As of the recent PR at #9043, after some testing we've realised that the
default values were not being used for `api_key` and `api_url`. Besides
that, the default for `api_key` was set to `argilla.apikey`, but since
the default values are intended for people using the Argilla Quickstart
(easy to run and setup), the defaults should be instead `owner.apikey`
if using Argilla 1.11.0 or higher, or `admin.apikey` if using a lower
version of Argilla.
Additionally, we've removed the f-string replacements from the
docstrings.
---------
Co-authored-by: Gabriel Martin <gabriel@argilla.io>
This MR corrects the IndexError arising in prep_prompts method when no
documents are returned from a similarity search.
Fixes#1733
Co-authored-by: Sam Groenjes <sam.groenjes@darkwolfsolutions.com>
Co-authored-by: Bagatur <baskaryan@gmail.com>
### Description:
`ConversationBufferTokenMemory` should have a simple way of returning
the conversation messages as a string.
Previously to complete this, you would only have the option to return
memory as an array through the buffer method and call
`get_buffer_string` by importing it from `langchain.schema`, or use the
`load_memory_variables` method and key into `self.memory_key`.
### Maintainer
@hwchase17
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
Now that we accept any runnable or arbitrary function to evaluate, we
don't always look up the input keys. If an evaluator requires
references, we should try to infer if there's one key present. We only
have delayed validation here but it's better than nothing
- **Description**: [BagelDB](bageldb.ai) a collaborative vector
database. Integrated the bageldb PyPi package with langchain with
related tests and code.
- **Issue**: Not applicable.
- **Dependencies**: `betabageldb` PyPi package.
- **Tag maintainer**: @rlancemartin, @eyurtsev, @baskaryan
- **Twitter handle**: bageldb_ai (https://twitter.com/BagelDB_ai)
We ran `make format`, `make lint` and `make test` locally.
Followed the contribution guideline thoroughly
https://github.com/hwchase17/langchain/blob/master/.github/CONTRIBUTING.md
---------
Co-authored-by: Towhid1 <nurulaktertowhid@gmail.com>
Description: updated BabyAGI examples and experimental to append the
iteration to the result id to fix error storing data to vectorstore.
Issue: 7445
Dependencies: no
Tag maintainer: @eyurtsev
This fix worked for me locally. Happy to take some feedback and iterate
on a better solution. I was considering appending a uuid instead but
didn't want to over complicate the example.
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
Add convenience methods to `ConversationBufferMemory` and
`ConversationBufferWindowMemory` to get buffer either as messages or as
string.
Helps when `return_messages` is set to `True` but you want access to the
messages as a string, and vice versa.
@hwchase17
One use case: Using a `MultiPromptRouter` where `default_chain` is
`ConversationChain`, but destination chains are `LLMChains`. Injecting
chat memory into prompts for destination chains prints a stringified
`List[Messages]` in the prompt, which creates a lot of noise. These
convenience methods allow caller to choose either as needed.
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
Description: Due to some issue on the test, this is a separate PR with
the test for #8502
Tag maintainer: @rlancemartin
---------
Co-authored-by: Lance Martin <lance@langchain.dev>
Co-authored-by: Bagatur <baskaryan@gmail.com>
Current regex only extracts agent's action between '` ``` ``` `', this
commit will extract action between both '` ```json ``` `' and '` ``` ```
`'
This is very similar to #7511
Co-authored-by: zjl <junlinzhou@yzbigdata.com>
## Description
This PR adds the `aembed_query` and `aembed_documents` async methods for
improving the embeddings generation for large documents. The
implementation uses asyncio tasks and gather to achieve concurrency as
there is no bedrock async API in boto3.
### Maintainers
@agola11
@aarora79
### Open questions
To avoid throttling from the Bedrock API, should there be an option to
limit the concurrency of the calls?
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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- DataLoaders / VectorStores / Retrievers: @rlancemartin, @eyurtsev
- Models / Prompts: @hwchase17, @baskaryan
- Memory: @hwchase17
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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>
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- Issue: the issue # it fixes (if applicable),
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Maintainer responsibilities:
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- DataLoaders / VectorStores / Retrievers: @rlancemartin, @eyurtsev
- Models / Prompts: @hwchase17, @baskaryan
- Memory: @hwchase17
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---------
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>
This addresses some issues with introducing the Nebula LLM to LangChain
in this PR:
https://github.com/langchain-ai/langchain/pull/8876
This fixes the following:
- Removes `SYMBLAI` from variable names
- Fixes bug with `Bearer` for the API KEY
Thanks again in advance for your help!
cc: @hwchase17, @baskaryan
---------
Co-authored-by: dvonthenen <david.vonthenen@gmail.com>
### Description
Now, we can pass information like a JWT token using user_context:
```python
self.retriever = AmazonKendraRetriever(index_id=kendraIndexId, user_context={"Token": jwt_token})
```
- [x] `make lint`
- [x] `make format`
- [x] `make test`
Also tested by pip installing in my own project, and it allows access
through the token.
### Maintainers
@rlancemartin, @eyurtsev
### My twitter handle
[girlknowstech](https://twitter.com/girlknowstech)
- Description: The API doc passed to LLM only included the content of
responses but did not include the content of requestBody, causing the
agent to be unable to construct the correct request parameters based on
the requestBody information. Add two lines of code fixed the bug,
- Issue: the issue # it fixes (if applicable),
- Dependencies: any dependencies required for this change,
- Tag maintainer: @hinthornw ,
- 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!
Adds Ollama as an LLM. Ollama can run various open source models locally
e.g. Llama 2 and Vicuna, automatically configuring and GPU-optimizing
them.
@rlancemartin @hwchase17
---------
Co-authored-by: Lance Martin <lance@langchain.dev>
## Description
I am excited to propose an integration with USearch, a lightweight
vector-search engine available for both Python and JavaScript, among
other languages.
## Dependencies
It introduces a new PyPi dependency - `usearch`. I am unsure if it must
be added to the Poetry file, as this would make the PR too clunky.
Please let me know.
## Profiles
- Maintainers: @ashvardanian @davvard
- Twitter handles: @ashvardanian @unum_cloud
---------
Co-authored-by: Davit Vardanyan <78792753+davvard@users.noreply.github.com>
Co-authored-by: Bagatur <baskaryan@gmail.com>
Update to #8528
Newlines and other special characters within markdown code blocks
returned as `action_input` should be handled correctly (in particular,
unescaped `"` => `\"` and `\n` => `\\n`) so they don't break JSON
parsing.
@baskaryan
when e.g. downloading a sitemap with a malformed url (e.g.
"ttp://example.com/index.html" with the h omitted at the beginning of
the url), this will ensure that the sitemap download does not crash, but
just emits a warning. (maybe should be optional with e.g. a
`skip_faulty_urls:bool=True` parameter, but this was the most
straightforward fix)
@rlancemartin, @eyurtsev
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
Added async parsing functions for RetryOutputParser,
RetryWithErrorOutputParser and OutputFixingParser.
The async parse functions call the arun methods of the used LLMChains.
Fix for #7989
---------
Co-authored-by: Benjamin May <benjamin.may94@gmail.com>
- Description: Adds the ChatAnyscale class with llama-2 7b, llama-2 13b,
and llama-2 70b on [Anyscale
Endpoints](https://app.endpoints.anyscale.com/)
- It inherits from ChatOpenAI and requires openai (probably unnecessary
but it made for a quick and easy implementation)
- Inspired by https://github.com/langchain-ai/langchain/pull/8434
(@kylehh and @baskaryan )
## Description
This PR adds Nebula to the available LLMs in LangChain.
Nebula is an LLM focused on conversation understanding and enables users
to extract conversation insights from video, audio, text, and chat-based
conversations. These conversations can occur between any mix of human or
AI participants.
Examples of some questions you could ask Nebula from a given
conversation are:
- What could be the customer’s pain points based on the conversation?
- What sales opportunities can be identified from this conversation?
- What best practices can be derived from this conversation for future
customer interactions?
You can read more about Nebula here:
https://symbl.ai/blog/extract-insights-symbl-ai-generative-ai-recall-ai-meetings/
#### Integration Test
An integration test is added, but it requires network access. Since
Nebula is fully managed like OpenAI, network access is required to
exercise the integration test.
#### Linting
- [x] make lint
- [x] make test (TODO: there seems to be a failure in another
non-related test??? Need to check on this.)
- [x] make format
### Dependencies
No new dependencies were introduced.
### Twitter handle
[@symbldotai](https://twitter.com/symbldotai)
[@dvonthenen](https://twitter.com/dvonthenen)
If you have any questions, please let me know.
cc: @hwchase17, @baskaryan
---------
Co-authored-by: dvonthenen <david.vonthenen@gmail.com>
Co-authored-by: Bagatur <baskaryan@gmail.com>
# What
- fix evaluation parse test
<!-- Thank you for contributing to LangChain!
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- Issue: None
- Dependencies: None
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Long-term, would be better to use the lower-level batch() method(s) but
it may take me a bit longer to clean up. This unblocks in the meantime,
though it may fail when the evaluated chain raises a
`NotImplementedError` for a corresponding async method
This adds support for [Xata](https://xata.io) (data platform based on
Postgres) as a vector store. We have recently added [Xata to
Langchain.js](https://github.com/hwchase17/langchainjs/pull/2125) and
would love to have the equivalent in the Python project as well.
The PR includes integration tests and a Jupyter notebook as docs. Please
let me know if anything else would be needed or helpful.
I have added the xata python SDK as an optional dependency.
## To run the integration tests
You will need to create a DB in xata (see the docs), then run something
like:
```
OPENAI_API_KEY=sk-... XATA_API_KEY=xau_... XATA_DB_URL='https://....xata.sh/db/langchain' poetry run pytest tests/integration_tests/vectorstores/test_xata.py
```
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- Models / Prompts: @hwchase17, @baskaryan
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- Async: @agola11
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---------
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
Co-authored-by: Philip Krauss <35487337+philkra@users.noreply.github.com>
Co-authored-by: Bagatur <baskaryan@gmail.com>
#7469
since 1.29.0, Vertex SDK supports a chat history provided to a codey
chat model.
Co-authored-by: Leonid Kuligin <kuligin@google.com>
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
Hello langchain maintainers,
this PR aims at integrating
[vllm](https://vllm.readthedocs.io/en/latest/#) into langchain. This PR
closes#8729.
This feature clearly depends on `vllm`, but I've seen other models
supported here depend on packages that are not included in the
pyproject.toml (e.g. `gpt4all`, `text-generation`) so I thought it was
the case for this as well.
@hwchase17, @baskaryan
---------
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
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See contribution guidelines for more information on how to write/run
tests, lint, etc:
https://github.com/hwchase17/langchain/blob/master/.github/CONTRIBUTING.md
-->
@hwchase17, @baskaryan
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Co-authored-by: Bagatur <baskaryan@gmail.com>
Co-authored-by: Nuno Campos <nuno@boringbits.io>
- Updated to use newer better function interaction
- Previous version had only one callback
- @hinthornw @hwchase17 Can you look into this
- Shout out to @MultiON_AI @DivGarg9 on twitter
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Co-authored-by: Naman Garg <ngarg3@binghamton.edu>
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
