# Description
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# Description
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# Description
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## Screenshots (if appropriate):
This pull request updates the docker-compose files to specify the
platform for the backend services as linux/amd64. This ensures that the
services are built and run specifically for the amd64 architecture.
# Description
Please include a summary of the changes and the related issue. Please
also include relevant motivation and context.
## Checklist before requesting a review
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my feature works
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## Screenshots (if appropriate):
Co-authored-by: Stan Girard <stan@quivr.app>
# Description
closes#2756
- Fixed torchvision cpu version in poetry
- Moved test dependencies to dev group
Co-authored-by: aminediro <aminediro@github.com>
# Description
- Added package manager
- Added precommit checks
- Rewrote dependency injection of Services and Repositories
- Integrate async SQL alchemy engine
- Migrate Chat repository to SQLModel
- Migrated ChatHistory repository to SQLModel
- User SQLModel
- Unit test methodology with db rollback
- Unit tests ChatRepository
- Test ChatService get_history
- Brain entity SQL Model
- Promp SQLModel
- Rewrite chat/{chat_id}/question route
- updated docker files and docker compose in dev and production
Added `quivr_core` subpackages:
- Refactored KnowledgebrainQa
- Added Rag service to interface with non-rag dependencies
---------
Co-authored-by: aminediro <aminediro@github.com>
Fixes#2718
# Description
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my feature works
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- [ ] Any dependent changes have been merged
## Screenshots (if appropriate):
---------
Co-authored-by: Stan Girard <stan@quivr.app>
# Description
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## Checklist before requesting a review
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my feature works
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## Screenshots (if appropriate):
Co-authored-by: Stan Girard <stan@quivr.app>
This commit adds timezone conversion to the premium user check in the
`check_if_is_premium_user` function. It imports the `pytz` library and
retrieves the current time in the "Europe/Paris" timezone. This ensures
that the comparison of the current period end with the current time is
accurate, taking into account the correct timezone. The Paris time is
logged for debugging purposes.
Co-authored-by: Stan Girard <stan@quivr.app>
# Description
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also include relevant motivation and context.
## Checklist before requesting a review
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my feature works
- [ ] New and existing unit tests pass locally with my changes
- [ ] Any dependent changes have been merged
## Screenshots (if appropriate):
---------
Co-authored-by: Stan Girard <stan@quivr.app>
"This pull request adds a new celery task called
`check_if_is_premium_user` that checks if a user is a premium user based
on their subscription status. The task retrieves the list of active
subscriptions and the list of customers from the Supabase database. It
then matches the subscriptions with the customers and updates the user
settings with the corresponding premium features if a match is found. If
a user is not found or their subscription is expired, the user settings
are deleted. This task will run periodically to keep the user settings
up to date with the subscription status.
---------
Co-authored-by: Stan Girard <stan@quivr.app>
# Description
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also include relevant motivation and context.
## Checklist before requesting a review
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- [ ] My code follows the style guidelines of this project
- [ ] I have performed a self-review of my code
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my feature works
- [ ] New and existing unit tests pass locally with my changes
- [ ] Any dependent changes have been merged
## Screenshots (if appropriate):
---------
Co-authored-by: Stan Girard <stan@quivr.app>
This pull request adds error handling for the syncing process in the
tasks.py file. Previously, if an error occurred during the syncing
process, the program would crash. With this change, any exceptions that
occur during syncing will be caught and logged, allowing the program to
continue running. This improves the stability and reliability of the
syncing functionality.
Co-authored-by: Stan Girard <stan@quivr.app>
This commit adds a new utility function `remove_special_characters` to
the `normalize.py` module in the `sync/utils` directory. The function
removes special characters from file names by normalizing the input
string and using regular expressions to remove non-alphanumeric
characters.
The function is then used in the `list_files.py` module in the
`sync/utils` directory to normalize the names of files retrieved from
Google Drive and Azure Drive. This ensures that the file names are free
of special characters, improving consistency and compatibility with
other parts of the system.
Co-authored-by: Stan Girard <stan@quivr.app>
The code changes in `google_sync_routes.py` update the authorization URL
for Google authentication. The `prompt` parameter is added with the
value "consent" to ensure that users are prompted to grant consent when
authorizing the application. This improves the user experience and
ensures that the necessary permissions are obtained.
# Description
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also include relevant motivation and context.
## Checklist before requesting a review
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- [ ] My code follows the style guidelines of this project
- [ ] I have performed a self-review of my code
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my feature works
- [ ] New and existing unit tests pass locally with my changes
- [ ] Any dependent changes have been merged
## Screenshots (if appropriate):
This pull request updates the license to include enterprise features.
The license now specifies that certain folders or files within the
distribution may be subject to different license terms and conditions,
which will be specified in a separate LICENSE file or within the file
itself. Additionally, a new Quivr Enterprise license has been added,
which restricts the use of the software to production environments only
if the user has a valid Quivr Enterprise license for the correct number
of user seats. The license also clarifies that modifications and patches
to the software can only be used with a valid Quivr Enterprise license.
Finally, the license includes a disclaimer of warranties and limitations
of liability.
This commit adds a new optional boolean field, force_sync, to the
SyncsActiveUpdateInput class in the sync module. The force_sync field
allows users to manually trigger a sync operation, bypassing the regular
sync interval. By default, force_sync is set to False.
The force_sync field is used in the GoogleSyncUtils and AzureSyncUtils
classes to determine whether to perform a sync operation even if the
regular sync interval has not elapsed. If force_sync is True, the
last_synced timestamp is updated and the sync operation is executed.
This enhancement provides more flexibility and control over the
synchronization process, allowing users to manually trigger sync
operations when needed.
# Description
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also include relevant motivation and context.
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my feature works
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- [ ] Any dependent changes have been merged
## Screenshots (if appropriate):
# Description
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## Checklist before requesting a review
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- [ ] Any dependent changes have been merged
## Screenshots (if appropriate):
# Description
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## Checklist before requesting a review
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- [ ] Any dependent changes have been merged
## Screenshots (if appropriate):
# Description
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my feature works
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- [ ] Any dependent changes have been merged
## Screenshots (if appropriate):
This pull request adds support for recursive folder retrieval in the
`get_files_folder_user_sync` method. Previously, the method only
retrieved files from the specified folder, but now it can also retrieve
files from all subfolders recursively. This enhancement improves the
functionality and flexibility of the method, allowing for more
comprehensive file retrieval in sync operations.
# Description
Please include a summary of the changes and the related issue. Please
also include relevant motivation and context.
## Checklist before requesting a review
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my feature works
- [ ] New and existing unit tests pass locally with my changes
- [ ] Any dependent changes have been merged
## Screenshots (if appropriate):
This pull request includes updates to the `docker-compose.dev.yml` and
`Dockerfile.dev` files. The changes aim to improve performance and fix
bugs. The updates include:
- Removing unnecessary workers configuration in the
`docker-compose.dev.yml` file.
- Updating the base image in the `Dockerfile.dev` to use a slim version.
- Adjusting the schedule for a specific task in the code.
- Modifying the time interval for retrieving active syncs.
- Changing the loader class for processing PowerPoint files.
- Refactoring the file existence check logic.
- Adding debug logs for file existence check and file removal.
- Adjusting the file synchronization logic.
These changes are intended to enhance the performance and stability of
the application.
# Description
Hey,
Here's a breakdown of what I've done:
- Reducing the number of opened fd and memory footprint: Previously, for
each uploaded file, we were opening a temporary NamedTemporaryFile to
write existing content read from Supabase. However, due to the
dependency on `langchain` loader classes, we couldn't use memory buffers
for the loaders. Now, with the changes made, we only open a single
temporary file for each `process_file_and_notify`, cutting down on
excessive file opening, read syscalls, and memory buffer usage. This
could cause stability issues when ingesting and processing large volumes
of documents. Unfortunately, there is still reopening of temporary files
in some code paths but this can be improved further in later work.
- Removing `UploadFile` class from File: The `UploadFile` ( a FastAPI
abstraction over a SpooledTemporaryFile for multipart upload) was
redundant in our `File` setup since we already downloaded the file from
remote storage and read it into memory + wrote the file into a temp
file. By removing this abstraction, we streamline our code and eliminate
unnecessary complexity.
- `async` function Adjustments: I've removed the async labeling from
functions where it wasn't truly asynchronous. For instance, calling
`filter_file` for processing files isn't genuinely async, ass async file
reading isn't actually asynchronous—it [uses a threadpool for reading
the
file](9f16bf5c25/starlette/datastructures.py (L458))
. Given that we're already leveraging `celery` for parallelism (one
worker per core), we need to ensure that reading and processing occur in
the same thread, or at least minimize thread spawning. Additionally,
since the rest of the code isn't inherently asynchronous, our bottleneck
lies in CPU operations rather than asynchronous processing.
These changes aim to improve performance and streamline our codebase.
Let me know if you have any questions or suggestions for further
improvements!
## Checklist before requesting a review
- [x] My code follows the style guidelines of this project
- [x] I have performed a self-review of my code
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my feature works
---------
Signed-off-by: aminediro <aminediro@github.com>
Co-authored-by: aminediro <aminediro@github.com>
Co-authored-by: Stan Girard <girard.stanislas@gmail.com>
# Description
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also include relevant motivation and context.
## Checklist before requesting a review
Please delete options that are not relevant.
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## Screenshots (if appropriate):
# Description
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## Checklist before requesting a review
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my feature works
- [ ] New and existing unit tests pass locally with my changes
- [ ] Any dependent changes have been merged
## Screenshots (if appropriate):
This pull request updates the parsing instructions in the `common.py`
file for the `llamaparse` feature. The previous parsing instruction for
transforming checkboxes into text has been modified to also extract
tables and transform them into key-value pairs. Additionally, the
instruction now allows for duplicate keys if needed. The example
instructions have also been updated to provide clearer examples for both
tables and checkboxes.
# Description
Change the prompt of the thoughts feature to have more steps.
## Checklist before requesting a review
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my feature works
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## Screenshots (if appropriate):
# Description
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also include relevant motivation and context.
## Checklist before requesting a review
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my feature works
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- [ ] Any dependent changes have been merged
## Screenshots (if appropriate):
---------
Co-authored-by: chloedia <chloedaems0@gmail.com>
This pull request refactors the generate_answer and generate_stream
functions in order to improve code readability and maintainability. It
also adds new fields to the cited_answer model and updates the system
message template.
# Description
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also include relevant motivation and context.
## Checklist before requesting a review
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my feature works
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- [ ] Any dependent changes have been merged
## Screenshots (if appropriate):
# Description
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- [ ] Any dependent changes have been merged
## Screenshots (if appropriate):
# Description
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my feature works
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- [ ] Any dependent changes have been merged
## Screenshots (if appropriate):
# Description
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my feature works
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- [ ] Any dependent changes have been merged
## Screenshots (if appropriate):
# Description
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my feature works
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## Screenshots (if appropriate):
# Description
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also include relevant motivation and context.
## Checklist before requesting a review
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my feature works
- [ ] New and existing unit tests pass locally with my changes
- [ ] Any dependent changes have been merged
## Screenshots (if appropriate):
This pull request fixes the sender email address in the
`resend_invitation_email` function in the `resend_invitation_email.py`
file. The `from` field has been changed to `sender` to ensure that the
correct email address is used when sending the invitation email.
This pull request updates the ChatLiteLLM model to "gpt-4o" and adds a
row-level security (RLS) optimization for notifications. It also
includes a new SQL script to drop and create a policy for allowing user
access to all notifications.
This pull request adds support for the gpt-4o model to the existing
codebase. It includes changes to the BrainConfig, openAiFreeModels,
defineMaxTokens, model_compatible_with_function_calling, create_graph,
main, and process_assistant functions.
This pull request fixes the import statements for OllamaEmbeddings in
multiple files. The import statements are updated to use the correct
package name "langchain_community.embeddings" instead of
"langchain.embeddings.ollama". This ensures that the code can be
compiled and executed without any import errors.
This pull request adds comprehensive docstrings to the Brain classes
within the `backend/modules/brain/integrations` directory, enhancing
code documentation and readability. The changes include:
- **BigBrain (`Big/Brain.py`)**: Adds a class-level docstring explaining
the purpose and functionality of the BigBrain class, along with
method-level docstrings detailing the operations performed by each
method.
- **ClaudeBrain (`Claude/Brain.py`)**: Introduces a class-level
docstring that describes the ClaudeBrain class's integration with the
Claude model for conversational AI capabilities, and method-level
docstrings that clarify the purpose of each method.
- **GPT4Brain (`GPT4/Brain.py`)**: Updates include a detailed
class-level docstring outlining the GPT4Brain's integration with GPT-4
for real-time answers and tool support, along with method-level
docstrings explaining the functionality of each method.
- **NotionBrain (`Notion/Brain.py`)**: Adds a class-level docstring that
describes the NotionBrain's role in leveraging Notion data for
knowledge-based responses.
- **ProxyBrain (`Proxy/Brain.py`)**: Incorporates a class-level
docstring explaining the ProxyBrain's function as a dynamic language
model selector and method-level docstrings detailing the operations of
each method.
These additions ensure that each Brain class and its methods are
well-documented, providing clear insights into their purposes and
functionalities.
---
For more details, open the [Copilot Workspace
session](https://copilot-workspace.githubnext.com/QuivrHQ/quivr?shareId=b4e301ad-828e-4424-95ec-6e378d5d3849).
Updates the GPT-4 documentation and the `GPT4Brain` class to include
detailed information about the tools available for GPT4Brain and their
use cases.
- **Documentation (`docs/brains/gpt4.mdx`):**
- Adds a new section titled "Tools Available for GPT4Brain" that
describes specific tools: WebSearchTool, ImageGeneratorTool,
URLReaderTool, and EmailSenderTool.
- Provides use cases for each tool, demonstrating how they can be
utilized within GPT4Brain for various scenarios, such as generating
images, reading content from URLs, and sending emails.
- **Code (`backend/modules/brain/integrations/GPT4/Brain.py`):**
- Updates the class documentation to include information about the tools
available for GPT4Brain and outlines use cases for WebSearchTool,
ImageGeneratorTool, URLReaderTool, and EmailSenderTool.
- Maintains the existing functionality of the `GPT4Brain` class,
ensuring compatibility with the newly documented tools and use cases.
---
For more details, open the [Copilot Workspace
session](https://copilot-workspace.githubnext.com/QuivrHQ/quivr?shareId=2c2c1666-e5fb-4a06-bb08-ca967f4fe276).
