- **Description:** add a paragraph to the GoogleDriveLoader doc on how
to bypass errors on authentication.
For some reason, specifying credential path via `credentials_path`
constructor parameter when creating `GoogleDriveLoader` makes it so that
the oAuth screen is never showing up when first using GoogleDriveLoader.
Instead, the `RefreshError: ('invalid_grant: Bad Request', {'error':
'invalid_grant', 'error_description': 'Bad Request'})` error happens.
Setting it via `os.environ["GOOGLE_APPLICATION_CREDENTIALS"] = ...`
solves the problem. Also, `token_path` constructor parameter is
mandatory, otherwise another error happens when trying to `load()` for
the first time.
These errors are tricky and time-consuming to figure out, so I believe
it's good to mention them in the docs.
---------
Co-authored-by: Erick Friis <erick@langchain.dev>
Description: Similar in concept to the `MarkdownHeaderTextSplitter`, the
`HTMLHeaderTextSplitter` is a "structure-aware" chunker that splits text
at the element level and adds metadata for each header "relevant" to any
given chunk. It can return chunks element by element or combine elements
with the same metadata, with the objectives of (a) keeping related text
grouped (more or less) semantically and (b) preserving context-rich
information encoded in document structures. It can be used with other
text splitters as part of a chunking pipeline.
Dependency: lxml python package
Maintainer: @hwchase17
Twitter handle: @MartinZirulnik
---------
Co-authored-by: PresidioVantage <github@presidiovantage.com>
Co-authored-by: Bagatur <baskaryan@gmail.com>
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- **Description:** Doc corrections and resolve notebook rendering issue
on GH
- **Issue:** N/A
- **Dependencies:** N/A
- **Tag maintainer:** @baskaryan
- **Twitter handle:** `@isaacchung1217`
---------
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
**Description:**
Examples in the "Select by similarity" section were not really
highlighting capabilities of similarity search.
E.g. "# Input is a measurement, so should select the tall/short example"
was still outputting the "mood" example.
I tweaked the inputs a bit and fixed the examples (checking that those
are indeed what the search outputs).
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
- **Description:** Fix typo about `RetrievalQAWithSourceChain` ->
`RetrievalQAWithSourcesChain`
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- **Description:** Adds Kotlin language to `TextSplitter`
---------
Co-authored-by: Eugene Yurtsev <eyurtsev@gmail.com>
- **Description:** use term keyword according to the official python doc
glossary, see https://docs.python.org/3/glossary.html
- **Issue:** not applicable
- **Dependencies:** not applicable
- **Tag maintainer:** @hwchase17
- **Twitter handle:** vreyespue
continuation of PR #8550
@hwchase17 please see and merge. And also close the PR #8550.
---------
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
Co-authored-by: Erick Friis <erick@langchain.dev>
therefor -> therefore
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### Description
When I was reading the document, I found that some examples had extra
spaces and violated "Unexpected spaces around keyword / parameter equals
(E251)" in pep8. I removed these extra spaces.
### Tag maintainer
@eyurtsev
### Twitter handle
[billvsme](https://twitter.com/billvsme)
### Description
renamed several repository links from `hwchase17` to `langchain-ai`.
### Why
I discovered that the README file in the devcontainer contains an old
repository name, so I took the opportunity to rename the old repository
name in all files within the repository, excluding those that do not
require changes.
### Dependencies
none
### Tag maintainer
@baskaryan
### Twitter handle
[kzk_maeda](https://twitter.com/kzk_maeda)
updated `YouTube` and `tutorial` videos with new links.
Removed couple of duplicates.
Reordered several links by view counters
Some formatting: emphasized the names of products
- updated titles and descriptions of the `integrations/memory` notebooks
into consistent and laconic format;
- removed
`docs/extras/integrations/memory/motorhead_memory_managed.ipynb` file as
a duplicate of the
`docs/extras/integrations/memory/motorhead_memory.ipynb`;
- added `integrations/providers` Integration Cards for `dynamodb`,
`motorhead`.
- updated `integrations/providers/redis.mdx` with links
- renamed several notebooks; updated `vercel.json` to reroute new names.
Enviroment -> Environment
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- **Description:** A Document Loader for MongoDB
- **Issue:** n/a
- **Dependencies:** Motor, the async driver for MongoDB
- **Tag maintainer:** n/a
- **Twitter handle:** pigpenblue
Note that an initial mongodb document loader was created 4 months ago,
but the [PR ](https://github.com/langchain-ai/langchain/pull/4285)was
never pulled in. @leo-gan had commented on that PR, but given it is
extremely far behind the master branch and a ton has changed in
Langchain since then (including repo name and structure), I rewrote the
branch and issued a new PR with the expectation that the old one can be
closed.
Please reference that old PR for comments/context, but it can be closed
in favor of this one. Thanks!
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
Co-authored-by: Eugene Yurtsev <eyurtsev@gmail.com>
Based on the customers' requests for native langchain integration,
SearchApi is ready to invest in AI and LLM space, especially in
open-source development.
- This is our initial PR and later we want to improve it based on
customers' and langchain users' feedback. Most likely changes will
affect how the final results string is being built.
- We are creating similar native integration in Python and JavaScript.
- The next plan is to integrate into Java, Ruby, Go, and others.
- Feel free to assign @SebastjanPrachovskij as a main reviewer for any
SearchApi-related searches. We will be glad to help and support
langchain development.
## Description
Expanded the upper bound for `networkx` dependency to allow installation
of latest stable version. Tested the included sample notebook with
version 3.1, and all steps ran successfully.
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
- **Description:** Bedrock updated boto service name to
"bedrock-runtime" for the InvokeModel and InvokeModelWithResponseStream
APIs. This update also includes new model identifiers for Titan text,
embedding and Anthropic.
Co-authored-by: Mani Kumar Adari <maniadar@amazon.com>
Fixed Typo Error in Update get_started.mdx file by addressing a minor
typographical error.
This improvement enhances the readability and correctness of the
notebook, making it easier for users to understand and follow the
demonstration. The commit aims to maintain the quality and accuracy of
the content within the repository.
please review the change at your convenience.
@baskaryan , @hwaking
The new Fireworks and FireworksChat implementations are awesome! Added
in this PR https://github.com/langchain-ai/langchain/pull/11117 thank
you @ZixinYang
However, I think stop words were not plumbed correctly. I've made some
simple changes to do that, and also updated the notebook to be a bit
clearer with what's needed to use both new models.
---------
Co-authored-by: Taqi Jaffri <tjaffri@docugami.com>
The intermediate steps example in docs has an example on how to retrieve
and display the intermediate steps.
But the intermediate steps object is of type AgentAction which cannot be
passed to json.dumps (it raises an error).
I replaced it with Langchain's dumps function (from langchain.load.dump
import dumps) which is the preferred way to do so.
Description
* Refactor Fireworks within Langchain LLMs.
* Remove FireworksChat within Langchain LLMs.
* Add ChatFireworks (which uses chat completion api) to Langchain chat
models.
* Users have to install `fireworks-ai` and register an api key to use
the api.
Issue - Not applicable
Dependencies - None
Tag maintainer - @rlancemartin @baskaryan
This enables bulk args like `chunk_size` to be passed down from the
ingest methods (from_text, from_documents) to be passed down to the bulk
API.
This helps alleviate issues where bulk importing a large amount of
documents into Elasticsearch was resulting in a timeout.
Contribution Shoutout
- @elastic
- [x] Updated Integration tests
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
Fixed navbar:
- renamed several files, so ToC is sorted correctly
- made ToC items consistent: formatted several Titles
- added several links
- reformatted several docs to a consistent format
- renamed several files (removed `_example` suffix)
- added renamed files to the `docs/docs_skeleton/vercel.json`
Sometimes you don't want the LLM to be aware of the whole graph schema,
and want it to ignore parts of the graph when it is constructing Cypher
statements.
- **Description**: Adding retrievers for [kay.ai](https://kay.ai) and
SEC filings powered by Kay and Cybersyn. Kay provides context as a
service: it's an API built for RAG.
- **Issue**: N/A
- **Dependencies**: Just added a dep to the
[kay](https://pypi.org/project/kay/) package
- **Tag maintainer**: @baskaryan @hwchase17 Discussed in slack
- **Twtter handle:** [@vishalrohra_](https://twitter.com/vishalrohra_)
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
The huggingface pipeline in langchain (used for locally hosted models)
does not support batching. If you send in a batch of prompts, it just
processes them serially using the base implementation of _generate:
https://github.com/docugami/langchain/blob/master/libs/langchain/langchain/llms/base.py#L1004C2-L1004C29
This PR adds support for batching in this pipeline, so that GPUs can be
fully saturated. I updated the accompanying notebook to show GPU batch
inference.
---------
Co-authored-by: Taqi Jaffri <tjaffri@docugami.com>
This PR aims at showcasing how to use vLLM's OpenAI-compatible chat API.
### Context
Lanchain already supports vLLM and its OpenAI-compatible `Completion`
API. However, the `ChatCompletion` API was not aligned with OpenAI and
for this reason I've waited for this
[PR](https://github.com/vllm-project/vllm/pull/852) to be merged before
adding this notebook to langchain.
LLMRails Embedding Integration
This PR provides integration with LLMRails. Implemented here are:
langchain/embeddings/llm_rails.py
docs/extras/integrations/text_embedding/llm_rails.ipynb
Hi @hwchase17 after adding our vectorstore integration to langchain with
confirmation of you and @baskaryan, now we want to add our embedding
integration
---------
Co-authored-by: Anar Aliyev <aaliyev@mgmt.cloudnet.services>
Co-authored-by: Bagatur <baskaryan@gmail.com>
Adds support for gradient.ai's embedding model.
This will remain a Draft, as the code will likely be refactored with the
`pip install gradientai` python sdk.
- chat vertex async
- vertex stream
- vertex full generation info
- vertex use server-side stopping
- model garden async
- update docs for all the above
in follow up will add
[] chat vertex full generation info
[] chat vertex retries
[] scheduled tests
- Description:
Updated JSONLoader usage documentation which was making it unusable
- Issue: JSONLoader if used with the documented arguments was failing on
various JSON documents.
- Dependencies:
no dependencies
- Twitter handle: @TheSlnArchitect
This adds a section on usage of `CassandraCache` and
`CassandraSemanticCache` to the doc notebook about caching LLMs, as
suggested in [this
comment](https://github.com/langchain-ai/langchain/pull/9772/#issuecomment-1710544100)
on a previous merged PR.
I also spotted what looks like a mismatch between different executions
and propose a fix (line 98).
Being the result of several runs, the cell execution numbers are
scrambled somewhat, so I volunteer to refine this PR by (manually)
re-numbering the cells to restore the appearance of a single, smooth
running (for the sake of orderly execution :)
**Description:**
This commit adds a vector store for the Postgres-based vector database
(`TimescaleVector`).
Timescale Vector(https://www.timescale.com/ai) is PostgreSQL++ for AI
applications. It enables you to efficiently store and query billions of
vector embeddings in `PostgreSQL`:
- Enhances `pgvector` with faster and more accurate similarity search on
1B+ vectors via DiskANN inspired indexing algorithm.
- Enables fast time-based vector search via automatic time-based
partitioning and indexing.
- Provides a familiar SQL interface for querying vector embeddings and
relational data.
Timescale Vector scales with you from POC to production:
- Simplifies operations by enabling you to store relational metadata,
vector embeddings, and time-series data in a single database.
- Benefits from rock-solid PostgreSQL foundation with enterprise-grade
feature liked streaming backups and replication, high-availability and
row-level security.
- Enables a worry-free experience with enterprise-grade security and
compliance.
Timescale Vector is available on Timescale, the cloud PostgreSQL
platform. (There is no self-hosted version at this time.) LangChain
users get a 90-day free trial for Timescale Vector.
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
Co-authored-by: Avthar Sewrathan <avthar@timescale.com>
- **Description:** This PR implements a new LLM API to
https://gradient.ai
- **Issue:** Feature request for LLM #10745
- **Dependencies**: No additional dependencies are introduced.
- **Tag maintainer:** I am opening this PR for visibility, once ready
for review I'll tag.
- ```make format && make lint && make test``` is running.
- added a `integration` and `mock unit` test.
Co-authored-by: michaelfeil <me@michaelfeil.eu>
Co-authored-by: Bagatur <baskaryan@gmail.com>
We are introducing the py integration to Javelin AI Gateway
www.getjavelin.io. Javelin is an enterprise-scale fast llm router &
gateway. Could you please review and let us know if there is anything
missing.
Javelin AI Gateway wraps Embedding, Chat and Completion LLMs. Uses
javelin_sdk under the covers (pip install javelin_sdk).
Author: Sharath Rajasekar, Twitter: @sharathr, @javelinai
Thanks!!
### Description
- Add support for streaming with `Bedrock` LLM and `BedrockChat` Chat
Model.
- Bedrock as of now supports streaming for the `anthropic.claude-*` and
`amazon.titan-*` models only, hence support for those have been built.
- Also increased the default `max_token_to_sample` for Bedrock
`anthropic` model provider to `256` from `50` to keep in line with the
`Anthropic` defaults.
- Added examples for streaming responses to the bedrock example
notebooks.
**_NOTE:_**: This PR fixes the issues mentioned in #9897 and makes that
PR redundant.
- **Description:** QianfanEndpoint bugs for SystemMessages. When the
`SystemMessage` is input as the messages to
`chat_models.QianfanEndpoint`. A `TypeError` will be raised.
- **Issue:** #10643
- **Dependencies:**
- **Tag maintainer:** @baskaryan
- **Twitter handle:** no
### Description
Implements synthetic data generation with the fields and preferences
given by the user. Adds showcase notebook.
Corresponding prompt was proposed for langchain-hub.
### Example
```
output = chain({"fields": {"colors": ["blue", "yellow"]}, "preferences": {"style": "Make it in a style of a weather forecast."}})
print(output)
# {'fields': {'colors': ['blue', 'yellow']},
'preferences': {'style': 'Make it in a style of a weather forecast.'},
'text': "Good morning! Today's weather forecast brings a beautiful combination of colors to the sky, with hues of blue and yellow gently blending together like a mesmerizing painting."}
```
### Twitter handle
@deepsense_ai @matt_wosinski
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
**Description**
Adds new output parser, this time enabling the output of LLM to be of an
XML format. Seems to be particularly useful together with Claude model.
Addresses [issue
9820](https://github.com/langchain-ai/langchain/issues/9820).
**Twitter handle**
@deepsense_ai @matt_wosinski
