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Qdrant logoQdrant · Feature trail

Data Ingestion and Integration

46 posts · Jan 2024 – Aug 2026

What this trail covers

Qdrant Hybrid Cloud offers flexible deployment across any environment (cloud, on-premise, edge), integrating with various infrastructure providers and AI frameworks like Airbyte and LangChain. This enables building AI applications such as RAG systems, chatbots, and recommendation engines, emphasizing data sovereignty and control. The platform supports scalable search engines for enterprises, handling millions of messages and evolving data models, including omnimodal data. The integration with Airbyte and LangChain simplifies data ingestion and application development.

All Qdrant trails
  1. How Bayer Built an Enterprise-Scale Search Engine with Qdrant - QdrantThis post details Bayer's journey building an enterprise-scale search engine using Qdrant. Key contributions include: 1. **Scalability and Compliance:** Transitioning from self-hosted Qdrant to Qdrant Hybrid Cloud to meet strict life sciences compliance requirements while managing operational burden for 116,000 employees. 2. **Omnimodal Data Handling:** Implementing a pipeline to ingest and search across diverse data formats (text, images, audio, video) by generating multi-vector embeddings from figures in scientific PDFs and indexing transcribed audio/video. 3. **Agentic Retrieval:** Exposing the Qdrant API directly to agentic applications (LangGraph-based harness) for deep retrieval loops, where each tool call is backed by a Qdrant collection, emphasizing low per-query latency. 4. **Hybrid Search for Grounding:** Utilizing hybrid search (keyword + semantic) as a core component for grounding LLMs and reducing hallucinations, crucial for agentic applications. 5. **Caching for Cost Control:** Implementing caching strategies to manage agent costs by reducing redundant search queries. 6. **Performance and Evolution:** Demonstrating the ability to scale to millions of messages and adapt to evolving data models and AI workloads, highlighting Qdrant's role as a foundational retrieval engine.
  2. How Sapu Indexed 28 Million PubMed Abstracts to Accelerate Cancer Research with Qdrant - QdrantThis post details Sapu's use of Qdrant to index 28 million PubMed abstracts into a single collection, enabling hybrid retrieval and per-document parallel querying. The architecture involves OpenAI's text-embedding-3-large model and LangChain for ingestion. Qdrant Cloud Premium was adopted for reliability and compliance. Future plans include evaluating Qdrant Edge for on-device vector search in air-gapped environments and Qdrant Cloud Inference for embedding pipelines.
  3. How Data Graphs Built a True Hybrid Graph RAG Platform - QdrantThis post details Data Graphs' implementation of a 'True Hybrid Graph RAG Platform' using Qdrant. Key technical contributions include: 1. Real-time embedding from a graph database into Qdrant collections with rich metadata for filtering. 2. An agentic AI layer that leverages Qdrant's payload filtering and graph queries to dynamically select the best retrieval path based on schema analysis and prompt requirements. 3. Blended retrieval from Qdrant (for semantic similarity) and the graph database (for empirical, structured data) with verifiable provenance. Qdrant's Hybrid Cloud deployment and payload filtering capabilities were critical for integrating with existing infrastructure and data models.
  4. How GlassDollar improved high-recall sourcing by migrating from Elasticsearch to Qdrant - QdrantGlassDollar migrated from Elasticsearch to Qdrant for their high-recall sourcing product. This migration improved retrieval speed, reduced infrastructure costs by 40%, and enabled more effective query expansion. The system leverages the Qdrant Node.js SDK for integration within their existing Node.js/TypeScript backend services. The improved retrieval quality led to a 3x increase in user engagement (bookmarks).
  5. Convolve 4.0 - IIT Hackathon Winners - QdrantThis post highlights the use of Qdrant in three winning projects from the Convolve 4.0 IIT Hackathon: Masthishq (multimodal AI agent for patient assistance), SignalWeave (temporal AI memory system for trend detection), and Demeter (autonomous multi-agent system for hydroponic farm management). All projects leverage Qdrant as a persistent vector memory layer for storing embeddings, enabling retrieval, and facilitating long-term memory and reasoning in AI systems.
  7. How My AskAI Built Self-Improving Support Agents - QdrantThis post details how My AskAI built its AI customer support agent platform using Qdrant Cloud. It highlights the transition to a customer support focus, the adoption of embeddings and RAG, and the reasons for choosing Qdrant (scalability, integrations, developer experience). The post also discusses the benefits of Qdrant Cloud for scaling and managing hybrid search, and introduces the concept of self-learning support agents where human agent responses are indexed in Qdrant to continuously update the knowledge base.
  8. How Anima Health scaled clinical document intelligence with Qdrant - QdrantThis post details how Anima Health uses Qdrant for clinical document intelligence, focusing on the challenges of unstructured data and the need for privacy-preserving AI. Qdrant serves as a retrieval layer in agentic pipelines for tasks like SNOMED code recommendation, leveraging vector embeddings and metadata. Key benefits highlighted include Qdrant's deployment flexibility (self-hosted, UK residency), cost predictability for retrieval-intensive pipelines, and vector-native features like payload filtering and multivector support for managing diverse EHR systems and embedding models. The system scaled without operational overhead, enabling Anima to build more agentic workflows and explore temporal relevance and flexible AI stacks.
  9. How Kakao Built an AI-Powered Internal Service Desk with Qdrant - QdrantThis post details how Kakao built an AI-powered internal service desk using Qdrant as the core vector store for their RAG system. Key technical contributions include: the use of Qdrant's hybrid search (dense and sparse vectors with RRF) to address both semantic and keyword-based queries; named vectors for managing multiple vector types within a collection; scalar quantization (INT8) for memory optimization; and an automated indexing pipeline for data ingestion. The system is deployed on Kubernetes with a replication-based setup for high availability and uses the async Python client for high query concurrency. Metadata filtering is used to narrow search scope, and a semantic cache speeds up repeated queries. The post highlights the operational fit of Qdrant for self-hosting within internal infrastructure and its ease of integration for rapid prototyping.
  10. How Xaver scaled personalized financial advice with Qdrant - QdrantThis post details how Xaver built an AI knowledge engine using Qdrant for personalized financial advice. It describes a two-tier retrieval architecture: a Condensed Knowledge Base (CKB) for common questions and a Full Knowledge Base for detailed queries. Qdrant was chosen for its high performance at low latency, developer simplicity, and flexible deployment, enabling sub-second retrieval for conversational use cases and improving advisor efficiency.
  11. Qdrant for Research: The Story Behind ETH & Stanford’s MIRIAD Dataset - QdrantThis post details the use of Qdrant in the creation of the MIRIAD dataset, a large-scale medical QA dataset. It highlights Qdrant's role in powering the dataset's storage and RAG experiments, emphasizing its simplicity of use, speed, scalability, and open-source nature as key factors for researchers. The post also mentions the development of MIRIAD Atlas, an interactive map visualizing UMAP-based dimensionality-reduced embeddings, similar to Qdrant's WebUI visualization capabilities. Future plans include migrating MIRIAD Atlas to Qdrant for full-scale dataset serving.
  12. How &AI scaled global legal retrieval with Qdrant - Qdrant&AI scaled global legal retrieval by using Qdrant Cloud to manage billions of patent documents. They employed OpenAI's embedding models to transform data into vectors and indexed them in Qdrant. Key technical decisions included using scalar quantization for a balance of retrieval speed and accuracy, and leveraging Qdrant's multi-tenancy feature for data isolation between firms. The architecture focuses on retrieval-first, using Qdrant's payload filtering and HNSW for refined search results.
  13. Introducing Qdrant Cloud Inference - QdrantIntroduces Qdrant Cloud Inference, a new capability that integrates model inference directly into Qdrant Cloud. This allows users to generate, store, and index embeddings within a single API call, eliminating the need for separate inference infrastructure and reducing latency. It supports multiple embedding models for multimodal and hybrid search, including CLIP-style models for text and images, and offers free token allowances for paid users. Inference is automatically enabled for paid accounts with version 1.14.0 or higher.
  14. How GoodData turbocharged AI analytics with Qdrant - QdrantThis post details GoodData's successful migration from a prototype RAG approach to Qdrant for their AI-powered analytics platform. They transitioned from an unsustainable method of loading the entire semantic model into LLM contexts to a RAG strategy using Qdrant. Key technical achievements include: deploying Qdrant via Helm chart in Kubernetes, achieving near real-time embedding updates (hundreds to thousands per minute), sub-100ms semantic search results, and negligible latency overhead from Qdrant, enabling AI assistant responses within 5-10 seconds. This migration addressed scalability limitations, high compute costs, and context size limits of their previous approach.
  15. How FAZ unlocked 75 years of journalism with Qdrant - QdrantFAZ has built a hybrid search engine using Qdrant and Azure OpenAI for their 75-year journalism archive, enabling semantic search with rich metadata filtering and real-time updates. They are planning to integrate sparse vectors for hybrid search capabilities.
  16. GraphRAG: How Lettria Unlocked 20% Accuracy Gains with Qdrant and Neo4j - QdrantLettria's GraphRAG system integrates Qdrant with Neo4j for enhanced accuracy in complex document intelligence, achieving >20% accuracy gains by combining vector search with graph-based semantic understanding. Key technical challenges addressed include maintaining data consistency between Qdrant and Neo4j through a custom ingest transaction mechanism with snapshotting and conflict resolution, and optimizing query performance via payload flattening and disk-based caching for large-scale vector datasets (>100M vectors).
  17. ​​Introducing the Official Qdrant Node for n8n - QdrantIntroduces an official Qdrant node for n8n, allowing direct integration of Qdrant's features (batch upserts, updates, hybrid search, recommendations, discovery search, distance matrix API) into n8n workflows without relying on HTTP nodes. Demonstrates a hybrid search example using Reciprocal Rank Fusion (RRF) and highlights support for advanced features like the Score-Boosting Reranker.
  18. How Qovery Accelerated Developer Autonomy with Qdrant - QdrantThis post details how Qovery integrated Qdrant Cloud into their DevOps automation platform to power an AI-driven DevOps Copilot. They utilized Qdrant for its real-time indexing and low-latency queries to manage a corpus of vectors derived from historical usage data, enabling developers to manage infrastructure tasks via natural language. The integration focused on seamlessness, scalability, and performance, allowing Qovery to reduce reliance on specialized DevOps expertise and accelerate infrastructure task execution.
  19. How Dust Scaled to 5,000+ Data Sources with Qdrant - QdrantThis post details Dust's successful scaling to over 5,000 data sources by migrating to Qdrant. Key technical contributions include: consolidating thousands of individual collections into a few shared, multi-tenant ones using Qdrant's sharding and payload filtering; implementing scalar quantization to reduce vector storage by 4x and enable in-memory data, drastically improving search latency; building a custom `DustQdrantClient` to abstract Qdrant operations; and deploying Qdrant in isolated VPCs across US and EU regions for high availability and compliance. The post also highlights the architectural flexibility gained by grouping data sources by embedder, enabling smoother migrations and model experimentation.
  20. How Mixpeek Uses Qdrant for Efficient Multimodal Feature Stores - QdrantThis post details how Mixpeek uses Qdrant as a specialized feature store for multimodal data. Key technical contributions include: 1. Simplifying hybrid retrievers by leveraging Qdrant's native Reciprocal Rank Fusion (RRF) support, reducing custom logic by 80%. 2. Achieving 40% faster query times (from ~2.5s to 1.3-1.6s) through Qdrant's prefetching capabilities for parallel retrieval across billions of features. 3. Optimizing SageMaker feature extraction workflows by reducing query overhead by 50%. 4. Implementing taxonomy (JOIN analogue) and clustering (GROUP BY analogue) architectures using Qdrant's payload filtering and batch vector search capabilities. The post quantifies improvements in retriever speed, code reduction, and developer productivity.
  21. Vibe Coding RAG with our MCP server - QdrantThis post details the integration of Qdrant with the Model Context Protocol (MCP) server to enhance AI coding assistants. It explains how the `mcp-server-qdrant` acts as a semantic memory layer, enabling AI agents to store and retrieve code snippets and documentation using `qdrant-store` and `qdrant-find` tools. The post provides configuration examples for Claude Code, demonstrating how to set up tool descriptions for frontend code generation and storage, and lists key environment variables for the MCP server configuration, including embedding provider and model selection.
  22. How Deutsche Telekom Built a Multi-Agent Enterprise Platform Leveraging Qdrant - QdrantDeutsche Telekom built a multi-agent enterprise platform (LMOS) as a PaaS for AI agents, leveraging Qdrant for vector search and retrieval. They chose Qdrant for its simplicity, Rust efficiency, memory management, and operational fit for multitenancy, contrasting it with previous solutions that had higher operational overhead and downtime during scaling. The platform reduced agent development time from 15 days to 2 and now processes over 2 million conversations.
  23. How to Build Intelligent Agentic RAG with CrewAI and Qdrant - QdrantThis post details the integration of Qdrant with CrewAI for building agentic RAG systems. It includes the development of a custom Qdrant storage implementation for CrewAI's entity and short-term memory, and outlines strategies for loading and semantically chunking data from sources like Obsidian notes into Qdrant for retrieval.
  24. Voiceflow & Qdrant: Powering No-Code AI Agent Creation with Scalable Vector Search - QdrantThis post details Voiceflow's migration from Pinecone to Qdrant for their RAG solution, highlighting Qdrant's advantages in scaling capabilities (horizontal scaling, multi-node setup), robust infrastructure support (AWS Private Links, KMS), and team responsiveness. Voiceflow transitioned from namespaces to Qdrant's collections, implemented quantization, and uses chunking search. They also leverage Qdrant's private link for SOC2 compliance and isolated data management for multitenancy. The outcome includes enhanced metadata tagging, optimized performance, and minimal operational overhead.
  25. Advanced Retrieval with ColPali & Qdrant Vector Database - QdrantIntroduces ColPali, a multimodal retrieval approach that uses Vision Language Models (VLMs) to process document images directly, creating multi-vector embeddings from both visual and textual content. Details the integration of ColPali with Qdrant, including the use of Binary Quantization for optimizing storage and computational load, and presents results showing a 2x faster search time compared to Scalar Quantization while maintaining accuracy.
  26. Qdrant 1.12 - Distance Matrix, Facet Counting & On-Disk Indexing - QdrantIntroduced Distance Matrix API for pairwise vector distance calculations with 'pairs' and 'offsets' output formats. Added Facet API for counting unique values in payload fields, with an 'exact' parameter for precise counts. Enabled 'on_disk' option for text and geo indexes to reduce memory footprint. Integrated Distance Matrix API into the Graph UI for data visualization.
  27. Qdrant and Shakudo: Secure & Performant Vector Search in VPC Environments - QdrantThis post details the partnership between Qdrant and Shakudo to offer Qdrant Hybrid Cloud within Shakudo's VPC environments. It highlights the technical aspects of deploying Qdrant on Kubernetes within a private cloud for enhanced data sovereignty, security, and performance. The integration leverages Qdrant's HNSW indexing for efficient handling of large vector datasets and its Kubernetes-native architecture for automated deployments and cluster management within Shakudo's pre-configured AI infrastructure.
  28. Data-Driven RAG Evaluation: Testing Qdrant Apps with Relari AI - QdrantThis post details the integration of Qdrant with Relari AI for data-driven RAG evaluation. It introduces two key evaluation methods: Top-K parameter optimization and Auto Prompt Optimization. The post provides a practical example using the GitLab legal policies dataset, demonstrating how to build a RAG app with Qdrant, FastEmbed, and LangChain, log retrieval results for different Top-K values, and submit these results to Relari for performance analysis using metrics like Precision/Recall and Rank-Aware methods. It also outlines the setup for Auto Prompt Optimization by analyzing prompts and setting up a base prompt.
  29. Kern AI & Qdrant: Precision AI Solutions for Finance and Insurance - QdrantThis post details Kern AI's use of Qdrant for their AI solutions in the finance and insurance sectors. Key technical contributions include the utilization of Qdrant's multi-vector storage to manage diverse embedding indexes, hybrid search/filters for nuanced data retrieval, and an on-premise deployment for their open-source and commercial frameworks. The results highlight achieving under 1% hallucination rates and reducing customer service response times from five minutes to under 30 seconds for Markel Insurance SE. Use cases include claims management and similarity search for incident handling.
  30. Qdrant 1.11 - The Vector Stronghold: Optimizing Data Structures for Scale and Efficiency - QdrantQdrant 1.11 introduces defragmentation of tenant storage to co-locate vectors from the same tenant for improved read performance and cost efficiency, configurable via `is_tenant=true` on payload indexes. It adds `on_disk=true` support for payload indexes to offload cold data, improving memory management for large datasets. A new UUID datatype for payload indexes reduces memory overhead by storing UUIDs as 16-byte values. The Query API now supports a Groups endpoint for result aggregation by payload fields, random sampling, and distribution-based score fusion. New Web UI tools include a Search Quality Tool and a Graph Exploration Tool.
  31. Qdrant 1.10 - Universal Query, Built-in IDF & ColBERT Support - QdrantIntroduced a Universal Query API to consolidate all search types (nearest, fusion, discover, context, recommend) into a single endpoint. Added native support for sparse/dense hybrid search with fusion strategies like RRF, and built-in IDF calculation for sparse vectors. Introduced native support for multivector models like ColBERT via the Query API. Added sparse vector compression. Introduced an Issues API. Added S3 snapshot storage support. Released a new Rust client.
  32. Developing Advanced RAG Systems with Qdrant Hybrid Cloud and LangChain - QdrantThis post details the collaboration between Qdrant and LangChain to launch Qdrant Hybrid Cloud, a Kubernetes-native deployment option for Qdrant. It highlights how this integration empowers engineers to build and scale GenAI applications, particularly RAG systems, by providing flexible deployment environments and seamless integration with LangChain's framework. A tutorial is provided for building a RAG-based employee onboarding system using Qdrant Hybrid Cloud, LangChain, and Cohere embeddings.
  33. Iveta Lohovska on Gen AI and Vector Search | Qdrant - QdrantThis post features insights from Iveta Lohovska on the importance of trustworthiness and explainability in Generative AI, particularly in high-stakes use cases. It highlights the role of vector databases in providing control and explainability by understanding foundational datasets. The post also touches upon data privacy, on-premises vs. cloud solutions, and the maturity of GenAI models. A specific example of a climate agent is discussed, emphasizing the need for accurate data sources and citations.
  34. Red Hat OpenShift and Qdrant Hybrid Cloud Offer Seamless and Scalable AI - QdrantThis post details the integration of Qdrant Hybrid Cloud with Red Hat OpenShift, enabling seamless and scalable AI deployments within enterprise Kubernetes environments. It highlights how this collaboration simplifies AI infrastructure, allows for data sovereignty, and leverages OpenShift's features like auto-scaling, load balancing, and security controls for managing vector database deployments. The post emphasizes benefits such as scalability, portability, automation, security, flexibility, and integration, and provides a tutorial for building a private RAG chatbot on this combined platform.
  35. Qdrant Hybrid Cloud and DigitalOcean for Scalable and Secure AI Solutions - QdrantThis post details the integration of Qdrant Hybrid Cloud with DigitalOcean, allowing users to deploy Qdrant on DigitalOcean Kubernetes (DOKS) clusters. It highlights the benefits of deploying within one's own infrastructure for data control and flexibility. A tutorial is provided for setting up a RAG application using Qdrant Hybrid Cloud on DigitalOcean with LlamaIndex and Jina AI.
  36. Enhance AI Data Sovereignty with Aleph Alpha and Qdrant Hybrid Cloud - QdrantThis post details the integration of Qdrant Hybrid Cloud with Aleph Alpha's foundation models, focusing on enabling data sovereignty and compliance with European standards. It highlights a tutorial for building a region-specific contract management system hosted within Germany for GDPR compliance, demonstrating how to leverage Qdrant's Kubernetes-native design and Aleph Alpha's models for secure, sensitive data processing in AI applications.
  37. Vultr and Qdrant Hybrid Cloud Support Next-Gen AI Projects - QdrantThis post announces and details the integration of Qdrant Hybrid Cloud with Vultr's infrastructure. It highlights the benefits of this partnership for AI development, including simple and flexible deployment, scalability, unified AI stack management, and global reach with local execution. A tutorial is provided for crafting a personalized AI assistant using RAG with Qdrant Hybrid Cloud on Vultr.
  38. STACKIT and Qdrant Hybrid Cloud for Best Data Privacy - QdrantThis post announces the integration of Qdrant Hybrid Cloud with STACKIT, allowing for the deployment of a fully managed vector database within STACKIT's German data centers. It emphasizes enhanced data privacy, GDPR compliance, and seamless integration via Kubernetes. A tutorial is provided for building an AI-enabled contract management platform using Qdrant Hybrid Cloud on STACKIT with Aleph Alpha, showcasing multitenancy and custom sharding.
  39. Qdrant and OVHcloud Bring Vector Search to All Enterprises - QdrantThis post announces the integration of Qdrant Hybrid Cloud with OVHcloud, allowing for deployment within OVHcloud's infrastructure. It emphasizes data sovereignty, trust, and cost-efficiency through OVHcloud's eco-friendly infrastructure and Qdrant's quantization. A tutorial for building a RAG-based recommendation system on OVHcloud is provided.
  40. New RAG Horizons with Qdrant Hybrid Cloud and LlamaIndex - QdrantThis post details the integration of Qdrant Hybrid Cloud with LlamaIndex to facilitate the development and scaling of GenAI applications. It highlights the benefits of deploying Qdrant in any environment using Kubernetes, enabling users to keep data sources, embedding models, and LLMs in the same hosting architecture. The integration supports RAG pipelines, offering advanced hybrid search functionality that combines sparse and dense vectors, and provides a tutorial for building a RAG system for complex PDF documentation using LlamaIndex and Qdrant Hybrid Cloud.
  41. Cutting-Edge GenAI with Jina AI and Qdrant Hybrid Cloud - QdrantThis post details the integration of Qdrant Hybrid Cloud with Jina AI's embedding models and APIs to streamline the development and scaling of AI applications, particularly RAG systems. It highlights seamless deployment on Kubernetes, scalable vector search, and cost efficiency through Jina AI's pricing and Qdrant's quantization. A tutorial is provided for building a GenAI application using Jina AI embeddings, Qdrant Hybrid Cloud, and LlamaIndex for hybrid search on PDF manuals.
  42. Qdrant Hybrid Cloud and Haystack for Enterprise RAG - QdrantThis post details the integration of Qdrant Hybrid Cloud with Haystack 2.0, focusing on simplifying RAG deployment for enterprise use cases. It highlights the mature integration, production readiness via Hayhooks for generating RESTful APIs from Haystack pipelines, and the flexible, customizable nature of both open-source products. A tutorial is provided for building a private RAG chatbot on RedHat OpenShift, emphasizing data privacy and sovereignty.
  43. Elevate Your Data With Airbyte and Qdrant Hybrid Cloud - QdrantThis post details the integration of Qdrant Hybrid Cloud with Airbyte, enabling seamless data ingestion from diverse sources into Qdrant's indexing system. It highlights the Kubernetes-native architecture of Qdrant Hybrid Cloud for flexible deployment and the synergy with Airbyte's data replication for up-to-date RAG applications. The post also provides a tutorial for building a RAG system for customer support queries and links to documentation for deploying Qdrant Hybrid Cloud.
  44. Teaching Vector Databases at Scale - Alfredo Deza | Vector Space Talks - QdrantThis post highlights Qdrant's suitability for educational purposes, specifically for teaching vector databases and retrieval augmented generation (RAG). It emphasizes the value of Qdrant's straightforward setup and in-memory implementation for learning experiences. The author, Alfredo Deza, a former Olympic athlete and current Adjunct Professor, uses Qdrant in his university courses, drawing parallels between athletic discipline and consistent learning in AI. The post also touches upon the use of a custom wine dataset for teaching and the broader context of AI learning acceleration in universities.
  45. How to meow on the long tail with Cheshire Cat AI? - Piero and Nicola | Vector Space Talks - QdrantThis post details the Cheshire Cat AI framework's native integration with Qdrant as its vector database. It highlights how Cheshire Cat utilizes Qdrant for storing quantized vectors in a hybrid mode to balance memory and performance, and explores Qdrant's alias feature for managing multiple embedders. The framework also leverages Qdrant for managing conversational memories and data.
  46. Talk with YouTube without paying a cent - Francesco Saverio Zuppichini | Vector Space Talks - QdrantThis post details the technical implementation of a RAG system that ingests data from YouTube video subtitles. It showcases the use of YouTube DL for subtitle extraction, Hugging Face for generating text embeddings, and Qdrant as the vector database. The author emphasizes running Qdrant and language models locally for privacy and cost-effectiveness, and highlights the importance of metadata in Qdrant for filtering and enhancing RAG query results. The talk also touches upon code organization using functions and the benefits of good documentation.
  47. Integrating Qdrant and LangChain for Advanced Vector Similarity Search - QdrantThis post details the integration of Qdrant with LangChain to facilitate Retrieval Augmented Generation (RAG) for GenAI applications. It explains how Qdrant serves as long-term memory for LLMs by storing and retrieving user data efficiently. The post outlines the RAG process, the benefits of using LangChain for simplifying development, and common use cases like chatbots and recommendation systems. It also provides a tutorial outline for building a RAG chatbot and discusses the scalability and production-readiness of the Qdrant-LangChain combination.
  48. IrisAgent and Qdrant: Redefining Customer Support with AI - QdrantThis post details the integration of Qdrant into IrisAgent's AI pipeline for customer support automation. It highlights IrisAgent's evaluation criteria for choosing Qdrant, including its open-source nature, performance, ease of use (SDKs, documentation), and maintainability (Rust). The post describes how IrisAgent uses Qdrant for RAG use cases to enhance proprietary AI models for tasks like hallucination detection, similarity detection, and customer intent classification, leading to performance and quality gains. Qdrant is deployed on Google Cloud.
  49. Unlocking AI Potential: Insights from Stanislas Polu - QdrantStanislas Polu, Co-Founder and Engineer at Dust, discusses their choice of Qdrant for their AI platform. He highlights Qdrant's open-source nature, performance, Rust implementation, and documentation as key decision factors. Polu specifically mentions Qdrant's indexing capabilities on top of vector search for precise data retrieval and its performance benefits for RAG applications. The post also touches on the challenges of enterprise data management and the use of customized AI assistants.
  50. Loading Unstructured.io Data into Qdrant from the Terminal - QdrantThis post demonstrates a practical workflow for ingesting unstructured data from Discord into Qdrant using the Unstructured.io CLI. It outlines the prerequisites, including setting up a Qdrant instance and obtaining a Discord bot token. The process involves retrieving data from Discord channels using `unstructured-ingest discord` and then ingesting this structured data into a Qdrant collection using `unstructured-ingest local qdrant`. The post provides example commands for setting up a Qdrant collection with specified vector dimensions and distance metrics, and for performing the ingestion, highlighting configurable options for data fields, partitioning, and chunking.