Build an Advanced RAG Pipeline with Qdrant, LlamaIndex, and Metadata Filtering

Learn how to build a high-performance RAG pipeline using smart ingestion, embeddings, Qdrant vector indexing, advanced metadata filtering, prompt enhancement, and orchestrated retrieval agents.

🚀 Introduction

In this post, we’ll explore how to build an advanced Retrieval-Augmented Generation (RAG) pipeline by combining several key technologies:

• 🧹 Smart ingestion: cleaning, chunking, and metadata enrichment.
• 🧠 Embedding generation: turning documents into vector representations.
• 🗃️ Vector indexing: using Qdrant for high-performance retrieval.
• 🧾 Advanced metadata filtering: refining search results.
• 🧪 Prompt enhancement: guiding language models effectively.
• 🤖 Orchestrated retrieval agents: coordinating the pipeline efficiently.

🧹 Step 1: Data Ingestion

Prepare your data with:

• Cleaning: Remove special characters, HTML, etc.
• Chunking: Split into manageable text segments.
• Metadata enrichment: Add source, date, author, etc.

Metadata are key for enabling powerful search filters.

🧠 Step 2: Embedding Generation

Convert text into vector format using:

• OpenAI
• Cohere
• Hugging Face Transformers

These embeddings allow semantic search based on meaning.

📃 Step 3: Indexing with Qdrant

Use Qdrant for fast and scalable vector searches. It supports:

• ⚡ High-speed indexing
• 🎯 Metadata filtering
• 🔀 Hybrid search (semantic + keyword)

🔍 Step 4: Advanced Metadata Filtering

Filter results with JSON-based queries. For example:

{
  "vector": [0.2, 0.1, 0.9, 0.7],
  "filter": {
    "must": [
      {"key": "category", "match": {"value": "laptop"}},
      {"key": "price", "range": {"lte": 1000}}
    ]
  },
  "limit": 3,
  "with_payload": true,
  "with_vector": false
}

🧪 Step 5: Prompt Enhancement

Improve response relevance with techniques like:

• 📘 Few-shot prompting
• 🧠 Chain-of-thought
• ❓ Self-ask prompting

These help guide models toward better answers.

🤖 Step 6: Orchestrated Retrieval Agents

Agents automate the RAG pipeline:

1. Query analysis
2. Vector + metadata search in Qdrant
3. Document retrieval
4. LLM-based answer generation

📈 Results and Performance

Benefits of this setup:

• 🎯 Higher accuracy
• 🚀 Faster responses
• 🔄 Scales well with data

🧹 Example Code with LlamaIndex and Qdrant

from llama_index.core import VectorStoreIndex, StorageContext
from llama_index.vector_stores.qdrant import QdrantVectorStore
from qdrant_client import QdrantClient

client = QdrantClient(path="./qdrant_data")

vector_store = QdrantVectorStore(
    "my_collection", client=client, enable_hybrid=True, batch_size=20
)

storage_context = StorageContext.from_defaults(vector_store=vector_store)

index = VectorStoreIndex.from_documents(
    documents,
    storage_context=storage_context,
)

📌 Conclusion

By integrating smart ingestion, embeddings, Qdrant indexing, metadata filtering, prompt engineering, and agent orchestration, you can build a powerful RAG pipeline tailored for real-world AI applications.

📚 References

1. Qdrant Filtering Guide
2. Hybrid Search with Qdrant
3. LlamaIndex Qdrant Integration
4. Qdrant RAG Use Case

💻 Development and Deployment

🛠️ You can modify this pipeline online via github.dev by pressing . in your GitHub repository.

📎 Download POST.md

📄 Download POST.md

Feel free to adapt this pipeline to your needs and test different setups to find what works best.