fix emaple and add pypi example
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35
README.md
35
README.md
@@ -114,32 +114,23 @@ Our declarative API makes RAG as easy as writing a config file.
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[Try in this ipynb file →](demo.ipynb) [](https://colab.research.google.com/github/yichuan-w/LEANN/blob/main/demo.ipynb)
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```python
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from leann.api import LeannBuilder, LeannSearcher, LeannChat
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from leann import LeannBuilder, LeannSearcher, LeannChat
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from pathlib import Path
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INDEX_PATH = str(Path("./").resolve() / "demo.leann")
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# 1. Build the index (no embeddings stored!)
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# Build an index
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builder = LeannBuilder(backend_name="hnsw")
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builder.add_text("C# is a powerful programming language")
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builder.add_text("Python is a powerful programming language and it is very popular")
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builder.add_text("Machine learning transforms industries")
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builder.add_text("Neural networks process complex data")
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builder.add_text("Leann is a great storage saving engine for RAG on your MacBook")
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builder.build_index("knowledge.leann")
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builder.add_text("LEANN saves 97% storage compared to traditional vector databases.")
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builder.add_text("Tung Tung Tung Sahur called—they need their banana‑crocodile hybrid back")
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builder.build_index(INDEX_PATH)
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# 2. Search with real-time embeddings
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searcher = LeannSearcher("knowledge.leann")
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results = searcher.search("programming languages", top_k=2)
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# Search
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searcher = LeannSearcher(INDEX_PATH)
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results = searcher.search("fantastical AI-generated creatures", top_k=1)
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# 3. Chat with LEANN using retrieved results
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llm_config = {
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"type": "ollama",
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"model": "llama3.2:1b"
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}
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chat = LeannChat(index_path="knowledge.leann", llm_config=llm_config)
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response = chat.ask(
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"Compare the two retrieved programming languages and say which one is more popular today.",
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top_k=2,
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)
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# Chat with your data
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chat = LeannChat(INDEX_PATH, llm_config={"type": "hf", "model": "Qwen/Qwen3-0.6B"})
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response = chat.ask("How much storage does LEANN save?", top_k=1)
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```
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## RAG on Everything!
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