feat: Adding sqlite-vec as a vectordb (#1040)

# 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>

llama_stack/providers/registry/vector_io.py

@@ -54,6 +54,14 @@ def available_providers() -> List[ProviderSpec]:
             config_class="llama_stack.providers.inline.vector_io.faiss.FaissImplConfig",
             api_dependencies=[Api.inference],
         ),
+        InlineProviderSpec(
+            api=Api.vector_io,
+            provider_type="inline::sqlite_vec",
+            pip_packages=EMBEDDING_DEPS + ["sqlite-vec"],
+            module="llama_stack.providers.inline.vector_io.sqlite_vec",
+            config_class="llama_stack.providers.inline.vector_io.sqlite_vec.SQLiteVectorIOConfig",
+            api_dependencies=[Api.inference],
+        ),
         remote_provider_spec(
             Api.vector_io,
             AdapterSpec(