# What does this PR do?
This PR implements hybrid search for Milvus DB based on the inbuilt
milvus support.
To test:
```
pytest tests/unit/providers/vector_io/remote/test_milvus.py -v -s
--tb=long --disable-warnings --asyncio-mode=auto
```
Signed-off-by: Varsha Prasad Narsing <varshaprasad96@gmail.com>
# What does this PR do?
some async test markers are in the codebase causing pre-commit to fail
due to #2744
remove these pytest fixtures
## Test Plan
pre-commit passes
Signed-off-by: Charlie Doern <cdoern@redhat.com>
# What does this PR do?
This PR adds the keyword search implementation for Milvus. Along with
the implementation for remote Milvus, the tests require us to start a
Milvus containers locally.
In order to verify the implementation, run:
```
pytest tests/unit/providers/vector_io/remote/test_milvus.py -v -s --tb=short --disable-warnings --asyncio-mode=auto
```
You can also test the changes using the below script:
```
#!/usr/bin/env python3
import asyncio
import os
import uuid
from typing import List
from llama_stack_client import (
Agent,
AgentEventLogger,
LlamaStackClient,
RAGDocument
)
class MilvusRAGDemo:
def __init__(self, base_url: str = "http://localhost:8321/"):
self.client = LlamaStackClient(base_url=base_url)
self.vector_db_id = f"milvus_rag_demo_{uuid.uuid4().hex[:8]}"
self.model_id = None
self.embedding_model_id = None
self.embedding_dimension = None
def setup_models(self):
"""Get available models and select appropriate ones for LLM and embeddings."""
models = self.client.models.list()
# Select embedding model
embedding_models = [m for m in models if m.model_type == "embedding"]
if not embedding_models:
raise ValueError("No embedding models found")
self.embedding_model_id = embedding_models[0].identifier
self.embedding_dimension = embedding_models[0].metadata["embedding_dimension"]
def register_vector_db(self):
print(f"Registering Milvus vector database: {self.vector_db_id}")
response = self.client.vector_dbs.register(
vector_db_id=self.vector_db_id,
embedding_model=self.embedding_model_id,
embedding_dimension=self.embedding_dimension,
provider_id="milvus-remote", # Use remote Milvus
)
print(f"Vector database registered successfully")
return response
def insert_documents(self):
"""Insert sample documents into the vector database."""
print("\nInserting sample documents...")
# Sample documents about different topics
documents = [
RAGDocument(
document_id="ai_ml_basics",
content="""
Artificial Intelligence (AI) and Machine Learning (ML) are transforming the world.
AI refers to the simulation of human intelligence in machines, while ML is a subset
of AI that enables computers to learn and improve from experience without being
explicitly programmed. Deep learning, a subset of ML, uses neural networks with
multiple layers to process complex patterns in data.
Key concepts in AI/ML include:
- Supervised Learning: Training with labeled data
- Unsupervised Learning: Finding patterns in unlabeled data
- Reinforcement Learning: Learning through trial and error
- Neural Networks: Computing systems inspired by biological brains
""",
mime_type="text/plain",
metadata={"topic": "technology", "category": "ai_ml"},
),
]
# Insert documents with chunking
self.client.tool_runtime.rag_tool.insert(
documents=documents,
vector_db_id=self.vector_db_id,
chunk_size_in_tokens=200, # Smaller chunks for better granularity
)
print(f"Inserted {len(documents)} documents with chunking")
def test_keyword_search(self):
"""Test keyword-based search using BM25."""
queries = [
"neural networks",
"Python frameworks",
"data cleaning",
]
for query in queries:
response = self.client.vector_io.query(
vector_db_id=self.vector_db_id,
query=query,
params={
"mode": "keyword", # Keyword search
"max_chunks": 3,
"score_threshold": 0.0,
}
)
for i, (chunk, score) in enumerate(zip(response.chunks, response.scores)):
print(f" {i+1}. Score: {score:.4f}")
print(f" Content: {chunk.content[:100]}...")
print(f" Metadata: {chunk.metadata}")
def run_demo(self):
try:
self.setup_models()
self.register_vector_db()
self.insert_documents()
self.test_keyword_search()
except Exception as e:
print(f"Error during demo: {e}")
raise
def main():
"""Main function to run the demo."""
# Check if Llama Stack server is running
demo = MilvusRAGDemo()
try:
demo.run_demo()
except Exception as e:
print(f"Demo failed: {e}")
if __name__ == "__main__":
main()
```
[//]: # (## Documentation)
---------
Signed-off-by: Varsha Prasad Narsing <varshaprasad96@gmail.com>
