mirror of https://github.com/danny-avila/LibreChat.git synced 2025-09-22 06:00:56 +02:00

feat(Google): Support all Text/Chat Models, Response streaming, PaLM -> Google 🤖 (#1316 )

* feat: update PaLM icons

* feat: add additional google models

* POC: formatting inputs for Vertex AI streaming

* refactor: move endpoints services outside of /routes dir to /services/Endpoints

* refactor: shorten schemas import

* refactor: rename PALM to GOOGLE

* feat: make Google editable endpoint

* feat: reusable Ask and Edit controllers based off Anthropic

* chore: organize imports/logic

* fix(parseConvo): include examples in googleSchema

* fix: google only allows odd number of messages to be sent

* fix: pass proxy to AnthropicClient

* refactor: change `google` altName to `Google`

* refactor: update getModelMaxTokens and related functions to handle maxTokensMap with nested endpoint model key/values

* refactor: google Icon and response sender changes (Codey and Google logo instead of PaLM in all cases)

* feat: google support for maxTokensMap

* feat: google updated endpoints with Ask/Edit controllers, buildOptions, and initializeClient

* feat(GoogleClient): now builds prompt for text models and supports real streaming from Vertex AI through langchain

* chore(GoogleClient): remove comments, left before for reference in git history

* docs: update google instructions (WIP)

* docs(apis_and_tokens.md): add images to google instructions

* docs: remove typo apis_and_tokens.md

* Update apis_and_tokens.md

* feat(Google): use default settings map, fully support context for both text and chat models, fully support examples for chat models

* chore: update more PaLM references to Google

* chore: move playwright out of workflows to avoid failing tests

2023-12-10 14:54:13 -05:00

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Hugging Face Deployment 🤗

⚠️ Note - Some features are not supported by HuggingFace:

Meilisearch

Social Logins

❗Also:

You will have to create an online MongoDB Atlas Database to be able to properly deploy

Create and Configure your Database (Required)

The first thing you need is to create a MongoDB Atlas Database and get your connection string.

Follow the instructions in this document: Online MongoDB Database

Getting Started

1. Login or Create an account on Hugging Face

2. Visit https://huggingface.co/spaces/LibreChat/LibreChat and click on Duplicate this Space to copy LibreChat into your profile

3. Name your Space and Fill the Secrets and Variables

You can also decide here to make it public or private

You will need to fill these values:

Secrets	Values
MONGO_URI	* use the string aquired in the previous step
OPENAI_API_KEY	`user_provided`
BINGAI_TOKEN	`user_provided`
CHATGPT_TOKEN	`user_provided`
ANTHROPIC_API_KEY	`user_provided`
GOOGLE_KEY	`user_provided`
CREDS_KEY	* see bellow
CREDS_IV	* see bellow
JWT_SECRET	* see bellow
JWT_REFRESH_SECRET	* see bellow

⬆️ Leave the value field blank for any endpoints that you wish to disable.

⚠️ setting the API keys and token to user_provided allows you to provide them safely from the webUI

For CREDS_KEY, CREDS_IV and JWT_SECRET use this tool: https://replit.com/@daavila/crypto#index.js.

Run the tool a second time and use the new JWT_SECRET value for the JWT_REFRESH_SECRET

Variables	Values
APP_TITLE	LibreChat
ALLOW_REGISTRATION	true

Deployment

1. When you're done filling the secrets and variables, click Duplicate Space in the bottom of that window

2. The project will now build, this will take a couple of minutes

3. When ready, Building will change to Running

And you will be able to access LibreChat!

Update

To update LibreChat, simply select Factory Reboot from the ⚙️Settings menu

Conclusion

You can now access it with from the current URL. If you want to access it without the Hugging Face overlay, you can modify this URL template with your info:

https://username-projectname.hf.space/

e.g. https://cooluser-librechat.hf.space/

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