phoenix-oss/llama-stack
1
0
Fork 0
forked from phoenix-oss/llama-stack-mirror
Code Pull requests Releases Packages 2 Activity Actions

Fix precommit check after moving to ruff (#927)

Browse source 
Lint check in main branch is failing. This fixes the lint check after we
moved to ruff in https://github.com/meta-llama/llama-stack/pull/921. We
need to move to a `ruff.toml` file as well as fixing and ignoring some
additional checks.

Signed-off-by: Yuan Tang <terrytangyuan@gmail.com>
This commit is contained in:
Yuan Tang 2025-02-02 09:46:45 -05:00 • committed by GitHub
parent 4773092dd1
commit 34ab7a3b6c
No known key found for this signature in database
GPG key ID: B5690EEEBB952194
217 changed files with 981 additions and 2681 deletions
Download patch file Download diff file Expand all files Collapse all files

32
docs/source/getting_started/index.md
View file

@ -96,18 +96,26 @@ Here is a simple example to perform chat completions using the SDK.
```python
import os
def create_http_client():
from llama_stack_client import LlamaStackClient
return LlamaStackClient(base_url=f"http://localhost:{os.environ['LLAMA_STACK_PORT']}")
return LlamaStackClient(
base_url=f"http://localhost:{os.environ['LLAMA_STACK_PORT']}"
)
def create_library_client(template="ollama"):
from llama_stack import LlamaStackAsLibraryClient
client = LlamaStackAsLibraryClient(template)
client.initialize()
return client
client = create_library_client() # or create_http_client() depending on the environment you picked
client = (
create_library_client()
) # or create_http_client() depending on the environment you picked
# List available models
models = client.models.list()
@ -120,8 +128,8 @@ response = client.inference.chat_completion(
model_id=os.environ["INFERENCE_MODEL"],
messages=[
{"role": "system", "content": "You are a helpful assistant."},
{"role": "user", "content": "Write a haiku about coding"}
]
{"role": "user", "content": "Write a haiku about coding"},
],
)
print(response.completion_message.content)
```
@ -139,7 +147,9 @@ from llama_stack_client.lib.agents.event_logger import EventLogger
from llama_stack_client.types.agent_create_params import AgentConfig
from llama_stack_client.types import Document
client = create_library_client() # or create_http_client() depending on the environment you picked
client = (
create_library_client()
) # or create_http_client() depending on the environment you picked
# Documents to be used for RAG
urls = ["chat.rst", "llama3.rst", "datasets.rst", "lora_finetune.rst"]
@ -174,12 +184,12 @@ agent_config = AgentConfig(
instructions="You are a helpful assistant",
enable_session_persistence=False,
# Define tools available to the agent
toolgroups = [
toolgroups=[
{
"name": "builtin::rag",
"args" : {
"vector_db_ids": [vector_db_id],
}
"name": "builtin::rag",
"args": {
"vector_db_ids": [vector_db_id],
},
}
],
)
@ -193,7 +203,7 @@ user_prompts = [
# Run the agent loop by calling the `create_turn` method
for prompt in user_prompts:
cprint(f'User> {prompt}', 'green')
cprint(f"User> {prompt}", "green")
response = rag_agent.create_turn(
messages=[{"role": "user", "content": prompt}],
session_id=session_id,