Add the ability to clean the metadata before
it goes into redis enabling document_loaders
that return lists of strings to create categorical
values for Tags in Redis indices.
Also, added docstrings and updated the jupyter notebook
⏳
- updated the top-level descriptions to a consistent format;
- changed several `ValueError` to `ImportError` in the import cases;
- changed the format of several internal functions from "name" to
"_name". So, these functions are not shown in the Top-level API
Reference page (with lists of classes/functions)
Currently, ChatOpenAI._stream does not reflect finish_reason to
generation_info. Change it to reflect that.
Same patch as https://github.com/langchain-ai/langchain/pull/9431 , but
also applies to _stream.
This PR adds a new document loader `AssemblyAIAudioTranscriptLoader`
that allows to transcribe audio files with the [AssemblyAI
API](https://www.assemblyai.com) and loads the transcribed text into
documents.
- Add new document_loader with class `AssemblyAIAudioTranscriptLoader`
- Add optional dependency `assemblyai`
- Add unit tests (using a Mock client)
- Add docs notebook
This is the equivalent to the JS integration already available in
LangChain.js. See the [LangChain JS docs AssemblyAI
page](https://js.langchain.com/docs/modules/data_connection/document_loaders/integrations/web_loaders/assemblyai_audio_transcription).
At its simplest, you can use the loader to get a transcript back from an
audio file like this:
```python
from langchain.document_loaders.assemblyai import AssemblyAIAudioTranscriptLoader
loader = AssemblyAIAudioTranscriptLoader(file_path="./testfile.mp3")
docs = loader.load()
```
To use it, it needs the `assemblyai` python package installed, and the
environment variable `ASSEMBLYAI_API_KEY` set with your API key.
Alternatively, the API key can also be passed as an argument.
Twitter handles to shout out if so kindly 🙇
[@AssemblyAI](https://twitter.com/AssemblyAI) and
[@patloeber](https://twitter.com/patloeber)
---------
Co-authored-by: Bagatur <22008038+baskaryan@users.noreply.github.com>
Co-authored-by: Eugene Yurtsev <eyurtsev@gmail.com>
This PR introduces a persistence layer to help with indexing workflows
into
vectostores.
The indexing code helps users to:
1. Avoid writing duplicated content into the vectostore
2. Avoid over-writing content if it's unchanged
Importantly, this keeps on working even if the content being written is
derived
via a set of transformations from some source content (e.g., indexing
children
documents that were derived from parent documents by chunking.)
The two main components are:
1. Persistence layer that keeps track of which keys were updated and
when.
Keeping track of the timestamp of updates, allows to clean up old
content
safely, and with minimal complexity.
2. HashedDocument which is used to hash the contents (including
metadata) of
the documents. We rely on the hashes for identifying duplicates.
The indexing code works with **ANY** document loader. To add
transformations
to the documents, users for now can add a custom document loader
that composes an existing loader together with document transformers.
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
- Description: ~~Creates a new root_validator in `_AnthropicCommon` that
allows the use of `model_name` and `max_tokens` keyword arguments.~~
Adds pydantic field aliases to support `model_name` and `max_tokens` as
keyword arguments. Ultimately, this makes `ChatAnthropic` more
consistent with `ChatOpenAI`, making the two classes more
interchangeable for the developer.
- Issue: https://github.com/langchain-ai/langchain/issues/9510
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
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
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
