feat: adding a Makefile and a test script for sqlite-vec

Signed-off-by: Francisco Javier Arceo <farceo@redhat.com>

Makefile

@@ -0,0 +1,33 @@
+ROOT_DIR := $(shell dirname $(realpath $(firstword $(MAKEFILE_LIST))))
+OS := linux
+ifeq ($(shell uname -s), Darwin)
+	OS = osx
+endif
+
+PYTHON_VERSION = ${shell python --version | grep -Eo '[0-9]\.[0-9]+'}
+PYTHON_VERSIONS := 3.10 3.11
+
+build-dev:
+	uv sync --extra dev --extra test
+	uv pip install -e .
+	. .venv/bin/activate
+	uv pip install sqlite-vec chardet datasets sentence_transformers pypdf
+
+build-ollama: fix-line-endings
+	llama stack build --template ollama --image-type venv
+
+fix-line-endings:
+	sed -i '' 's/\r$$//' llama_stack/distribution/common.sh
+	sed -i '' 's/\r$$//' llama_stack/distribution/build_venv.sh
+
+test-sqlite-vec:
+	pytest llama_stack/providers/tests/vector_io/test_sqlite_vec.py \
+	-v -s --tb=short --disable-warnings --asyncio-mode=auto
+
+test-ollama-vector-integration:
+	INFERENCE_MODEL=llama3.2:3b-instruct-fp16 LLAMA_STACK_CONFIG=ollama \
+	pytest -s -v tests/client-sdk/vector_io/test_vector_io.py
+
+
+make serve-ollama:
+	ollama run llama3.2:3b-instruct-fp16 --keepalive 24h

sqlite_vec_test.py

@@ -0,0 +1,112 @@
+#!/usr/bin/env python3
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the terms described in the LICENSE file in
+# the root directory of this source tree.
+
+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_client.lib.agents.agent import Agent
+from llama_stack_client.lib.agents.event_logger import EventLogger
+from llama_stack_client.types import Document
+from llama_stack_client.types.agent_create_params import AgentConfig
+
+from llama_stack.distribution.library_client import LlamaStackAsLibraryClient
+
+
+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()