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docs: Some aesthetic changes to the Building AI Applicaitons to make them read a little easier

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Signed-off-by: Francisco Javier Arceo <farceo@redhat.com>
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Francisco Javier Arceo 2025-04-03 22:33:21 -04:00
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28
docs/source/building_applications/rag.md
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@ -3,9 +3,9 @@
RAG enables your applications to reference and recall information from previous interactions or external documents.
Llama Stack organizes the APIs that enable RAG into three layers:
- the lowermost APIs deal with raw storage and retrieval. These include Vector IO, KeyValue IO (coming soon) and Relational IO (also coming soon.)
- next is the "Rag Tool", a first-class tool as part of the Tools API that allows you to ingest documents (from URLs, files, etc) with various chunking strategies and query them smartly.
- finally, it all comes together with the top-level "Agents" API that allows you to create agents that can use the tools to answer questions, perform tasks, and more.
1. The lowermost APIs deal with raw storage and retrieval. These include Vector IO, KeyValue IO (coming soon) and Relational IO (also coming soon.).
2. The next is the "Rag Tool", a first-class tool as part of the [Tools API](tools.md) that allows you to ingest documents (from URLs, files, etc) with various chunking strategies and query them smartly.
3. Finally, it all comes together with the top-level ["Agents" API](agent.md) that allows you to create agents that can use the tools to answer questions, perform tasks, and more.
<img src="rag.png" alt="RAG System" width="50%">
@ -17,14 +17,19 @@ We may add more storage types like Graph IO in the future.
### Setting up Vector DBs
For this guide, we will use [Ollama](https://ollama.com/) as the inference provider.
Ollama is an LLM runtime that allows you to run Llama models locally.
Here's how to set up a vector database for RAG:
```python
# Create http client
import os
from llama_stack_client import LlamaStackClient
client = LlamaStackClient(base_url=f"http://localhost:{os.environ['LLAMA_STACK_PORT']}")
# Register a vector db
vector_db_id = "my_documents"
response = client.vector_dbs.register(
@ -33,17 +38,27 @@ response = client.vector_dbs.register(
embedding_dimension=384,
provider_id="faiss",
)
```
### Ingesting Documents
You can ingest documents into the vector database using two methods: directly inserting pre-chunked
documents or using the RAG Tool.
```python
# You can insert a pre-chunked document directly into the vector db
chunks = [
{
"document_id": "doc1",
"content": "Your document text here",
"mime_type": "text/plain",
"metadata": {
"document_id": "doc1",
},
},
]
client.vector_io.insert(vector_db_id=vector_db_id, chunks=chunks)
```
### Retrieval
You can query the vector database to retrieve documents based on their embeddings.
```python
# You can then query for these chunks
chunks_response = client.vector_io.query(
vector_db_id=vector_db_id, query="What do you know about..."
@ -52,7 +67,8 @@ chunks_response = client.vector_io.query(
### Using the RAG Tool
A better way to ingest documents is to use the RAG Tool. This tool allows you to ingest documents from URLs, files, etc. and automatically chunks them into smaller pieces.
A better way to ingest documents is to use the RAG Tool. This tool allows you to ingest documents from URLs, files, etc.
and automatically chunks them into smaller pieces.
```python
from llama_stack_client import RAGDocument