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Zilliz

Zilliz is the company behind Milvus, an open-source vector database built for AI applications and similarity search. By integrating Milvus with HoneyHive, you can:

  • Track vector database operations
  • Monitor embedding quality and relevance
  • Analyze retrieval performance in your RAG pipelines
  • Identify opportunities for optimization

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Prerequisites

  • A HoneyHive account and API key
  • Python 3.7+
  • Basic understanding of vector databases and RAG pipelines

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Installation

Install the required packages:

pip install openai pymilvus honeyhive

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Basic Integration Example

The following example demonstrates a complete RAG pipeline with HoneyHive tracing for Milvus operations. We’ll break down each component step by step.

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Step 1: Initialize Clients

First, set up the necessary clients for HoneyHive, OpenAI, and Milvus:

from openai import OpenAI
from pymilvus import MilvusClient
from honeyhive.tracer import HoneyHiveTracer
from honeyhive.tracer.custom import trace

# Initialize HoneyHive Tracer
HoneyHiveTracer.init(
    api_key="your_honeyhive_api_key",
    project="your_project_name",
)

# Initialize OpenAI client
openai_client = OpenAI(api_key="your_openai_api_key")

# Initialize Milvus client
milvus_client = MilvusClient("milvus_demo.db")  # Using Milvus Lite for demo

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Step 2: Create Embedding Function

def embed_text(text):
    """Generate embeddings using OpenAI's text-embedding-ada-002 model"""
    res = openai_client.embeddings.create(
        model="text-embedding-ada-002",
        input=text
    )
    return res.data[0].embedding

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Step 3: Set Up Milvus Collection with Tracing

@trace(
    config={
        "collection_name": "demo_collection",
        "dimension": 1536,  # text-embedding-ada-002 dimension
    }
)
def setup_collection():
    """Set up Milvus collection with tracing"""
    # Drop collection if it exists
    if milvus_client.has_collection(collection_name="demo_collection"):
        milvus_client.drop_collection(collection_name="demo_collection")

    # Create new collection
    milvus_client.create_collection(
        collection_name="demo_collection",
        dimension=1536  # text-embedding-ada-002 dimension
    )

The @trace decorator logs this operation to HoneyHive with metadata about the collection name and dimension. The function itself creates a fresh collection for our vectors, with the dimension matching our embedding model’s output size.

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Step 4: Insert Documents with Tracing

@trace(
    config={
        "embedding_model": "text-embedding-ada-002"
    }
)
def insert_documents(documents):
    """Insert documents with tracing"""
    vectors = [embed_text(doc) for doc in documents]
    data = [
        {
            "id": i,
            "vector": vectors[i],
            "text": documents[i],
            "subject": "general"
        }
        for i in range(len(vectors))
    ]

    res = milvus_client.insert(
        collection_name="demo_collection",
        data=data
    )
    return res

This function converts a list of text documents into vectors using our embedding function, then inserts them into Milvus. The @trace decorator logs information about the embedding model used, allowing you to compare different models’ performance.

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Step 5: Search for Similar Documents with Tracing

@trace(
    config={
        "embedding_model": "text-embedding-ada-002",
        "top_k": 3
    }
)
def search_similar_documents(query, top_k=3):
    """Search for similar documents with tracing"""
    query_vector = embed_text(query)

    results = milvus_client.search(
        collection_name="demo_collection",
        data=[query_vector],
        limit=top_k,
        output_fields=["text", "subject"]
    )

    return [match["entity"]["text"] for match in results[0]]

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Step 6: Generate Response with Tracing

Create a function to generate a response using OpenAI with tracing:

@trace(
    config={
        "model": "gpt-4o",
        "prompt": "You are a helpful assistant"
    }
)
def generate_response(context, query):
    """Generate response using OpenAI with tracing"""
    prompt = f"Context: {context}\n\nQuestion: {query}\n\nAnswer:"
    response = openai_client.chat.completions.create(
        model="gpt-4o",
        messages=[
            {"role": "system", "content": "You are a helpful assistant."},
            {"role": "user", "content": prompt}
        ]
    )
    return response.choices[0].message.content

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Step 7: Complete RAG Pipeline with Tracing

Create a function that combines all the previous steps into a complete RAG pipeline:

@trace()
def rag_pipeline(query):
    """Complete RAG pipeline with tracing"""
    # Get relevant documents
    relevant_docs = search_similar_documents(query)
    # Generate response
    response = generate_response("\n".join(relevant_docs), query)
    return response

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Step 8: Run the Example

Finally, create a main function to run the example:

def main():
    # Sample documents
    documents = [
        "Artificial intelligence was founded as an academic discipline in 1956.",
        "Machine learning is a subset of artificial intelligence.",
        "Deep learning is a type of machine learning based on artificial neural networks.",
        "Natural Language Processing (NLP) is a branch of AI that helps computers understand human language.",
    ]

    # Set up collection
    setup_collection()

    # Insert documents
    print("Inserting documents...")
    insert_documents(documents)

    # Test RAG pipeline
    query = "What is the relationship between AI and machine learning?"
    print(f"\nQuery: {query}")
    response = rag_pipeline(query)
    print(f"Response: {response}")

if __name__ == "__main__":
    main()

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Advanced Configuration

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Using Zilliz Cloud

Milvus Lite with local file for demo, specify uri and token for Milvus server or Zilliz Cloud

milvus_client = MilvusClient("milvus_demo.db")

Connect to Zilliz Cloud or Milvus server To connect to Zilliz Cloud or Milvus server instead of Milvus Lite:

milvus_client = MilvusClient(
    uri="your_zilliz_cloud_endpoint_or_milvus_server_address",
    token="your_zilliz_api_key_or_milvus_username_and_password"
)

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Custom Metadata

Add custom metadata to your traces for better analysis:

@trace(
    config={
        "embedding_model": "text-embedding-ada-002",
        "top_k": 3,
        "custom_metadata": {
            "environment": "production",
            "version": "1.0.0",
            "dataset": "knowledge_base_v2"
        }
    }
)
def search_similar_documents(query, top_k=3):
    # Vector search code
    ...

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Analyzing Results in HoneyHive

After running your application with tracing enabled, you can analyze the results in the HoneyHive dashboard:

  1. Navigate to your project in the HoneyHive dashboard
  2. View traces for your Milvus operations
  3. Analyze retrieval performance metrics
  4. Compare different embedding models and configurations

By integrating Zilliz into your workflow, you can easily track and improve the performance of your AI applications. Keep an eye on what’s working, spot issues quickly, and fine-tune your embeddings to boost accuracy

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Additional Resources

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