Commit graph

18 commits

Author SHA1 Message Date
Ashwin Bharambe
8de8eb03c8
Update the "InterleavedTextMedia" type (#635)
## What does this PR do?

This is a long-pending change and particularly important to get done
now.

Specifically:
- we cannot "localize" (aka download) any URLs from media attachments
anywhere near our modeling code. it must be done within llama-stack.
- `PIL.Image` is infesting all our APIs via `ImageMedia ->
InterleavedTextMedia` and that cannot be right at all. Anything in the
API surface must be "naturally serializable". We need a standard `{
type: "image", image_url: "<...>" }` which is more extensible
- `UserMessage`, `SystemMessage`, etc. are moved completely to
llama-stack from the llama-models repository.

See https://github.com/meta-llama/llama-models/pull/244 for the
corresponding PR in llama-models.

## Test Plan

```bash
cd llama_stack/providers/tests

pytest -s -v -k "fireworks or ollama or together" inference/test_vision_inference.py
pytest -s -v -k "(fireworks or ollama or together) and llama_3b" inference/test_text_inference.py
pytest -s -v -k chroma memory/test_memory.py \
  --env EMBEDDING_DIMENSION=384 --env CHROMA_DB_PATH=/tmp/foobar

pytest -s -v -k fireworks agents/test_agents.py  \
   --safety-shield=meta-llama/Llama-Guard-3-8B \
   --inference-model=meta-llama/Llama-3.1-8B-Instruct
```

Updated the client sdk (see PR ...), installed the SDK in the same
environment and then ran the SDK tests:

```bash
cd tests/client-sdk
LLAMA_STACK_CONFIG=together pytest -s -v agents/test_agents.py
LLAMA_STACK_CONFIG=ollama pytest -s -v memory/test_memory.py

# this one needed a bit of hacking in the run.yaml to ensure I could register the vision model correctly
INFERENCE_MODEL=llama3.2-vision:latest LLAMA_STACK_CONFIG=ollama pytest -s -v inference/test_inference.py
```
2024-12-17 11:18:31 -08:00
Dinesh Yeduguru
516e1a3e59
add embedding model by default to distribution templates (#617)
# What does this PR do?
Adds the sentence transformer provider and the `all-MiniLM-L6-v2`
embedding model to the default models to register in the run.yaml for
all providers.

## Test Plan
llama stack build --template together --image-type conda
llama stack run
~/.llama/distributions/llamastack-together/together-run.yaml
2024-12-13 12:48:00 -08:00
Dinesh Yeduguru
96e158eaac
Make embedding generation go through inference (#606)
This PR does the following:
1) adds the ability to generate embeddings in all supported inference
providers.
2) Moves all the memory providers to use the inference API and improved
the memory tests to setup the inference stack correctly and use the
embedding models

This is a merge from #589 and #598
2024-12-12 11:47:50 -08:00
Ashwin Bharambe
14f973a64f
Make LlamaStackLibraryClient work correctly (#581)
This PR does a few things:

- it moves "direct client" to llama-stack repo instead of being in the
llama-stack-client-python repo
- renames it to `LlamaStackLibraryClient`
- actually makes synchronous generators work 
- makes streaming and non-streaming work properly

In many ways, this PR makes things finally "work"

## Test Plan

See a `library_client_test.py` I added. This isn't really quite a test
yet but it demonstrates that this mode now works. Here's the invocation
and the response:

```
INFERENCE_MODEL=meta-llama/Llama-3.2-3B-Instruct python llama_stack/distribution/tests/library_client_test.py ollama
```


![image](https://github.com/user-attachments/assets/17d4e116-4457-4755-a14e-d9a668801fe0)
2024-12-07 14:59:36 -08:00
Kai Wu
b6500974ec
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
Martin Hickey
76fc5d9f31
Update Ollama supported llama model list (#483)
# What does this PR do?

Update the llama model supported list for Ollama.

- [x] Addresses issue (#462)

Signed-off-by: Martin Hickey <martin.hickey@ie.ibm.com>
2024-11-22 21:56:43 -08:00
Dinesh Yeduguru
6395dadc2b
use logging instead of prints (#499)
# What does this PR do?

This PR moves all print statements to use logging. Things changed:
- Had to add `await start_trace("sse_generator")` to server.py to
actually get tracing working. else was not seeing any logs
- If no telemetry provider is provided in the run.yaml, we will write to
stdout
- by default, the logs are going to be in JSON, but we expose an option
to configure to output in a human readable way.
2024-11-21 11:32:53 -08:00
Ashwin Bharambe
84d5f35a48 Update the model alias for llama guard models in ollama 2024-11-19 00:22:24 -08:00
Dinesh Yeduguru
02f1c47416
support adding alias for models without hf repo/sku entry (#481)
# What does this PR do?

adds a new method build_model_alias_with_just_llama_model which is
needed for cases like ollama's quantized models which do not really have
a repo in hf and an entry in SKU list.


## Test Plan

pytest -v -s -m "ollama"
llama_stack/providers/tests/inference/test_text_inference.py

---------

Co-authored-by: Dinesh Yeduguru <dineshyv@fb.com>
2024-11-18 23:50:18 -08:00
Kai Wu
d2b7c5aeae
add quantized model ollama support (#471)
# What does this PR do?
add more quantized model support for ollama.


- [ ] Addresses issue (#issue)


## Test Plan
Tested with ollama docker that run llama3.2 3b 4bit model.
```
root@docker-desktop:/# ollama ps
NAME           ID              SIZE      PROCESSOR    UNTIL
llama3.2:3b    a80c4f17acd5    3.5 GB    100% CPU     3 minutes from now
```
## 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.
2024-11-18 18:55:23 -08:00
Ashwin Bharambe
2a31163178
Auto-generate distro yamls + docs (#468)
# What does this PR do?

Automatically generates
- build.yaml
- run.yaml
- run-with-safety.yaml
- parts of markdown docs

for the distributions.

## Test Plan

At this point, this only updates the YAMLs and the docs. Some testing
(especially with ollama and vllm) has been performed but needs to be
much more tested.
2024-11-18 14:57:06 -08:00
Dinesh Yeduguru
0850ad656a
unregister for memory banks and remove update API (#458)
The semantics of an Update on resources is very tricky to reason about
especially for memory banks and models. The best way to go forward here
is for the user to unregister and register a new resource. We don't have
a compelling reason to support update APIs.


Tests:
pytest -v -s llama_stack/providers/tests/memory/test_memory.py -m
"chroma" --env CHROMA_HOST=localhost --env CHROMA_PORT=8000

pytest -v -s llama_stack/providers/tests/memory/test_memory.py -m
"pgvector" --env PGVECTOR_DB=postgres --env PGVECTOR_USER=postgres --env
PGVECTOR_PASSWORD=mysecretpassword --env PGVECTOR_HOST=0.0.0.0

$CONDA_PREFIX/bin/pytest -v -s -m "ollama"
llama_stack/providers/tests/inference/test_model_registration.py

---------

Co-authored-by: Dinesh Yeduguru <dineshyv@fb.com>
2024-11-14 17:12:11 -08:00
Dinesh Yeduguru
787e2034b7
model registration in ollama and vllm check against the available models in the provider (#446)
tests:
pytest -v -s -m "ollama"
llama_stack/providers/tests/inference/test_text_inference.py

pytest -v -s -m vllm_remote
llama_stack/providers/tests/inference/test_text_inference.py --env
VLLM_URL="http://localhost:9798/v1"

---------
2024-11-13 13:04:06 -08:00
Ashwin Bharambe
12947ac19e
Kill "remote" providers and fix testing with a remote stack properly (#435)
# What does this PR do?

This PR kills the notion of "pure passthrough" remote providers. You
cannot specify a single provider you must specify a whole distribution
(stack) as remote.

This PR also significantly fixes / upgrades testing infrastructure so
you can now test against a remotely hosted stack server by just doing

```bash
pytest -s -v -m remote  test_agents.py \
  --inference-model=Llama3.1-8B-Instruct --safety-shield=Llama-Guard-3-1B \
  --env REMOTE_STACK_URL=http://localhost:5001
```

Also fixed `test_agents_persistence.py` (which was broken) and killed
some deprecated testing functions.

## Test Plan

All the tests.
2024-11-12 21:51:29 -08:00
Dinesh Yeduguru
fdff24e77a
Inference to use provider resource id to register and validate (#428)
This PR changes the way model id gets translated to the final model name
that gets passed through the provider.
Major changes include:
1) Providers are responsible for registering an object and as part of
the registration returning the object with the correct provider specific
name of the model provider_resource_id
2) To help with the common look ups different names a new ModelLookup
class is created.



Tested all inference providers including together, fireworks, vllm,
ollama, meta reference and bedrock
2024-11-12 20:02:00 -08:00
Ashwin Bharambe
c1f7ba3aed
Split safety into (llama-guard, prompt-guard, code-scanner) (#400)
Splits the meta-reference safety implementation into three distinct providers:

- inline::llama-guard
- inline::prompt-guard
- inline::code-scanner

Note that this PR is a backward incompatible change to the llama stack server. I have added deprecation_error field to ProviderSpec -- the server reads it and immediately barfs. This is used to direct the user with a specific message on what action to perform. An automagical "config upgrade" is a bit too much work to implement right now :/

(Note that we will be gradually prefixing all inline providers with inline:: -- I am only doing this for this set of new providers because otherwise existing configuration files will break even more badly.)
2024-11-11 09:29:18 -08:00
Dinesh Yeduguru
ec644d3418
migrate model to Resource and new registration signature (#410)
* resource oriented object design for models

* add back llama_model field

* working tests

* register singature fix

* address feedback

---------

Co-authored-by: Dinesh Yeduguru <dineshyv@fb.com>
2024-11-08 16:12:57 -08:00
Ashwin Bharambe
994732e2e0
impls -> inline, adapters -> remote (#381) 2024-11-06 14:54:05 -08:00
Renamed from llama_stack/providers/adapters/inference/ollama/ollama.py (Browse further)