# What does this PR do?
This fixes an import introduced due to merging #1079 before #1039, and
thus the changes from #1039 needing to update `QdrantConfig` to
`QdrantVectorIOConfig`.
## Test Plan
I ran the remote vllm provider inference tests against the latest main:
```
VLLM_URL="http://localhost:8001/v1" python -m pytest -s -v llama_stack/providers/tests/inference/test_text_inference.py --providers "inference=vllm_remote"
```
That failed with:
```
File "/home/bbrownin/src/llama-stack/llama_stack/providers/tests/vector_io/fixtures.py", line 20, in <module>
from llama_stack.providers.remote.vector_io.qdrant import QdrantConfig
ImportError: Error importing plugin "llama_stack.providers.tests.vector_io.fixtures": cannot import name 'QdrantConfig' from 'llama_stack.providers.remote.vector_io.qdrant' (/home/bbrownin/src/llama-stack/llama_stack/providers/remote/vector_io/qdrant/__init__.py)
```
After this change, the import no longer fails and the tests pass.
Signed-off-by: Ben Browning <bbrownin@redhat.com>
# What does this PR do?
This is a follow on to #1022 . It includes the changes I needed to be
able to test the Qdrant support as requested by @terrytangyuan .
I uncovered a lot of bigger, more systemic issues with the vector DB
testing and I will open a new issue for those. For now, I am just
delivering the work I already did on that.
## Test Plan
As discussed on #1022:
```
podman pull qdrant/qdrant
mkdir qdrant-data
podman run -p 6333:6333 -v $(pwd)/qdrant-data:/qdrant/storage qdrant/qdrant
```
```
ollama pull all-minilm:l6-v2
curl http://localhost:11434/api/embeddings -d '{"model": "all-minilm", "prompt": "Hello world"}'
```
```
EMBEDDING_DIMENSION=384 QDRANT_URL=http://localhost pytest llama_stack/providers/tests/vector_io/test_vector_io.py -m "qdrant" -v -s --tb=short --embedding-model all-minilm:latest --disable-warnings
```
These show 3 tests passing and 15 deselected which is presumably working
as intended.
---------
Signed-off-by: Bill Murdock <bmurdock@redhat.com>
# What does this PR do?
This changes all VectorIO providers classes to follow the pattern
`<ProviderName>VectorIOConfig` and `<ProviderName>VectorIOAdapter`. All
API endpoints for VectorIOs are currently consistent with `/vector-io`.
Note that API endpoint for VectorDB stay unchanged as `/vector-dbs`.
## Test Plan
I don't have a way to test all providers. This is a simple renaming so
things should work as expected.
---------
Signed-off-by: Yuan Tang <terrytangyuan@gmail.com>
# What does this PR do?
- Configured ruff linter to automatically fix import sorting issues.
- Set --exit-non-zero-on-fix to ensure non-zero exit code when fixes are
applied.
- Enabled the 'I' selection to focus on import-related linting rules.
- Ran the linter, and formatted all codebase imports accordingly.
- Removed the black dep from the "dev" group since we use ruff
Signed-off-by: Sébastien Han <seb@redhat.com>
[//]: # (If resolving an issue, uncomment and update the line below)
[//]: # (Closes #[issue-number])
## Test Plan
[Describe the tests you ran to verify your changes with result
summaries. *Provide clear instructions so the plan can be easily
re-executed.*]
[//]: # (## Documentation)
[//]: # (- [ ] Added a Changelog entry if the change is significant)
Signed-off-by: Sébastien Han <seb@redhat.com>
# What does this PR do?
This PR adds `sqlite_vec` as an additional inline vectordb.
Tested with `ollama` by adding the `vector_io` object in
`./llama_stack/templates/ollama/run.yaml` :
```yaml
vector_io:
- provider_id: sqlite_vec
provider_type: inline::sqlite_vec
config:
kvstore:
type: sqlite
namespace: null
db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/ollama}/sqlite_vec.db
db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/ollama}/sqlite_vec.db
```
I also updated the `./tests/client-sdk/vector_io/test_vector_io.py` test
file with:
```python
INLINE_VECTOR_DB_PROVIDERS = ["faiss", "sqlite_vec"]
```
And parameterized the relevant tests.
[//]: # (If resolving an issue, uncomment and update the line below)
# Closes
https://github.com/meta-llama/llama-stack/issues/1005
## Test Plan
I ran the tests with:
```bash
INFERENCE_MODEL=llama3.2:3b-instruct-fp16 LLAMA_STACK_CONFIG=ollama pytest -s -v tests/client-sdk/vector_io/test_vector_io.py
```
Which outputs:
```python
...
PASSED
tests/client-sdk/vector_io/test_vector_io.py::test_vector_db_retrieve[all-MiniLM-L6-v2-sqlite_vec] PASSED
tests/client-sdk/vector_io/test_vector_io.py::test_vector_db_list PASSED
tests/client-sdk/vector_io/test_vector_io.py::test_vector_db_register[all-MiniLM-L6-v2-faiss] PASSED
tests/client-sdk/vector_io/test_vector_io.py::test_vector_db_register[all-MiniLM-L6-v2-sqlite_vec] PASSED
tests/client-sdk/vector_io/test_vector_io.py::test_vector_db_unregister[faiss] PASSED
tests/client-sdk/vector_io/test_vector_io.py::test_vector_db_unregister[sqlite_vec] PASSED
```
In addition, I ran the `rag_with_vector_db.py`
[example](https://github.com/meta-llama/llama-stack-apps/blob/main/examples/agents/rag_with_vector_db.py)
using the script below with `uv run rag_example.py`.
<details>
<summary>CLICK TO SHOW SCRIPT 👋 </summary>
```python
#!/usr/bin/env python3
import os
import uuid
from termcolor import cprint
# Set environment variables
os.environ['INFERENCE_MODEL'] = 'llama3.2:3b-instruct-fp16'
os.environ['LLAMA_STACK_CONFIG'] = 'ollama'
# Import libraries after setting environment variables
from llama_stack.distribution.library_client import LlamaStackAsLibraryClient
from llama_stack_client.lib.agents.agent import Agent
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
def main():
# Initialize the client
client = LlamaStackAsLibraryClient("ollama")
vector_db_id = f"test-vector-db-{uuid.uuid4().hex}"
_ = client.initialize()
model_id = 'llama3.2:3b-instruct-fp16'
# Define the list of document URLs and create Document objects
urls = [
"chat.rst",
"llama3.rst",
"memory_optimizations.rst",
"lora_finetune.rst",
]
documents = [
Document(
document_id=f"num-{i}",
content=f"https://raw.githubusercontent.com/pytorch/torchtune/main/docs/source/tutorials/{url}",
mime_type="text/plain",
metadata={},
)
for i, url in enumerate(urls)
]
# (Optional) Use the documents as needed with your client here
client.vector_dbs.register(
provider_id='sqlite_vec',
vector_db_id=vector_db_id,
embedding_model="all-MiniLM-L6-v2",
embedding_dimension=384,
)
client.tool_runtime.rag_tool.insert(
documents=documents,
vector_db_id=vector_db_id,
chunk_size_in_tokens=512,
)
# Create agent configuration
agent_config = AgentConfig(
model=model_id,
instructions="You are a helpful assistant",
enable_session_persistence=False,
toolgroups=[
{
"name": "builtin::rag",
"args": {
"vector_db_ids": [vector_db_id],
}
}
],
)
# Instantiate the Agent
agent = Agent(client, agent_config)
# List of user prompts
user_prompts = [
"What are the top 5 topics that were explained in the documentation? Only list succinct bullet points.",
"Was anything related to 'Llama3' discussed, if so what?",
"Tell me how to use LoRA",
"What about Quantization?",
]
# Create a session for the agent
session_id = agent.create_session("test-session")
# Process each prompt and display the output
for prompt in user_prompts:
cprint(f"User> {prompt}", "green")
response = agent.create_turn(
messages=[
{
"role": "user",
"content": prompt,
}
],
session_id=session_id,
)
# Log and print events from the response
for log in EventLogger().log(response):
log.print()
if __name__ == "__main__":
main()
```
</details>
Which outputs a large summary of RAG generation.
# Documentation
Will handle documentation updates in follow-up PR.
# (- [ ] Added a Changelog entry if the change is significant)
---------
Signed-off-by: Francisco Javier Arceo <farceo@redhat.com>
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>
Making a few small naming changes as per feedback:
- RAGToolRuntime methods are called `insert` and `query` to keep them
more general
- The tool names are changed to non-namespaced forms
`insert_into_memory` and `query_from_memory`
- The REST endpoints are more REST-ful
See https://github.com/meta-llama/llama-stack/issues/827 for the broader
design.
Third part:
- we need to make `tool_runtime.rag_tool.query_context()` and
`tool_runtime.rag_tool.insert_documents()` methods work smoothly with
complete type safety. To that end, we introduce a sub-resource path
`tool-runtime/rag-tool/` and make changes to the resolver to make things
work.
- the PR updates the agents implementation to directly call these typed
APIs for memory accesses rather than going through the complex, untyped
"invoke_tool" API. the code looks much nicer and simpler (expectedly.)
- there are a number of hacks in the server resolver implementation
still, we will live with some and fix some
Note that we must make sure the client SDKs are able to handle this
subresource complexity also. Stainless has support for subresources, so
this should be possible but beware.
## Test Plan
Our RAG test is sad (doesn't actually test for actual RAG output) but I
verified that the implementation works. I will work on fixing the RAG
test afterwards.
```bash
pytest -s -v tests/agents/test_agents.py -k "rag and together" --safety-shield=meta-llama/Llama-Guard-3-8B
```
See https://github.com/meta-llama/llama-stack/issues/827 for the broader
design.
Second part:
- updates routing table / router code
- updates the faiss implementation
## Test Plan
```
pytest -s -v -k sentence test_vector_io.py --env EMBEDDING_DIMENSION=384
```
