I was initially confused weather to use create_vectorstore_agent or
create_vectorstore_router_agent due to lack of documentation so I
created a simple documentation for each of the function about their
different usecase.
Replace this comment with:
- Description: Added the doc_strings in create_vectorstore_agent and
create_vectorstore_router_agent to point out the difference in their
usecase
- Tag maintainer: @rlancemartin, @eyurtsev
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
Hi @agola11, or whoever is reviewing this PR 😄
## What's in this PR?
As of the latest Argilla release, we'll change and refactor some things
to make some workflows easier, one of those is how everything's pushed
to Argilla, so that now there's no need to call `push_to_argilla` over a
`FeedbackDataset` when either `push_to_argilla` is called for the first
time, or `from_argilla` is called; among others.
We also add some class variables to make sure those are easy to update
in case we update those internally in the future, also to make the
`warnings.warn` message lighter from the code view.
P.S. Regarding the Twitter/X mention feel free to do so at either
https://twitter.com/argilla_io or https://twitter.com/alvarobartt, or
both if applicable, otherwise, just the first Twitter/X handle.
## Description:
This PR adds the Titan Takeoff Server to the available LLMs in
LangChain.
Titan Takeoff is an inference server created by
[TitanML](https://www.titanml.co/) that allows you to deploy large
language models locally on your hardware in a single command. Most
generative model architectures are included, such as Falcon, Llama 2,
GPT2, T5 and many more.
Read more about Titan Takeoff here:
-
[Blog](https://medium.com/@TitanML/introducing-titan-takeoff-6c30e55a8e1e)
- [Docs](https://docs.titanml.co/docs/titan-takeoff/getting-started)
#### Testing
As Titan Takeoff runs locally on port 8000 by default, no network access
is needed. Responses are mocked for testing.
- [x] Make Lint
- [x] Make Format
- [x] Make Test
#### Dependencies
No new dependencies are introduced. However, users will need to install
the titan-iris package in their local environment and start the Titan
Takeoff inferencing server in order to use the Titan Takeoff
integration.
Thanks for your help and please let me know if you have any questions.
cc: @hwchase17 @baskaryan
- Description: Fixes an issue with Metaphor Search Tool throwing when
missing keys in API response.
- Issue: #9048
- Tag maintainer: @hinthornw @hwchase17
- Twitter handle: @pelaseyed
This PR adds the ability to temporarily cache or persistently store
embeddings.
A notebook has been included showing how to set up the cache and how to
use it with a vectorstore.
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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
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If no one reviews your PR within a few days, feel free to @-mention the
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FileCallbackHandler cannot handle some language, for example: Chinese.
Open file using UTF-8 encoding can fix it.
@agola11
**Issue**: #6919
**Dependencies**: NO dependencies,
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
DirectoryLoader can now return a random sample of files in a directory.
Parameters added are:
sample_size
randomize_sample
sample_seed
@rlancemartin, @eyurtsev
---------
Co-authored-by: Andrew Oseen <amovfx@protonmail.com>
Co-authored-by: Bagatur <baskaryan@gmail.com>
- Description: Allow GoogleDriveLoader to handle empty spreadsheets
- Issue: Currently GoogleDriveLoader will crash if it tries to load a
spreadsheet with an empty sheet
- Dependencies: n/a
- Tag maintainer: @rlancemartin, @eyurtsev
This pull request aims to ensure that the `OpenAICallbackHandler` can
properly calculate the total cost for Azure OpenAI chat models. The
following changes have resolved this issue:
- The `model_name` has been added to the ChatResult llm_output. Without
this, the default values of `gpt-35-turbo` were applied. This was
causing the total cost for Azure OpenAI's GPT-4 to be significantly
inaccurate.
- A new parameter `model_version` has been added to `AzureChatOpenAI`.
Azure does not include the model version in the response. With the
addition of `model_name`, this is not a significant issue for GPT-4
models, but it's an issue for GPT-3.5-Turbo. Version 0301 (default) of
GPT-3.5-Turbo on Azure has a flat rate of 0.002 per 1k tokens for both
prompt and completion. However, version 0613 introduced a split in
pricing for prompt and completion tokens.
- The `OpenAICallbackHandler` implementation has been updated with the
proper model names, versions, and cost per 1k tokens.
Unit tests have been added to ensure the functionality works as
expected; the Azure ChatOpenAI notebook has been updated with examples.
Maintainers: @hwchase17, @baskaryan
Twitter handle: @jjczopek
---------
Co-authored-by: Jerzy Czopek <jerzy.czopek@avanade.com>
Co-authored-by: Bagatur <baskaryan@gmail.com>
<!-- 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: jacoblee93 <jacoblee93@gmail.com>
Co-authored-by: Eugene Yurtsev <eyurtsev@gmail.com>
Co-authored-by: Bagatur <baskaryan@gmail.com>
Description: Adds Rockset as a chat history store
Dependencies: no changes
Tag maintainer: @hwchase17
This PR passes linting and testing.
I added a test for the integration and an example notebook showing its
use.
This PR adds 8 new loaders:
* `AirbyteCDKLoader` This reader can wrap and run all python-based
Airbyte source connectors.
* Separate loaders for the most commonly used APIs:
* `AirbyteGongLoader`
* `AirbyteHubspotLoader`
* `AirbyteSalesforceLoader`
* `AirbyteShopifyLoader`
* `AirbyteStripeLoader`
* `AirbyteTypeformLoader`
* `AirbyteZendeskSupportLoader`
## Documentation and getting started
I added the basic shape of the config to the notebooks. This increases
the maintenance effort a bit, but I think it's worth it to make sure
people can get started quickly with these important connectors. This is
also why I linked the spec and the documentation page in the readme as
these two contain all the information to configure a source correctly
(e.g. it won't suggest using oauth if that's avoidable even if the
connector supports it).
## Document generation
The "documents" produced by these loaders won't have a text part
(instead, all the record fields are put into the metadata). If a text is
required by the use case, the caller needs to do custom transformation
suitable for their use case.
## Incremental sync
All loaders support incremental syncs if the underlying streams support
it. By storing the `last_state` from the reader instance away and
passing it in when loading, it will only load updated records.
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
This PR defines an abstract interface for key value stores.
It provides 2 implementations:
1. Local File System
2. In memory -- used to facilitate testing
It also provides an encoder utility to help take care of serialization
from arbitrary data to data that can be stored by the given store
Proposal for an internal API to deprecate LangChain code.
This PR is heavily based on:
https://github.com/matplotlib/matplotlib/blob/main/lib/matplotlib/_api/deprecation.py
This PR only includes deprecation functionality (no renaming etc.).
Additional functionality can be added on a need basis (e.g., renaming
parameters), but best to roll out as an MVP to test this
out.
DeprecationWarnings are ignored by default. We can change the policy for
the deprecation warnings, but we'll need to make sure we're not creating
noise for users due to internal code invoking deprecated functionality.
- Description: consistent timeout at 60s for all calls to Vectara API
- Tag maintainer: @rlancemartin, @eyurtsev
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
Replace this comment with:
- Description: Improved query of BGE embeddings after talking with the
devs of BGE embeddings ,
- Dependencies: any dependencies required for this change,
- Tag maintainer: @hwchase17 ,
- Twitter handle: @ManabChetia3
---------
Co-authored-by: Bagatur <22008038+baskaryan@users.noreply.github.com>
- Description: added filter to query methods in VectorStoreIndexWrapper
for filtering by metadata (i.e. search_kwargs)
- Tag maintainer: @rlancemartin, @eyurtsev
Updated the doc snippet on this topic as well. It took me a long while
to figure out how to filter the vectorstore by filename, so this might
help someone else out.
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
