This pull request includes a feature update to the newest version of
litellm. This update brings in the latest enhancements and bug fixes to
improve the functionality and performance of the software.
# Description
With this pull request, I add a fix that **enables Ollama**.
Running quivr in a Docker container, Ollama needs to be accessed via
http://host.docker.internal:11434.
This URL is defined as `api_base` of the `ChatLiteLLM` constructor. But
there are calls that are missing `api_base`. This leads to fallback URLs
being used. This is now fixed.
Fixes#2270#2267#2217#2204
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## Screenshots (if appropriate):
None
Co-authored-by: Niels Garve <niels.garve@experienceone.com>
This pull request updates the integration types and classes in the
codebase. It includes the following commits:
- Update logo_url field in brain entity and integration_brain entity
- Update integration references in BrainsUsers and IntegrationBrain
classes
- Revert "Update integration references in BrainsUsers and
IntegrationBrain classes"
- Revert "Update logo_url field in brain entity and integration_brain
entity"
- Add 'doc' integration type and KnowledgeBrainQA class
Please review and merge these changes.
This pull request adds a custom integration feature and sync
functionality to the application. It includes the following changes:
- Added a new integration entity for custom integrations.
- Implemented the ability to load and poll the custom integration.
- Added a task to sync the custom integration with the user's brain.
- Updated the celery beat schedule to include the new task.
Please review and merge this pull request.
This pull request updates the origins in cors.py, origin in
resend_invitation_email.py, and referring_site in
add-new-email/index.ts. It also refactors the code in home/page.tsx and
next.config.js.
This pull request removes the healthz endpoint from the Sentry
integration. The `before_send` function has been updated to drop
transaction events that contain 'healthz' in the transaction name. This
change ensures that healthz transactions are not sent to Sentry.
This pull request adds the `max_input` and `max_tokens` parameters to
the `KnowledgeBrainQA` constructor. These parameters allow for setting
the maximum input length and maximum number of tokens for the
KnowledgeBrainQA model. This provides more flexibility and control over
the input size and tokenization process.
This pull request reduces the sampling rate for Sentry traces to 0.1.
This change ensures that only a small percentage of traces are captured,
which helps to reduce the impact on performance and storage.
This pull request fixes the `get_brain_details` API by adding a
`user_id` parameter. Previously, the API did not include the `user_id`
parameter, which caused issues when retrieving brain details. This fix
ensures that the `user_id` is passed correctly to the API, allowing for
accurate retrieval of brain details.
# Description
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## Screenshots (if appropriate):
This pull request adds a new attribute, max_files, to the
MinimalUserBrainEntity class in the BrainsUsers repository. The
max_files attribute represents the maximum number of files allowed for a
brain integration.
This pull request adds Redis configuration to the `celery_config.py`
file. The `CELERY_BROKER_URL` has been replaced with `REDIS_HOST`,
`REDIS_PORT`, and `REDIS_PASS` environment variables to configure the
Redis broker and backend. This change allows for better integration with
Redis in the application.
# Description
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## Screenshots (if appropriate):
This pull request updates the brain authorization and integration
entities. It includes changes to the `retrieve_brain_by_id`,
`update_existing_brain`, `update_existing_brain_secrets`,
`CreateApiBrainDefinition`, `CreateIntegrationBrain`,
`BrainIntegrationSettings`, `BrainIntegrationUpdateSettings`,
`CreateBrainProperties`, and `BrainUpdatableProperties` classes.
This pull request adds a new method called `calculate_pricing` to the
`GPT4Brain` class in the codebase. This method calculates the pricing
for the GPT4Brain model. Additionally, the user usage in the chat
controller has been updated to include the new pricing calculation
method.
This pull request updates the system templates to include support for
custom personality. It adds a new parameter called "custom_personality"
to the generate_stream function, which allows users to provide a custom
personality for the AI assistant. This feature enhances the user
experience by allowing them to personalize the assistant's responses.
# Description
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## Screenshots (if appropriate):
---------
Co-authored-by: Antoine Dewez <44063631+Zewed@users.noreply.github.com>
added ocr
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## Screenshots (if appropriate):
# Description
* This just corrects the word response in the function that reads chat
history.
* I have also noticed a little automatic change due to formatting. But
the update seems to be inline with the accepted formatting of this
project so I kept it.
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## Screenshots (if appropriate):
improve speed by using memoisation
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## Screenshots (if appropriate):
moved to brains
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## Screenshots (if appropriate):
---------
Co-authored-by: Antoine Dewez <44063631+Zewed@users.noreply.github.com>
now you can download & view pdf
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## Screenshots (if appropriate):
selection now fixed
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## Screenshots (if appropriate):
optimized number of calls
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## Screenshots (if appropriate):
backend test fixed
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## Screenshots (if appropriate):
fixed when brainful is empty
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## Screenshots (if appropriate):
added metadata object a bit bigger
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## Screenshots (if appropriate):
now updates
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## Screenshots (if appropriate):
adding metadata to api
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## Screenshots (if appropriate):
notification id issue when not in chat fixed
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## Screenshots (if appropriate):
error
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## Screenshots (if appropriate):
increased
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## Screenshots (if appropriate):
fixed where monthly was used in daily
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## Screenshots (if appropriate):
now monthly and better reading database
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## Screenshots (if appropriate):
moving from daily to monthly
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## Screenshots (if appropriate):
update vectors at launch if missign
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## Screenshots (if appropriate):
fixed public brains avaiable
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## Screenshots (if appropriate):
added preview env
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## Screenshots (if appropriate):
user settings to premium
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## Screenshots (if appropriate):
fixed when model is not choosen
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## Screenshots (if appropriate):
updated available models
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## Screenshots (if appropriate):
added check on customer if still valid
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## Screenshots (if appropriate):
# DONE
- generate_stream, generate and save answer in BE
# TODO
- Create an intermediary make_streaming_recursive_tool_calls async
function
- Save intermediary answers in new message logs column then fetch and
display in front
logic
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## Screenshots (if appropriate):
to return a list of DocumentAnswer objects
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## Screenshots (if appropriate):
- Add `QAInterface` which should be implemented by all custom answer
generator to be compatible with Quivr
- Add `RAGInterface` which should be implemented by all RAG classes
# Description
Please include a summary of the changes and the related issue. Please
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## Screenshots (if appropriate):
# Description
There was a typo on one of the celery's config variables.
The `CELEBRY_BROKER_QUEUE_NAME` variable has been replaced by
`CELERY_BROKER_QUEUE_NAME`
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## Screenshots (if appropriate):
now made easy
# Description
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## Screenshots (if appropriate):
# Description
- Refactor knowledge to a module
- This PR breaks the Github Processor function -> need to comment brain
and file imports as it creates a circular dependency issue. Should be
fixed and reverted in next PR.
upgraded
# Description
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## Screenshots (if appropriate):
# Description
- Notification module
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## Screenshots (if appropriate):
fixed
# Description
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## Screenshots (if appropriate):
reduce sampling
# Description
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## Screenshots (if appropriate):
# Description
```
======================================================================
backend-core | --- Logging error ---
backend-core | Traceback (most recent call last):
backend-core | File "/usr/local/lib/python3.11/logging/__init__.py", line 1110, in emit
backend-core | msg = self.format(record)
backend-core | ^^^^^^^^^^^^^^^^^^^
backend-core | File "/usr/local/lib/python3.11/logging/__init__.py", line 953, in format
backend-core | return fmt.format(record)
backend-core | ^^^^^^^^^^^^^^^^^^
backend-core | File "/usr/local/lib/python3.11/logging/__init__.py", line 687, in format
backend-core | record.message = record.getMessage()
backend-core | ^^^^^^^^^^^^^^^^^^^
backend-core | File "/usr/local/lib/python3.11/logging/__init__.py", line 377, in getMessage
backend-core | msg = msg % self.args
backend-core | ~~~~^~~~~~~~~~~
backend-core | TypeError: not all arguments converted during string formatting
backend-core | Call stack:
backend-core | File "<string>", line 1, in <module>
backend-core | File "/usr/local/lib/python3.11/multiprocessing/spawn.py", line 122, in spawn_main
backend-core | exitcode = _main(fd, parent_sentinel)
backend-core | File "/usr/local/lib/python3.11/multiprocessing/spawn.py", line 135, in _main
backend-core | return self._bootstrap(parent_sentinel)
backend-core | File "/usr/local/lib/python3.11/multiprocessing/process.py", line 314, in _bootstrap
backend-core | self.run()
backend-core | File "/usr/local/lib/python3.11/multiprocessing/process.py", line 108, in run
backend-core | self._target(*self._args, **self._kwargs)
backend-core | File "/usr/local/lib/python3.11/site-packages/uvicorn/_subprocess.py", line 76, in subprocess_started
backend-core | target(sockets=sockets)
backend-core | File "/usr/local/lib/python3.11/site-packages/uvicorn/server.py", line 61, in run
backend-core | return asyncio.run(self.serve(sockets=sockets))
backend-core | File "/usr/local/lib/python3.11/asyncio/runners.py", line 190, in run
backend-core | return runner.run(main)
backend-core | File "/usr/local/lib/python3.11/asyncio/runners.py", line 118, in run
backend-core | return self._loop.run_until_complete(task)
backend-core | File "/usr/local/lib/python3.11/asyncio/base_events.py", line 640, in run_until_complete
backend-core | self.run_forever()
backend-core | File "/usr/local/lib/python3.11/asyncio/base_events.py", line 607, in run_forever
backend-core | self._run_once()
backend-core | File "/usr/local/lib/python3.11/asyncio/base_events.py", line 1922, in _run_once
backend-core | handle._run()
backend-core | File "/usr/local/lib/python3.11/asyncio/events.py", line 80, in _run
backend-core | self._context.run(self._callback, *self._args)
backend-core | File "/usr/local/lib/python3.11/site-packages/uvicorn/protocols/http/h11_impl.py", line 428, in run_asgi
backend-core | result = await app( # type: ignore[func-returns-value]
backend-core | File "/usr/local/lib/python3.11/site-packages/uvicorn/middleware/proxy_headers.py", line 78, in __call__
backend-core | return await self.app(scope, receive, send)
backend-core | File "/usr/local/lib/python3.11/site-packages/fastapi/applications.py", line 276, in __call__
backend-core | await super().__call__(scope, receive, send)
backend-core | File "/usr/local/lib/python3.11/site-packages/starlette/applications.py", line 122, in __call__
backend-core | await self.middleware_stack(scope, receive, send)
backend-core | File "/usr/local/lib/python3.11/site-packages/starlette/middleware/errors.py", line 162, in __call__
backend-core | await self.app(scope, receive, _send)
backend-core | File "/usr/local/lib/python3.11/site-packages/starlette/middleware/cors.py", line 91, in __call__
backend-core | await self.simple_response(scope, receive, send, request_headers=headers)
backend-core | File "/usr/local/lib/python3.11/site-packages/starlette/middleware/cors.py", line 146, in simple_response
backend-core | await self.app(scope, receive, send)
backend-core | File "/usr/local/lib/python3.11/site-packages/starlette/middleware/exceptions.py", line 68, in __call__
backend-core | await self.app(scope, receive, sender)
backend-core | File "/usr/local/lib/python3.11/site-packages/fastapi/middleware/asyncexitstack.py", line 18, in __call__
backend-core | await self.app(scope, receive, send)
backend-core | File "/usr/local/lib/python3.11/site-packages/starlette/routing.py", line 718, in __call__
backend-core | await route.handle(scope, receive, send)
backend-core | File "/usr/local/lib/python3.11/site-packages/starlette/routing.py", line 276, in handle
backend-core | await self.app(scope, receive, send)
backend-core | File "/usr/local/lib/python3.11/site-packages/starlette/routing.py", line 66, in app
backend-core | response = await func(request)
backend-core | File "/usr/local/lib/python3.11/site-packages/fastapi/routing.py", line 237, in app
backend-core | raw_response = await run_endpoint_function(
backend-core | File "/usr/local/lib/python3.11/site-packages/fastapi/routing.py", line 163, in run_endpoint_function
backend-core | return await dependant.call(**values)
backend-core | File "/code/routes/knowledge_routes.py", line 37, in list_knowledge_in_brain_endpoint
backend-core | logger.info("List of knowledge from knowledge table", knowledges)
backend-core | Message: 'List of knowledge from knowledge table'
backend-core | Arguments: ([Knowledge(id=UUID('b5ae6a25-1e8e-4fb5-a0c1-0039a8318ec3'), brain_id=UUID('7af64537-61cb-48e1-b3d1-c700d748b373'), file_name='Cradle to Cradle Criteria for the built environmen.pdf', url=None, extension='.pdf'), Knowledge(id=UUID('efb7ad5a-a13e-46f3-a580-4c574710ad15'), brain_id=UUID('7af64537-61cb-48e1-b3d1-c700d748b373'), file_name='GEG_2023_gebaeudeenergiegesetz_kompakt_und_praktisch.pdf', url=None, extension='.pdf'), Knowledge(id=UUID('9442f898-0fbe-46d2-b6ae-a726d62717bd'), brain_id=UUID('7af64537-61cb-48e1-b3d1-c700d748b373'), file_name='dgnb-kriterienkatalog-gebaeude-neubau-version-2023-auflage-2.pdf', url=None, extension='.pdf')],)
======================================================================
```
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# Description
Prompt module with Service
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## Screenshots (if appropriate):
To enable the ingestion of copy protected PDF via OCR instead of text
extraction
# Description
Copy protected PDF can't be properly imported via the standard langchain
loader.
See the following errors:
```
2023-11-15 14:16:31,927 [INFO] models.files: Computing documents from file Cradle to Cradle Criteria for the built environmen.pdf
[nltk_data] Downloading package punkt to
[nltk_data] /home/pascal_gula_luccid_ai/nltk_data...
[nltk_data] Unzipping tokenizers/punkt.zip.
[nltk_data] Downloading package averaged_perceptron_tagger to
[nltk_data] /home/pascal_gula_luccid_ai/nltk_data...
[nltk_data] Unzipping taggers/averaged_perceptron_tagger.zip.
Error processing file: detectron2 is not installed, pytesseract is not installed and the text of the PDF is not extractable. To process this file, install detectron2, install pytesseract, or remove copy protection from the PDF.
```
```
2023-11-15 15:04:14,624 [INFO] models.files: Computing documents from file Cradle to Cradle Criteria for the built environmen.pdf
Error processing file: Unable to get page count. Is poppler installed and in PATH?
```
```
023-11-15 15:59:11,886 [INFO] models.files: Computing documents from file Cradle to Cradle Criteria for the built environmen.pdf
Error processing file: tesseract is not installed or it's not in your PATH. See README file for more information.
```
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## Screenshots (if appropriate):
None
Co-authored-by: Stan Girard <girard.stanislas@gmail.com>
increased default value to 1000
# Description
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## Screenshots (if appropriate):
truncating long response
# Description
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## Screenshots (if appropriate):
cleaning old code to introduce better patern
# Description
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## Screenshots (if appropriate):
added explore button and removed unused feature openai key
# Description
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## Screenshots (if appropriate):
reduced size by 2
# Description
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## Screenshots (if appropriate):
# Description
Please include a summary of the changes and the related issue. Please
also include relevant motivation and context.
## Checklist before requesting a review
fix/brain-management-shared-brain-access
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## Screenshots (if appropriate):
# Description
New Modules folder with "user" module:
- controller: contains the current route
- entity: contains the current Models (TO be renamed DTO)
- repository: contains the current repo
- service: methods used by other modules
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## Screenshots (if appropriate):
fixed
# Description
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## Screenshots (if appropriate):
# Description
Currently, the production Docker images are very large, sitting at 4.17
GB for the frontend image, and 3.49 GB for backend images. This change
adds multi-stage builds, to optimize the image sizes, which results in
the following improvements:
- frontend image size: 4.17 GB -> 1.64 GB
- backend image size: 3.49 GB -> 1.71 GB
I hope this is appropriate as there is no open issue for this that I
know of.
I implemented this change and tested it locally, and would be glad to
discuss this and open an issue if necessary.
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## Screenshots (if appropriate):
Image sizes before:
<img width="1416" alt="image"
src="https://github.com/StanGirard/quivr/assets/8296549/fcbb020f-8165-4549-ae30-823318691ec6">
Image sizes after:
<img width="1416" alt="image"
src="https://github.com/StanGirard/quivr/assets/8296549/d3f43c78-be26-4c38-9d23-9c1b0e9e37f2">
# Description
https://github.com/StanGirard/quivr/issues/1551
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## Screenshots (if appropriate):
are kept only once
# Description
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## Screenshots (if appropriate):
added loaders and compatibility
# Description
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## Screenshots (if appropriate):
# Description
- Refactor useSettingsTab by extracting usePrompt and methods in utils
- Fix bug #1545
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## Screenshots (if appropriate):
added limit to 4k for gpt4
# Description
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also include relevant motivation and context.
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- [ ] I have performed a self-review of my code
- [ ] I have commented hard-to-understand areas
- [ ] I have ideally added tests that prove my fix is effective or that
my feature works
- [ ] New and existing unit tests pass locally with my changes
- [ ] Any dependent changes have been merged
## Screenshots (if appropriate):
# Description
Please include a summary of the changes and the related issue. Please
also include relevant motivation and context.
## Checklist before requesting a review
Please delete options that are not relevant.
- [ ] My code follows the style guidelines of this project
- [ ] I have performed a self-review of my code
- [ ] I have commented hard-to-understand areas
- [ ] I have ideally added tests that prove my fix is effective or that
my feature works
- [ ] New and existing unit tests pass locally with my changes
- [ ] Any dependent changes have been merged
## Screenshots (if appropriate):
# Description
Please include a summary of the changes and the related issue. Please
also include relevant motivation and context.
## Checklist before requesting a review
Please delete options that are not relevant.
- [ ] My code follows the style guidelines of this project
- [ ] I have performed a self-review of my code
- [ ] I have commented hard-to-understand areas
- [ ] I have ideally added tests that prove my fix is effective or that
my feature works
- [ ] New and existing unit tests pass locally with my changes
- [ ] Any dependent changes have been merged
## Screenshots (if appropriate):
# Description
Please include a summary of the changes and the related issue. Please
also include relevant motivation and context.
## Checklist before requesting a review
Please delete options that are not relevant.
- [ ] My code follows the style guidelines of this project
- [ ] I have performed a self-review of my code
- [ ] I have commented hard-to-understand areas
- [ ] I have ideally added tests that prove my fix is effective or that
my feature works
- [ ] New and existing unit tests pass locally with my changes
- [ ] Any dependent changes have been merged
## Screenshots (if appropriate):
