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ReMe/docs/installation.md
jinli.yl ac72e6e021 feat(vector_store): add QdrantVectorStore implementation with async support 
- Added QdrantVectorStore backend with native async operations
- Implemented advanced filtering capabilities for metadata queries
- Added support for Qdrant Cloud and local deployments- Updated vector store comparison table with Qdrant features
- Enhanced documentation with Qdrant setup and usage examples
- Fixed code block formatting in vector store API guide
- Updated embedding model integration for Qdrant compatibility
2025-10-23 17:15:12 +08:00

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Python 3 python python3

Installation

Install from PyPI (Recommended)

pip install reme-ai

Install from Source

git clone https://github.com/modelscope/ReMe.git
cd ReMe
pip install .

Environment Configuration

Copy example.env to .env and modify the corresponding parameters:

FLOW_LLM_API_KEY=sk-xxxx
FLOW_LLM_BASE_URL=https://xxxx/v1
FLOW_EMBEDDING_API_KEY=sk-xxxx
FLOW_EMBEDDING_BASE_URL=https://xxxx/v1

You’ve completed the installation! Head over to quick start to see how to start using Reme quickly.