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fix some readme

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yichuan520030910320
2025-07-27 21:50:09 -07:00
parent 5c8921673a
commit 6f5d5e4a77
3 changed files with 9 additions and 8 deletions
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9
README.md
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@@ -33,6 +33,8 @@ LEANN achieves this through *graph-based selective recomputation* with *high-deg
🪶 **Lightweight:** Graph-based recomputation eliminates heavy embedding storage, while smart graph pruning and CSR format minimize graph storage overhead. Always less storage, less memory usage! 🪶 **Lightweight:** Graph-based recomputation eliminates heavy embedding storage, while smart graph pruning and CSR format minimize graph storage overhead. Always less storage, less memory usage!
📦 **Portable:** Transfer your entire knowledge base between devices (even with others) with minimal cost - your personal AI memory travels with you.
📈 **Scalability:** Handle messy personal data that would crash traditional vector DBs, easily managing your growing personalized data and agent generated memory! 📈 **Scalability:** Handle messy personal data that would crash traditional vector DBs, easily managing your growing personalized data and agent generated memory!
✨ **No Accuracy Loss:** Maintain the same search quality as heavyweight solutions while using 97% less storage. ✨ **No Accuracy Loss:** Maintain the same search quality as heavyweight solutions while using 97% less storage.
@@ -85,7 +87,7 @@ uv sync
## Quick Start in 30s ## Quick Start
Our declarative API makes RAG as easy as writing a config file. Our declarative API makes RAG as easy as writing a config file.
[Try in this ipynb file →](demo.ipynb) [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/yichuan-w/LEANN/blob/main/demo.ipynb) [Try in this ipynb file →](demo.ipynb) [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/yichuan-w/LEANN/blob/main/demo.ipynb)
@@ -116,7 +118,6 @@ LEANN supports RAG on various data sources including documents (.pdf, .txt, .md)
> **Generation Model Setup** > **Generation Model Setup**
>
> LEANN supports multiple LLM providers for text generation (OpenAI API, HuggingFace, Ollama). > LEANN supports multiple LLM providers for text generation (OpenAI API, HuggingFace, Ollama).
<details> <details>
@@ -467,10 +468,10 @@ If you find Leann useful, please cite:
## ✨ [Detailed Features →](docs/features.md) ## ✨ [Detailed Features →](docs/features.md)
## 🤝 [Contributing →](docs/contributing.md) ## 🤝 [CONTRIBUTING →](docs/CONTRIBUTING.md)
## [FAQ →](docs/faq.md) ## ❓ [FAQ →](docs/faq.md)
## 📈 [Roadmap →](docs/roadmap.md) ## 📈 [Roadmap →](docs/roadmap.md)

6
docs/CONTRIBUTING.md
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@@ -26,7 +26,7 @@ We welcome contributions! Leann is built by the community, for the community.
``` ```
3. **Install system dependencies**: 3. **Install system dependencies**:
**macOS:** **macOS:**
```bash ```bash
brew install llvm libomp boost protobuf zeromq pkgconf brew install llvm libomp boost protobuf zeromq pkgconf
@@ -42,7 +42,7 @@ We welcome contributions! Leann is built by the community, for the community.
```bash ```bash
# macOS # macOS
CC=$(brew --prefix llvm)/bin/clang CXX=$(brew --prefix llvm)/bin/clang++ uv sync CC=$(brew --prefix llvm)/bin/clang CXX=$(brew --prefix llvm)/bin/clang++ uv sync
# Ubuntu/Debian # Ubuntu/Debian
uv sync uv sync
``` ```
@@ -182,7 +182,7 @@ Make sure your code passes these checks locally before pushing!
```bash ```bash
git commit -m "feat: add new search algorithm" git commit -m "feat: add new search algorithm"
``` ```
Follow [Conventional Commits](https://www.conventionalcommits.org/): Follow [Conventional Commits](https://www.conventionalcommits.org/):
- `feat:` for new features - `feat:` for new features
- `fix:` for bug fixes - `fix:` for bug fixes

2
docs/normalized_embeddings.md
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@@ -72,4 +72,4 @@ Using the wrong distance metric with normalized embeddings can lead to:
- **Incorrect ranking** of search results - **Incorrect ranking** of search results
- **Suboptimal performance** compared to using the correct metric - **Suboptimal performance** compared to using the correct metric
For more details on why this happens, see our analysis of [OpenAI embeddings with MIPS](../examples/main_cli_example.py). For more details on why this happens, see our analysis of [OpenAI embeddings with MIPS](../examples/main_cli_example.py).