chore: Adding demo script and importing it into the docs (#2848)
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# What does this PR do?
This PR adds the quickstart as a file to the docs so that it can be more
easily maintained and run, as mentioned in
https://github.com/meta-llama/llama-stack/pull/2800.

## Test Plan
I could add this as a test in the CI but I wasn't sure if we wanted to
add additional jobs there. 😅

Signed-off-by: Francisco Javier Arceo <farceo@redhat.com>
This commit is contained in:
Francisco Arceo 2025-07-21 22:53:32 -04:00 committed by GitHub
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commit 2bc96613f9
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2 changed files with 64 additions and 57 deletions

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@ -0,0 +1,62 @@
# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
#
# This source code is licensed under the terms described in the LICENSE file in
# the root directory of this source tree.
from llama_stack_client import Agent, AgentEventLogger, RAGDocument, LlamaStackClient
vector_db_id = "my_demo_vector_db"
client = LlamaStackClient(base_url="http://localhost:8321")
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",
)
source = "https://www.paulgraham.com/greatwork.html"
print("rag_tool> Ingesting document:", source)
document = RAGDocument(
document_id="document_1",
content=source,
mime_type="text/html",
metadata={},
)
client.tool_runtime.rag_tool.insert(
documents=[document],
vector_db_id=vector_db_id,
chunk_size_in_tokens=50,
)
agent = Agent(
client,
model=model_id,
instructions="You are a helpful assistant",
tools=[
{
"name": "builtin::rag/knowledge_search",
"args": {"vector_db_ids": [vector_db_id]},
}
],
)
prompt = "How do you do great work?"
print("prompt>", prompt)
response = agent.create_turn(
messages=[{"role": "user", "content": prompt}],
session_id=agent.create_session("rag_session"),
stream=True,
)
for log in AgentEventLogger().log(response):
log.print()

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@ -24,63 +24,8 @@ ENABLE_OLLAMA=ollama OLLAMA_INFERENCE_MODEL=llama3.2:3b uv run --with llama-stac
#### Step 3: Run the demo
Now open up a new terminal and copy the following script into a file named `demo_script.py`.
```python
from llama_stack_client import Agent, AgentEventLogger, RAGDocument, LlamaStackClient
vector_db_id = "my_demo_vector_db"
client = LlamaStackClient(base_url="http://localhost:8321")
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",
)
source = "https://www.paulgraham.com/greatwork.html"
print("rag_tool> Ingesting document:", source)
document = RAGDocument(
document_id="document_1",
content=source,
mime_type="text/html",
metadata={},
)
client.tool_runtime.rag_tool.insert(
documents=[document],
vector_db_id=vector_db_id,
chunk_size_in_tokens=50,
)
agent = Agent(
client,
model=model_id,
instructions="You are a helpful assistant",
tools=[
{
"name": "builtin::rag/knowledge_search",
"args": {"vector_db_ids": [vector_db_id]},
}
],
)
prompt = "How do you do great work?"
print("prompt>", prompt)
response = agent.create_turn(
messages=[{"role": "user", "content": prompt}],
session_id=agent.create_session("rag_session"),
stream=True,
)
for log in AgentEventLogger().log(response):
log.print()
```{literalinclude} ./demo_script.py
:language: python
```
We will use `uv` to run the script
```