# Beam
Calls the Beam API wrapper to deploy and make subsequent calls to an
instance of the gpt2 LLM in a cloud deployment. Requires installation of
the Beam library and registration of Beam Client ID and Client Secret.
Additional calls can then be made through the instance of the large
language model in your code or by calling the Beam API.
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Co-authored-by: Dev 2049 <dev.dev2049@gmail.com>
# Add MosaicML inference endpoints
This PR adds support in langchain for MosaicML inference endpoints. We
both serve a select few open source models, and allow customers to
deploy their own models using our inference service. Docs are here
(https://docs.mosaicml.com/en/latest/inference.html), and sign up form
is here (https://forms.mosaicml.com/demo?utm_source=langchain). I'm not
intimately familiar with the details of langchain, or the contribution
process, so please let me know if there is anything that needs fixing or
this is the wrong way to submit a new integration, thanks!
I'm also not sure what the procedure is for integration tests. I have
tested locally with my api key.
## Who can review?
@hwchase17
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Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
OpenLM is a zero-dependency OpenAI-compatible LLM provider that can call
different inference endpoints directly via HTTP. It implements the
OpenAI Completion class so that it can be used as a drop-in replacement
for the OpenAI API. This changeset utilizes BaseOpenAI for minimal added
code.
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Co-authored-by: Dev 2049 <dev.dev2049@gmail.com>
Let user inspect the token ids in addition to getting th enumber of tokens
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Co-authored-by: Zach Schillaci <40636930+zachschillaci27@users.noreply.github.com>
# Add summarization task type for HuggingFace APIs
Add summarization task type for HuggingFace APIs.
This task type is described by [HuggingFace inference
API](https://huggingface.co/docs/api-inference/detailed_parameters#summarization-task)
My project utilizes LangChain to connect multiple LLMs, including
various HuggingFace models that support the summarization task.
Integrating this task type is highly convenient and beneficial.
Fixes#4720
Handle duplicate and incorrectly specified OpenAI params
Thanks @PawelFaron for the fix! Made small update
Closes#4331
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Co-authored-by: PawelFaron <42373772+PawelFaron@users.noreply.github.com>
Co-authored-by: Pawel Faron <ext-pawel.faron@vaisala.com>
Seems the pyllamacpp package is no longer the supported bindings from
gpt4all. Tested that this works locally.
Given that the older models weren't very performant, I think it's better
to migrate now without trying to include a lot of try / except blocks
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Co-authored-by: Nissan Pow <npow@users.noreply.github.com>
Co-authored-by: Nissan Pow <pownissa@amazon.com>
- Add langchain.llms.GooglePalm for text completion,
- Add langchain.chat_models.ChatGooglePalm for chat completion,
- Add langchain.embeddings.GooglePalmEmbeddings for sentence embeddings,
- Add example field to HumanMessage and AIMessage so that users can feed
in examples into the PaLM Chat API,
- Add system and unit tests.
Note async completion for the Text API is not yet supported and will be
included in a future PR.
Happy for feedback on any aspect of this PR, especially our choice of
adding an example field to Human and AI Message objects to enable
passing example messages to the API.
* Adds an Anthropic ChatModel
* Factors out common code in our LLMModel and ChatModel
* Supports streaming llm-tokens to the callbacks on a delta basis (until
a future V2 API does that for us)
* Some fixes
Given that different models have very different latencies and pricings,
it's benefitial to pass the information about the model that generated
the response. Such information allows implementing custom callback
managers and track usage and price per model.
Addresses https://github.com/hwchase17/langchain/issues/1557.
Supporting asyncio in langchain primitives allows for users to run them
concurrently and creates more seamless integration with
asyncio-supported frameworks (FastAPI, etc.)
Summary of changes:
**LLM**
* Add `agenerate` and `_agenerate`
* Implement in OpenAI by leveraging `client.Completions.acreate`
**Chain**
* Add `arun`, `acall`, `_acall`
* Implement them in `LLMChain` and `LLMMathChain` for now
**Agent**
* Refactor and leverage async chain and llm methods
* Add ability for `Tools` to contain async coroutine
* Implement async SerpaPI `arun`
Create demo notebook.
Open questions:
* Should all the async stuff go in separate classes? I've seen both
patterns (keeping the same class and having async and sync methods vs.
having class separation)
https://github.com/hwchase17/langchain/issues/354
Add support for running your own HF pipeline locally. This would allow
you to get a lot more dynamic with what HF features and models you
support since you wouldn't be beholden to what is hosted in HF hub. You
could also do stuff with HF Optimum to quantize your models and stuff to
get pretty fast inference even running on a laptop.
