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.
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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.
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This pull request fixes the parsing instruction in the common.py file.
The result_type has been corrected to "markdown" and the
parsing_instruction has been updated to handle checkboxes, tables, and
other elements that are hard to parse in a meaningful way.
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This pull request fixes the citation handling in the ChatItem component.
Previously, if the citation did not contain the word "Content:", the
component would throw an error. This PR adds a check for the presence of
"Content:" in the citation and handles it accordingly.
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This pull request adds Llama Parse integration for complex document
parsing in Quivr. Llama Parse is a tool from Llama Index that allows you
to read complex documents in Quivr. It provides an API key that needs to
be added to the `.env` file as `LLAMA_CLOUD_API_KEY`. Once configured,
you can use the Llama Parse tool to read `pdf`, `docx`, and `doc` files
in Quivr.
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This pull request adds caching for the Supabase client and database
instances in order to improve performance and reduce unnecessary API
calls. The `get_supabase_client()` and `get_supabase_db()` functions now
check if the instances have already been created and return the cached
instances if available. This avoids creating new instances for every
function call, resulting in faster execution times.
This pull request adds the pyinstrument package and updates the Makefile
and backend code. The pyinstrument package is used for profiling and the
Makefile and backend code have been modified to support profiling.
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