a74f3a4979
**Description:** Batch update of alt text and title attributes for images in `md` & `mdx` files across the repo using [alttexter](https://github.com/jonathanalgar/alttexter)/[alttexter-ghclient](https://github.com/jonathanalgar/alttexter-ghclient) (built using LangChain/LangSmith). **Limitation:** cannot update `ipynb` files because of [this issue](https://github.com/langchain-ai/langchain/pull/15357#issuecomment-1885037250). Can revisit when Docusaurus is bumped to v3. I checked all the generated alt texts and titles and didn't find any technical inaccuracies. That's not to say they're _perfect_, but a lot better than what's there currently. [Deployed](https://langchain-819yf1tbk-langchain.vercel.app/docs/modules/model_io/) image example:  You can see LangSmith traces for all the calls out to the LLM in the PRs merged into this one: * https://github.com/jonathanalgar/langchain/pull/6 * https://github.com/jonathanalgar/langchain/pull/4 * https://github.com/jonathanalgar/langchain/pull/3 I didn't add the following files to the PR as the images already have OK alt texts: *27dca2d92f/docs/docs/integrations/providers/argilla.mdx (L3)*27dca2d92f/docs/docs/integrations/providers/apify.mdx (L11)--------- Co-authored-by: github-actions <github-actions@github.com>
3.5 KiB
Bedrock JCVD 🕺🥋
Overview
LangChain template that uses Anthropic's Claude on Amazon Bedrock to behave like JCVD.
I am the Fred Astaire of Chatbots! 🕺
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Environment Setup
AWS Credentials
This template uses Boto3, the AWS SDK for Python, to call Amazon Bedrock. You must configure both AWS credentials and an AWS Region in order to make requests.
For information on how to do this, see AWS Boto3 documentation (Developer Guide > Credentials).
Foundation Models
By default, this template uses Anthropic's Claude v2 (anthropic.claude-v2).
To request access to a specific model, check out the Amazon Bedrock User Guide (Model access)
To use a different model, set the environment variable BEDROCK_JCVD_MODEL_ID. A list of base models is available in the Amazon Bedrock User Guide (Use the API > API operations > Run inference > Base Model IDs).
The full list of available models (including base and custom models) is available in the Amazon Bedrock Console under Foundation Models or by calling
aws bedrock list-foundation-models.
Usage
To use this package, you should first have the LangChain CLI installed:
pip install -U langchain-cli
To create a new LangChain project and install this as the only package, you can do:
langchain app new my-app --package bedrock-jcvd
If you want to add this to an existing project, you can just run:
langchain app add bedrock-jcvd
And add the following code to your server.py file:
from bedrock_jcvd import chain as bedrock_jcvd_chain
add_routes(app, bedrock_jcvd_chain, path="/bedrock-jcvd")
(Optional) Let's now configure LangSmith. LangSmith will help us trace, monitor and debug LangChain applications. LangSmith is currently in private beta, you can sign up here. If you don't have access, you can skip this section
export LANGCHAIN_TRACING_V2=true
export LANGCHAIN_API_KEY=<your-api-key>
export LANGCHAIN_PROJECT=<your-project> # if not specified, defaults to "default"
If you are inside this directory, then you can spin up a LangServe instance directly by:
langchain serve
This will start the FastAPI app with a server is running locally at http://localhost:8000
We can see all templates at http://127.0.0.1:8000/docs.
We can also access the playground at http://127.0.0.1:8000/bedrock-jcvd/playground
