Follow up to https://github.com/langchain-ai/langsmith-sdk/pull/1696,
I've bumped the `langsmith` version where applicable in `uv.lock`.
Type checking problems here because deps have been updated in
`pyproject.toml` and `uv lock` hasn't been run - we should enforce that
in the future - goes with the other dependabot todos :).
## Changes
- `/Makefile` - added extra step to `make format` and `make lint` to
ensure the lint dep-group is installed before running ruff (documented
in issue #30069)
- `/pyproject.toml` - removed ruff exceptions for files that no longer
exist or no longer create formatting/linting errors in ruff
## Testing
**running `make format` on this branch/PR**
<img width="435" alt="image"
src="https://github.com/user-attachments/assets/82751788-f44e-4591-98ed-95ce893ce623"
/>
## Issue
fixes#30069
---------
Co-authored-by: Chester Curme <chester.curme@gmail.com>
### Description:
This PR introduces Google-style docstring linting for the
ModelLaboratory class in libs/langchain/langchain/model_laboratory.py.
It also updates the pyproject.toml file to comply with the latest Ruff
configuration standards (deprecating top-level lint settings in favor of
lint).
### Changes include:
- [x] Added detailed Google-style docstrings to all methods in
ModelLaboratory.
- [x] Updated pyproject.toml to move select and pydocstyle settings
under the [tool.ruff.lint] section.
- [x] Ensured all files pass Ruff linting.
Issue:
Closes#25154
### Dependencies:
No additional dependencies are required for this change.
### Checklist
- [x] Files passes ruff linting.
- [x] Docstrings conform to the Google-style convention.
- [x] pyproject.toml updated to avoid deprecation warnings.
- [x] My PR is ready to review, please review.
1. Move dependencies for running notebooks into monorepo poetry test
deps;
2. Add script to update cassettes for a single notebook;
3. Add cassettes for some how-to guides.
---
To update cassettes for a single notebook, run
`docs/scripts/update_cassettes.sh`. For example:
```
./docs/scripts/update_cassettes.sh docs/docs/how_to/binding.ipynb
```
Requires:
1. monorepo dev and test dependencies installed;
2. env vars required by notebook are set.
Note: How-to guides are not currently run in [scheduled
job](https://github.com/langchain-ai/langchain/actions/workflows/run_notebooks.yml).
Will add cassettes for more how-to guides in subsequent PRs before
adding them to scheduled job.
security scanners can't distinguish monorepo sources from each other.
this will resolve issues for folks trying to use e.g. langchain-core but
getting security issues from experimental flagged!
We need to use a different version of numpy for py3.8 and py3.12 in
pyproject.
And so do projects that use that Python version range and import
langchain.
- **Twitter handle:** _cbornet
## Summary
No new diagnostics (given that the set of enabled rules hasn't changed),
but gains access to our new parser (much faster) and reduced false
positives all around.
- **Description:** [CVE
2024-21503](https://www.cve.org/CVERecord?id=CVE-2024-21503) was
recently identified. The python linter "black" suffers from a potential
Regex-related denial of service attack. Updated version from the
vulnerable 24.2.0 to the patched 24.3.0.
- **Issue:** N/A
- **Dependencies:** The 'black' package in both `langchain` (top-level)
and `templates/python-lint`.
Co-authored-by: Bagatur <22008038+baskaryan@users.noreply.github.com>
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Please title your PR "<package>: <description>", where <package> is
whichever of langchain, community, core, experimental, etc. is being
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Replace this entire comment with:
- **Description:** a description of the change,
- **Issue:** the issue # it fixes if applicable,
- **Dependencies:** any dependencies required for this change,
- **Twitter handle:** we announce bigger features on Twitter. If your PR
gets announced, and you'd like a mention, we'll gladly shout you out!
Please make sure your PR is passing linting and testing before
submitting. Run `make format`, `make lint` and `make test` from the root
of the package you've modified to check this locally.
See contribution guidelines for more information on how to write/run
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If you're adding a new integration, please include:
1. a test for the integration, preferably unit tests that do not rely on
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2. an example notebook showing its use. It lives in
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If no one reviews your PR within a few days, please @-mention one of
@baskaryan, @eyurtsev, @hwchase17.
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Many jupyter notebooks didn't pass linting. List of these files are
presented in the [tool.ruff.lint.per-file-ignores] section of the
pyproject.toml . Addressed these bugs:
- fixed bugs; added missed imports; updated pyproject.toml
Only the `document_loaders/tensorflow_datasets.ipyn`,
`cookbook/gymnasium_agent_simulation.ipynb` are not completely fixed.
I'm not sure about imports.
---------
Co-authored-by: Erick Friis <erick@langchain.dev>
The new ruff version fixed the blocking bugs, and I was able to fairly
easily us to a passing state: ruff fixed some issues on its own, I fixed
a handful by hand, and I added a list of narrowly-targeted exclusions
for files that are currently failing ruff rules that we probably should
look into eventually.
I went pretty lenient on the docs / cookbooks rules, allowing dead code
and such things. Perhaps in the future we may want to tighten the rules
further, but this is already a good set of checks that found real issues
and will prevent them going forward.
Best to review one commit at a time, since two of the commits are 100%
autogenerated changes from running `ruff format`:
- Install and use `ruff format` instead of black for code formatting.
- Output of `ruff format .` in the `langchain` package.
- Use `ruff format` in experimental package.
- Format changes in experimental package by `ruff format`.
- Manual formatting fixes to make `ruff .` pass.
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
Co-authored-by: Lance Martin <lance@langchain.dev>
Co-authored-by: Jacob Lee <jacoblee93@gmail.com>
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Replace this entire comment with:
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- **Issue:** the issue # it fixes (if applicable),
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Issue: closes#9855
* consolidates `from_texts` and `add_texts` functions for pinecone
upsert
* adds two types of batching (one for embeddings and one for index
upsert)
* adds thread pool size when instantiating pinecone index
- [Xorbits
Inference(Xinference)](https://github.com/xorbitsai/inference) is a
powerful and versatile library designed to serve language, speech
recognition, and multimodal models. Xinference supports a variety of
GGML-compatible models including chatglm, whisper, and vicuna, and
utilizes heterogeneous hardware and a distributed architecture for
seamless cross-device and cross-server model deployment.
- This PR integrates Xinference models and Xinference embeddings into
LangChain.
- Dependencies: To install the depenedencies for this integration, run
`pip install "xinference[all]"`
- Example Usage:
To start a local instance of Xinference, run `xinference`.
To deploy Xinference in a distributed cluster, first start an Xinference
supervisor using `xinference-supervisor`:
`xinference-supervisor -H "${supervisor_host}"`
Then, start the Xinference workers using `xinference-worker` on each
server you want to run them on.
`xinference-worker -e "http://${supervisor_host}:9997"`
To use Xinference with LangChain, you also need to launch a model. You
can use command line interface (CLI) to do so. Fo example: `xinference
launch -n vicuna-v1.3 -f ggmlv3 -q q4_0`. This launches a model named
vicuna-v1.3 with `model_format="ggmlv3"` and `quantization="q4_0"`. A
model UID is returned for you to use.
Now you can use Xinference with LangChain:
```python
from langchain.llms import Xinference
llm = Xinference(
server_url="http://0.0.0.0:9997", # suppose the supervisor_host is "0.0.0.0"
model_uid = {model_uid} # model UID returned from launching a model
)
llm(
prompt="Q: where can we visit in the capital of France? A:",
generate_config={"max_tokens": 1024},
)
```
You can also use RESTful client to launch a model:
```python
from xinference.client import RESTfulClient
client = RESTfulClient("http://0.0.0.0:9997")
model_uid = client.launch_model(model_name="vicuna-v1.3", model_size_in_billions=7, quantization="q4_0")
```
The following code block demonstrates how to use Xinference embeddings
with LangChain:
```python
from langchain.embeddings import XinferenceEmbeddings
xinference = XinferenceEmbeddings(
server_url="http://0.0.0.0:9997",
model_uid = model_uid
)
```
```python
query_result = xinference.embed_query("This is a test query")
```
```python
doc_result = xinference.embed_documents(["text A", "text B"])
```
Xinference is still under rapid development. Feel free to [join our
Slack
community](https://xorbitsio.slack.com/join/shared_invite/zt-1z3zsm9ep-87yI9YZ_B79HLB2ccTq4WA)
to get the latest updates!
- Request for review: @hwchase17, @baskaryan
- Twitter handle: https://twitter.com/Xorbitsio
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
## Background
With the addition on email and calendar tools, LangChain is continuing
to complete its functionality to automate business processes.
## Challenge
One of the pieces of business functionality that LangChain currently
doesn't have is the ability to search for flights and travel in order to
book business travel.
## Changes
This PR implements an integration with the
[Amadeus](https://developers.amadeus.com/) travel search API for
LangChain, enabling seamless search for flights with a single
authentication process.
## Who can review?
@hinthornw
## Appendix
@tsolakoua and @minjikarin, I utilized your
[amadeus-python](https://github.com/amadeus4dev/amadeus-python) library
extensively. Given the rising popularity of LangChain and similar AI
frameworks, the convergence of libraries like amadeus-python and tools
like this one is likely. So, I wanted to keep you updated on our
progress.
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
