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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Maintainer responsibilities:
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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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Replace this comment with:
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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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- Tracing / Callbacks: @agola11
- Async: @agola11
If no one reviews your PR within a few days, feel free to @-mention the
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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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- Description: Fix evaluation parse test
- Issue: None
- Dependencies: None
- Tag maintainer: @baskaryan
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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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- Description: a description of the change,
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- DataLoaders / VectorStores / Retrievers: @rlancemartin, @eyurtsev
- Models / Prompts: @hwchase17, @baskaryan
- Memory: @hwchase17
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- Tracing / Callbacks: @agola11
- 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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- Memory: @hwchase17
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If no one reviews your PR within a few days, feel free to @-mention the
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@hwchase17, @baskaryan
---------
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
---------
Co-authored-by: Naman Garg <ngarg3@binghamton.edu>
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
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Description: The lines I have changed looks like incorrectly escaped for
regex. In python 3.11, I receive DeprecationWarning for these lines.
You don't see any warnings unless you explicitly run python with `-W
always::DeprecationWarning` flag. So, this is my attempt to fix it.
Here are the warnings from log files:
```
/usr/local/lib/python3.11/site-packages/langchain/text_splitter.py:919: DeprecationWarning: invalid escape sequence '\s'
/usr/local/lib/python3.11/site-packages/langchain/text_splitter.py:918: DeprecationWarning: invalid escape sequence '\s'
/usr/local/lib/python3.11/site-packages/langchain/text_splitter.py:917: DeprecationWarning: invalid escape sequence '\s'
/usr/local/lib/python3.11/site-packages/langchain/text_splitter.py:916: DeprecationWarning: invalid escape sequence '\c'
/usr/local/lib/python3.11/site-packages/langchain/text_splitter.py:903: DeprecationWarning: invalid escape sequence '\*'
/usr/local/lib/python3.11/site-packages/langchain/text_splitter.py:804: DeprecationWarning: invalid escape sequence '\*'
/usr/local/lib/python3.11/site-packages/langchain/text_splitter.py:804: DeprecationWarning: invalid escape sequence '\*'
```
cc @baskaryan
---------
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
Description: This PR improves the function of recursive_url_loader, such
as limiting the depth of the access, and customizable extractors(from
the raw webpage to the text of the Document object), so that users can
use other tools to extract the webpage. This PR also includes the
document and test for the new loader.
Old PR closed due to project structure change. #7756
Because socket requests are not allowed, the old unit test was removed.
Issue: N/A
Dependencies: asyncio, aiohttp
Tag maintainer: @rlancemartin
Twitter handle: @ Zend_Nihility
---------
Co-authored-by: Lance Martin <lance@langchain.dev>
<!-- Thank you for contributing to LangChain!
Replace this comment with:
- Description: docstore had two main method: add and search, however,
dealing with docstore sometimes requires deleting an entry from
docstore. So I have added a simple delete method that deletes items from
docstore. Additionally, I have added the delete method to faiss
vectorstore for the very same reason.
- Issue: NA
- Dependencies: NA
- Tag maintainer: @rlancemartin, @eyurtsev
- Twitter handle: we announce bigger features on Twitter. If your PR
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Maintainer responsibilities:
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- Models / Prompts: @hwchase17, @baskaryan
- Memory: @hwchase17
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- Tracing / Callbacks: @agola11
- Async: @agola11
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---------
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
Fix Issue #7616 with a simpler approach to extract function names (use
`__name__` attribute)
@hwchase17
---------
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
Fixes for #8786 @agola11
- Description: The flow of callback is breaking till the last chain, as
callbacks are missed in between chain along nested path. This will help
get full trace and correlate parent child relationship in all nested
chains.
- Issue: the issue #8786
- Dependencies: NA
- Tag maintainer: @agola11
- Twitter handle: Agarwal_Ankur
Description: When using a ReAct Agent with tools and no tool is found,
the InvalidTool gets called. Previously it just asked for a different
action, but I've found that if you list the available actions it
improves the chances of getting a valid action in the next round. I've
added a UnitTest for it also.
@hinthornw
# What
- Add missing test for retrievers self_query
- Add missing import validation
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- Description: Add missing test for retrievers self_query
- Issue: None
- Dependencies: None
- Tag maintainer: @rlancemartin, @eyurtsev
- Twitter handle: @MlopsJ
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- Description: we expose Kendra result item id and document id as
document metadata.
- Tag maintainer: @3coins @baskaryan
- Twitter handle: wilsonleao
**Why**
The result item id and document id might be used to keep track of the
retrieved resources.
Added a couple of "integration tests" for these that I ran.
Main design point of feedback: at this point, would it just be better to
have separate arguments for each type? Little confusing what is or isn't
supported and what is the intended usage at this point since I try to
wrap the function as runnable or pack or unpack chains/llms.
```
run_on_dataset(
...
llm_or_chain_factory = None,
llm = None,
chain = NOne,
runnable=None,
function=None
):
# raise error if none set
```
Downside with runnables and arbitrary function support is that you get
much less helpful validation and error messages, but I don't think we
should block you from this, at least.
Description: Adding support for [Amazon
Textract](https://aws.amazon.com/textract/) as a PDF document loader
---------
Co-authored-by: schadem <45048633+schadem@users.noreply.github.com>
Co-authored-by: Bagatur <baskaryan@gmail.com>
Resolves occasional JSON parsing error when some predictions are passed
through a `MultiPromptChain`.
Makes [this
modification](https://github.com/langchain-ai/langchain/issues/5163#issuecomment-1652220401)
to `multi_prompt_prompt.py`, which is much cleaner than appending an
entire example object, which is another community-reported solution.
@hwchase17, @baskaryan
cc: @SimasJan
llamacpp params (per their own code) are unstable, so instead of
adding/deleting them constantly adding a model_kwargs parameter that
allows for arbitrary additional kwargs
cc @jsjolund and @zacps re #8599 and #8704
There is already a `loads()` function which takes a JSON string and
loads it using the Reviver
But in the callbacks system, there is a `serialized` object that is
passed in and that object is already a deserialized JSON-compatible
object. This allows you to call `load(serialized)` and bypass
intermediate JSON encoding.
I found one other place in the code that benefited from this
short-circuiting (string_run_evaluator.py) so I fixed that too.
Tagging @baskaryan for general/utility stuff.
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- DataLoaders / VectorStores / Retrievers: @rlancemartin, @eyurtsev
- Models / Prompts: @hwchase17, @baskaryan
- Memory: @hwchase17
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- Tracing / Callbacks: @agola11
- Async: @agola11
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---------
Co-authored-by: Nuno Campos <nuno@boringbits.io>
Description: Add ScaNN vectorstore to langchain.
ScaNN is a Open Source, high performance vector similarity library
optimized for AVX2-enabled CPUs.
https://github.com/google-research/google-research/tree/master/scann
- Dependencies: scann
Python notebook to illustrate the usage:
docs/extras/integrations/vectorstores/scann.ipynb
Integration test:
libs/langchain/tests/integration_tests/vectorstores/test_scann.py
@rlancemartin, @eyurtsev for review.
Thanks!
This PR updates _load_reduce_documents_chain to handle
`reduce_documents_chain` and `combine_documents_chain` config
Please review @hwchase17, @baskaryan
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
# What
- This is to add filter option to sklearn vectore store functions
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- Description: Add filter to sklearn vectore store functions.
- Issue: None
- Dependencies: None
- Tag maintainer: @rlancemartin, @eyurtsev
- Twitter handle: @MlopsJ
If you're adding a new integration, please include:
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- Memory: @hwchase17
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- Async: @agola11
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same people again.
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---------
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
This is to add save_local and load_local to tfidf_vectorizer and docs in
tfidf_retriever to make the vectorizer reusable.
<!-- Thank you for contributing to LangChain!
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- Description: add save_local and load_local to tfidf_vectorizer and
docs in tfidf_retriever
- Issue: None
- Dependencies: None
- Tag maintainer: @rlancemartin, @eyurtsev
- Twitter handle: @MlopsJ
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- Async: @agola11
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---------
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
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Removing score threshold parameter of faiss
_similarity_search_with_relevance_scores as the thresholding part is
implemented in similarity_search_with_relevance_scores method which
calls this method.
As this method is supposed to be a private method of faiss.py this will
never receive the score threshold parameter as it is popped in the super
method similarity_search_with_relevance_scores.
@baskaryan @hwchase17
Just a tiny change to use `list.append(...)` and `list.extend(...)`
instead of `list += [...]` so that no unnecessary temporary lists are
created.
Since its a tiny miscellaneous thing I guess @baskaryan is the
maintainer to tag?
---------
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
Simple retriever that applies an LLM between the user input and the
query pass the to retriever.
It can be used to pre-process the user input in any way.
The default prompt:
```
DEFAULT_QUERY_PROMPT = PromptTemplate(
input_variables=["question"],
template="""You are an assistant tasked with taking a natural languge query from a user
and converting it into a query for a vectorstore. In this process, you strip out
information that is not relevant for the retrieval task. Here is the user query: {question} """
)
```
---------
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
- Description:
- Provides a new attribute in the AmazonKendraRetriever which processes
a ResultItem and returns a string that will be used as page_content;
- The excerpt metadata should not be changed, it will be kept as was
retrieved. But it is cleaned when composing the page_content;
- Refactors the AmazonKendraRetriever to improve code reusability;
- Issue: #7787
- Tag maintainer: @3coins @baskaryan
- Twitter handle: wilsonleao
**Why?**
Some use cases need to adjust the page_content by dynamically combining
the ResultItem attributes depending on the context of the item.
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- Description: a description of the change,
- Issue: the issue # it fixes (if applicable),
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(see below),
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- 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
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-->
#7854
Added the ability to use the `separator` ase a regex or a simple
character.
Fixed a bug where `start_index` was incorrectly counting from -1.
Who can review?
@eyurtsev
@hwchase17
@mmz-001
When using AzureChatOpenAI the openai_api_type defaults to "azure". The
utils' get_from_dict_or_env() function triggered by the root validator
does not look for user provided values from environment variables
OPENAI_API_TYPE, so other values like "azure_ad" are replaced with
"azure". This does not allow the use of token-based auth.
By removing the "default" value, this allows environment variables to be
pulled at runtime for the openai_api_type and thus enables the other
api_types which are expected to work.
This fixes#6650
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
This lets you pass callbacks when you create the summarize chain:
```
summarize = load_summarize_chain(llm, chain_type="map_reduce", callbacks=[my_callbacks])
summary = summarize(documents)
```
See #5572 for a similar surgical fix.
tagging @hwchase17 for callbacks work
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This is another case, similar to #5572 and #7565 where the callbacks are
getting dropped during construction of the chains.
tagging @hwchase17 and @agola11 for callbacks propagation
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-->
Description: I have added two methods serializer and deserializer
methods. There was method called save local but it saves the to the
local disk. I wanted the vectorstore in the format using which i can
push it to the sql database's blob field. I have used this while i was
working on something
@rlancemartin, @eyurtsev
---------
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
It fails currently because the event loop is already running.
The `retry` decorator alraedy infers an `AsyncRetrying` handler for
coroutines (see [tenacity
line](aa6f8f0a24/tenacity/__init__.py (L535)))
However before_sleep always gets called synchronously (see [tenacity
line](aa6f8f0a24/tenacity/__init__.py (L338))).
Instead, check for a running loop and use that it exists. Of course,
it's running an async method synchronously which is not _nice_. Given
how important LLMs are, it may make sense to have a task list or
something but I'd want to chat with @nfcampos on where that would live.
This PR also fixes the unit tests to check the handler is called and to
make sure the async test is run (it looks like it's just been being
skipped). It would have failed prior to the proposed fixes but passes
now.
Replace this comment with:
- Description: added a document loader for a list of RSS feeds or OPML.
It iterates through the list and uses NewsURLLoader to load each
article.
- Issue: N/A
- Dependencies: feedparser, listparser
- Tag maintainer: @rlancemartin, @eyurtsev
- Twitter handle: @ruze
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
Solves #8644
This embedding models output identical random embedding vectors, given
the input texts are identical.
Useful when used in unittest.
@baskaryan
## Description:
1)Map reduce example in docs is missing an important import statement.
Figured other people would benefit from being able to copy 🍝 the code.
2)RefineDocumentsChain example also broken.
## Issue:
None
## Dependencies:
None. One liner.
## Tag maintainer:
@baskaryan
## Twitter handle:
I mean, it's a one line fix lol. But @will_thompson_k is my twitter
handle.
This small PR introduces new parameters into Qdrant (`on_disk`), fixes
some tests and changes the error message to be more clear.
Tagging: @baskaryan, @rlancemartin, @eyurtsev
- Description: run the poetry dependencies
- Issue: #7329
- Dependencies: any dependencies required for this change,
- Tag maintainer: @rlancemartin
---------
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
Co-authored-by: Bagatur <baskaryan@gmail.com>
### Description
OpenSearch supports validation using both Master Credentials (Username
and password) and IAM. For Master Credentials users will not pass the
argument `service` in `http_auth` and the existing code will break. To
fix this, I have updated the condition to check if service attribute is
present in http_auth before accessing it.
### Maintainers
@baskaryan @navneet1v
Signed-off-by: Naveen Tatikonda <navtat@amazon.com>
Description - Integrates Fireworks within Langchain LLMs to allow users
to use Fireworks models with Langchain, mainly for summarization.
Issue - Not applicable
Dependencies - None
Tag maintainer - @rlancemartin
---------
Co-authored-by: Raj Janardhan <rajjanardhan@Rajs-Laptop.attlocal.net>
Existing implementation requires that you install `firebase-admin`
package, and prevents you from using an existing Firestore client
instance if available.
This adds optional `firestore_client` param to
`FirestoreChatMessageHistory`, so users can just use their existing
client/settings. If not passed, existing logic executes to initialize a
`firestore_client`.
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
Add a StreamlitChatMessageHistory class that stores chat messages in
[Streamlit's Session
State](https://docs.streamlit.io/library/api-reference/session-state).
Note: The integration test uses a currently-experimental Streamlit
testing framework to simulate the execution of a Streamlit app. Marking
this PR as draft until I confirm with the Streamlit team that we're
comfortable supporting it.
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
- Description: added memgraph_graph.py which defines the MemgraphGraph
class, subclassing off the existing Neo4jGraph class. This lets you
query the Memgraph graph database using natural language. It leverages
the Neo4j drivers and the bolt protocol.
- Dependencies: since it is a subclass off of Neo4jGraph, it is
dependent on it and the GraphCypherQA Chain implementations. It is
dependent on the Neo4j drivers being present. It is dependent on having
a running Memgraph instance to connect to.
- Tag maintainer: @baskaryan
- Twitter handle: @villageideate
- example usage can be seen in this repo
https://github.com/brettdbrewer/MemgraphGraph/
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
## Description
This PR implements a callback handler for SageMaker Experiments which is
similar to that of mlflow.
* When creating the callback handler, it takes the experiment's run
object as an argument. All the callback outputs are then logged to the
run object.
* The output of each callback action (e.g., `on_llm_start`) is saved to
S3 bucket as json file.
* Optionally, you can also log additional information such as the LLM
hyper-parameters to the same run object.
* Once the callback object is no more needed, you will need to call the
`flush_tracker()` method. This makes sure that any intermediate files
are deleted.
* A separate notebook example is provided to show how the callback is
used.
@3coins @agola11
---------
Co-authored-by: Tesfagabir Meharizghi <mehariz@amazon.com>
Description: Made Chroma constructor more robust when client_settings is
provided. Otherwise, existing embeddings will not be loaded correctly
from Chroma.
Issue: #7804
Dependencies: None
Tag maintainer: @rlancemartin, @eyurtsev
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
Description:
This PR adds support for loading documents from Huawei OBS (Object
Storage Service) in Langchain. OBS is a cloud-based object storage
service provided by Huawei Cloud. With this enhancement, Langchain users
can now easily access and load documents stored in Huawei OBS directly
into the system.
Key Changes:
- Added a new document loader module specifically for Huawei OBS
integration.
- Implemented the necessary logic to authenticate and connect to Huawei
OBS using access credentials.
- Enabled the loading of individual documents from a specified bucket
and object key in Huawei OBS.
- Provided the option to specify custom authentication information or
obtain security tokens from Huawei Cloud ECS for easy access.
How to Test:
1. Ensure the required package "esdk-obs-python" is installed.
2. Configure the endpoint, access key, secret key, and bucket details
for Huawei OBS in the Langchain settings.
3. Load documents from Huawei OBS using the updated document loader
module.
4. Verify that documents are successfully retrieved and loaded into
Langchain for further processing.
Please review this PR and let us know if any further improvements are
needed. Your feedback is highly appreciated!
@rlancemartin, @eyurtsev
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
- allow overriding run_type in on_chain_start
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from my understanding, the `check_repeated_memory_variable` validator
will raise an error if any of the variables in the `memories` list are
repeated. However, the `load_memory_variables` method does not check for
repeated variables. This means that it is possible for the
`CombinedMemory` instance to return a dictionary of memory variables
that contains duplicate values. This code will check for repeated
variables in the `data` dictionary returned by the
`load_memory_variables` method of each sub-memory. If a repeated
variable is found, an error will be raised.
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---------
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
…call, it needs retry
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-->
Co-authored-by: yangdihang <yangdihang@bytedance.com>
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
Works just like the GenericLoader but concurrently for those who choose
to optimize their workflow.
@rlancemartin @eyurtsev
---------
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
Description: Using Azure Cognitive Search as a VectorStore. Calling the
`add_texts` method throws an error if there is no metadata property
specified. The `additional_fields` field is set in an `if` statement and
then is used later outside the if statement. This PR just moves the
declaration of `additional_fields` below and puts the usage of it in
context.
Issue: https://github.com/langchain-ai/langchain/issues/8544
Tagging @rlancemartin, @eyurtsev as this is related to Vector stores.
`make format`, `make lint`, `make spellcheck`, and `make test` have been
run
- Description: This pull request (PR) includes two minor changes:
1. Updated the default prompt for SQL Query Checker: The current prompt
does not clearly specify the final response that the LLM (Language
Model) should provide when checking for the query if `use_query_checker`
is enabled in SQLDatabase Chain. As a result, the LLM adds extra words
like "Here is your updated query" to the response. However, this causes
a syntax error when executing the SQL command in SQLDatabaseChain, as
these additional words are also included in the SQL query.
2. Moved the query's execution part into a separate method for
SQLDatabase: The purpose of this change is to provide users with more
flexibility when obtaining the result of an SQL query in the original
form returned by sqlalchemy. In the previous implementation, the run
method returned the results as a string. By creating a distinct method
for execution, users can now receive the results in original format,
which proves helpful in various scenarios. For example, during the
development of a tool, I found it advantageous to obtain results in
original format rather than a string, as currently done by the run
method.
- Tag maintainer: @hinthornw
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
This PR makes minor improvements to our python notebook, and adds
support for `Rockset` workspaces in our vectorstore client.
@rlancemartin, @eyurtsev
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
**Description: a description of the change**
In this pull request, GitLoader has been updated to handle multiple load
calls, provided the same repository is being cloned. Previously, calling
`load` multiple times would raise an error if a clone URL was provided.
Additionally, a check has been added to raise a ValueError when
attempting to clone a different repository into an existing path.
New tests have also been introduced to verify the correct behavior of
the GitLoader class when `load` is called multiple times.
Lastly, the GitPython package, a dependency for the GitLoader class, has
been added to the project dependencies (pyproject.toml and poetry.lock).
**Issue: the issue # it fixes (if applicable)**
None
**Dependencies: any dependencies required for this change**
GitPython
**Tag maintainer: for a quicker response, tag the relevant maintainer
(see below)**
- DataLoaders / VectorStores / Retrievers: @rlancemartin, @eyurtsev
## Description
The imports for `NeptuneOpenCypherQAChain` are failing. This PR adds the
chain class to the `__init__.py` file to fix this issue.
## Maintainers
@dev2049
@krlawrence
### Description
In the LangChain Documentation and Comments, I've Noticed that `pip
install faiss` was mentioned, instead of `pip install faiss-gpu`, since
installing `pip install faiss` results in an error. I've gone ahead and
updated the Documentation, and `faiss.ipynb`. This Change will ensure
ease of use for the end user, trying to install `faiss-gpu`.
### Issue:
Documentation / Comments Related.
### Dependencies:
No Dependencies we're changed only updated the files with the wrong
reference.
### Tag maintainer:
@rlancemartin, @eyurtsev (Thank You for your contributions 😄 )
# What
- add test to ensure values in time weighted retriever are updated
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- Description: add test to ensure values in time weighted retriever are
updated
- Issue: None
- Dependencies: None
- Tag maintainer: @rlancemartin, @eyurtsev
- Twitter handle: @MlopsJ
Please make sure you're PR is passing linting and testing before
submitting. Run `make format`, `make lint` and `make test` to check this
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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
- 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
-->
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- Memory: @hwchase17
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- 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: Harrison Chase <hw.chase.17@gmail.com>
- Make _arun optional
- Pass run_manager to inner chains in tools that have them
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- DataLoaders / VectorStores / Retrievers: @rlancemartin, @eyurtsev
- Models / Prompts: @hwchase17, @baskaryan
- Memory: @hwchase17
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- Async: @agola11
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-->
**Description:**
Add support for Meilisearch vector store.
Resolve#7603
- No external dependencies added
- A notebook has been added
@rlancemartin
https://twitter.com/meilisearch
Co-authored-by: Bagatur <baskaryan@gmail.com>
# PromptTemplate
* Update documentation to highlight the classmethod for instantiating a
prompt template.
* Expand kwargs in the classmethod to make parameters easier to discover
This PR got reverted here:
https://github.com/langchain-ai/langchain/pull/8395/files
* Expands support for a variety of message formats in the
`from_messages` classmethod. Ideally, we could deprecate the other
on-ramps to reduce the amount of classmethods users need to know about.
* Expand documentation with code examples.
- Description: Minimax is a great AI startup from China, recently they
released their latest model and chat API, and the API is widely-spread
in China. As a result, I'd like to add the Minimax llm model to
Langchain.
- Tag maintainer: @hwchase17, @baskaryan
---------
Co-authored-by: the <tao.he@hulu.com>
Co-authored-by: Bagatur <baskaryan@gmail.com>
Micro convenience PR to avoid warning regarding missing `client`
parameter. It is always set during initialization.
@baskaryan
Co-authored-by: Bagatur <baskaryan@gmail.com>
- [Xorbits
Inference(Xinference)](https://github.com/xorbitsai/inference) is a
powerful and versatile library designed to serve language, speech
recognition, and multimodal models. Xinference supports a variety of
GGML-compatible models including chatglm, whisper, and vicuna, and
utilizes heterogeneous hardware and a distributed architecture for
seamless cross-device and cross-server model deployment.
- This PR integrates Xinference models and Xinference embeddings into
LangChain.
- Dependencies: To install the depenedencies for this integration, run
`pip install "xinference[all]"`
- Example Usage:
To start a local instance of Xinference, run `xinference`.
To deploy Xinference in a distributed cluster, first start an Xinference
supervisor using `xinference-supervisor`:
`xinference-supervisor -H "${supervisor_host}"`
Then, start the Xinference workers using `xinference-worker` on each
server you want to run them on.
`xinference-worker -e "http://${supervisor_host}:9997"`
To use Xinference with LangChain, you also need to launch a model. You
can use command line interface (CLI) to do so. Fo example: `xinference
launch -n vicuna-v1.3 -f ggmlv3 -q q4_0`. This launches a model named
vicuna-v1.3 with `model_format="ggmlv3"` and `quantization="q4_0"`. A
model UID is returned for you to use.
Now you can use Xinference with LangChain:
```python
from langchain.llms import Xinference
llm = Xinference(
server_url="http://0.0.0.0:9997", # suppose the supervisor_host is "0.0.0.0"
model_uid = {model_uid} # model UID returned from launching a model
)
llm(
prompt="Q: where can we visit in the capital of France? A:",
generate_config={"max_tokens": 1024},
)
```
You can also use RESTful client to launch a model:
```python
from xinference.client import RESTfulClient
client = RESTfulClient("http://0.0.0.0:9997")
model_uid = client.launch_model(model_name="vicuna-v1.3", model_size_in_billions=7, quantization="q4_0")
```
The following code block demonstrates how to use Xinference embeddings
with LangChain:
```python
from langchain.embeddings import XinferenceEmbeddings
xinference = XinferenceEmbeddings(
server_url="http://0.0.0.0:9997",
model_uid = model_uid
)
```
```python
query_result = xinference.embed_query("This is a test query")
```
```python
doc_result = xinference.embed_documents(["text A", "text B"])
```
Xinference is still under rapid development. Feel free to [join our
Slack
community](https://xorbitsio.slack.com/join/shared_invite/zt-1z3zsm9ep-87yI9YZ_B79HLB2ccTq4WA)
to get the latest updates!
- Request for review: @hwchase17, @baskaryan
- Twitter handle: https://twitter.com/Xorbitsio
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
Added a new tool to the Github toolkit called **Create Pull Request.**
Now we can make our own langchain contributor in langchain 😁
In order to have somewhere to pull from, I also added a new env var,
"GITHUB_BASE_BRANCH." This will allow the existing env var,
"GITHUB_BRANCH," to be a working branch for the bot (so that it doesn't
have to always commit on the main/master). For example, if you want the
bot to work in a branch called `bot_dev` and your repo base is `main`,
you would set up the vars like:
```
GITHUB_BASE_BRANCH = "main"
GITHUB_BRANCH = "bot_dev"
```
Maintainer responsibilities:
- Agents / Tools / Toolkits: @hinthornw
# PromptTemplate
* Update documentation to highlight the classmethod for instantiating a
prompt template.
* Expand kwargs in the classmethod to make parameters easier to discover
In this PR:
- Removed restricted model loading logic for Petals-Bloom
- Removed petals imports (DistributedBloomForCausalLM,
BloomTokenizerFast)
- Instead imported more generalized versions of loader
(AutoDistributedModelForCausalLM, AutoTokenizer)
- Updated the Petals example notebook to allow for a successful
installation of Petals in Apple Silicon Macs
- Tag maintainer: @hwchase17, @baskaryan
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
Description:
This PR will enable the Open API chain to work with valid Open API
specifications missing `description` and `summary` properties for path
and operation nodes in open api specs.
Since both `description` and `summary` property are declared optional we
cannot be sure they are defined. This PR resolves this problem by
providing an empty (`''`) description as fallback.
The previous behavior of the Open API chain was that the underlying LLM
(OpenAI) throw ed an exception since `None` is not of type string:
```
openai.error.InvalidRequestError: None is not of type 'string' - 'functions.0.description'
```
Using this PR the Open API chain will succeed also using Open API specs
lacking `description` and `summary` properties for path and operation
nodes.
Thanks for your amazing work !
Tag maintainer: @baskaryan
---------
Co-authored-by: Lars Gersmann <lars.gersmann@cm4all.com>
Co-authored-by: Bagatur <baskaryan@gmail.com>
1. Upgrade the AwaDB from v0.3.7 to v0.3.9
2. Change the default embedding to AwaEmbedding
---------
Co-authored-by: ljeagle <awadb.vincent@gmail.com>
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
- Description: Adds AwaEmbeddings class for embeddings, which provides
users with a convenient way to do fine-tuning, as well as the potential
need for multimodality
- Tag maintainer: @baskaryan
Create `Awa.ipynb`: an example notebook for AwaEmbeddings class
Modify `embeddings/__init__.py`: Import the class
Create `embeddings/awa.py`: The embedding class
Create `embeddings/test_awa.py`: The test file.
---------
Co-authored-by: taozhiwang <taozhiwa@gmail.com>
Full set of params are missing from Vertex* LLMs when `dict()` method is
called.
```
>>> from langchain.chat_models.vertexai import ChatVertexAI
>>> from langchain.llms.vertexai import VertexAI
>>> chat_llm = ChatVertexAI()
l>>> llm = VertexAI()
>>> chat_llm.dict()
{'_type': 'vertexai'}
>>> llm.dict()
{'_type': 'vertexai'}
```
This PR just uses the same mechanism used elsewhere to expose the full
params.
Since `_identifying_params()` is on the `_VertexAICommon` class, it
should cover the chat and non-chat cases.
<!-- Thank you for contributing to LangChain!
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- 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
-->
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(see below),
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Please make sure you're PR is passing linting and testing before
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- 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:
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-->
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locally.
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- 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
-->
## Description
This commit introduces the `DropboxLoader` class, a new document loader
that allows loading files from Dropbox into the application. The loader
relies on a Dropbox app, which requires creating an app on Dropbox,
obtaining the necessary scope permissions, and generating an access
token. Additionally, the dropbox Python package is required.
The `DropboxLoader` class is designed to be used as a document loader
for processing various file types, including text files, PDFs, and
Dropbox Paper files.
## Dependencies
`pip install dropbox` and `pip install unstructured` for PDF reading.
## Tag maintainer
@rlancemartin, @eyurtsev (from Data Loaders). I'd appreciate some
feedback here 🙏 .
## Social Networks
https://github.com/rubenbarraganhttps://www.linkedin.com/in/rgbarragan/https://twitter.com/RubenBarraganP
---------
Co-authored-by: Ruben Barragan <rbarragan@Rubens-MacBook-Air.local>
Since the refactoring into sub-projects `libs/langchain` and
`libs/experimental`, the `make` targets `format_diff` and `lint_diff` do
not work anymore when running `make` from these subdirectories. Reason
is that
```
PYTHON_FILES=$(shell git diff --name-only --diff-filter=d master | grep -E '\.py$$|\.ipynb$$')
```
generates paths from the project's root directory instead of the
corresponding subdirectories. This PR fixes this by adding a
`--relative` command line option.
- Tag maintainer: @baskaryan
# [WIP] Tree of Thought introducing a new ToTChain.
This PR adds a new chain called ToTChain that implements the ["Large
Language Model Guided
Tree-of-Though"](https://arxiv.org/pdf/2305.08291.pdf) paper.
There's a notebook example `docs/modules/chains/examples/tot.ipynb` that
shows how to use it.
Implements #4975
## Who can review?
Community members can review the PR once tests pass. Tag
maintainers/contributors who might be interested:
- @hwchase17
- @vowelparrot
---------
Co-authored-by: Vadim Gubergrits <vgubergrits@outbox.com>
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
Optimizing important numerical code and making it run faster.
Performance went up by 1.48x (148%). Runtime went down from 138715us to
56020us
Optimization explanation:
The `cosine_similarity_top_k` function is where we made the most
significant optimizations.
Instead of sorting the entire score_array which needs considering all
elements, `np.argpartition` is utilized to find the top_k largest scores
indices, this operation has a time complexity of O(n), higher
performance than sorting. Remember, `np.argpartition` doesn't guarantee
the order of the values. So we need to use argsort() to get the indices
that would sort our top-k values after partitioning, which is much more
efficient because it only sorts the top-K elements, not the entire
array. Then to get the row and column indices of sorted top_k scores in
the original score array, we use `np.unravel_index`. This operation is
more efficient and cleaner than a list comprehension.
The code has been tested for correctness by running the following
snippet on both the original function and the optimized function and
averaged over 5 times.
```
def test_cosine_similarity_top_k_large_matrices():
X = np.random.rand(1000, 1000)
Y = np.random.rand(1000, 1000)
top_k = 100
score_threshold = 0.5
gc.disable()
counter = time.perf_counter_ns()
return_value = cosine_similarity_top_k(X, Y, top_k, score_threshold)
duration = time.perf_counter_ns() - counter
gc.enable()
```
@hwaking @hwchase17 @jerwelborn
Unit tests pass, I also generated more regression tests which all
passed.
Description: Adding support for custom index and scoring profile support
in Azure Cognitive Search
@hwchase17
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
This PR introduces async API support for Cohere, both LLM and
embeddings. It requires updating `cohere` package to `^4`.
Tagging @hwchase17, @baskaryan, @agola11
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
# Description:
**Add the possibility to keep text as Markdown in the ConfluenceLoader**
Add a bool variable that allows to keep the Markdown format of the
Confluence pages.
It is useful because it allows to use MarkdownHeaderTextSplitter as a
DataSplitter.
If this variable in set to True in the load() method, the pages are
extracted using the markdownify library.
# Issue:
[4407](https://github.com/langchain-ai/langchain/issues/4407)
# Dependencies:
Add the markdownify library
# Tag maintainer:
@rlancemartin, @eyurtsev
# Twitter handle:
FloBastinHeyI - https://twitter.com/FloBastinHeyI
---------
Co-authored-by: Florian Bastin <florian.bastin@octo.com>
Co-authored-by: Bagatur <baskaryan@gmail.com>
Objects implementing Runnable: BasePromptTemplate, LLM, ChatModel,
Chain, Retriever, OutputParser
- [x] Implement Runnable in base Retriever
- [x] Raise TypeError in operator methods for unsupported things
- [x] Implement dict which calls values in parallel and outputs dict
with results
- [x] Merge in `+` for prompts
- [x] Confirm precedence order for operators, ideal would be `+` `|`,
https://docs.python.org/3/reference/expressions.html#operator-precedence
- [x] Add support for openai functions, ie. Chat Models must return
messages
- [x] Implement BaseMessageChunk return type for BaseChatModel, a
subclass of BaseMessage which implements __add__ to return
BaseMessageChunk, concatenating all str args
- [x] Update implementation of stream/astream for llm and chat models to
use new `_stream`, `_astream` optional methods, with default
implementation in base class `raise NotImplementedError` use
https://stackoverflow.com/a/59762827 to see if it is implemented in base
class
- [x] Delete the IteratorCallbackHandler (leave the async one because
people using)
- [x] Make BaseLLMOutputParser implement Runnable, accepting either str
or BaseMessage
---------
Co-authored-by: Eugene Yurtsev <eyurtsev@gmail.com>
ElasticsearchVectorStore.as_retriever() method is returning
`RecursionError: maximum recursion depth exceeded`
because of incorrect field reference in
`embeddings()` method
- Description: Fix RecursionError because of a typo
- Issue: the issue #8310
- Dependencies: None,
- Tag maintainer: @eyurtsev
- Twitter handle: bpatel
Description:
I wanted to use the DuckDuckGoSearch tool in an agent to let him get the
latest news for a topic. DuckDuckGoSearch has already an implemented
function for retrieving news articles. But there wasn't a tool to use
it. I simply adapted the SearchResult class with an extra argument
"backend". You can set it to "news" to only get news articles.
Furthermore, I added an example to the DuckDuckGo Notebook on how to
further customize the results by using the DuckDuckGoSearchAPIWrapper.
Dependencies: no new dependencies
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
Description: in the .devcontainer, docker-compose build is currently
failing due to the src paths in the COPY command. This change adds the
full path to the pyproject.toml and poetry.toml to allow the build to
run.
Issue:
You can see the issue if you try to build the dev docker image with:
```
cd .devcontainer
docker-compose build
```
Dependencies: none
Twitter handle: byronsalty
- Description: During streaming, the first chunk may only contain the
name of an OpenAI function and not any arguments. In this case, the
current code presumes there is a streaming response and tries to append
to it, but gets a KeyError. This fixes that case by checking if the
arguments key exists, and if not, creates a new entry instead of
appending.
- Issue: Related to #6462
Sample Code:
```python
llm = AzureChatOpenAI(
deployment_name=deployment_name,
model_name=model_name,
streaming=True
)
tools = [PythonREPLTool()]
callbacks = [StreamingStdOutCallbackHandler()]
agent = initialize_agent(
tools=tools,
llm=llm,
agent=AgentType.OPENAI_FUNCTIONS,
callbacks=callbacks
)
agent('Run some python code to test your interpreter')
```
Previous Result:
```
File ...langchain/chat_models/openai.py:344, in ChatOpenAI._generate(self, messages, stop, run_manager, **kwargs)
342 function_call = _function_call
343 else:
--> 344 function_call["arguments"] += _function_call["arguments"]
345 if run_manager:
346 run_manager.on_llm_new_token(token)
KeyError: 'arguments'
```
New Result:
```python
{'input': 'Run some python code to test your interpreter',
'output': "The Python code `print('Hello, World!')` has been executed successfully, and the output `Hello, World!` has been printed."}
```
Co-authored-by: jswe <jswe@polencapital.com>
- Description: Fix mangling issue affecting a couple of VectorStore
classes including Redis.
- Issue: https://github.com/langchain-ai/langchain/issues/8185
- @rlancemartin
This is a simple issue but I lack of some context in the original
implementation.
My changes perhaps are not the definitive fix but to start a quick
discussion.
@hinthornw Tagging you since one of your changes introduced this
[here.](c38965fcba)
I have some Prompt subclasses in my project that I'd like to be able to
deserialize in callbacks. Right now `loads()`/`load()` will bail when it
encounters my object, but I know I can trust the objects because they're
in my own projects.
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- 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),
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If you're adding a new integration, please include:
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Maintainer responsibilities:
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- DataLoaders / VectorStores / Retrievers: @rlancemartin, @eyurtsev
- Models / Prompts: @hwchase17, @baskaryan
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### Description
This PR includes the following changes:
- Adds AOSS (Amazon OpenSearch Service Serverless) support to
OpenSearch. Please refer to the documentation on how to use it.
- While creating an index, AOSS only supports Approximate Search with
`nmslib` and `faiss` engines. During Search, only Approximate Search and
Script Scoring (on doc values) are supported.
- This PR also adds support to `efficient_filter` which can be used with
`faiss` and `lucene` engines.
- The `lucene_filter` is deprecated. Instead please use the
`efficient_filter` for the lucene engine.
Signed-off-by: Naveen Tatikonda <navtat@amazon.com>
Given a user question, this will -
* Use LLM to generate a set of queries.
* Query for each.
* The URLs from search results are stored in self.urls.
* A check is performed for any new URLs that haven't been processed yet
(not in self.url_database).
* Only these new URLs are loaded, transformed, and added to the
vectorstore.
* The vectorstore is queried for relevant documents based on the
questions generated by the LLM.
* Only unique documents are returned as the final result.
This code will avoid reprocessing of URLs across multiple runs of
similar queries, which should improve the performance of the retriever.
It also keeps track of all URLs that have been processed, which could be
useful for debugging or understanding the retriever's behavior.
---------
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
Added a quick check to make integration easier with Databricks; another
option would be to make a new class, but this seemed more
straightfoward.
cc: @liangz1 Can this be done in a more straightfoward way?
This PR removes operator overloading for base message.
Removing the `+` operating from base message will help make sure that:
1) There's no need to re-define `+` for message chunks
2) That there's no unexpected behavior in terms of types changing
(adding two messages yields a ChatPromptTemplate which is not a message)
- Description: Small change to fix broken Azure streaming. More complete
migration probably still necessary once the new API behavior is
finalized.
- Issue: Implements fix by @rock-you in #6462
- Dependencies: N/A
There don't seem to be any tests specifically for this, and I was having
some trouble adding some. This is just a small temporary fix to allow
for the new API changes that OpenAI are releasing without breaking any
other code.
---------
Co-authored-by: Jacob Swe <jswe@polencapital.com>
Co-authored-by: Bagatur <baskaryan@gmail.com>
# What
- This is to add test for faiss vector store with score threshold
<!-- Thank you for contributing to LangChain!
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- Description: This is to add test for faiss vector store with score
threshold
- Issue: None
- Dependencies: None
- Tag maintainer: @rlancemartin, @eyurtsev
- Twitter handle: @MlopsJ
Please make sure you're PR is passing linting and testing before
submitting. Run `make format`, `make lint` and `make test` to check this
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If you're adding a new integration, please include:
1. a test for the integration, preferably unit tests that do not rely on
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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
- 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:
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-->
# What
- Use `logger` instead of using logging directly.
<!-- Thank you for contributing to LangChain!
Replace this comment with:
- Description: Use `logger` instead of using logging directly.
- Issue: None
- Dependencies: None
- Tag maintainer: @baskaryan
- Twitter handle: @MlopsJ
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
-->
Refactored `requests.py`. The same as
https://github.com/langchain-ai/langchain/pull/7961#8098#8099
requests.py is in the root code folder. This creates the
`langchain.requests: Requests` group on the API Reference navigation
ToC, on the same level as Chains and Agents which is incorrect.
Refactoring:
- copied requests.py content into utils/requests.py
- I added the backwards compatibility ref in the original requests.py.
- updated imports to requests objects
@hwchase17, @baskaryan
