docs: Updated docs to show minimal RAG example and some other minor changes

Signed-off-by: Francisco Javier Arceo <farceo@redhat.com>

docs/_static/css/my_theme.css

@@ -17,9 +17,13 @@
     display: none;
 }
 
-h3 {
+h2, h3, h4 {
     font-weight: normal;
 }
 html[data-theme="dark"] .rst-content div[class^="highlight"] {
   background-color: #0b0b0b;
 }
+pre {
+    white-space: pre-wrap !important;
+    word-break: break-all;
+}

docs/source/getting_started/detailed_tutorial.md

@@ -173,9 +173,8 @@ You will see the below:
 Done! You can now use the Llama Stack Client CLI with endpoint http://localhost:8321
 ```
 
-#### iii. List Available Models
 List the models
-```
+```bash
 llama-stack-client models list
 Available Models
 
@@ -190,15 +189,6 @@ Available Models
 Total models: 2
 
 ```
-
-## Step 4: Run the Demos
-
-Note that these demos show the [Python Client SDK](../references/python_sdk_reference/index.md).
-Other SDKs are also available, please refer to the [Client SDK](../index.md#client-sdks) list for the complete options.
-
-::::{tab-set}
-
-:::{tab-item} Basic Inference with the CLI
 You can test basic Llama inference completion using the CLI.
 
 ```bash
@@ -221,9 +211,15 @@ ChatCompletionResponse(
     ],
 )
 ```
-:::
 
-:::{tab-item} Basic Inference with a Script
+## Step 4: Run the Demos
+
+Note that these demos show the [Python Client SDK](../references/python_sdk_reference/index.md).
+Other SDKs are also available, please refer to the [Client SDK](../index.md#client-sdks) list for the complete options.
+
+::::{tab-set}
+
+:::{tab-item} Basic Inference
 Alternatively, you can run inference using the Llama Stack client SDK.
 
 ### i. Create the Script

docs/source/getting_started/index.md

@@ -12,9 +12,8 @@ as the inference [provider](../providers/index.md#inference) for a Llama Model.
 Install [uv](https://docs.astral.sh/uv/), setup your virtual environment, and run inference on a Llama model with
 [Ollama](https://ollama.com/download).
 ```bash
-uv pip install llama-stack aiosqlite faiss-cpu ollama openai datasets opentelemetry-exporter-otlp-proto-http mcp autoevals
+uv pip install llama-stack
 source .venv/bin/activate
-export INFERENCE_MODEL="llama3.2:3b"
 ollama run llama3.2:3b --keepalive 60m
 ```
 ## Step 2: Run the Llama Stack Server
@@ -24,70 +23,62 @@ INFERENCE_MODEL=llama3.2:3b llama stack build --template ollama --image-type ven
 ## Step 3: Run the Demo
 Now open up a new terminal using the same virtual environment and you can run this demo as a script using `uv run demo_script.py` or in an interactive shell.
 ```python
-from termcolor import cprint
+from llama_stack_client import Agent, AgentEventLogger, RAGDocument, LlamaStackClient
-from llama_stack_client.types import Document
-from llama_stack_client import LlamaStackClient
-
-
-vector_db = "faiss"
-vector_db_id = "test-vector-db"
-model_id = "llama3.2:3b-instruct-fp16"
-query = "Can you give me the arxiv link for Lora Fine Tuning in Pytorch?"
-documents = [
-    Document(
-        document_id="document_1",
-        content=f"https://raw.githubusercontent.com/pytorch/torchtune/main/docs/source/tutorials/lora_finetune.rst",
-        mime_type="text/plain",
-        metadata={},
-    )
-]
 
+vector_db_id = "my_demo_vector_db"
 client = LlamaStackClient(base_url="http://localhost:8321")
-client.vector_dbs.register(
-    provider_id=vector_db,
-    vector_db_id=vector_db_id,
-    embedding_model="all-MiniLM-L6-v2",
-    embedding_dimension=384,
-)
 
+models = client.models.list()
+
+# Select the first LLM and first embedding models
+model_id = next(m for m in models if m.model_type == "llm").identifier
+embedding_model_id = (
+    em := next(m for m in models if m.model_type == "embedding")
+).identifier
+embedding_dimension = em.metadata["embedding_dimension"]
+
+_ = client.vector_dbs.register(
+    vector_db_id=vector_db_id,
+    embedding_model=embedding_model_id,
+    embedding_dimension=embedding_dimension,
+    provider_id="faiss",
+)
+document = RAGDocument(
+    document_id="document_1",
+    content="https://www.paulgraham.com/greatwork.html",
+    mime_type="text/html",
+    metadata={},
+)
 client.tool_runtime.rag_tool.insert(
-    documents=documents,
+    documents=[document],
     vector_db_id=vector_db_id,
     chunk_size_in_tokens=50,
 )
-
+agent = Agent(
-response = client.tool_runtime.rag_tool.query(
+    client,
-    vector_db_ids=[vector_db_id],
+    model=model_id,
-    content=query,
+    instructions="You are a helpful assistant",
+    tools=[
+        {
+            "name": "builtin::rag/knowledge_search",
+            "args": {"vector_db_ids": [vector_db_id]},
+        }
+    ],
 )
 
-cprint("" + "-" * 50, "yellow")
+response = agent.create_turn(
-cprint(f"Query> {query}", "red")
+    messages=[{"role": "user", "content": "How do you do great work?"}],
-cprint("" + "-" * 50, "yellow")
+    session_id=agent.create_session("rag_session"),
-for chunk in response.content:
+)
-    cprint(f"Chunk ID> {chunk.text}", "green")
+
-    cprint("" + "-" * 50, "yellow")
+for log in AgentEventLogger().log(response):
+    log.print()
 ```
 And you should see output like below.
-```
+```bash
---------------------------------------------------
+inference> [knowledge_search(query="What does it mean to do great work")]
-Query> Can you give me the arxiv link for Lora Fine Tuning in Pytorch?
+tool_execution> Tool:knowledge_search Args:{'query': 'What does it mean to do great work'}
---------------------------------------------------
+tool_execution> Tool:knowledge_search Response:[TextContentItem(text='knowledge_search tool found 5 chunks:\nBEGIN of knowledge_search tool results.\n', type='text'), TextContentItem(text="Result 1:\nDocument_id:docum\nContent:  work. Doing great work means doing something important\nso well that you expand people's ideas of what's possible. But\nthere's no threshold for importance. It's a matter of degree, and\noften hard to judge at the time anyway.\n", type='text'), TextContentItem(text='Result 2:\nDocument_id:docum\nContent: [<a name="f1n"><font color=#000000>1</font></a>]\nI don\'t think you could give a precise definition of what\ncounts as great work. Doing great work means doing something important\nso well\n', type='text'), TextContentItem(text="Result 3:\nDocument_id:docum\nContent: . And if so\nyou're already further along than you might realize, because the\nset of people willing to want to is small.<br /><br />The factors in doing great work are factors in the literal,\nmathematical sense, and\n", type='text'), TextContentItem(text="Result 4:\nDocument_id:docum\nContent: \nincreases your morale and helps you do even better work. But this\ncycle also operates in the other direction: if you're not doing\ngood work, that can demoralize you and make it even harder to. Since\nit matters\n", type='text'), TextContentItem(text="Result 5:\nDocument_id:docum\nContent:  to try to do\ngreat work. But that's what's going on subconsciously; they shy\naway from the question.<br /><br />So I'm going to pull a sneaky trick on you. Do you want to do great\n", type='text'), TextContentItem(text='END of knowledge_search tool results.\n', type='text')]
-Chunk ID> knowledge_search tool found 5 chunks:
-BEGIN of knowledge_search tool results.
-
---------------------------------------------------
-Chunk ID> Result 1:
-Document_id:docum
-Content: .. _lora_finetune_label:
-
-============================
-Fine-Tuning Llama2 with LoRA
-============================
-
-This guide will teach you about `LoRA <https://arxiv.org/abs/2106.09685>`_, a
-
---------------------------------------------------
 ```
 Congratulations! You've successfully built your first RAG application using Llama Stack! 🎉🥳