Thank you for contributing to LangChain!
- [ ] **PR title**: "langchain: Fix chain_filter.py to be compatible
with async"
- [ ] **PR message**:
- **Description:** chain_filter is not compatible with async.
- **Twitter handle:** pprados
- [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/
---------
Signed-off-by: zhangwangda <zhangwangda94@163.com>
Co-authored-by: Prakul <discover.prakul@gmail.com>
Co-authored-by: Lei Zhang <zhanglei@apache.org>
Co-authored-by: Gin <ictgtvt@gmail.com>
Co-authored-by: wangda <38549158+daziz@users.noreply.github.com>
Co-authored-by: Max Mulatz <klappradla@posteo.net>
Thank you for contributing to LangChain!
### Description
Fix the example in the docstring of redis store.
Change the initilization logic and remove redundant check, enhance error
message.
### Issue
The example in docstring of how to use redis store was wrong.
### Dependencies
Nothing
- [ ] **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/
If no one reviews your PR within a few days, please @-mention one of
baskaryan, efriis, eyurtsev, ccurme, vbarda, hwchase17.
---------
Co-authored-by: Eugene Yurtsev <eyurtsev@gmail.com>
- [ ] **Miscellaneous updates and fixes**:
- **Description:** Handled error in querying; quotes in table names;
updated gpudb API
- **Issue:** Threw an error with an error message difficult to
understand if a query failed or returned no records
- **Dependencies:** Updated GPUDB API version to `7.2.0.9`
@baskaryan @hwchase17
- **Description:** allow to use partial variables to pass `top_k` and
`table_info`
- **Issue:** no
- **Dependencies:** no
- **Twitter handle:** @gymnstcs
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
- **Description:** This PR updates the `WandbTracer` to work with the
new RunV2 API so that wandb Traces logging works correctly for new
LangChain versions. Here's an example
[run](https://wandb.ai/parambharat/langchain-tracing/runs/wpm99ftq) from
the existing tests
- **Issue:** https://github.com/wandb/wandb/issues/7762
- **Twitter handle:** @ParamBharat
_If no one reviews your PR within a few days, please @-mention one of
baskaryan, efriis, eyurtsev, ccurme, vbarda, hwchase17._
**Updated ChatHuggingFace doc string as per issue #22296**:
"langchain_huggingface: updated docstring for ChatHuggingFace in
langchain_huggingface to match that of the description (in the appendix)
provided in issue #22296. "
**Issue:** This PR is in response to issue #22296, and more specifically
ChatHuggingFace model. In particular, this PR updates the docstring for
langchain/libs/partners/hugging_face/langchain_huggingface/chat_models/huggingface.py
by adding the following sections: Instantiate, Invoke, Stream, Async,
Tool calling, and Response metadata. I used the template from the
Anthropic implementation and referenced the Appendix of the original
issue post. I also noted that: langchain_community hugging face llms do
not work with langchain_huggingface's ChatHuggingFace model (at least
for me); the .stream(messages) functionality of ChatHuggingFace only
returned a block of response.
---------
Co-authored-by: lucast2021 <lucast2021@headroyce.org>
Co-authored-by: Bagatur <baskaryan@gmail.com>
LLMs struggle with Graph RAG, because it's different from vector RAG in
a way that you don't provide the whole context, only the answer and the
LLM has to believe. However, that doesn't really work a lot of the time.
However, if you wrap the context as function response the accuracy is
much better.
btw... `union[LLMChain, Runnable]` is linting fun, that's why so many
ignores
**Description:** this PR adds Volcengine Rerank capability to Langchain,
you can find Volcengine Rerank API from
[here](https://www.volcengine.com/docs/84313/1254474) &
[here](https://www.volcengine.com/docs/84313/1254605).
[Volcengine](https://www.volcengine.com/) is a cloud service platform
developed by ByteDance, the parent company of TikTok. You can obtain
Volcengine API AK/SK from
[here](https://www.volcengine.com/docs/84313/1254553).
**Dependencies:** VolcengineRerank depends on `volcengine` python
package.
**Twitter handle:** my twitter/x account is https://x.com/LastMonopoly
and I'd like a mention, thank you!
**Tests and docs**
1. integration test: `test_volcengine_rerank.py`
2. example notebook: `volcengine_rerank.ipynb`
**Lint and test**: I have run `make format`, `make lint` and `make test`
from the root of the package I've modified.
Hi 👋
First off, thanks a ton for your work on this 💚 Really appreciate what
you're providing here for the community.
## Description
This PR adds a basic language parser for the
[Elixir](https://elixir-lang.org/) programming language. The parser code
is based upon the approach outlined in
https://github.com/langchain-ai/langchain/pull/13318: it's using
`tree-sitter` under the hood and aligns with all the other `tree-sitter`
based parses added that PR.
The `CHUNK_QUERY` I'm using here is probably not the most sophisticated
one, but it worked for my application. It's a starting point to provide
"core" parsing support for Elixir in LangChain. It enables people to use
the language parser out in real world applications which may then lead
to further tweaking of the queries. I consider this PR just the ground
work.
- **Dependencies:** requires `tree-sitter` and `tree-sitter-languages`
from the extended dependencies
- **Twitter handle:**`@bitcrowd`
## Checklist
- [x] **PR title**: "package: description"
- [x] **Add tests and docs**
- [x] **Lint and test**: Run `make format`, `make lint` and `make test`
from the root of the package(s) you've modified.
<!-- If no one reviews your PR within a few days, please @-mention one
of baskaryan, efriis, eyurtsev, ccurme, vbarda, hwchase17. -->
The fact that we outsourced pgvector to another project has an
unintended effect. The mapping dictionary found by
`_get_builtin_translator()` cannot recognize the new version of pgvector
because it comes from another package.
`SelfQueryRetriever` no longer knows `PGVector`.
I propose to fix this by creating a global dictionary that can be
populated by various database implementations. Thus, importing
`langchain_postgres` will allow the registration of the `PGvector`
mapping.
But for the moment I'm just adding a lazy import
Furthermore, the implementation of _get_builtin_translator()
reconstructs the BUILTIN_TRANSLATORS variable with each invocation,
which is not very efficient. A global map would be an optimization.
- **Twitter handle:** pprados
@eyurtsev, can you review this PR? And unlock the PR [Add async mode for
pgvector](https://github.com/langchain-ai/langchain-postgres/pull/32)
and PR [community[minor]: Add SQL storage
implementation](https://github.com/langchain-ai/langchain/pull/22207)?
Are you in favour of a global dictionary-based implementation of
Translator?
## Description
This PR addresses a logging inconsistency in the `get_user_agent`
function. Previously, the function was using the root logger to log a
warning message when the "USER_AGENT" environment variable was not set.
This bypassed the custom logger `log` that was created at the start of
the module, leading to potential inconsistencies in logging behavior.
Changes:
- Replaced `logging.warning` with `log.warning` in the `get_user_agent`
function to ensure that the custom logger is used.
This change ensures that all logging in the `get_user_agent` function
respects the configurations of the custom logger, leading to more
consistent and predictable logging behavior.
## Dependencies
None
## Issue
None
## Tests and docs
☝🏻 see description
## `make format`, `make lint` & `cd libs/community; make test`
```shell
> make format
poetry run ruff format docs templates cookbook
1417 files left unchanged
poetry run ruff check --select I --fix docs templates cookbook
All checks passed!
```
```shell
> make lint
poetry run ruff check docs templates cookbook
All checks passed!
poetry run ruff format docs templates cookbook --diff
1417 files already formatted
poetry run ruff check --select I docs templates cookbook
All checks passed!
git grep 'from langchain import' docs/docs templates cookbook | grep -vE 'from langchain import (hub)' && exit 1 || exit 0
```
~cd libs/community; make test~ too much dependencies for integration ...
```shell
> poetry run pytest tests/unit_tests
....
==== 884 passed, 466 skipped, 4447 warnings in 15.93s ====
```
I choose you randomly : @ccurme
Adding `UpstashRatelimitHandler` callback for rate limiting based on
number of chain invocations or LLM token usage.
For more details, see [upstash/ratelimit-py
repository](https://github.com/upstash/ratelimit-py) or the notebook
guide included in this PR.
Twitter handle: @cahidarda
---------
Co-authored-by: Eugene Yurtsev <eyurtsev@gmail.com>
- Refactor streaming to use raw events;
- Add `stream_usage` class attribute and kwarg to stream methods that,
if True, will include separate chunks in the stream containing usage
metadata.
There are two ways to implement streaming with anthropic's python sdk.
They have slight differences in how they surface usage metadata.
1. [Use helper
functions](https://github.com/anthropics/anthropic-sdk-python?tab=readme-ov-file#streaming-helpers).
This is what we are doing now.
```python
count = 1
with client.messages.stream(**params) as stream:
for text in stream.text_stream:
snapshot = stream.current_message_snapshot
print(f"{count}: {snapshot.usage} -- {text}")
count = count + 1
final_snapshot = stream.get_final_message()
print(f"{count}: {final_snapshot.usage}")
```
```
1: Usage(input_tokens=8, output_tokens=1) -- Hello
2: Usage(input_tokens=8, output_tokens=1) -- !
3: Usage(input_tokens=8, output_tokens=1) -- How
4: Usage(input_tokens=8, output_tokens=1) -- can
5: Usage(input_tokens=8, output_tokens=1) -- I
6: Usage(input_tokens=8, output_tokens=1) -- assist
7: Usage(input_tokens=8, output_tokens=1) -- you
8: Usage(input_tokens=8, output_tokens=1) -- today
9: Usage(input_tokens=8, output_tokens=1) -- ?
10: Usage(input_tokens=8, output_tokens=12)
```
To do this correctly, we need to emit a new chunk at the end of the
stream containing the usage metadata.
2. [Handle raw
events](https://github.com/anthropics/anthropic-sdk-python?tab=readme-ov-file#streaming-responses)
```python
stream = client.messages.create(**params, stream=True)
count = 1
for event in stream:
print(f"{count}: {event}")
count = count + 1
```
```
1: RawMessageStartEvent(message=Message(id='msg_01Vdyov2kADZTXqSKkfNJXcS', content=[], model='claude-3-haiku-20240307', role='assistant', stop_reason=None, stop_sequence=None, type='message', usage=Usage(input_tokens=8, output_tokens=1)), type='message_start')
2: RawContentBlockStartEvent(content_block=TextBlock(text='', type='text'), index=0, type='content_block_start')
3: RawContentBlockDeltaEvent(delta=TextDelta(text='Hello', type='text_delta'), index=0, type='content_block_delta')
4: RawContentBlockDeltaEvent(delta=TextDelta(text='!', type='text_delta'), index=0, type='content_block_delta')
5: RawContentBlockDeltaEvent(delta=TextDelta(text=' How', type='text_delta'), index=0, type='content_block_delta')
6: RawContentBlockDeltaEvent(delta=TextDelta(text=' can', type='text_delta'), index=0, type='content_block_delta')
7: RawContentBlockDeltaEvent(delta=TextDelta(text=' I', type='text_delta'), index=0, type='content_block_delta')
8: RawContentBlockDeltaEvent(delta=TextDelta(text=' assist', type='text_delta'), index=0, type='content_block_delta')
9: RawContentBlockDeltaEvent(delta=TextDelta(text=' you', type='text_delta'), index=0, type='content_block_delta')
10: RawContentBlockDeltaEvent(delta=TextDelta(text=' today', type='text_delta'), index=0, type='content_block_delta')
11: RawContentBlockDeltaEvent(delta=TextDelta(text='?', type='text_delta'), index=0, type='content_block_delta')
12: RawContentBlockStopEvent(index=0, type='content_block_stop')
13: RawMessageDeltaEvent(delta=Delta(stop_reason='end_turn', stop_sequence=None), type='message_delta', usage=MessageDeltaUsage(output_tokens=12))
14: RawMessageStopEvent(type='message_stop')
```
Here we implement the second option, in part because it should make
things easier when implementing streaming tool calls in the near future.
This would add two new chunks to the stream-- one at the beginning and
one at the end-- with blank content and containing usage metadata. We
add kwargs to the stream methods and a class attribute allowing for this
behavior to be toggled. I enabled it by default. If we merge this we can
add the same kwargs / attribute to OpenAI.
Usage:
```python
from langchain_anthropic import ChatAnthropic
model = ChatAnthropic(
model="claude-3-haiku-20240307",
temperature=0
)
full = None
for chunk in model.stream("hi"):
full = chunk if full is None else full + chunk
print(chunk)
print(f"\nFull: {full}")
```
```
content='' id='run-8a20843f-25c7-4025-ad72-9add395899e3' usage_metadata={'input_tokens': 8, 'output_tokens': 0, 'total_tokens': 8}
content='Hello' id='run-8a20843f-25c7-4025-ad72-9add395899e3'
content='!' id='run-8a20843f-25c7-4025-ad72-9add395899e3'
content=' How' id='run-8a20843f-25c7-4025-ad72-9add395899e3'
content=' can' id='run-8a20843f-25c7-4025-ad72-9add395899e3'
content=' I' id='run-8a20843f-25c7-4025-ad72-9add395899e3'
content=' assist' id='run-8a20843f-25c7-4025-ad72-9add395899e3'
content=' you' id='run-8a20843f-25c7-4025-ad72-9add395899e3'
content=' today' id='run-8a20843f-25c7-4025-ad72-9add395899e3'
content='?' id='run-8a20843f-25c7-4025-ad72-9add395899e3'
content='' id='run-8a20843f-25c7-4025-ad72-9add395899e3' usage_metadata={'input_tokens': 0, 'output_tokens': 12, 'total_tokens': 12}
Full: content='Hello! How can I assist you today?' id='run-8a20843f-25c7-4025-ad72-9add395899e3' usage_metadata={'input_tokens': 8, 'output_tokens': 12, 'total_tokens': 20}
```
They cause `poetry lock` to take a ton of time, and `uv pip install` can
resolve the constraints from these toml files in trivial time
(addressing problem with #19153)
This allows us to properly upgrade lockfile dependencies moving forward,
which revealed some issues that were either fixed or type-ignored (see
file comments)
