docs: Updating contribution docs to source README.md directly

Signed-off-by: Francisco Javier Arceo <farceo@redhat.com>
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Francisco Javier Arceo 2025-04-01 00:42:29 -04:00
parent b440a1dc42
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## Using Retrieval Augmented Generation (RAG) ## Retrieval Augmented Generation (RAG)
RAG enables your applications to reference and recall information from previous interactions or external documents. RAG enables your applications to reference and recall information from previous interactions or external documents.
@ -162,3 +162,8 @@ for vector_db_id in client.vector_dbs.list():
print(f"Unregistering vector database: {vector_db_id.identifier}") print(f"Unregistering vector database: {vector_db_id.identifier}")
client.vector_dbs.unregister(vector_db_id=vector_db_id.identifier) client.vector_dbs.unregister(vector_db_id=vector_db_id.identifier)
``` ```
### Supported Data Types
You can ingest data into the vector database using `RAGDocument` objects.
The supported mime types (https://en.wikipedia.org/wiki/Media_type#mime.types) data types are:

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# Contributing to Llama Stack
Start with the [Contributing Guide](https://github.com/meta-llama/llama-stack/blob/main/CONTRIBUTING.md) for some general tips. This section covers a few key topics in more detail. ```{include} ../../../CONTRIBUTING.md
```
See the [Adding a New API Provider](new_api_provider.md) which describes how to add new API providers to the Stack.
- [Adding a New API Provider](new_api_provider.md) describes adding new API providers to the Stack.
- [Testing Llama Stack](testing.md) provides details about the testing framework and how to test providers and distributions.
```{toctree} ```{toctree}
:maxdepth: 1 :maxdepth: 1
:hidden: :hidden:
new_api_provider new_api_provider
testing
``` ```