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.
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.
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.
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:** 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>
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>
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.
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
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>
Add ruff rules PGH: https://docs.astral.sh/ruff/rules/#pygrep-hooks-pgh
Except PGH003 which will be dealt in a dedicated PR.
---------
Co-authored-by: Eugene Yurtsev <eyurtsev@gmail.com>
Co-authored-by: Eugene Yurtsev <eugene@langchain.dev>
**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>
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>
**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:**
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 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 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!
- **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>
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)
```
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!
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>
**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>
# 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 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
**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
# 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>
From function calling to Ollama's [dedicated structured output
feature](https://ollama.com/blog/structured-outputs).
---------
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.
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>
**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>
## 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.
```
```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>
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
Thank you for contributing to LangChain!
- [ ] **PR title**: "community: chinese doc extracting"
- [ ] **PR message**:
- **Description:** add jieba_link_extractor.py for chinese doc
extracting
- **Dependencies:** jieba
- [ ] **Add tests and docs**: If you're adding a new integration, please
include
/doc/doc/integrations/providers/jieba.md
/doc/doc/integrations/vectorstores/jieba_link_extractor.ipynb
/libs/packages.yml
---------
Co-authored-by: Chester Curme <chester.curme@gmail.com>
Groq is retiring `mixtral-8x7b-32768`, which is currently the default
model for ChatGroq, on March 20. Here we emit a warning if the model is
not specified explicitly.
A version 0.3.0 will be released ahead of March 20 that removes the
default altogether.
docs: New integration for LangChain - ads4gpts-langchain
Description: Tools and Toolkit for Agentic integration natively within
LangChain with ADS4GPTs, in order to help applications monetize with
advertising.
Twitter handle: @ads4gpts
Co-authored-by: knitlydevaccount <loom+github@knitly.app>
- **Description: a notebook showing langchain and langraph agents using
the new langchain_tableau tool
- **Twitter handle: @joe_constantin0
---------
Co-authored-by: Joe Constantino <joe@constantino.com>
Co-authored-by: Chester Curme <chester.curme@gmail.com>
- Support thinking blocks in core's `convert_to_openai_messages` (pass
through instead of error)
- Ignore thinking blocks in ChatOpenAI (instead of error)
- Support Anthropic-style image blocks in ChatOpenAI
---
Standard integration tests include a `supports_anthropic_inputs`
property which is currently enabled only for tests on `ChatAnthropic`.
This test enforces compatibility with message histories of the form:
```
- system message
- human message
- AI message with tool calls specified only through `tool_use` content blocks
- human message containing `tool_result` and an additional `text` block
```
It additionally checks support for Anthropic-style image inputs if
`supports_image_inputs` is enabled.
Here we change this test, such that if you enable
`supports_anthropic_inputs`:
- You support AI messages with text and `tool_use` content blocks
- You support Anthropic-style image inputs (if `supports_image_inputs`
is enabled)
- You support thinking content blocks.
That is, we add a test case for thinking content blocks, but we also
remove the requirement of handling tool results within HumanMessages
(motivated by existing agent abstractions, which should all return
ToolMessage). We move that requirement to a ChatAnthropic-specific test.
**Description:**
This PR adds a call to `guard_import()` to fix an AttributeError raised
when creating LanceDB vectorstore instance with an existing LanceDB
table.
**Issue:**
This PR fixes issue #30124.
**Dependencies:**
No additional dependencies.
**Twitter handle:**
[@metadaddy](https://x.com/metadaddy), but I spend more time at
[@metadaddy.net](https://bsky.app/profile/metadaddy.net) these days.
---------
Co-authored-by: Chester Curme <chester.curme@gmail.com>
## Description
make DashScope models support Partial Mode for text continuation.
For text continuation in ChatTongYi, it supports text continuation with
a prefix by adding a "partial" argument in AIMessage. The document is
[Partial Mode
](https://help.aliyun.com/zh/model-studio/user-guide/partial-mode?spm=a2c4g.11186623.help-menu-2400256.d_1_0_0_8.211e5b77KMH5Pn&scm=20140722.H_2862210._.OR_help-T_cn~zh-V_1).
The API example is:
```py
import os
import dashscope
messages = [{
"role": "user",
"content": "请对“春天来了，大地”这句话进行续写，来表达春天的美好和作者的喜悦之情"
},
{
"role": "assistant",
"content": "春天来了，大地",
"partial": True
}]
response = dashscope.Generation.call(
api_key=os.getenv("DASHSCOPE_API_KEY"),
model='qwen-plus',
messages=messages,
result_format='message',
)
print(response.output.choices[0].message.content)
```
---------
Co-authored-by: Chester Curme <chester.curme@gmail.com>
- **Description**: Added the request_id field to the check_response
function to improve request tracking and debugging, applicable for the
Tongyi model.
- **Issue**: None
- **Dependencies**: None
- **Twitter handle**: None
- **Add tests and docs**: None
- **Lint and test**: Ran `make format`, `make lint`, and `make test` to
ensure the code meets formatting and testing requirements.
### **Description**
Converts the boolean `jira_cloud` parameter in the Jira API Wrapper to a
string before initializing the Jira Client. Also adds tests for the
same.
### **Issue**
[Jira API Wrapper
Bug](8abb65e138/libs/community/langchain_community/utilities/jira.py (L47))
```python
jira_cloud_str = get_from_dict_or_env(values, "jira_cloud", "JIRA_CLOUD")
jira_cloud = jira_cloud_str.lower() == "true"
```
The above code has a bug where the value of `"jira_cloud"` is a boolean.
If it is passed, calling `.lower()` on a boolean raises an error.
Additionally, `False` cannot be passed explicitly since
`get_from_dict_or_env` falls back to environment variables.
Relevant code in `langchain_core`:
[Source](https://github.com/thesmallstar/langchain/blob/master/.venv/lib/python3.13/site-packages/langchain_core/utils/env.py#L46)
```python
if isinstance(key, str) and key in data and data[key]: # Here, data[key] is False
```
This PR fixes both issues.
### **Twitter Handle**
[Manthan Surkar](https://x.com/manthan_surkar)
This PR adds documentation for the langchain-taiga Tool integration,
including an example notebook at
'docs/docs/integrations/tools/taiga.ipynb' and updates to
'libs/packages.yml' to track the new package.
Issue:
N/A
Dependencies:
None
Twitter handle:
N/A
---------
Co-authored-by: ccurme <chester.curme@gmail.com>
PR Title:
langchain: add attachments support in OpenAIAssistantRunnable
PR Description:
This PR fixes an issue with the "retrieval" tool (internally named
"file_search") in the OpenAI Assistant by adding support for the
"attachments" parameter in the invoke method. This change allows files
to be linked to messages when they are inserted into threads, which is
essential for utilizing OpenAI's Retrieval Augmented Generation (RAG)
feature.
Issue:
N/A
Dependencies:
None
Twitter handle:
N/A
---------
Co-authored-by: Chester Curme <chester.curme@gmail.com>
- **Description:** Fix typo in code samples for max_tokens_for_prompt.
Code blocks had singular "token" but the method has plural "tokens".
- **Issue:** N/A
- **Dependencies:** N/A
- **Twitter handle:** N/A
**Description:**
5 fix of example from function with_alisteners() in
libs/core/langchain_core/runnables/base.py
Replace incoherent example output with workable example's output.
1. SyntaxError: unterminated string literal
print(f"on start callback starts at {format_t(time.time())}
correct as
print(f"on start callback starts at {format_t(time.time())}")
2. SyntaxError: unterminated string literal
print(f"on end callback starts at {format_t(time.time())}
correct as
print(f"on end callback starts at {format_t(time.time())}")
3. NameError: name 'Runnable' is not defined
Fix as
from langchain_core.runnables import Runnable
4. NameError: name 'asyncio' is not defined
Fix as
import asyncio
5. NameError: name 'format_t' is not defined.
Implement format_t() as
from datetime import datetime, timezone
def format_t(timestamp: float) -> str:
return datetime.fromtimestamp(timestamp, tz=timezone.utc).isoformat()
add batch_size to fix oom when embed large amount texts
Thank you for contributing to LangChain!
- [ ] **PR title**: "package: description"
- Where "package" is whichever of langchain, community, core, etc. is
being modified. Use "docs: ..." for purely docs changes, "infra: ..."
for CI changes.
- Example: "community: add foobar LLM"
- [ ] **PR message**: ***Delete this entire checklist*** and replace
with
- **Description:** a description of the change
- **Issue:** the issue # it fixes, if applicable
- **Dependencies:** any dependencies required for this change
- **Twitter handle:** if your PR gets announced, and you'd like a
mention, we'll gladly shout you out!
- [ ] **Add tests and docs**: If you're adding a new integration, please
include
1. a test for the integration, preferably unit tests that do not rely on
network access,
2. an example notebook showing its use. It lives in
`docs/docs/integrations` directory.
- [ ] **Lint and test**: Run `make format`, `make lint` and `make test`
from the root of the package(s) you've modified. See contribution
guidelines for more: https://python.langchain.com/docs/contributing/
Additional guidelines:
- Make sure optional dependencies are imported within a function.
- Please do not add dependencies to pyproject.toml files (even optional
ones) unless they are required for unit tests.
- Most PRs should not touch more than one package.
- Changes should be backwards compatible.
- If you are adding something to community, do not re-import it in
langchain.
If no one reviews your PR within a few days, please @-mention one of
baskaryan, efriis, eyurtsev, ccurme, vbarda, hwchase17.
Structured output will currently always raise a BadRequestError when
Claude 3.7 Sonnet's `thinking` is enabled, because we rely on forced
tool use for structured output and this feature is not supported when
`thinking` is enabled.
Here we:
- Emit a warning if `with_structured_output` is called when `thinking`
is enabled.
- Raise `OutputParserException` if no tool calls are generated.
This is arguably preferable to raising an error in all cases.
```python
from langchain_anthropic import ChatAnthropic
from pydantic import BaseModel
class Person(BaseModel):
name: str
age: int
llm = ChatAnthropic(
model="claude-3-7-sonnet-latest",
max_tokens=5_000,
thinking={"type": "enabled", "budget_tokens": 2_000},
)
structured_llm = llm.with_structured_output(Person) # <-- this generates a warning
```
```python
structured_llm.invoke("Alice is 30.") # <-- works
```
```python
structured_llm.invoke("Hello!") # <-- raises OutputParserException
```
Took a "census" of models supported by init_chat_model-- of those that
return model names in response metadata, these were the only two that
had it keyed under `"model"` instead of `"model_name"`.
- [ ] **PR title**: [langchain_community.llms.xinference]: Add
asynchronous generate interface
- [ ] **PR message**: The asynchronous generate interface support stream
data and non-stream data.
chain = prompt | llm
async for chunk in chain.astream(input=user_input):
yield chunk
- [ ] **Add tests and docs**:
from langchain_community.llms import Xinference
from langchain.prompts import PromptTemplate
llm = Xinference(
server_url="http://0.0.0.0:9997", # replace your xinference server url
model_uid={model_uid} # replace model_uid with the model UID return from
launching the model
stream = True
)
prompt = PromptTemplate(input=['country'], template="Q: where can we
visit in the capital of {country}? A:")
chain = prompt | llm
async for chunk in chain.astream(input=user_input):
yield chunk
Thank you for contributing to LangChain!
- **Implementing the MMR algorithm for OLAP vector storage**:
- Support Apache Doris and StarRocks OLAP database.
- Example: "vectorstore.as_retriever(search_type="mmr",
search_kwargs={"k": 10})"
- **Implementing the MMR algorithm for OLAP vector storage**:
- **Apache Doris
- **StarRocks
- **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**:
- Example: "vectorstore.as_retriever(search_type="mmr",
search_kwargs={"k": 10})"
- [ ] **Lint and test**: Run `make format`, `make lint` and `make test`
from the root of the package(s) you've modified. See contribution
guidelines for more: https://python.langchain.com/docs/contributing/
Additional guidelines:
- Make sure optional dependencies are imported within a function.
- Please do not add dependencies to pyproject.toml files (even optional
ones) unless they are required for unit tests.
- Most PRs should not touch more than one package.
- Changes should be backwards compatible.
- If you are adding something to community, do not re-import it in
langchain.
If no one reviews your PR within a few days, please @-mention one of
baskaryan, efriis, eyurtsev, ccurme, vbarda, hwchase17.
---------
Co-authored-by: fakzhao <fakzhao@cisco.com>
This pull request includes a change to the `TavilySearchResults` class
in the `tool.py` file, which updates the code block format in the
documentation.
Documentation update:
*
[`libs/community/langchain_community/tools/tavily_search/tool.py`](diffhunk://#diff-e3b6a980979268b639c6a86e9b182756b0f7c7e9e5605e613bc0a72ea6aa5301L54-R59):
Changed the code block format from Python to JSON in the example
provided in the docstring.Thank you for contributing to LangChain!
## **Description:**
When using the Tavily retriever with include_raw_content=True, the
retriever occasionally fails with a Pydantic ValidationError because
raw_content can be None.
The Document model in langchain_core/documents/base.py requires
page_content to be a non-None value, but the Tavily API sometimes
returns None for raw_content.
This PR fixes the issue by ensuring that even when raw_content is None,
an empty string is used instead:
```python
page_content=result.get("content", "")
if not self.include_raw_content
else (result.get("raw_content") or ""),
This pull request includes updates to the
`libs/community/langchain_community/callbacks/bedrock_anthropic_callback.py`
file to add a new model version to the list of supported models.
Updates to supported models:
* Added support for the `anthropic.claude-3-7-sonnet-20250219-v1:0`
model with a rate of `0.003` for 1000 input tokens.
* Added support for the `anthropic.claude-3-7-sonnet-20250219-v1:0`
model with a rate of `0.015` for 1000 output tokens.
AWS Bedrock pricing reference : https://aws.amazon.com/bedrock/pricing
## PyMuPDF4LLM integration to LangChain for PDF content extraction in
Markdown format
### Description
[PyMuPDF4LLM](https://github.com/pymupdf/RAG) makes it easier to extract
PDF content in Markdown format, needed for LLM & RAG applications.
(License: GNU Affero General Public License v3.0)
[langchain-pymupdf4llm](https://github.com/lakinduboteju/langchain-pymupdf4llm)
integrates PyMuPDF4LLM to LangChain as a Document Loader.
(License: MIT License)
This pull request introduces the integration of
[PyMuPDF4LLM](https://pymupdf.readthedocs.io/en/latest/pymupdf4llm) into
the LangChain project as an integration package:
[`langchain-pymupdf4llm`](https://github.com/lakinduboteju/langchain-pymupdf4llm).
The most important changes include adding new Jupyter notebooks to
document the integration and updating the package configuration file to
include the new package.
### Documentation:
* `docs/docs/integrations/providers/pymupdf4llm.ipynb`: Added a new
Jupyter notebook to document the integration of `PyMuPDF4LLM` with
LangChain, including installation instructions and class imports.
* `docs/docs/integrations/document_loaders/pymupdf4llm.ipynb`: Added a
new Jupyter notebook to document the usage of `langchain-pymupdf4llm` as
a LangChain integration package in detail.
### Package registration:
* `libs/packages.yml`: Updated the package configuration file to include
the `langchain-pymupdf4llm` package.
### Additional information
* Related to: https://github.com/langchain-ai/langchain/pull/29848
---------
Co-authored-by: Chester Curme <chester.curme@gmail.com>
- **Description:** Same changes as #26593 but for FileCallbackHandler
- **Issue:** Fixes#29941
- **Dependencies:** None
- **Twitter handle:** None
- [x] **Lint and test**: Run `make format`, `make lint` and `make test`
from the root of the package(s) you've modified. See contribution
guidelines for more: https://python.langchain.com/docs/contributing/
See https://docs.astral.sh/ruff/rules/#flake8-type-checking-tc
Some fixes done for TC001,TC002 and TC003 but these rules are excluded
since they don't play well with Pydantic.
---------
Co-authored-by: Chester Curme <chester.curme@gmail.com>
**Issue**: This trigger can only be used by the first table created.
Cannot create additional triggers for other tables.
**fixed**: Update the trigger name so that it can be used for new
tables.
---------
Co-authored-by: Chester Curme <chester.curme@gmail.com>
**Description:**
Tavily search results returned from API include useful information like
title, score and (optionally) raw_content that is missed in wrapper
although it's documented there properly. Add this data to the result
structure.
---------
Co-authored-by: Chester Curme <chester.curme@gmail.com>
Resolves https://github.com/langchain-ai/langchain/issues/29951
Was able to reproduce the issue with Anthropic installing from pydantic
`main` and correct it with the fix recommended in the issue.
Thanks very much @Viicos for finding the bug and the detailed writeup!
Resolves https://github.com/langchain-ai/langchain/issues/29003,
https://github.com/langchain-ai/langchain/issues/27264
Related: https://github.com/langchain-ai/langchain-redis/issues/52
```python
from langchain.chat_models import init_chat_model
from langchain.globals import set_llm_cache
from langchain_community.cache import SQLiteCache
from pydantic import BaseModel
cache = SQLiteCache()
set_llm_cache(cache)
class Temperature(BaseModel):
value: int
city: str
llm = init_chat_model("openai:gpt-4o-mini")
structured_llm = llm.with_structured_output(Temperature)
```
```python
# 681 ms
response = structured_llm.invoke("What is the average temperature of Rome in May?")
```
```python
# 6.98 ms
response = structured_llm.invoke("What is the average temperature of Rome in May?")
```
Some o-series models will raise a 400 error for `"role": "system"`
(`o1-mini` and `o1-preview` will raise, `o1` and `o3-mini` will not).
Here we update `ChatOpenAI` to update the role to `"developer"` for all
model names matching `^o\d`.
We only make this change on the ChatOpenAI class (not BaseChatOpenAI).
For Context please check #29626
The Deepseek is using langchain_openai. The error happens that it show
`json decode error`.
I added a handler for this to give a more sensible error message which
is DeepSeek API returned empty/invalid json.
Reproducing the issue is a bit challenging as it is inconsistent,
sometimes DeepSeek returns valid data and in other times it returns
invalid data which triggers the JSON Decode Error.
This PR is an exception handling, but not an ultimate fix for the issue.
---------
Co-authored-by: Chester Curme <chester.curme@gmail.com>
**Description:** As commented on the commit
[41b6a86](41b6a86bbe)
it introduced a bug for when we do an embedding request and the model
returns a non-nested list. Typically it's the case for model
**_nomic-embed-text_**.
- I added the unit test, and ran `make format`, `make lint` and `make
test` from the `community` package.
- No new dependency.
---------
Co-authored-by: Chester Curme <chester.curme@gmail.com>
- [x] **PR title**: docs: (community) update ChatLiteLLM
- [x] **PR message**:
- **Description:** updated description of model_kwargs parameter which
was wrongly describing for temperature.
- **Issue:** #29862
- **Dependencies:** N/A
- [x] **Add tests and docs**: N/A
- [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: Chester Curme <chester.curme@gmail.com>
See https://docs.astral.sh/ruff/rules/#flake8-annotations-ann
The interest compared to only mypy is that ruff is very fast at
detecting missing annotations.
ANN101 and ANN102 are deprecated so we ignore them
ANN401 (no Any type) ignored to be in sync with mypy config
---------
Co-authored-by: ccurme <chester.curme@gmail.com>
## Which area of LangChain is being modified?
- This PR adds a new "Permit" integration to the `docs/integrations/`
folder.
- Introduces two new Tools (`LangchainJWTValidationTool` and
`LangchainPermissionsCheckTool`)
- Introduces two new Retrievers (`PermitSelfQueryRetriever` and
`PermitEnsembleRetriever`)
- Adds demo scripts in `examples/` showcasing usage.
## Description of Changes
- Created `langchain_permit/tools.py` for JWT validation and permission
checks with Permit.
- Created `langchain_permit/retrievers.py` for custom Permit-based
retrievers.
- Added documentation in `docs/integrations/providers/permit.ipynb` (or
`.mdx`) to explain setup, usage, and examples.
- Provided sample scripts in `examples/demo_scripts/` to illustrate
usage of these tools and retrievers.
- Ensured all code is linted and tested locally.
Thank you again for reviewing!
---------
Co-authored-by: Chester Curme <chester.curme@gmail.com>
- **Description:**
Since mlx_lm 0.20, all calls to mlx crash due to deprecation of the way
parameters are passed to methods generate and generate_step.
Parameters top_p, temp, repetition_penalty and repetition_context_size
are not passed directly to those method anymore but wrapped into
"sampler" and "logit_processor".
- **Dependencies:** mlx_lm (optional)
- **Tests:**
I've had a new test to existing test file:
tests/integration_tests/llms/test_mlx_pipeline.py
---------
Co-authored-by: Jean-Philippe Dournel <jp@insightkeeper.io>
# community: Fix AttributeError in RankLLMRerank (`list` object has no
attribute `candidates`)
## **Description**
This PR fixes an issue in `RankLLMRerank` where reranking fails with the
following error:
```
AttributeError: 'list' object has no attribute 'candidates'
```
The issue arises because `rerank_batch()` returns a `List[Result]`
instead of an object containing `.candidates`.
### **Changes Introduced**
- Adjusted `compress_documents()` to support both:
- Old API format: `rerank_results.candidates`
- New API format: `rerank_results` as a list
- Also fix wrong .txt location parsing while I was at it.
---
## **Issue**
Fixes **AttributeError** in `RankLLMRerank` when using
`compression_retriever.invoke()`. The issue is observed when
`rerank_batch()` returns a list instead of an object with `.candidates`.
**Relevant log:**
```
AttributeError: 'list' object has no attribute 'candidates'
```
## **Dependencies**
- No additional dependencies introduced.
---
## **Checklist**
- [x] **Backward compatible** with previous API versions
- [x] **Tested** locally with different RankLLM models
- [x] **No new dependencies introduced**
- [x] **Linted** with `make format && make lint`
- [x] **Ready for review**
---
## **Testing**
- Ran `compression_retriever.invoke(query)`
## **Reviewers**
If no review within a few days, please **@mention** one of:
- @baskaryan
- @efriis
- @eyurtsev
- @ccurme
- @vbarda
- @hwchase17
Thank you for contributing to LangChain!
- [ ] **PR title**: "package: description"
- Where "package" is whichever of langchain, community, core, etc. is
being modified. Use "docs: ..." for purely docs changes, "infra: ..."
for CI changes.
- Example: "community: add foobar LLM"
- [ ] **Add tests and docs**: If you're adding a new integration, please
include
1. a test for the integration, preferably unit tests that do not rely on
network access,
2. an example notebook showing its use. It lives in
`docs/docs/integrations` directory.
- [ ] **Lint and test**: Run `make format`, `make lint` and `make test`
from the root of the package(s) you've modified. See contribution
guidelines for more: https://python.langchain.com/docs/contributing/
Additional guidelines:
- Make sure optional dependencies are imported within a function.
- Please do not add dependencies to pyproject.toml files (even optional
ones) unless they are required for unit tests.
- Most PRs should not touch more than one package.
- Changes should be backwards compatible.
- If you are adding something to community, do not re-import it in
langchain.
If no one reviews your PR within a few days, please @-mention one of
baskaryan, efriis, eyurtsev, ccurme, vbarda, hwchase17.
---------
Co-authored-by: Chester Curme <chester.curme@gmail.com>
This PR adds a new cognee integration, knowledge graph based retrieval
enabling developers to ingest documents into cognee’s knowledge graph,
process them, and then retrieve context via CogneeRetriever.
It includes:
- langchain_cognee package with a CogneeRetriever class
- a test for the integration, demonstrating how to create, process, and
retrieve with cognee
- an example notebook showing its use. It lives in
`docs/docs/integrations` directory.
Followed 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.
Thank you for the review!
---------
Co-authored-by: Chester Curme <chester.curme@gmail.com>
**Description:** Two small changes have been proposed here:
(1)
Previous code assumes that every issue has a priority field. If an issue
lacks this field, the code will raise a KeyError.
Now, the code checks if priority exists before accessing it. If priority
is missing, it assigns None instead of crashing. This prevents runtime
errors when processing issues without a priority.
(2)
Also If the "style" field is missing, the code throws a KeyError.
`.get("style", None)` safely retrieves the value if present.
**Issue:** #29875
**Dependencies:** N/A
Thank you for contributing to LangChain!
- [ ] **Handled query records properly**: "community:
vectorstores/kinetica"
- [ ] **Bugfix for empty query results handling**:
- **Description:** checked for the number of records returned by a query
before processing further
- **Issue:** resulted in an `AttributeError` earlier which has now been
fixed
@efriis
This PR adds documentation for the Azure AI package in Langchain to the
main mono-repo
No issue connected or updated dependencies.
Utilises existing tests and makes updates to the docs
---------
Co-authored-by: Chester Curme <chester.curme@gmail.com>
**Description:** Update docstring for `reasoning_effort` argument to
specify that it applies to reasoning models only (e.g., OpenAI o1 and
o3-mini), clarifying its supported models.
**Issue:** None
**Dependencies:** None
Adds a `attachment_filter_func` parameter to the ConfluenceLoader class
which can be used to determine which files are indexed. This is useful
if you are interested in excluding files based on their media type or
other metadata.
https://docs.x.ai/docs/guides/structured-outputs
Interface appears identical to OpenAI's.
```python
from langchain.chat_models import init_chat_model
from pydantic import BaseModel
class Joke(BaseModel):
setup: str
punchline: str
llm = init_chat_model("xai:grok-2").with_structured_output(
Joke, method="json_schema"
)
llm.invoke("Tell me a joke about cats.")
```
- **Description:** add deprecation warning when using weaviate from
langchain_community
- **Issue:** NA
- **Dependencies:** NA
- **Twitter handle:** NA
---------
Signed-off-by: hsm207 <hsm207@users.noreply.github.com>
Co-authored-by: Chester Curme <chester.curme@gmail.com>
Add `model` properties for OpenAIWhisperParser. Defaulted to `whisper-1`
(previous value).
Please help me update the docs and other related components of this
repo.
**Description:**
This PR adds a Jupyter notebook that explains the features,
installation, and usage of the
[`langchain-salesforce`](https://github.com/colesmcintosh/langchain-salesforce)
package. The notebook includes:
- Setup instructions for configuring Salesforce credentials
- Example code demonstrating common operations such as querying,
describing objects, creating, updating, and deleting records
**Issue:**
N/A
**Dependencies:**
No new dependencies are required.
**Tests and Docs:**
- Added an example notebook demonstrating the usage of the
`langchain-salesforce` package, located in `docs/docs/integrations`.
**Lint and Test:**
- Ran `make format`, `make lint`, and `make test` successfully.
---------
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, community, core, etc. is
being modified. Use "docs: ..." for purely docs changes, "infra: ..."
for CI changes.
- Example: "community: add foobar LLM"
- [x] **PR message**:
This PR adds top_k as a param to the Needle Retriever. By default we use
top 10.
- [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, efriis, eyurtsev, ccurme, vbarda, hwchase17.
- ** Description**: I have added a new operator in the operator map with
key `$in` and value `IN`, so that you can define filters using lists as
values. This was already contemplated but as IN operator was not in the
map they cannot be used.
- **Issue**: Fixes#29804.
- **Dependencies**: No extra.
This PR adds documentation for the `langchain-discord-shikenso`
integration, including an example notebook at
`docs/docs/integrations/tools/discord.ipynb` and updates to
`libs/packages.yml` to track the new package.
**Issue:**
N/A
**Dependencies:**
None
**Twitter handle:**
N/A
---------
Co-authored-by: Chester Curme <chester.curme@gmail.com>
- [ ] **PR title**: langchain_community: add image support to
DuckDuckGoSearchAPIWrapper
- **Description:** This PR enhances the DuckDuckGoSearchAPIWrapper
within the langchain_community package by introducing support for image
searches. The enhancement includes:
- Adding a new method _ddgs_images to handle image search queries.
- Updating the run and results methods to process and return image
search results appropriately.
- Modifying the source parameter to accept "images" as a valid option,
alongside "text" and "news".
- **Dependencies:** No additional dependencies are required for this
change.
