forked from phoenix-oss/llama-stack-mirror

removed assertion in ollama.py and fixed typo in the readme (#563 )

# What does this PR do?
1. removed [incorrect
assertion](435f34b05e/llama_stack/providers/remote/inference/ollama/ollama.py (L183))
in ollama.py
2. fixed a typo in [this
line](435f34b05e/docs/source/distributions/importing_as_library.md (L24)),
as `model=` should be `model_id=` .

- [x] Addresses issue
([#issue562](https://github.com/meta-llama/llama-stack/issues/562))


## Test Plan

tested with code:

```python
import asyncio
import os

# pip install aiosqlite ollama faiss
from llama_stack_client.lib.direct.direct import LlamaStackDirectClient
from llama_stack_client.types import SystemMessage, UserMessage


async def main():
    os.environ["INFERENCE_MODEL"] = "meta-llama/Llama-3.2-1B-Instruct"
    client = await LlamaStackDirectClient.from_template("ollama")
    await client.initialize()
    response = await client.models.list()
    print(response)
    model_name = response[0].identifier
    response = await client.inference.chat_completion(
        messages=[
            SystemMessage(content="You are a friendly assistant.", role="system"),
            UserMessage(
                content="hello world, write me a 2 sentence poem about the moon",
                role="user",
            ),
        ],
        model_id=model_name,
        stream=False,
    )
    print("\nChat completion response:")
    print(response, type(response))


asyncio.run(main())

```
OUTPUT:
```
python test.py
Using template ollama with config:
apis:
- agents
- inference
- memory
- safety
- telemetry
conda_env: ollama
datasets: []
docker_image: null
eval_tasks: []
image_name: ollama
memory_banks: []
metadata_store:
  db_path: /Users/kaiwu/.llama/distributions/ollama/registry.db
  namespace: null
  type: sqlite
models:
- metadata: {}
  model_id: meta-llama/Llama-3.2-1B-Instruct
  provider_id: ollama
  provider_model_id: null
providers:
  agents:
  - config:
      persistence_store:
        db_path:
/Users/kaiwu/.llama/distributions/ollama/agents_store.db
        namespace: null
        type: sqlite
    provider_id: meta-reference
    provider_type: inline::meta-reference
  inference:
  - config:
      url: http://localhost:11434
    provider_id: ollama
    provider_type: remote::ollama
  memory:
  - config:
      kvstore:
        db_path:
/Users/kaiwu/.llama/distributions/ollama/faiss_store.db
        namespace: null
        type: sqlite
    provider_id: faiss
    provider_type: inline::faiss
  safety:
  - config: {}
    provider_id: llama-guard
    provider_type: inline::llama-guard
  telemetry:
  - config: {}
    provider_id: meta-reference
    provider_type: inline::meta-reference
scoring_fns: []
shields: []
version: '2'

[Model(identifier='meta-llama/Llama-3.2-1B-Instruct', provider_resource_id='llama3.2:1b-instruct-fp16', provider_id='ollama', type='model', metadata={})]

Chat completion response:
completion_message=CompletionMessage(role='assistant', content='Here is a short poem about the moon:\n\nThe moon glows bright in the midnight sky,\nA silver crescent shining, catching the eye.', stop_reason=<StopReason.end_of_turn: 'end_of_turn'>, tool_calls=[]) logprobs=None <class 'llama_stack.apis.inference.inference.ChatCompletionResponse'>
```

## Sources

Please link relevant resources if necessary.


## Before submitting

- [ ] This PR fixes a typo or improves the docs (you can dismiss the
other checks if that's the case).
- [ ] Ran pre-commit to handle lint / formatting issues.
- [ ] Read the [contributor
guideline](https://github.com/meta-llama/llama-stack/blob/main/CONTRIBUTING.md),
      Pull Request section?
- [ ] Updated relevant documentation.
- [ ] Wrote necessary unit or integration tests.

2024-12-03 20:11:19 -08:00

1.3 KiB

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Using Llama Stack as a Library

If you are planning to use an external service for Inference (even Ollama or TGI counts as external), it is often easier to use Llama Stack as a library. This avoids the overhead of setting up a server. For example:

from llama_stack_client.lib.direct.direct import LlamaStackDirectClient

client = await LlamaStackDirectClient.from_template('ollama')
await client.initialize()

This will parse your config and set up any inline implementations and remote clients needed for your implementation.

Then, you can access the APIs like models and inference on the client and call their methods directly:

response = await client.models.list()
print(response)

response = await client.inference.chat_completion(
    messages=[UserMessage(content="What is the capital of France?", role="user")],
    model_id="Llama3.1-8B-Instruct",
    stream=False,
)
print("\nChat completion response:")
print(response)

If you've created a custom distribution, you can also use the run.yaml configuration file directly:

client = await LlamaStackDirectClient.from_config(config_path)
await client.initialize()

1.3 KiB Raw Blame History

Using Llama Stack as a Library

1.3 KiB

Raw Blame History