The Docugami loader was not returning the source metadata key. This was
triggering this exception when used with retrievers, per
https://github.com/langchain-ai/langchain/blob/master/libs/langchain/langchain/schema/prompt_template.py#L193C1-L195C41
The fix is simple and just updates the metadata key name for the
document each chunk is sourced from, from "name" to "source" as
expected.
I tested by running the python notebook that has an end to end scenario
in it.
Tagging DataLoader maintainers @rlancemartin @eyurtsev
This pull request corrects the URL links in the Async API documentation
to align with the updated project layout. The links had not been updated
despite the changes in layout.
Not obvious what the error is when you cannot index. This pr adds the
ability to log the first errors reason, to help the user diagnose the
issue.
Also added some more documentation for when you want to use the
vectorstore with an embedding model deployed in elasticsearch.
Credit: @elastic and @phoey1
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Replace this comment with:
- Description: Added the capability to handles structured data from
google enterprise search,
- Issue: Retriever failed when underline search engine was integrated
with structured data,
- Dependencies: google-api-core
- Tag maintainer: @jarokaz
- Twitter handle: anifort
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
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---------
Co-authored-by: Christos Aniftos <aniftos@google.com>
Co-authored-by: Holt Skinner <13262395+holtskinner@users.noreply.github.com>
Co-authored-by: Eugene Yurtsev <eyurtsev@gmail.com>
# Description
This PR introduces a new toolkit for interacting with the AINetwork
blockchain. The toolkit provides a set of tools for performing various
operations on the AINetwork blockchain, such as transferring AIN,
reading and writing values to the blockchain database, managing apps,
setting rules and owners.
# Dependencies
[ain-py](https://github.com/ainblockchain/ain-py) >= 1.0.2
# Misc
The example notebook
(langchain/docs/extras/integrations/toolkits/ainetwork.ipynb) is in the
PR
---------
Co-authored-by: kriii <kriii@users.noreply.github.com>
Co-authored-by: Bagatur <baskaryan@gmail.com>
Description: Link an example of deploying a Langchain app to an AzureML
online endpoint to the deployments documentation page.
Co-authored-by: Vanessa Arndorfer <vaarndor@microsoft.com>
### Description
Polars is a DataFrame interface on top of an OLAP Query Engine
implemented in Rust.
Polars is faster to read than pandas, so I'm looking forward to seeing
it added to the document loader.
### Dependencies
polars (https://pola-rs.github.io/polars-book/user-guide/)
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
Add PromptGuard integration
-------
There are two approaches to integrate PromptGuard with a LangChain
application.
1. PromptGuardLLMWrapper
2. functions that can be used in LangChain expression.
-----
- Dependencies
`promptguard` python package, which is a runtime requirement if you'd
try out the demo.
- @baskaryan @hwchase17 Thanks for the ideas and suggestions along the
development process.
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
- Description: added graph_memgraph_qa.ipynb which shows how to use LLMs
to provide a natural language interface to a Memgraph database using
[MemgraphGraph](https://github.com/langchain-ai/langchain/pull/8591)
class.
- Dependencies: given that the notebook utilizes the MemgraphGraph
class, it relies on both this class and several Python packages that are
installed in the notebook using pip (langchain, openai, neo4j,
gqlalchemy). The notebook is dependent on having a functional Memgraph
instance running, as it requires this instance to establish a
connection.
- Improved docs
- Improved performance in multiple ways through batching, threading,
etc.
- fixed error message
- Added support for metadata filtering during similarity search.
@baskaryan PTAL
Made the notion document of how Langchain executes agents method by
method in the codebase.
Can be helpful for developers that just started working with the
Langchain codebase.
The current Collab URL returns a 404, since there is no `chatbots`
directory under `use_cases`.
<!-- Thank you for contributing to LangChain!
If no one reviews your PR within a few days, please @-mention one of
@baskaryan, @eyurtsev, @hwchase17, @rlancemartin.
-->
### Summary
Fixes a bug from #7850 where post processing functions in Unstructured
loaders were not apply. Adds a assertion to the test to verify the post
processing function was applied and also updates the explanation in the
example notebook.
Fix spelling errors in the text: 'Therefore' and 'Retrying
I want to stress that your feedback is invaluable to us and is genuinely
cherished.
With gratitude,
@baskaryan @hwchase17
Removed extra "the" in the sentence about the chicken crossing the road
in fallbacks.ipynb. The sentence now reads correctly: "Why did the
chicken cross the road?" This resolves the grammatical error and
improves the overall quality of the content.
@baskaryan , @hinthornw , @hwchase17
