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📥 feat: Import Conversations from LibreChat, ChatGPT, Chatbot UI (#2355)

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* Basic implementation of ChatGPT conversation import

* remove debug code

* Handle citations

* Fix updatedAt in import

* update default model

* Use job scheduler to handle import requests

* import job status endpoint

* Add wrapper around Agenda

* Rate limits for import endpoint

* rename import api path

* Batch save import to mongo

* Improve naming

* Add documenting comments

* Test for importers

* Change button for importing conversations

* Frontend changes

* Import job status endpoint

* Import endpoint response

* Add translations to new phrases

* Fix conversations refreshing

* cleanup unused functions

* set timeout for import job status polling

* Add documentation

* get extra spaces back

* Improve error message

* Fix translation files after merge

* fix translation files 2

* Add zh translation for import functionality

* Sync mailisearch index after import

* chore: add dummy uri for jest tests, as MONGO_URI should only be real for E2E tests

* docs: fix links

* docs: fix conversationsImport section

* fix: user role issue for librechat imports

* refactor: import conversations from json
- organize imports
- add additional jsdocs
- use multer with diskStorage to avoid loading file into memory outside of job
- use filepath instead of loading data string for imports
- replace console logs and some logger.info() with logger.debug
- only use multer for import route

* fix: undefined metadata edge case and replace ChatGtp -> ChatGpt

* Refactor importChatGptConvo function to handle undefined metadata edge case and replace ChatGtp with ChatGpt

* fix: chatgpt importer

* feat: maintain tree relationship for librechat messages

* chore: use enum

* refactor: saveMessage to use single object arg, replace console logs, add userId to log message

* chore: additional comment

* chore: multer edge case

* feat: first pass, maintain tree relationship

* chore: organize

* chore: remove log

* ci: add heirarchy test for chatgpt

* ci: test maintaining of heirarchy for librechat

* wip: allow non-text content type messages

* refactor: import content part object json string

* refactor: more content types to format

* chore: consolidate messageText formatting

* docs: update on changes, bump data-provider/config versions, update readme

* refactor(indexSync): singleton pattern for MeiliSearchClient

* refactor: debug log after batch is done

* chore: add back indexSync error handling

---------

Co-authored-by: jakubmieszczak <jakub.mieszczak@zendesk.com>
Co-authored-by: Danny Avila <danny@librechat.ai>
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10
docs/install/configuration/azure_openai.md
View file

@ -239,7 +239,7 @@ Applying these setup requirements thoughtfully will ensure a correct and efficie
### Model Deployments
The list of models available to your users are determined by the model groupings specified in your [`azureOpenAI` endpoint config.](./custom_config.md#models-1)
The list of models available to your users are determined by the model groupings specified in your [`azureOpenAI` endpoint config.](./custom_config.md#models_1)
For example:
@ -408,7 +408,7 @@ endpoints:
To use Vision (image analysis) with Azure OpenAI, you need to make sure `gpt-4-vision-preview` is a specified model [in one of your groupings](#model-deployments)
This will work seamlessly as it does with the [OpenAI endpoint](#openai) (no need to select the vision model, it will be switched behind the scenes)
This will work seamlessly as it does with the [OpenAI endpoint](./ai_setup.md#openai) (no need to select the vision model, it will be switched behind the scenes)
### Generate images with Azure OpenAI Service (DALL-E)
@ -639,15 +639,15 @@ In any case, you can adjust the title model as such: `OPENAI_TITLE_MODEL=your-ti
Currently, the best way to setup Vision is to use your deployment names as the model names, as [shown here](#model-deployments)
This will work seamlessly as it does with the [OpenAI endpoint](#openai) (no need to select the vision model, it will be switched behind the scenes)
This will work seamlessly as it does with the [OpenAI endpoint](./ai_setup.md#openai) (no need to select the vision model, it will be switched behind the scenes)
Alternatively, you can set the [required variables](#required-variables) to explicitly use your vision deployment, but this may limit you to exclusively using your vision deployment for all Azure chat settings.
Alternatively, you can set the [required variables](#required-fields) to explicitly use your vision deployment, but this may limit you to exclusively using your vision deployment for all Azure chat settings.
**Notes:**
- If using `AZURE_OPENAI_BASEURL`, you should not specify instance and deployment names instead of placeholders as the vision request will fail.
- As of December 18th, 2023, Vision models seem to have degraded performance with Azure OpenAI when compared to [OpenAI](#openai)
- As of December 18th, 2023, Vision models seem to have degraded performance with Azure OpenAI when compared to [OpenAI](./ai_setup.md#openai)
![image](https://github.com/danny-avila/LibreChat/assets/110412045/7306185f-c32c-4483-9167-af514cc1c2dd)