package is missing a pyproject.toml file which should be
added.Aborting the build.
make: *** [Makefile:35: api_docs_build] Error 1
**Description**:
Add a `async_client_kwargs` field to ollama chat/llm/embeddings adapters
that is passed to async httpx client constructor.
**Motivation:**
In my use-case:
- chat/embedding model adapters may be created frequently, sometimes to
be called just once or to never be called at all
- they may be used in bots sunc and async mode (not known at the moment
they are created)
So, I want to keep a static transport instance maintaining connection
pool, so model adapters can be created and destroyed freely. But that
doesn't work when both sync and async functions are in use as I can only
pass one transport instance for both sync and async client, while
transport types must be different for them. So I can't make both sync
and async calls use shared transport with current model adapter
interfaces.
In this PR I add a separate `async_client_kwargs` that gets passed to
async client constructor, so it will be possible to pass a separate
transport instance. For sake of backwards compatibility, it is merged
with `client_kwargs`, so nothing changes when it is not set.
I am unable to run linter right now, but the changes look ok.
Bumps
[Ana06/get-changed-files](https://github.com/ana06/get-changed-files)
from 2.2.0 to 2.3.0.
<details>
<summary>Release notes</summary>
<p><em>Sourced from <a
href="https://github.com/ana06/get-changed-files/releases">Ana06/get-changed-files's
releases</a>.</em></p>
<blockquote>
<h2>v2.3.0</h2>
<p>This project is a fork of <a
href="https://github.com/jitterbit/get-changed-files">jitterbit/get-changed-files</a>,
which:</p>
<ul>
<li>Supports <code>pull_request_target</code></li>
<li>Allows to filter files using regular expressions</li>
<li>Removes the ahead check</li>
<li>Considers renamed modified files as modified</li>
<li>Adds <code>added_modified_renamed</code> that includes renamed
non-modified files and all files in <code>added_modified</code></li>
<li>Uses Node 20</li>
</ul>
<h2>Changes</h2>
<ul>
<li>Update to Node 20</li>
<li>Update dependencies</li>
</ul>
<h2>Raw diff</h2>
<p><a
href="https://github.com/Ana06/get-changed-files/compare/v2.2.0...v2.3.0">https://github.com/Ana06/get-changed-files/compare/v2.2.0...v2.3.0</a></p>
</blockquote>
</details>
<details>
<summary>Commits</summary>
<ul>
<li><a
href="25f79e676e"><code>25f79e6</code></a>
Update version in package.json</li>
<li><a
href="6f5373eb01"><code>6f5373e</code></a>
Merge pull request <a
href="https://redirect.github.com/ana06/get-changed-files/issues/42">#42</a>
from Ana06/release2-3</li>
<li><a
href="64dffb46a4"><code>64dffb4</code></a>
Prepare 2.3.0 release</li>
<li><a
href="791f7645b7"><code>791f764</code></a>
[CI] Update actions/checkout</li>
<li><a
href="5a4a136e91"><code>5a4a136</code></a>
[CI] Ensure GH action uses node version 20</li>
<li><a
href="38bdb2e498"><code>38bdb2e</code></a>
Update to Node 20</li>
<li><a
href="5558be5781"><code>5558be5</code></a>
Merge pull request <a
href="https://redirect.github.com/ana06/get-changed-files/issues/30">#30</a>
from Ana06/dependabot/npm_and_yarn/decode-uri-componen...</li>
<li><a
href="6a376fdbb3"><code>6a376fd</code></a>
Merge pull request <a
href="https://redirect.github.com/ana06/get-changed-files/issues/31">#31</a>
from Ana06/dependabot/npm_and_yarn/qs-6.5.3</li>
<li><a
href="ace6e7bcbb"><code>ace6e7b</code></a>
Merge pull request <a
href="https://redirect.github.com/ana06/get-changed-files/issues/32">#32</a>
from brtrick/main</li>
<li><a
href="a102fae9bf"><code>a102fae</code></a>
Merge pull request <a
href="https://redirect.github.com/ana06/get-changed-files/issues/33">#33</a>
from Ana06/dependabot/npm_and_yarn/json5-2.2.3</li>
<li>Additional commits viewable in <a
href="https://github.com/ana06/get-changed-files/compare/v2.2.0...v2.3.0">compare
view</a></li>
</ul>
</details>
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Dependabot will resolve any conflicts with this PR as long as you don't
alter it yourself. You can also trigger a rebase manually by commenting
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Updates dependencies to Chroma to integrate the major release of Chroma
with improved performance, and to fix issues users have been seeing
using the latest chroma docker image with langchain-chroma
https://github.com/langchain-ai/langchain/issues/31047#issuecomment-2850790841
Updates chromadb dependency to >=1.0.9
This also removes the dependency of chroma-hnswlib, meaning it can run
against python 3.13 runners for tests as well.
Tested this by pulling the latest Chroma docker image, running
langchain-chroma using client mode
```
httpClient = chromadb.HttpClient(host="localhost", port=8000)
vector_store = Chroma(
client=httpClient,
collection_name="test",
embedding_function=embeddings,
)
```
"Alanis Morissette" spelling error
Thank you for contributing to LangChain!
- [ ] **PR title**: "package: description"
- Where "package" is whichever of langchain, core, etc. is being
modified. Use "docs: ..." for purely docs changes, "infra: ..." for CI
changes.
- Example: "core: 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 no one reviews your PR within a few days, please @-mention one of
baskaryan, eyurtsev, ccurme, vbarda, hwchase17.
Bumps [astral-sh/setup-uv](https://github.com/astral-sh/setup-uv) from 5
to 6.
<details>
<summary>Release notes</summary>
<p><em>Sourced from <a
href="https://github.com/astral-sh/setup-uv/releases">astral-sh/setup-uv's
releases</a>.</em></p>
<blockquote>
<h2>v6.0.0 🌈 activate-environment and working-directory</h2>
<h2>Changes</h2>
<p>This version contains some breaking changes which have been gathering
up for a while. Lets dive into them:</p>
<ul>
<li><a
href="https://github.com/astral-sh/setup-uv/blob/HEAD/#activate-environment">Activate
environment</a></li>
<li><a
href="https://github.com/astral-sh/setup-uv/blob/HEAD/#working-directory">Working
Directory</a></li>
<li><a
href="https://github.com/astral-sh/setup-uv/blob/HEAD/#default-cache-dependency-glob">Default
<code>cache-dependency-glob</code></a></li>
<li><a
href="https://github.com/astral-sh/setup-uv/blob/HEAD/#use-default-cache-dir-on-self-hosted-runners">Use
default cache dir on self hosted runners</a></li>
</ul>
<h3>Activate environment</h3>
<p>In previous versions using the input <code>python-version</code>
automatically activated a venv at the repository root.
This led to some unwanted side-effects, was sometimes unexpected and not
flexible enough.</p>
<p>The venv activation is now explicitly controlled with the new input
<code>activate-environment</code> (false by default):</p>
<pre lang="yaml"><code>- name: Install the latest version of uv and
activate the environment
uses: astral-sh/setup-uv@v6
with:
activate-environment: true
- run: uv pip install pip
</code></pre>
<p>The venv gets created by the <a
href="https://docs.astral.sh/uv/pip/environments/"><code>uv
venv</code></a> command so the python version is controlled by the
<code>python-version</code> input or the files
<code>pyproject.toml</code>, <code>uv.toml</code>,
<code>.python-version</code> in the <code>working-directory</code>.</p>
<h3>Working Directory</h3>
<p>The new input <code>working-directory</code> controls where we look
for <code>pyproject.toml</code>, <code>uv.toml</code> and
<code>.python-version</code> files
which are used to determine the version of uv and python to install.</p>
<p>It can also be used to control where the venv gets created.</p>
<pre lang="yaml"><code>- name: Install uv based on the config files in
the working-directory
uses: astral-sh/setup-uv@v6
with:
working-directory: my/subproject/dir
</code></pre>
<blockquote>
<p>[!CAUTION]</p>
<p>The inputs <code>pyproject-file</code> and <code>uv-file</code> have
been removed.</p>
</blockquote>
<h3>Default <code>cache-dependency-glob</code></h3>
<p><a href="https://github.com/ssbarnea"><code>@ssbarnea</code></a>
found out that the default <code>cache-dependency-glob</code> was not
suitable for a lot of users.</p>
<p>The old default</p>
<!-- raw HTML omitted -->
</blockquote>
<p>... (truncated)</p>
</details>
<details>
<summary>Commits</summary>
<ul>
<li><a
href="6b9c6063ab"><code>6b9c606</code></a>
Bump dependencies (<a
href="https://redirect.github.com/astral-sh/setup-uv/issues/389">#389</a>)</li>
<li><a
href="ef6bcdff59"><code>ef6bcdf</code></a>
Fix default cache dependency glob (<a
href="https://redirect.github.com/astral-sh/setup-uv/issues/388">#388</a>)</li>
<li><a
href="9a311713f4"><code>9a31171</code></a>
chore: update known checksums for 0.6.17 (<a
href="https://redirect.github.com/astral-sh/setup-uv/issues/384">#384</a>)</li>
<li><a
href="c7f87aa956"><code>c7f87aa</code></a>
bump to v6 in README (<a
href="https://redirect.github.com/astral-sh/setup-uv/issues/382">#382</a>)</li>
<li><a
href="aadfaf08d6"><code>aadfaf0</code></a>
Change default cache-dependency-glob (<a
href="https://redirect.github.com/astral-sh/setup-uv/issues/352">#352</a>)</li>
<li><a
href="a0f9da6273"><code>a0f9da6</code></a>
No default UV_CACHE_DIR on selfhosted runners (<a
href="https://redirect.github.com/astral-sh/setup-uv/issues/380">#380</a>)</li>
<li><a
href="ec4c691628"><code>ec4c691</code></a>
new inputs activate-environment and working-directory (<a
href="https://redirect.github.com/astral-sh/setup-uv/issues/381">#381</a>)</li>
<li><a
href="aa1290542e"><code>aa12905</code></a>
chore: update known checksums for 0.6.16 (<a
href="https://redirect.github.com/astral-sh/setup-uv/issues/378">#378</a>)</li>
<li><a
href="fcaddda076"><code>fcaddda</code></a>
chore: update known checksums for 0.6.15 (<a
href="https://redirect.github.com/astral-sh/setup-uv/issues/377">#377</a>)</li>
<li><a
href="fb3a0a97fa"><code>fb3a0a9</code></a>
log info on venv activation (<a
href="https://redirect.github.com/astral-sh/setup-uv/issues/375">#375</a>)</li>
<li>See full diff in <a
href="https://github.com/astral-sh/setup-uv/compare/v5...v6">compare
view</a></li>
</ul>
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**Description:** This is a document change regarding integration with
package `langchain-anthropic` for newly released websearch tool ([Claude
doc](https://docs.anthropic.com/en/docs/build-with-claude/tool-use/web-search-tool)).
Issue 1: The sample in [Web Search
section](https://python.langchain.com/docs/integrations/chat/anthropic/#web-search)
did not run. You would get an error as below:
```
File "my_file.py", line 170, in call
model_with_tools = model.bind_tools([websearch_tool])
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/langchain_anthropic/chat_models.py", line 1363, in bind_tools
tool if _is_builtin_tool(tool) else convert_to_anthropic_tool(tool)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/langchain_anthropic/chat_models.py", line 1645, in convert_to_anthropic_tool
input_schema=oai_formatted["parameters"],
~~~~~~~~~~~~~^^^^^^^^^^^^^^
KeyError: 'parameters'
```
This is because websearch tool is only recently supported in
langchain-anthropic==0.3.13`, in [0.3.13
release](https://github.com/langchain-ai/langchain/releases?q=tag%3A%22langchain-anthropic%3D%3D0%22&expanded=true)
mentioning:
> anthropic[patch]: support web search
(https://github.com/langchain-ai/langchain/pull/31157)
Issue 2: The current doc has outdated package requirements for Websearch
tool: "This guide requires langchain-anthropic>=0.3.10".
Changes:
- Updated the required `langchain-anthropic` package version (0.3.10 ->
0.3.13).
- Added notes to user when using websearch sample.
I believe this will help avoid future confusion from readers.
**Issue:** N/A
**Dependencies:** N/A
**Twitter handle:** N/A
* Remove unnecessary cast of id -> str (can do with a field setting)
* Remove unnecessary `set_text` model validator (can be done with a
computed field - though we had to make some changes to the `Generation`
class to make this possible
Before: ~2.4s
Blue circles represent time spent in custom validators :(
<img width="1337" alt="Screenshot 2025-05-14 at 10 10 12 AM"
src="https://github.com/user-attachments/assets/bb4f477f-4ee3-4870-ae93-14ca7f197d55"
/>
After: ~2.2s
<img width="1344" alt="Screenshot 2025-05-14 at 10 11 03 AM"
src="https://github.com/user-attachments/assets/99f97d80-49de-462f-856f-9e7e8662adbc"
/>
We still want to optimize the backwards compatible tool calls model
validator, though I think this might involve breaking changes, so wanted
to separate that into a different PR. This is circled in green.
**Description:** Before this commit, if one record is batched in more
than 32k rows for sqlite3 >= 3.32 or more than 999 rows for sqlite3 <
3.31, the `record_manager.delete_keys()` will fail, as we are creating a
query with too many variables.
This commit ensures that we are batching the delete operation leveraging
the `cleanup_batch_size` as it is already done for `full` cleanup.
Added unit tests for incremental mode as well on different deleting
batch size.
1. Removes summation of `ChatGenerationChunk` from hot loops in `stream`
and `astream`
2. Removes run id gen from loop as well (minor impact)
Again, benchmarking on processing ~200k chunks (a poem about broccoli).
Before: ~4.2s
Blue circle is all the time spent adding up gen chunks
<img width="1345" alt="Screenshot 2025-05-14 at 7 48 33 AM"
src="https://github.com/user-attachments/assets/08a59d78-134d-4cd3-9d54-214de689df51"
/>
After: ~2.3s
Blue circle is remaining time spent on adding chunks, which can be
minimized in a future PR by optimizing the `merge_content`,
`merge_dicts`, and `merge_lists` utilities.
<img width="1353" alt="Screenshot 2025-05-14 at 7 50 08 AM"
src="https://github.com/user-attachments/assets/df6b3506-929e-4b6d-b198-7c4e992c6d34"
/>
1. Remove `shielded` decorator from non-end event handlers
2. Exit early with a `self.handlers` check instead of doing unnecessary
asyncio work
Using a benchmark that processes ~200k chunks (a poem about broccoli).
Before: ~15s
Circled in blue is unnecessary event handling time. This is addressed by
point 2 above
<img width="1347" alt="Screenshot 2025-05-14 at 7 37 53 AM"
src="https://github.com/user-attachments/assets/675e0fed-8f37-46c0-90b3-bef3cb9a1e86"
/>
After: ~4.2s
The total time is largely reduced by the removal of the `shielded`
decorator, which holds little significance for non-end handlers.
<img width="1348" alt="Screenshot 2025-05-14 at 7 37 22 AM"
src="https://github.com/user-attachments/assets/54be8a3e-5827-4136-a87b-54b0d40fe331"
/>
Extend Google parameters in the embeddings tab to include Google GenAI
(Gemini)
**Description:** Update embeddings tab to include example for Google
GenAI (Gemini)
**Issue:** N/A
**Dependencies:** N/A
**Twitter handle:** N/A
- [ ] **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:
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If no one reviews your PR within a few days, please @-mention one of
baskaryan, eyurtsev, ccurme, vbarda, hwchase17.
---------
Co-authored-by: Chester Curme <chester.curme@gmail.com>
- **Description:** Updates two notebooks in the how_to documentation to
reflect new loader interfaces and functionalities.
- **Issue:** Some how_to notebooks were still using loader interfaces
from previous versions of LangChain and did not demonstrate the latest
loader functionalities (e.g., extracting images with `ImageBlobParser`,
extracting tables in specific output formats, parsing documents using
Vision-Language Models with `ZeroxPDFLoader`, and using
`CloudBlobLoader` in the `GenericLoader`, etc.).
- **Dependencies:** `py-zerox`
- **Twitter handle:** @MarcMedlock2
---------
Co-authored-by: Marc Medlock <marc.medlock@octo.com>
Co-authored-by: Chester Curme <chester.curme@gmail.com>
Thank you for contributing to LangChain!
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- [ ] **Docs Update**: "langchain-cloudflare: add env var references in
example notebooks"
- We've updated our Cloudflare integration example notebooks with
examples showing environmental variables to initialize the class
instances.
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If no one reviews your PR within a few days, please @-mention one of
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Co-authored-by: SiddharthAnandShorthillsAI <siddharth.anand@shorthills.ai>
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---------
Co-authored-by: Chester Curme <chester.curme@gmail.com>
…map.ipynb
Update openweathermap markdown file for tools
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Replace the deprecated load_tools
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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.
**Description**: The 'inspect' package in python skips over the aliases
set in the schema of a pydantic model. This is a workound to include the
aliases from the original input.
**issue**: #31035
Cc: @ccurme @eyurtsev
---------
Co-authored-by: Chester Curme <chester.curme@gmail.com>
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changes.
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- **Description:** Integrated the Bright Data package to enable
Langchain users to seamlessly incorporate Bright Data into their agents.
- **Dependencies:** None
- **LinkedIn handle**:[Bright
Data](https://www.linkedin.com/company/bright-data)
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---------
Co-authored-by: Chester Curme <chester.curme@gmail.com>
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---------
Co-authored-by: Chester Curme <chester.curme@gmail.com>
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**Description:** The Aerospike Vector Search vector store integration
has moved out of langchain-community and to its own repository,
https://github.com/aerospike/langchain-aerospike. This PR updates
langchain documentation to reference it.
If no one reviews your PR within a few days, please @-mention one of
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**Description:**
Updated the import path for `DoctranPropertyExtractor` from
`langchain_community.document_loaders` to
`langchain_community.document_transformers` in multiple locations to
reflect recent package structure changes. Also corrected a minor typo in
the word "variable".
**Issue:**
N/A
**Dependencies:**
N/A
**LinkedIn handle:** For shout out if announced [Asif
Mehmood](https://www.linkedin.com/in/asifmehmood1997/).
**Description:**
Fix the merge logic in `CharacterTextSplitter.split_text` so that when
using a regex lookahead separator (`is_separator_regex=True`) with
`keep_separator=False`, the raw pattern is not re-inserted between
chunks.
**Issue:**
Fixes#31136
**Dependencies:**
None
**Twitter handle:**
None
Since this is my first open-source PR, please feel free to point out any
mistakes, and I'll be eager to make corrections.
Anthropic updated how they report token counts during streaming today.
See changes to `MessageDeltaUsage` in [this
commit](2da00f26c5 (diff-1a396eba0cd9cd8952dcdb58049d3b13f6b7768ead1411888d66e28211f7bfc5)).
It's clean and simple to grab these fields from the final
`message_delta` event. However, some of them are typed as Optional, and
language
[here](e42451ab3f/src/anthropic/lib/streaming/_messages.py (L462))
suggests they may not always be present. So here we take the required
field from the `message_delta` event as we were doing previously, and
ignore the rest.
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>
**Library Repo Path Update **: "langchain-cloudflare"
We recently changed our `langchain-cloudflare` repo to allow for future
libraries.
Created a `libs` folder to hold `langchain-cloudflare` python package.
https://github.com/cloudflare/langchain-cloudflare/tree/main/libs/langchain-cloudflare
On `langchain`, updating `packages.yaml` to point to new
`libs/langchain-cloudflare` library folder.
This PR fixes a grammar issue in the sentence:
"A chat prompt is made up a of a list of messages..." → "A chat prompt
is made up of a list of messages. "
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If no one reviews your PR within a few days, please @-mention one of
baskaryan, eyurtsev, ccurme, vbarda, hwchase17.
---------
Co-authored-by: Chester Curme <chester.curme@gmail.com>
Thank you for contributing to LangChain!
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---------
Co-authored-by: Chester Curme <chester.curme@gmail.com>
- **Description:** Update Pinecone notebook example
- **Issue:** N\A
- **Dependencies:** N\A
- **Twitter handle:** N\A
- [ x ] **Add tests and docs**: Just notebook updates
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If no one reviews your PR within a few days, please @-mention one of
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---------
Co-authored-by: Chester Curme <chester.curme@gmail.com>
- **Description:** The deprecated initialize_agent functionality is
replaced with create_react_agent for the google tools. Also noticed a
potential issue with the non-existent "google-drive-search" which was
used in the old `google-drive.ipynb`. If this should be a by default
available tool, an issue should be opened to modify
langchain-community's `load_tools` accordingly.
- **Issue:** #29277
- **Dependencies:** No added dependencies
- **Twitter handle:** No Twitter account
This PR brings several improvements and modernizations to the
documentation around the Astra DB partner package.
- language alignment for better matching with the terms used in the
Astra DB docs
- updated several links to pages on said documentation
- for the `AstraDBVectorStore`, added mentions of the new features in
the overall `astra.mdx`
- for the vector store, rewritten/upgraded most of the usage example
notebook for a more straightforward experience able to highlight the
main usage patterns (including new ones such as the newly-introduced
"autodetect feature")
---------
Co-authored-by: ccurme <chester.curme@gmail.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.
- **Description:** `ChatAnthropic.get_num_tokens_from_messages` does not
currently receive `kwargs` and pass those on to
`self._client.beta.messages.count_tokens`. This is a problem if you need
to pass specific options to `count_tokens`, such as the `thinking`
option. This PR fixes that.
- **Issue:** N/A
- **Dependencies:** None
- **Twitter handle:** @bengladwell
Co-authored-by: ccurme <chester.curme@gmail.com>
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**:
* Starting to put together some PR's to fix the typing around
`langchain-chroma` `filter` and `where_document` query filtering, as
mentioned:
https://github.com/langchain-ai/langchain/issues/30879https://github.com/langchain-ai/langchain/issues/30507
The typing of `dict[str, str]` is on the one hand too restrictive (marks
valid filter expressions as ill-typed) and also too permissive (allows
illegal filter expressions). That's not what this PR addresses though.
This PR just removes from the documentation some examples of filters
that are illegal, and also syntactically incorrect: (a) dictionaries
with keys like `$contains` but the key is missing quotation marks; (b)
dictionaries with multiple entries - this is illegal in Chroma filter
syntax and will raise an exception. (`{"foo": "bar", "qux": "baz"}`).
Filter dictionaries in Chroma must have one and one key only. Again this
is just the documentation issue, which is the lowest hanging fruit. I
also think we need to update the types for `filter` and `where_document`
to be (at the very least `dict[str, Any]`), or, since we have access to
Chroma's types, they should be `Where` and `WhereDocument` types. This
has a wider blast radius though, so I'm starting small.
This PR does not fix the issues mentioned above, it's just starting to
get the ball rolling, and cleaning up the documentation.
- [x] **Lint and test**: Run `make format`, `make lint` and `make test`
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Additional guidelines:
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- Please do not add dependencies to pyproject.toml files (even optional
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langchain.
If no one reviews your PR within a few days, please @-mention one of
baskaryan, eyurtsev, ccurme, vbarda, hwchase17.
---------
Co-authored-by: Really Him <hesereallyhim@proton.me>
This PR includes the following documentation fixes for the SAP HANA
Cloud vector store integration:
- Removed stale output from the `%pip install` code cell.
- Replaced an unrelated vectorstore documentation link on the provider
overview page.
- Renamed the provider from "SAP HANA" to "SAP HANA Cloud"
# What's Changed?
- [x] 1. docs: **docs/docs/integrations/chat/litellm.ipynb** : Updated
with docs for litellm_router since it has been moved into the
[langchain-litellm](https://github.com/Akshay-Dongare/langchain-litellm)
package along with ChatLiteLLM
- [x] 2. docs: **docs/docs/integrations/chat/litellm_router.ipynb** :
Deleted to avoid redundancy
- [x] 3. docs: **docs/docs/integrations/providers/litellm.mdx** :
Updated to reflect inclusion of ChatLiteLLMRouter class
- [x] Lint and test: Done
# Issue:
- [x] Related to the issue
https://github.com/langchain-ai/langchain/issues/30368
# About me
- [x] 🔗 LinkedIn:
[akshay-dongare](https://www.linkedin.com/in/akshay-dongare/)
Hi there, I'm Célina from 🤗,
This PR introduces support for Hugging Face's serverless Inference
Providers (documentation
[here](https://huggingface.co/docs/inference-providers/index)), allowing
users to specify different providers for chat completion and text
generation tasks.
This PR also removes the usage of `InferenceClient.post()` method in
`HuggingFaceEndpoint`, in favor of the task-specific `text_generation`
method. `InferenceClient.post()` is deprecated and will be removed in
`huggingface_hub v0.31.0`.
---
## Changes made
- bumped the minimum required version of the `huggingface-hub` package
to ensure compatibility with the latest API usage.
- added a `provider` field to `HuggingFaceEndpoint`, enabling users to
select the inference provider (e.g., 'cerebras', 'together',
'fireworks-ai'). Defaults to `hf-inference` (HF Inference API).
- replaced the deprecated `InferenceClient.post()` call in
`HuggingFaceEndpoint` with the task-specific `text_generation` method
for future-proofing, `post()` will be removed in huggingface-hub
v0.31.0.
- updated the `ChatHuggingFace` component:
- added async and streaming support.
- added support for tool calling.
- exposed underlying chat completion parameters for more granular
control.
- Added integration tests for `ChatHuggingFace` and updated the
corresponding unit tests.
✅ All changes are backward compatible.
---------
Co-authored-by: ccurme <chester.curme@gmail.com>
Follow up to https://github.com/langchain-ai/langsmith-sdk/pull/1696,
I've bumped the `langsmith` version where applicable in `uv.lock`.
Type checking problems here because deps have been updated in
`pyproject.toml` and `uv lock` hasn't been run - we should enforce that
in the future - goes with the other dependabot todos :).
Thank you for contributing to LangChain!
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- 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"
- [x] **PR message**: ***Delete this entire checklist*** and replace
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- **Issue:** the issue # it fixes, if applicable
- **Dependencies:** any dependencies required for this change
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- [x] **Lint and test**: Run `make format`, `make lint` and `make test`
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Additional guidelines:
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- 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, eyurtsev, ccurme, vbarda, hwchase17.
---------
Co-authored-by: Chester Curme <chester.curme@gmail.com>
## Description
Added support for retrieving column comments in the SQL Database
utility. This feature allows users to see comments associated with
database columns when querying table information. Column comments
provide valuable metadata that helps LLMs better understand the
semantics and purpose of database columns.
A new optional parameter `get_col_comments` was added to the
`get_table_info` method, defaulting to `False` for backward
compatibility. When set to `True`, it retrieves and formats column
comments for each table.
Currently, this feature is supported on PostgreSQL, MySQL, and Oracle
databases.
## Implementation
You should create Table with column comments before.
```python
db = SQLDatabase.from_uri("YOUR_DB_URI")
print(db.get_table_info(get_col_comments=True))
```
## Result
```
CREATE TABLE test_table (
name VARCHAR
school VARCHAR)
/*
Column Comments: {'name': person name, 'school":school_name}
*/
/*
3 rows from test_table:
name
a
b
c
*/
```
## Benefits
1. Enhances LLM's understanding of database schema semantics
2. Preserves valuable domain knowledge embedded in database design
3. Improves accuracy of SQL query generation
4. Provides more context for data interpretation
Tests are available in
`langchain/libs/community/tests/test_sql_get_table_info.py`.
---------
Co-authored-by: chbae <chbae@gcsc.co.kr>
Co-authored-by: Chester Curme <chester.curme@gmail.com>
- **Description:**
This PR marks the `HanaDB` vector store (and related utilities) in
`langchain_community` as deprecated using the `@deprecated` annotation.
- Set `since="0.1.0"` and `removal="1.0"`
- Added a clear migration path and a link to the SAP-maintained
replacement in the
[`langchain_hana`](https://github.com/SAP/langchain-integration-for-sap-hana-cloud)
package.
Additionally, the example notebook has been updated to use the new
`HanaDB` class from `langchain_hana`, ensuring users follow the
recommended integration moving forward.
- **Issue:** None
- **Dependencies:** None
---------
Co-authored-by: Chester Curme <chester.curme@gmail.com>
The `_chunk_size` has not changed by method `self._tokenize`, So i think
these is duplicate code.
Signed-off-by: zhanluxianshen <zhanluxianshen@163.com>
This PR brings some much-needed updates to some of the Astra DB shorter
example notebooks,
- ensuring imports are from the partner package instead of the
(deprecated) community legacy package
- improving the wording in a few related places
- updating the constructor signature introduced with the latest partner
package's AstraDBLoader
- marking the community package counterpart of the LLM caches as
deprecated in the summary table at the end of the page.
This is a PR to return the message attachments in _get_response, as when
files are generated these attachments are not returned thus generated
files cannot be retrieved
Fixes issue: https://github.com/langchain-ai/langchain/issues/30851
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"
community: fix browserbase integration
docs: update docs
- [ ] **PR message**: ***Delete this entire checklist*** and replace
with
- **Description:** Updated BrowserbaseLoader to use the new python sdk.
- **Issue:** update browserbase integration with langchain
- **Dependencies:** n/a
- **Twitter handle:** @kylejeong21
- [ ] **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/
Following https://github.com/langchain-ai/langchain/pull/30909: need to
retain "empty" reasoning output when streaming, e.g.,
```python
{'id': 'rs_...', 'summary': [], 'type': 'reasoning'}
```
Tested by existing integration tests, which are currently failing.
This PR adds Google Gemini (via AI Studio and Gemini API). Feel free to
change the ordering, if needed.
---------
Co-authored-by: Chester Curme <chester.curme@gmail.com>
This PR restructures the main Google integrations documentation page
(`docs/docs/integrations/providers/google.mdx`) for better clarity and
updates content.
**Key changes:**
* **Separated Sections:** Divided integrations into distinct `Google
Generative AI (Gemini API & AI Studio)`, `Google Cloud`, and `Other
Google Products` sections.
* **Updated Generative AI:** Refreshed the introduction and the `Google
Generative AI` section with current information and quickstart examples
for the Gemini API via `langchain-google-genai`.
* **Reorganized Content:** Moved non-Cloud Platform specific
integrations (e.g., Drive, GMail, Search tools, ScaNN) to the `Other
Google Products` section.
* **Cleaned Up:** Minor improvements to descriptions and code snippets.
This aims to make it easier for users to find the relevant Google
integrations based on whether they are using the Gemini API directly or
Google Cloud services.
| Before | After |
|-----------------------|------------|
| 
| 
|
---------
Co-authored-by: Chester Curme <chester.curme@gmail.com>
- [x] **PR title**: "community: add indexname to other functions in
opensearch"
- [x] **PR message**:
- **Description:** add ability to over-ride index-name if provided in
the kwargs of sub-functions. When used in WSGI application it's crucial
to be able to dynamically change parameters.
- [ ] **Add tests and docs**:
- [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/
Chat models currently implement support for:
- images in OpenAI Chat Completions format
- other multimodal types (e.g., PDF and audio) in a cross-provider
[standard
format](https://python.langchain.com/docs/how_to/multimodal_inputs/)
Here we update core to extend support to PDF and audio input in Chat
Completions format. **If an OAI-format PDF or audio content block is
passed into any chat model, it will be transformed to the LangChain
standard format**. We assume that any chat model supporting OAI-format
PDF or audio has implemented support for the standard format.
`mixtral-8x-7b-instruct` was recently retired from Fireworks Serverless.
Here we remove the default model altogether, so that the model must be
explicitly specified on init:
```python
ChatFireworks(model="accounts/fireworks/models/llama-v3p1-70b-instruct") # for example
```
We also set a null default for `temperature`, which previously defaulted
to 0.0. This parameter will no longer be included in request payloads
unless it is explicitly provided.
**langchain_openai: Support of reasoning summary streaming**
**Description:**
OpenAI API now supports streaming reasoning summaries for reasoning
models (o1, o3, o3-mini, o4-mini). More info about it:
https://platform.openai.com/docs/guides/reasoning#reasoning-summaries
It is supported only in Responses API (not Completion API), so you need
to create LangChain Open AI model as follows to support reasoning
summaries streaming:
```
llm = ChatOpenAI(
model="o4-mini", # also o1, o3, o3-mini support reasoning streaming
use_responses_api=True, # reasoning streaming works only with responses api, not completion api
model_kwargs={
"reasoning": {
"effort": "high", # also "low" and "medium" supported
"summary": "auto" # some models support "concise" summary, some "detailed", but auto will always work
}
}
)
```
Now, if you stream events from llm:
```
async for event in llm.astream_events(prompt, version="v2"):
print(event)
```
or
```
for chunk in llm.stream(prompt):
print (chunk)
```
OpenAI API will send you new types of events:
`response.reasoning_summary_text.added`
`response.reasoning_summary_text.delta`
`response.reasoning_summary_text.done`
These events are new, so they were ignored. So I have added support of
these events in function `_convert_responses_chunk_to_generation_chunk`,
so reasoning chunks or full reasoning added to the chunk
additional_kwargs.
Example of how this reasoning summary may be printed:
```
async for event in llm.astream_events(prompt, version="v2"):
if event["event"] == "on_chat_model_stream":
chunk: AIMessageChunk = event["data"]["chunk"]
if "reasoning_summary_chunk" in chunk.additional_kwargs:
print(chunk.additional_kwargs["reasoning_summary_chunk"], end="")
elif "reasoning_summary" in chunk.additional_kwargs:
print("\n\nFull reasoning step summary:", chunk.additional_kwargs["reasoning_summary"])
elif chunk.content and chunk.content[0]["type"] == "text":
print(chunk.content[0]["text"], end="")
```
or
```
for chunk in llm.stream(prompt):
if "reasoning_summary_chunk" in chunk.additional_kwargs:
print(chunk.additional_kwargs["reasoning_summary_chunk"], end="")
elif "reasoning_summary" in chunk.additional_kwargs:
print("\n\nFull reasoning step summary:", chunk.additional_kwargs["reasoning_summary"])
elif chunk.content and chunk.content[0]["type"] == "text":
print(chunk.content[0]["text"], end="")
```
---------
Co-authored-by: Chester Curme <chester.curme@gmail.com>
PR title:
docs: add Valyu integration documentation
Description:
This PR adds documentation and example notebooks for the Valyu
integration, including retriever and tool usage.
Issue:
N/A
Dependencies:
No new dependencies.
---------
Co-authored-by: ccurme <chester.curme@gmail.com>
- [x] **PR message**:
- **Description:** Updates the documentation for the
langchain-predictionguard package, adding tool calling functionality and
some new parameters.
PR Summary
This change adds a fallback in ChatAnthropic.with_structured_output() to
handle Pydantic models that don’t include a docstring. Without it,
calling:
```py
from pydantic import BaseModel
from langchain_anthropic import ChatAnthropic
class SampleModel(BaseModel):
sample_field: str
llm = ChatAnthropic(
model="claude-3-7-sonnet-latest"
).with_structured_output(SampleModel.model_json_schema())
llm.invoke("test")
```
will raise a
```
KeyError: 'description'
```
because Pydantic omits the description field when no docstring is
present.
This issue doesn’t occur when using ChatOpenAI or if you add a docstring
to the model:
```py
from pydantic import BaseModel
from langchain_openai import ChatOpenAI
class SampleModel(BaseModel):
"""Schema for sample_field output."""
sample_field: str
llm = ChatOpenAI(
model="gpt-4o-mini"
).with_structured_output(SampleModel.model_json_schema())
llm.invoke("test")
```
---------
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, eyurtsev, ccurme, vbarda, hwchase17.
Addresses #30158
When using the output parser—either in a chain or standalone—hitting
max_tokens triggers a misleading “missing variable” error instead of
indicating the output was truncated. This subtle bug often surfaces with
Anthropic models.
---------
Co-authored-by: Chester Curme <chester.curme@gmail.com>
PR title:
Community: Add bind variable support for oracle adb docloader
Description:
This PR adds support of using bind variable to oracle adb doc loader
class, including minor document change.
Issue:
N/A
Dependencies:
No new dependencies.
This is a follow-on PR to go with the identical changes that were made
in parters/openai.
Previous PR: https://github.com/langchain-ai/langchain/pull/30757
When calling embed_documents and providing a chunk_size argument, that
argument is ignored when OpenAIEmbeddings is instantiated with its
default configuration (where check_embedding_ctx_length=True).
_get_len_safe_embeddings specifies a chunk_size parameter but it's not
being passed through in embed_documents, which is its only caller. This
appears to be an oversight, especially given that the
_get_len_safe_embeddings docstring states it should respect "the set
embedding context length and chunk size."
Developers typically expect method parameters to take effect (also, take
precedence) when explicitly provided, especially when instantiating
using defaults. I was confused as to why my API calls were being
rejected regardless of the chunk size I provided.
When calling `embed_documents` and providing a `chunk_size` argument,
that argument is ignored when `OpenAIEmbeddings` is instantiated with
its default configuration (where `check_embedding_ctx_length=True`).
`_get_len_safe_embeddings` specifies a `chunk_size` parameter but it's
not being passed through in `embed_documents`, which is its only caller.
This appears to be an oversight, especially given that the
`_get_len_safe_embeddings` docstring states it should respect "the set
embedding context length and chunk size."
Developers typically expect method parameters to take effect (also, take
precedence) when explicitly provided, especially when instantiating
using defaults. I was confused as to why my API calls were being
rejected regardless of the chunk size I provided.
This bug also exists in langchain_community package. I can add that to
this PR if requested otherwise I will create a new one once this passes.
**Description:**
partners-anthropic: ChatAnthropic supports b64 and urls in the
part[image_url][url] message variable
**Issue**:
ChatAnthropic right now only supports b64 encoded images in the
part[image_url][url] message variable. This PR enables ChatAnthropic to
also accept image urls in said variable and makes it compatible with
OpenAI messages to make model switching easier.
---------
Co-authored-by: Chester Curme <chester.curme@gmail.com>
SingleStore integration now has its package `langchain-singlestore', so
the community implementation will no longer be maintained.
Added `deprecated` decorator to `SingleStoreDBChatMessageHistory`,
`SingleStoreDBSemanticCache`, and `SingleStoreDB` classes in the
community package.
**Dependencies:** https://github.com/langchain-ai/langchain/pull/30841
---------
Co-authored-by: ccurme <chester.curme@gmail.com>
Support "usage_metadata" for LiteLLM streaming calls.
This is a follow-up to
https://github.com/langchain-ai/langchain/pull/30625, which tackled
non-streaming calls.
If no one reviews your PR within a few days, please @-mention one of
baskaryan, eyurtsev, ccurme, vbarda, hwchase17.
- [ ] **PR message**:
- **Description:** including metadata_field in
max_marginal_relevance_search() would result in error, changed the logic
to be similar to how it's handled in similarity_search, where it can be
any field or simply a "*" to include every field
* Remove unused ignores
* Add type ignore codes
* Add mypy rule `warn_unused_ignores`
* Add ruff rule PGH003
NB: some `type: ignore[unused-ignore]` are added because the ignores are
needed when `extended_testing_deps.txt` deps are installed.
We only need to rebuild model schemas if type annotation information
isn't available during declaration - that shouldn't be the case for
these types corrected here.
Need to do more thorough testing to make sure these structures have
complete schemas, but hopefully this boosts startup / import time.
- [ ] **PR title**: "docs: adding Smabbler's Galaxia integration"
- [ ] **PR message**: **Twitter handle:** @Galaxia_graph
I'm adding docs here + added the package to the packages.yml. I didn't
add a unit test, because this integration is just a thin wrapper on top
of our API. There isn't much left to test if you mock it away.
---------
Co-authored-by: Chester Curme <chester.curme@gmail.com>
**Description:** This PR adds provider inference logic to
`init_chat_model` for Perplexity models that use the "sonar..." prefix
(`sonar`, `sonar-pro`, `sonar-reasoning`, `sonar-reasoning-pro` or
`sonar-deep-research`).
This allows users to initialize these models by simply passing the model
name, without needing to explicitly set `model_provider="perplexity"`.
The docstring for `init_chat_model` has also been updated to reflect
this new inference rule.
https://github.com/langchain-ai/langchain/pull/30778 (not released)
broke all invocation modes of ChatOllama (intent was to remove
`"message"` from `generation_info`, but we turned `generation_info` into
`stream_resp["message"]`), resulting in validation errors.
On core releases, we check out the latest published package for
langchain-openai and langchain-anthropic and run their tests against the
candidate version of langchain-core.
Because these packages have a local install of langchain-tests, we also
need to check out the previous version of langchain-tests.
TL;DR: you can't optimize imports with a lazy `__getattr__` if there is
a namespace conflict with a module name and an attribute name. We should
avoid introducing conflicts like this in the future.
This PR fixes a bug introduced by my lazy imports PR:
https://github.com/langchain-ai/langchain/pull/30769.
In `langchain_core`, we have utilities for loading and dumping data.
Unfortunately, one of those utilities is a `load` function, located in
`langchain_core/load/load.py`. To make this function more visible, we
make it accessible at the top level `langchain_core.load` module via
importing the function in `langchain_core/load/__init__.py`.
So, either of these imports should work:
```py
from langchain_core.load import load
from langchain_core.load.load import load
```
As you can tell, this is already a bit confusing. You'd think that the
first import would produce the module `load`, but because of the
`__init__.py` shortcut, both produce the function `load`.
<details> More on why the lazy imports PR broke this support...
All was well, except when the absolute import was run first, see the
last snippet:
```
>>> from langchain_core.load import load
>>> load
<function load at 0x101c320c0>
```
```
>>> from langchain_core.load.load import load
>>> load
<function load at 0x1069360c0>
```
```
>>> from langchain_core.load import load
>>> load
<function load at 0x10692e0c0>
>>> from langchain_core.load.load import load
>>> load
<function load at 0x10692e0c0>
```
```
>>> from langchain_core.load.load import load
>>> load
<function load at 0x101e2e0c0>
>>> from langchain_core.load import load
>>> load
<module 'langchain_core.load.load' from '/Users/sydney_runkle/oss/langchain/libs/core/langchain_core/load/load.py'>
```
In this case, the function `load` wasn't stored in the globals cache for
the `langchain_core.load` module (by the lazy import logic), so Python
defers to a module import.
</details>
New `langchain` tongue twister 😜: we've created a problem for ourselves
because you have to load the load function from the load file in the
load module 😨.
Fix CI to trigger benchmarks on `run-codspeed-benchmarks` label addition
Reduce scope of async benchmark to save time on CI
Waiting to merge this PR until we figure out how to use walltime on
local runners.
Most easily reviewed with the "hide whitespace" option toggled.
Seeing 10-50% speed ups in import time for common structures 🚀
The general purpose of this PR is to lazily import structures within
`langchain_core.XXX_module.__init__.py` so that we're not eagerly
importing expensive dependencies (`pydantic`, `requests`, etc).
Analysis of flamegraphs generated with `importtime` motivated these
changes. For example, the one below demonstrates that importing
`HumanMessage` accidentally triggered imports for `importlib.metadata`,
`requests`, etc.
There's still much more to do on this front, and we can start digging
into our own internal code for optimizations now that we're less
concerned about external imports.
<img width="1210" alt="Screenshot 2025-04-11 at 1 10 54 PM"
src="https://github.com/user-attachments/assets/112a3fe7-24a9-4294-92c1-d5ae64df839e"
/>
I've tracked the improvements with some local benchmarks:
## `pytest-benchmark` results
| Name | Before (s) | After (s) | Delta (s) | % Change |
|-----------------------------|------------|-----------|-----------|----------|
| Document | 2.8683 | 1.2775 | -1.5908 | -55.46% |
| HumanMessage | 2.2358 | 1.1673 | -1.0685 | -47.79% |
| ChatPromptTemplate | 5.5235 | 2.9709 | -2.5526 | -46.22% |
| Runnable | 2.9423 | 1.7793 | -1.163 | -39.53% |
| InMemoryVectorStore | 3.1180 | 1.8417 | -1.2763 | -40.93% |
| RunnableLambda | 2.7385 | 1.8745 | -0.864 | -31.55% |
| tool | 5.1231 | 4.0771 | -1.046 | -20.42% |
| CallbackManager | 4.2263 | 3.4099 | -0.8164 | -19.32% |
| LangChainTracer | 3.8394 | 3.3101 | -0.5293 | -13.79% |
| BaseChatModel | 4.3317 | 3.8806 | -0.4511 | -10.41% |
| PydanticOutputParser | 3.2036 | 3.2995 | 0.0959 | 2.99% |
| InMemoryRateLimiter | 0.5311 | 0.5995 | 0.0684 | 12.88% |
Note the lack of change for `InMemoryRateLimiter` and
`PydanticOutputParser` is just random noise, I'm getting comparable
numbers locally.
## Local CodSpeed results
We're still working on configuring CodSpeed on CI. The local usage
produced similar results.
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, eyurtsev, ccurme, vbarda, hwchase17.
This PR fixes an issue where ChatPerplexity would raise an
AttributeError when the citations attribute was missing from the model
response (e.g., when using offline models like r1-1776).
The fix checks for the presence of citations, images, and
related_questions before attempting to access them, avoiding crashes in
models that don't provide these fields.
Tested locally with models that omit citations, and the fix works as
expected.
Hey LangChain community! 👋 Excited to propose official documentation for
our new openGauss integration that brings powerful vector capabilities
to the stack!
### What's Inside 📦
1. **Full Integration Guide**
Introducing
[langchain-opengauss](https://pypi.org/project/langchain-opengauss/) on
PyPI - your new toolkit for:
🔍 Native hybrid search (vectors + metadata)
🚀 Production-grade connection pooling
🧩 Automatic schema management
2. **Rigorous Testing Passed** ✅
- 100% non-async test coverage
ps: Current implementation resides in my personal repository:
https://github.com/mpb159753/langchain-opengauss, How can I transfer
process to langchain-ai org?? *Keen to hear your thoughts and make this
integration shine!* ✨
---------
Co-authored-by: Eugene Yurtsev <eugene@langchain.dev>
Co-authored-by: Eugene Yurtsev <eyurtsev@gmail.com>
Looks like `pyupgrade` was already used here but missed some docs and
tests.
This helps to keep our docs looking professional and up to date.
Eventually, we should lint / format our inline docs.
**Description:** Replaced the example with the deprecated
`intialize_agent` function with `create_react_agent` from
`langgraph.prebuild`
**Issue:** #29277
**Dependencies:** N/A
**Twitter handle:** N/A
**Description:** add support for oauth2 in Jira tool by adding the
possibility to pass a dictionary with oauth parameters. I also adapted
the documentation to show this new behavior
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, eyurtsev, ccurme, vbarda, hwchase17.
Signed-off-by: zhanluxianshen <zhanluxianshen@163.com>
This PR aims to reduce import time of `langchain-core` tools by removing
the `importlib.metadata` import previously used in `__init__.py`. This
is the first in a sequence of PRs to reduce import time delays for
`langchain-core` features and structures 🚀.
Because we're now hard coding the version, we need to make sure
`version.py` and `pyproject.toml` stay in sync, so I've added a new CI
job that runs whenever either of those files are modified. [This
run](https://github.com/langchain-ai/langchain/actions/runs/14358012706/job/40251952044?pr=30744)
demonstrates the failure that occurs whenever the version gets out of
sync (thus blocking a PR).
Before, note the ~15% of time spent on the `importlib.metadata` /related
imports
<img width="1081" alt="Screenshot 2025-04-09 at 9 06 15 AM"
src="https://github.com/user-attachments/assets/59f405ec-ee8d-4473-89ff-45dea5befa31"
/>
After (note, lack of `importlib.metadata` time sink):
<img width="1245" alt="Screenshot 2025-04-09 at 9 01 23 AM"
src="https://github.com/user-attachments/assets/9c32e77c-27ce-485e-9b88-e365193ed58d"
/>
Description:
This PR adds documentation for the langchain-cloudflare integration
package.
Issue:
N/A
Dependencies:
No new dependencies are required.
Tests and Docs:
Added an example notebook demonstrating the usage of the
langchain-cloudflare package, located in docs/docs/integrations.
Added a new package to libs/packages.yml.
Lint and Format:
Successfully ran make format and make lint.
---------
Co-authored-by: Collier King <collier@cloudflare.com>
Co-authored-by: Collier King <collierking99@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, eyurtsev, ccurme, vbarda, hwchase17.
Signed-off-by: zhanluxianshen <zhanluxianshen@163.com>
Hi there, This is a complementary PR to #30733.
This PR introduces support for Hugging Face's serverless Inference
Providers (documentation
[here](https://huggingface.co/docs/inference-providers/index)), allowing
users to specify different providers
This PR also removes the usage of `InferenceClient.post()` method in
`HuggingFaceEndpointEmbeddings`, in favor of the task-specific
`feature_extraction` method. `InferenceClient.post()` is deprecated and
will be removed in `huggingface_hub` v0.31.0.
## Changes made
- bumped the minimum required version of the `huggingface_hub` package
to ensure compatibility with the latest API usage.
- added a provider field to `HuggingFaceEndpointEmbeddings`, enabling
users to select the inference provider.
- replaced the deprecated `InferenceClient.post()` call in
`HuggingFaceEndpointEmbeddings` with the task-specific
`feature_extraction` method for future-proofing, `post()` will be
removed in `huggingface-hub` v0.31.0.
✅ All changes are backward compatible.
---------
Co-authored-by: Lucain <lucainp@gmail.com>
Co-authored-by: ccurme <chester.curme@gmail.com>
* Only run codspeed logic when `libs/core` is changed (for now, we'll
want to add other benchmarks later
* Also run on `master` so that we can get a reference :)
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, eyurtsev, ccurme, vbarda, hwchase17.
The first in a sequence of PRs focusing on improving performance in
core. We're starting with reducing import times for common structures,
hence the benchmarks here.
The benchmark looks a little bit complicated - we have to use a process
so that we don't suffer from Python's import caching system. I tried
doing manual modification of `sys.modules` between runs, but that's
pretty tricky / hacky to get right, hence the subprocess approach.
Motivated by extremely slow baseline for common imports (we're talking
2-5 seconds):
<img width="633" alt="Screenshot 2025-04-09 at 12 48 12 PM"
src="https://github.com/user-attachments/assets/994616fe-1798-404d-bcbe-48ad0eb8a9a0"
/>
Also added a `make benchmark` command to make local runs easy :).
Currently using walltimes so that we can track total time despite using
a manual proces.
Google vertex ai search will now return the title of the found website
as part of the document metadata, if available.
Thank you for contributing to LangChain!
- **Description**: Vertex AI Search can be used to index websites and
then develop chatbots that use these websites to answer questions. At
present, the document metadata includes an `id` and `source` (which is
the URL). While the URL is enough to create a link, the ID is not
descriptive enough to show users. Therefore, I propose we return `title`
as well, when available (e.g., it will not be available in `.txt`
documents found during the website indexing).
- **Issue**: No bug in particular, but it would be better if this was
here.
- **Dependencies**: None
- I do not use twitter.
Format, Lint and Test seem to be all good.
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, eyurtsev, ccurme, vbarda, hwchase17.
Generally, this PR is CI performance focused + aims to clean up some
dependencies at the same time.
1. Unpins upper bounds for `numpy` in all `pyproject.toml` files where
`numpy` is specified
2. Requires `numpy >= 2.1.0` for Python 3.13 and `numpy > v1.26.0` for
Python 3.12, plus a `numpy` min version bump for `chroma`
3. Speeds up CI by minutes - linting on Python 3.13, installing `numpy <
2.1.0` was taking [~3
minutes](https://github.com/langchain-ai/langchain/actions/runs/14316342925/job/40123305868?pr=30713),
now the entire env setup takes a few seconds
4. Deleted the `numpy` test dependency from partners where that was not
used, specifically `huggingface`, `voyageai`, `xai`, and `nomic`.
It's a bit unfortunate that `langchain-community` depends on `numpy`, we
might want to try to fix that in the future...
Closes https://github.com/langchain-ai/langchain/issues/26026
Fixes https://github.com/langchain-ai/langchain/issues/30555
Resolves https://github.com/langchain-ai/langchain/issues/30724
The [prompt in
langchain-hub](https://smith.langchain.com/hub/langchain-ai/sql-query-system-prompt)
used in this guide was composed of just a system message, but the guide
did not add a human message to it. This was incompatible with some
providers (and is generally not a typical usage pattern).
The prompt in prompt hub has been updated to split the question into a
separate HumanMessage. Here we update the guide to reflect this.
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, eyurtsev, ccurme, vbarda, hwchase17.
Signed-off-by: zhanluxianshen <zhanluxianshen@163.com>
Tool-calling tests started intermittently failing with
> groq.APIError: Failed to call a function. Please adjust your prompt.
See 'failed_generation' for more details.
**Description:** The error message was supposed to display the missing
vector name, but instead, it includes only the existing collection
configs.
This simple PR just includes the correct variable name, so that the user
knows the requested vector does not exist in the collection.
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, eyurtsev, ccurme, vbarda, hwchase17.
Signed-off-by: Tin Lai <tin@tinyiu.com>
Description
This PR updates the docs for the
[langchain-hyperbrowser](https://pypi.org/project/langchain-hyperbrowser/)
package. It adds a few tools
- Scrape Tool
- Crawl Tool
- Extract Tool
- Browser Agents
- Claude Computer Use
- OpenAI CUA
- Browser Use
[Hyperbrowser](https://hyperbrowser.ai/) is a platform for running and
scaling headless browsers. It lets you launch and manage browser
sessions at scale and provides easy to use solutions for any webscraping
needs, such as scraping a single page or crawling an entire site.
Issue
None
Dependencies
None
Twitter Handle
`@hyperbrowser`
## Docs: Add Google Calendar Toolkit Documentation
### Description:
This PR adds documentation for the Google Calendar Toolkit as part of
the `langchain-google` repository. Refer to the related PR: [community:
Add Google Calendar
Toolkit](https://github.com/langchain-ai/langchain-google/pull/688).
### Issue:
N/A
### Twitter handle:
@jorgejrzz
**Description:**
Fixed a bug in `BaseCallbackManager.remove_handler()` that caused a
`ValueError` when removing a handler added via the constructor's
`handlers` parameter. The issue occurred because handlers passed to the
constructor were added only to the `handlers` list and not automatically
to `inheritable_handlers` unless explicitly specified. However,
`remove_handler()` attempted to remove the handler from both lists
unconditionally, triggering a `ValueError` when it wasn't in
`inheritable_handlers`.
The fix ensures the method checks for the handler’s presence in each
list before attempting removal, making it more robust while preserving
its original behavior.
**Issue:** Fixes#30640
**Dependencies:** None
---------
Co-authored-by: Eugene Yurtsev <eyurtsev@gmail.com>
- **Description:** We do not need to set parser in `scrape` since it is
already been done in `_scrape`
- **Issue:** #30629, not directly related but makes sure xml parser is
used
This pull request includes various changes to the `langchain_core`
library, focusing on improving compatibility with different versions of
Pydantic. The primary change involves replacing checks for Pydantic
major versions with boolean flags, which simplifies the code and
improves readability.
This also solves ruff rule checks for
[RUF048](https://docs.astral.sh/ruff/rules/map-int-version-parsing/) and
[PLR2004](https://docs.astral.sh/ruff/rules/magic-value-comparison/).
Key changes include:
### Compatibility Improvements:
*
[`libs/core/langchain_core/output_parsers/json.py`](diffhunk://#diff-5add0cf7134636ae4198a1e0df49ee332ae0c9123c3a2395101e02687c717646L22-R24):
Replaced `PYDANTIC_MAJOR_VERSION` with `IS_PYDANTIC_V1` to check for
Pydantic version 1.
*
[`libs/core/langchain_core/output_parsers/pydantic.py`](diffhunk://#diff-2364b5b4aee01c462aa5dbda5dc3a877dcd20f29df173ad540dc8adf8b192361L14-R14):
Updated version checks from `PYDANTIC_MAJOR_VERSION` to `IS_PYDANTIC_V2`
in the `PydanticOutputParser` class.
[[1]](diffhunk://#diff-2364b5b4aee01c462aa5dbda5dc3a877dcd20f29df173ad540dc8adf8b192361L14-R14)
[[2]](diffhunk://#diff-2364b5b4aee01c462aa5dbda5dc3a877dcd20f29df173ad540dc8adf8b192361L27-R27)
### Utility Enhancements:
*
[`libs/core/langchain_core/utils/pydantic.py`](diffhunk://#diff-ff28020c5f1073a8b63bcd9d8b756a187fd682cb81935295120c63b207071896R23):
Introduced `IS_PYDANTIC_V1` and `IS_PYDANTIC_V2` flags and deprecated
the `get_pydantic_major_version` function. Updated various functions to
use these flags instead of version numbers.
[[1]](diffhunk://#diff-ff28020c5f1073a8b63bcd9d8b756a187fd682cb81935295120c63b207071896R23)
[[2]](diffhunk://#diff-ff28020c5f1073a8b63bcd9d8b756a187fd682cb81935295120c63b207071896R42-R78)
[[3]](diffhunk://#diff-ff28020c5f1073a8b63bcd9d8b756a187fd682cb81935295120c63b207071896L90-R89)
[[4]](diffhunk://#diff-ff28020c5f1073a8b63bcd9d8b756a187fd682cb81935295120c63b207071896L104-R101)
[[5]](diffhunk://#diff-ff28020c5f1073a8b63bcd9d8b756a187fd682cb81935295120c63b207071896L120-R122)
[[6]](diffhunk://#diff-ff28020c5f1073a8b63bcd9d8b756a187fd682cb81935295120c63b207071896L135-R132)
[[7]](diffhunk://#diff-ff28020c5f1073a8b63bcd9d8b756a187fd682cb81935295120c63b207071896L149-R151)
[[8]](diffhunk://#diff-ff28020c5f1073a8b63bcd9d8b756a187fd682cb81935295120c63b207071896L164-R161)
[[9]](diffhunk://#diff-ff28020c5f1073a8b63bcd9d8b756a187fd682cb81935295120c63b207071896L248-R250)
[[10]](diffhunk://#diff-ff28020c5f1073a8b63bcd9d8b756a187fd682cb81935295120c63b207071896L330-R335)
[[11]](diffhunk://#diff-ff28020c5f1073a8b63bcd9d8b756a187fd682cb81935295120c63b207071896L356-R357)
[[12]](diffhunk://#diff-ff28020c5f1073a8b63bcd9d8b756a187fd682cb81935295120c63b207071896L393-R390)
[[13]](diffhunk://#diff-ff28020c5f1073a8b63bcd9d8b756a187fd682cb81935295120c63b207071896L403-R400)
### Test Updates:
*
[`libs/core/tests/unit_tests/output_parsers/test_openai_tools.py`](diffhunk://#diff-694cc0318edbd6bbca34f53304934062ad59ba9f5a788252ce6c5f5452489d67L19-R22):
Updated tests to use `IS_PYDANTIC_V1` and `IS_PYDANTIC_V2` for version
checks.
[[1]](diffhunk://#diff-694cc0318edbd6bbca34f53304934062ad59ba9f5a788252ce6c5f5452489d67L19-R22)
[[2]](diffhunk://#diff-694cc0318edbd6bbca34f53304934062ad59ba9f5a788252ce6c5f5452489d67L532-R535)
[[3]](diffhunk://#diff-694cc0318edbd6bbca34f53304934062ad59ba9f5a788252ce6c5f5452489d67L567-R570)
[[4]](diffhunk://#diff-694cc0318edbd6bbca34f53304934062ad59ba9f5a788252ce6c5f5452489d67L602-R605)
*
[`libs/core/tests/unit_tests/prompts/test_chat.py`](diffhunk://#diff-3e60e744842086a4f3c4b21bc83e819c3435720eab210078e77e2430fb8c7e84R7):
Replaced version tuple checks with `PYDANTIC_VERSION` comparisons.
[[1]](diffhunk://#diff-3e60e744842086a4f3c4b21bc83e819c3435720eab210078e77e2430fb8c7e84R7)
[[2]](diffhunk://#diff-3e60e744842086a4f3c4b21bc83e819c3435720eab210078e77e2430fb8c7e84L35-R38)
[[3]](diffhunk://#diff-3e60e744842086a4f3c4b21bc83e819c3435720eab210078e77e2430fb8c7e84L924-R927)
[[4]](diffhunk://#diff-3e60e744842086a4f3c4b21bc83e819c3435720eab210078e77e2430fb8c7e84L935-R938)
*
[`libs/core/tests/unit_tests/runnables/test_graph.py`](diffhunk://#diff-99a290330ef40103d0ce02e52e21310d6fadea142bfdea13c94d23fc81c0bb5dR3):
Simplified version checks using `PYDANTIC_VERSION`.
[[1]](diffhunk://#diff-99a290330ef40103d0ce02e52e21310d6fadea142bfdea13c94d23fc81c0bb5dR3)
[[2]](diffhunk://#diff-99a290330ef40103d0ce02e52e21310d6fadea142bfdea13c94d23fc81c0bb5dL15-R18)
[[3]](diffhunk://#diff-99a290330ef40103d0ce02e52e21310d6fadea142bfdea13c94d23fc81c0bb5dL234-L239)
*
[`libs/core/tests/unit_tests/runnables/test_runnable.py`](diffhunk://#diff-06bed920c0dad0cfd41d57a8d9e47a7b56832409649c10151061a791860d5bb5L18-R20):
Introduced `PYDANTIC_VERSION_AT_LEAST_29` and
`PYDANTIC_VERSION_AT_LEAST_210` for more readable version checks.
[[1]](diffhunk://#diff-06bed920c0dad0cfd41d57a8d9e47a7b56832409649c10151061a791860d5bb5L18-R20)
[[2]](diffhunk://#diff-06bed920c0dad0cfd41d57a8d9e47a7b56832409649c10151061a791860d5bb5L92-R99)
[[3]](diffhunk://#diff-06bed920c0dad0cfd41d57a8d9e47a7b56832409649c10151061a791860d5bb5L230-R233)
[[4]](diffhunk://#diff-06bed920c0dad0cfd41d57a8d9e47a7b56832409649c10151061a791860d5bb5L652-R655)
Add ruff rules:
* FIX: https://docs.astral.sh/ruff/rules/#flake8-fixme-fix
* TD: https://docs.astral.sh/ruff/rules/#flake8-todos-td
Code cleanup:
*
[`libs/core/langchain_core/outputs/chat_generation.py`](diffhunk://#diff-a1017ee46f58fa4005b110ffd4f8e1fb08f6a2a11d6ca4c78ff8be641cbb89e5L56-R56):
Removed the "HACK" prefix from a comment in the `set_text` method.
Configuration adjustments:
*
[`libs/core/pyproject.toml`](diffhunk://#diff-06baaee12b22a370fef9f170c9ed13e2727e377d3b32f5018430f4f0a39d3537R85-R93):
Added new rules `FIX002`, `TD002`, and `TD003` to the ignore list.
*
[`libs/core/pyproject.toml`](diffhunk://#diff-06baaee12b22a370fef9f170c9ed13e2727e377d3b32f5018430f4f0a39d3537L102-L108):
Removed the `FIX` and `TD` rules from the ignore list.
Test refinement:
*
[`libs/core/tests/unit_tests/runnables/test_runnable.py`](diffhunk://#diff-06bed920c0dad0cfd41d57a8d9e47a7b56832409649c10151061a791860d5bb5L3231-R3232):
Updated a TODO comment to improve clarity in the `test_map_stream`
function.
- [ ] **PR title**: "community: Removes pandas dependency for using
DuckDB for similarity search"
- [ ] **PR message**:
- **Description:** Removes pandas dependency for using DuckDB for
similarity search. The old function still exists as
`similarity_search_pd`, while the new one is at `similarity_search` and
requires no code changes. Return format remains the same.
- **Issue:** Issue #29933 and update on PR #30435
- **Dependencies:** No dependencies
LangChain QwQ allows non-Tongyi users to access thinking models with
extra capabilities which serve as an extension to Alibaba Cloud.
Hi @ccurme I'm back with the updated PR this time with documentation and
a finished package.
- [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:** adds documentation of `langchain-qwq` integration
package. Also adds it to Alibaba Cloud provider
- **Issue:** #30580#30317#30579
- **Dependencies:** openai, json-repair
- **Twitter handle:** YigitBekir
- [x] **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.
- [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, eyurtsev, ccurme, vbarda, hwchase17.
**Description:**
Adds support for Riza custom runtimes to the two Riza code interpreter
tools, allowing users to run LLM-generated code that depends on
libraries outside stdlib.
**Issue:** N/A
**Dependencies:** None
**Twitter handle:** @rizaio
## Description:
This PR adds the necessary documentation for the `langchain-runpod`
partner package integration. It includes:
* A provider page (`docs/docs/integrations/providers/runpod.ipynb`)
explaining the overall setup.
* An LLM component page (`docs/docs/integrations/llms/runpod.ipynb`)
detailing the `RunPod` class usage.
* A Chat Model component page
(`docs/docs/integrations/chat/runpod.ipynb`) detailing the `ChatRunPod`
class usage, including a feature support table.
These documentation files reflect the latest features of the
`langchain-runpod` package (v0.2.0+) such as async support and API
polling logic.
This work also addresses the review feedback provided on the previous
attempt in PR #30246 by:
* Removing all TODOs from documentation.
* Adding the required links between provider and component pages.
* Completing the feature support table in the chat documentation.
* Linking to the source code on GitHub for API reference.
Finally, it registers the `langchain-runpod` package in
`libs/packages.yml`.
## Dependencies:
None added to the core LangChain repository by these documentation
changes. The required dependency (`langchain-runpod`) is managed as a
separate package.
## Twitter handle:
@runpod_io
---------
Co-authored-by: Max Forsey <maxpod@maxpod.local>
Co-authored-by: Chester Curme <chester.curme@gmail.com>
Thank you for contributing to LangChain!
- [x] Fix Tool description of SerpAPI tool: "docs: Fix SerpAPI tool
description"
- [ ] Fix SerpAPI tool description:
- Tool description + name in example initialization of the SerpAPI tool
was still that of the python repl tool.
- @RLHoeppi
---------
Co-authored-by: Chester Curme <chester.curme@gmail.com>
Plus, some accompanying docs updates
Some compelling usage:
```py
from langchain_perplexity import ChatPerplexity
chat = ChatPerplexity(model="llama-3.1-sonar-small-128k-online")
response = chat.invoke(
"What were the most significant newsworthy events that occurred in the US recently?",
extra_body={"search_recency_filter": "week"},
)
print(response.content)
# > Here are the top significant newsworthy events in the US recently: ...
```
Also, some confirmation of structured outputs:
```py
from langchain_perplexity import ChatPerplexity
from pydantic import BaseModel
class AnswerFormat(BaseModel):
first_name: str
last_name: str
year_of_birth: int
num_seasons_in_nba: int
messages = [
{"role": "system", "content": "Be precise and concise."},
{
"role": "user",
"content": (
"Tell me about Michael Jordan. "
"Please output a JSON object containing the following fields: "
"first_name, last_name, year_of_birth, num_seasons_in_nba. "
),
},
]
llm = ChatPerplexity(model="llama-3.1-sonar-small-128k-online")
structured_llm = llm.with_structured_output(AnswerFormat)
response = structured_llm.invoke(messages)
print(repr(response))
#> AnswerFormat(first_name='Michael', last_name='Jordan', year_of_birth=1963, num_seasons_in_nba=15)
```
Perplexity's importance in the space has been growing, so we think it's
time to add an official integration!
Note: following the release of `langchain-perplexity` to `pypi`, we
should be able to add `perplexity` as an extra in
`libs/langchain/pyproject.toml`, but we're blocked by a circular import
for now.
---------
Co-authored-by: Eugene Yurtsev <eyurtsev@gmail.com>
Co-authored-by: Chester Curme <chester.curme@gmail.com>
Support "usage_metadata" for LiteLLM.
If no one reviews your PR within a few days, please @-mention one of
baskaryan, eyurtsev, ccurme, vbarda, hwchase17.
Related to https://github.com/langchain-ai/langchain/issues/30344https://github.com/langchain-ai/langchain/pull/30542 introduced an
erroneous test for token counts for o-series models. tiktoken==0.8 does
not support o-series models in
`tiktoken.encoding_for_model(model_name)`, and this is the version of
tiktoken we had in the lock file. So we would default to `cl100k_base`
for o-series, which is the wrong encoding model. The test tested against
this wrong encoding (so it passed with tiktoken 0.8).
Here we update tiktoken to 0.9 in the lock file, and fix the expected
counts in the test. Verified that we are pulling
[o200k_base](https://github.com/openai/tiktoken/blob/main/tiktoken/model.py#L8),
as expected.
Description:
This PR adds documentation for the langchain-oxylabs integration
package.
The documentation includes instructions for configuring Oxylabs
credentials and provides example code demonstrating how to use the
package.
Issue:
N/A
Dependencies:
No new dependencies are required.
Tests and Docs:
Added an example notebook demonstrating the usage of the
Langchain-Oxylabs package, located in docs/docs/integrations.
Added a provider page in docs/docs/providers.
Added a new package to libs/packages.yml.
Lint and Test:
Successfully ran make format, make lint, and make test.
- **Description:** Propagates config_factories when calling decoration
methods for RunnableBinding--e.g. bind, with_config, with_types,
with_retry, and with_listeners. This ensures that configs attached to
the original RunnableBinding are kept when creating the new
RunnableBinding and the configs are merged during invocation. Picks up
where #30551 left off.
- **Issue:** #30531
Co-authored-by: ccurme <chester.curme@gmail.com>
## Description
This PR adds a new `sitemap_url` parameter to the `GitbookLoader` class
that allows users to specify a custom sitemap URL when loading content
from a GitBook site. This is particularly useful for GitBook sites that
use non-standard sitemap file names like `sitemap-pages.xml` instead of
the default `sitemap.xml`.
The standard `GitbookLoader` assumes that the sitemap is located at
`/sitemap.xml`, but some GitBook instances (including GitBook's own
documentation) use different paths for their sitemaps. This parameter
makes the loader more flexible and helps users extract content from a
wider range of GitBook sites.
## Issue
Fixes bug
[30473](https://github.com/langchain-ai/langchain/issues/30473) where
the `GitbookLoader` would fail to find pages on GitBook sites that use
custom sitemap URLs.
## Dependencies
No new dependencies required.
*I've added*:
* Unit tests to verify the parameter works correctly
* Integration tests to confirm the parameter is properly used with real
GitBook sites
* Updated docstrings with parameter documentation
The changes are fully backward compatible, as the parameter is optional
with a sensible default.
---------
Co-authored-by: andrasfe <andrasf94@gmail.com>
Co-authored-by: Eugene Yurtsev <eugene@langchain.dev>
This pull request updates the `pyproject.toml` configuration file to
modify the linting rules and ignored warnings for the project. The most
important changes include switching to a more comprehensive selection of
linting rules and updating the list of ignored rules to better align
with the project's requirements.
Linting rules update:
* Changed the `select` option to include all available linting rules by
setting it to `["ALL"]`.
Ignored rules update:
* Updated the `ignore` option to include specific rules that interfere
with the formatter, are incompatible with Pydantic, or are temporarily
excluded due to project constraints.
This PR addresses two key issues:
- **Prevent history errors from failing silently**: Previously, errors
in message history were only logged and not raised, which can lead to
inconsistent state and downstream failures (e.g., ValidationError from
Bedrock due to malformed message history). This change ensures that such
errors are raised explicitly, making them easier to detect and debug.
(Side note: I’m using AWS Lambda Powertools Logger but hadn’t configured
it properly with the standard Python logger—my bad. If the error had
been raised, I would’ve seen it in the logs 😄) This is a **BREAKING
CHANGE**
- **Add messages in bulk instead of iteratively**: This introduces a
custom add_messages method to add all messages at once. The previous
approach failed silently when individual messages were too large,
resulting in partial history updates and inconsistent state. With this
change, either all messages are added successfully, or none are—helping
avoid obscure history-related errors from Bedrock.
---------
Co-authored-by: Kacper Wlodarczyk <kacper.wlodarczyk@chaosgears.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, eyurtsev, ccurme, vbarda, hwchase17.
**Description:**
Fixes a bug in the YoutubeLoader where FetchedTranscript objects were
not properly processed. The loader was only extracting the 'text'
attribute from FetchedTranscriptSnippet objects while ignoring 'start'
and 'duration' attributes. This would cause a TypeError when the code
later tried to access these missing keys, particularly when using the
CHUNKS format or any code path that needed timestamp information.
This PR modifies the conversion of FetchedTranscriptSnippet objects to
include all necessary attributes, ensuring that the loader works
correctly with all transcript formats.
**Issue:** Fixes#30309
**Dependencies:** None
**Testing:**
- Tested the fix with multiple YouTube videos to confirm it resolves the
issue
- Verified that both regular loading and CHUNKS format work correctly
- **Description:**
- Make Brave Search Tool consistent with other tools and allow reading
its api key from `BRAVE_SEARCH_API_KEY` instead of having to pass the
api key manually (no breaking changes)
- Improve Brave Search Tool by storing api key in `SecretStr` instead of
plain `str`.
- Add unit test for `BraveSearchWrapper`
- Reflect the changes in the documentation
- **Issue:** N/A
- **Dependencies:** N/A
- **Twitter handle:** ivan_brko
Release notes: https://pydantic.dev/articles/pydantic-v2-11-release
Covered here:
- We no longer access `model_fields` on class instances (that is now
deprecated);
- Update schema normalization for Pydantic version testing to reflect
changes to generated JSON schema (addition of `"additionalProperties":
True` for dict types with value Any or object).
## Considerations:
### Changes to JSON schema generation
#### Tool-calling / structured outputs
This may impact tool-calling + structured outputs for some providers,
but schema generation only changes if you have parameters of the form
`dict`, `dict[str, Any]`, `dict[str, object]`, etc. If dict parameters
are typed my understanding is there are no changes.
For OpenAI for example, untyped dicts work for structured outputs with
default settings before and after updating Pydantic, and error both
before/after if `strict=True`.
### Use of `model_fields`
There is one spot where we previously accessed `super(cls,
self).model_fields`, where `cls` is an object in the MRO. This was done
for the purpose of tracking aliases in secrets. I've updated this to
always be `type(self).model_fields`-- see comment in-line for detail.
---------
Co-authored-by: Sydney Runkle <54324534+sydney-runkle@users.noreply.github.com>
- **Description:** Add samba nova cloud embeddings docs, only
samabastudio embeddings were supported, now in the latest release of
langchan_sambanova sambanova cloud embeddings is also available
Broken source/docs links for Runnable methods
### What was changed
Added the `with_config` method to the method lists in both Runnable
template files:
- docs/api_reference/templates/runnable_non_pydantic.rst
- docs/api_reference/templates/runnable_pydantic.rst
# Community: update RankLLM integration and fix LangChain deprecation
- [x] **Description:**
- Removed `ModelType` enum (`VICUNA`, `ZEPHYR`, `GPT`) to align with
RankLLM's latest implementation.
- Updated `chain({query})` to `chain.invoke({query})` to resolve
LangChain 0.1.0 deprecation warnings from
https://github.com/langchain-ai/langchain/pull/29840.
- [x] **Dependencies:** No new dependencies added.
- [x] **Tests and Docs:**
- Updated RankLLM documentation
(`docs/docs/integrations/document_transformers/rankllm-reranker.ipynb`).
- Fixed LangChain usage in related code examples.
- [x] **Lint and Test:**
- Ran `make format`, `make lint`, and verified functionality after
updates.
- No breaking changes introduced.
```
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.
```
---------
Co-authored-by: Chester Curme <chester.curme@gmail.com>
**Description:**
This PR addresses the loss of partially initialised variables when
composing different prompts. I.e. it allows the following snippet to
run:
```python
from langchain_core.prompts import ChatPromptTemplate
prompt = ChatPromptTemplate.from_messages([('system', 'Prompt {x} {y}')]).partial(x='1')
appendix = ChatPromptTemplate.from_messages([('system', 'Appendix {z}')])
(prompt + appendix).invoke({'y': '2', 'z': '3'})
```
Previously, this would have raised a `KeyError`, stating that variable
`x` remains undefined.
**Issue**
References issue #30049
**Todo**
- [x] **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.
- [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/
---------
Co-authored-by: Eugene Yurtsev <eyurtsev@gmail.com>
Please see PR #27678 for context
## Overview
This pull request presents a refactor of the `HTMLHeaderTextSplitter`
class aimed at improving its maintainability and readability. The
primary enhancements include simplifying the internal structure by
consolidating multiple private helper functions into a single private
method, thereby reducing complexity and making the codebase easier to
understand and extend. Importantly, all existing functionalities and
public interfaces remain unchanged.
## PR Goals
1. **Simplify Internal Logic**:
- **Consolidation of Private Methods**: The original implementation
utilized multiple private helper functions (`_header_level`,
`_dom_depth`, `_get_elements`) to manage different aspects of HTML
parsing and document generation. This fragmentation increased cognitive
load and potential maintenance overhead.
- **Streamlined Processing**: By merging these functionalities into a
single private method (`_generate_documents`), the class now offers a
more straightforward flow, making it easier for developers to trace and
understand the processing steps. (Thanks to @eyurtsev)
2. **Enhance Readability**:
- **Clearer Method Responsibilities**: With fewer private methods, each
method now has a more focused responsibility. The primary logic resides
within `_generate_documents`, which handles both HTML traversal and
document creation in a cohesive manner.
- **Reduced Redundancy**: Eliminating redundant checks and consolidating
logic reduces the code's verbosity, making it more concise without
sacrificing clarity.
3. **Improve Maintainability**:
- **Easier Debugging and Extension**: A simplified internal structure
allows for quicker identification of issues and easier implementation of
future enhancements or feature additions.
- **Consistent Header Management**: The new implementation ensures that
headers are managed consistently within a single context, reducing the
likelihood of bugs related to header scope and hierarchy.
4. **Maintain Backward Compatibility**:
- **Unchanged Public Interface**: All public methods (`split_text`,
`split_text_from_url`, `split_text_from_file`) and their signatures
remain unchanged, ensuring that existing integrations and usage patterns
are unaffected.
- **Preserved Docstrings**: Comprehensive docstrings are retained,
providing clear documentation for users and developers alike.
## Detailed Changes
1. **Removed Redundant Private Methods**:
- **Eliminated `_header_level`, `_dom_depth`, and `_get_elements`**:
These methods were merged into the `_generate_documents` method,
centralizing the logic for HTML parsing and document generation.
2. **Consolidated Document Generation Logic**:
- **Single Private Method `_generate_documents`**: This method now
handles the entire process of parsing HTML, tracking active headers,
managing document chunks, and yielding `Document` instances. This
consolidation reduces the number of moving parts and simplifies the
overall processing flow.
3. **Simplified Header Management**:
- **Immediate Header Scope Handling**: Headers are now managed within
the traversal loop of `_generate_documents`, ensuring that headers are
added or removed from the active headers dictionary in real-time based
on their DOM depth and hierarchy.
- **Removed `chunk_dom_depth` Attribute**: The need to track chunk DOM
depth separately has been eliminated, as header scopes are now directly
managed within the traversal logic.
4. **Streamlined Chunk Finalization**:
- **Enhanced `finalize_chunk` Function**: The chunk finalization process
has been simplified to directly yield a single `Document` when needed,
without maintaining an intermediate list. This change reduces
unnecessary list operations and makes the logic more straightforward.
5. **Improved Variable Naming and Flow**:
- **Descriptive Variable Names**: Variables such as `current_chunk` and
`node_text` provide clear insights into their roles within the
processing logic.
- **Direct Header Removal Logic**: Headers that are out of scope are
removed immediately during traversal, ensuring that the active headers
dictionary remains accurate and up-to-date.
6. **Preserved Comprehensive Docstrings**:
- **Unchanged Documentation**: All existing docstrings, including
class-level and method-level documentation, remain intact. This ensures
that users and developers continue to have access to detailed usage
instructions and method explanations.
## Testing
All existing test cases from `test_html_header_text_splitter.py` have
been executed against the refactored code. The results confirm that:
- **Functionality Remains Intact**: The splitter continues to accurately
parse HTML content, respect header hierarchies, and produce the expected
`Document` objects with correct metadata.
- **Backward Compatibility is Maintained**: No changes were required in
the test cases, and all tests pass without modifications, demonstrating
that the refactor does not introduce any regressions or alter existing
behaviors.
This example remains fully operational and behaves as before, returning
a list of `Document` objects with the expected metadata and content
splits.
## Conclusion
This refactor achieves a more maintainable and readable codebase by
simplifying the internal structure of the `HTMLHeaderTextSplitter`
class. By consolidating multiple private methods into a single, cohesive
private method, the class becomes easier to understand, debug, and
extend. All existing functionalities are preserved, and comprehensive
tests confirm that the refactor maintains the expected behavior. These
changes align with LangChain’s standards for clean, maintainable, and
efficient code.
---
---------
Co-authored-by: Eugene Yurtsev <eyurtsev@gmail.com>
This pull request adds documentation and a tutorial for integrating the
[Vectorize](https://vectorize.io/) service with LangChain. The most
important changes include adding a new documentation page for Vectorize
and creating a Jupyter notebook that demonstrates how to use the
Vectorize retriever.
The source code for the langchain-vectorize package can be found
[here](https://github.com/vectorize-io/integrations-python/tree/main/langchain).
Previews:
*
https://langchain-git-fork-cbornet-vectorize-langchain.vercel.app/docs/integrations/providers/vectorize/
*
https://langchain-git-fork-cbornet-vectorize-langchain.vercel.app/docs/integrations/retrievers/vectorize/
Documentation updates:
*
[`docs/docs/integrations/providers/vectorize.mdx`](diffhunk://#diff-7e00d4ce4768f73b4d381a7c7b1f94d138f1b27ebd08e3666b942630a0285606R1-R40):
Added a new documentation page for Vectorize, including an overview of
its features, installation instructions, and a basic usage example.
Tutorial updates:
*
[`docs/docs/integrations/retrievers/vectorize.ipynb`](diffhunk://#diff-ba5bb9a1b4586db7740944b001bcfeadc88be357640ded0c82a329b11d8d6e29R1-R294):
Created a Jupyter notebook tutorial that shows how to set up the
Vectorize environment, create a RAG pipeline, and use the LangChain
Vectorize retriever. The notebook includes steps for account creation,
token generation, environment setup, and pipeline deployment.
This can only be reviewed by [hiding
whitespaces](https://github.com/langchain-ai/langchain/pull/30302/files?diff=unified&w=1).
The motivation behind this PR is to get my hands on the docs and make
the LangSmith teasing short and clear.
Right now I don't know how to do it, but this could be an include in the
future.
---------
Co-authored-by: Eugene Yurtsev <eyurtsev@gmail.com>
This PR includes support for HANA dialect in SQLDatabase, which is a
wrapper class for SQLAlchemy.
Currently, it is unable to set schema name when using HANA DB with
Langchain. And, it does not show any message to user so that it makes
hard for user to figure out why the SQL does not work as expected.
Here is the reference document for HANA DB to set schema for the
session.
- [SET SCHEMA Statement (Session
Management)](https://help.sap.com/docs/SAP_HANA_PLATFORM/4fe29514fd584807ac9f2a04f6754767/20fd550375191014b886a338afb4cd5f.html)
**partners: Enable max_retries in ChatMistralAI**
**Description**
- This pull request reactivates the retry logic in the
completion_with_retry method of the ChatMistralAI class, restoring the
intended functionality of the previously ineffective max_retries
parameter. New unit test that mocks failed/successful retry calls and an
integration test to confirm end-to-end functionality.
**Issue**
- Closes#30362
**Dependencies**
- No additional dependencies required
Co-authored-by: andrasfe <andrasf94@gmail.com>
This pull request includes fixes in documentation for PDF loaders to
correct the names of the loaders and the required installations. The
most important changes include updating the loader names and
installation instructions in the Jupyter notebooks.
Documentation fixes:
*
[`docs/docs/integrations/document_loaders/pdfminer.ipynb`](diffhunk://#diff-a4a0561cd4a6e876ea34b7182de64a452060b921bb32d37b02e6a7980a41729bL34-R34):
Changed references from `PyMuPDFLoader` to `PDFMinerLoader` and updated
the installation instructions to replace `pymupdf` with `pdfminer`.
[[1]](diffhunk://#diff-a4a0561cd4a6e876ea34b7182de64a452060b921bb32d37b02e6a7980a41729bL34-R34)
[[2]](diffhunk://#diff-a4a0561cd4a6e876ea34b7182de64a452060b921bb32d37b02e6a7980a41729bL63-R63)
[[3]](diffhunk://#diff-a4a0561cd4a6e876ea34b7182de64a452060b921bb32d37b02e6a7980a41729bL330-R330)
*
[`docs/docs/integrations/document_loaders/pymupdf.ipynb`](diffhunk://#diff-8487995f457e33daa2a08fdcff3b42e144eca069eeadfad5651c7c08cce7a5cdL292-R292):
Corrected the loader name from `PDFPlumberLoader` to `PyMuPDFLoader`.
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"
- [x] **PR message**: ***Delete this entire checklist*** and replace
with
- **Description:** Adding back a section of the Elasticsearch
vectorstore documentation that was deleted in [this
commit]([a72fddbf8d (diff-4988344c6ccc08191f89ac1ebf1caab5185e13698d7567fde5352038cd950d77))).
The only change I've made is to update the example RRF request, which
was out of date.
- [ ] **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.
- [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, eyurtsev, ccurme, vbarda, hwchase17.
This pull request includes enhancements to the `perplexity.py` file in
the `chat_models` module, focusing on improving the handling of
additional keyword arguments (`additional_kwargs`) in message processing
methods. Additionally, new unit tests have been added to ensure the
correct inclusion of citations, images, and related questions in the
`additional_kwargs`.
Issue: resolves https://github.com/langchain-ai/langchain/issues/30439
Enhancements to `perplexity.py`:
*
[`libs/community/langchain_community/chat_models/perplexity.py`](diffhunk://#diff-d3e4d7b277608683913b53dcfdbd006f0f4a94d110d8b9ac7acf855f1f22207fL208-L212):
Modified the `_convert_delta_to_message_chunk`, `_stream`, and
`_generate` methods to handle `additional_kwargs`, which include
citations, images, and related questions.
[[1]](diffhunk://#diff-d3e4d7b277608683913b53dcfdbd006f0f4a94d110d8b9ac7acf855f1f22207fL208-L212)
[[2]](diffhunk://#diff-d3e4d7b277608683913b53dcfdbd006f0f4a94d110d8b9ac7acf855f1f22207fL277-L286)
[[3]](diffhunk://#diff-d3e4d7b277608683913b53dcfdbd006f0f4a94d110d8b9ac7acf855f1f22207fR324-R331)
New unit tests:
*
[`libs/community/tests/unit_tests/chat_models/test_perplexity.py`](diffhunk://#diff-dab956d79bd7d17a0f5dea3f38ceab0d583b43b63eb1b29138ee9b6b271ba1d9R119-R275):
Added new tests `test_perplexity_stream_includes_citations_and_images`
and `test_perplexity_stream_includes_citations_and_related_questions` to
verify that the `stream` method correctly includes citations, images,
and related questions in the `additional_kwargs`.
When OpenAI originally released `stream_options` to enable token usage
during streaming, it was not supported in AzureOpenAI. It is now
supported.
Like the [OpenAI
SDK](f66d2e6fdc/src/openai/resources/completions.py (L68)),
ChatOpenAI does not return usage metadata during streaming by default
(which adds an extra chunk to the stream). The OpenAI SDK requires users
to pass `stream_options={"include_usage": True}`. ChatOpenAI implements
a convenience argument `stream_usage: Optional[bool]`, and an attribute
`stream_usage: bool = False`.
Here we extend this to AzureChatOpenAI by moving the `stream_usage`
attribute and `stream_usage` kwarg (on `_(a)stream`) from ChatOpenAI to
BaseChatOpenAI.
---
Additional consideration: we must be sensitive to the number of users
using BaseChatOpenAI to interact with other APIs that do not support the
`stream_options` parameter.
Suppose OpenAI in the future updates the default behavior to stream
token usage. Currently, BaseChatOpenAI only passes `stream_options` if
`stream_usage` is True, so there would be no way to disable this new
default behavior.
To address this, we could update the `stream_usage` attribute to
`Optional[bool] = None`, but this is technically a breaking change (as
currently values of False are not passed to the client). IMO: if / when
this change happens, we could accompany it with this update in a minor
bump.
---
Related previous PRs:
- https://github.com/langchain-ai/langchain/pull/22628
- https://github.com/langchain-ai/langchain/pull/22854
- https://github.com/langchain-ai/langchain/pull/23552
---------
Co-authored-by: Eugene Yurtsev <eyurtsev@gmail.com>
Thank you for contributing to LangChain!
**PR title**: Docs Update for vectara
**Description:** Vectara is moved as langchain partner package and
updating the docs according to that.
Thank you for contributing to LangChain!
- **Description:** Azure Document Intelligence OCR solution has a
*feature* parameter that enables some features such as high-resolution
document analysis, key-value pairs extraction, ... In langchain parser,
you could be provided as a `analysis_feature` parameter to the
constructor that was passed on the `DocumentIntelligenceClient`.
However, according to the `DocumentIntelligenceClient` [API
Reference](https://learn.microsoft.com/en-us/python/api/azure-ai-documentintelligence/azure.ai.documentintelligence.documentintelligenceclient?view=azure-python),
this is not a valid constructor parameter. It was therefore remove and
instead stored as a parser property that is used in the
`begin_analyze_document`'s `features` parameter (see [API
Reference](https://learn.microsoft.com/en-us/python/api/azure-ai-formrecognizer/azure.ai.formrecognizer.documentanalysisclient?view=azure-python#azure-ai-formrecognizer-documentanalysisclient-begin-analyze-document)).
I also removed the check for "Supported features" since all features are
supported out-of-the-box. Also I did not check if the provided `str`
actually corresponds to the Azure package enumeration of features, since
the `ValueError` when creating the enumeration object is pretty
explicit.
Last caveat, is that some features are not supported for some kind of
documents. This is documented inside Microsoft documentation and
exception are also explicit.
- **Issue:** N/A
- **Dependencies:** No
- **Twitter handle:** @Louis___A
---------
Co-authored-by: Louis Auneau <louis@handshakehealth.co>
## **Description:**
The Jupyter notebooks in the docs section are extremely useful and
critical for widespread adoption of LangChain amongst new developers.
However, because they are also converted to MDX and used to build the
HTML for the Docusaurus site, they contain JSX code that degrades
readability when opened in a "notebook" setting (local notebook server,
google colab, etc.). For instance, here we see the website, with a nice
React tab component for installation instructions (`pip` vs `conda`):
Now, here is the same notebook viewed in colab:
Note that the text following "To install LangChain run:" contains
snippets of JSX code that is (i) confusing, (ii) bad for readability,
(iii) potentially misleading for a novice developer, who might take it
literally to mean that "to install LangChain I should run `import Tabs
from...`" and then an ill-formed command which mixes the `pip` and
`conda` installation instructions.
Ideally, we would like to have a system that presents a
similar/equivalent UI when viewing the notebooks on the documentation
site, or when interacting with them in a notebook setting - or, at a
minimum, we should not present ill-formed JSX snippets to someone trying
to execute the notebooks. As the documentation itself states, running
the notebooks yourself is a great way to learn the tools. Therefore,
these distracting and ill-formed snippets are contrary to that goal.
## **Fixes:**
* Comment out the JSX code inside the notebook
`docs/tutorials/llm_chain` with a special directive `<!-- HIDE_IN_NB`
(closed with `HIDE_IN_NB -->`). This makes the JSX code "invisible" when
viewed in a notebook setting.
* Add a custom preprocessor that runs process_cell and just erases these
comment strings. This makes sure they are rendered when converted to
MDX.
* Minor tweak: Refactor some of the Markdown instructions into an
executable codeblock for better experience when running as a notebook.
* Minor tweak: Optionally try to get the environment variables from a
`.env` file in the repo so the user doesn't have to enter it every time.
Depends on the user installing `python-dotenv` and adding their own
`.env` file.
* Add an environment variable for "LANGSMITH_PROJECT"
(default="default"), per the LangSmith docs, so a local user can target
a specific project in their LangSmith account.
**NOTE:** If this PR is approved, and the maintainers agree with the
general goal of aligning the notebook execution experience and the doc
site UI, I would plan to implement this on the rest of the JSX snippets
that are littered in the notebooks.
**NOTE:** I wasn't able to/don't know how to run the linkcheck Makefile
commands.
- [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/
---------
Co-authored-by: Really Him <hesereallyhim@proton.me>
We are implementing a token-counting callback handler in
`langchain-core` that is intended to work with all chat models
supporting usage metadata. The callback will aggregate usage metadata by
model. This requires responses to include the model name in its
metadata.
To support this, if a model `returns_usage_metadata`, we check that it
includes a string model name in its `response_metadata` in the
`"model_name"` key.
More context: https://github.com/langchain-ai/langchain/pull/30487
Thank you for contributing to LangChain!
**Description:**
Since we just implemented
[langchain-memgraph](https://pypi.org/project/langchain-memgraph/)
integration, we are adding basic docs to [your site based on this
comment](https://github.com/langchain-ai/langchain/pull/30197#pullrequestreview-2671616410)
from @ccurme .
**Twitter handle:**
[@memgraphdb](https://x.com/memgraphdb)
- [x] **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.
- [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, eyurtsev, ccurme, vbarda, hwchase17.
---------
Co-authored-by: Chester Curme <chester.curme@gmail.com>
Stripped-down version of
[OpenAICallbackHandler](https://github.com/langchain-ai/langchain/blob/master/libs/community/langchain_community/callbacks/openai_info.py)
that just tracks `AIMessage.usage_metadata`.
```python
from langchain_core.callbacks import get_usage_metadata_callback
from langgraph.prebuilt import create_react_agent
def get_weather(location: str) -> str:
"""Get the weather at a location."""
return "It's sunny."
tools = [get_weather]
agent = create_react_agent("openai:gpt-4o-mini", tools)
with get_usage_metadata_callback() as cb:
result = await agent.ainvoke({"messages": "What's the weather in Boston?"})
print(cb.usage_metadata)
```
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"
- [x] **PR message**: ***Delete this entire checklist*** and replace
with
- **Description:** fix typo
- **Issue:** -
- **Dependencies:** -
- **Twitter handle:** -
- [x] **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.
- [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, eyurtsev, ccurme, vbarda, hwchase17.
Description: Extend the gremlin graph schema to include the edge
properties, grouped by its triples; i.e: `inVLabel` and `outVLabel`.
This should give more context when crafting queries to run against a
gremlin graph db
This pull request includes extensive documentation updates for the
`ChatPerplexity` class in the
`libs/community/langchain_community/chat_models/perplexity.py` file. The
changes provide detailed setup instructions, key initialization
arguments, and usage examples for various functionalities of the
`ChatPerplexity` class.
Documentation improvements:
* Added setup instructions for installing the `openai` package and
setting the `PPLX_API_KEY` environment variable.
* Documented key initialization arguments for completion parameters and
client parameters, including `model`, `temperature`, `max_tokens`,
`streaming`, `pplx_api_key`, `request_timeout`, and `max_retries`.
* Provided examples for instantiating the `ChatPerplexity` class,
invoking it with messages, using structured output, invoking with
perplexity-specific parameters, streaming responses, and accessing token
usage and response metadata.Thank you for contributing to LangChain!
This pull request includes updates to the
`docs/docs/integrations/chat/perplexity.ipynb` file to enhance the
documentation for `ChatPerplexity`. The changes focus on demonstrating
the use of Perplexity-specific parameters and supporting structured
outputs for Tier 3+ users.
Enhancements to documentation:
* Added a new markdown cell explaining the use of Perplexity-specific
parameters through the `ChatPerplexity` class, including parameters like
`search_domain_filter`, `return_images`, `return_related_questions`, and
`search_recency_filter` using the `extra_body` parameter.
* Added a new code cell demonstrating how to invoke `ChatPerplexity`
with the `extra_body` parameter to filter search recency.
Support for structured outputs:
* Added a new markdown cell explaining that `ChatPerplexity` supports
structured outputs for Tier 3+ users.
* Added a new code cell demonstrating how to use `ChatPerplexity` with
structured outputs by defining a `BaseModel` class and invoking the chat
with structured output.[Copilot is generating a summary...]Thank you for
contributing to LangChain!
---------
Co-authored-by: Chester Curme <chester.curme@gmail.com>
Hello!
I have reopened a pull request for tool integration.
Please refer to the previous
[PR](https://github.com/langchain-ai/langchain/pull/30248).
I understand that for the tool integration, a separate package should be
created, and only the documentation should be added under docs/docs/. If
there are any other procedures, please let me know.
[langchain-naver-community](https://github.com/e7217/langchain-naver-community)
cc: @ccurme
---------
Co-authored-by: Chester Curme <chester.curme@gmail.com>
Hi @ccurme!
Thanks so much for helping with getting the Contextual documentation
merged last time. We added the reranker to our provider's documentation!
Please let me know if there's any issues with it! Would love to also
work with your team on an announcement for this! 🙏
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"
- [x] **PR message**: ***Delete this entire checklist*** and replace
with
- **Description:** updates contextual provider documentation to include
information about our reranker, also includes documentation for
contextual's reranker in the retrievers section
- **Twitter handle:** https://x.com/ContextualAI/highlights
docs have been added
- [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, eyurtsev, ccurme, vbarda, hwchase17.
---------
Co-authored-by: ccurme <chester.curme@gmail.com>
Description: Update vector store tab inits to match either the docs or
api_ref (whichever was more comprehensive)
List of changes per vector stores:
- In-memory
- no change
- AstraDB
- match to docs - docs/api_refs match (excluding embeddings)
- Chroma
- match to docs - api_refs is less descriptive
- FAISS
- match to docs - docs/api_refs match (excluding embeddings)
- Milvus
- match to docs to use Milvus Lite with Flat index - api_refs does not
have index_param for generalization
- MongoDB
- match to docs - api_refs are sparser
- PGVector
- match to api_ref
- changed to include docker cmd directly in code
- docs/api_ref has comment to view docker command in separate code block
- Pinecone
- match to api_refs - docs have code dispersed
- Qdrant
- match to api_ref - docs has size=3072, api_ref has size=1536
---------
Co-authored-by: Chester Curme <chester.curme@gmail.com>
**Description:**
a third party package not listed in the default valid namespaces cannot
pass test_serdes because the load() does not allow for extending the
valid_namespaces.
test_serdes will fail with -
ValueError: Invalid namespace: {'lc': 1, 'type': 'constructor', 'id':
['langchain_other', 'chat_models', 'ChatOther'], 'kwargs':
{'model_name': '...', 'api_key': '...'}, 'name': 'ChatOther'}
this change has test_serdes automatically extend valid_namespaces based
off the ChatModel under test's namespace.
this_row_id previously used UUID v1. However, since UUID v1 can be
predicted if the MAC address and timestamp are known, it poses a
potential security risk. Therefore, it has been changed to UUID v4.
added warning when duckdb is used as a vectorstore without pandas being
installed (currently used for similarity search result processing)
Thank you for contributing to LangChain!
- [ ] **PR title**: "community: added warning when duckdb is used as a
vectorstore without pandas"
- [ ] **PR message**: ***Delete this entire checklist*** and replace
with
- **Description:** displays a warning when using duckdb as a vector
store without pandas being installed, as it is used by the
`similarity_search` function
- **Issue:** #29933
- **Dependencies:** None
---------
Co-authored-by: Chester Curme <chester.curme@gmail.com>
Deepseek model does not return reasoning when hosted on openrouter
(Issue [30067](https://github.com/langchain-ai/langchain/issues/30067))
the following code did not return reasoning:
```python
llm = ChatDeepSeek( model = 'deepseek/deepseek-r1:nitro', api_base="https://openrouter.ai/api/v1", api_key=os.getenv("OPENROUTER_API_KEY"))
messages = [
{"role": "system", "content": "You are an assistant."},
{"role": "user", "content": "9.11 and 9.8, which is greater? Explain the reasoning behind this decision."}
]
response = llm.invoke(messages, extra_body={"include_reasoning": True})
print(response.content)
print(f"REASONING: {response.additional_kwargs.get('reasoning_content', '')}")
print(response)
```
The fix is to extract reasoning from
response.choices[0].message["model_extra"] and from
choices[0].delta["reasoning"]. and place in response additional_kwargs.
Change is really just the addition of a couple one-sentence if
statements.
---------
Co-authored-by: andrasfe <andrasf94@gmail.com>
Co-authored-by: Chester Curme <chester.curme@gmail.com>
Fix several typos in docs/docs/how_to/split_html.ipynb
* `structered` should be `structured`
* `signifcant` should be `significant`
* `seperator` should be `separator`
# Description
This PR adds reasoning model support for `langchain-ollama` by
extracting reasoning token blocks, like those used in deepseek. It was
inspired by
[ollama-deep-researcher](https://github.com/langchain-ai/ollama-deep-researcher),
specifically the parsing of [thinking
blocks](6d1aaf2139/src/assistant/graph.py (L91)):
```python
# TODO: This is a hack to remove the <think> tags w/ Deepseek models
# It appears very challenging to prompt them out of the responses
while "<think>" in running_summary and "</think>" in running_summary:
start = running_summary.find("<think>")
end = running_summary.find("</think>") + len("</think>")
running_summary = running_summary[:start] + running_summary[end:]
```
This notes that it is very hard to remove the reasoning block from
prompting, but we actually want the model to reason in order to increase
model performance. This implementation extracts the thinking block, so
the client can still expect a proper message to be returned by
`ChatOllama` (and use the reasoning content separately when desired).
This implementation takes the same approach as
[ChatDeepseek](5d581ba22c/libs/partners/deepseek/langchain_deepseek/chat_models.py (L215)),
which adds the reasoning content to
chunk.additional_kwargs.reasoning_content;
```python
if hasattr(response.choices[0].message, "reasoning_content"): # type: ignore
rtn.generations[0].message.additional_kwargs["reasoning_content"] = (
response.choices[0].message.reasoning_content # type: ignore
)
```
This should probably be handled upstream in ollama + ollama-python, but
this seems like a reasonably effective solution. This is a standalone
example of what is happening;
```python
async def deepseek_message_astream(
llm: BaseChatModel,
messages: list[BaseMessage],
config: RunnableConfig | None = None,
*,
model_target: str = "deepseek-r1",
**kwargs: Any,
) -> AsyncIterator[BaseMessageChunk]:
"""Stream responses from Deepseek models, filtering out <think> tags.
Args:
llm: The language model to stream from
messages: The messages to send to the model
Yields:
Filtered chunks from the model response
"""
# check if the model is deepseek based
if (llm.name and model_target not in llm.name) or (hasattr(llm, "model") and model_target not in llm.model):
async for chunk in llm.astream(messages, config=config, **kwargs):
yield chunk
return
# Yield with a buffer, upon completing the <think></think> tags, move them to the reasoning content and start over
buffer = ""
async for chunk in llm.astream(messages, config=config, **kwargs):
# start or append
if not buffer:
buffer = chunk.content
else:
buffer += chunk.content if hasattr(chunk, "content") else chunk
# Process buffer to remove <think> tags
if "<think>" in buffer or "</think>" in buffer:
if hasattr(chunk, "tool_calls") and chunk.tool_calls:
raise NotImplementedError("tool calls during reasoning should be removed?")
if "<think>" in chunk.content or "</think>" in chunk.content:
continue
chunk.additional_kwargs["reasoning_content"] = chunk.content
chunk.content = ""
# upon block completion, reset the buffer
if "<think>" in buffer and "</think>" in buffer:
buffer = ""
yield chunk
```
# Issue
Integrating reasoning models (e.g. deepseek-r1) into existing LangChain
based workflows is hard due to the thinking blocks that are included in
the message contents. To avoid this, we could match the `ChatOllama`
integration with `ChatDeepseek` to return the reasoning content inside
`message.additional_arguments.reasoning_content` instead.
# Dependenices
None
---------
Co-authored-by: Chester Curme <chester.curme@gmail.com>
- Test if models support forcing tool calls via `tool_choice`. If they
do, they should support
- `"any"` to specify any tool
- the tool name as a string to force calling a particular tool
- Add `tool_choice` to signature of `BaseChatModel.bind_tools` in core
- Deprecate `tool_choice_value` in standard tests in favor of a boolean
`has_tool_choice`
Will follow up with PRs in external repos (tested in AWS and Google
already).
- **Description:** This PR updates the [MLflow
integration](https://python.langchain.com/docs/integrations/providers/mlflow_tracking/)
docs. This PR is based on feedback and suggestions from @efriis on
#29612 . This proposed revision is much shorter, does not contain
images, and links out to the MLflow docs rather than providing lengthy
descriptions directly within these docs. Thank you for taking another
look!
- **Issue:** NA
- **Dependencies:** NA
---------
Co-authored-by: Chester Curme <chester.curme@gmail.com>
**Description**
This contribution adds a retriever for the Zotero API.
[Zotero](https://www.zotero.org/) is an open source reference management
for bibliographic data and related research materials. A retriever will
allow langchain applications to retrieve relevant documents from
personal or shared group libraries, which I believe will be helpful for
numerous applications, such as RAG systems, personal research
assistants, etc. Tests and docs were added.
The documentation provided assumes the retriever will be part of the
langchain-community package, as this seemed customary. Please let me
know if this is not the preferred way to do it. I also uploaded the
implementation to PyPI.
**Dependencies**
The retriever requires the `pyzotero` package for API access. This
dependency is stated in the docs, and the retriever will return an error
if the package is not found. However, this dependency is not added to
the langchain package itself.
**Twitter handle**
I'm no longer using Twitter, but I'd appreciate a shoutout on
[Bluesky](https://bsky.app/profile/koenigt.bsky.social) or
[LinkedIn](https://www.linkedin.com/in/dr-tim-k%C3%B6nig-534aa2324/)!
Let me know if there are any issues, I'll gladly try and sort them out!
---------
Co-authored-by: Chester Curme <chester.curme@gmail.com>
This pull request includes a change to the following
- docs/docs/integrations/tools/tavily_search.ipynb
- docs/docs/integrations/tools/tavily_extract.ipynb
- added docs/docs/integrations/providers/tavily.mdx
---------
Co-authored-by: pulvedu <dustin@tavily.com>
**Description:**
Implements an additional `browser_session` parameter on
PlaywrightURLLoader which can be used to initialize the browser context
by providing a stored playwright context.
**Description:**
This PR fixes a minor typo in the comments within
`libs/partners/openai/langchain_openai/chat_models/base.py`. The word
"ben" has been corrected to "be" for clarity and professionalism.
**Issue:**
N/A
**Dependencies:**
None
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"
- [x] **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, eyurtsev, ccurme, vbarda, hwchase17.
---------
Signed-off-by: pudongair <744355276@qq.com>
Co-authored-by: Chester Curme <chester.curme@gmail.com>
**Description:**
Since `ChatLiteLLM` is forwarding most parameters to
`litellm.completion(...)`, there is no reason to set other default
values than the ones defined by `litellm`.
In the case of parameter 'n', it also provokes an issue when trying to
call a serverless endpoint on Azure, as it is considered an extra
parameter. So we need to keep it optional.
We can debate about backward compatibility of this change: in my
opinion, there should not be big issues since from my experience,
calling `litellm.completion()` without these parameters works fine.
**Issue:**
- #29679
**Dependencies:** None
- **Description:** Adding keep_newlines parameter to process_pages
method with page_ids on Confluence document loader
- **Issue:** N/A (This is an enhancement rather than a bug fix)
- **Dependencies:** N/A
- **Twitter handle:** N/A
- [x] **PR title**
- [x] **PR message**:
- **Description:** Updated the sparse and hybrid vector search due to
changes in the Qdrant API, and cleaned up the notebook
- [x] **Add tests and docs**:
- N/A
- [x] **Lint and test**
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, eyurtsev, ccurme, vbarda, hwchase17.
Co-authored-by: Mark Perfect <mark.anthony.perfect1@gmail.com>
# Description
Adds documentation on LangChain website for a Dell specific document
loader for on-prem storage devices. Additional details on what the
document loader is described in the PR as well as on our github repo:
[https://github.com/dell/powerscale-rag-connector](https://github.com/dell/powerscale-rag-connector)
This PR also creates a category on the document loader webpage as no
existing category exists for on-prem. This follows the existing pattern
already established as the website has a category for cloud providers.
# Issue:
New release, no issue.
# Dependencies:
None
# Twitter handle:
DellTech
---------
Signed-off-by: Adam Brenner <adam@aeb.io>
Co-authored-by: Chester Curme <chester.curme@gmail.com>
- **Description:** I was testing out `init_chat` and saw that chat
models can now be inferred. Azure OpenAI is currently only supported but
we would like to add support for Azure AI which is a different package.
This PR edits the `base.py` file to add the chat implementation.
- I don't think this adds any additional dependencies
- Will add a test and lint, but starting an initial draft PR.
cc @santiagxf
---------
Co-authored-by: Chester Curme <chester.curme@gmail.com>
The default model for `ChatGroq`, `"mixtral-8x7b-32768"`, is being
retired on March 20, 2025. Here we remove the default, such that model
names must be explicitly specified (being explicit is a good practice
here, and avoids the need for breaking changes down the line). This
change will be released in a minor version bump to 0.3.
This follows https://github.com/langchain-ai/langchain/pull/30161
(released in version 0.2.5), where we began generating warnings to this
effect.
## Description:
- Removed deprecated `initialize_agent()` usage in AWS Lambda
integration.
- Replaced it with `AgentExecutor` for compatibility with LangChain
v0.3.
- Fixed documentation linting errors.
## Issue:
- No specific issue linked, but this resolves the use of deprecated
agent initialization.
## Dependencies:
- No new dependencies added.
## Request for Review:
- Please verify if the implementation is correct.
- If approved and merged, I will proceed with updating other related
files.
## Twitter Handle (Optional):
I don't have a Twitter but here is my LinkedIn instead
(https://www.linkedin.com/in/aryan1227/)
OpenAIWhisperParser, OpenAIWhisperParserLocal, YandexSTTParser do not
handle in-memory audio data (loaded via Blob.from_data) correctly. They
require Blob.path to be set and AudioSegment is always read from the
file system. In-memory data is handled correctly only for
FasterWhisperParser so far. I changed OpenAIWhisperParser,
OpenAIWhisperParserLocal, YandexSTTParser accordingly to match
FasterWhisperParser.
Thanks for reviewing the PR!
Co-authored-by: qonnop <qonnop@users.noreply.github.com>
**description:** the ChatModel[Integration]Tests classes are powerful
and helpful, this change allows sub-classes to add additional tests.
for instance,
```
class TestChatMyServiceIntegration(ChatModelIntegrationTests):
...
def test_myservice(self, model: BaseChatModel) -> None:
...
```
---------
Co-authored-by: ccurme <chester.curme@gmail.com>
## Description
This pull request introduces a new text splitter,
`JSFrameworkTextSplitter`, to the Langchain library. The
`JSFrameworkTextSplitter` extends the `RecursiveCharacterTextSplitter`
to handle JavaScript framework code effectively, including React (JSX),
Vue, and Svelte. It identifies and utilizes framework-specific component
tags and syntax elements as splitting points, alongside standard
JavaScript syntax. This ensures that code is divided at natural
boundaries, enhancing the parsing and processing of JavaScript and
framework-specific code.
### Key Features
- Supports React (JSX), Vue, and Svelte frameworks.
- Identifies and uses framework-specific tags and syntax elements as
natural splitting points.
- Extends the existing `RecursiveCharacterTextSplitter` for seamless
integration.
## Issue
No specific issue addressed.
## Dependencies
No additional dependencies required.
---------
Co-authored-by: ccurme <chester.curme@gmail.com>
The former link led to a site that explains that the docs have moved,
but did not redirect the user to the actual site automatically. I just
copied the provided url, checked that it works and updated the link to
the current version.
**Description:** Updated the link to Unstructured Docs at
https://docs.unstructured.io
**Issue:** #30315
**Dependencies:** None
**Twitter handle:** @lahoramaker
**Description:**
Added an 'extract' mode to FireCrawlLoader that enables structured data
extraction from web pages. This feature allows users to Extract
structured data from a single URLs, or entire websites using Large
Language Models (LLMs).
You can show more params and usage on [firecrawl
docs](https://docs.firecrawl.dev/features/extract-beta).
You can extract from only one url now.(it depends on firecrawl's extract
method)
**Dependencies:**
No new dependencies required. Uses existing FireCrawl API capabilities.
---------
Co-authored-by: chbae <chbae@gcsc.co.kr>
Co-authored-by: ccurme <chester.curme@gmail.com>
FasterWhisperParser fails on a machine without an NVIDIA GPU: "Requested
float16 compute type, but the target device or backend do not support
efficient float16 computation." This problem arises because the
WhisperModel is called with compute_type="float16", which works only for
NVIDIA GPU.
According to the [CTranslate2
docs](https://opennmt.net/CTranslate2/quantization.html#bit-floating-points-float16)
float16 is supported only on NVIDIA GPUs. Removing the compute_type
parameter solves the problem for CPUs. According to the [CTranslate2
docs](https://opennmt.net/CTranslate2/quantization.html#quantize-on-model-loading)
setting compute_type to "default" (standard when omitting the parameter)
uses the original compute type of the model or performs implicit
conversion for the specific computation device (GPU or CPU). I suggest
to remove compute_type="float16".
@hulitaitai you are the original author of the FasterWhisperParser - is
there a reason for setting the parameter to float16?
Thanks for reviewing the PR!
Co-authored-by: qonnop <qonnop@users.noreply.github.com>
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:** Do not load non-public dimensions and measures
(public: false) with Cube semantic loader
- **Issue:** Currently, non-public dimensions and measures are loaded by
the Cube document loader which leads to downstream applications using
these which is not allowed by Cube.
- [x] **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.
- [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, eyurtsev, ccurme, vbarda, hwchase17.
- Support features from recent update:
https://www.anthropic.com/news/token-saving-updates (mostly adding
support for built-in tools in `bind_tools`
- Add documentation around prompt caching, token-efficient tool use, and
built-in tools.
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:** Fix bad log message on line#56 and replace f-string
logs with format specifiers
- **Issue:** Log messages such as this one
`INFO:langchain_community.document_loaders.cube_semantic:Loading
dimension values for: {dimension_name}...`
- [ ] **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.
- [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, eyurtsev, ccurme, vbarda, hwchase17.
PR Title:
community: Fix Pass API_KEY as argument
PR Message:
Description:
This PR fixes validation error "Value error, Did not find
tavily_api_key, please add an environment variable `TAVILY_API_KEY`
which contains it, or pass `tavily_api_key` as a named parameter."
Dependencies:
No new dependencies introduced.
---------
Co-authored-by: pulvedu <dustin@tavily.com>
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, eyurtsev, ccurme, vbarda, hwchase17.
Here we add a job to the release workflow that, when releasing
`langchain-core`, tests prior published versions of select packages
against the new version of core. We limit the testing to the most recent
published versions of langchain-anthropic and langchain-openai.
This is designed to catch backward-incompatible updates to core. We
sometimes update core and downstream packages simultaneously, so there
may not be any commit in the history at which tests would fail. So
although core and latest downstream packages could be consistent, we can
benefit from testing prior versions of downstream packages against core.
I tested the workflow by simulating a [breaking
change](d7287248cf)
in core and running it with publishing steps disabled:
https://github.com/langchain-ai/langchain/actions/runs/13741876345. The
workflow correctly caught the issue.
## Description
The models in DashScope support multiple SystemMessage. Here is the
[Doc](https://bailian.console.aliyun.com/model_experience_center/text#/model-market/detail/qwen-long?tabKey=sdk),
and the example code on the document page:
```python
import os
from openai import OpenAI
client = OpenAI(
api_key=os.getenv("DASHSCOPE_API_KEY"), # 如果您没有配置环境变量，请在此处替换您的API-KEY
base_url="https://dashscope.aliyuncs.com/compatible-mode/v1", # 填写DashScope服务base_url
)
# 初始化messages列表
completion = client.chat.completions.create(
model="qwen-long",
messages=[
{'role': 'system', 'content': 'You are a helpful assistant.'},
# 请将 'file-fe-xxx'替换为您实际对话场景所使用的 file-id。
{'role': 'system', 'content': 'fileid://file-fe-xxx'},
{'role': 'user', 'content': '这篇文章讲了什么？'}
],
stream=True,
stream_options={"include_usage": True}
)
full_content = ""
for chunk in completion:
if chunk.choices and chunk.choices[0].delta.content:
# 拼接输出内容
full_content += chunk.choices[0].delta.content
print(chunk.model_dump())
print({full_content})
```
Tip: The example code is for OpenAI, but the document said that it also
supports the DataScope API, and I tested it, and it works.
```
Is the Dashscope SDK invocation method compatible?
Yes, the Dashscope SDK remains compatible for model invocation. However, file uploads and file-ID retrieval are currently only supported via the OpenAI SDK. The file-ID obtained through this method is also compatible with Dashscope for model invocation.
```
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, eyurtsev, ccurme, vbarda, hwchase17.
```markdown
**Description:**
This PR integrates Valthera into LangChain, introducing an framework designed to send highly personalized nudges by an LLM agent. This is modeled after Dr. BJ Fogg's Behavior Model. This integration includes:
- Custom data connectors for HubSpot, PostHog, and Snowflake.
- A unified data aggregator that consolidates user data.
- Scoring configurations to compute motivation and ability scores.
- A reasoning engine that determines the appropriate user action.
- A trigger generator to create personalized messages for user engagement.
**Issue:**
N/A
**Dependencies:**
N/A
**Twitter handle:**
- `@vselvarajijay`
**Tests and Docs:**
- `docs/docs/integrations/tools/valthera`
- `https://github.com/valthera/langchain-valthera/tree/main/tests`
```
---------
Co-authored-by: Chester Curme <chester.curme@gmail.com>
## Changes
- `/Makefile` - added extra step to `make format` and `make lint` to
ensure the lint dep-group is installed before running ruff (documented
in issue #30069)
- `/pyproject.toml` - removed ruff exceptions for files that no longer
exist or no longer create formatting/linting errors in ruff
## Testing
**running `make format` on this branch/PR**
<img width="435" alt="image"
src="https://github.com/user-attachments/assets/82751788-f44e-4591-98ed-95ce893ce623"
/>
## Issue
fixes#30069
---------
Co-authored-by: Chester Curme <chester.curme@gmail.com>
Thank you for contributing to LangChain!
**Description:** adds ContextualAI's `langchain-contextual` package's
documentation
If no one reviews your PR within a few days, please @-mention one of
baskaryan, efriis, eyurtsev, ccurme, vbarda, hwchase17.
---------
Co-authored-by: Chester Curme <chester.curme@gmail.com>
The OpenAI API requires function names to match the pattern
'^[a-zA-Z0-9_-]+$'. This updates the JIRA toolkit's tool names to use
underscores instead of spaces to comply with this requirement and
prevent BadRequestError when using the tools with OpenAI functions.
Error fixed:
```
File "langgraph-bug-fix/.venv/lib/python3.13/site-packages/openai/_base_client.py", line 1023, in _request
raise self._make_status_error_from_response(err.response) from None
openai.BadRequestError: Error code: 400 - {'error': {'message': "Invalid 'tools[0].function.name': string does not match pattern. Expected a string that matches the pattern '^[a-zA-Z0-9_-]+$'.", 'type': 'invalid_request_error', 'param': 'tools[0].function.name', 'code': 'invalid_value'}}
During task with name 'agent' and id 'aedd7537-e8d5-6678-d0c5-98129586d3ac'
```
Issue:#30182
- 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"
- Where "package" is whichever of langchain, core, etc. is being modified. Use "docs: ..." for purely docs changes, "infra: ..." for CI changes.
- Example: "core: add foobar LLM"
- [ ]**PR message**: ***Delete this entire checklist*** and replace with
@@ -24,6 +24,5 @@ Additional guidelines:
- 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, eyurtsev, ccurme, vbarda, hwchase17.
[](https://vscode.dev/redirect?url=vscode://ms-vscode-remote.remote-containers/cloneInVolume?url=https://github.com/langchain-ai/langchain)
[](https://codespaces.new/langchain-ai/langchain)
[<img src="https://github.com/codespaces/badge.svg" title="Open in Github Codespace" width="150" height="20">](https://codespaces.new/langchain-ai/langchain)
Use [LangGraph](https://langchain-ai.github.io/langgraph/) to build stateful agents with first-class streaming and human-in-the-loop support.
- **Productionization**: Inspect, monitor, and evaluate your apps with [LangSmith](https://docs.smith.langchain.com/) so that you can constantly optimize and deploy with confidence.
- **Deployment**: Turn your LangGraph applications into production-ready APIs and Assistants with [LangGraph Platform](https://langchain-ai.github.io/langgraph/cloud/).
-[LangSmith](http://www.langchain.com/langsmith) - Helpful for agent evals and
observability. Debug poor-performing LLM app runs, evaluate agent trajectories, gain
visibility in production, and improve performance over time.
- [LangGraph](https://langchain-ai.github.io/langgraph/) - Build agents that can
reliably handle complex tasks with LangGraph, our low-level agent orchestration
framework. LangGraph offers customizable architecture, long-term memory, and
human-in-the-loop workflows — and is trusted in production by companies like LinkedIn,
-**Integration packages** (e.g. **`langchain-openai`**, **`langchain-anthropic`**, etc.): Important integrations have been split into lightweight packages that are co-maintained by the LangChain team and the integration developers.
- **`langchain`**: Chains, agents, and retrieval strategies that make up an application's cognitive architecture.
-**`langchain-community`**: Third-party integrations that are community maintained.
- **[LangGraph](https://langchain-ai.github.io/langgraph)**: LangGraph powers production-grade agents, trusted by Linkedin, Uber, Klarna, GitLab, and many more. Build robust and stateful multi-actor applications with LLMs by modeling steps as edges and nodes in a graph. Integrates smoothly with LangChain, but can be used without it. To learn more about LangGraph, check out our first LangChain Academy course, *Introduction to LangGraph*, available [here](https://academy.langchain.com/courses/intro-to-langgraph).
### Productionization:
- **[LangSmith](https://docs.smith.langchain.com/)**: A developer platform that lets you debug, test, evaluate, and monitor chains built on any LLM framework and seamlessly integrates with LangChain.
### Deployment:
- **[LangGraph Platform](https://langchain-ai.github.io/langgraph/cloud/)**: Turn your LangGraph applications into production-ready APIs and Assistants.
- End-to-end Example: [Web LangChain (web researcher chatbot)](https://weblangchain.vercel.app) and [repo](https://github.com/langchain-ai/weblangchain)
And much more! Head to the [Tutorials](https://python.langchain.com/docs/tutorials/) section of the docs for more.
## 🚀 How does LangChain help?
The main value props of the LangChain libraries are:
1.**Components**: composable building blocks, tools and integrations for working with language models. Components are modular and easy-to-use, whether you are using the rest of the LangChain framework or not.
2.**Easy orchestration with LangGraph**: [LangGraph](https://langchain-ai.github.io/langgraph/),
built on top of `langchain-core`, has built-in support for [messages](https://python.langchain.com/docs/concepts/messages/), [tools](https://python.langchain.com/docs/concepts/tools/),
and other LangChain abstractions. This makes it easy to combine components into
production-ready applications with persistence, streaming, and other key features.
Check out the LangChain [tutorials page](https://python.langchain.com/docs/tutorials/#orchestration) for examples.
## Components
Components fall into the following **modules**:
**📃 Model I/O**
This includes [prompt management](https://python.langchain.com/docs/concepts/prompt_templates/)
and a generic interface for [chat models](https://python.langchain.com/docs/concepts/chat_models/), including a consistent interface for [tool-calling](https://python.langchain.com/docs/concepts/tool_calling/) and [structured output](https://python.langchain.com/docs/concepts/structured_outputs/) across model providers.
**📚 Retrieval**
Retrieval Augmented Generation involves [loading data](https://python.langchain.com/docs/concepts/document_loaders/) from a variety of sources, [preparing it](https://python.langchain.com/docs/concepts/text_splitters/), then [searching over (a.k.a. retrieving from)](https://python.langchain.com/docs/concepts/retrievers/) it for use in the generation step.
**🤖 Agents**
Agents allow an LLM autonomy over how a task is accomplished. Agents make decisions about which Actions to take, then take that Action, observe the result, and repeat until the task is complete. [LangGraph](https://langchain-ai.github.io/langgraph/) makes it easy to use
LangChain components to build both [custom](https://langchain-ai.github.io/langgraph/tutorials/)
and [built-in](https://langchain-ai.github.io/langgraph/how-tos/create-react-agent/)
LLM agents.
## 📖 Documentation
Please see [here](https://python.langchain.com) for full documentation, which includes:
- [Introduction](https://python.langchain.com/docs/introduction/): Overview of the framework and the structure of the docs.
- [Tutorials](https://python.langchain.com/docs/tutorials/): If you're looking to build something specific or are more of a hands-on learner, check out our tutorials. This is the best place to get started.
- [How-to guides](https://python.langchain.com/docs/how_to/): Answers to “How do I….?” type questions. These guides are goal-oriented and concrete; they're meant to help you complete a specific task.
- [Conceptual guide](https://python.langchain.com/docs/concepts/): Conceptual explanations of the key parts of the framework.
- [API Reference](https://python.langchain.com/api_reference/): Thorough documentation of every class and method.
## 🌐 Ecosystem
- [🦜🛠️ LangSmith](https://docs.smith.langchain.com/): Trace and evaluate your language model applications and intelligent agents to help you move from prototype to production.
- [🦜🕸️ LangGraph](https://langchain-ai.github.io/langgraph/): Create stateful, multi-actor applications with LLMs. Integrates smoothly with LangChain, but can be used without it.
- [🦜🕸️ LangGraph Platform](https://langchain-ai.github.io/langgraph/concepts/#langgraph-platform): Deploy LLM applications built with LangGraph into production.
## 💁 Contributing
As an open-source project in a rapidly developing field, we are extremely open to contributions, whether it be in the form of a new feature, improved infrastructure, or better documentation.
For detailed information on how to contribute, see [here](https://python.langchain.com/docs/contributing/).
@@ -7,8 +7,8 @@ LangChain has a large ecosystem of integrations with various external resources
When building such applications developers should remember to follow good security practices:
* [**Limit Permissions**](https://en.wikipedia.org/wiki/Principle_of_least_privilege): Scope permissions specifically to the application's need. Granting broad or excessive permissions can introduce significant security vulnerabilities. To avoid such vulnerabilities, consider using read-only credentials, disallowing access to sensitive resources, using sandboxing techniques (such as running inside a container), specifying proxy configurations to control external requests, etc. as appropriate for your application.
* **Anticipate Potential Misuse**: Just as humans can err, so can Large Language Models (LLMs). Always assume that any system access or credentials may be used in any way allowed by the permissions they are assigned. For example, if a pair of database credentials allows deleting data, it’s safest to assume that any LLM able to use those credentials may in fact delete data.
* [**Defense in Depth**](https://en.wikipedia.org/wiki/Defense_in_depth_(computing)): No security technique is perfect. Fine-tuning and good chain design can reduce, but not eliminate, the odds that a Large Language Model (LLM) may make a mistake. It’s best to combine multiple layered security approaches rather than relying on any single layer of defense to ensure security. For example: use both read-only permissions and sandboxing to ensure that LLMs are only able to access data that is explicitly meant for them to use.
* **Anticipate Potential Misuse**: Just as humans can err, so can Large Language Models (LLMs). Always assume that any system access or credentials may be used in any way allowed by the permissions they are assigned. For example, if a pair of database credentials allows deleting data, it's safest to assume that any LLM able to use those credentials may in fact delete data.
* [**Defense in Depth**](https://en.wikipedia.org/wiki/Defense_in_depth_(computing)): No security technique is perfect. Fine-tuning and good chain design can reduce, but not eliminate, the odds that a Large Language Model (LLM) may make a mistake. It's best to combine multiple layered security approaches rather than relying on any single layer of defense to ensure security. For example: use both read-only permissions and sandboxing to ensure that LLMs are only able to access data that is explicitly meant for them to use.
Risks of not doing so include, but are not limited to:
* Data corruption or loss.
@@ -39,7 +39,7 @@ Before reporting a vulnerability, please review:
1) In-Scope Targets and Out-of-Scope Targets below.
2) The [langchain-ai/langchain](https://python.langchain.com/docs/contributing/repo_structure) monorepo structure.
3) The [Best practicies](#best-practices) above to
3) The [Best practices](#best-practices) above to
understand what we consider to be a security vulnerability vs. developer
"Go to the VertexAI Model Garden on Google Cloud [console](https://pantheon.corp.google.com/vertex-ai/publishers/google/model-garden/335), and deploy the desired version of Gemma to VertexAI. It will take a few minutes, and after the endpoint it ready, you need to copy its number."
"Go to the VertexAI Model Garden on Google Cloud [console](https://pantheon.corp.google.com/vertex-ai/publishers/google/model-garden/335), and deploy the desired version of Gemma to VertexAI. It will take a few minutes, and after the endpoint is ready, you need to copy its number."
" description=\"useful for when you need to answer questions about the most recent state of the union address. Input should be a fully formed question.\",\n",
" ),\n",
" Tool(\n",
" name=\"Ruff QA System\",\n",
" name=\"ruff_qa_system\",\n",
" func=ruff.run,\n",
" description=\"useful for when you need to answer questions about ruff (a python linter). Input should be a fully formed question.\",\n",
" ),\n",
@@ -186,94 +192,116 @@
},
{
"cell_type": "code",
"execution_count": 45,
"id": "fc47f230",
"execution_count": 11,
"id": "70c461d8-aaca-4f2a-9a93-bf35841cc615",
"metadata": {},
"outputs": [],
"source": [
"# Construct the agent. We will use the default agent type here.\n",
"# See documentation for a full list of options.\n",
"\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
"\u001b[32;1m\u001b[1;3m I need to find out what Biden said about Ketanji Brown Jackson in the State of the Union address.\n",
"Action: State of Union QA System\n",
"Action Input: What did Biden say about Ketanji Brown Jackson in the State of the Union address?\u001b[0m\n",
"Observation: \u001b[36;1m\u001b[1;3m Biden said that Jackson is one of the nation's top legal minds and that she will continue Justice Breyer's legacy of excellence.\u001b[0m\n",
"Thought:\u001b[32;1m\u001b[1;3m I now know the final answer\n",
"Final Answer: Biden said that Jackson is one of the nation's top legal minds and that she will continue Justice Breyer's legacy of excellence.\u001b[0m\n",
" Biden said that he nominated Ketanji Brown Jackson for the United States Supreme Court and praised her as one of the nation's top legal minds who will continue Justice Breyer's legacy of excellence.\n",
"==================================\u001b[1m Ai Message \u001b[0m==================================\n",
"\n",
"\u001b[1m> Finished chain.\u001b[0m\n"
"In the State of the Union address, Biden said that he nominated Ketanji Brown Jackson for the United States Supreme Court and praised her as one of the nation's top legal minds who will continue Justice Breyer's legacy of excellence.\n"
]
},
{
"data": {
"text/plain": [
"\"Biden said that Jackson is one of the nation's top legal minds and that she will continue Justice Breyer's legacy of excellence.\""
]
},
"execution_count": 46,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"agent.run(\n",
" \"What did biden say about ketanji brown jackson in the state of the union address?\"\n",
")"
"input_message = {\n",
" \"role\": \"user\",\n",
" \"content\": \"What did biden say about ketanji brown jackson in the state of the union address?\",\n",
"}\n",
"\n",
"for step in agent.stream(\n",
" {\"messages\": [input_message]},\n",
" stream_mode=\"values\",\n",
"):\n",
" step[\"messages\"][-1].pretty_print()"
]
},
{
"cell_type": "code",
"execution_count": 47,
"id": "4e91b811",
"execution_count": 13,
"id": "e836b4cd-abf7-49eb-be0e-b9ad501213f3",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"================================\u001b[1m Human Message \u001b[0m=================================\n",
"\n",
"Why use ruff over flake8?\n",
"==================================\u001b[1m Ai Message \u001b[0m==================================\n",
"\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
"\u001b[32;1m\u001b[1;3m I need to find out the advantages of using ruff over flake8\n",
"Action: Ruff QA System\n",
"Action Input: What are the advantages of using ruff over flake8?\u001b[0m\n",
"Observation: \u001b[33;1m\u001b[1;3m Ruff can be used as a drop-in replacement for Flake8 when used (1) without or with a small number of plugins, (2) alongside Black, and (3) on Python 3 code. It also re-implements some of the most popular Flake8 plugins and related code quality tools natively, including isort, yesqa, eradicate, and most of the rules implemented in pyupgrade. Ruff also supports automatically fixing its own lint violations, which Flake8 does not.\u001b[0m\n",
"Thought:\u001b[32;1m\u001b[1;3m I now know the final answer\n",
"Final Answer: Ruff can be used as a drop-in replacement for Flake8 when used (1) without or with a small number of plugins, (2) alongside Black, and (3) on Python 3 code. It also re-implements some of the most popular Flake8 plugins and related code quality tools natively, including isort, yesqa, eradicate, and most of the rules implemented in pyupgrade. Ruff also supports automatically fixing its own lint violations, which Flake8 does not.\u001b[0m\n",
"There are a few reasons why someone might choose to use Ruff over Flake8:\n",
"\n",
"\u001b[1m> Finished chain.\u001b[0m\n"
"1. Larger rule set: Ruff implements over 800 rules, while Flake8 only implements around 200. This means that Ruff can catch more potential issues in your code.\n",
"\n",
"2. Better compatibility with other tools: Ruff is designed to work well with other tools like Black, isort, and type checkers like Mypy. This means that you can use Ruff alongside these tools to get more comprehensive feedback on your code.\n",
"\n",
"3. Automatic fixing of lint violations: Unlike Flake8, Ruff is capable of automatically fixing its own lint violations. This can save you time and effort when fixing issues in your code.\n",
"\n",
"4. Native implementation of popular Flake8 plugins: Ruff re-implements some of the most popular Flake8 plugins natively, which means you don't have to install and configure multiple plugins to get the same functionality.\n",
"\n",
"Overall, Ruff offers a more comprehensive and user-friendly experience compared to Flake8, making it a popular choice for many developers.\n",
"==================================\u001b[1m Ai Message \u001b[0m==================================\n",
"\n",
"You might choose to use Ruff over Flake8 for several reasons:\n",
"\n",
"1. Ruff has a much larger rule set, implementing over 800 rules compared to Flake8's roughly 200, so it can catch more potential issues.\n",
"2. Ruff is designed to work better with other tools like Black, isort, and type checkers like Mypy, providing more comprehensive code feedback.\n",
"3. Ruff can automatically fix its own lint violations, which Flake8 cannot, saving time and effort.\n",
"4. Ruff natively implements some popular Flake8 plugins, so you don't need to install and configure multiple plugins separately.\n",
"\n",
"Overall, Ruff offers a more comprehensive and user-friendly experience compared to Flake8.\n"
]
},
{
"data": {
"text/plain": [
"'Ruff can be used as a drop-in replacement for Flake8 when used (1) without or with a small number of plugins, (2) alongside Black, and (3) on Python 3 code. It also re-implements some of the most popular Flake8 plugins and related code quality tools natively, including isort, yesqa, eradicate, and most of the rules implemented in pyupgrade. Ruff also supports automatically fixing its own lint violations, which Flake8 does not.'"
]
},
"execution_count": 47,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"agent.run(\"Why use ruff over flake8?\")"
"input_message = {\n",
" \"role\": \"user\",\n",
" \"content\": \"Why use ruff over flake8?\",\n",
"}\n",
"\n",
"for step in agent.stream(\n",
" {\"messages\": [input_message]},\n",
" stream_mode=\"values\",\n",
"):\n",
" step[\"messages\"][-1].pretty_print()"
]
},
{
@@ -296,20 +324,20 @@
},
{
"cell_type": "code",
"execution_count": 48,
"execution_count": 14,
"id": "f59b377e",
"metadata": {},
"outputs": [],
"source": [
"tools = [\n",
" Tool(\n",
" name=\"Stateof Union QA System\",\n",
" name=\"state_of_union_qa_system\",\n",
" func=state_of_union.run,\n",
" description=\"useful for when you need to answer questions about the most recent state of the union address. Input should be a fully formed question.\",\n",
" return_direct=True,\n",
" ),\n",
" Tool(\n",
" name=\"Ruff QA System\",\n",
" name=\"ruff_qa_system\",\n",
" func=ruff.run,\n",
" description=\"useful for when you need to answer questions about ruff (a python linter). Input should be a fully formed question.\",\n",
"\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
"\u001b[32;1m\u001b[1;3m I need to find out what Biden said about Ketanji Brown Jackson in the State of the Union address.\n",
"Action: State of Union QA System\n",
"Action Input: What did Biden say about Ketanji Brown Jackson in the State of the Union address?\u001b[0m\n",
"Observation: \u001b[36;1m\u001b[1;3m Biden said that Jackson is one of the nation's top legal minds and that she will continue Justice Breyer's legacy of excellence.\u001b[0m\n",
"\u001b[32;1m\u001b[1;3m\u001b[0m\n",
"\n",
"\u001b[1m> Finished chain.\u001b[0m\n"
" Biden said that he nominated Ketanji Brown Jackson for the United States Supreme Court and praised her as one of the nation's top legal minds who will continue Justice Breyer's legacy of excellence.\n"
]
},
{
"data": {
"text/plain": [
"\" Biden said that Jackson is one of the nation's top legal minds and that she will continue Justice Breyer's legacy of excellence.\""
]
},
"execution_count": 50,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"agent.run(\n",
" \"What did biden say about ketanji brown jackson in the state of the union address?\"\n",
")"
"input_message = {\n",
" \"role\": \"user\",\n",
" \"content\": \"What did biden say about ketanji brown jackson in the state of the union address?\",\n",
"}\n",
"\n",
"for step in agent.stream(\n",
" {\"messages\": [input_message]},\n",
" stream_mode=\"values\",\n",
"):\n",
" step[\"messages\"][-1].pretty_print()"
]
},
{
"cell_type": "code",
"execution_count": 51,
"id": "edfd0a1a",
"execution_count": 17,
"id": "88f08d86-7972-4148-8128-3ac8898ad68a",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"================================\u001b[1m Human Message \u001b[0m=================================\n",
"\n",
"Why use ruff over flake8?\n",
"==================================\u001b[1m Ai Message \u001b[0m==================================\n",
"\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
"\u001b[32;1m\u001b[1;3m I need to find out the advantages of using ruff over flake8\n",
"Action: Ruff QA System\n",
"Action Input: What are the advantages of using ruff over flake8?\u001b[0m\n",
"Observation: \u001b[33;1m\u001b[1;3m Ruff can be used as a drop-in replacement for Flake8 when used (1) without or with a small number of plugins, (2) alongside Black, and (3) on Python 3 code. It also re-implements some of the most popular Flake8 plugins and related code quality tools natively, including isort, yesqa, eradicate, and most of the rules implemented in pyupgrade. Ruff also supports automatically fixing its own lint violations, which Flake8 does not.\u001b[0m\n",
"\u001b[32;1m\u001b[1;3m\u001b[0m\n",
"\n",
"\u001b[1m> Finished chain.\u001b[0m\n"
" Ruff has a larger rule set, supports automatic fixing of lint violations, and does not require the installation of additional plugins. It also has better compatibility with Black and can be used alongside a type checker for more comprehensive code analysis.\n"
]
},
{
"data": {
"text/plain": [
"' Ruff can be used as a drop-in replacement for Flake8 when used (1) without or with a small number of plugins, (2) alongside Black, and (3) on Python 3 code. It also re-implements some of the most popular Flake8 plugins and related code quality tools natively, including isort, yesqa, eradicate, and most of the rules implemented in pyupgrade. Ruff also supports automatically fixing its own lint violations, which Flake8 does not.'"
]
},
"execution_count": 51,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"agent.run(\"Why use ruff over flake8?\")"
"input_message = {\n",
" \"role\": \"user\",\n",
" \"content\": \"Why use ruff over flake8?\",\n",
"}\n",
"\n",
"for step in agent.stream(\n",
" {\"messages\": [input_message]},\n",
" stream_mode=\"values\",\n",
"):\n",
" step[\"messages\"][-1].pretty_print()"
]
},
{
@@ -417,19 +447,19 @@
},
{
"cell_type": "code",
"execution_count": 57,
"execution_count": 18,
"id": "d397a233",
"metadata": {},
"outputs": [],
"source": [
"tools = [\n",
" Tool(\n",
" name=\"Stateof Union QA System\",\n",
" name=\"state_of_union_qa_system\",\n",
" func=state_of_union.run,\n",
" description=\"useful for when you need to answer questions about the most recent state of the union address. Input should be a fully formed question, not referencing any obscure pronouns from the conversation before.\",\n",
" ),\n",
" Tool(\n",
" name=\"Ruff QA System\",\n",
" name=\"ruff_qa_system\",\n",
" func=ruff.run,\n",
" description=\"useful for when you need to answer questions about ruff (a python linter). Input should be a fully formed question, not referencing any obscure pronouns from the conversation before.\",\n",
" ),\n",
@@ -438,60 +468,60 @@
},
{
"cell_type": "code",
"execution_count": 58,
"id": "06157240",
"execution_count": 19,
"id": "41743f29-150d-40ba-aa8e-3a63c32216aa",
"metadata": {},
"outputs": [],
"source": [
"# Construct the agent. We will use the default agent type here.\n",
"# See documentation for a full list of options.\n",
"\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
"\u001b[32;1m\u001b[1;3m I need to find out what tool ruff uses to run over Jupyter Notebooks, and if the president mentioned it in the state of the union.\n",
"Action: Ruff QA System\n",
"Action Input: What tool does ruff use to run over Jupyter Notebooks?\u001b[0m\n",
"Observation: \u001b[33;1m\u001b[1;3m Ruff is integrated into nbQA, a tool for running linters and code formatters over Jupyter Notebooks. After installing ruff and nbqa, you can run Ruff over a notebook like so: > nbqa ruff Untitled.html\u001b[0m\n",
"Thought:\u001b[32;1m\u001b[1;3m I now need to find out if the president mentioned this tool in the state of the union.\n",
"Action: State of Union QA System\n",
"Action Input: Did the president mention nbQA in the state of the union?\u001b[0m\n",
"Observation: \u001b[36;1m\u001b[1;3m No, the president did not mention nbQA in the state of the union.\u001b[0m\n",
"Thought:\u001b[32;1m\u001b[1;3m I now know the final answer.\n",
"Final Answer: No, the president did not mention nbQA in the state of the union.\u001b[0m\n",
" No, the president did not mention the tool that ruff uses to run over Jupyter Notebooks in the state of the union.\n",
"==================================\u001b[1m Ai Message \u001b[0m==================================\n",
"\n",
"\u001b[1m> Finished chain.\u001b[0m\n"
"Ruff does not support source.organizeImports and source.fixAll code actions in Jupyter Notebooks. Additionally, the president did not mention the tool that ruff uses to run over Jupyter Notebooks in the state of the union.\n"
]
},
{
"data": {
"text/plain": [
"'No, the president did not mention nbQA in the state of the union.'"
]
},
"execution_count": 59,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"agent.run(\n",
" \"What tool does ruff use to run over Jupyter Notebooks? Did the president mention that tool in the state of the union?\"\n",
")"
"input_message = {\n",
" \"role\": \"user\",\n",
" \"content\": \"What tool does ruff use to run over Jupyter Notebooks? Did the president mention that tool in the state of the union?\",\n",
..NOTE:: {{objname}} implements the standard :py:class:`Runnable Interface <langchain_core.runnables.base.Runnable>`. 🏃
The :py:class:`Runnable Interface <langchain_core.runnables.base.Runnable>` has additional methods that are available on runnables, such as :py:meth:`with_types <langchain_core.runnables.base.Runnable.with_types>`, :py:meth:`with_retry <langchain_core.runnables.base.Runnable.with_retry>`, :py:meth:`assign <langchain_core.runnables.base.Runnable.assign>`, :py:meth:`bind <langchain_core.runnables.base.Runnable.bind>`, :py:meth:`get_graph <langchain_core.runnables.base.Runnable.get_graph>`, and more.
The :py:class:`Runnable Interface <langchain_core.runnables.base.Runnable>` has additional methods that are available on runnables, such as :py:meth:`with_config <langchain_core.runnables.base.Runnable.with_config>`, :py:meth:`with_types <langchain_core.runnables.base.Runnable.with_types>`, :py:meth:`with_retry <langchain_core.runnables.base.Runnable.with_retry>`, :py:meth:`assign <langchain_core.runnables.base.Runnable.assign>`, :py:meth:`bind <langchain_core.runnables.base.Runnable.bind>`, :py:meth:`get_graph <langchain_core.runnables.base.Runnable.get_graph>`, and more.
..NOTE:: {{objname}} implements the standard :py:class:`Runnable Interface <langchain_core.runnables.base.Runnable>`. 🏃
The :py:class:`Runnable Interface <langchain_core.runnables.base.Runnable>` has additional methods that are available on runnables, such as :py:meth:`with_types <langchain_core.runnables.base.Runnable.with_types>`, :py:meth:`with_retry <langchain_core.runnables.base.Runnable.with_retry>`, :py:meth:`assign <langchain_core.runnables.base.Runnable.assign>`, :py:meth:`bind <langchain_core.runnables.base.Runnable.bind>`, :py:meth:`get_graph <langchain_core.runnables.base.Runnable.get_graph>`, and more.
The :py:class:`Runnable Interface <langchain_core.runnables.base.Runnable>` has additional methods that are available on runnables, such as :py:meth:`with_config <langchain_core.runnables.base.Runnable.with_config>`, :py:meth:`with_types <langchain_core.runnables.base.Runnable.with_types>`, :py:meth:`with_retry <langchain_core.runnables.base.Runnable.with_retry>`, :py:meth:`assign <langchain_core.runnables.base.Runnable.assign>`, :py:meth:`bind <langchain_core.runnables.base.Runnable.bind>`, :py:meth:`get_graph <langchain_core.runnables.base.Runnable.get_graph>`, and more.
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Reference in New Issue
Block a user
Blocking a user prevents them from interacting with repositories, such as opening or commenting on pull requests or issues. Learn more about blocking a user.
