Build Semantic Search and Chatbot Retrieval Systems with Qdrant on Google Cloud C4A Axion processors

content/learning-paths/servers-and-cloud-computing/qdrant-on-axion/_index.md

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+---
+title: Build Semantic Search and Chatbot Retrieval Systems with Qdrant on Google Cloud C4A Axion processors
+
+minutes_to_complete: 30
+
+who_is_this_for: This is an introductory topic for developers, data engineers, and platform engineers who want to build semantic search systems and chatbot retrieval pipelines on Arm64-based Google Cloud C4A Axion processors using the Qdrant vector database.
+
+learning_objectives:
+ - Deploy and run the Qdrant vector database on Google Cloud C4A Axion processors
+ - Generate vector embeddings using transformer models
+ - Store and index embeddings efficiently using Qdrant
+ - Perform semantic similarity search using vector queries
+ - Build a simple chatbot retrieval system powered by vector search
+
+prerequisites:
+  - A [Google Cloud Platform (GCP)](https://cloud.google.com/free) account with billing enabled
+  - Basic familiarity with Python
+  - Basic understanding of machine learning embeddings
+  - Familiarity with Linux command-line operations
+
+author: Pareena Verma
+
+##### Tags
+skilllevels: Introductory
+subjects: Databases
+cloud_service_providers: 
+- Google Cloud
+
+armips:
+- Neoverse
+
+tools_software_languages:
+- Qdrant
+- Python
+- Sentence Transformers
+- Docker
+
+operatingsystems:
+- Linux
+
+# ================================================================================
+#       FIXED, DO NOT MODIFY
+# ================================================================================
+
+further_reading:
+  - resource:
+      title: Google Cloud documentation
+      link: https://cloud.google.com/docs
+      type: documentation
+
+  - resource:
+      title: Qdrant documentation
+      link: https://qdrant.tech/documentation/
+      type: documentation
+
+  - resource:
+      title: Sentence Transformers documentation
+      link: https://www.sbert.net/
+      type: documentation
+
+  - resource:
+      title: Vector Databases Explained
+      link: https://qdrant.tech/articles/what-is-a-vector-database/
+      type: documentation
+
+weight: 1
+layout: "learningpathall"
+learning_path_main_page: yes
+---

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+# ================================================================================
+#       FIXED, DO NOT MODIFY THIS FILE
+# ================================================================================
+weight: 21                  # Set to always be larger than the content in this path to be at the end of the navigation.
+title: "Next Steps"         # Always the same, html page title.
+layout: "learningpathall"   # All files under learning paths have this same wrapper for Hugo processing.
+---

content/learning-paths/servers-and-cloud-computing/qdrant-on-axion/architecture.md

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+---
+title: Architecture
+weight: 8
+
+### FIXED, DO NOT MODIFY
+layout: learningpathall
+---
+
+# Chatbot Architecture Using Qdrant
+
+In this section, you explore the architecture behind the chatbot retrieval system built using Qdrant on Google Axion Arm-based infrastructure.
+
+This architecture demonstrates how modern AI systems perform **semantic similarity search** to retrieve relevant information from stored knowledge.
+
+Unlike traditional keyword search systems, vector databases allow applications to retrieve information based on **semantic meaning and contextual similarity**.
+
+
+## System architecture
+
+The chatbot system retrieves relevant information through vector embeddings and similarity search.
+
+```text
+User Question
+      │
+      ▼
+Embedding Model
+(Sentence Transformer)
+      │
+      ▼
+Vector Representation
+      │
+      ▼
+Qdrant Vector Database
+(Vector Similarity Search)
+      │
+      ▼
+Top Matching Knowledge
+      │
+      ▼
+Chatbot Response
+```
+
+
+
+## Components
+
+**Embedding Model**
+
+The embedding model converts text into numerical vectors representing semantic meaning.
+
+**Example model used:**
+
+```text
+sentence-transformers/all-MiniLM-L6-v2
+```
+
+This lightweight transformer model is commonly used for semantic search and AI retrieval workloads.
+
+## Vector Database (Qdrant)
+Qdrant stores and indexes vector embeddings generated from documents and user queries.
+
+It enables fast **nearest-neighbor similarity search**, which finds the most relevant vectors based on semantic similarity.
+
+Key capabilities:
+
+- high performance vector indexing
+- semantic similarity search
+- scalable vector storage
+
+## Knowledge Base
+
+The system stores knowledge documents such as:
+
+- technical documentation
+- support articles
+- FAQs
+- internal company knowledge
+
+During ingestion, these documents are converted into embeddings and stored in Qdrant.
+
+## Chatbot Query Engine
+
+When the user asks a question:
+
+1. The query is converted into an embedding
+2. Qdrant searches for the closest vectors
+3. The chatbot returns relevant information
+
+This process enables the chatbot to understand intent and meaning, rather than relying solely on keyword matching.
+
+## Benefits of This Architecture
+
+This design provides several advantages:
+
+- semantic search instead of keyword matching
+- scalable knowledge retrieval
+- faster query responses
+- efficient AI workloads on Arm infrastructure
+
+## Running on Axion
+
+This example demonstrates that Axion Arm infrastructure can efficiently run vector search workloads.
+
+- Benefits include:
+- energy-efficient compute
+- scalable cloud infrastructure
+- optimized performance for AI workloads
+
+## What you've learned
+
+In this section, you learned how the chatbot retrieval system works using vector search.
+
+You explored:
+
+- How embeddings represent semantic meaning
+- How Qdrant stores and indexes vectors
+- How similarity search retrieves relevant knowledge
+- How this architecture supports chatbot and RAG systems
+
+Together, these components form the foundation for modern AI-powered search and knowledge retrieval systems running on Arm-based cloud infrastructure.

content/learning-paths/servers-and-cloud-computing/qdrant-on-axion/background.md

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+---
+title: Get started with Qdrant on Google Axion C4A
+weight: 2
+
+layout: "learningpathall"
+---
+
+## Explore Axion C4A Arm instances in Google Cloud
+
+Google Axion C4A is a family of Arm-based virtual machines built on Google’s custom Axion CPU, which is based on Arm Neoverse-V2 cores. Designed for high-performance and energy-efficient computing, these virtual machines offer strong performance for data-intensive and analytics workloads such as big data processing, in-memory analytics, columnar data processing, and high-throughput data services.
+
+The C4A series provides a cost-effective alternative to x86 virtual machines while leveraging the scalability, SIMD acceleration, and memory bandwidth advantages of the Arm architecture in Google Cloud.
+
+These characteristics make Axion C4A instances well-suited for modern analytics stacks that rely on columnar data formats and memory-efficient execution engines.
+
+To learn more, see the Google blog [Introducing Google Axion Processors, our new Arm-based CPUs](https://cloud.google.com/blog/products/compute/introducing-googles-new-arm-based-cpu).
+
+## Explore Qdrant Vector Search on Google Axion C4A (Arm Neoverse V2)
+
+Qdrant is an open-source vector database designed for efficient similarity search and high-performance vector indexing. It enables applications to store and retrieve embeddings—numerical representations of data such as text, images, or audio—allowing systems to perform semantic search and AI-powered retrieval.
+
+Vector databases like Qdrant are commonly used in modern AI systems to support applications such as semantic search, recommendation systems, anomaly detection, and Retrieval-Augmented Generation (RAG) pipelines. By storing embeddings and performing nearest-neighbor search, Qdrant allows applications to retrieve the most relevant information based on semantic meaning rather than simple keyword matching.
+
+Running Qdrant on Google Axion C4A Arm-based infrastructure enables efficient execution of AI and vector search workloads. Axion processors, based on the Arm Neoverse V2 architecture, provide high performance and improved energy efficiency for modern cloud-native applications and data services.
+
+Using Qdrant on Axion allows you to achieve:
+
+- High-performance vector similarity search for AI applications
+- Efficient embedding, storage, and indexing for semantic retrieval
+- Low-latency data access for chatbots and AI assistants
+- Scalable infrastructure for Retrieval-Augmented Generation (RAG) pipelines
+- Cost-efficient execution of vector database workloads on Arm-based cloud infrastructure
+
+Common use cases include AI chatbots, semantic search engines, recommendation systems, enterprise knowledge assistants, document retrieval systems, and machine learning feature stores.
+
+To learn more, visit the [Qdrant documentation](https://qdrant.tech/documentation/) and explore how vector databases enable modern AI applications.
+
+## What you've learned and what's next
+
+In this section, you learned about:
+
+* Google Axion C4A Arm-based VMs and their performance characteristics
+* Qdrant as a vector database for storing and retrieving embeddings
+* Semantic similarity search and how it powers AI retrieval systems
+* How vector search enables chatbot and RAG-style knowledge retrieval 
+
+Next, you can explore how to extend this setup by integrating large language models (LLMs) to build a full Retrieval-Augmented Generation (RAG) pipeline, enabling AI systems to generate context-aware responses using information retrieved from the Qdrant vector database.