yangdx
commited on
Commit
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c5022d3
1
Parent(s):
74d902f
Settign LLM cache option for entity extraction from env
Browse files- env.example +1 -0
- lightrag/api/README.md +3 -0
- lightrag/api/lightrag_server.py +5 -2
env.example
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@@ -50,6 +50,7 @@
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# MAX_TOKEN_SUMMARY=500 # Max tokens for entity or relations summary
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# SUMMARY_LANGUAGE=English
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# MAX_EMBED_TOKENS=8192
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### LLM Configuration (Use valid host. For local services installed with docker, you can use host.docker.internal)
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LLM_BINDING=ollama
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# MAX_TOKEN_SUMMARY=500 # Max tokens for entity or relations summary
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# SUMMARY_LANGUAGE=English
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# MAX_EMBED_TOKENS=8192
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+
# ENABLE_LLM_CACHE_FOR_EXTRACT=false # Enable LLM cache for entity extraction, defaults to false
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### LLM Configuration (Use valid host. For local services installed with docker, you can use host.docker.internal)
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LLM_BINDING=ollama
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lightrag/api/README.md
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@@ -223,6 +223,9 @@ 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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### Storage Types Supported
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LightRAG uses 4 types of storage for difference purposes:
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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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### Storage Types Supported
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LightRAG uses 4 types of storage for difference purposes:
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lightrag/api/lightrag_server.py
CHANGED
@@ -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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# Initialize config parser
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config = configparser.ConfigParser()
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config.read("config.ini")
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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=
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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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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=
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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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# 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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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=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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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=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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