- **Description:** Add a new format, `CHUNKS`, to
`langchain_community.document_loaders.youtube.YoutubeLoader` which
creates multiple `Document` objects from YouTube video transcripts
(captions), each of a fixed duration. The metadata of each chunk
`Document` includes the start time of each one and a URL to that time in
the video on the YouTube website.
I had implemented this for UMich (@umich-its-ai) in a local module, but
it makes sense to contribute this to LangChain community for all to
benefit and to simplify maintenance.
- **Issue:** N/A
- **Dependencies:** N/A
- **Twitter:** lsloan_umich
- **Mastodon:**
[lsloan@mastodon.social](https://mastodon.social/@lsloan)
With regards to **tests and documentation**, most existing features of
the `YoutubeLoader` class are not tested. Only the
`YoutubeLoader.extract_video_id()` static method had a test. However,
while I was waiting for this PR to be reviewed and merged, I had time to
add a test for the chunking feature I've proposed in this PR.
I have added an example of using chunking to the
`docs/docs/integrations/document_loaders/youtube_transcript.ipynb`
notebook.
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
This PR add supports for Azure Cosmos DB for NoSQL vector store.
Summary:
Description: added vector store integration for Azure Cosmos DB for
NoSQL Vector Store,
Dependencies: azure-cosmos dependency,
Tag maintainer: @hwchase17, @baskaryan @efriis @eyurtsev
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
Co-authored-by: Bagatur <22008038+baskaryan@users.noreply.github.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
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. -->
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>
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)
This PR adds support for using Databricks Unity Catalog functions as
LangChain tools, which runs inside a Databricks SQL warehouse.
* An example notebook is provided.
Thank you for contributing to LangChain!
- [x] **PR title**: "package: description"
- Where "package" is whichever of langchain, community, core,
experimental, etc. is being modified. Use "docs: ..." for purely docs
changes, "templates: ..." for template changes, "infra: ..." for CI
changes.
- Example: "community: add foobar LLM"
- [x] **PR message**:
- **Description:** This PR corrects the return type in the docstring of
the `docs/api_reference/create_api_rst.py/_load_package_modules`
function. The return type was previously described as a list of
Co-authored-by: suganthsolamanraja <suganth.solamanraja@techjays..com>
More direct entrypoint for a common use-case. Meant to give people a
more hands-on intro to document loaders/loading data from different data
sources as well.
Some duplicate content for RAG and extraction (to show what you can do
with the loaded documents), but defers to the appropriate sections
rather than going too in-depth.
@baskaryan @hwchase17
decisions to discuss
- only chat models
- model_provider isn't based on any existing values like llm-type,
package names, class names
- implemented as function not as a wrapper ChatModel
- function name (init_model)
- in langchain as opposed to community or core
- marked beta
