Merge branch 'HKUDS:main' into main
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@@ -223,6 +223,11 @@ LightRAG supports binding to various LLM/Embedding backends:
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Use environment variables `LLM_BINDING` or CLI argument `--llm-binding` to select LLM backend type. Use environment variables `EMBEDDING_BINDING` or CLI argument `--embedding-binding` to select LLM backend type.
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### Entity Extraction Configuration
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* ENABLE_LLM_CACHE_FOR_EXTRACT: Enable LLM cache for entity extraction (default: false)
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It's very common to set `ENABLE_LLM_CACHE_FOR_EXTRACT` to true for test environment to reduce the cost of LLM calls.
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### Storage Types Supported
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LightRAG uses 4 types of storage for difference purposes:
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@@ -50,6 +50,9 @@ from .auth import auth_handler
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# This update allows the user to put a different.env file for each lightrag folder
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load_dotenv(".env", override=True)
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# Read entity extraction cache config
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enable_llm_cache = os.getenv("ENABLE_LLM_CACHE_FOR_EXTRACT", "false").lower() == "true"
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# Initialize config parser
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config = configparser.ConfigParser()
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config.read("config.ini")
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@@ -323,7 +326,7 @@ def create_app(args):
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vector_db_storage_cls_kwargs={
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"cosine_better_than_threshold": args.cosine_threshold
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},
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enable_llm_cache_for_entity_extract=False, # set to True for debuging to reduce llm fee
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enable_llm_cache_for_entity_extract=enable_llm_cache, # Read from environment variable
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embedding_cache_config={
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"enabled": True,
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"similarity_threshold": 0.95,
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@@ -352,7 +355,7 @@ def create_app(args):
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vector_db_storage_cls_kwargs={
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"cosine_better_than_threshold": args.cosine_threshold
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},
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enable_llm_cache_for_entity_extract=False, # set to True for debuging to reduce llm fee
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enable_llm_cache_for_entity_extract=enable_llm_cache, # Read from environment variable
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embedding_cache_config={
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"enabled": True,
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"similarity_threshold": 0.95,
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