update readme and main example
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@@ -85,6 +85,8 @@ uv sync
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**Ollama Setup (Optional for Local LLM):**
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**Ollama Setup (Optional for Local LLM):**
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*macOS:*
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*macOS:*
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First, [download Ollama for macOS](https://ollama.com/download/mac).
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```bash
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```bash
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# Install Ollama
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# Install Ollama
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brew install ollama
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brew install ollama
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9790
demo.ipynb
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demo.ipynb
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@@ -10,7 +10,7 @@ from pathlib import Path
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dotenv.load_dotenv()
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dotenv.load_dotenv()
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node_parser = SentenceSplitter(
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node_parser = SentenceSplitter(
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chunk_size=256, chunk_overlap=64, separator=" ", paragraph_separator="\n\n"
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chunk_size=256, chunk_overlap=128, separator=" ", paragraph_separator="\n\n"
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)
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)
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print("Loading documents...")
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print("Loading documents...")
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documents = SimpleDirectoryReader(
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documents = SimpleDirectoryReader(
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@@ -58,17 +58,16 @@ async def main(args):
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print(f"\n[PHASE 2] Starting Leann chat session...")
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print(f"\n[PHASE 2] Starting Leann chat session...")
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llm_config = {"type": "hf", "model": "Qwen/Qwen3-4B"}
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# llm_config = {"type": "hf", "model": "Qwen/Qwen3-4B"}
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llm_config = {"type": "ollama", "model": "qwen3:8b"}
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chat = LeannChat(index_path=INDEX_PATH, llm_config=llm_config)
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chat = LeannChat(index_path=INDEX_PATH, llm_config=llm_config)
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query = "Based on the paper, what are the main techniques LEANN explores to reduce the storage overhead and DLPM explore to achieve Fairness and Efiiciency trade-off?"
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query = "Based on the paper, what are the main techniques LEANN explores to reduce the storage overhead and DLPM explore to achieve Fairness and Efiiciency trade-off?"
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query = (
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"What is the main idea of RL and give me 5 exapmle of classic RL algorithms?"
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# query = (
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)
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# "什么是盘古大模型以及盘古开发过程中遇到了什么阴暗面,任务令一般在什么城市颁发"
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query = (
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# )
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"什么是盘古大模型以及盘古开发过程中遇到了什么阴暗面,任务令一般在什么城市颁发"
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)
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print(f"You: {query}")
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print(f"You: {query}")
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chat_response = chat.ask(
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chat_response = chat.ask(
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@@ -103,7 +102,7 @@ if __name__ == "__main__":
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parser.add_argument(
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parser.add_argument(
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"--index-dir",
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"--index-dir",
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type=str,
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type=str,
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default="./test_pdf_index_pangu_test",
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default="./test_doc_files",
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help="Directory where the Leann index will be stored.",
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help="Directory where the Leann index will be stored.",
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)
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)
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args = parser.parse_args()
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args = parser.parse_args()
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