Day 7

Integrating with Haystack

Build end-to-end agentic pipelines with Qdrant.

What You’ll Learn

• Haystack pipeline integration
• Document processing workflows
• Question answering systems
• Search and retrieval optimization
• Sparse vector search and metadata filtering
• LLM-based agent development
• Movie recommendation system architecture

Haystack Movie Recommendation Assistant

Haystack provides a powerful framework for building sophisticated recommendation systems that combine multiple search strategies. The movie recommendation assistant demonstrates how to leverage sparse vector search, metadata filtering, and LLM-based agents to handle complex natural language queries like “find me a highly-rated action movie about car racing” or “recommend five Japanese thrillers.”

Core Architecture

The Haystack recommendation system uses a multi-layered approach to deliver accurate and relevant results:

• Sparse Vector Search: Utilizes sparse embeddings to capture keyword-based relevance and semantic meaning
• Metadata Filtering: Enables precise filtering by movie attributes like genre, rating, year, and language
• LLM-Based Agents: Intelligent agents that can interpret complex queries and dynamically choose between search strategies
• Qdrant Integration: Seamless storage and retrieval of both dense and sparse vector representations

Implementation Workflow

The movie recommendation system follows these key steps:

1. Data Preparation:

   • Convert movie data into Haystack documents with rich metadata
   • Structure information including title, genre, rating, year, language, and plot descriptions

2. Sparse Embedding Creation:

   • Generate sparse embeddings that capture both semantic and keyword-based relevance
   • Optimize embeddings for movie recommendation use cases

3. Qdrant Cloud Integration:

   • Write sparse embeddings and metadata to Qdrant Cloud
   • Configure collections for optimal retrieval performance
   • Set up proper indexing for fast metadata filtering

4. Query Pipeline Development:

   • Build retrieval pipelines that combine semantic search and metadata filtering
   • Implement intelligent routing based on query complexity and intent

5. Agent Implementation:

   • Create LLM-based agents that can interpret natural language queries
   • Enable dynamic strategy selection between semantic search and metadata filtering
   • Implement query understanding for complex requests

Advanced Query Handling

The system excels at processing sophisticated queries by:

• Natural Language Understanding: Interpreting queries like “highly-rated action movie about car racing”
• Multi-Criteria Filtering: Combining genre, rating, and thematic requirements
• Dynamic Strategy Selection: Choosing between semantic search, metadata filtering, or hybrid approaches
• Contextual Recommendations: Providing relevant suggestions based on user preferences and movie characteristics

Real-World Applications

This architecture extends beyond movie recommendations to various domains:

• E-commerce: Product recommendations with complex attribute filtering
• Content Discovery: Finding relevant articles, videos, or resources
• Enterprise Search: Intelligent document retrieval with metadata constraints
• Personalized Recommendations: User-specific content suggestions

Resources

• Haystack Qdrant Integration:
  Official Haystack documentation for using Qdrant as a document store. Learn about installation, usage, and connecting to Qdrant Cloud clusters.

• Qdrant & Haystack Integration Guide:
  Official Qdrant documentation on integrating with Haystack. Learn how to build powerful NLP pipelines with vector search capabilities.

⭐ Show your support! Give Haystack a star on their GitHub repository: github.com/deepset-ai/haystack