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Didier Durand committed Feb 27, 2024
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Expand Up @@ -21,11 +21,12 @@ We will add new scripts based on your demand: feel free to cut a ticket
[here](https://github.com/didier-durand/qstensils/issues) if you have a need or idea!

We currently provide the following utilities:
1. [q_list_applications](doc/q_list_applications.md) to inventory the applications existing in a given AWS account. The returned json structure
details the various components (indices, data source, retrievers, etc.) of those Amazon Q applications.
2. [q_list_data_source_sync_jobs](doc/q_list_data_source_sync_jobs.md) to list the history of index synchronization jobs executed on a given
Q data source. This script adds additional metric like total job duration, document scan rate and average scan duration
per document.
1. [q_list_applications](doc/q_list_applications.md) to inventory the applications existing in a given region of an AWS account. The returned
json structure details the various components (indices, data source, retrievers, etc.) of those Amazon Q
applications.
2. [q_list_data_source_sync_jobs](doc/q_list_data_source_sync_jobs.md) to list the history of index synchronization
jobs executed on a given Q data source. This script adds additional metric like total job duration, document scan rate
and average scan duration per document.
3. [q_list_documents](doc/q_list_documents.md) to list all the documents of an Amazon Q index and get all their associated metadata,
in particular their status. The returned list can be filtered (via inclusion or exclusion) to return
only a fraction of those documents for example based on their indexing status.
Expand All @@ -52,8 +53,8 @@ by the assistant will be prepared through the leverage of RAG technology.

[Retrieval-Augmented Generation](https://www.promptingguide.ai/techniques/rag) (RAG) is a natural language processing (NLP) technique. It is composed of a
language model-based system, usually a [Large Language Model](https://en.wikipedia.org/wiki/Large_language_model) (LLM), that accesses external knowledge sources
to complete tasks. This enables more contextuality, factual consistency, improves reliability of the generated responses, and helps
to mitigate the problem of "hallucinations".
to complete tasks. This enables more contextuality, factual consistency, improves reliability of the generated
responses, and helps to mitigate the problem of "hallucinations".

### Security

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