forked from phoenix-oss/llama-stack-mirror
docs: Updated docs to show minimal RAG example and some other minor changes (#1935)
# What does this PR do?
Incorporating some feedback into the docs.
- **`docs/source/getting_started/index.md`:**
- Demo actually does RAG now
- Simplified the installation command for dependencies.
- Updated demo script examples to align with the latest API changes.
- Replaced manual document manipulation with `RAGDocument` for clarity
and maintainability.
- Introduced new logic for model and embedding selection using the Llama
Stack Client SDK.
- Enhanced examples to showcase proper agent initialization and logging.
- **`docs/source/getting_started/detailed_tutorial.md`:**
- Updated the section for listing models to include proper code
formatting with `bash`.
- Removed and reorganized the "Run the Demos" section for clarity.
- Adjusted tab-item structures and added new instructions for demo
scripts.
- **`docs/_static/css/my_theme.css`:**
- Updated heading styles to include `h2`, `h3`, and `h4` for consistent
font weight.
- Added a new style for `pre` tags to wrap text and break long words,
this is particularly useful for rendering long output from generation.
## Test Plan
Tested locally. Screenshot for reference:
<img width="1250" alt="Screenshot 2025-04-10 at 10 12 12 PM"
src="https://github.com/user-attachments/assets/ce1c8986-e072-4c6f-a697-ed0d8fb75b34"
/>
---------
Signed-off-by: Francisco Javier Arceo <farceo@redhat.com>
This commit is contained in:
Francisco Arceo
GitHub
parent
c1cb6aad11
commit
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3 changed files with 62 additions and 71 deletions
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docs/_static/css/my_theme.css
vendored
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docs/_static/css/my_theme.css
vendored
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@ -17,9 +17,13 @@
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display: none;
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}
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h3 {
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h2, h3, h4 {
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font-weight: normal;
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}
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html[data-theme="dark"] .rst-content div[class^="highlight"] {
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background-color: #0b0b0b;
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}
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pre {
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white-space: pre-wrap !important;
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word-break: break-all;
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}
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@ -173,9 +173,8 @@ You will see the below:
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Done! You can now use the Llama Stack Client CLI with endpoint http://localhost:8321
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```
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#### iii. List Available Models
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List the models
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```
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```bash
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llama-stack-client models list
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Available Models
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@ -190,15 +189,6 @@ Available Models
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Total models: 2
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```
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## Step 4: Run the Demos
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Note that these demos show the [Python Client SDK](../references/python_sdk_reference/index.md).
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Other SDKs are also available, please refer to the [Client SDK](../index.md#client-sdks) list for the complete options.
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::::{tab-set}
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:::{tab-item} Basic Inference with the CLI
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You can test basic Llama inference completion using the CLI.
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```bash
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@ -221,10 +211,16 @@ ChatCompletionResponse(
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],
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)
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```
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:::
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:::{tab-item} Basic Inference with a Script
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Alternatively, you can run inference using the Llama Stack client SDK.
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## Step 4: Run the Demos
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Note that these demos show the [Python Client SDK](../references/python_sdk_reference/index.md).
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Other SDKs are also available, please refer to the [Client SDK](../index.md#client-sdks) list for the complete options.
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::::{tab-set}
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:::{tab-item} Basic Inference
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Now you can run inference using the Llama Stack client SDK.
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### i. Create the Script
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Create a file `inference.py` and add the following code:
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@ -269,7 +265,7 @@ Beauty in the bits
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:::
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:::{tab-item} Build a Simple Agent
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Now we can move beyond simple inference and build an agent that can perform tasks using the Llama Stack server.
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Next we can move beyond simple inference and build an agent that can perform tasks using the Llama Stack server.
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### i. Create the Script
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Create a file `agent.py` and add the following code:
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@ -12,9 +12,8 @@ as the inference [provider](../providers/index.md#inference) for a Llama Model.
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Install [uv](https://docs.astral.sh/uv/), setup your virtual environment, and run inference on a Llama model with
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[Ollama](https://ollama.com/download).
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```bash
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uv pip install llama-stack aiosqlite faiss-cpu ollama openai datasets opentelemetry-exporter-otlp-proto-http mcp autoevals
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uv pip install llama-stack
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source .venv/bin/activate
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export INFERENCE_MODEL="llama3.2:3b"
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ollama run llama3.2:3b --keepalive 60m
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```
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## Step 2: Run the Llama Stack Server
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@ -24,70 +23,62 @@ INFERENCE_MODEL=llama3.2:3b llama stack build --template ollama --image-type ven
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## Step 3: Run the Demo
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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.
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```python
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from termcolor import cprint
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from llama_stack_client.types import Document
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from llama_stack_client import LlamaStackClient
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from llama_stack_client import Agent, AgentEventLogger, RAGDocument, LlamaStackClient
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vector_db_id = "my_demo_vector_db"
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client = LlamaStackClient(base_url="http://localhost:8321")
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vector_db = "faiss"
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vector_db_id = "test-vector-db"
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model_id = "llama3.2:3b-instruct-fp16"
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query = "Can you give me the arxiv link for Lora Fine Tuning in Pytorch?"
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documents = [
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Document(
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models = client.models.list()
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# Select the first LLM and first embedding models
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model_id = next(m for m in models if m.model_type == "llm").identifier
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embedding_model_id = (
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em := next(m for m in models if m.model_type == "embedding")
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).identifier
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embedding_dimension = em.metadata["embedding_dimension"]
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_ = client.vector_dbs.register(
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vector_db_id=vector_db_id,
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embedding_model=embedding_model_id,
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embedding_dimension=embedding_dimension,
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provider_id="faiss",
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)
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document = RAGDocument(
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document_id="document_1",
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content=f"https://raw.githubusercontent.com/pytorch/torchtune/main/docs/source/tutorials/lora_finetune.rst",
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mime_type="text/plain",
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content="https://www.paulgraham.com/greatwork.html",
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mime_type="text/html",
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metadata={},
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)
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]
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client = LlamaStackClient(base_url="http://localhost:8321")
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client.vector_dbs.register(
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provider_id=vector_db,
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vector_db_id=vector_db_id,
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embedding_model="all-MiniLM-L6-v2",
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embedding_dimension=384,
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)
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client.tool_runtime.rag_tool.insert(
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documents=documents,
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documents=[document],
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vector_db_id=vector_db_id,
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chunk_size_in_tokens=50,
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)
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response = client.tool_runtime.rag_tool.query(
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vector_db_ids=[vector_db_id],
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content=query,
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agent = Agent(
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client,
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model=model_id,
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instructions="You are a helpful assistant",
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tools=[
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{
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"name": "builtin::rag/knowledge_search",
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"args": {"vector_db_ids": [vector_db_id]},
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}
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],
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)
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cprint("" + "-" * 50, "yellow")
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cprint(f"Query> {query}", "red")
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cprint("" + "-" * 50, "yellow")
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for chunk in response.content:
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cprint(f"Chunk ID> {chunk.text}", "green")
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cprint("" + "-" * 50, "yellow")
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response = agent.create_turn(
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messages=[{"role": "user", "content": "How do you do great work?"}],
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session_id=agent.create_session("rag_session"),
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)
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for log in AgentEventLogger().log(response):
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log.print()
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```
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And you should see output like below.
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```
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--------------------------------------------------
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Query> Can you give me the arxiv link for Lora Fine Tuning in Pytorch?
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--------------------------------------------------
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Chunk ID> knowledge_search tool found 5 chunks:
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BEGIN of knowledge_search tool results.
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--------------------------------------------------
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Chunk ID> Result 1:
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Document_id:docum
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Content: .. _lora_finetune_label:
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============================
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Fine-Tuning Llama2 with LoRA
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============================
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This guide will teach you about `LoRA <https://arxiv.org/abs/2106.09685>`_, a
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--------------------------------------------------
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```bash
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inference> [knowledge_search(query="What does it mean to do great work")]
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tool_execution> Tool:knowledge_search Args:{'query': 'What does it mean to do great work'}
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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')]
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```
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Congratulations! You've successfully built your first RAG application using Llama Stack! 🎉🥳
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