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Merge branch 'main' into feat-node-expand

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yangdx
2025-03-17 00:08:12 +08:00
parent e7456c8579 9875037e6c
commit 1c19290979
7 changed files with 13 additions and 10 deletions
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2
lightrag/api/README.md
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@@ -224,7 +224,7 @@ LightRAG supports binding to various LLM/Embedding backends:
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.
### Entity Extraction Configuration
* ENABLE_LLM_CACHE_FOR_EXTRACT: Enable LLM cache for entity extraction (default: false)
* ENABLE_LLM_CACHE_FOR_EXTRACT: Enable LLM cache for entity extraction (default: true)
It's very common to set `ENABLE_LLM_CACHE_FOR_EXTRACT` to true for test environment to reduce the cost of LLM calls.

2
lightrag/api/docs/LightRagWithPostGRESQL.md
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@@ -141,7 +141,7 @@ Start the LightRAG server using specified options:
lightrag-server --port 9621 --key sk-somepassword --kv-storage PGKVStorage --graph-storage PGGraphStorage --vector-storage PGVectorStorage --doc-status-storage PGDocStatusStorage
```
Replace `the-port-number` with your desired port number (default is 9621) and `your-secret-key` with a secure key.
Replace the `port` number with your desired port number (default is 9621) and `your-secret-key` with a secure key.
## Conclusion

2
lightrag/api/utils_api.py
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@@ -364,7 +364,7 @@ def parse_args(is_uvicorn_mode: bool = False) -> argparse.Namespace:
# Inject LLM cache configuration
args.enable_llm_cache_for_extract = get_env_value(
"ENABLE_LLM_CACHE_FOR_EXTRACT", False, bool
"ENABLE_LLM_CACHE_FOR_EXTRACT", True, bool
)
# Select Document loading tool (DOCLING, DEFAULT)