Attixa + Qdrant + OpenAI = 🔥: A Full RAG Stack in 20 Minutes

Building a robust RAG system requires careful integration of multiple components. This guide shows how to combine Attixa's memory capabilities with Qdrant's efficient vector search and OpenAI's powerful generation to create a production-ready RAG stack in minutes.

Why This Stack?

Each component brings unique strengths:

1. Attixa: Sophisticated memory and context management
2. Qdrant: Fast and scalable vector similarity search
3. OpenAI: State-of-the-art text generation
4. Together: A complete, production-ready RAG solution

Quick Start Implementation

Here's how to set up the full stack:

from attixa import MemorySystem
from qdrant_client import QdrantClient
from openai import OpenAI
from attixa.rag import RAGSystem

# Initialize components
memory = MemorySystem()
qdrant = QdrantClient("localhost", port=6333)
openai_client = OpenAI(api_key="your-api-key")

# Create RAG system
rag = RAGSystem(
    memory_system=memory,
    vector_store=qdrant,
    llm=openai_client
)

# Use the system
response = await rag.query(
    "What are the best practices for RAG systems?",
    context={
        "domain": "AI",
        "recency": "last 6 months"
    }
)

Core Components Integration

1. Document Processing Pipeline

async def process_documents(documents):
    # Split documents
    chunks = await rag.split_documents(documents)
    
    # Generate embeddings
    embeddings = await rag.generate_embeddings(chunks)
    
    # Store in Qdrant
    await rag.vector_store.upsert(
        points=[
            {
                "id": chunk.id,
                "vector": embedding,
                "payload": {
                    "text": chunk.text,
                    "metadata": chunk.metadata
                }
            }
            for chunk, embedding in zip(chunks, embeddings)
        ]
    )
    
    # Store in Attixa
    await rag.memory_system.store(
        content=chunks,
        context={
            "source": "document_processing",
            "timestamp": datetime.now()
        }
    )

2. Query Processing

async def process_query(query, context=None):
    # Generate query embedding
    query_embedding = await rag.generate_embeddings([query])[0]
    
    # Search in Qdrant
    vector_results = await rag.vector_store.search(
        query_vector=query_embedding,
        limit=5
    )
    
    # Get context from Attixa
    memory_context = await rag.memory_system.retrieve(
        query=query,
        filters=context
    )
    
    # Combine results
    combined_context = await rag.combine_contexts(
        vector_results=vector_results,
        memory_context=memory_context
    )
    
    # Generate response
    response = await rag.generate_response(
        query=query,
        context=combined_context
    )
    
    return response

3. Memory Integration

async def update_memory(query, response, context):
    # Store interaction
    await rag.memory_system.store(
        content={
            "query": query,
            "response": response,
            "context": context
        },
        metadata={
            "type": "interaction",
            "timestamp": datetime.now()
        }
    )
    
    # Update salience scores
    await rag.memory_system.update_salience(
        content_ids=[result.id for result in context["vector_results"]],
        interaction_type="query_response"
    )

Advanced Features

1. Hybrid Search

async def hybrid_search(query):
    # Vector search
    vector_results = await rag.vector_store.search(
        query_vector=await rag.generate_embeddings([query])[0],
        limit=3
    )
    
    # Keyword search
    keyword_results = await rag.memory_system.keyword_search(
        query=query,
        limit=3
    )
    
    # Combine results
    return await rag.rank_results(
        vector_results=vector_results,
        keyword_results=keyword_results,
        weights=[0.6, 0.4]
    )

2. Contextual Reranking

async def rerank_results(results, query, context):
    # Get memory context
    memory_context = await rag.memory_system.retrieve(
        query=query,
        filters=context
    )
    
    # Rerank based on memory
    return await rag.rerank(
        results=results,
        context=memory_context,
        scoring_function="relevance_with_memory"
    )

3. Feedback Loop

async def process_feedback(query, response, feedback):
    # Update memory based on feedback
    await rag.memory_system.store(
        content={
            "query": query,
            "response": response,
            "feedback": feedback
        },
        context={
            "type": "feedback",
            "timestamp": datetime.now()
        }
    )
    
    # Adjust salience scores
    await rag.memory_system.adjust_salience(
        content_ids=[result.id for result in response["context"]],
        feedback_score=feedback["score"]
    )

Best Practices

1. System Configuration

   • Optimize chunk sizes
   • Set appropriate limits
   • Configure caching
   • Monitor performance

2. Memory Management

   • Implement cleanup routines
   • Set retention policies
   • Monitor memory usage
   • Optimize storage

3. Performance Optimization

   • Use batch processing
   • Implement caching
   • Optimize queries
   • Monitor latency

Real-World Examples

Here are some production use cases:

• Knowledge Bases: Enterprise documentation
• Customer Support: Automated assistance
• Research Tools: Academic literature
• Content Creation: Writing assistance

Next Steps

Ready to build your RAG stack? Check out our documentation or try our quickstart guide.