670 lines
15 KiB
Markdown
670 lines
15 KiB
Markdown
# Vietnamese Embedding Integration - Complete Guide
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## 🎯 Overview
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This guide provides complete information about the Vietnamese Embedding integration for LightRAG. The **AITeamVN/Vietnamese_Embedding** model enhances LightRAG's retrieval capabilities for Vietnamese text while maintaining multilingual support.
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---
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## 📋 Table of Contents
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1. [Quick Start](#quick-start)
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2. [Installation](#installation)
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3. [Configuration](#configuration)
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4. [Usage Examples](#usage-examples)
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5. [API Reference](#api-reference)
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6. [Advanced Topics](#advanced-topics)
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7. [Performance Tuning](#performance-tuning)
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8. [Troubleshooting](#troubleshooting)
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9. [FAQ](#faq)
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10. [Resources](#resources)
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---
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## 🚀 Quick Start
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### 5-Minute Setup
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```bash
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# 1. Navigate to LightRAG directory
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cd LightRAG
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# 2. Install (if not already installed)
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pip install -e .
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# 3. Set your tokens
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export HUGGINGFACE_API_KEY=
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export OPENAI_API_KEY="your_openai_key"
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# 4. Run the simple example
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python examples/lightrag_vietnamese_embedding_simple.py
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```
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### Verify Installation
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```bash
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python -c "
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import asyncio
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from lightrag.llm.vietnamese_embed import vietnamese_embed
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async def test():
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result = await vietnamese_embed(['Test'])
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print(f'✓ Success! Shape: {result.shape}')
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asyncio.run(test())
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"
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```
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Expected output: `✓ Success! Shape: (1, 1024)`
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---
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## 📦 Installation
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### Prerequisites
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- Python 3.10+
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- pip
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- 4-8 GB RAM
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- 2 GB free disk space
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- (Optional) CUDA-capable GPU
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### Install LightRAG
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```bash
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cd LightRAG
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pip install -e .
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```
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### Dependencies
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The following are automatically installed when you first use the Vietnamese embedding:
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- `transformers`
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- `torch`
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- `numpy`
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### GPU Support (Recommended)
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For significantly faster performance:
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```bash
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# CUDA 11.8
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pip install torch --index-url https://download.pytorch.org/whl/cu118
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# CUDA 12.1
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pip install torch --index-url https://download.pytorch.org/whl/cu121
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```
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---
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## ⚙️ Configuration
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### Environment Variables
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#### Required
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```bash
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# HuggingFace token for model access
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export HUGGINGFACE_API_KEY="your_hf_token"
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# OR
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export HF_TOKEN="your_hf_token"
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# LLM API key (OpenAI example)
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export OPENAI_API_KEY="your_openai_key"
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```
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#### Optional
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```bash
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# Embedding configuration
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export EMBEDDING_MODEL="AITeamVN/Vietnamese_Embedding"
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export EMBEDDING_DIM=1024
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# Working directory
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export WORKING_DIR="./vietnamese_rag_storage"
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```
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### Using `.env` File
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Create `.env` in your project root:
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```env
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# HuggingFace
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HUGGINGFACE_API_KEY=hf_your_token_here
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# LLM
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OPENAI_API_KEY=sk_your_key_here
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LLM_BINDING=openai
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LLM_MODEL=gpt-4o-mini
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# Embedding
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EMBEDDING_MODEL=AITeamVN/Vietnamese_Embedding
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EMBEDDING_DIM=1024
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```
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### Getting Tokens
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1. **HuggingFace Token:**
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- Visit: https://huggingface.co/settings/tokens
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- Create token with "Read" permission
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- Copy and use
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2. **OpenAI API Key:**
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- Visit: https://platform.openai.com/api-keys
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- Create new key
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- Copy and use
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---
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## 💻 Usage Examples
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### Example 1: Minimal Code
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```python
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import os
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import asyncio
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from lightrag import LightRAG, QueryParam
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from lightrag.llm.openai import gpt_4o_mini_complete
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from lightrag.llm.vietnamese_embed import vietnamese_embed
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from lightrag.kg.shared_storage import initialize_pipeline_status
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from lightrag.utils import EmbeddingFunc
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async def main():
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rag = LightRAG(
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working_dir="./vietnamese_rag",
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llm_model_func=gpt_4o_mini_complete,
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embedding_func=EmbeddingFunc(
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embedding_dim=1024,
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max_token_size=2048,
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func=vietnamese_embed
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)
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)
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await rag.initialize_storages()
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await initialize_pipeline_status()
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await rag.ainsert("Việt Nam là quốc gia ở Đông Nam Á.")
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result = await rag.aquery("Việt Nam ở đâu?", param=QueryParam(mode="hybrid"))
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print(result)
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await rag.finalize_storages()
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asyncio.run(main())
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```
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### Example 2: With Custom Configuration
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```python
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import os
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import asyncio
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from lightrag import LightRAG, QueryParam
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from lightrag.llm.openai import gpt_4o_mini_complete
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from lightrag.llm.vietnamese_embed import vietnamese_embed
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from lightrag.kg.shared_storage import initialize_pipeline_status
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from lightrag.utils import EmbeddingFunc, setup_logger
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# Enable debug logging
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setup_logger("lightrag", level="DEBUG")
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async def main():
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hf_token = os.getenv("HUGGINGFACE_API_KEY")
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rag = LightRAG(
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working_dir="./vietnamese_rag",
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llm_model_func=gpt_4o_mini_complete,
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embedding_func=EmbeddingFunc(
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embedding_dim=1024,
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max_token_size=2048,
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func=lambda texts: vietnamese_embed(
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texts,
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model_name="AITeamVN/Vietnamese_Embedding",
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token=hf_token
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)
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),
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# Optional: customize chunk size
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chunk_token_size=1200,
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chunk_overlap_token_size=100,
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)
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await rag.initialize_storages()
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await initialize_pipeline_status()
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# Insert from file
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with open("data.txt", "r", encoding="utf-8") as f:
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await rag.ainsert(f.read())
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# Query with different modes
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for mode in ["naive", "local", "global", "hybrid"]:
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result = await rag.aquery(
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"Your question here",
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param=QueryParam(mode=mode)
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)
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print(f"\n{mode.upper()} mode result:\n{result}\n")
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await rag.finalize_storages()
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asyncio.run(main())
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```
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### Example 3: Batch Processing
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```python
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import asyncio
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from lightrag import LightRAG, QueryParam
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from lightrag.llm.openai import gpt_4o_mini_complete
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from lightrag.llm.vietnamese_embed import vietnamese_embed
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from lightrag.kg.shared_storage import initialize_pipeline_status
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from lightrag.utils import EmbeddingFunc
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async def main():
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rag = LightRAG(
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working_dir="./vietnamese_rag",
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llm_model_func=gpt_4o_mini_complete,
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embedding_func=EmbeddingFunc(
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embedding_dim=1024,
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max_token_size=2048,
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func=vietnamese_embed
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)
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)
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await rag.initialize_storages()
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await initialize_pipeline_status()
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# Batch insert multiple documents
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documents = [
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"Document 1 content...",
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"Document 2 content...",
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"Document 3 content...",
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]
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await rag.ainsert(documents)
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# Batch queries
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queries = [
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"Question 1?",
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"Question 2?",
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"Question 3?",
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]
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for query in queries:
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result = await rag.aquery(query, param=QueryParam(mode="hybrid"))
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print(f"Q: {query}\nA: {result}\n")
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await rag.finalize_storages()
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asyncio.run(main())
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```
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---
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## 📚 API Reference
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### Main Functions
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#### `vietnamese_embed(texts, model_name, token)`
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Generate embeddings for texts.
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**Parameters:**
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- `texts` (list[str]): List of texts to embed
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- `model_name` (str, optional): Model identifier. Default: "AITeamVN/Vietnamese_Embedding"
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- `token` (str, optional): HuggingFace token. Reads from env if None
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**Returns:**
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- `np.ndarray`: Embeddings array, shape (len(texts), 1024)
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**Example:**
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```python
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embeddings = await vietnamese_embed(["Text 1", "Text 2"])
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print(embeddings.shape) # (2, 1024)
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```
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#### `vietnamese_embedding_func(texts)`
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Convenience wrapper that reads token from environment.
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**Parameters:**
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- `texts` (list[str]): List of texts to embed
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**Returns:**
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- `np.ndarray`: Embeddings array
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**Example:**
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```python
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embeddings = await vietnamese_embedding_func(["Test text"])
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```
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### Configuration Classes
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#### `EmbeddingFunc`
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Wrapper for embedding functions in LightRAG.
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**Parameters:**
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- `embedding_dim` (int): Output dimensions (1024 for Vietnamese_Embedding)
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- `max_token_size` (int): Maximum tokens per input (2048 recommended)
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- `func` (callable): The embedding function
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**Example:**
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```python
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from lightrag.utils import EmbeddingFunc
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from lightrag.llm.vietnamese_embed import vietnamese_embed
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embedding_func = EmbeddingFunc(
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embedding_dim=1024,
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max_token_size=2048,
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func=vietnamese_embed
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)
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```
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#### `QueryParam`
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Parameters for querying LightRAG.
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**Parameters:**
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- `mode` (str): Query mode - "naive", "local", "global", "hybrid", "mix"
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- `top_k` (int): Number of top results to retrieve
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- `stream` (bool): Enable streaming response
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**Example:**
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```python
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from lightrag import QueryParam
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param = QueryParam(
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mode="hybrid",
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top_k=60,
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stream=False
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)
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```
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---
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## 🔧 Advanced Topics
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### Custom Model Configuration
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Use a different HuggingFace model:
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```python
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embeddings = await vietnamese_embed(
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texts=["Sample text"],
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model_name="BAAI/bge-m3", # Use base model
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token=your_token
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)
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```
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### Device Management
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The model automatically uses the best available device:
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1. CUDA (if available)
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2. MPS (for Apple Silicon)
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3. CPU (fallback)
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Check which device is being used:
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```python
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from lightrag.utils import setup_logger
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setup_logger("lightrag", level="DEBUG")
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# Will log: "Using CUDA device for embedding"
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```
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### Memory Optimization
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For limited memory environments:
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```python
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# Reduce batch size
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embedding_func = EmbeddingFunc(
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embedding_dim=1024,
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max_token_size=512, # Reduce if texts are short
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func=vietnamese_embed
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)
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# Process documents one at a time
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for doc in documents:
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await rag.ainsert(doc)
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```
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---
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## ⚡ Performance Tuning
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### Hardware Requirements
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| Component | Minimum | Recommended |
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|-----------|---------|-------------|
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| RAM | 4 GB | 8 GB |
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| GPU Memory | N/A | 4 GB |
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| Disk Space | 2 GB | 10 GB |
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| CPU | 2 cores | 4+ cores |
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### Performance Metrics
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**GPU (NVIDIA RTX 3090):**
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- Short texts (< 512 tokens): ~1000 texts/second
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- Long texts (1024-2048 tokens): ~400 texts/second
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**CPU (Intel i7):**
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- Short texts: ~50 texts/second
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- Long texts: ~20 texts/second
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### Optimization Tips
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1. **Use GPU:**
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```bash
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pip install torch --index-url https://download.pytorch.org/whl/cu118
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```
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2. **Batch Processing:**
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```python
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# Good: Process in batch
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await rag.ainsert(multiple_documents)
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# Avoid: One by one
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for doc in multiple_documents:
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await rag.ainsert(doc)
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```
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3. **Adjust Token Size:**
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```python
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# If your texts are typically < 512 tokens
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embedding_func = EmbeddingFunc(
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embedding_dim=1024,
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max_token_size=512, # Faster processing
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func=vietnamese_embed
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)
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```
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4. **Cache Model:**
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Model is cached after first download in `~/.cache/huggingface/`
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---
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## 🔍 Troubleshooting
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### Common Issues
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#### 1. "No HuggingFace token found"
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**Symptom:** Error when initializing
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**Solution:**
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```bash
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export HUGGINGFACE_API_KEY="your_token"
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```
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#### 2. "Model download fails"
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**Symptoms:** Timeout, network error
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**Solutions:**
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- Check internet connection
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- Verify HuggingFace token
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- Ensure 2GB+ free disk space
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- Try again (network might be temporary issue)
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#### 3. "Out of memory error"
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**Symptoms:** CUDA OOM, system freezes
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**Solutions:**
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- Use CPU: System will auto-fallback
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- Reduce batch size
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- Close other GPU applications
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- Use smaller max_token_size
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#### 4. "Slow performance"
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**Symptoms:** Takes minutes for simple queries
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**Solutions:**
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- Install CUDA-enabled PyTorch
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- Verify GPU is being used (check logs)
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- Reduce max_token_size if texts are short
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- Use batch processing
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#### 5. "Import errors"
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**Symptoms:** ModuleNotFoundError
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**Solutions:**
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```bash
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pip install -e .
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pip install transformers torch numpy
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```
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### Debug Mode
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Enable detailed logging:
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```python
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from lightrag.utils import setup_logger
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setup_logger("lightrag", level="DEBUG")
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```
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### Getting Help
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1. Check documentation
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2. Run test suite:
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```bash
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python tests/test_vietnamese_embedding_integration.py
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```
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3. Review examples
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4. Open GitHub issue with `vietnamese-embedding` tag
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---
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## ❓ FAQ
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### Q: Does this work with languages other than Vietnamese?
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**A:** Yes! The model is based on BGE-M3 which supports 100+ languages. It's optimized for Vietnamese but works well with English, Chinese, and other languages.
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### Q: Do I need GPU?
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**A:** No, but highly recommended. CPU works but is 10-50x slower.
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### Q: How much does it cost?
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**A:** The embedding model is free. You only pay for the LLM API (e.g., OpenAI).
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### Q: Can I use this offline?
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**A:** After the first run (model download), the model is cached locally. You still need LLM API access though.
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### Q: What's the difference from BGE-M3?
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**A:** Vietnamese_Embedding is fine-tuned specifically for Vietnamese with 300K Vietnamese query-document pairs, providing better Vietnamese retrieval.
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### Q: Can I fine-tune this model further?
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**A:** Yes, you can fine-tune using HuggingFace transformers. See the model page for details.
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### Q: Is my HuggingFace token safe?
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**A:** The token is only used to download the model from HuggingFace. It's not sent anywhere else.
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### Q: How do I switch back to other embeddings?
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**A:** Just use a different embedding function in your configuration. No other changes needed.
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---
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## 📖 Resources
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### Documentation Files
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- **English Full Guide:** `docs/VietnameseEmbedding.md`
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- **Vietnamese Guide:** `docs/VietnameseEmbedding_VI.md`
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- **Quick Reference:** `docs/VietnameseEmbedding_QuickRef.md`
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- **Examples Guide:** `examples/VIETNAMESE_EXAMPLES_README.md`
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### Example Scripts
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- **Simple:** `examples/lightrag_vietnamese_embedding_simple.py`
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- **Comprehensive:** `examples/vietnamese_embedding_demo.py`
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### Testing
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- **Test Suite:** `tests/test_vietnamese_embedding_integration.py`
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### External Links
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- **Model Page:** https://huggingface.co/AITeamVN/Vietnamese_Embedding
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- **Base Model:** https://huggingface.co/BAAI/bge-m3
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- **LightRAG:** https://github.com/HKUDS/LightRAG
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- **HuggingFace Tokens:** https://huggingface.co/settings/tokens
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---
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## 🎓 Learning Path
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### Beginner
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1. Read Quick Start section
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2. Run `lightrag_vietnamese_embedding_simple.py`
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3. Modify the example for your data
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4. Read FAQ section
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### Intermediate
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1. Run `vietnamese_embedding_demo.py`
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2. Try different query modes
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3. Experiment with your own Vietnamese data
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4. Read Performance Tuning section
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### Advanced
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1. Study API Reference
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2. Customize model configuration
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3. Implement batch processing
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4. Optimize for your specific use case
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5. Read Advanced Topics section
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---
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## 🤝 Contributing
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Found an issue or want to improve the integration?
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1. Open an issue on GitHub
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2. Use tag: `vietnamese-embedding`
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3. Include:
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- Python version
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- OS
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- Error message
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- Minimal code to reproduce
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---
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## 📄 License
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This integration follows LightRAG's license. The Vietnamese_Embedding model may have separate terms - check the [model page](https://huggingface.co/AITeamVN/Vietnamese_Embedding).
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---
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## 🙏 Acknowledgments
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- **AITeamVN** - Vietnamese_Embedding model
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- **BAAI** - BGE-M3 base model
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- **LightRAG Team** - Excellent RAG framework
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- **HuggingFace** - Model hosting
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---
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**Last Updated:** October 25, 2025
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**Version:** 1.0.0
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**Status:** Production Ready ✅
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