# Description
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## Checklist before requesting a review
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## Screenshots (if appropriate):
This pull request adds a new config parameter to the
`conversational_qa_chain` function. The config parameter allows for
passing metadata, specifically the conversation ID, to the function.
This change ensures that the conversation ID is included in the metadata
when invoking the `conversational_qa_chain` function.
This pull request adds the ProxyBrain integration to the project. The
ProxyBrain class is responsible for handling conversational QA and
generating answers based on the provided chat history and question.
# Description
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## Checklist before requesting a review
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## Screenshots (if appropriate):
# Description
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## Checklist before requesting a review
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## Screenshots (if appropriate):
This pull request removes the citation metadata from the generate_answer
and generate_stream functions. The citation metadata was previously
being added to the streamed_chat_history and metadata dictionaries, but
it is no longer necessary. This change improves the efficiency and
clarity of the code.
This pull request updates the chunk size and overlap parameters in the
File class to improve performance. It also increases the top_n value in
the compressor for both the CohereRerank and FlashrankRerank models.
Additionally, it ensures that the page content is encoded in UTF-8
before processing.
# Description
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## Screenshots (if appropriate):
# Description
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## Checklist before requesting a review
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my feature works
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## Screenshots (if appropriate):
This pull request adds the flashrank and contextual compression
retriever to the codebase. The flashrank reranker model is used for
compression, and the contextual compression retriever combines the base
compressor and base retriever to improve document retrieval.
This pull request updates the API documentation to include new sections
on configuring Quivr and contacting the Quivr team. It also removes the
"API Brains" section from the documentation.
This pull request fixes the issue of duplicate sources in the model
response and adds metadata to the response. It removes duplicate sources
with the same name and creates a list of unique sources. Additionally,
it includes the generated URLs and sources in the metadata of the model
response.
This pull request refactors the GPT4Brain and KnowledgeBrainQA classes
to add the functionality of saving non-streaming answers. It includes
changes to the `generate_answer` method and the addition of the
`save_non_streaming_answer` method. This enhancement improves the
overall functionality and performance of the code.
# Description
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## Screenshots (if appropriate):
# Description
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# Description
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## Screenshots (if appropriate):
<!--
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----
| 🚀 This description was created by
[Ellipsis](https://www.ellipsis.dev) for commit
997576d577 |
|--------|
### Summary:
This PR involves a significant refactoring of the codebase, with file
and class relocations and renames, and updated import paths, without
introducing new functionality.
**Key points**:
- Significant refactoring of the codebase for improved organization and
clarity.
- File and class relocations and renames.
- Updated import paths to reflect new locations and names.
- No new functionality introduced.
----
Generated with ❤️ by [ellipsis.dev](https://www.ellipsis.dev)
<!--
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This pull request refactors the DEFAULT_DOCUMENT_PROMPT in quivr_rag.py
to improve code readability and maintainability. The unnecessary file
name information has been removed from the prompt template as it is now
included in the vector embedding
<!--
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----
| <a href="https://ellipsis.dev" target="_blank"><img
src="https://avatars.githubusercontent.com/u/80834858?s=400&u=31e596315b0d8f7465b3ee670f25cea677299c96&v=4"
alt="Ellipsis" width="30px" height="30px"/></a> | 🚀 This PR
description was created by [Ellipsis](https://www.ellipsis.dev) for
commit 81939457e6. |
|--------|--------|
### Summary:
This PR refactors the `DEFAULT_DOCUMENT_PROMPT` in `quivr_rag.py` by
removing the file name information from the prompt template, enhancing
code readability and maintainability.
**Key points**:
- Refactored `DEFAULT_DOCUMENT_PROMPT` in `quivr_rag.py`.
- Removed file name information from the prompt template.
- File name information is now included in the vector embedding.
----
Generated with ❤️ by [ellipsis.dev](https://www.ellipsis.dev)
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# 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):
# 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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## Screenshots (if appropriate):
---------
Co-authored-by: Stan Girard <girard.stanislas@gmail.com>
# Description
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my feature works
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## Screenshots (if appropriate):
# Description
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my feature works
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## Screenshots (if appropriate):
# Description
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## Screenshots (if appropriate):
# Description
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my feature works
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## Screenshots (if appropriate):
This pull request updates the langchain.prompts and
langchain_core.messages modules. It includes changes to the code that
improve functionality and fix any existing issues.
# Description
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also include relevant motivation and context.
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# Description
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my feature works
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## Screenshots (if appropriate):
# 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
## Checklist before requesting a review
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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 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.
# Description
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also include relevant motivation and context.
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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.
# 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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# Description
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# Description
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## Screenshots (if appropriate):
