HoneyHive Docs

Learn how to integrate Qdrant with HoneyHive for vector database monitoring and tracing in RAG applications.

Qdrant

Qdrant is an open-source vector database optimized for storing and searching high-dimensional vectors. By integrating Qdrant with HoneyHive, you can:

Trace vector database operations
Monitor latency, embedding quality, and context relevance
Evaluate retrieval performance in your RAG pipelines
Optimize paramaters such as chunk_size or chunk_overlap

Prerequisites

A HoneyHive account and API key
Python 3.8+
Basic understanding of vector databases and RAG pipelines

Installation

Install the required packages:

pip install qdrant-client openai honeyhive

Basic Integration Example

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

Initialize Clients and Setup

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

from qdrant_client import QdrantClient
from qdrant_client.http.models import PointStruct, VectorParams, Distance
import openai
import os
from honeyhive.tracer import HoneyHiveTracer
from honeyhive.tracer.custom import trace
from openai import OpenAI

# Set API Keys
openai.api_key = os.getenv("OPENAI_API_KEY")
honeyhive_api_key = os.getenv("HONEYHIVE_API_KEY")

# Initialize HoneyHive Tracer
HoneyHiveTracer.init(
    api_key=honeyhive_api_key,
    project="qdrant-rag-example",
    session_name="qdrant-integration-demo"
)

# Initialize OpenAI client
openai_client = OpenAI(api_key=openai.api_key)

Connect to Qdrant

You can connect to Qdrant in two ways: self-hosted (local) or cloud-hosted (Qdrant Cloud):

Option 1: Self-Hosted Qdrant (Local)

To run Qdrant locally, you need to have Docker installed and run the following command:

docker pull qdrant/qdrant
docker run -p 6333:6333 -p 6334:6334 -v "$(pwd)/qdrant_storage:/qdrant/storage" qdrant/qdrant

Then connect to the local Qdrant instance:

# Connect to local Qdrant
client = QdrantClient(url="http://localhost:6333")
print("Connected to local Qdrant instance")

Option 2: Qdrant Cloud

For Qdrant Cloud, you’ll need your cluster host and API key:

# Qdrant Cloud configuration
QDRANT_HOST = os.getenv("QDRANT_HOST")  # e.g., "your-cluster-id.eu-central.aws.cloud.qdrant.io"
QDRANT_API_KEY = os.getenv("QDRANT_API_KEY")

# Connect to Qdrant Cloud
client = QdrantClient(url=QDRANT_HOST, api_key=QDRANT_API_KEY)
print("Connected to Qdrant Cloud")

Create a Collection

Create a collection to store document embeddings:

collection_name = "documents"
vector_size = 1536  # For text-embedding-3-small
vector_distance = Distance.COSINE

# Create collection if it doesn't exist
if not client.collection_exists(collection_name):
    client.create_collection(
        collection_name=collection_name,
        vectors_config=VectorParams(size=vector_size, distance=vector_distance)
    )

Define Embedding Function with Tracing

Create a function to generate embeddings with HoneyHive tracing:

@trace()
def embed_text(text: str) -> list:
    """Generate embeddings for a text using OpenAI's API."""
    response = openai_client.embeddings.create(
        model="text-embedding-3-small",
        input=text
    )
    return response.data[0].embedding

Insert Documents with Tracing

Create a function to insert documents into Qdrant with tracing:

@trace()
def insert_documents(docs):
    """Insert documents into Qdrant collection."""
    points = []
    for idx, doc in enumerate(docs):
        vector = embed_text(doc)
        points.append(PointStruct(
            id=idx + 1,
            vector=vector,
            payload={"text": doc}
        ))
    
    client.upsert(
        collection_name=collection_name,
        points=points
    )
    return len(points)

# Sample documents
documents = [
    "Qdrant is a vector database optimized for storing and searching high-dimensional vectors.",
    "HoneyHive provides observability for AI applications, including RAG pipelines.",
    "Retrieval-Augmented Generation (RAG) combines retrieval systems with generative models.",
    "Vector databases like Qdrant are essential for efficient similarity search in RAG systems.",
    "OpenAI's embedding models convert text into high-dimensional vectors for semantic search."
]

# Insert documents
num_inserted = insert_documents(documents)

Retrieve Documents with Tracing

Create a function to retrieve relevant documents from Qdrant with tracing:

@trace()
def get_relevant_docs(query: str, top_k: int = 3) -> list:
    """Retrieve relevant documents for a query."""
    # Embed the query
    q_vector = embed_text(query)
    
    # Search in Qdrant
    search_response = client.query_points(
        collection_name=collection_name,
        query=q_vector,
        limit=top_k,
        with_payload=True
    )
    
    # Extract results
    docs = []
    for point in search_response.points:
        docs.append({
            "id": point.id,
            "text": point.payload.get("text"),
            "score": point.score
        })
    
    return docs

Generate Response with Tracing

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

@trace()
def answer_query(query: str, relevant_docs: list) -> str:
    """Generate an answer for a query using retrieved documents."""
    if not relevant_docs:
        return "Could not retrieve relevant documents to answer the query."

    # Format context from retrieved documents
    context_parts = []
    for i, doc in enumerate(relevant_docs):
        context_parts.append(f"Document {i+1} (ID: {doc['id']}, Score: {doc['score']:.4f}):\n{doc['text']}")
    context = "\n\n".join(context_parts)

    # Create prompt
    prompt = f"""Answer the question based ONLY on the following context:

Context:
{context}

Question: {query}

Answer:"""

    # Generate answer
    completion = openai_client.chat.completions.create(
        model="gpt-4o-mini",
        messages=[
            {"role": "system", "content": "You are a helpful assistant that answers questions based strictly on the provided context. If the answer is not in the context, say so clearly."},
            {"role": "user", "content": prompt}
        ],
        temperature=0.2
    )

    return completion.choices[0].message.content.strip()

Complete RAG Pipeline

Create a function to run the complete RAG pipeline with tracing:

@trace()
def rag_pipeline(query: str) -> dict:
    """End-to-end RAG pipeline."""
    # Get relevant documents
    relevant_docs = get_relevant_docs(query)
    
    # Generate answer
    answer = answer_query(query, relevant_docs)
    
    return {
        "query": query,
        "answer": answer,
        "retrieved_documents": relevant_docs
    }

Batch Processing

For larger document sets, you can use batch processing to improve performance:

@trace()
def batch_insert_documents(documents_to_insert, batch_size=10, start_id_offset=0):
    """Insert documents in batches."""
    total_inserted = 0
    
    for i in range(0, len(documents_to_insert), batch_size):
        batch_docs = documents_to_insert[i:i+batch_size]
        points = []
        
        for local_idx, doc in enumerate(batch_docs):
            relative_idx = i + local_idx
            vector = embed_text(doc)
            point_id = relative_idx + start_id_offset + 1
            points.append(PointStruct(
                id=point_id,
                vector=vector,
                payload={"text": doc}
            ))
        
        if points:
            client.upsert(
                collection_name=collection_name,
                points=points
            )
            total_inserted += len(points)
    
    return total_inserted

Test the RAG Pipeline

Here’s how to test the complete RAG pipeline:

# Test query
test_query = "What is Qdrant used for?"
result = rag_pipeline(test_query)

print(f"Query: {result['query']}")
print(f"Answer: {result['answer']}")
print("\nRetrieved Documents:")
for i, doc in enumerate(result['retrieved_documents']):
    print(f"Document {i+1} (ID: {doc['id']}, Score: {doc['score']:.4f}): {doc['text']}")

Viewing Traces in HoneyHive

After running your RAG pipeline with Qdrant, you can view the traces in the HoneyHive UI:

Navigate to your project in the HoneyHive dashboard
Click on the “Traces” tab to see all the traces from your RAG pipeline
Click on a specific trace to see detailed information about each step in the pipeline
Analyze the performance of your vector operations, embeddings, and retrieval processes

With HoneyHive, you can easily monitor and optimize your Qdrant-powered RAG pipeline, ensuring that it delivers the best possible results for your users.

Visit the Qdrant documentation and the HoneyHive documentation.

Integrations

​Qdrant

​Prerequisites

​Installation

​Basic Integration Example

​Initialize Clients and Setup

​Connect to Qdrant

​Option 1: Self-Hosted Qdrant (Local)

​Option 2: Qdrant Cloud

​Create a Collection

​Define Embedding Function with Tracing

​Insert Documents with Tracing

​Retrieve Documents with Tracing

​Generate Response with Tracing

​Complete RAG Pipeline

​Batch Processing

​Test the RAG Pipeline

​Viewing Traces in HoneyHive