pathwaycom/llm-app
Ready-to-run cloud templates for building real-time RAG, AI pipelines, and enterprise search applications that synchronize with various live data sources.
Awesome Infra for AI › Vector Databases & Retrieval Infrastructure
qdrant/qdrant-client
This project is the official Python client library for Qdrant, a high-performance vector search engine. It provides a comprehensive interface for interacting with Qdrant instances, whether running locally, as a self-hosted server, or in the cloud. The library includes type definitions for the entire Qdrant API, enabling both synchronous and asynchronous requests. Beyond direct API calls, it offers helper methods for common operations like initial collection uploading. A key feature is its 'local mode,' which allows developers to use the same API without needing to run a separate Qdrant server, ideal for development, testing, and prototyping in CI/CD pipelines, Jupyter notebooks, or Google Colab. The client also supports connecting to self-hosted Qdrant servers or Qdrant Cloud instances. Further enhancing its utility for AI practitioners, the client incorporates an Inference API. This API enables seamless creation of embeddings, either locally using the FastEmbed library (with CPU or optional GPU support) or remotely via Qdrant Cloud's managed inference services. This integration simplifies the process of generating vector representations from text and instantly using them within Qdrant, streamlining the workflow for developing and deploying AI applications that rely on semantic search and similarity retrieval.
https://github.com/qdrant/qdrant-client
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