wehub-resource-sync
114 lines
4.7 KiB
Python
114 lines
4.7 KiB
Python
"""
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Example: Configuring Milvus Index Parameters via vector_db_storage_cls_kwargs
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This example demonstrates how to configure Milvus indexing parameters through
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vector_db_storage_cls_kwargs, which is the recommended approach when using
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frameworks that build on top of LightRAG (like RAGAnything).
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This approach allows configuration to be passed through framework layers without
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requiring environment variable changes or direct code modifications.
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"""
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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 openai_complete_if_cache, openai_embed
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async def main():
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# Configure Milvus connection
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os.environ["MILVUS_URI"] = "http://localhost:19530"
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# os.environ["MILVUS_USER"] = "root"
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# os.environ["MILVUS_PASSWORD"] = "your_password"
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# os.environ["MILVUS_DB_NAME"] = "lightrag"
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# Initialize LightRAG with Milvus index configuration via vector_db_storage_cls_kwargs
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# This is the recommended approach for framework integration (e.g., RAGAnything)
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rag = LightRAG(
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working_dir="./demo_index",
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llm_model_func=openai_complete_if_cache,
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embedding_func=openai_embed,
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# Specify Milvus as the vector storage backend
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vector_storage="MilvusVectorDBStorage",
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# Configure Milvus indexing parameters via vector_db_storage_cls_kwargs
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# These parameters are extracted and passed to MilvusIndexConfig
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vector_db_storage_cls_kwargs={
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# Required parameter for all vector storage backends
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"cosine_better_than_threshold": 0.2,
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# Milvus index configuration parameters
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# All of these can be configured via vector_db_storage_cls_kwargs
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# Index type (AUTOINDEX, HNSW, HNSW_SQ, IVF_FLAT, etc.)
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"index_type": "HNSW",
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# Distance metric (COSINE, L2, IP)
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"metric_type": "COSINE",
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# HNSW parameters
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"hnsw_m": 32, # Number of connections per layer (2-2048)
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"hnsw_ef_construction": 256, # Size of dynamic candidate list during construction
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"hnsw_ef": 150, # Size of dynamic candidate list during search
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# IVF parameters (used when index_type is IVF_FLAT, IVF_SQ8, IVF_PQ)
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# "ivf_nlist": 2048, # Number of cluster units
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# "ivf_nprobe": 32, # Number of units to query
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# HNSW_SQ parameters (requires Milvus 2.6.8+)
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# "sq_type": "SQ8", # Quantization type (SQ4U, SQ6, SQ8, BF16, FP16)
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# "sq_refine": True, # Enable refinement
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# "sq_refine_type": "FP32", # Refinement type
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# "sq_refine_k": 20, # Number of candidates to refine
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},
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)
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# Initialize storage backends
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await rag.initialize_storages()
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print(
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"✅ LightRAG initialized with Milvus index configuration via vector_db_storage_cls_kwargs"
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)
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print(
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f" Index Type: {rag.vector_db_storages['entities'].index_config.index_type}"
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)
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print(
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f" Metric Type: {rag.vector_db_storages['entities'].index_config.metric_type}"
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)
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print(f" HNSW M: {rag.vector_db_storages['entities'].index_config.hnsw_m}")
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print(
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f" HNSW EF Construction: {rag.vector_db_storages['entities'].index_config.hnsw_ef_construction}"
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)
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print(f" HNSW EF: {rag.vector_db_storages['entities'].index_config.hnsw_ef}")
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# Example: Insert some text
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sample_text = """
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LightRAG is a Retrieval-Augmented Generation framework that uses graph-based
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knowledge representation for enhanced information retrieval. It supports multiple
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vector storage backends including Milvus, which offers advanced indexing options
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for optimal performance.
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"""
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await rag.ainsert(sample_text)
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print("\n✅ Sample text inserted")
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# Example: Query with different modes
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result = await rag.aquery("What is LightRAG?", param=QueryParam(mode="hybrid"))
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print(f"\n✅ Query result: {result[:200]}...")
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# Cleanup
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await rag.finalize_storages()
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if __name__ == "__main__":
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print("=" * 80)
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print("Milvus Configuration via vector_db_storage_cls_kwargs Example")
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print("=" * 80)
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print()
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print("This example shows how to configure Milvus indexing parameters through")
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print("vector_db_storage_cls_kwargs, which is ideal for framework integration.")
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print()
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print("Key Benefits:")
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print(" • No environment variable changes required")
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print(" • Configuration can be passed through framework layers")
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print(" • Perfect for RAGAnything and similar frameworks")
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print(" • All 11 index parameters are supported")
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print()
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print("=" * 80)
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print()
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asyncio.run(main())
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