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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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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---------
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
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- Issue: 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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- Description: a description of the change,
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- Models / Prompts: @hwchase17, @baskaryan
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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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- 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.
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tests, lint, etc:
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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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- Issue: the issue # it fixes (if applicable),
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- DataLoaders / VectorStores / Retrievers: @rlancemartin, @eyurtsev
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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.
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tests, lint, etc:
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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
gets announced and you'd like a mention, we'll gladly shout you out!
Please make sure you're PR is passing linting and testing before
submitting. Run `make format`, `make lint` and `make test` to check this
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If you're adding a new integration, please include:
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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
-->
---------
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
<!-- Thank you for contributing to LangChain!
Replace this comment with:
- Description: Add missing test for retrievers self_query
- Issue: None
- Dependencies: None
- Tag maintainer: @rlancemartin, @eyurtsev
- Twitter handle: @MlopsJ
Please make sure you're PR is passing linting and testing before
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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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---------
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
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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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- Memory: @hwchase17
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- Async: @agola11
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tests, lint, etc:
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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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- Async: @agola11
If no one reviews your PR within a few days, feel free to @-mention the
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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>
