The Github utilities are fantastic, so I'm adding support for deeper
interaction with pull requests. Agents should read "regular" comments
and review comments, and the content of PR files (with summarization or
`ctags` abbreviations).
Progress:
- [x] Add functions to read pull requests and the full content of
modified files.
- [x] Function to use Github's built in code / issues search.
Out of scope:
- Smarter summarization of file contents of large pull requests (`tree`
output, or ctags).
- Smarter functions to checkout PRs and edit the files incrementally
before bulk committing all changes.
- Docs example for creating two agents:
- One watches issues: For every new issue, open a PR with your best
attempt at fixing it.
- The other watches PRs: For every new PR && every new comment on a PR,
check the status and try to finish the job.
<!-- Thank you for contributing to LangChain!
Replace this comment with:
- Description: a description of the change,
- Issue: the issue # it fixes (if applicable),
- Dependencies: any dependencies required for this change,
- Tag maintainer: for a quicker response, tag the relevant maintainer
(see below),
- 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 you're PR is passing linting and testing before
submitting. Run `make format`, `make lint` and `make test` to check this
locally.
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.
Maintainer responsibilities:
- General / Misc / if you don't know who to tag: @baskaryan
- DataLoaders / VectorStores / Retrievers: @rlancemartin, @eyurtsev
- Models / Prompts: @hwchase17, @baskaryan
- Memory: @hwchase17
- Agents / Tools / Toolkits: @hinthornw
- Tracing / Callbacks: @agola11
- Async: @agola11
If no one reviews your PR within a few days, feel free to @-mention the
same people again.
See contribution guidelines for more information on how to write/run
tests, lint, etc:
https://github.com/hwchase17/langchain/blob/master/.github/CONTRIBUTING.md
-->
---------
Co-authored-by: Erick Friis <erick@langchain.dev>
Allow users to pass a generic `BaseStore[str, bytes]` to
MultiVectorRetriever, removing the need to use the `create_kv_docstore`
method. This encoding will now happen internally.
@rlancemartin @eyurtsev
---------
Co-authored-by: Eugene Yurtsev <eyurtsev@gmail.com>
**Description:**
When a RunnableLambda only receives a synchronous callback, this
callback is wrapped into an async one since #13408. However, this
wrapping with `(*args, **kwargs)` causes the `accepts_config` check at
[/libs/core/langchain_core/runnables/config.py#L342](ee94ef55ee/libs/core/langchain_core/runnables/config.py (L342))
to fail, as this checks for the presence of a "config" argument in the
method signature.
Adding a `functools.wraps` around it, resolves it.
If we are not going to make the existing Docstore class also implement
`BaseStore[str, Document]`, IMO all base store implementations should
always be `[str, bytes]` so that they are more interchangeable.
CC @rlancemartin @eyurtsev
<!-- Thank you for contributing to LangChain!
Replace this entire comment with:
- **Description:** The existing version hardcoded search.windows.net in
the base url. This is not compatible with the gov cloud. I am allowing
the user to override the default for gov cloud support.,
- **Issue:** N/A, did not write up in an issue,
- **Dependencies:** None
Please make sure your PR is passing linting and testing before
submitting. Run `make format`, `make lint` and `make test` to check this
locally.
See contribution guidelines for more information on how to write/run
tests, lint, etc:
https://github.com/langchain-ai/langchain/blob/master/.github/CONTRIBUTING.md
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/extras`
directory.
If no one reviews your PR within a few days, please @-mention one of
@baskaryan, @eyurtsev, @hwchase17.
-->
---------
Co-authored-by: Nicholas Ceccarelli <nceccarelli2@moog.com>
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
- **Description:** Obsidian templates can include
[variables](https://help.obsidian.md/Plugins/Templates#Template+variables)
using double curly braces. `ObsidianLoader` uses PyYaml to parse the
frontmatter of documents. This parsing throws an error when encountering
variables' curly braces. This is avoided by temporarily substituting
safe strings before parsing.
- **Issue:** #13887
- **Tag maintainer:** @hwchase17
**Description:**
Adds the document loader for [Couchbase](http://couchbase.com/), a
distributed NoSQL database.
**Dependencies:**
Added the Couchbase SDK as an optional dependency.
**Twitter handle:** nithishr
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
- **Description:** Our PR is an integration of a Steam API Tool that
makes recommendations on steam games based on user's Steam profile and
provides information on games based on user provided queries.
- **Issue:** the issue # our PR implements:
https://github.com/langchain-ai/langchain/issues/12120
- **Dependencies:** python-steam-api library, steamspypi library and
decouple library
- **Tag maintainer:** @baskaryan, @hwchase17
- **Twitter handle:** N/A
Hello langchain Maintainers,
We are a team of 4 University of Toronto students contributing to
langchain as part of our course [CSCD01 (link to course
page)](https://cscd01.com/work/open-source-project). We hope our changes
help the community. We have run make format, make lint and make test
locally before submitting the PR. To our knowledge, our changes do not
introduce any new errors.
Our PR integrates the python-steam-api, steamspypi and decouple
packages. We have added integration tests to test our python API
integration into langchain and an example notebook is also provided.
Our amazing team that contributed to this PR: @JohnY2002, @shenceyang,
@andrewqian2001 and @muntaqamahmood
Thank you in advance to all the maintainers for reviewing our PR!
---------
Co-authored-by: Shence <ysc1412799032@163.com>
Co-authored-by: JohnY2002 <johnyuan0526@gmail.com>
Co-authored-by: Andrew Qian <andrewqian2001@gmail.com>
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
Co-authored-by: JohnY <94477598+JohnY2002@users.noreply.github.com>
### Description
Starting from [openai version
1.0.0](17ac677995 (module-level-client)),
the camel case form of `openai.ChatCompletion` is no longer supported
and has been changed to lowercase `openai.chat.completions`. In
addition, the returned object only accepts attribute access instead of
index access:
```python
import openai
# optional; defaults to `os.environ['OPENAI_API_KEY']`
openai.api_key = '...'
# all client options can be configured just like the `OpenAI` instantiation counterpart
openai.base_url = "https://..."
openai.default_headers = {"x-foo": "true"}
completion = openai.chat.completions.create(
model="gpt-4",
messages=[
{
"role": "user",
"content": "How do I output all files in a directory using Python?",
},
],
)
print(completion.choices[0].message.content)
```
So I implemented a compatible adapter that supports both attribute
access and index access:
```python
In [1]: from langchain.adapters import openai as lc_openai
...: messages = [{"role": "user", "content": "hi"}]
In [2]: result = lc_openai.chat.completions.create(
...: messages=messages, model="gpt-3.5-turbo", temperature=0
...: )
In [3]: result.choices[0].message
Out[3]: {'role': 'assistant', 'content': 'Hello! How can I assist you today?'}
In [4]: result["choices"][0]["message"]
Out[4]: {'role': 'assistant', 'content': 'Hello! How can I assist you today?'}
In [5]: result = await lc_openai.chat.completions.acreate(
...: messages=messages, model="gpt-3.5-turbo", temperature=0
...: )
In [6]: result.choices[0].message
Out[6]: {'role': 'assistant', 'content': 'Hello! How can I assist you today?'}
In [7]: result["choices"][0]["message"]
Out[7]: {'role': 'assistant', 'content': 'Hello! How can I assist you today?'}
In [8]: for rs in lc_openai.chat.completions.create(
...: messages=messages, model="gpt-3.5-turbo", temperature=0, stream=True
...: ):
...: print(rs.choices[0].delta)
...: print(rs["choices"][0]["delta"])
...:
{'role': 'assistant', 'content': ''}
{'role': 'assistant', 'content': ''}
{'content': 'Hello'}
{'content': 'Hello'}
{'content': '!'}
{'content': '!'}
In [20]: async for rs in await lc_openai.chat.completions.acreate(
...: messages=messages, model="gpt-3.5-turbo", temperature=0, stream=True
...: ):
...: print(rs.choices[0].delta)
...: print(rs["choices"][0]["delta"])
...:
{'role': 'assistant', 'content': ''}
{'role': 'assistant', 'content': ''}
{'content': 'Hello'}
{'content': 'Hello'}
{'content': '!'}
{'content': '!'}
...
```
### Twitter handle
[lin_bob57617](https://twitter.com/lin_bob57617)
- **Description:** to support not only publicly available Hugging Face
endpoints, but also protected ones (created with "Inference Endpoints"
Hugging Face feature), I have added ability to specify custom api_url.
But if not specified, default behaviour won't change
- **Issue:** #9181,
- **Dependencies:** no extra dependencies
**Description:** The way the condition is checked in the
`return_stopped_response` function of `OpenAIAgent` may not be correct,
when the value returned is `AgentFinish` from the tools it does not work
properly.
Thanks for review, @baskaryan, @eyurtsev, @hwchase17.
- **Description:** Adds `llm_chain_kwargs` to `BaseRetrievalQA.from_llm`
so these can be passed to the LLM at runtime,
- **Issue:** https://github.com/langchain-ai/langchain/issues/14216,
---------
Signed-off-by: ugm2 <unaigaraymaestre@gmail.com>
- **Description:** As part of my conversation with Cerebrium team,
`model_api_request` will be no longer available in cerebrium lib so it
needs to be replaced.
- **Issue:** #12705 12705,
- **Dependencies:** Cerebrium team (agreed)
- **Tag maintainer:** @eyurtsev
- **Twitter handle:** No official Twitter account sorry :D
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
**Description:** Adding a possibility to use asynchronous callback
handler in human-in-the-loop validation tool. Very useful, for example,
if you want to implement a validation over Telegram bot.
**Issue:** -
**Dependencies:** -
---------
Co-authored-by: Daniyar_Supiyev <daniyar_supiyev@epam.com>
Co-authored-by: Bagatur <baskaryan@gmail.com>
- **Description** An integration to allow the Yellowbrick Data Warehouse
to function as a vector store
---------
Co-authored-by: markcusack <markcusack@markcusacksmac.lan>
Co-authored-by: markcusack <markcusack@Mark-Cusack-sMac.local>
- **Description**: This PR addresses an issue with the OpenAI API
streaming response, where initially the key (arguments) is provided but
the value is None. Subsequently, it updates with {"arguments": "{\n"},
leading to a type inconsistency that causes an exception. The specific
error encountered is ValueError: additional_kwargs["arguments"] already
exists in this message, but with a different type. This change aims to
resolve this inconsistency and ensure smooth API interactions.
- **Issue**: None.
- **Dependencies**: None.
- **Tag maintainer**: @eyurtsev
This is an updated version of #13229 based on the refactored code.
Credit goes to @superken01.
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
- **Description:** some vector stores have a flag for try deleting the
collection before creating it (such as ´vectorpg´). This is a useful
flag when prototyping indexing pipelines and also for integration tests.
Added the bool flag `pre_delete_collection ` to the constructor (default
False)
- **Tag maintainer:** @hemidactylus
- **Twitter handle:** nicoloboschi
---------
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
<!-- Thank you for contributing to LangChain!
Replace this entire comment with:
- **Description:** This extends `OpenAIEmbeddings` to add support for
non-`tiktoken` based embeddings, specifically for use with the new
`text-generation-webui` API (`--extensions openai`) which does not
support `tiktoken` encodings, but rather strings
- **Issue:** Not found,
- **Dependencies:** HuggingFace `transformers.AutoTokenizer` is new
dependency for running the model without `tiktoken`
- **Tag maintainer:** @baskaryan based on last commit for
`langchain-core` refactor
- **Twitter handle:** @xychelsea
Modified the tokenization process to be model-agnostic, allowing for
both OpenAI and non-OpenAI model tokenizations, by setting the new
default `bool` flag `tiktoken_enabled` to `False`. This requeires
HuggingFace’s AutoTokenizer and handling tokenization for models
requiring different preprocessing steps to generate a chunked string
request rather than a list of integers.
Updated the embeddings generation process to accommodate non-OpenAI
models. This includes converting tokenized text into embeddings using
OpenAI’s and Hugging Face’s model architectures.
-->
Hi,
I made some code changes on the Hologres vector store to improve the
data insertion performance.
Also, this version of the code uses `hologres-vector` library. This
library is more convenient for us to update, and more efficient in
performance.
The code has passed the format/lint/spell check. I have run the unit
test for Hologres connecting to my own database.
Please check this PR again and tell me if anything needs to change.
Best,
Changgeng,
Developer @ Alibaba Cloud
Co-authored-by: Changgeng Zhao <zhaochanggeng.zcg@alibaba-inc.com>
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
- **Description:** Fixes the Mathpix PDF loader API integration.
Specifically, ensures that Mathpix auth headers are provided for every
request, and ensures that we recognize all errors that can occur during
a request. Also, the option to provide API keys as kwargs never actually
worked before, but now that's fixed too.
- **Issue:** #11249
- **Dependencies:** None
- **Description:**
This PR introduces the Slack toolkit to LangChain, which allows users to
read and write to Slack using the Slack API. Specifically, we've added
the following tools.
1. get_channel: Provides a summary of all the channels in a workspace.
2. get_message: Gets the message history of a channel.
3. send_message: Sends a message to a channel.
4. schedule_message: Sends a message to a channel at a specific time and
date.
- **Issue:** This pull request addresses [Add Slack Toolkit
#11747](https://github.com/langchain-ai/langchain/issues/11747)
- **Dependencies:** package`slack_sdk`
Note: For this toolkit to function you will need to add a Slack app to
your workspace. Additional info can be found
[here](https://slack.com/help/articles/202035138-Add-apps-to-your-Slack-workspace).
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
Co-authored-by: ArianneLavada <ariannelavada@gmail.com>
Co-authored-by: ArianneLavada <84357335+ArianneLavada@users.noreply.github.com>
Co-authored-by: ariannelavada@gmail.com <you@example.com>
Unnecessarily overridden methods:
- Give the idea the subclass is doing something special (when it isn't)
- Block CTRL-click to the actual method
This PR removes some unnecessarily overridden methods in
`StdOutCallbackHandler`
Supercedes https://github.com/langchain-ai/langchain/pull/12858
<!-- Thank you for contributing to LangChain!
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,
- **Tag maintainer:** for a quicker response, tag the relevant
maintainer (see below),
- **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` to check this
locally.
See contribution guidelines for more information on how to write/run
tests, lint, etc:
https://github.com/langchain-ai/langchain/blob/master/.github/CONTRIBUTING.md
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/extras`
directory.
If no one reviews your PR within a few days, please @-mention one of
@baskaryan, @eyurtsev, @hwchase17.
-->
Hi,
There is some unintended behavior in Html2TextTransformer.
The current code is **directly modifying the original documents that are
passed as arguments to the function.**
Therefore, not only the return of the function but also the input
variables are being modified simultaneously.
**To resolve this, I added unit test code as well.**
reference link: [Shallow vs Deep Copying of Python
Objects](https://realpython.com/copying-python-objects/)
Thanks! ☺️
Before, we need to use `params` to pass extra parameters:
```python
from langchain.llms import Databricks
Databricks(..., params={"temperature": 0.0})
```
Now, we can directly specify extra params:
```python
from langchain.llms import Databricks
Databricks(..., temperature=0.0)
```
This PR adds an "Azure AI data" document loader, which allows Azure AI
users to load their registered data assets as a document object in
langchain.
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
See PR title.
From what I can see, `poetry` will auto-include this. Please let me know
if I am missing something here.
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
… properly
Fixed a bug that was causing the streaming transfer to not work
properly.
- **Description:
1、The on_llm_new_token method in the streaming callback can now be
called properly in streaming transfer mode.
2、In streaming transfer mode, LLM can now correctly output the complete
response instead of just the first token.
- **Tag maintainer: @wangxuqi
- **Twitter handle: @kGX7XJjuYxzX9Km
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
* Add support for passing a specific file to the file system blob loader
* Allow specifying a class parameter for the parser for the generic
loader
```python
class AudioLoader(GenericLoader):
@staticmethod
def get_parser(**kwargs):
return MyAudioParser(**kwargs):
```
The intent of the GenericLoader is to provide on-ramps from different
sources (e.g., web, s3, file system).
An alternative is to use pipelining syntax or creating a Pipeline
```
FileSystemBlobLoader(...) | MyAudioParser
```
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
- **Description:** just a little change of ErnieChatBot class
description, sugguesting user to use more suitable class
- **Issue:** none,
- **Dependencies:** none,
- **Tag maintainer:** @baskaryan ,
- **Twitter handle:** none
**Description**
`embed_with_retry` is for sync operations and not for async operations.
Use `async_embed_with_retry` for appropriate async operations.
I'm using `OpenAIEmbedding(http_client=httpx.AsyncClient())` with only
async operations.
However, I got an error when I use `embedding.aembed_documents` because
`embed_with_retry` uses sync OpenAI client with async http client.
